diff --git a/README.md b/README.md
index c3ba1ca..58c17c4 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,18 @@
 
 该资源为作者在CSDN的撰写Python图像处理文章的支撑，主要是Python实现图像处理、图像识别、图像分类等算法代码实现。该系列文章是讲解Python OpenCV图像处理知识，前期主要讲解图像入门、OpenCV基础用法，中期讲解图像处理的各种算法，包括图像锐化算子、图像增强技术、图像分割等，后期结合深度学习研究图像识别、图像分类应用。希望该资源对您有所帮助，一起加油！
 
-前文参考：
+强烈推荐另一个资源，这是作者归纳的一本著作《Python中的图像处理》，非常系统，希望对您有所帮助，记得点赞喔，感谢。
+- https://github.com/eastmountyxz/Book3-Python-ImageProcessing
+
+
+
+华为云《从零到壹•Python图像处理及识别》电子书开源：
+- https://bbs.huaweicloud.com/blogs/336932
+- https://github.com/eastmountyxz/HWCloudImageRecognition
+
+
+
+**图像处理：**
 
 [[Python图像处理] 一.图像处理基础知识及OpenCV入门函数](https://blog.csdn.net/Eastmount/article/details/81748802) <br />
 [[Python图像处理] 二.OpenCV+Numpy库读取与修改像素](https://blog.csdn.net/eastmount/article/details/82120114)  <br />
@@ -35,6 +46,29 @@
 [[Python图像处理] 二十七.OpenGL入门及绘制基本图形（一）](https://blog.csdn.net/Eastmount/article/details/104267905)  <br />
 [[Python图像处理] 二十八.OpenCV快速实现人脸检测及视频中的人脸](https://blog.csdn.net/Eastmount/article/details/104463173) <br />
 [[Python图像处理] 二十九.MoviePy视频编辑库实现抖音短视频剪切合并操作](https://blog.csdn.net/eastmount/article/details/108929561) <br />
+[[Python图像处理] 三十.图像量化及采样处理万字详细总结（推荐）](https://blog.csdn.net/Eastmount/article/details/109605161) <br />
+[[Python图像处理] 三十一.图像点运算处理两万字详细总结（灰度化处理、阈值化处理）](https://blog.csdn.net/Eastmount/article/details/109649659) <br />
+[[Python图像处理] 三十二.傅里叶变换（图像去噪）与霍夫变换（特征识别）万字详细总结](https://blog.csdn.net/Eastmount/article/details/110487868) <br />
+[[Python图像处理] 三十三.图像各种特效处理及原理万字详解（毛玻璃、浮雕、素描、怀旧、流年、滤镜等）](https://blog.csdn.net/Eastmount/article/details/111568397) <br />
+[[Python图像处理] 三十四.数字图像处理基础与几何图形绘制万字详解（推荐）](https://blog.csdn.net/Eastmount/article/details/112547228) <br />
+[[Python图像处理] 三十五.OpenCV图像处理入门、算数逻辑运算与图像融合（推荐）](https://blog.csdn.net/Eastmount/article/details/113192978) <br />
+[[Python图像处理] 三十六.OpenCV图像几何变换万字详解（平移缩放旋转、镜像仿射透视）](https://blog.csdn.net/Eastmount/article/details/113529948) <br />
+[[Python图像处理] 三十七.OpenCV直方图统计两万字详解（掩膜直方图、灰度直方图对比、黑夜白天预测）](https://blog.csdn.net/Eastmount/article/details/113700710) <br />
+[[Python图像处理] 三十八.OpenCV图像增强和图像去雾万字详解（直方图均衡化、局部直方图均衡化、自动色彩均衡化）](https://blog.csdn.net/Eastmount/article/details/114706950) <br />
+[[Python图像处理] 三十九.Python图像分类万字详解（贝叶斯图像分类、KNN图像分类、DNN图像分类）](https://blog.csdn.net/Eastmount/article/details/115382688) <br />
+[[Python图像处理] 四十.全网首发Python图像分割万字详解（阈值分割、边缘分割、纹理分割、分水岭算法、K-Means分割、漫水填充分割、区域定位）](https://blog.csdn.net/Eastmount/article/details/116952580) <br />
+[[Python图像处理] 四十一.Python图像平滑万字详解（均值滤波、方框滤波、高斯滤波、中值滤波、双边滤波）](https://blog.csdn.net/Eastmount/article/details/117665503) <br />
+[[Python图像处理] 四十二.Python图像锐化及边缘检测万字详解（Roberts、Prewitt、Sobel、Laplacian、Canny、LOG）](https://blog.csdn.net/Eastmount/article/details/118897338) <br />
+[[Python图像处理] 四十三.Python图像形态学处理万字详解（腐蚀膨胀、开闭运算、梯度顶帽黑帽运算）](https://blog.csdn.net/Eastmount/article/details/119257935) <br />
+[万字长文告诉新手如何学习Python图像处理（上篇完结 四十四）](https://blog.csdn.net/Eastmount/article/details/119324956) <br />
+
+**图像识别：**
+
+[[Python图像识别] 四十五.目标检测入门普及和ImageAI“傻瓜式”对象检测案例详解](https://blog.csdn.net/Eastmount/article/details/119107913) <br />
+[[Python图像识别] 四十六.图像预处理之图像去雾详解（ACE算法和暗通道先验去雾算法）](https://blog.csdn.net/Eastmount/article/details/120239889)  <br />
+[[Python图像识别] 四十七.Keras深度学习构建CNN识别阿拉伯手写文字图像](https://blog.csdn.net/Eastmount/article/details/120593269) <br />
+
+
 
 希望文章对您有所帮助，如果有不足之处，还请海涵~
 
@@ -43,9 +77,10 @@
 
 后续会尝试结合深度学习进行更深入的研究。
 
+[[图像处理] Python+OpenCV实现车牌区域识别及Sobel算子](https://blog.csdn.net/Eastmount/article/details/81461679)
 
 CSDN Eastmount 杨秀璋
-2020-10-07
+2021-05-09
 
 
 ---
diff --git a/blog01-start/blog01-image02.py b/blog01-start/blog01-image02.py
index f59e606..c6d60b9 100644
--- a/blog01-start/blog01-image02.py
+++ b/blog01-start/blog01-image02.py
@@ -4,18 +4,17 @@
 #读取图片
 img = cv2.imread("flower.png", cv2.IMREAD_UNCHANGED)
 test = img[88,142]
-print test
-
+print(test)
 img[88,142] = [255, 255, 255]
-print test
+print(test)
 
 #分别获取BGR通道像素
 blue = img[88,142,0]
-print blue
+print(blue)
 green = img[88,142,1]
-print green
+print(green)
 red = img[88,142,2]
-print red
+print(red)
 
 #显示图像
 cv2.imshow("Demo", img)
diff --git a/blog04-blur/blog04-image02.py b/blog04-blur/blog04-image02.py
index 8ce53b0..35c54da 100644
--- a/blog04-blur/blog04-image02.py
+++ b/blog04-blur/blog04-image02.py
@@ -13,7 +13,7 @@
 #显示图形
 titles = ['Source Image', 'Blur Image']  
 images = [source, result]  
-for i in xrange(2):  
+for i in range(2):  
    plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
    plt.title(titles[i])  
    plt.xticks([]),plt.yticks([])  
diff --git a/blog04-blur/blog04-image03.py b/blog04-blur/blog04-image03.py
index 6d68563..e1ef143 100644
--- a/blog04-blur/blog04-image03.py
+++ b/blog04-blur/blog04-image03.py
@@ -13,7 +13,7 @@
 #显示图形
 titles = ['Source Image', 'BoxFilter Image']  
 images = [source, result]  
-for i in xrange(2):  
+for i in range(2):  
    plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
    plt.title(titles[i])  
    plt.xticks([]),plt.yticks([])  
diff --git a/blog04-blur/blog04-image04.py b/blog04-blur/blog04-image04.py
index e1f7a24..5a0eb0a 100644
--- a/blog04-blur/blog04-image04.py
+++ b/blog04-blur/blog04-image04.py
@@ -13,7 +13,7 @@
 #显示图形
 titles = ['Source Image', 'BoxFilter Image']  
 images = [source, result]  
-for i in xrange(2):  
+for i in range(2):  
    plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
    plt.title(titles[i])  
    plt.xticks([]),plt.yticks([])  
diff --git a/blog04-blur/blog04-image05.py b/blog04-blur/blog04-image05.py
index 2c521f1..3fedd38 100644
--- a/blog04-blur/blog04-image05.py
+++ b/blog04-blur/blog04-image05.py
@@ -13,7 +13,7 @@
 #显示图形
 titles = ['Source Image', 'GaussianBlur Image']  
 images = [source, result]  
-for i in xrange(2):  
+for i in range(2):  
    plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
    plt.title(titles[i])  
    plt.xticks([]),plt.yticks([])  
diff --git a/blog06-resize/blog06-image01.py b/blog06-resize/blog06-image01.py
index b052f12..9b896e3 100644
--- a/blog06-resize/blog06-image01.py
+++ b/blog06-resize/blog06-image01.py
@@ -7,7 +7,7 @@
 
 #图像缩放
 result = cv2.resize(src, (200,100))
-print result.shape
+print(result.shape)
 
 #显示图像
 cv2.imshow("src", src)
diff --git a/blog06-resize/blog06-image02.py b/blog06-resize/blog06-image02.py
index d51fb6f..3194a04 100644
--- a/blog06-resize/blog06-image02.py
+++ b/blog06-resize/blog06-image02.py
@@ -5,7 +5,7 @@
 #读取图片
 src = cv2.imread('scenery.png')
 rows, cols = src.shape[:2]
-print rows, cols
+print(rows, cols)
 
 #图像缩放 dsize(列,行)
 result = cv2.resize(src, (int(cols*0.6), int(rows*1.2)))
diff --git a/blog06-resize/blog06-image03.py b/blog06-resize/blog06-image03.py
index cf12a03..62b5c02 100644
--- a/blog06-resize/blog06-image03.py
+++ b/blog06-resize/blog06-image03.py
@@ -5,7 +5,7 @@
 #读取图片
 src = cv2.imread('scenery.png')
 rows, cols = src.shape[:2]
-print rows, cols
+print(rows, cols)
 
 #图像缩放
 result = cv2.resize(src, None, fx=0.3, fy=0.3)
diff --git a/blog06-resize/blog06-image05.py b/blog06-resize/blog06-image05.py
index f7c392d..fbf1d61 100644
--- a/blog06-resize/blog06-image05.py
+++ b/blog06-resize/blog06-image05.py
@@ -16,7 +16,7 @@
 #显示图形
 titles = ['Source', 'Image1', 'Image2', 'Image3']  
 images = [src, img1, img2, img3]  
-for i in xrange(4):  
+for i in range(4):  
    plt.subplot(2,2,i+1), plt.imshow(images[i], 'gray')  
    plt.title(titles[i])  
    plt.xticks([]),plt.yticks([])  
diff --git a/blog06-resize/blog06-image06.py b/blog06-resize/blog06-image06.py
index b93ead4..92f6cc5 100644
--- a/blog06-resize/blog06-image06.py
+++ b/blog06-resize/blog06-image06.py
@@ -23,7 +23,7 @@
 #显示图形
 titles = [ 'Image1', 'Image2', 'Image3', 'Image4']  
 images = [img1, img2, img3, img4]  
-for i in xrange(4):  
+for i in range(4):  
    plt.subplot(2,2,i+1), plt.imshow(images[i], 'gray')  
    plt.title(titles[i])  
    plt.xticks([]),plt.yticks([])  
diff --git a/blog07-threshold/blog07-image01.py b/blog07-threshold/blog07-image01.py
index 4eb2677..1d15ec3 100644
--- a/blog07-threshold/blog07-image01.py
+++ b/blog07-threshold/blog07-image01.py
@@ -10,7 +10,7 @@
 
 #二进制阈值化处理
 r, b = cv2.threshold(GrayImage, 127, 255, cv2.THRESH_BINARY)
-print r
+print(r)
 
 #显示图像
 cv2.imshow("src", src)
diff --git a/blog07-threshold/blog07-image02.py b/blog07-threshold/blog07-image02.py
index 8b19c0e..945f473 100644
--- a/blog07-threshold/blog07-image02.py
+++ b/blog07-threshold/blog07-image02.py
@@ -10,7 +10,7 @@
 
 #反二进制阈值化处理
 r, b = cv2.threshold(GrayImage, 127, 255, cv2.THRESH_BINARY_INV)
-print r
+print(r)
 
 #显示图像
 cv2.imshow("src", src)
diff --git a/blog07-threshold/blog07-image03.py b/blog07-threshold/blog07-image03.py
index d76eaf1..4c1ddc9 100644
--- a/blog07-threshold/blog07-image03.py
+++ b/blog07-threshold/blog07-image03.py
@@ -10,7 +10,7 @@
 
 #截断阈值化处理
 r, b = cv2.threshold(GrayImage, 127, 255, cv2.THRESH_TRUNC)
-print r
+print(r)
 
 #显示图像
 cv2.imshow("src", src)
diff --git a/blog07-threshold/blog07-image04.py b/blog07-threshold/blog07-image04.py
index 3e10252..eb5efce 100644
--- a/blog07-threshold/blog07-image04.py
+++ b/blog07-threshold/blog07-image04.py
@@ -10,7 +10,7 @@
 
 #反阈值化为0处理
 r, b = cv2.threshold(GrayImage, 127, 255, cv2.THRESH_TOZERO_INV)
-print r
+print(r)
 
 #显示图像
 cv2.imshow("src", src)
diff --git a/blog07-threshold/blog07-image05.py b/blog07-threshold/blog07-image05.py
index 6d7d63b..a2ea0be 100644
--- a/blog07-threshold/blog07-image05.py
+++ b/blog07-threshold/blog07-image05.py
@@ -10,7 +10,7 @@
 
 #阈值化为0处理
 r, b = cv2.threshold(GrayImage, 127, 255, cv2.THRESH_TOZERO)
-print r
+print(r)
 
 #显示图像
 cv2.imshow("src", src)
diff --git a/blog07-threshold/blog07-image06.py b/blog07-threshold/blog07-image06.py
index 039d991..8933416 100644
--- a/blog07-threshold/blog07-image06.py
+++ b/blog07-threshold/blog07-image06.py
@@ -18,7 +18,7 @@
 #显示结果
 titles = ['Gray Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']  
 images = [GrayImage, thresh1, thresh2, thresh3, thresh4, thresh5]  
-for i in xrange(6):  
+for i in range(6):  
    plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')  
    plt.title(titles[i])  
    plt.xticks([]),plt.yticks([])  
diff --git a/blog30-lh&cy/blog30_01.py b/blog30-lh&cy/blog30_01.py
new file mode 100644
index 0000000..f9b0240
--- /dev/null
+++ b/blog30-lh&cy/blog30_01.py
@@ -0,0 +1,33 @@
+# -*- coding: utf-8 -*-
+# BY:Eastmount CSDN 2020-11-10
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('lena.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#创建一幅图像
+new_img = np.zeros((height, width, 3), np.uint8)
+
+#图像量化操作 量化等级为2
+for i in range(height):
+    for j in range(width):
+        for k in range(3): #对应BGR三分量
+            if img[i, j][k] < 128:
+                gray = 0
+            else:
+                gray = 128
+            new_img[i, j][k] = np.uint8(gray)
+        
+#显示图像
+cv2.imshow("src", img)
+cv2.imshow("Quantization", new_img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog30-lh&cy/blog30_02.py b/blog30-lh&cy/blog30_02.py
new file mode 100644
index 0000000..56e4fb2
--- /dev/null
+++ b/blog30-lh&cy/blog30_02.py
@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+# BY:Eastmount CSDN 2020-11-10
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('lena.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#创建一幅图像
+new_img1 = np.zeros((height, width, 3), np.uint8)
+new_img2 = np.zeros((height, width, 3), np.uint8)
+new_img3 = np.zeros((height, width, 3), np.uint8)
+
+#图像量化等级为2的量化处理
+for i in range(height):
+    for j in range(width):
+        for k in range(3): #对应BGR三分量
+            if img[i, j][k] < 128:
+                gray = 0
+            else:
+                gray = 128
+            new_img1[i, j][k] = np.uint8(gray)
+
+#图像量化等级为4的量化处理
+for i in range(height):
+    for j in range(width):
+        for k in range(3): #对应BGR三分量
+            if img[i, j][k] < 64:
+                gray = 0
+            elif img[i, j][k] < 128:
+                gray = 64
+            elif img[i, j][k] < 192:
+                gray = 128
+            else:
+                gray = 192
+            new_img2[i, j][k] = np.uint8(gray)
+
+#图像量化等级为8的量化处理
+for i in range(height):
+    for j in range(width):
+        for k in range(3): #对应BGR三分量
+            if img[i, j][k] < 32:
+                gray = 0
+            elif img[i, j][k] < 64:
+                gray = 32
+            elif img[i, j][k] < 96:
+                gray = 64
+            elif img[i, j][k] < 128:
+                gray = 96
+            elif img[i, j][k] < 160:
+                gray = 128
+            elif img[i, j][k] < 192:
+                gray = 160
+            elif img[i, j][k] < 224:
+                gray = 192
+            else:
+                gray = 224
+            new_img3[i, j][k] = np.uint8(gray)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = ['(a) 原始图像', '(b) 量化-L2', '(c) 量化-L4', '(d) 量化-L8']  
+images = [img, new_img1, new_img2, new_img3]  
+for i in range(4):  
+   plt.subplot(2,2,i+1), plt.imshow(images[i], 'gray'), 
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog30-lh&cy/blog30_03.py b/blog30-lh&cy/blog30_03.py
new file mode 100644
index 0000000..6d2c834
--- /dev/null
+++ b/blog30-lh&cy/blog30_03.py
@@ -0,0 +1,45 @@
+# coding: utf-8
+# BY:Eastmount CSDN 2020-11-10
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('nv.png') 
+
+#图像二维像素转换为一维
+data = img.reshape((-1,3))
+data = np.float32(data)
+
+#定义中心 (type,max_iter,epsilon)
+criteria = (cv2.TERM_CRITERIA_EPS +
+            cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+
+#设置标签
+flags = cv2.KMEANS_RANDOM_CENTERS
+
+#K-Means聚类 聚集成4类
+compactness, labels, centers = cv2.kmeans(data, 4, None, criteria, 10, flags)
+
+
+#图像转换回uint8二维类型
+centers = np.uint8(centers)
+res = centers[labels.flatten()]
+dst = res.reshape((img.shape))
+
+#图像转换为RGB显示
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
+
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = ['原始图像', '聚类量化 K=4']  
+images = [img, dst]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray'), 
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog30-lh&cy/blog30_04.py b/blog30-lh&cy/blog30_04.py
new file mode 100644
index 0000000..a8f3213
--- /dev/null
+++ b/blog30-lh&cy/blog30_04.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+# BY:Eastmount CSDN 2020-11-10
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('lena.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#采样转换成16*16区域
+numHeight = int(height/16)
+numWidth = int(width/16)
+
+#创建一幅图像
+new_img = np.zeros((height, width, 3), np.uint8)
+
+#图像循环采样16*16区域
+for i in range(16):
+    #获取Y坐标
+    y = i*numHeight
+    for j in range(16):
+        #获取X坐标
+        x = j*numWidth
+        #获取填充颜色 左上角像素点
+        b = img[y, x][0]
+        g = img[y, x][1]
+        r = img[y, x][2]
+        
+        #循环设置小区域采样
+        for n in range(numHeight):
+            for m in range(numWidth):
+                new_img[y+n, x+m][0] = np.uint8(b)
+                new_img[y+n, x+m][1] = np.uint8(g)
+                new_img[y+n, x+m][2] = np.uint8(r)
+        
+#显示图像
+cv2.imshow("src", img)
+cv2.imshow("Sampling", new_img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog30-lh&cy/blog30_05.py b/blog30-lh&cy/blog30_05.py
new file mode 100644
index 0000000..ea2fe45
--- /dev/null
+++ b/blog30-lh&cy/blog30_05.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+# BY:Eastmount CSDN 2020-11-10
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('scenery.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#采样转换成8*8区域
+numHeight = int(height/8)
+numwidth = int(width/8)
+
+#创建一幅图像
+new_img = np.zeros((height, width, 3), np.uint8)
+
+#图像循环采样8*8区域
+for i in range(8):
+    #获取Y坐标
+    y = i*numHeight
+    for j in range(8):
+        #获取X坐标
+        x = j*numwidth
+        #获取填充颜色 左上角像素点
+        b = img[y, x][0]
+        g = img[y, x][1]
+        r = img[y, x][2]
+        
+        #循环设置小区域采样
+        for n in range(numHeight):
+            for m in range(numwidth):
+                new_img[y+n, x+m][0] = np.uint8(b)
+                new_img[y+n, x+m][1] = np.uint8(g)
+                new_img[y+n, x+m][2] = np.uint8(r)
+        
+#显示图像
+cv2.imshow("src", img)
+cv2.imshow("Sampling", new_img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog30-lh&cy/blog30_06.py b/blog30-lh&cy/blog30_06.py
new file mode 100644
index 0000000..1cd0883
--- /dev/null
+++ b/blog30-lh&cy/blog30_06.py
@@ -0,0 +1,56 @@
+# -- coding:utf-8 --
+# BY:Eastmount CSDN 2020-11-10
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+im = cv2.imread('people.png', 1)
+
+#设置鼠标左键开启
+en = False
+
+#鼠标事件
+def draw(event, x, y, flags, param):
+    global en
+    #鼠标左键按下开启en值
+    if event==cv2.EVENT_LBUTTONDOWN:
+        en = True
+    #鼠标左键按下并且移动
+    elif event==cv2.EVENT_MOUSEMOVE and flags==cv2.EVENT_LBUTTONDOWN:
+        #调用函数打马赛克
+        if en:
+            drawMask(y,x)
+        #鼠标左键弹起结束操作
+        elif event==cv2.EVENT_LBUTTONUP:
+            en = False
+          
+#图像局部采样操作         
+def drawMask(x, y, size=10):
+    #size*size采样处理
+    m = int(x / size * size)
+    n = int(y / size * size)
+    print(m, n)
+    #10*10区域设置为同一像素值
+    for i in range(size):
+        for j in range(size):
+            im[m+i][n+j] = im[m][n]
+
+#打开对话框
+cv2.namedWindow('image')
+
+#调用draw函数设置鼠标操作
+cv2.setMouseCallback('image', draw)
+
+#循环处理
+while(1):
+    cv2.imshow('image', im)
+    #按ESC键退出
+    if cv2.waitKey(10)&0xFF==27:
+        break
+    #按s键保存图片
+    elif cv2.waitKey(10)&0xFF==115:
+        cv2.imwrite('sava.png', im)
+
+#退出窗口
+cv2.destroyAllWindows()
diff --git a/blog30-lh&cy/blog30_07.py b/blog30-lh&cy/blog30_07.py
new file mode 100644
index 0000000..def9220
--- /dev/null
+++ b/blog30-lh&cy/blog30_07.py
@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+# BY:Eastmount CSDN 2020-11-10
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#图像向下取样
+r = cv2.pyrDown(img)
+
+#显示图像
+cv2.imshow('original', img)
+cv2.imshow('PyrDown', r)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog30-lh&cy/blog30_08.py b/blog30-lh&cy/blog30_08.py
new file mode 100644
index 0000000..54ede71
--- /dev/null
+++ b/blog30-lh&cy/blog30_08.py
@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#图像向下取样
+r1 = cv2.pyrDown(img)
+r2 = cv2.pyrDown(r1)
+r3 = cv2.pyrDown(r2)
+
+#显示图像
+cv2.imshow('original', img)
+cv2.imshow('PyrDown1', r1)
+cv2.imshow('PyrDown2', r2)
+cv2.imshow('PyrDown3', r3)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog30-lh&cy/blog30_09.py b/blog30-lh&cy/blog30_09.py
new file mode 100644
index 0000000..decf8f6
--- /dev/null
+++ b/blog30-lh&cy/blog30_09.py
@@ -0,0 +1,17 @@
+# -*- coding: utf-8 -*-
+# BY:Eastmount CSDN 2020-11-10
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('lena.png')
+
+#图像向上取样
+r = cv2.pyrUp(img)
+
+#显示图像
+cv2.imshow('original', img)
+cv2.imshow('PyrUp', r)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog30-lh&cy/blog30_10.py b/blog30-lh&cy/blog30_10.py
new file mode 100644
index 0000000..70146af
--- /dev/null
+++ b/blog30-lh&cy/blog30_10.py
@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('lena2.png')
+
+#图像向上取样
+r1 = cv2.pyrUp(img)
+r2 = cv2.pyrUp(r1)
+r3 = cv2.pyrUp(r2)
+
+#显示图像
+cv2.imshow('original', img)
+cv2.imshow('PyrUp1', r1)
+cv2.imshow('PyrUp2', r2)
+cv2.imshow('PyrUp3', r3)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog30-lh&cy/figure_1.png b/blog30-lh&cy/figure_1.png
new file mode 100644
index 0000000..f89c2f1
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diff --git a/blog30-lh&cy/figure_2.png b/blog30-lh&cy/figure_2.png
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index 0000000..c02a043
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diff --git a/blog30-lh&cy/lena.png b/blog30-lh&cy/lena.png
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index 0000000..c0723e8
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diff --git a/blog30-lh&cy/lena2.png b/blog30-lh&cy/lena2.png
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index 0000000..1093210
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index 0000000..36d21fe
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diff --git a/blog30-lh&cy/sava.png b/blog30-lh&cy/sava.png
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index 0000000..d705eec
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diff --git a/blog30-lh&cy/scenery.png b/blog30-lh&cy/scenery.png
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index 0000000..e38f809
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diff --git a/blog31-dys/2016-.png b/blog31-dys/2016-.png
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diff --git a/blog31-dys/2017.png b/blog31-dys/2017.png
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diff --git a/blog31-dys/2019.png b/blog31-dys/2019.png
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diff --git a/blog31-dys/2020.png b/blog31-dys/2020.png
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diff --git a/blog31-dys/2023.png b/blog31-dys/2023.png
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diff --git a/blog31-dys/blog31_01.py b/blog31-dys/blog31_01.py
new file mode 100644
index 0000000..86aac62
--- /dev/null
+++ b/blog31-dys/blog31_01.py
@@ -0,0 +1,17 @@
+#encoding:utf-8
+import cv2  
+import numpy as np  
+
+#读取原始图片
+src = cv2.imread('miao.png')
+
+#图像灰度化处理
+grayImage = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", grayImage)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_02.py b/blog31-dys/blog31_02.py
new file mode 100644
index 0000000..112e04a
--- /dev/null
+++ b/blog31-dys/blog31_02.py
@@ -0,0 +1,42 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img_BGR = cv2.imread('miao.png')
+
+#BGR转换为RGB
+img_RGB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2RGB)
+
+#灰度化处理
+img_GRAY = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2GRAY)
+
+#BGR转HSV
+img_HSV = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2HSV)
+
+#BGR转YCrCb
+img_YCrCb = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YCrCb)
+
+#BGR转HLS
+img_HLS = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2HLS)
+
+#BGR转XYZ
+img_XYZ = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2XYZ)
+
+#BGR转LAB
+img_LAB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2LAB)
+
+#BGR转YUV
+img_YUV = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YUV)
+
+#调用matplotlib显示处理结果
+titles = ['BGR', 'RGB', 'GRAY', 'HSV', 'YCrCb', 'HLS', 'XYZ', 'LAB', 'YUV']  
+images = [img_BGR, img_RGB, img_GRAY, img_HSV, img_YCrCb,
+          img_HLS, img_XYZ, img_LAB, img_YUV]  
+for i in range(9):  
+   plt.subplot(3, 3, i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog31-dys/blog31_03.py b/blog31-dys/blog31_03.py
new file mode 100644
index 0000000..34a86a3
--- /dev/null
+++ b/blog31-dys/blog31_03.py
@@ -0,0 +1,31 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('miao.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#创建一幅图像
+grayimg = np.zeros((height, width, 3), np.uint8)
+
+#图像最大值灰度处理
+for i in range(height):
+    for j in range(width):
+        #获取图像R G B最大值
+        gray = max(img[i,j][0], img[i,j][1], img[i,j][2])
+        #灰度图像素赋值 gray=max(R,G,B)
+        grayimg[i,j] = np.uint8(gray)
+
+#显示图像
+cv2.imshow("src", img)
+cv2.imshow("gray", grayimg)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_04.py b/blog31-dys/blog31_04.py
new file mode 100644
index 0000000..b76d68f
--- /dev/null
+++ b/blog31-dys/blog31_04.py
@@ -0,0 +1,31 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('miao.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#创建一幅图像
+grayimg = np.zeros((height, width, 3), np.uint8)
+print grayimg
+
+#图像平均灰度处理方法
+for i in range(height):
+    for j in range(width):
+        #灰度值为RGB三个分量的平均值
+        gray = (int(img[i,j][0]) + int(img[i,j][1]) + int(img[i,j][2]))  /  3
+        grayimg[i,j] = np.uint8(gray)
+
+#显示图像
+cv2.imshow("src", img)
+cv2.imshow("gray", grayimg)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_05.py b/blog31-dys/blog31_05.py
new file mode 100644
index 0000000..19ee51f
--- /dev/null
+++ b/blog31-dys/blog31_05.py
@@ -0,0 +1,30 @@
+#encoding:utf-8
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('miao.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#创建一幅图像
+grayimg = np.zeros((height, width, 3), np.uint8)
+print(grayimg)
+
+#图像平均灰度处理方法
+for i in range(height):
+    for j in range(width):
+        #灰度加权平均法
+        gray = 0.30 * img[i,j][0] + 0.59 * img[i,j][1] + 0.11 * img[i,j][2]
+        grayimg[i,j] = np.uint8(gray)
+
+#显示图像
+cv2.imshow("src", img)
+cv2.imshow("gray", grayimg)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_06.py b/blog31-dys/blog31_06.py
new file mode 100644
index 0000000..ddf630c
--- /dev/null
+++ b/blog31-dys/blog31_06.py
@@ -0,0 +1,37 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('miao.png')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+
+#创建一幅图像
+result = np.zeros((height, width, 3), np.uint8)
+
+#图像灰度上移变换 DB=DA+50
+for i in range(height):
+    for j in range(width):
+        
+        if (int(grayImage[i,j]+50) > 255):
+            gray = 255
+        else:
+            gray = int(grayImage[i,j]+50)
+            
+        result[i,j] = np.uint8(gray)
+
+#显示图像
+cv2.imshow("Gray Image", grayImage)
+cv2.imshow("Result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_07.py b/blog31-dys/blog31_07.py
new file mode 100644
index 0000000..adbeb1c
--- /dev/null
+++ b/blog31-dys/blog31_07.py
@@ -0,0 +1,37 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('miao.png')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+
+#创建一幅图像
+result = np.zeros((height, width, 3), np.uint8)
+
+#图像对比度增强变换 DB=DA*1.5
+for i in range(height):
+    for j in range(width):
+        
+        if (int(grayImage[i,j]*1.5) > 255):
+            gray = 255
+        else:
+            gray = int(grayImage[i,j]*1.5)
+            
+        result[i,j] = np.uint8(gray)
+
+#显示图像
+cv2.imshow("Gray Image", grayImage)
+cv2.imshow("Result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_08.py b/blog31-dys/blog31_08.py
new file mode 100644
index 0000000..c601070
--- /dev/null
+++ b/blog31-dys/blog31_08.py
@@ -0,0 +1,32 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('miao.png')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+
+#创建一幅图像
+result = np.zeros((height, width, 3), np.uint8)
+
+#图像对比度减弱变换 DB=DA*0.8
+for i in range(height):
+    for j in range(width):
+        gray = int(grayImage[i,j]*0.8)
+        result[i,j] = np.uint8(gray)
+
+#显示图像
+cv2.imshow("Gray Image", grayImage)
+cv2.imshow("Result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_09.py b/blog31-dys/blog31_09.py
new file mode 100644
index 0000000..bab0972
--- /dev/null
+++ b/blog31-dys/blog31_09.py
@@ -0,0 +1,31 @@
+#encoding:utf-8
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('miao.png')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+
+#创建一幅图像
+result = np.zeros((height, width), np.uint8)
+
+#图像灰度反色变换 DB=255-DA
+for i in range(height):
+    for j in range(width):
+        gray = 255 - grayImage[i,j]
+        result[i,j] = np.uint8(gray)
+
+#显示图像
+cv2.imshow("Gray Image", grayImage)
+cv2.imshow("Result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_10.py b/blog31-dys/blog31_10.py
new file mode 100644
index 0000000..002ca7d
--- /dev/null
+++ b/blog31-dys/blog31_10.py
@@ -0,0 +1,32 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('miao.png')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+
+#创建一幅图像
+result = np.zeros((height, width), np.uint8)
+
+#图像灰度非线性变换：DB=DA×DA/255
+for i in range(height):
+    for j in range(width):
+        gray = int(grayImage[i,j])*int(grayImage[i,j]) / 255
+        result[i,j] = np.uint8(gray)
+
+#显示图像
+cv2.imshow("Gray Image", grayImage)
+cv2.imshow("Result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_11(best).py b/blog31-dys/blog31_11(best).py
new file mode 100644
index 0000000..b618200
--- /dev/null
+++ b/blog31-dys/blog31_11(best).py
@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+
+#绘制曲线
+def log_plot(c):
+    x = np.arange(0, 256, 0.01)
+    y = c * np.log(1 + x)
+    plt.plot(x, y, 'r', linewidth=1)
+    plt.rcParams['font.sans-serif']=['SimHei'] #正常显示中文标签
+    plt.title(u'对数变换函数')
+    plt.xlim(0, 255), plt.ylim(0, 255)
+    plt.show()
+
+#对数变换
+def log(c, img):
+    output = c * np.log(1.0 + img)
+    output = np.uint8(output + 0.5)
+    return output
+
+#读取原始图像
+img = cv2.imread('2016-.png')
+
+#绘制对数变换曲线
+log_plot(42)
+
+#图像灰度对数变换
+output = log(42, img)
+
+#显示图像
+cv2.imshow('Input', img)
+cv2.imshow('Output', output)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
diff --git a/blog31-dys/blog31_12(best).py b/blog31-dys/blog31_12(best).py
new file mode 100644
index 0000000..850ed68
--- /dev/null
+++ b/blog31-dys/blog31_12(best).py
@@ -0,0 +1,38 @@
+#encoding:utf-8
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+
+#绘制曲线
+def gamma_plot(c, v):
+    x = np.arange(0, 256, 0.01)
+    y = c*x**v
+    plt.plot(x, y, 'r', linewidth=1)
+    plt.rcParams['font.sans-serif']=['SimHei'] #正常显示中文标签
+    plt.title('伽马变换函数')
+    plt.xlim([0, 255]), plt.ylim([0, 255])
+    plt.show()
+
+#伽玛变换
+def gamma(img, c, v):
+    lut = np.zeros(256, dtype=np.float32)
+    for i in range(256):
+        lut[i] = c * i ** v
+    output_img = cv2.LUT(img, lut) #像素灰度值的映射
+    output_img = np.uint8(output_img+0.5)  
+    return output_img
+
+#读取原始图像
+img = cv2.imread('2019.png')
+
+#绘制伽玛变换曲线
+gamma_plot(0.00000005, 4.0)
+
+#图像灰度伽玛变换
+output = gamma(img, 0.00000005, 4.0)
+
+#显示图像
+cv2.imshow('Imput', img)
+cv2.imshow('Output', output)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_13.py b/blog31-dys/blog31_13.py
new file mode 100644
index 0000000..864c307
--- /dev/null
+++ b/blog31-dys/blog31_13.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('miao.png')
+
+#灰度图像处理
+GrayImage = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
+
+#二进制阈值化处理
+r, b = cv2.threshold(GrayImage, 127, 255, cv2.THRESH_BINARY)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", b)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_14.py b/blog31-dys/blog31_14.py
new file mode 100644
index 0000000..21b25e7
--- /dev/null
+++ b/blog31-dys/blog31_14.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('miao.png')
+
+#灰度图像处理
+grayImage = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
+
+#反二进制阈值化处理
+r, b = cv2.threshold(grayImage, 127, 255, cv2.THRESH_BINARY_INV)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", b)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_15.py b/blog31-dys/blog31_15.py
new file mode 100644
index 0000000..ccd73cd
--- /dev/null
+++ b/blog31-dys/blog31_15.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('miao.png')
+
+#灰度图像处理
+grayImage = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
+
+#截断阈值化处理
+r, b = cv2.threshold(grayImage, 127, 255, cv2.THRESH_TRUNC)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", b)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_16.py b/blog31-dys/blog31_16.py
new file mode 100644
index 0000000..751e948
--- /dev/null
+++ b/blog31-dys/blog31_16.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('miao.png')
+
+#灰度图像处理
+grayImage = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
+
+#阈值化为0处理
+r, b = cv2.threshold(grayImage, 127, 255, cv2.THRESH_TOZERO)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", b)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_17.py b/blog31-dys/blog31_17.py
new file mode 100644
index 0000000..0e34a13
--- /dev/null
+++ b/blog31-dys/blog31_17.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('miao.png')
+
+#灰度图像处理
+GrayImage = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
+
+#二进制阈值化处理
+r, b = cv2.threshold(GrayImage, 127, 255, cv2.THRESH_TOZERO_INV)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", b)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog31-dys/blog31_18.py b/blog31-dys/blog31_18.py
new file mode 100644
index 0000000..3ba4669
--- /dev/null
+++ b/blog31-dys/blog31_18.py
@@ -0,0 +1,25 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img=cv2.imread('miao.png')
+grayImage=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  
+
+#阈值化处理
+ret,thresh1=cv2.threshold(grayImage,127,255,cv2.THRESH_BINARY)  
+ret,thresh2=cv2.threshold(grayImage,127,255,cv2.THRESH_BINARY_INV)  
+ret,thresh3=cv2.threshold(grayImage,127,255,cv2.THRESH_TRUNC)  
+ret,thresh4=cv2.threshold(grayImage,127,255,cv2.THRESH_TOZERO)  
+ret,thresh5=cv2.threshold(grayImage,127,255,cv2.THRESH_TOZERO_INV)
+
+#显示结果
+titles = ['Gray Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']  
+images = [grayImage, thresh1, thresh2, thresh3, thresh4, thresh5]  
+for i in range(6):  
+   plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog31-dys/blog31_19.py b/blog31-dys/blog31_19.py
new file mode 100644
index 0000000..bbefabf
--- /dev/null
+++ b/blog31-dys/blog31_19.py
@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2020-11-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv2.imread('miao.png')
+
+#图像灰度化处理
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  
+
+#固定值阈值化处理
+r, thresh1 = cv2.threshold(grayImage, 127, 255, cv2.THRESH_BINARY)  
+
+#自适应阈值化处理 方法一
+thresh2 = cv2.adaptiveThreshold(grayImage, 255, 
+cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 2)
+
+#自适应阈值化处理 方法二
+thresh3 = cv2.adaptiveThreshold(grayImage, 255, 
+cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = ['灰度图像', '全局阈值', '自适应平均阈值', '自适应高斯阈值']
+#显示图像
+titles = ['Gray Image',
+          'Global Thresholding',
+          'Adaptive Mean Thresholding',
+          'Adaptive Gaussian Thresholding']
+images = [grayImage, thresh1, thresh2, thresh3]
+for i in range(4):
+   plt.subplot(2, 2, i+1), plt.imshow(images[i], 'gray')
+   plt.title(titles[i])
+   plt.xticks([]),plt.yticks([])
+plt.show()
diff --git a/blog32-Hough_Transform/Hough_Transform_Circle.py b/blog32-Hough_Transform/Hough_Transform_Circle.py
new file mode 100644
index 0000000..5ddf73f
--- /dev/null
+++ b/blog32-Hough_Transform/Hough_Transform_Circle.py
@@ -0,0 +1,21 @@
+import cv2
+import numpy as np
+
+img=cv2.imread("circles.png")
+gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #convert to grayscale
+
+blurr=cv2.blur(gray,(3,3)) #blur the image to reduce noice
+
+detected_circles=cv2.HoughCircles(blurr,cv2.HOUGH_GRADIENT,1,20,param1=50,param2=30,minRadius=0,maxRadius=100)
+
+circles = np.uint16(np.around(detected_circles))
+for i in circles[0,:]:
+    # draw the outer circle
+    cv2.circle(img,(i[0],i[1]),i[2],(0,255,0),2)
+    # draw the center of the circle
+    cv2.circle(img,(i[0],i[1]),2,(0,0,255),3)
+
+cv2.imshow('detected circles',img)
+cv2.imwrite("Circle detected.png",img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog32-Hough_Transform/Lena.bmp b/blog32-Hough_Transform/Lena.bmp
new file mode 100644
index 0000000..782dd7a
Binary files /dev/null and b/blog32-Hough_Transform/Lena.bmp differ
diff --git a/blog32-Hough_Transform/Lena.png b/blog32-Hough_Transform/Lena.png
new file mode 100644
index 0000000..e1f2d71
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diff --git a/blog32-Hough_Transform/Road_Lane_Lines_detection.ipynb b/blog32-Hough_Transform/Road_Lane_Lines_detection.ipynb
new file mode 100644
index 0000000..55e35c1
--- /dev/null
+++ b/blog32-Hough_Transform/Road_Lane_Lines_detection.ipynb
@@ -0,0 +1,388 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cv2\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1a70e537988>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "img=cv2.imread(r\"E:\\NOTES\\CVG_Class\\road.jfif\")\n",
+    "gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n",
+    "\n",
+    "#cv2.imshow(\"original image\",img)\n",
+    "#cv2.waitKey(0)\n",
+    "#cv2.destroyAllWindows()\n",
+    "plt.imshow(img,cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Gaussian blur"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1a70e4d1ac8>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "gauss=cv2.GaussianBlur(gray,(9,9),0)\n",
+    "plt.imshow(gauss,cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Canny Edge detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1a70e7e7b88>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "canny=cv2.Canny(gray,100,205)\n",
+    "#cv2.imshow(\"Canny_edge_detected_image\",canny)\n",
+    "#cv2.waitKey(0)\n",
+    "#cv2.destroyAllWindows()\n",
+    "plt.imshow(canny,cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finding region of interest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1a70ea44c08>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "roi=np.array([[(0,650),(420,400),(590,420),(1000,600)]],dtype=np.int32)\n",
+    "mask=np.zeros_like(gray)    #returns an array of zeros with same shape and type of gray\n",
+    "cv2.fillPoly(mask,roi,255)\n",
+    "m_img=cv2.bitwise_and(gray,mask) #calculates the bitwise_and of 2 array,here gray image and the mask\n",
+    "plt.figure()\n",
+    "plt.imshow(m_img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1a70fc98a08>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "m_img=cv2.bitwise_and(canny,mask)\n",
+    "plt.figure()\n",
+    "plt.imshow(m_img)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hough Tranform for line detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rho=3\n",
+    "theta=np.pi/180\n",
+    "threshold=80\n",
+    "\n",
+    "lines=cv2.HoughLinesP(m_img,rho,theta,threshold,np.array([]),minLineLength=100,maxLineGap=50)\n",
+    "\n",
+    "l_img=np.zeros((m_img.shape[0],m_img.shape[1],3),dtype=np.uint8)\n",
+    "\n",
+    "for line in lines:\n",
+    "    for x1,y1,x2,y2 in line:\n",
+    "        cv2.line(l_img,(x1,y1),(x2,y2),[0,255,0],6)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[140, 576, 497, 409]],\n",
+       "\n",
+       "       [[479, 408, 803, 566]],\n",
+       "\n",
+       "       [[ 58, 625, 299, 507]],\n",
+       "\n",
+       "       [[481, 408, 751, 533]],\n",
+       "\n",
+       "       [[418, 401, 667, 454]],\n",
+       "\n",
+       "       [[670, 490, 834, 570]],\n",
+       "\n",
+       "       [[592, 444, 782, 505]],\n",
+       "\n",
+       "       [[456, 404, 632, 438]],\n",
+       "\n",
+       "       [[293, 509, 460, 427]],\n",
+       "\n",
+       "       [[430, 401, 655, 449]],\n",
+       "\n",
+       "       [[679, 491, 807, 553]],\n",
+       "\n",
+       "       [[331, 453, 482, 407]],\n",
+       "\n",
+       "       [[ 98, 596, 199, 551]],\n",
+       "\n",
+       "       [[410, 410, 540, 414]],\n",
+       "\n",
+       "       [[306, 470, 421, 401]],\n",
+       "\n",
+       "       [[399, 416, 559, 416]],\n",
+       "\n",
+       "       [[424, 401, 614, 438]],\n",
+       "\n",
+       "       [[685, 492, 834, 564]],\n",
+       "\n",
+       "       [[302, 470, 492, 408]],\n",
+       "\n",
+       "       [[ 38, 638, 151, 571]],\n",
+       "\n",
+       "       [[344, 459, 445, 403]],\n",
+       "\n",
+       "       [[321, 464, 428, 402]],\n",
+       "\n",
+       "       [[ 36, 639, 208, 551]],\n",
+       "\n",
+       "       [[428, 406, 651, 454]],\n",
+       "\n",
+       "       [[745, 515, 848, 576]],\n",
+       "\n",
+       "       [[316, 463, 487, 408]],\n",
+       "\n",
+       "       [[169, 566, 270, 521]],\n",
+       "\n",
+       "       [[487, 417, 494, 557]],\n",
+       "\n",
+       "       [[332, 461, 433, 403]],\n",
+       "\n",
+       "       [[742, 528, 912, 604]],\n",
+       "\n",
+       "       [[399, 413, 533, 413]],\n",
+       "\n",
+       "       [[392, 457, 494, 409]],\n",
+       "\n",
+       "       [[386, 420, 589, 420]],\n",
+       "\n",
+       "       [[313, 465, 420, 401]],\n",
+       "\n",
+       "       [[342, 446, 465, 408]],\n",
+       "\n",
+       "       [[408, 410, 578, 440]],\n",
+       "\n",
+       "       [[539, 438, 655, 450]]], dtype=int32)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1a710a31f88>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "imgf=cv2.addWeighted(img,1,l_img,1,0)\n",
+    "\n",
+    "plt.figure()\n",
+    "plt.imshow(imgf)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/blog32-Hough_Transform/blog32_01.py b/blog32-Hough_Transform/blog32_01.py
new file mode 100644
index 0000000..6fbf6d1
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_01.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+import cv2 as cv
+import numpy as np
+from matplotlib import pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv.imread('test.png', 0)
+
+#快速傅里叶变换算法得到频率分布
+f = np.fft.fft2(img)
+
+#默认结果中心点位置是在左上角,
+#调用fftshift()函数转移到中间位置
+fshift = np.fft.fftshift(f)       
+
+#fft结果是复数, 其绝对值结果是振幅
+fimg = np.log(np.abs(fshift))
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#展示结果
+plt.subplot(121), plt.imshow(img, 'gray'), plt.title('(a)原始图像')
+plt.axis('off')
+plt.subplot(122), plt.imshow(fimg, 'gray'), plt.title('(b)傅里叶变换处理')
+plt.axis('off')
+plt.show()
diff --git a/blog32-Hough_Transform/blog32_02.py b/blog32-Hough_Transform/blog32_02.py
new file mode 100644
index 0000000..e0d5029
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_02.py
@@ -0,0 +1,21 @@
+# -*- coding: utf-8 -*-
+import cv2 as cv
+import numpy as np
+from matplotlib import pyplot as plt
+
+#读取图像
+img = cv.imread('Na.png', 0)
+
+#傅里叶变换
+f = np.fft.fft2(img)
+
+#转移像素做幅度普
+fshift = np.fft.fftshift(f)       
+
+#取绝对值：将复数变化成实数取对数的目的为了将数据变化到0-255
+res = np.log(np.abs(fshift))
+
+#展示结果
+plt.subplot(121), plt.imshow(img, 'gray'), plt.title('Original Image')
+plt.subplot(122), plt.imshow(res, 'gray'), plt.title('Fourier Image')
+plt.show()
diff --git a/blog32-Hough_Transform/blog32_03.py b/blog32-Hough_Transform/blog32_03.py
new file mode 100644
index 0000000..095f5b1
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_03.py
@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+import cv2 as cv
+import numpy as np
+from matplotlib import pyplot as plt
+
+#读取图像
+img = cv.imread('Lena.png', 0)
+
+#傅里叶变换
+f = np.fft.fft2(img)
+fshift = np.fft.fftshift(f)
+res = np.log(np.abs(fshift))
+
+#傅里叶逆变换
+ishift = np.fft.ifftshift(fshift)
+iimg = np.fft.ifft2(ishift)
+iimg = np.abs(iimg)
+
+#展示结果
+plt.subplot(131), plt.imshow(img, 'gray'), plt.title('Original Image')
+plt.axis('off')
+plt.subplot(132), plt.imshow(res, 'gray'), plt.title('Fourier Image')
+plt.axis('off')
+plt.subplot(133), plt.imshow(iimg, 'gray'), plt.title('Inverse Fourier Image')
+plt.axis('off')
+plt.show()
+
+rows, cols = img.shape
+crow,ccol = int(rows/2), int(cols/2)
+fshift[crow-30:crow+30, ccol-30:ccol+30] = 0
+
+
+
+
+
diff --git a/blog32-Hough_Transform/blog32_04.py b/blog32-Hough_Transform/blog32_04.py
new file mode 100644
index 0000000..7566729
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_04.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv2.imread('Lena.png', 0)
+
+#傅里叶变换
+dft = cv2.dft(np.float32(img), flags = cv2.DFT_COMPLEX_OUTPUT)
+
+#将频谱低频从左上角移动至中心位置
+dft_shift = np.fft.fftshift(dft)
+
+#频谱图像双通道复数转换为0-255区间
+result = 20*np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1]))
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+plt.subplot(121), plt.imshow(img, cmap = 'gray')
+plt.title(u'(a)原始图像'), plt.xticks([]), plt.yticks([])
+plt.subplot(122), plt.imshow(result, cmap = 'gray')
+plt.title(u'(b)傅里叶变换处理'), plt.xticks([]), plt.yticks([])
+plt.show()
diff --git a/blog32-Hough_Transform/blog32_05.py b/blog32-Hough_Transform/blog32_05.py
new file mode 100644
index 0000000..f8821cb
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_05.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv2.imread('Lena.png', 0)
+
+#傅里叶变换
+dft = cv2.dft(np.float32(img), flags = cv2.DFT_COMPLEX_OUTPUT)
+dftshift = np.fft.fftshift(dft)
+res1= 20*np.log(cv2.magnitude(dftshift[:,:,0], dftshift[:,:,1]))
+
+#傅里叶逆变换
+ishift = np.fft.ifftshift(dftshift)
+iimg = cv2.idft(ishift)
+res2 = cv2.magnitude(iimg[:,:,0], iimg[:,:,1])
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+plt.subplot(131), plt.imshow(img, 'gray'), plt.title(u'(a)原始图像')
+plt.axis('off')
+plt.subplot(132), plt.imshow(res1, 'gray'), plt.title(u'(b)傅里叶变换处理')
+plt.axis('off')
+plt.subplot(133), plt.imshow(res2, 'gray'), plt.title(u'(b)傅里叶变换逆处理')
+plt.axis('off')
+plt.show()
diff --git a/blog32-Hough_Transform/blog32_06.py b/blog32-Hough_Transform/blog32_06.py
new file mode 100644
index 0000000..4cf6eae
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_06.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+import cv2 as cv
+import numpy as np
+from matplotlib import pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv.imread('Na.png', 0)
+
+#傅里叶变换
+f = np.fft.fft2(img)
+fshift = np.fft.fftshift(f)
+
+#设置高通滤波器
+rows, cols = img.shape
+crow,ccol = int(rows/2), int(cols/2)
+fshift[crow-30:crow+30, ccol-30:ccol+30] = 0
+
+#傅里叶逆变换
+ishift = np.fft.ifftshift(fshift)
+iimg = np.fft.ifft2(ishift)
+iimg = np.abs(iimg)
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示原始图像和高通滤波处理图像
+plt.subplot(121), plt.imshow(img, 'gray'), plt.title(u'(a)原始图像')
+plt.axis('off')
+plt.subplot(122), plt.imshow(iimg, 'gray'), plt.title(u'(b)结果图像')
+plt.axis('off')
+plt.show()
diff --git a/blog32-Hough_Transform/blog32_07.py b/blog32-Hough_Transform/blog32_07.py
new file mode 100644
index 0000000..91b17dd
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_07.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+#读取图像
+img = cv2.imread('Na.png', 0)
+
+#傅里叶变换
+dft = cv2.dft(np.float32(img), flags = cv2.DFT_COMPLEX_OUTPUT)
+fshift = np.fft.fftshift(dft)
+
+#设置低通滤波器
+rows, cols = img.shape
+crow,ccol = int(rows/2), int(cols/2) #中心位置
+mask = np.zeros((rows, cols, 2), np.uint8)
+mask[crow-30:crow+30, ccol-30:ccol+30] = 1
+
+#掩膜图像和频谱图像乘积
+f = fshift * mask
+print(f.shape, fshift.shape, mask.shape)
+
+
+#傅里叶逆变换
+ishift = np.fft.ifftshift(f)
+iimg = cv2.idft(ishift)
+res = cv2.magnitude(iimg[:,:,0], iimg[:,:,1])
+
+#显示原始图像和低通滤波处理图像
+plt.subplot(121), plt.imshow(img, 'gray'), plt.title('Original Image')
+plt.axis('off')
+plt.subplot(122), plt.imshow(res, 'gray'), plt.title('Result Image')
+plt.axis('off')
+plt.show()
diff --git a/blog32-Hough_Transform/blog32_08.py b/blog32-Hough_Transform/blog32_08.py
new file mode 100644
index 0000000..4dccda4
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_08.py
@@ -0,0 +1,47 @@
+# -*- coding: utf-8 -*-
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv2.imread('lines.png')
+
+#灰度变换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#转换为二值图像
+edges = cv2.Canny(gray, 50, 150)
+
+#显示原始图像
+plt.subplot(121), plt.imshow(edges, 'gray'), plt.title(u'(a)原始图像')
+plt.axis('off')
+
+#霍夫变换检测直线
+lines = cv2.HoughLines(edges, 1, np.pi / 180, 160)
+
+#转换为二维
+line = lines[:, 0, :] 
+
+#将检测的线在极坐标中绘制 
+for rho,theta in line[:]: 
+    a = np.cos(theta)
+    b = np.sin(theta)
+    x0 = a * rho
+    y0 = b * rho
+    print(x0, y0)
+    x1 = int(x0 + 1000 * (-b))
+    y1 = int(y0 + 1000 * (a))
+    x2 = int(x0 - 1000 * (-b))
+    y2 = int(y0 - 1000 * (a))
+    print(x1, y1, x2, y2)
+    #绘制直线
+    cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 1)
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示处理图像
+plt.subplot(122), plt.imshow(img, 'gray'), plt.title(u'(b)结果图像')
+plt.axis('off')
+plt.show()
diff --git a/blog32-Hough_Transform/blog32_09.py b/blog32-Hough_Transform/blog32_09.py
new file mode 100644
index 0000000..e1c4fd1
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_09.py
@@ -0,0 +1,38 @@
+# -*- coding: utf-8 -*-
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv2.imread('judge.png')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#转换为二值图像
+edges = cv2.Canny(gray, 50, 200)
+
+#显示原始图像
+plt.subplot(121), plt.imshow(edges, 'gray'), plt.title(u'(a)原始图像')
+plt.axis('off')
+
+#霍夫变换检测直线
+minLineLength = 60
+maxLineGap = 10
+lines = cv2.HoughLinesP(edges, 1, np.pi/180, 30, minLineLength, maxLineGap)
+
+#绘制直线
+lines1 = lines[:, 0, :]
+for x1,y1,x2,y2 in lines1[:]:
+    cv2.line(img, (x1,y1), (x2,y2), (255,0,0), 2)
+
+res = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示处理图像
+plt.subplot(122), plt.imshow(res), plt.title(u'(b)结果图像')
+plt.axis('off')
+plt.show()
diff --git a/blog32-Hough_Transform/blog32_10.py b/blog32-Hough_Transform/blog32_10.py
new file mode 100644
index 0000000..f21dc3a
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_10.py
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+#读取图像
+img = cv2.imread('test01.png')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#显示原始图像
+plt.subplot(121), plt.imshow(gray, 'gray'), plt.title('Input Image')
+plt.axis('off')
+
+#霍夫变换检测圆
+#circles1 = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 100,
+#                           param1=100, param2=30, minRadius=200, maxRadius=300)
+
+circles1 = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param2=30)
+
+print(circles1)
+
+#提取为二维
+circles = circles1[0, :, :]
+
+#四舍五入取整
+circles = np.uint16(np.around(circles))
+
+#绘制圆
+for i in circles[:]: 
+    cv2.circle(img, (i[0],i[1]), i[2], (255,0,0), 5) #画圆
+    cv2.circle(img, (i[0],i[1]), 2, (255,0,255), 10) #画圆心
+
+#显示处理图像
+plt.subplot(122), plt.imshow(img), plt.title('Result Image')
+plt.axis('off')
+plt.show()
+
+
+
diff --git a/blog32-Hough_Transform/blog32_11.py b/blog32-Hough_Transform/blog32_11.py
new file mode 100644
index 0000000..0a6a990
--- /dev/null
+++ b/blog32-Hough_Transform/blog32_11.py
@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+#读取图像
+img = cv2.imread('eyes.png')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#显示原始图像
+plt.subplot(121), plt.imshow(gray, 'gray'), plt.title('Input Image')
+plt.axis('off')
+
+#霍夫变换检测圆
+circles1 = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20,
+                            param1=100, param2=30,
+                            minRadius=160, maxRadius=200)
+print(circles1)
+
+#提取为二维
+circles = circles1[0, :, :]
+
+#四舍五入取整
+circles = np.uint16(np.around(circles))
+
+#绘制圆
+for i in circles[:]: 
+    cv2.circle(img, (i[0],i[1]), i[2], (255,0,0), 5) #画圆
+    cv2.circle(img, (i[0],i[1]), 2, (255,0,255), 8) #画圆心
+
+#显示处理图像
+plt.subplot(122), plt.imshow(img), plt.title('Result Image')
+plt.axis('off')
+plt.show()
+
+
+
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diff --git a/blog32-Hough_Transform/judge.png b/blog32-Hough_Transform/judge.png
new file mode 100644
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diff --git a/blog32-Hough_Transform/lines.png b/blog32-Hough_Transform/lines.png
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diff --git a/blog32-Hough_Transform/readme.md b/blog32-Hough_Transform/readme.md
new file mode 100644
index 0000000..44c425c
--- /dev/null
+++ b/blog32-Hough_Transform/readme.md
@@ -0,0 +1,4 @@
+# Hough_Transforms_Feature_Extraction
+
+Hough transform is a feature extraction techniques to detect simple shapes like lines,circles etc.,
+Simple shape is one whose parametric equations are known
diff --git a/blog32-Hough_Transform/road.jfif b/blog32-Hough_Transform/road.jfif
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diff --git a/blog32-Hough_Transform/test01.png b/blog32-Hough_Transform/test01.png
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diff --git a/blog32-Hough_Transform/test02.png b/blog32-Hough_Transform/test02.png
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diff --git a/blog33-Special-effects/blog33_01.py b/blog33-Special-effects/blog33_01.py
new file mode 100644
index 0000000..250dd65
--- /dev/null
+++ b/blog33-Special-effects/blog33_01.py
@@ -0,0 +1,31 @@
+#coding:utf-8
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取原始图像
+src = cv2.imread('scenery.png')
+
+#新建目标图像
+dst = np.zeros_like(src)
+
+#获取图像行和列
+rows, cols = src.shape[:2]
+
+#定义偏移量和随机数
+offsets = 5
+random_num = 0
+
+#毛玻璃效果: 像素点邻域内随机像素点的颜色替代当前像素点的颜色
+for y in range(rows - offsets):
+    for x in range(cols - offsets):
+        random_num = np.random.randint(0,offsets)
+        dst[y,x] = src[y + random_num,x + random_num]
+
+#显示图像
+cv2.imshow('src',src)
+cv2.imshow('dst',dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
diff --git a/blog33-Special-effects/blog33_02.py b/blog33-Special-effects/blog33_02.py
new file mode 100644
index 0000000..7439e4d
--- /dev/null
+++ b/blog33-Special-effects/blog33_02.py
@@ -0,0 +1,36 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('scenery.png', 1)
+
+#获取图像的高度和宽度
+height, width = img.shape[:2]
+
+#图像灰度处理
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+
+#创建目标图像
+dstImg = np.zeros((height,width,1),np.uint8)
+
+#浮雕特效算法：newPixel = grayCurrentPixel - grayNextPixel + 150
+for i in range(0,height):
+    for j in range(0,width-1):
+        grayCurrentPixel = int(gray[i,j])
+        grayNextPixel = int(gray[i,j+1])
+        newPixel = grayCurrentPixel - grayNextPixel + 150
+        if newPixel > 255:
+            newPixel = 255
+        if newPixel < 0:
+            newPixel = 0
+        dstImg[i,j] = newPixel
+        
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst',dstImg)
+
+#等待显示
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog33-Special-effects/blog33_03.py b/blog33-Special-effects/blog33_03.py
new file mode 100644
index 0000000..325a583
--- /dev/null
+++ b/blog33-Special-effects/blog33_03.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import numpy as np
+
+#读取原始图像
+src = cv2.imread('scenery.png')
+
+#图像灰度处理
+gray = cv2.cvtColor(src,cv2.COLOR_BGR2GRAY)
+
+#自定义卷积核
+kernel = np.array([[-1,-1,-1],[-1,10,-1],[-1,-1,-1]])
+#kernel = np.array([[0,-1,0],[-1,5,-1],[0,-1,0]])
+
+#图像浮雕效果
+output = cv2.filter2D(gray, -1, kernel)
+
+#显示图像
+cv2.imshow('Original Image', src)
+cv2.imshow('Emboss_1',output)
+
+#等待显示
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog33-Special-effects/blog33_04.py b/blog33-Special-effects/blog33_04.py
new file mode 100644
index 0000000..b488681
--- /dev/null
+++ b/blog33-Special-effects/blog33_04.py
@@ -0,0 +1,27 @@
+#coding:utf-8
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('scenery.png')
+
+#图像灰度处理
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+
+#高斯滤波降噪
+gaussian = cv2.GaussianBlur(gray, (5,5), 0)
+ 
+#Canny算子
+canny = cv2.Canny(gaussian, 50, 150)
+
+#阈值化处理
+ret, result = cv2.threshold(canny, 100, 255, cv2.THRESH_BINARY_INV)
+
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('result', result)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
diff --git a/blog33-Special-effects/blog33_05_sumiao .py b/blog33-Special-effects/blog33_05_sumiao .py
new file mode 100644
index 0000000..04d2f7d
--- /dev/null
+++ b/blog33-Special-effects/blog33_05_sumiao .py	
@@ -0,0 +1,61 @@
+#coding:utf-8
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import numpy as np
+ 
+def dodgeNaive(image, mask):
+    # determine the shape of the input image
+    width, height = image.shape[:2]
+ 
+    # prepare output argument with same size as image
+    blend = np.zeros((width, height), np.uint8)
+ 
+    for col in range(width):
+        for row in range(height):
+            # do for every pixel
+            if mask[col, row] == 255:
+                # avoid division by zero
+                blend[col, row] = 255
+            else:
+                # shift image pixel value by 8 bits
+                # divide by the inverse of the mask
+                tmp = (image[col, row] << 8) / (255 - mask)
+                # print('tmp={}'.format(tmp.shape))
+                # make sure resulting value stays within bounds
+                if tmp.any() > 255:
+                    tmp = 255
+                    blend[col, row] = tmp
+ 
+    return blend
+ 
+def dodgeV2(image, mask):
+    return cv2.divide(image, 255 - mask, scale=256)
+ 
+def burnV2(image, mask):
+    return 255 - cv2.divide(255 - image, 255 - mask, scale=256)
+ 
+def rgb_to_sketch(src_image_name, dst_image_name):
+    img_rgb = cv2.imread(src_image_name)
+    img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
+    # 读取图片时直接转换操作
+    # img_gray = cv2.imread('example.jpg', cv2.IMREAD_GRAYSCALE)
+ 
+    img_gray_inv = 255 - img_gray
+    img_blur = cv2.GaussianBlur(img_gray_inv, ksize=(21, 21),
+                                sigmaX=0, sigmaY=0)
+    img_blend = dodgeV2(img_gray, img_blur)
+ 
+    cv2.imshow('original', img_rgb)
+    cv2.imshow('gray', img_gray)
+    cv2.imshow('gray_inv', img_gray_inv)
+    cv2.imshow('gray_blur', img_blur)
+    cv2.imshow("pencil sketch", img_blend)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    cv2.imwrite(dst_image_name, img_blend)
+ 
+ 
+if __name__ == '__main__':
+    src_image_name = 'scenery.png'
+    dst_image_name = 'sketch_example.jpg'
+    rgb_to_sketch(src_image_name, dst_image_name)
diff --git a/blog33-Special-effects/blog33_06_huaijiu.py b/blog33-Special-effects/blog33_06_huaijiu.py
new file mode 100644
index 0000000..6242d02
--- /dev/null
+++ b/blog33-Special-effects/blog33_06_huaijiu.py
@@ -0,0 +1,35 @@
+#coding:utf-8
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#新建目标图像
+dst = np.zeros((rows, cols, 3), dtype="uint8")
+
+#图像怀旧特效
+for i in range(rows):
+    for j in range(cols):
+        B = 0.272*img[i,j][2] + 0.534*img[i,j][1] + 0.131*img[i,j][0]
+        G = 0.349*img[i,j][2] + 0.686*img[i,j][1] + 0.168*img[i,j][0]
+        R = 0.393*img[i,j][2] + 0.769*img[i,j][1] + 0.189*img[i,j][0]
+        if B>255:
+            B = 255
+        if G>255:
+            G = 255
+        if R>255:
+            R = 255
+        dst[i,j] = np.uint8((B, G, R))
+        
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
diff --git a/blog33-Special-effects/blog33_07_guang.py b/blog33-Special-effects/blog33_07_guang.py
new file mode 100644
index 0000000..1c36950
--- /dev/null
+++ b/blog33-Special-effects/blog33_07_guang.py
@@ -0,0 +1,55 @@
+#coding:utf-8
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import math
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#设置中心点
+centerX = rows / 2
+centerY = cols / 2
+print(centerX, centerY)
+radius = min(centerX, centerY)
+print(radius)
+
+#设置光照强度
+strength = 200
+
+#新建目标图像
+dst = np.zeros((rows, cols, 3), dtype="uint8")
+
+#图像光照特效
+for i in range(rows):
+    for j in range(cols):
+        #计算当前点到光照中心的距离(平面坐标系中两点之间的距离)
+        distance = math.pow((centerY-j), 2) + math.pow((centerX-i), 2)
+        #获取原始图像
+        B =  img[i,j][0]
+        G =  img[i,j][1]
+        R = img[i,j][2]
+        if (distance < radius * radius):
+            #按照距离大小计算增强的光照值
+            result = (int)(strength*( 1.0 - math.sqrt(distance) / radius ))
+            B = img[i,j][0] + result
+            G = img[i,j][1] + result
+            R = img[i,j][2] + result
+            #判断边界 防止越界
+            B = min(255, max(0, B))
+            G = min(255, max(0, G))
+            R = min(255, max(0, R))
+            dst[i,j] = np.uint8((B, G, R))
+        else:
+            dst[i,j] = np.uint8((B, G, R))
+        
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
diff --git a/blog33-Special-effects/blog33_08_liunian.py b/blog33-Special-effects/blog33_08_liunian.py
new file mode 100644
index 0000000..d1e6123
--- /dev/null
+++ b/blog33-Special-effects/blog33_08_liunian.py
@@ -0,0 +1,33 @@
+#coding:utf-8
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import math
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#新建目标图像
+dst = np.zeros((rows, cols, 3), dtype="uint8")
+
+#图像流年特效
+for i in range(rows):
+    for j in range(cols):
+        #B通道的数值开平方乘以参数12
+        B = math.sqrt(img[i,j][0]) * 12
+        G =  img[i,j][1]
+        R =  img[i,j][2]
+        if B>255:
+            B = 255
+        dst[i,j] = np.uint8((B, G, R))
+        
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
diff --git a/blog33-Special-effects/blog33_09_sb.py b/blog33-Special-effects/blog33_09_sb.py
new file mode 100644
index 0000000..e593f58
--- /dev/null
+++ b/blog33-Special-effects/blog33_09_sb.py
@@ -0,0 +1,74 @@
+#coding:utf-8
+#By:Eastmount CSDN 2020-12-22
+#该效果有点丑，读者可以设置透明度进一步优化，可能效果会好看些。
+import cv2
+import math
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#新建目标图像
+dst = np.zeros((rows, cols, 3), dtype="uint8")
+
+#定义水波特效参数
+wavelength = 20
+amplitude = 30
+phase = math.pi / 4
+
+#获取中心点
+centreX = 0.5
+centreY = 0.5
+radius = min(rows, cols) / 2
+
+#设置水波覆盖面积
+icentreX = cols*centreX
+icentreY = rows*centreY
+    
+#图像水波特效
+for i in range(rows):
+    for j in range(cols):
+        dx = j - icentreX
+        dy = i - icentreY
+        distance = dx*dx + dy*dy
+        
+        if distance>radius*radius:
+            x = j
+            y = i
+        else:
+            #计算水波区域
+            distance = math.sqrt(distance)
+            
+            amount = amplitude * math.sin(distance / wavelength * 2*math.pi - phase)
+            amount = amount *  (radius-distance) / radius
+            amount = amount * wavelength / (distance+0.0001)
+            x = j + dx * amount
+            y = i + dy * amount
+
+        #边界判断
+        if x<0:
+            x = 0
+        if x>=cols-1:
+            x = cols - 2
+        if y<0:
+            y = 0
+        if y>=rows-1:
+            y = rows - 2
+
+        p = x - int(x)
+        q = y - int(y)
+        
+        #图像水波赋值
+        dst[i, j, :] = (1-p)*(1-q)*img[int(y),int(x),:] + p*(1-q)*img[int(y),int(x),:]
+        + (1-p)*q*img[int(y),int(x),:] + p*q*img[int(y),int(x),:]
+        
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
diff --git a/blog33-Special-effects/blog33_10_kt.py b/blog33-Special-effects/blog33_10_kt.py
new file mode 100644
index 0000000..f73067f
--- /dev/null
+++ b/blog33-Special-effects/blog33_10_kt.py
@@ -0,0 +1,42 @@
+#coding:utf-8
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#定义双边滤波的数目
+num_bilateral = 7
+
+#用高斯金字塔降低取样
+img_color = img
+
+#双边滤波处理
+for i in range(num_bilateral):
+    img_color = cv2.bilateralFilter(img_color, d=9, sigmaColor=9, sigmaSpace=7)
+
+#灰度图像转换
+img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+#中值滤波处理
+img_blur = cv2.medianBlur(img_gray, 7)
+
+#边缘检测及自适应阈值化处理
+img_edge = cv2.adaptiveThreshold(img_blur, 255,
+                                 cv2.ADAPTIVE_THRESH_MEAN_C,
+                                 cv2.THRESH_BINARY,
+                                 blockSize=9,
+                                 C=2)
+
+#转换回彩色图像
+img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)
+
+#与运算
+img_cartoon = cv2.bitwise_and(img_color, img_edge)
+
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', img_cartoon)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog33-Special-effects/blog33_11_lj.py b/blog33-Special-effects/blog33_11_lj.py
new file mode 100644
index 0000000..89ca156
--- /dev/null
+++ b/blog33-Special-effects/blog33_11_lj.py
@@ -0,0 +1,37 @@
+#coding:utf-8
+import cv2
+import math
+import numpy as np
+
+#获取滤镜颜色
+def getBGR(img, table, i, j):
+    #获取图像颜色
+    b, g, r = img[i][j]
+    #计算标准颜色表中颜色的位置坐标
+    x = int(g/4 + int(b/32) * 64)
+    y = int(r/4 + int((b%32) / 4) * 64)
+    #返回滤镜颜色表中对应的颜色
+    return lj_map[x][y]
+
+#读取原始图像
+img = cv2.imread('nv.png')
+lj_map = cv2.imread('table.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#新建目标图像
+dst = np.zeros((rows, cols, 3), dtype="uint8")
+
+#循环设置滤镜颜色
+for i in range(rows):
+    for j in range(cols):
+        dst[i][j] = getBGR(img, lj_map, i, j)
+        
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
diff --git a/blog33-Special-effects/blog33_12_jh.py b/blog33-Special-effects/blog33_12_jh.py
new file mode 100644
index 0000000..28cf2b4
--- /dev/null
+++ b/blog33-Special-effects/blog33_12_jh.py
@@ -0,0 +1,30 @@
+#coding:utf-8
+#By:Eastmount CSDN 2020-12-22
+import cv2
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#新建目标图像
+dst = np.zeros((rows, cols, 3), dtype="uint8")
+
+#提取三个颜色通道
+(b, g, r) = cv2.split(img)
+
+#彩色图像均衡化
+bH = cv2.equalizeHist(b)
+gH = cv2.equalizeHist(g)
+rH = cv2.equalizeHist(r)
+
+#合并通道
+dst = cv2.merge((bH, gH, rH))
+
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog33-Special-effects/blog33_13_gs.py b/blog33-Special-effects/blog33_13_gs.py
new file mode 100644
index 0000000..0714353
--- /dev/null
+++ b/blog33-Special-effects/blog33_13_gs.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2020-12-22
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('scenery.png')
+source = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+ 
+#高斯滤波
+result = cv2.GaussianBlur(source, (11,11), 0)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'高斯滤波']  
+images = [source, result]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog33-Special-effects/dd.jpg b/blog33-Special-effects/dd.jpg
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index 0000000..88de9bf
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diff --git a/blog33-Special-effects/pp.png b/blog33-Special-effects/pp.png
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index 0000000..e516068
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diff --git a/blog33-Special-effects/scenery.png b/blog33-Special-effects/scenery.png
new file mode 100644
index 0000000..e38f809
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diff --git a/blog33-Special-effects/sketch_example.jpg b/blog33-Special-effects/sketch_example.jpg
new file mode 100644
index 0000000..e43f9c6
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diff --git a/blog33-Special-effects/table.png b/blog33-Special-effects/table.png
new file mode 100644
index 0000000..ad02c0a
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diff --git a/blog33-Special-effects/table2.png b/blog33-Special-effects/table2.png
new file mode 100644
index 0000000..ed814df
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diff --git a/blog34-based-opencv/Lena.png b/blog34-based-opencv/Lena.png
new file mode 100644
index 0000000..e1f2d71
Binary files /dev/null and b/blog34-based-opencv/Lena.png differ
diff --git a/blog34-based-opencv/blog34_01.py b/blog34-based-opencv/blog34_01.py
new file mode 100644
index 0000000..aaabf19
--- /dev/null
+++ b/blog34-based-opencv/blog34_01.py
@@ -0,0 +1,12 @@
+# -*- coding:utf-8 -*-
+import cv2
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#显示图像
+cv2.imshow("Demo", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_02.py b/blog34-based-opencv/blog34_02.py
new file mode 100644
index 0000000..80e9cf9
--- /dev/null
+++ b/blog34-based-opencv/blog34_02.py
@@ -0,0 +1,15 @@
+# -*- coding:utf-8 -*-
+import cv2
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#显示图像
+cv2.imshow("Demo", img)
+
+#无限期等待输入
+k=cv2.waitKey(0)
+
+#如果输入ESC按键退出
+if k==27:
+    cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_03.py b/blog34-based-opencv/blog34_03.py
new file mode 100644
index 0000000..8f0468e
--- /dev/null
+++ b/blog34-based-opencv/blog34_03.py
@@ -0,0 +1,11 @@
+# -*- coding:utf-8 -*-
+import numpy as np
+
+#定义一维数组
+a = np.array([2, 0, 1, 5, 8, 3])
+print('原始数据:', a)
+
+#输出最大、最小值及形状
+print('最小值:', a.min())
+print('最大值:', a.max())
+print('形状', a.shape)
diff --git a/blog34-based-opencv/blog34_04.py b/blog34-based-opencv/blog34_04.py
new file mode 100644
index 0000000..5468024
--- /dev/null
+++ b/blog34-based-opencv/blog34_04.py
@@ -0,0 +1,13 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#显示图像
+cv2.imshow("image", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_05.py b/blog34-based-opencv/blog34_05.py
new file mode 100644
index 0000000..1ccfa79
--- /dev/null
+++ b/blog34-based-opencv/blog34_05.py
@@ -0,0 +1,30 @@
+# coding: utf-8
+import numpy as np
+import matplotlib.pyplot as plt
+
+#生成随机数表示点的坐标
+x = np.random.randn(200)
+y = np.random.randn(200)
+
+#生成随机点的大小及颜色
+size = 50*np.random.randn(200) 
+colors = np.random.rand(200)
+
+#用来正常显示中文标签
+plt.rc('font', family='SimHei', size=13)
+
+#用来正常显示负号
+plt.rcParams['axes.unicode_minus'] = False  
+
+#绘制散点图
+plt.scatter(x, y, s=size, c=colors)
+
+#设置x、y轴名称
+plt.xlabel(u"x坐标")  
+plt.ylabel(u"y坐标")
+
+#绘制标题
+plt.title(u"Matplotlib绘制散点图")
+
+#显示图像
+plt.show()
diff --git a/blog34-based-opencv/blog34_06.py b/blog34-based-opencv/blog34_06.py
new file mode 100644
index 0000000..c92b680
--- /dev/null
+++ b/blog34-based-opencv/blog34_06.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img1 = cv2.imread('lena.png')
+img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
+
+img2 = cv2.imread('people.png')
+img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
+
+img3 = cv2.imread('flower.png')
+img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2RGB)
+
+img4 = cv2.imread('scenery.png')
+img4 = cv2.cvtColor(img4, cv2.COLOR_BGR2RGB)
+
+#显示四张图像
+titles = ['lena', 'people', 'flower', 'scenery']
+images = [img1, img2, img3, img4]
+for i in range(4):
+   plt.subplot(2, 2, i+1), plt.imshow(images[i], 'gray')
+   plt.title(titles[i])
+   plt.xticks([]),plt.yticks([])
+plt.show()
diff --git a/blog34-based-opencv/blog34_07.py b/blog34-based-opencv/blog34_07.py
new file mode 100644
index 0000000..972f212
--- /dev/null
+++ b/blog34-based-opencv/blog34_07.py
@@ -0,0 +1,16 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#绘制直线
+cv2.line(img, (0,0), (255,255), (55,255,155), 5)
+
+#显示图像
+cv2.imshow("line", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_08.py b/blog34-based-opencv/blog34_08.py
new file mode 100644
index 0000000..b9d0459
--- /dev/null
+++ b/blog34-based-opencv/blog34_08.py
@@ -0,0 +1,19 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#绘制直线
+i = 0
+while i<255:
+    cv2.line(img, (0,i), (255,255-i), (55,255,155), 5)
+    i = i + 1
+
+#显示图像
+cv2.imshow("line", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_09.py b/blog34-based-opencv/blog34_09.py
new file mode 100644
index 0000000..d229e34
--- /dev/null
+++ b/blog34-based-opencv/blog34_09.py
@@ -0,0 +1,16 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#绘制矩形
+cv2.rectangle(img, (20,20), (150,250), (255,0,0), 2)
+
+#显示图像
+cv2.imshow("rectangle", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_10.py b/blog34-based-opencv/blog34_10.py
new file mode 100644
index 0000000..e69d080
--- /dev/null
+++ b/blog34-based-opencv/blog34_10.py
@@ -0,0 +1,16 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#绘制圆形
+cv2.circle(img, (100,100), 50, (255,255,0), 4)
+
+#显示图像
+cv2.imshow("circle", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_11.py b/blog34-based-opencv/blog34_11.py
new file mode 100644
index 0000000..ebee3f5
--- /dev/null
+++ b/blog34-based-opencv/blog34_11.py
@@ -0,0 +1,20 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#绘制椭圆
+#椭圆中心(120,100) 长轴和短轴为(100,50)
+#偏转角度为20
+#圆弧起始角的角度0 圆弧终结角的角度360
+#颜色(255,0,255) 线条粗细2
+cv2.ellipse(img, (120, 100), (100, 50), 20, 0, 360, (255, 0, 255), 2)
+
+#显示图像
+cv2.imshow("ellipse", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_12.py b/blog34-based-opencv/blog34_12.py
new file mode 100644
index 0000000..ec01d60
--- /dev/null
+++ b/blog34-based-opencv/blog34_12.py
@@ -0,0 +1,16 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#绘制椭圆
+cv2.ellipse(img, (120, 120), (120, 80), 40, 0, 360, (255, 0, 255), -1)
+
+#显示图像
+cv2.imshow("ellipse", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_13.py b/blog34-based-opencv/blog34_13.py
new file mode 100644
index 0000000..91cf610
--- /dev/null
+++ b/blog34-based-opencv/blog34_13.py
@@ -0,0 +1,17 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#绘制多边形
+pts = np.array([[10,80], [120,80], [120,200], [30,250]])
+cv2.polylines(img, [pts], True, (255, 255, 255), 5)
+
+#显示图像
+cv2.imshow("ellipse", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_14.py b/blog34-based-opencv/blog34_14.py
new file mode 100644
index 0000000..0abf59f
--- /dev/null
+++ b/blog34-based-opencv/blog34_14.py
@@ -0,0 +1,17 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((512,512,3), np.uint8)
+
+#绘制多边形
+pts = np.array([[50, 190], [380, 420], [255, 50], [120, 420], [450, 190]])
+cv2.polylines(img, [pts], True, (0, 255, 255), 10)
+
+#显示图像
+cv2.imshow("ellipse", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/blog34_15.py b/blog34-based-opencv/blog34_15.py
new file mode 100644
index 0000000..a1aa265
--- /dev/null
+++ b/blog34-based-opencv/blog34_15.py
@@ -0,0 +1,18 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((256,256,3), np.uint8)
+
+#绘制文字
+font = cv2.FONT_HERSHEY_SIMPLEX
+cv2.putText(img, 'I love Python!!!',
+            (10, 100), font, 0.6, (255, 255, 0), 2)
+
+#显示图像
+cv2.imshow("polylines", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog34-based-opencv/flower.png b/blog34-based-opencv/flower.png
new file mode 100644
index 0000000..e516068
Binary files /dev/null and b/blog34-based-opencv/flower.png differ
diff --git a/blog34-based-opencv/lena-hd.png b/blog34-based-opencv/lena-hd.png
new file mode 100644
index 0000000..c0723e8
Binary files /dev/null and b/blog34-based-opencv/lena-hd.png differ
diff --git a/blog34-based-opencv/people.png b/blog34-based-opencv/people.png
new file mode 100644
index 0000000..2964f76
Binary files /dev/null and b/blog34-based-opencv/people.png differ
diff --git a/blog34-based-opencv/scenery.png b/blog34-based-opencv/scenery.png
new file mode 100644
index 0000000..e38f809
Binary files /dev/null and b/blog34-based-opencv/scenery.png differ
diff --git a/blog35-ljys/Lena.png b/blog35-ljys/Lena.png
new file mode 100644
index 0000000..e1f2d71
Binary files /dev/null and b/blog35-ljys/Lena.png differ
diff --git a/blog35-ljys/blog35_01.py b/blog35-ljys/blog35_01.py
new file mode 100644
index 0000000..63edf4e
--- /dev/null
+++ b/blog35-ljys/blog35_01.py
@@ -0,0 +1,13 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#显示图像
+cv2.imshow("Demo", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_02.py b/blog35-ljys/blog35_02.py
new file mode 100644
index 0000000..9ba136b
--- /dev/null
+++ b/blog35-ljys/blog35_02.py
@@ -0,0 +1,29 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#读取像素
+test = img[88,142]
+print("读取的像素值:", test)
+
+#修改像素
+img[88,142] = [255, 255, 255]
+print("修改后的像素值:", test)
+
+#分别获取BGR通道像素
+blue = img[88,142,0]
+print("蓝色分量", blue)
+green = img[88,142,1]
+print("绿色分量", green)
+red = img[88,142,2]
+print("红色分量", red)
+
+#显示图像
+cv2.imshow("Demo", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_03.py b/blog35-ljys/blog35_03.py
new file mode 100644
index 0000000..a7785e6
--- /dev/null
+++ b/blog35-ljys/blog35_03.py
@@ -0,0 +1,16 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#该区域设置为白色
+img[100:200, 150:250] = [255,255,255]
+
+#显示图像
+cv2.imshow("Demo", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_04.py b/blog35-ljys/blog35_04.py
new file mode 100644
index 0000000..894873c
--- /dev/null
+++ b/blog35-ljys/blog35_04.py
@@ -0,0 +1,21 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+import numpy
+
+#读取图片
+img = cv2.imread("Lena.png")
+print(type(img))
+
+#Numpy读取像素
+print(img.item(78, 100, 0))
+print(img.item(78, 100, 1))
+print(img.item(78, 100, 2))
+
+#Numpy修改像素
+img.itemset((78, 100, 0), 100)
+img.itemset((78, 100, 1), 100)
+img.itemset((78, 100, 2), 100)
+print(img.item(78, 100, 0))
+print(img.item(78, 100, 1))
+print(img.item(78, 100, 2))
diff --git a/blog35-ljys/blog35_05.py b/blog35-ljys/blog35_05.py
new file mode 100644
index 0000000..4e33c3d
--- /dev/null
+++ b/blog35-ljys/blog35_05.py
@@ -0,0 +1,22 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+import numpy as np
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#创建空图像
+emptyImage = np.zeros(img.shape, np.uint8)
+
+#复制图像
+emptyImage2 = img.copy()
+
+#显示图像
+cv2.imshow("Demo1", img)
+cv2.imshow("Demo2", emptyImage)
+cv2.imshow("Demo3", emptyImage2)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_06.py b/blog35-ljys/blog35_06.py
new file mode 100644
index 0000000..8223135
--- /dev/null
+++ b/blog35-ljys/blog35_06.py
@@ -0,0 +1,20 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+import numpy as np
+
+#读取图像
+img = cv2.imread("Lena.png")
+
+#显示图像
+cv2.imshow("Demo", img)
+
+#保存图像
+cv2.imwrite("dst1.jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), 5])  
+cv2.imwrite("dst2.jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), 100])  
+cv2.imwrite("dst3.png", img, [int(cv2.IMWRITE_PNG_COMPRESSION), 0])  
+cv2.imwrite("dst4.png", img, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_07.py b/blog35-ljys/blog35_07.py
new file mode 100644
index 0000000..ed153f4
--- /dev/null
+++ b/blog35-ljys/blog35_07.py
@@ -0,0 +1,16 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#获取图像形状
+print(img.shape)
+
+#显示图像
+cv2.imshow("Demo", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_08.py b/blog35-ljys/blog35_08.py
new file mode 100644
index 0000000..d3929d0
--- /dev/null
+++ b/blog35-ljys/blog35_08.py
@@ -0,0 +1,12 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#获取图像形状
+print(img.shape)
+
+#获取像素数目
+print(img.size)
diff --git a/blog35-ljys/blog35_09.py b/blog35-ljys/blog35_09.py
new file mode 100644
index 0000000..9b9eac8
--- /dev/null
+++ b/blog35-ljys/blog35_09.py
@@ -0,0 +1,15 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#获取图像形状
+print(img.shape)
+
+#获取像素数目
+print(img.size)
+
+#获取图像数据类型
+print(img.dtype)
diff --git a/blog35-ljys/blog35_10.py b/blog35-ljys/blog35_10.py
new file mode 100644
index 0000000..32a5def
--- /dev/null
+++ b/blog35-ljys/blog35_10.py
@@ -0,0 +1,23 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+import numpy
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#拆分通道
+b, g, r = cv2.split(img)
+
+#b = img[:, :, 0]
+#g = img[:, :, 1]
+#r = img[:, :, 2]
+
+#显示原始图像
+cv2.imshow("B", b)
+cv2.imshow("G", g)
+cv2.imshow("R", r)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_11.py b/blog35-ljys/blog35_11.py
new file mode 100644
index 0000000..d8c6170
--- /dev/null
+++ b/blog35-ljys/blog35_11.py
@@ -0,0 +1,18 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+import numpy as np
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#拆分通道
+b, g, r = cv2.split(img)
+
+#合并通道
+m = cv2.merge([b, g, r])
+cv2.imshow("Merge", m)
+           
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_12.py b/blog35-ljys/blog35_12.py
new file mode 100644
index 0000000..174bce7
--- /dev/null
+++ b/blog35-ljys/blog35_12.py
@@ -0,0 +1,23 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+import numpy as np
+
+#读取图片
+img = cv2.imread("Lena.png")
+rows, cols, chn = img.shape
+
+#拆分通道
+b = cv2.split(img)[0]
+
+#设置g、r通道为0
+g = np.zeros((rows,cols), dtype=img.dtype)
+r = np.zeros((rows,cols), dtype=img.dtype)
+
+#合并通道
+m = cv2.merge([b, g, r])
+cv2.imshow("Merge", m)
+           
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_13.py b/blog35-ljys/blog35_13.py
new file mode 100644
index 0000000..535fff0
--- /dev/null
+++ b/blog35-ljys/blog35_13.py
@@ -0,0 +1,20 @@
+#coding:utf-8
+import cv2  
+import numpy as np  
+ 
+#读取图片
+img = cv2.imread("Lena.png")
+
+#图像各像素加100
+m = np.ones(img.shape, dtype="uint8")*100
+
+#OpenCV加法运算
+result = cv2.add(img, m)
+
+#显示图像
+cv2.imshow("original", img)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_14.py b/blog35-ljys/blog35_14.py
new file mode 100644
index 0000000..7d7a81a
--- /dev/null
+++ b/blog35-ljys/blog35_14.py
@@ -0,0 +1,21 @@
+#coding:utf-8
+# By: Eastmount CSDN 2021-01-26
+import cv2  
+import numpy as np  
+ 
+#读取图片 
+img = cv2.imread("Lena.png")
+
+#图像各像素减50
+m = np.ones(img.shape, dtype="uint8")*50
+
+#OpenCV减法运算
+result = cv2.subtract(img, m)
+
+#显示图像
+cv2.imshow("original", img)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_15.py b/blog35-ljys/blog35_15.py
new file mode 100644
index 0000000..9107fa4
--- /dev/null
+++ b/blog35-ljys/blog35_15.py
@@ -0,0 +1,29 @@
+#coding:utf-8
+# By: Eastmount CSDN 2021-01-26
+import cv2  
+import numpy as np  
+ 
+#读取图片 
+img = cv2.imread("Lena.png", cv2.IMREAD_GRAYSCALE)
+
+#获取图像宽和高
+rows, cols = img.shape[:2]
+print(rows, cols)
+
+#画圆形
+circle = np.zeros((rows, cols), dtype="uint8")
+cv2.circle(circle, (int(rows/2.0), int(cols/2)), 80, 255, -1)
+print(circle.shape)
+print(img.size, circle.size)
+
+#OpenCV图像与运算
+result = cv2.bitwise_and(img, circle)
+
+#显示图像
+cv2.imshow("original", img)
+cv2.imshow("circle", circle)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_16.py b/blog35-ljys/blog35_16.py
new file mode 100644
index 0000000..98bad2d
--- /dev/null
+++ b/blog35-ljys/blog35_16.py
@@ -0,0 +1,29 @@
+#coding:utf-8
+# By: Eastmount CSDN 2021-01-26
+import cv2  
+import numpy as np  
+ 
+#读取图片 
+img = cv2.imread("Lena.png", cv2.IMREAD_GRAYSCALE)
+
+#获取图像宽和高
+rows, cols = img.shape[:2]
+print(rows, cols)
+
+#画圆形
+circle = np.zeros((rows, cols), dtype="uint8")
+cv2.circle(circle, (int(rows/2), int(cols/2)), 80, 255, -1)
+print(circle.shape)
+print(img.size, circle.size)
+
+#OpenCV图像或运算
+result = cv2.bitwise_or(img, circle)
+
+#显示图像
+cv2.imshow("original", img)
+cv2.imshow("circle", circle)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_17.py b/blog35-ljys/blog35_17.py
new file mode 100644
index 0000000..303c04f
--- /dev/null
+++ b/blog35-ljys/blog35_17.py
@@ -0,0 +1,29 @@
+#coding:utf-8
+# By: Eastmount CSDN 2021-01-26
+import cv2  
+import numpy as np  
+ 
+#读取图片 
+img = cv2.imread("Lena.png", cv2.IMREAD_GRAYSCALE)
+
+#获取图像宽和高
+rows, cols = img.shape[:2]
+print(rows, cols)
+
+#画圆形
+circle = np.zeros((rows, cols), dtype="uint8")
+cv2.circle(circle, (int(rows/2), int(cols/2)), 80, 255, -1)
+print(circle.shape)
+print(img.size, circle.size)
+
+#OpenCV图像异或运算
+result = cv2.bitwise_xor(img, circle)
+
+#显示图像
+cv2.imshow("original", img)
+cv2.imshow("circle", circle)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_18.py b/blog35-ljys/blog35_18.py
new file mode 100644
index 0000000..c877c52
--- /dev/null
+++ b/blog35-ljys/blog35_18.py
@@ -0,0 +1,17 @@
+#coding:utf-8
+import cv2  
+import numpy as np  
+ 
+#读取图片 
+img = cv2.imread("Lena.png", cv2.IMREAD_GRAYSCALE)
+
+#OpenCV图像非运算
+result = cv2.bitwise_not(img)
+
+#显示图像
+cv2.imshow("original", img)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_19.py b/blog35-ljys/blog35_19.py
new file mode 100644
index 0000000..9dd974c
--- /dev/null
+++ b/blog35-ljys/blog35_19.py
@@ -0,0 +1,22 @@
+#encoding:utf-8
+# By: Eastmount CSDN 2021-01-26
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+src1 = cv2.imread('lena-hd.png')
+src2 = cv2.imread('na.png')
+
+#图像融合
+#result = cv2.addWeighted(src1, 1, src2, 1, 0)
+result = cv2.addWeighted(src1, 0.6, src2, 0.8, 10)
+
+#显示图像
+cv2.imshow("src1", src1)
+cv2.imshow("src2", src2)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_20.py b/blog35-ljys/blog35_20.py
new file mode 100644
index 0000000..5b389c8
--- /dev/null
+++ b/blog35-ljys/blog35_20.py
@@ -0,0 +1,20 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#读取图片
+img = cv2.imread("Lena.png")
+
+#定义200×200矩阵 3对应BGR
+face = np.ones((200, 200, 3))
+
+#显示原始图像
+cv2.imshow("Demo", img)
+
+#显示ROI区域
+face = img[150:350, 150:350]
+cv2.imshow("face", face)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_21.py b/blog35-ljys/blog35_21.py
new file mode 100644
index 0000000..5029e81
--- /dev/null
+++ b/blog35-ljys/blog35_21.py
@@ -0,0 +1,22 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2
+import numpy as np
+
+#读取图片
+img = cv2.imread("Lena.png")
+test = cv2.imread("test.jpg")
+
+#定义150×150矩阵 3对应BGR
+face = np.ones((150, 150, 3))
+
+#显示ROI区域
+face = img[170:320, 170:320]
+cv2.imshow("Demo", face)
+
+test[220:370, 220:370] = face
+cv2.imshow("Result", test)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_22.py b/blog35-ljys/blog35_22.py
new file mode 100644
index 0000000..7d54666
--- /dev/null
+++ b/blog35-ljys/blog35_22.py
@@ -0,0 +1,18 @@
+#encoding:utf-8
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+src = cv2.imread('Lena.png')
+
+#图像类型转换
+result = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog35-ljys/blog35_23.py b/blog35-ljys/blog35_23.py
new file mode 100644
index 0000000..6d4d35c
--- /dev/null
+++ b/blog35-ljys/blog35_23.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+# By: Eastmount CSDN 2021-01-26
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img_BGR = cv2.imread('na.png')
+
+#BGR转换为RGB
+img_RGB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2RGB)
+
+#灰度化处理
+img_GRAY = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2GRAY)
+
+#BGR转HSV
+img_HSV = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2HSV)
+
+#BGR转YCrCb
+img_YCrCb = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YCrCb)
+
+#BGR转HLS
+img_HLS = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2HLS)
+
+#BGR转XYZ
+img_XYZ = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2XYZ)
+
+#BGR转LAB
+img_LAB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2LAB)
+
+#BGR转YUV
+img_YUV = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YUV)
+
+#调用matplotlib显示处理结果
+titles = ['BGR', 'RGB', 'GRAY', 'HSV', 'YCrCb', 'HLS', 'XYZ', 'LAB', 'YUV']  
+images = [img_BGR, img_RGB, img_GRAY, img_HSV, img_YCrCb,
+          img_HLS, img_XYZ, img_LAB, img_YUV]  
+for i in range(9):  
+   plt.subplot(3, 3, i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog35-ljys/flower.png b/blog35-ljys/flower.png
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index 0000000..e516068
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diff --git a/blog35-ljys/lena-hd.png b/blog35-ljys/lena-hd.png
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index 0000000..c0723e8
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diff --git a/blog35-ljys/na.png b/blog35-ljys/na.png
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diff --git a/blog35-ljys/scenery.png b/blog35-ljys/scenery.png
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diff --git a/blog35-ljys/test.jpg b/blog35-ljys/test.jpg
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index 0000000..8ba4069
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diff --git a/blog36-jhbh/blog36-01-py.py b/blog36-jhbh/blog36-01-py.py
new file mode 100644
index 0000000..183cd07
--- /dev/null
+++ b/blog36-jhbh/blog36-01-py.py
@@ -0,0 +1,25 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2
+import numpy as np
+
+#读取图片
+src = cv2.imread('test.bmp')
+
+#图像平移矩阵
+M = np.float32([[1, 0, 100], [0, 1, 50]])
+
+#获取原始图像列数和行数
+rows, cols = src.shape[:2]
+
+#图像平移
+result = cv2.warpAffine(src, M, (cols, rows)) 
+
+#显示图像
+cv2.imshow("original", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
diff --git a/blog36-jhbh/blog36-02-py.py b/blog36-jhbh/blog36-02-py.py
new file mode 100644
index 0000000..a2c9543
--- /dev/null
+++ b/blog36-jhbh/blog36-02-py.py
@@ -0,0 +1,35 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('test.bmp')
+image = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#图像平移
+#垂直方向 向下平移100
+M = np.float32([[1, 0, 0], [0, 1, 100]])
+img1 = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
+
+#垂直方向 向上平移100
+M = np.float32([[1, 0, 0], [0, 1, -100]])
+img2 = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
+
+#水平方向 向右平移100
+M = np.float32([[1, 0, 100], [0, 1, 0]])
+img3 = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
+
+#水平方向 向左平移100
+M = np.float32([[1, 0, -100], [0, 1, 0]])
+img4 = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
+
+#循环显示图形
+titles = [ 'Image1', 'Image2', 'Image3', 'Image4']  
+images = [img1, img2, img3, img4]  
+for i in range(4):  
+   plt.subplot(2,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog36-jhbh/blog36-03-sf.py b/blog36-jhbh/blog36-03-sf.py
new file mode 100644
index 0000000..67d43d0
--- /dev/null
+++ b/blog36-jhbh/blog36-03-sf.py
@@ -0,0 +1,19 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np  
+ 
+#读取图片
+src = cv2.imread('test.bmp')
+
+#图像缩放
+result = cv2.resize(src, (200,100))
+print(result.shape)
+
+#显示图像
+cv2.imshow("original", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog36-jhbh/blog36-04-sf.py b/blog36-jhbh/blog36-04-sf.py
new file mode 100644
index 0000000..83013af
--- /dev/null
+++ b/blog36-jhbh/blog36-04-sf.py
@@ -0,0 +1,20 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np  
+ 
+#读取图片
+src = cv2.imread('test.bmp')
+rows, cols = src.shape[:2]
+print(rows, cols)
+
+#图像缩放 dsize(列,行)
+result = cv2.resize(src, (int(cols*0.6), int(rows*1.2)))
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog36-jhbh/blog36-05-sf.py b/blog36-jhbh/blog36-05-sf.py
new file mode 100644
index 0000000..9afb521
--- /dev/null
+++ b/blog36-jhbh/blog36-05-sf.py
@@ -0,0 +1,20 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np  
+ 
+#读取图片
+src = cv2.imread('test.bmp')
+rows, cols = src.shape[:2]
+print(rows, cols)
+
+#图像缩放
+result = cv2.resize(src, None, fx=0.3, fy=0.3)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog36-jhbh/blog36-06-xz.py b/blog36-jhbh/blog36-06-xz.py
new file mode 100644
index 0000000..a717243
--- /dev/null
+++ b/blog36-jhbh/blog36-06-xz.py
@@ -0,0 +1,22 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np  
+ 
+#读取图片
+src = cv2.imread('test.bmp')
+
+#源图像的高、宽 以及通道数
+rows, cols, channel = src.shape
+
+#绕图像的中心旋转
+M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1) #旋转中心 旋转度数 scale
+rotated = cv2.warpAffine(src, M, (cols, rows))  #原始图像 旋转参数 元素图像宽高
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("rotated", rotated)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog36-jhbh/blog36-07-xz.py b/blog36-jhbh/blog36-07-xz.py
new file mode 100644
index 0000000..dc42af4
--- /dev/null
+++ b/blog36-jhbh/blog36-07-xz.py
@@ -0,0 +1,25 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np  
+ 
+#读取图片
+src = cv2.imread('test.bmp')
+
+#源图像的高、宽 以及通道数
+rows, cols, channel = src.shape
+
+#绕图像的中心旋转
+#函数参数：旋转中心 旋转度数 scale
+M = cv2.getRotationMatrix2D((cols/2, rows/2), -90, 1)
+
+#函数参数：原始图像 旋转参数 元素图像宽高
+rotated = cv2.warpAffine(src, M, (cols, rows))  
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("rotated", rotated)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog36-jhbh/blog36-08-jx.py b/blog36-jhbh/blog36-08-jx.py
new file mode 100644
index 0000000..457d39b
--- /dev/null
+++ b/blog36-jhbh/blog36-08-jx.py
@@ -0,0 +1,23 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('test.bmp')
+src = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#图像翻转
+img1 = cv2.flip(src, 0)   #参数=0以X轴为对称轴翻转 
+img2 = cv2.flip(src, 1)   #参数>0以Y轴为对称轴翻转
+img3 = cv2.flip(src, -1)  #参数<0X轴和Y轴翻转
+
+#显示图形
+titles = ['Source', 'Image1', 'Image2', 'Image3']  
+images = [src, img1, img2, img3]  
+for i in range(4):  
+   plt.subplot(2,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog36-jhbh/blog36-09-fs.py b/blog36-jhbh/blog36-09-fs.py
new file mode 100644
index 0000000..c36442e
--- /dev/null
+++ b/blog36-jhbh/blog36-09-fs.py
@@ -0,0 +1,27 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图片
+src = cv2.imread('test.bmp')
+
+#获取图像大小
+rows, cols = src.shape[:2]
+
+#设置图像仿射变换矩阵
+pos1 = np.float32([[50,50], [200,50], [50,200]])
+pos2 = np.float32([[10,100], [200,50], [100,250]])
+M = cv2.getAffineTransform(pos1, pos2)
+
+#图像仿射变换
+result = cv2.warpAffine(src, M, (cols, rows))
+
+#显示图像
+cv2.imshow("original", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog36-jhbh/blog36-10-ts.py b/blog36-jhbh/blog36-10-ts.py
new file mode 100644
index 0000000..10da445
--- /dev/null
+++ b/blog36-jhbh/blog36-10-ts.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2021-02-01
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图片
+src = cv2.imread('test01.jpg')
+
+#获取图像大小
+rows, cols = src.shape[:2]
+
+#设置图像透视变换矩阵
+pos1 = np.float32([[114, 82], [287, 156], [8, 322], [216, 333]])
+pos2 = np.float32([[0, 0], [188, 0], [0, 262], [188, 262]])
+M = cv2.getPerspectiveTransform(pos1, pos2)
+
+#图像透视变换
+result = cv2.warpPerspective(src, M, (190, 272))
+
+#显示图像
+cv2.imshow("original", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog36-jhbh/blog36-11-ts.py b/blog36-jhbh/blog36-11-ts.py
new file mode 100644
index 0000000..d1e9cd1
--- /dev/null
+++ b/blog36-jhbh/blog36-11-ts.py
@@ -0,0 +1,49 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图片
+src = cv2.imread('test01.jpg')
+
+#获取图像大小
+rows, cols = src.shape[:2]
+
+#将源图像高斯模糊
+img = cv2.GaussianBlur(src, (3,3), 0)
+#进行灰度化处理
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+
+#边缘检测（检测出图像的边缘信息）
+edges = cv2.Canny(gray,50,250,apertureSize = 3)
+cv2.imwrite("canny.jpg", edges)
+
+#通过霍夫变换得到A4纸边缘
+lines = cv2.HoughLinesP(edges,1,np.pi/180,50,minLineLength=90,maxLineGap=10)
+
+#下面输出的四个点分别为四个顶点
+for x1,y1,x2,y2 in lines[0]:
+    print((x1,y1),(x2,y2))
+for x1,y1,x2,y2 in lines[1]:
+    print((x1,y1),(x2,y2))
+
+#绘制边缘
+for x1,y1,x2,y2 in lines[0]:
+    cv2.line(gray, (x1,y1), (x2,y2), (0,0,255), 1)
+
+#根据四个顶点设置图像透视变换矩阵
+pos1 = np.float32([[114, 82], [287, 156], [8, 322], [216, 333]])
+pos2 = np.float32([[0, 0], [188, 0], [0, 262], [188, 262]])
+M = cv2.getPerspectiveTransform(pos1, pos2)
+
+#图像透视变换
+result = cv2.warpPerspective(src, M, (190, 272))
+
+#显示图像
+cv2.imshow("original", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog36-jhbh/blog36-12-all.py b/blog36-jhbh/blog36-12-all.py
new file mode 100644
index 0000000..77df00e
--- /dev/null
+++ b/blog36-jhbh/blog36-12-all.py
@@ -0,0 +1,56 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('test.bmp')
+image = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#图像平移矩阵
+M = np.float32([[1, 0, 100], [0, 1, 80]])
+rows, cols = image.shape[:2]
+img1 = cv2.warpAffine(image, M, (cols, rows))
+
+#图像缩小
+img2 = cv2.resize(image, (200,100))
+
+#图像放大
+img3 = cv2.resize(image, None, fx=1.1, fy=1.1)
+
+#绕图像的中心旋转
+#源图像的高、宽 以及通道数
+rows, cols, channel = image.shape
+#函数参数：旋转中心 旋转度数 scale
+M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1) 
+#函数参数：原始图像 旋转参数 元素图像宽高
+img4 = cv2.warpAffine(image, M, (cols, rows))
+
+#图像翻转
+img5 = cv2.flip(image, 0)   #参数=0以X轴为对称轴翻转 
+img6 = cv2.flip(image, 1)   #参数>0以Y轴为对称轴翻转
+
+#图像的仿射
+pts1 = np.float32([[50,50],[200,50],[50,200]])
+pts2 = np.float32([[10,100],[200,50],[100,250]])
+M = cv2.getAffineTransform(pts1,pts2)
+img7 = cv2.warpAffine(image, M, (rows,cols))
+
+#图像的透射
+pts1 = np.float32([[56,65],[238,52],[28,237],[239,240]])
+pts2 = np.float32([[0,0],[200,0],[0,200],[200,200]])
+M = cv2.getPerspectiveTransform(pts1,pts2)
+img8 = cv2.warpPerspective(image,M,(200,200))
+
+
+#循环显示图形
+titles = [ 'source', 'shift', 'reduction', 'enlarge', 'rotation', 'flipX', 'flipY', 'affine', 'transmission']  
+images = [image, img1, img2, img3, img4, img5, img6, img7, img8]  
+for i in range(9):  
+   plt.subplot(3, 3, i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
+
+
diff --git a/blog36-jhbh/canny.jpg b/blog36-jhbh/canny.jpg
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index 0000000..dc4d413
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diff --git a/blog36-jhbh/test.bmp b/blog36-jhbh/test.bmp
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index 0000000..3857312
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diff --git a/blog36-jhbh/test01.jpg b/blog36-jhbh/test01.jpg
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index 0000000..cb87225
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diff --git a/blog37-histogram/Figure_1.png b/blog37-histogram/Figure_1.png
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diff --git a/blog37-histogram/Figure_2.png b/blog37-histogram/Figure_2.png
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diff --git a/blog37-histogram/Figure_3.png b/blog37-histogram/Figure_3.png
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diff --git a/blog37-histogram/Figure_4.png b/blog37-histogram/Figure_4.png
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index 0000000..f3b06ba
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diff --git a/blog37-histogram/Lena.png b/blog37-histogram/Lena.png
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index 0000000..e1f2d71
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diff --git a/blog37-histogram/blog37-01.py b/blog37-histogram/blog37-01.py
new file mode 100644
index 0000000..f4e35a2
--- /dev/null
+++ b/blog37-histogram/blog37-01.py
@@ -0,0 +1,20 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+src = cv2.imread('lena.bmp')
+
+#绘制直方图
+#plt.hist(src.ravel(), 256)
+plt.hist(src.ravel(), bins=256, density=1, facecolor='green', alpha=0.75)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.show()
+
+#显示原始图像
+cv2.imshow("src", src)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog37-histogram/blog37-02.py b/blog37-histogram/blog37-02.py
new file mode 100644
index 0000000..4f3758d
--- /dev/null
+++ b/blog37-histogram/blog37-02.py
@@ -0,0 +1,29 @@
+#coding:utf-8
+# By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+src = cv2.imread('Lena.png')
+
+#获取BGR三个通道的像素值
+b, g, r = cv2.split(src)
+print(r,g,b)
+
+#绘制直方图
+plt.figure("Lena")
+#蓝色分量
+plt.hist(b.ravel(), bins=256, density=1, facecolor='b', edgecolor='b', alpha=0.75)
+#绿色分量
+plt.hist(g.ravel(), bins=256, density=1, facecolor='g', edgecolor='g', alpha=0.75)
+#红色分量
+plt.hist(r.ravel(), bins=256, density=1, facecolor='r', edgecolor='r', alpha=0.75)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.show()
+
+#显示原始图像
+cv2.imshow("src", src)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog37-histogram/blog37-03.py b/blog37-histogram/blog37-03.py
new file mode 100644
index 0000000..c17a6f3
--- /dev/null
+++ b/blog37-histogram/blog37-03.py
@@ -0,0 +1,49 @@
+#coding:utf-8
+# By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+#读取图像
+src = cv2.imread('Lena.png')
+
+#转换为RGB图像
+img_rgb = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
+
+#获取BGR三个通道的像素值
+b, g, r = cv2.split(src)
+print(r,g,b)
+
+plt.figure(figsize=(8, 6))
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#原始图像
+plt.subplot(221)
+plt.imshow(img_rgb)
+plt.axis('off')
+plt.title("(a)原图像")
+
+#绘制蓝色分量直方图
+plt.subplot(222)
+plt.hist(b.ravel(), bins=256, density=1, facecolor='b', edgecolor='b', alpha=0.75)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(b)蓝色分量直方图")
+
+#绘制绿色分量直方图
+plt.subplot(223)
+plt.hist(g.ravel(), bins=256, density=1, facecolor='g', edgecolor='g', alpha=0.75)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(c)绿色分量直方图")
+
+#绘制红色分量直方图
+plt.subplot(224)
+plt.hist(r.ravel(), bins=256, density=1, facecolor='r', edgecolor='r', alpha=0.75)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(d)红色分量直方图")
+plt.show()
diff --git a/blog37-histogram/blog37-04.py b/blog37-histogram/blog37-04.py
new file mode 100644
index 0000000..f3538a8
--- /dev/null
+++ b/blog37-histogram/blog37-04.py
@@ -0,0 +1,33 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+#读取图像
+src = cv2.imread('lena.bmp')
+
+#计算256灰度级的图像直方图
+hist = cv2.calcHist([src], [0], None, [256], [0,255])
+
+#输出直方图大小、形状、数量
+print(hist.size)
+print(hist.shape)
+print(hist)
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示原始图像和绘制的直方图
+plt.subplot(121)
+plt.imshow(src, 'gray')
+plt.axis('off')
+plt.title("(a)Lena灰度图像")
+
+plt.subplot(122)
+plt.plot(hist, color='r')
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(b)直方图曲线")
+plt.show()
diff --git a/blog37-histogram/blog37-05.py b/blog37-histogram/blog37-05.py
new file mode 100644
index 0000000..4966d23
--- /dev/null
+++ b/blog37-histogram/blog37-05.py
@@ -0,0 +1,35 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+#读取图像
+src = cv2.imread('Lena.png')
+
+#转换为RGB图像
+img_rgb = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
+
+#计算直方图
+histb = cv2.calcHist([src], [0], None, [256], [0,255])
+histg = cv2.calcHist([src], [1], None, [256], [0,255])
+histr = cv2.calcHist([src], [2], None, [256], [0,255])
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示原始图像和绘制的直方图
+plt.subplot(121)
+plt.imshow(img_rgb, 'gray')
+plt.axis('off')
+plt.title("(a)Lena原始图像")
+
+plt.subplot(122)
+plt.plot(histb, color='b')
+plt.plot(histg, color='g')
+plt.plot(histr, color='r')
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(b)直方图曲线")
+plt.show()
diff --git a/blog37-histogram/blog37-06.py b/blog37-histogram/blog37-06.py
new file mode 100644
index 0000000..d7b48b8
--- /dev/null
+++ b/blog37-histogram/blog37-06.py
@@ -0,0 +1,55 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv2.imread('yxz.png')
+
+#转换为RGB图像
+img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#设置掩膜
+mask = np.zeros(img.shape[:2], np.uint8)
+mask[100:300, 100:300] = 255
+masked_img = cv2.bitwise_and(img, img, mask=mask)
+
+#图像直方图计算
+hist_full = cv2.calcHist([img], [0], None, [256], [0,256]) #通道[0]-灰度图
+
+#图像直方图计算(含掩膜)
+hist_mask = cv2.calcHist([img], [0], mask, [256], [0,256])
+
+plt.figure(figsize=(8, 6))
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#原始图像
+plt.subplot(221)
+plt.imshow(img_rgb, 'gray')
+plt.axis('off')
+plt.title("(a)原始图像")
+
+#绘制掩膜
+plt.subplot(222)
+plt.imshow(mask, 'gray')
+plt.axis('off')
+plt.title("(b)掩膜")
+
+#绘制掩膜设置后的图像
+plt.subplot(223)
+plt.imshow(masked_img, 'gray')
+plt.axis('off')
+plt.title("(c)图像掩膜处理")
+
+#绘制直方图
+plt.subplot(224)
+plt.plot(hist_full)
+plt.plot(hist_mask)
+plt.title("(d)直方图曲线")
+plt.xlabel("x")
+plt.ylabel("y")
+plt.show()
diff --git a/blog37-histogram/blog37-07.py b/blog37-histogram/blog37-07.py
new file mode 100644
index 0000000..e383e62
--- /dev/null
+++ b/blog37-histogram/blog37-07.py
@@ -0,0 +1,46 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('lena.bmp')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+result = np.zeros((height, width), np.uint8)
+
+#图像灰度上移变换 DB=DA+50
+for i in range(height):
+    for j in range(width):
+        if (int(grayImage[i,j]+50) > 255):
+            gray = 255
+        else:
+            gray = int(grayImage[i,j]+50)
+            
+        result[i,j] = np.uint8(gray)
+
+#计算原图的直方图
+hist = cv2.calcHist([img], [0], None, [256], [0,255])
+
+#计算灰度变换的直方图
+hist_res = cv2.calcHist([result], [0], None, [256], [0,255])
+
+#原始图像
+plt.figure(figsize=(8, 6))
+plt.subplot(221), plt.imshow(img, 'gray'), plt.title("(a)"), plt.axis('off')
+
+#绘制掩膜
+plt.subplot(222), plt.plot(hist), plt.title("(b)"), plt.xlabel("x"), plt.ylabel("y")
+
+#绘制掩膜设置后的图像
+plt.subplot(223), plt.imshow(result, 'gray'), plt.title("(c)"), plt.axis('off')
+
+#绘制直方图
+plt.subplot(224), plt.plot(hist_res), plt.title("(d)"), plt.xlabel("x"), plt.ylabel("y")
+plt.show()
diff --git a/blog37-histogram/blog37-08.py b/blog37-histogram/blog37-08.py
new file mode 100644
index 0000000..a4c85b2
--- /dev/null
+++ b/blog37-histogram/blog37-08.py
@@ -0,0 +1,42 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('lena.bmp')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+result = np.zeros((height, width), np.uint8)
+
+#图像对比度减弱变换 DB=DA×0.8
+for i in range(height):
+    for j in range(width):
+        gray = int(grayImage[i,j]*0.8)
+        result[i,j] = np.uint8(gray)
+
+#计算原图的直方图
+hist = cv2.calcHist([img], [0], None, [256], [0,255])
+
+#计算灰度变换的直方图
+hist_res = cv2.calcHist([result], [0], None, [256], [0,255])
+
+#原始图像
+plt.figure(figsize=(8, 6))
+plt.subplot(221), plt.imshow(img, 'gray'), plt.title("(a)"), plt.axis('off')
+
+#绘制掩膜
+plt.subplot(222), plt.plot(hist), plt.title("(b)"), plt.xlabel("x"), plt.ylabel("y")
+
+#绘制掩膜设置后的图像
+plt.subplot(223), plt.imshow(result, 'gray'), plt.title("(c)"), plt.axis('off')
+
+#绘制直方图
+plt.subplot(224), plt.plot(hist_res), plt.title("(d)"), plt.xlabel("x"), plt.ylabel("y")
+plt.show()
diff --git a/blog37-histogram/blog37-09.py b/blog37-histogram/blog37-09.py
new file mode 100644
index 0000000..56c08ea
--- /dev/null
+++ b/blog37-histogram/blog37-09.py
@@ -0,0 +1,42 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('lena.bmp')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+result = np.zeros((height, width), np.uint8)
+
+#图像灰度反色变换 DB=255-DA
+for i in range(height):
+    for j in range(width):
+        gray = 255 - grayImage[i,j]
+        result[i,j] = np.uint8(gray)
+
+#计算原图的直方图
+hist = cv2.calcHist([img], [0], None, [256], [0,255])
+
+#计算灰度变换的直方图
+hist_res = cv2.calcHist([result], [0], None, [256], [0,255])
+
+#原始图像
+plt.figure(figsize=(8, 6))
+plt.subplot(221), plt.imshow(img, 'gray'), plt.title("(a)"), plt.axis('off')
+
+#绘制掩膜
+plt.subplot(222), plt.plot(hist), plt.title("(b)"), plt.xlabel("x"), plt.ylabel("y")
+
+#绘制掩膜设置后的图像
+plt.subplot(223), plt.imshow(result, 'gray'), plt.title("(c)"), plt.axis('off')
+
+#绘制直方图
+plt.subplot(224), plt.plot(hist_res), plt.title("(d)"), plt.xlabel("x"), plt.ylabel("y")
+plt.show()
diff --git a/blog37-histogram/blog37-10.py b/blog37-histogram/blog37-10.py
new file mode 100644
index 0000000..96adcc5
--- /dev/null
+++ b/blog37-histogram/blog37-10.py
@@ -0,0 +1,42 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('lena.bmp')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+result = np.zeros((height, width), np.uint8)
+
+#图像灰度对数变换
+for i in range(height):
+    for j in range(width):
+        gray = 42 * np.log(1.0 + grayImage[i,j])
+        result[i,j] = np.uint8(gray)
+
+#计算原图的直方图
+hist = cv2.calcHist([img], [0], None, [256], [0,255])
+
+#计算灰度变换的直方图
+hist_res = cv2.calcHist([result], [0], None, [256], [0,255])
+
+#原始图像
+plt.figure(figsize=(8, 6))
+plt.subplot(221), plt.imshow(img, 'gray'), plt.title("(a)"), plt.axis('off')
+
+#绘制原始图像直方图
+plt.subplot(222), plt.plot(hist), plt.title("(b)"), plt.xlabel("x"), plt.ylabel("y")
+
+#灰度变换后的图像
+plt.subplot(223), plt.imshow(result, 'gray'), plt.title("(c)"), plt.axis('off')
+
+#灰度变换图像的直方图
+plt.subplot(224), plt.plot(hist_res), plt.title("(d)"), plt.xlabel("x"), plt.ylabel("y")
+plt.show()
diff --git a/blog37-histogram/blog37-11.py b/blog37-histogram/blog37-11.py
new file mode 100644
index 0000000..2130fd1
--- /dev/null
+++ b/blog37-histogram/blog37-11.py
@@ -0,0 +1,34 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('lena.bmp')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#二进制阈值化处理
+r, result = cv2.threshold(grayImage, 127, 255, cv2.THRESH_BINARY)
+
+#计算原图的直方图
+hist = cv2.calcHist([img], [0], None, [256], [0,256])
+
+#计算阈值化处理的直方图
+hist_res = cv2.calcHist([result], [0], None, [256], [0,256])
+
+#原始图像
+plt.figure(figsize=(8, 6))
+plt.subplot(221), plt.imshow(img, 'gray'), plt.title("(a)"), plt.axis('off')
+
+#绘制原始图像直方图
+plt.subplot(222), plt.plot(hist), plt.title("(b)"), plt.xlabel("x"), plt.ylabel("y")
+
+#阈值化处理后的图像
+plt.subplot(223), plt.imshow(result, 'gray'), plt.title("(c)"), plt.axis('off')
+
+#阈值化处理图像的直方图
+plt.subplot(224), plt.plot(hist_res), plt.title("(d)"), plt.xlabel("x"), plt.ylabel("y")
+plt.show()
diff --git a/blog37-histogram/blog37-12.py b/blog37-histogram/blog37-12.py
new file mode 100644
index 0000000..9968dfa
--- /dev/null
+++ b/blog37-histogram/blog37-12.py
@@ -0,0 +1,26 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('Lena.png')
+
+#转换为RGB图像
+img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#图像HSV转换
+hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)
+
+#计算H-S直方图
+hist = cv2.calcHist(hsv, [0,1], None, [180,256], [0,180,0,256])
+
+#原始图像
+plt.figure(figsize=(8, 6))
+plt.subplot(121), plt.imshow(img_rgb, 'gray'), plt.title("(a)"), plt.axis('off')
+
+#绘制H-S直方图
+plt.subplot(122), plt.imshow(hist, interpolation='nearest'), plt.title("(b)")
+plt.xlabel("x"), plt.ylabel("y")
+plt.show()
diff --git a/blog37-histogram/blog37-13.py b/blog37-histogram/blog37-13.py
new file mode 100644
index 0000000..23043d7
--- /dev/null
+++ b/blog37-histogram/blog37-13.py
@@ -0,0 +1,48 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#函数: 获取图像的灰度平均值
+def fun_mean(img, height, width):
+    sum_img = 0
+    for i in range(height):
+        for j in range(width):
+            sum_img = sum_img + int(img[i,j])
+    mean = sum_img / (height * width)
+    return mean
+
+#函数: 获取中位数
+def fun_median(data):
+    length = len(data)
+    data.sort()
+    if (length % 2)== 1: 
+        z = length // 2
+        y = data[z]
+    else:
+        y = (int(data[length//2]) + int(data[length//2-1])) / 2
+    return y
+
+#读取图像
+img = cv2.imread('lena.bmp')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+
+#计算图像的灰度平均值
+mean = fun_mean(grayImage, height, width)
+print("灰度平均值：", mean)
+
+#计算图像的灰度中位数
+value = grayImage.ravel() #获取所有像素值
+median = fun_median(value)
+print("灰度中值：", median)
+
+#计算图像的灰度标准差
+std = np.std(value, ddof = 1)
+print("灰度标准差", std)
diff --git a/blog37-histogram/blog37-14.py b/blog37-histogram/blog37-14.py
new file mode 100644
index 0000000..fb08cf8
--- /dev/null
+++ b/blog37-histogram/blog37-14.py
@@ -0,0 +1,48 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-05
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#函数: 判断黑夜或白天
+def func_judge(img):
+    #获取图像高度和宽度
+    height = grayImage.shape[0]
+    width = grayImage.shape[1]
+    piexs_sum = height * width
+    dark_sum = 0  #偏暗像素个数
+    dark_prop = 0 #偏暗像素所占比例
+    
+    for i in range(height):
+        for j in range(width):
+            if img[i, j] < 50: #阈值为50
+                dark_sum += 1
+
+    #计算比例
+    print(dark_sum)
+    print(piexs_sum)
+    dark_prop = dark_sum * 1.0 / piexs_sum 
+    if dark_prop >=0.8:
+        print("This picture is dark!", dark_prop)
+    else:
+        print("This picture is bright!", dark_prop)
+               
+#读取图像
+img = cv2.imread('day.png')
+
+#转换为RGB图像
+img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#计算256灰度级的图像直方图
+hist = cv2.calcHist([grayImage], [0], None, [256], [0,255])
+
+#判断黑夜或白天
+func_judge(grayImage)
+
+#显示原始图像和绘制的直方图
+plt.subplot(121), plt.imshow(img_rgb, 'gray'), plt.axis('off'), plt.title("(a)")
+plt.subplot(122), plt.plot(hist, color='r'), plt.xlabel("x"), plt.ylabel("y"), plt.title("(b)")
+plt.show()
diff --git a/blog37-histogram/blog37-15-3d.py b/blog37-histogram/blog37-15-3d.py
new file mode 100644
index 0000000..c73ae16
--- /dev/null
+++ b/blog37-histogram/blog37-15-3d.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib.ticker import LinearLocator, FormatStrFormatter
+
+#读取图像
+img = cv.imread("yxz.png")
+img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
+imgd = np.array(img)      #image类转numpy
+
+#准备数据
+sp = img.shape
+h = int(sp[0])        #图像高度(rows)
+w = int(sp[1])        #图像宽度(colums) of image
+
+#绘图初始处理
+fig = plt.figure(figsize=(16,12))
+ax = fig.gca(projection="3d")
+
+x = np.arange(0, w, 1)
+y = np.arange(0, h, 1)
+x, y = np.meshgrid(x,y)
+z = imgd
+surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm)  
+
+#自定义z轴
+ax.set_zlim(-10, 255)
+ax.zaxis.set_major_locator(LinearLocator(10))   #设置z轴网格线的疏密
+#将z的value字符串转为float并保留2位小数
+ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) 
+
+# 设置坐标轴的label和标题
+ax.set_xlabel('x', size=15)
+ax.set_ylabel('y', size=15)
+ax.set_zlabel('z', size=15)
+ax.set_title("surface plot", weight='bold', size=20)
+
+#添加右侧的色卡条
+fig.colorbar(surf, shrink=0.6, aspect=8)  
+plt.show()
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diff --git a/blog38-ImageEnhancement/blog38-01.py b/blog38-ImageEnhancement/blog38-01.py
new file mode 100644
index 0000000..2347d27
--- /dev/null
+++ b/blog38-ImageEnhancement/blog38-01.py
@@ -0,0 +1,21 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN 2021-03-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('test.png')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#直方图均衡化处理
+result = cv2.equalizeHist(gray)
+
+#显示图像
+cv2.imshow("Input", gray)
+cv2.imshow("Result", result)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
diff --git a/blog38-ImageEnhancement/blog38-02.py b/blog38-ImageEnhancement/blog38-02.py
new file mode 100644
index 0000000..b97d123
--- /dev/null
+++ b/blog38-ImageEnhancement/blog38-02.py
@@ -0,0 +1,25 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN 2021-03-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena.bmp')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#直方图均衡化处理
+result = cv2.equalizeHist(gray)
+
+#显示图像
+plt.subplot(221)
+plt.imshow(gray, cmap=plt.cm.gray), plt.axis("off"), plt.title('(a)') 
+plt.subplot(222)
+plt.imshow(result, cmap=plt.cm.gray), plt.axis("off"), plt.title('(b)') 
+plt.subplot(223)
+plt.hist(img.ravel(), 256), plt.title('(c)') 
+plt.subplot(224)
+plt.hist(result.ravel(), 256), plt.title('(d)') 
+plt.show()
diff --git a/blog38-ImageEnhancement/blog38-03.py b/blog38-ImageEnhancement/blog38-03.py
new file mode 100644
index 0000000..4f7f7ce
--- /dev/null
+++ b/blog38-ImageEnhancement/blog38-03.py
@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN 2021-03-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图片
+img = cv2.imread('yxz.jpg')
+
+# 彩色图像均衡化 需要分解通道 对每一个通道均衡化
+(b, g, r) = cv2.split(img)
+bH = cv2.equalizeHist(b)
+gH = cv2.equalizeHist(g)
+rH = cv2.equalizeHist(r)
+
+# 合并每一个通道
+result = cv2.merge((bH, gH, rH))
+cv2.imshow("Input", img)
+cv2.imshow("Result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+#绘制直方图
+plt.figure("Hist")
+#蓝色分量
+plt.hist(bH.ravel(), bins=256, normed=1, facecolor='b', edgecolor='b')
+#绿色分量
+plt.hist(gH.ravel(), bins=256, normed=1, facecolor='g', edgecolor='g')
+#红色分量
+plt.hist(rH.ravel(), bins=256, normed=1, facecolor='r', edgecolor='r')
+plt.xlabel("x")
+plt.ylabel("y")
+plt.show()
diff --git a/blog38-ImageEnhancement/blog38-04.py b/blog38-ImageEnhancement/blog38-04.py
new file mode 100644
index 0000000..cbca170
--- /dev/null
+++ b/blog38-ImageEnhancement/blog38-04.py
@@ -0,0 +1,28 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN 2021-03-12
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena.bmp')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#局部直方图均衡化处理
+clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(10,10))
+
+#将灰度图像和局部直方图相关联, 把直方图均衡化应用到灰度图 
+result = clahe.apply(gray)
+
+#显示图像
+plt.subplot(221)
+plt.imshow(gray, cmap=plt.cm.gray), plt.axis("off"), plt.title('(a)') 
+plt.subplot(222)
+plt.imshow(result, cmap=plt.cm.gray), plt.axis("off"), plt.title('(b)') 
+plt.subplot(223)
+plt.hist(img.ravel(), 256), plt.title('(c)') 
+plt.subplot(224)
+plt.hist(result.ravel(), 256), plt.title('(d)') 
+plt.show()
diff --git a/blog38-ImageEnhancement/blog38-05.py b/blog38-ImageEnhancement/blog38-05.py
new file mode 100644
index 0000000..32ebee2
--- /dev/null
+++ b/blog38-ImageEnhancement/blog38-05.py
@@ -0,0 +1,114 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN 2021-03-12
+# 参考zmshy2128老师文章
+import cv2
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+
+#线性拉伸处理
+#去掉最大最小0.5%的像素值 线性拉伸至[0,1]
+def stretchImage(data, s=0.005, bins = 2000):   
+    ht = np.histogram(data, bins);
+    d = np.cumsum(ht[0])/float(data.size)
+    lmin = 0; lmax=bins-1
+    while lmin<bins:
+        if d[lmin]>=s:
+            break
+        lmin+=1
+    while lmax>=0:
+        if d[lmax]<=1-s:
+            break
+        lmax-=1
+    return np.clip((data-ht[1][lmin])/(ht[1][lmax]-ht[1][lmin]), 0,1)
+
+#根据半径计算权重参数矩阵
+g_para = {}
+def getPara(radius = 5):                        
+    global g_para
+    m = g_para.get(radius, None)
+    if m is not None:
+        return m
+    size = radius*2+1
+    m = np.zeros((size, size))
+    for h in range(-radius, radius+1):
+        for w in range(-radius, radius+1):
+            if h==0 and w==0:
+                continue
+            m[radius+h, radius+w] = 1.0/math.sqrt(h**2+w**2)
+    m /= m.sum()
+    g_para[radius] = m
+    return m
+
+#常规的ACE实现
+def zmIce(I, ratio=4, radius=300):                     
+    para = getPara(radius)
+    height,width = I.shape
+    
+    #Python3报错 列表append
+    """
+    #TypeError: can only concatenate list (not "range") to list
+    #增加max
+    print(range(height),type(range(height)))
+    print([0]*radius,type([0]*radius))
+    print([height-1]*radius,type([height-1]*radius))
+    zh = [0]*radius + max(range(height)) + [height-1]*radius
+    zw = [0]*radius + max(range(width))  + [width -1]*radius
+    #[0,0,0] + [0,1,2,3] + [3,3,3]
+    #[0, 0, 0, 0, 1, 2, 3, 3, 3, 3]
+    """
+
+    zh = []
+    zw = []
+    n = 0
+    while n < radius:
+        zh.append(0)
+        zw.append(0)
+        n += 1
+    for n in range(height):
+        zh.append(n)
+    for n in range(width):
+        zw.append(n)
+    n = 0
+    while n < radius:
+        zh.append(height-1)
+        zw.append(width-1)
+        n += 1
+
+    #print(zh)
+    #print(zw)
+    
+    Z = I[np.ix_(zh, zw)]
+    res = np.zeros(I.shape)
+    for h in range(radius*2+1):
+        for w in range(radius*2+1):
+            if para[h][w] == 0:
+                continue
+            res += (para[h][w] * np.clip((I-Z[h:h+height, w:w+width])*ratio, -1, 1))
+    return res
+
+#单通道ACE快速增强实现
+def zmIceFast(I, ratio, radius):
+    print(I)
+    height, width = I.shape[:2]
+    if min(height, width) <=2:
+        return np.zeros(I.shape)+0.5
+    Rs = cv2.resize(I, (int((width+1)/2), int((height+1)/2)))
+    Rf = zmIceFast(Rs, ratio, radius)             #递归调用
+    Rf = cv2.resize(Rf, (width, height))
+    Rs = cv2.resize(Rs, (width, height))
+ 
+    return Rf+zmIce(I,ratio, radius)-zmIce(Rs,ratio,radius)   
+
+#rgb三通道分别增强 ratio是对比度增强因子 radius是卷积模板半径          
+def zmIceColor(I, ratio=4, radius=3):               
+    res = np.zeros(I.shape)
+    for k in range(3):
+        res[:,:,k] = stretchImage(zmIceFast(I[:,:,k], ratio, radius))
+    return res
+
+#主函数
+if __name__ == '__main__':
+    img = cv2.imread('test02.png')
+    res = zmIceColor(img/255.0)*255
+    cv2.imwrite('Ice.jpg', res)
diff --git a/blog38-ImageEnhancement/lena.bmp b/blog38-ImageEnhancement/lena.bmp
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index 0000000..782dd7a
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diff --git a/blog38-ImageEnhancement/test.png b/blog38-ImageEnhancement/test.png
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diff --git a/blog38-ImageEnhancement/test01.png b/blog38-ImageEnhancement/test01.png
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diff --git a/blog38-ImageEnhancement/yxz.jpg b/blog38-ImageEnhancement/yxz.jpg
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index 0000000..2e6a80d
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diff --git a/blog38-ImageEnhancement/zmIce1.jpg b/blog38-ImageEnhancement/zmIce1.jpg
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index 0000000..2406613
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diff --git a/blog38-ImageEnhancement/zmIce2.jpg b/blog38-ImageEnhancement/zmIce2.jpg
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index 0000000..697ab88
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diff --git a/blog39-ImageClassification/blog39-DNN.py b/blog39-ImageClassification/blog39-DNN.py
new file mode 100644
index 0000000..4b80e19
--- /dev/null
+++ b/blog39-ImageClassification/blog39-DNN.py
@@ -0,0 +1,128 @@
+# -*- coding: utf-8 -*-
+# By: Eastmount CSDN 2021-04-01
+import os
+import cv2
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#----------------------------------------------------------------------------------
+# 第一步 图像读取及转换为像素直方图
+#----------------------------------------------------------------------------------
+
+X = []
+Y = []
+
+for i in range(0, 10):
+    #遍历文件夹，读取图片
+    for f in os.listdir("photo/%s" % i):
+        #获取图像像素
+        Images = cv2.imread("photo/%s/%s" % (i, f)) 
+        image=cv2.resize(Images,(256,256),interpolation=cv2.INTER_CUBIC)
+        hist = cv2.calcHist([image], [0,1], None, [256,256], [0.0,255.0,0.0,255.0]) 
+        X.append((hist/255).flatten())
+        Y.append(i)
+        
+X = np.array(X)
+Y = np.array(Y)
+
+#切分训练集和测试集
+X_train, X_test, y_train, y_test = train_test_split(X, Y,
+                                                    test_size=0.3, random_state=1)
+
+#----------------------------------------------------------------------------------
+# 第二步 定义神经网络函数
+#----------------------------------------------------------------------------------
+
+from sklearn.preprocessing import LabelBinarizer
+import random
+
+def logistic(x):
+    return 1 / (1 + np.exp(-x))
+
+def logistic_derivative(x):
+    return logistic(x) * (1 - logistic(x))
+
+class NeuralNetwork:
+    def predict(self, x):
+        for b, w in zip(self.biases, self.weights):
+            # 计算权重相加再加上偏向的结果
+            z = np.dot(x, w) + b
+            # 计算输出值
+            x = self.activation(z)
+        return self.classes_[np.argmax(x, axis=1)]
+    
+class BP(NeuralNetwork):
+    
+    def __init__(self,layers,batch):
+                
+        self.layers = layers
+        self.batch = batch
+        self.activation = logistic
+        self.activation_deriv = logistic_derivative
+        
+        self.num_layers = len(layers)
+        self.biases = [np.random.randn(x) for x in layers[1:]]
+        self.weights = [np.random.randn(x, y) for x, y in zip(layers[:-1], layers[1:])]
+        
+    def fit(self, X, y, learning_rate=0.1, epochs=1):
+    
+        labelbin = LabelBinarizer()
+        y = labelbin.fit_transform(y)
+        self.classes_ = labelbin.classes_
+        training_data = [(x,y) for x, y in zip(X, y)]
+        n = len(training_data)
+        for k in range(epochs):
+        #每次迭代都循环一次训练
+            #训练集乱序
+            random.shuffle(training_data)
+            batches = [training_data[k:k+self.batch] for k in range(0, n, self.batch)]
+            #批量梯度下降
+            for mini_batch in batches:
+                x = []
+                y = []
+                for a,b in mini_batch:
+                    x.append(a)
+                    y.append(b)
+                activations = [np.array(x)]
+                #向前一层一层的走
+                for b, w in zip(self.biases, self.weights):
+                    #计算激活函数的参数,计算公式：权重.dot(输入)+偏向 
+                    z = np.dot(activations[-1],w)+b 
+                    #计算输出值
+                    output = self.activation(z)
+                    #将本次输出放进输入列表 后面更新权重的时候备用
+                    activations.append(output)
+                #计算误差值
+                error = activations[-1]-np.array(y)
+                #计算输出层误差率
+                deltas = [error * self.activation_deriv(activations[-1])]
+                
+                #循环计算隐藏层的误差率 从倒数第2层开始
+                for l in range(self.num_layers-2, 0, -1):
+                    deltas.append(self.activation_deriv(activations[l]) * np.dot(deltas[-1],self.weights[l].T))
+
+                #将各层误差率顺序颠倒 准备逐层更新权重和偏向
+                deltas.reverse()
+                #更新权重和偏向
+                for j in range(self.num_layers-1):
+                    # 权重的增长量 计算公式为: 增长量 = 学习率 × (错误率.dot(输出值))
+                    delta = learning_rate/self.batch*((np.atleast_2d(activations[j].sum(axis=0)).T).dot(np.atleast_2d(deltas[j].sum(axis=0))))
+                    #更新权重
+                    self.weights[j] -= delta
+                    #偏向增加量 计算公式为: 学习率 × 错误率
+                    delta = learning_rate/self.batch * deltas[j].sum(axis=0)
+                    #更新偏向
+                    self.biases[j] -= delta
+        return self   
+
+#----------------------------------------------------------------------------------
+# 第三步 基于神经网络的图像分类处理
+#----------------------------------------------------------------------------------
+
+clf = BP([X_train.shape[1],10],10).fit(X_train,y_train,epochs=100)
+predictions_labels = clf.predict(X_test)
+print('预测结果:')
+print(predictions_labels)
+print('算法评价:')
+print(classification_report(y_test, predictions_labels))
diff --git a/blog39-ImageClassification/blog39-DTC.py b/blog39-ImageClassification/blog39-DTC.py
new file mode 100644
index 0000000..3d4881e
--- /dev/null
+++ b/blog39-ImageClassification/blog39-DTC.py
@@ -0,0 +1,97 @@
+# -*- coding: utf-8 -*-
+import os
+import cv2
+import numpy as np
+#from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#----------------------------------------------------------------------------------
+# 第一步 切分训练集和测试集
+#----------------------------------------------------------------------------------
+
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+for i in range(0, 10):
+    #遍历文件夹，读取图片
+    for f in os.listdir("photo/%s" % i):
+        #获取图像名称
+        X.append("photo//" +str(i) + "//" + str(f))
+        #获取图像类标即为文件夹名称
+        Y.append(i)
+
+X = np.array(X)
+Y = np.array(Y)
+
+#随机率为100% 选取其中的30%作为测试集
+X_train, X_test, y_train, y_test = train_test_split(X, Y,                                                   
+test_size=0.3, random_state=1)
+
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+
+#----------------------------------------------------------------------------------
+# 第二步 图像读取及转换为像素直方图
+#----------------------------------------------------------------------------------
+
+#训练集
+XX_train = []
+for i in X_train:
+    #读取图像
+    #print i
+    image = cv2.imread(i)
+    
+    #图像像素大小一致
+    img = cv2.resize(image, (256,256),
+                     interpolation=cv2.INTER_CUBIC)
+
+    #计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    #读取图像
+    #print i
+    image = cv2.imread(i)
+    
+    #图像像素大小一致
+    img = cv2.resize(image, (256,256),
+                     interpolation=cv2.INTER_CUBIC)
+
+    #计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+
+    XX_test.append(((hist/255).flatten()))
+
+#----------------------------------------------------------------------------------
+# 第三步 基于决策树的图像分类处理
+#----------------------------------------------------------------------------------
+
+from sklearn.tree import DecisionTreeClassifier
+clf = DecisionTreeClassifier().fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+
+print(u'预测结果:')
+print(predictions_labels)
+
+print(u'算法评价:')
+print((classification_report(y_test, predictions_labels)))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    #读取图像
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    #显示图像
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
diff --git a/blog39-ImageClassification/blog39-NB.py b/blog39-ImageClassification/blog39-NB.py
new file mode 100644
index 0000000..7ad7a04
--- /dev/null
+++ b/blog39-ImageClassification/blog39-NB.py
@@ -0,0 +1,97 @@
+# -*- coding: utf-8 -*-
+# By: Eastmount CSDN 2021-04-01
+import os
+import cv2
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#----------------------------------------------------------------------------------
+# 第一步 切分训练集和测试集
+#----------------------------------------------------------------------------------
+
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+for i in range(0, 10):
+    #遍历文件夹，读取图片
+    for f in os.listdir("photo/%s" % i):
+        #获取图像名称
+        X.append("photo//" +str(i) + "//" + str(f))
+        #获取图像类标即为文件夹名称
+        Y.append(i)
+
+X = np.array(X)
+Y = np.array(Y)
+
+#随机率为100% 选取其中的30%作为测试集
+X_train, X_test, y_train, y_test = train_test_split(X, Y,                                                   
+test_size=0.3, random_state=1)
+
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+
+#----------------------------------------------------------------------------------
+# 第二步 图像读取及转换为像素直方图
+#----------------------------------------------------------------------------------
+
+#训练集
+XX_train = []
+for i in X_train:
+    #读取图像
+    #print i
+    image = cv2.imread(i)
+    
+    #图像像素大小一致
+    img = cv2.resize(image, (256,256),
+                     interpolation=cv2.INTER_CUBIC)
+
+    #计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    #读取图像
+    #print i
+    image = cv2.imread(i)
+    
+    #图像像素大小一致
+    img = cv2.resize(image, (256,256),
+                     interpolation=cv2.INTER_CUBIC)
+
+    #计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+
+    XX_test.append(((hist/255).flatten()))
+
+#----------------------------------------------------------------------------------
+# 第三步 基于朴素贝叶斯的图像分类处理
+#----------------------------------------------------------------------------------
+
+from sklearn.naive_bayes import BernoulliNB
+clf = BernoulliNB().fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+
+print('预测结果:')
+print(predictions_labels)
+
+print('算法评价:')
+print(classification_report(y_test, predictions_labels))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    #读取图像
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    #显示图像
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
diff --git a/blog39-ImageClassification/blog39-knn.py b/blog39-ImageClassification/blog39-knn.py
new file mode 100644
index 0000000..07b47ef
--- /dev/null
+++ b/blog39-ImageClassification/blog39-knn.py
@@ -0,0 +1,97 @@
+# -*- coding: utf-8 -*-
+# By: Eastmount CSDN 2021-04-01
+import os
+import cv2
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#----------------------------------------------------------------------------------
+# 第一步 切分训练集和测试集
+#----------------------------------------------------------------------------------
+
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+for i in range(0, 10):
+    #遍历文件夹，读取图片
+    for f in os.listdir("photo/%s" % i):
+        #获取图像名称
+        X.append("photo//" +str(i) + "//" + str(f))
+        #获取图像类标即为文件夹名称
+        Y.append(i)
+
+X = np.array(X)
+Y = np.array(Y)
+
+#随机率为100% 选取其中的30%作为测试集
+X_train, X_test, y_train, y_test = train_test_split(X, Y,
+                                                    test_size=0.3, random_state=1)
+
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+
+#----------------------------------------------------------------------------------
+# 第二步 图像读取及转换为像素直方图
+#----------------------------------------------------------------------------------
+
+#训练集
+XX_train = []
+for i in X_train:
+    #读取图像
+    #print i
+    image = cv2.imread(i)
+    
+    #图像像素大小一致
+    img = cv2.resize(image, (256,256),
+                     interpolation=cv2.INTER_CUBIC)
+
+    #计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    #读取图像
+    #print i
+    image = cv2.imread(i)
+    
+    #图像像素大小一致
+    img = cv2.resize(image, (256,256),
+                     interpolation=cv2.INTER_CUBIC)
+
+    #计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+
+    XX_test.append(((hist/255).flatten()))
+
+#----------------------------------------------------------------------------------
+# 第三步 基于KNN的图像分类处理
+#----------------------------------------------------------------------------------
+
+from sklearn.neighbors import KNeighborsClassifier
+clf = KNeighborsClassifier(n_neighbors=11).fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+
+print('预测结果:')
+print(predictions_labels)
+
+print('算法评价:')
+print((classification_report(y_test, predictions_labels)))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    #读取图像
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    #显示图像
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
diff --git a/blog39-ImageClassification/photo.rar b/blog39-ImageClassification/photo.rar
new file mode 100644
index 0000000..70264e1
Binary files /dev/null and b/blog39-ImageClassification/photo.rar differ
diff --git a/blog40-ImageSegmentation/blog40-01-yzfg.py b/blog40-ImageSegmentation/blog40-01-yzfg.py
new file mode 100644
index 0000000..b9a2861
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-01-yzfg.py
@@ -0,0 +1,26 @@
+# -*- coding: utf-8 -*-
+# 2021-05-17 Eastmount CSDN
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img=cv2.imread('scenery.png')
+grayImage=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  
+
+#阈值化处理
+ret,thresh1=cv2.threshold(grayImage,127,255,cv2.THRESH_BINARY)  
+ret,thresh2=cv2.threshold(grayImage,127,255,cv2.THRESH_BINARY_INV)  
+ret,thresh3=cv2.threshold(grayImage,127,255,cv2.THRESH_TRUNC)  
+ret,thresh4=cv2.threshold(grayImage,127,255,cv2.THRESH_TOZERO)  
+ret,thresh5=cv2.threshold(grayImage,127,255,cv2.THRESH_TOZERO_INV)
+
+#显示结果
+titles = ['Gray Image','BINARY','BINARY_INV','TRUNC',
+'TOZERO','TOZERO_INV']  
+images = [grayImage, thresh1, thresh2, thresh3, thresh4, thresh5]  
+for i in range(6):  
+   plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog40-ImageSegmentation/blog40-02-byjc.py b/blog40-ImageSegmentation/blog40-02-byjc.py
new file mode 100644
index 0000000..b664408
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-02-byjc.py
@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+# 2021-05-17 Eastmount CSDN
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('scenery.png')
+rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#阈值处理
+ret, binary = cv2.threshold(grayImage, 127, 255, cv2.THRESH_BINARY)
+
+#Roberts算子
+kernelx = np.array([[-1,0],[0,1]], dtype=int)
+kernely = np.array([[0,-1],[1,0]], dtype=int)
+x = cv2.filter2D(binary, cv2.CV_16S, kernelx)
+y = cv2.filter2D(binary, cv2.CV_16S, kernely)
+absX = cv2.convertScaleAbs(x)     
+absY = cv2.convertScaleAbs(y)    
+Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#Prewitt算子
+kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]], dtype=int)
+kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]], dtype=int)
+x = cv2.filter2D(binary, cv2.CV_16S, kernelx)
+y = cv2.filter2D(binary, cv2.CV_16S, kernely)
+absX = cv2.convertScaleAbs(x)  
+absY = cv2.convertScaleAbs(y)    
+Prewitt = cv2.addWeighted(absX,0.5,absY,0.5,0)
+
+#Sobel算子
+x = cv2.Sobel(binary, cv2.CV_16S, 1, 0)
+y = cv2.Sobel(binary, cv2.CV_16S, 0, 1)    
+absX = cv2.convertScaleAbs(x)   
+absY = cv2.convertScaleAbs(y)    
+Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#拉普拉斯算法
+dst = cv2.Laplacian(binary, cv2.CV_16S, ksize = 3)
+Laplacian = cv2.convertScaleAbs(dst)
+
+# Scharr算子
+x = cv2.Scharr(binary, cv2.CV_32F, 1, 0) #X方向
+y = cv2.Scharr(binary, cv2.CV_32F, 0, 1) #Y方向
+absX = cv2.convertScaleAbs(x)       
+absY = cv2.convertScaleAbs(y)
+Scharr = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#Canny算子
+gaussianBlur = cv2.GaussianBlur(binary, (3,3), 0) #高斯滤波
+Canny = cv2.Canny(gaussianBlur , 50, 150) 
+
+#LOG算子
+gaussianBlur = cv2.GaussianBlur(binary, (3,3), 0) #高斯滤波
+dst = cv2.Laplacian(gaussianBlur, cv2.CV_16S, ksize = 3)
+LOG = cv2.convertScaleAbs(dst)
+
+#效果图
+titles = ['Source Image', 'Binary Image', 'Roberts Image',
+          'Prewitt Image','Sobel Image', 'Laplacian Image',
+          'Scharr Image', 'Canny Image', 'LOG Image']  
+images = [rgb_img, binary, Roberts, Prewitt,
+          Sobel, Laplacian, Scharr, Canny, LOG]  
+for i in np.arange(9):  
+   plt.subplot(3,3,i+1),plt.imshow(images[i],'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog40-ImageSegmentation/blog40-03-lk.py b/blog40-ImageSegmentation/blog40-03-lk.py
new file mode 100644
index 0000000..a896ac3
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-03-lk.py
@@ -0,0 +1,29 @@
+# -*- coding: utf-8 -*-
+# 2021-05-17 Eastmount CSDN
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('scenery.png')
+rgb_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#阈值化处理
+ret, binary = cv2.threshold(grayImage, 0, 255,
+                            cv2.THRESH_BINARY+cv2.THRESH_OTSU) 
+
+#边缘检测
+contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE,
+                                              cv2.CHAIN_APPROX_SIMPLE) 
+
+#轮廓绘制
+cv2.drawContours(img, contours, -1, (0, 255, 0), 1)
+
+#显示图像5
+cv2.imshow('gray', binary)
+cv2.imshow('res', img)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog40-ImageSegmentation/blog40-04-peoloe-get.py b/blog40-ImageSegmentation/blog40-04-peoloe-get.py
new file mode 100644
index 0000000..3b111d6
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-04-peoloe-get.py
@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# 2021-05-17 Eastmount CSDN
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('nv.png')
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#设置掩码、fgbModel、bgModel
+mask = np.zeros(img.shape[:2], np.uint8)
+bgdModel = np.zeros((1,65), np.float64)
+fgdModel = np.zeros((1,65), np.float64)
+
+#矩形坐标
+rect = (100, 100, 500, 800)
+
+#图像分割
+cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5,
+            cv2.GC_INIT_WITH_RECT)
+
+#设置新掩码：0和2做背景
+mask2 = np.where((mask==2)|(mask==0), 0, 1).astype('uint8')
+
+#显示原图
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+plt.subplot(1,2,1)
+plt.imshow(img)
+plt.title('Original Image')
+plt.xticks([]), plt.yticks([])
+
+#使用蒙板来获取前景区域
+img = img*mask2[:, :, np.newaxis]
+plt.subplot(1,2,2)
+plt.imshow(img)
+plt.title('Target Image')
+plt.colorbar()
+plt.xticks([]), plt.yticks([])
+plt.show()
+
diff --git a/blog40-ImageSegmentation/blog40-05-kmeans.py b/blog40-ImageSegmentation/blog40-05-kmeans.py
new file mode 100644
index 0000000..b6689b6
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-05-kmeans.py
@@ -0,0 +1,41 @@
+# coding: utf-8
+# 2021-05-17 Eastmount CSDN
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取原始图像灰度颜色
+img = cv2.imread('scenery.png', 0) 
+print(img.shape)
+
+#获取图像高度、宽度和深度
+rows, cols = img.shape[:]
+
+#图像二维像素转换为一维
+data = img.reshape((rows * cols, 1))
+data = np.float32(data)
+
+#定义中心 (type,max_iter,epsilon)
+criteria = (cv2.TERM_CRITERIA_EPS +
+            cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+
+#设置标签
+flags = cv2.KMEANS_RANDOM_CENTERS
+
+#K-Means聚类 聚集成4类
+compactness, labels, centers = cv2.kmeans(data, 4, None, criteria, 10, flags)
+
+#生成最终图像
+dst = labels.reshape((img.shape[0], img.shape[1]))
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = [u'原始图像', u'聚类图像']  
+images = [img, dst]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray'), 
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog40-ImageSegmentation/blog40-06-kmeans(best).py b/blog40-ImageSegmentation/blog40-06-kmeans(best).py
new file mode 100644
index 0000000..70ca92d
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-06-kmeans(best).py
@@ -0,0 +1,77 @@
+# coding: utf-8
+# 2021-05-17 Eastmount CSDN
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('scenery.png') 
+print(img.shape)
+
+#图像二维像素转换为一维
+data = img.reshape((-1,3))
+data = np.float32(data)
+
+#定义中心 (type,max_iter,epsilon)
+criteria = (cv2.TERM_CRITERIA_EPS +
+            cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+
+#设置标签
+flags = cv2.KMEANS_RANDOM_CENTERS
+
+#K-Means聚类 聚集成2类
+compactness, labels2, centers2 = cv2.kmeans(data, 2, None, criteria, 10, flags)
+
+#K-Means聚类 聚集成4类
+compactness, labels4, centers4 = cv2.kmeans(data, 4, None, criteria, 10, flags)
+
+#K-Means聚类 聚集成8类
+compactness, labels8, centers8 = cv2.kmeans(data, 8, None, criteria, 10, flags)
+
+#K-Means聚类 聚集成16类
+compactness, labels16, centers16 = cv2.kmeans(data, 16, None, criteria, 10, flags)
+
+#K-Means聚类 聚集成64类
+compactness, labels64, centers64 = cv2.kmeans(data, 64, None, criteria, 10, flags)
+
+#图像转换回uint8二维类型
+centers2 = np.uint8(centers2)
+res = centers2[labels2.flatten()]
+dst2 = res.reshape((img.shape))
+
+centers4 = np.uint8(centers4)
+res = centers4[labels4.flatten()]
+dst4 = res.reshape((img.shape))
+
+centers8 = np.uint8(centers8)
+res = centers8[labels8.flatten()]
+dst8 = res.reshape((img.shape))
+
+centers16 = np.uint8(centers16)
+res = centers16[labels16.flatten()]
+dst16 = res.reshape((img.shape))
+
+centers64 = np.uint8(centers64)
+res = centers64[labels64.flatten()]
+dst64 = res.reshape((img.shape))
+
+#图像转换为RGB显示
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+dst2 = cv2.cvtColor(dst2, cv2.COLOR_BGR2RGB)
+dst4 = cv2.cvtColor(dst4, cv2.COLOR_BGR2RGB)
+dst8 = cv2.cvtColor(dst8, cv2.COLOR_BGR2RGB)
+dst16 = cv2.cvtColor(dst16, cv2.COLOR_BGR2RGB)
+dst64 = cv2.cvtColor(dst64, cv2.COLOR_BGR2RGB)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = [u'原始图像', u'聚类图像 K=2', u'聚类图像 K=4',
+          u'聚类图像 K=8', u'聚类图像 K=16',  u'聚类图像 K=64']  
+images = [img, dst2, dst4, dst8, dst16, dst64]  
+for i in range(6):  
+   plt.subplot(2,3,i+1), plt.imshow(images[i], 'gray'), 
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog40-ImageSegmentation/blog40-07-shift.py b/blog40-ImageSegmentation/blog40-07-shift.py
new file mode 100644
index 0000000..63ec6f7
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-07-shift.py
@@ -0,0 +1,21 @@
+# coding: utf-8
+# 2021-05-17 Eastmount CSDN
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取原始图像灰度颜色
+img = cv2.imread('scenery.png') 
+
+spatialRad = 100   #空间窗口大小
+colorRad = 100     #色彩窗口大小
+maxPyrLevel = 2    #金字塔层数
+
+#图像均值漂移分割
+dst = cv2.pyrMeanShiftFiltering( img, spatialRad, colorRad, maxPyrLevel)
+
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog40-ImageSegmentation/blog40-08-shift(best).py b/blog40-ImageSegmentation/blog40-08-shift(best).py
new file mode 100644
index 0000000..5d2e45b
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-08-shift(best).py
@@ -0,0 +1,32 @@
+# coding: utf-8
+# 2021-05-17 Eastmount CSDN
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取原始图像灰度颜色
+img = cv2.imread('scenery.png') 
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#mask必须行和列都加2且必须为uint8单通道阵列
+mask = np.zeros([rows+2, cols+2], np.uint8) 
+
+spatialRad = 100 #空间窗口大小
+colorRad = 100   #色彩窗口大小
+maxPyrLevel = 2  #金字塔层数
+
+#图像均值漂移分割
+dst = cv2.pyrMeanShiftFiltering( img, spatialRad, colorRad, maxPyrLevel)
+
+#图像漫水填充处理
+cv2.floodFill(dst, mask, (30, 30), (0, 255, 255),
+              (100, 100, 100), (50, 50, 50),
+              cv2.FLOODFILL_FIXED_RANGE)
+
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog40-ImageSegmentation/blog40-09-fsl.py b/blog40-ImageSegmentation/blog40-09-fsl.py
new file mode 100644
index 0000000..a615e31
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-09-fsl.py
@@ -0,0 +1,21 @@
+# coding: utf-8
+# 2021-05-17 Eastmount CSDN
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+#读取原始图像
+img = cv2.imread('test01.png')
+
+#图像灰度化处理
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+
+#图像阈值化处理
+ret, thresh = cv2.threshold(gray, 0, 255, 
+cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('res', thresh)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog40-ImageSegmentation/blog40-10-water.py b/blog40-ImageSegmentation/blog40-10-water.py
new file mode 100644
index 0000000..a1ccc34
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-10-water.py
@@ -0,0 +1,43 @@
+# coding: utf-8
+# 2021-05-17 Eastmount CSDN
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+#读取原始图像
+img = cv2.imread('test01.png')
+
+#图像灰度化处理
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+
+#图像阈值化处理
+ret, thresh = cv2.threshold(gray, 0, 255,
+                            cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+
+#图像开运算消除噪声
+kernel = np.ones((3,3),np.uint8)
+opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
+
+#图像膨胀操作确定背景区域
+sure_bg = cv2.dilate(opening,kernel,iterations=3)
+
+#距离运算确定前景区域
+dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
+ret, sure_fg = cv2.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0)
+
+#寻找未知区域
+sure_fg = np.uint8(sure_fg)
+unknown = cv2.subtract(sure_bg, sure_fg)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = [u'原始图像', u'阈值化', u'开运算',
+          u'背景区域', u'前景区域', u'未知区域']  
+images = [img, thresh, opening, sure_bg, sure_fg, unknown]  
+for i in range(6):  
+   plt.subplot(2,3,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog40-ImageSegmentation/blog40-11-water-final.py b/blog40-ImageSegmentation/blog40-11-water-final.py
new file mode 100644
index 0000000..0b83b44
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-11-water-final.py
@@ -0,0 +1,55 @@
+# coding: utf-8
+# 2021-05-17 Eastmount CSDN
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+#读取原始图像
+img = cv2.imread('test01.png')
+
+#图像灰度化处理
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+
+#图像阈值化处理
+ret, thresh = cv2.threshold(gray, 0, 255,
+                            cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+
+#图像开运算消除噪声
+kernel = np.ones((3,3),np.uint8)
+opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
+
+#图像膨胀操作确定背景区域
+sure_bg = cv2.dilate(opening,kernel,iterations=3)
+
+#距离运算确定前景区域
+dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
+ret, sure_fg = cv2.threshold(dist_transform, 0.7*dist_transform.max(), 255, 0)
+
+#寻找未知区域
+sure_fg = np.uint8(sure_fg)
+unknown = cv2.subtract(sure_bg, sure_fg)
+
+#标记变量
+ret, markers = cv2.connectedComponents(sure_fg)
+
+#所有标签加一，以确保背景不是0而是1
+markers = markers+1
+
+#用0标记未知区域
+markers[unknown==255]=0
+
+#分水岭算法实现图像分割
+markers = cv2.watershed(img, markers)
+img[markers == -1] = [255,0,0]
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = [u'标记区域', u'图像分割']  
+images = [markers, img]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog40-ImageSegmentation/blog40-12-mstc.py b/blog40-ImageSegmentation/blog40-12-mstc.py
new file mode 100644
index 0000000..795f6ae
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-12-mstc.py
@@ -0,0 +1,30 @@
+#coding:utf-8
+# 2021-05-17 Eastmount CSDN
+import cv2
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('test.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#目标图像
+dst = img.copy()
+
+#mask必须行和列都加2且必须为uint8单通道阵列
+#mask多出来的2可以保证扫描的边界上的像素都会被处理
+mask = np.zeros([rows+2, cols+2], np.uint8)  
+
+#图像漫水填充处理
+#种子点位置(30,30) 设置颜色(0,255,255) 连通区范围设定loDiff upDiff
+#src(seed.x, seed.y) - loDiff <= src(x, y) <= src(seed.x, seed.y) +upDiff
+cv2.floodFill(dst, mask, (30, 30), (0, 255, 255),
+              (100, 100, 100), (50, 50, 50),
+              cv2.FLOODFILL_FIXED_RANGE)
+
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog40-ImageSegmentation/blog40-13-mstc(best).py b/blog40-ImageSegmentation/blog40-13-mstc(best).py
new file mode 100644
index 0000000..c2a249c
--- /dev/null
+++ b/blog40-ImageSegmentation/blog40-13-mstc(best).py
@@ -0,0 +1,104 @@
+# coding:utf-8
+# 2021-05-17 Eastmount CSDN
+import cv2
+import random
+import sys
+import numpy as np
+
+#使用说明 点击鼠标选择种子点
+help_message = '''USAGE: floodfill.py [<image>]
+Click on the image to set seed point
+Keys:
+  f - toggle floating range
+  c - toggle 4/8 connectivity
+  ESC - exit
+'''
+ 
+if __name__ == '__main__':
+
+    #输出提示文本
+    print(help_message)
+
+    #读取原始图像
+    img = cv2.imread('nv.png')
+
+    #获取图像高和宽
+    h, w = img.shape[:2]
+
+    #设置掩码 长和宽都比输入图像多两个像素点 
+    mask = np.zeros((h+2, w+2), np.uint8)
+
+    #设置种子节点和4邻接
+    seed_pt = None
+    fixed_range = True
+    connectivity = 4 
+
+    #图像漫水填充分割更新函数
+    def update(dummy=None):
+        if seed_pt is None:
+            cv2.imshow('floodfill', img)
+            return
+        
+        #建立图像副本并漫水填充
+        flooded = img.copy()
+        mask[:] = 0 #掩码初始为全0
+        lo = cv2.getTrackbarPos('lo', 'floodfill') #观察点像素邻域负差最大值
+        hi = cv2.getTrackbarPos('hi', 'floodfill') #观察点像素邻域正差最大值
+        print('lo=', lo, 'hi=', hi)
+
+        #低位比特包含连通值 4 (缺省) 或 8
+        flags = connectivity
+        
+        #考虑当前象素与种子象素之间的差（高比特也可以为0）
+        if fixed_range:
+            flags |= cv2.FLOODFILL_FIXED_RANGE
+            
+        #以白色进行漫水填充
+        cv2.floodFill(flooded, mask, seed_pt,
+                      (random.randint(0,255), random.randint(0,255),
+                       random.randint(0,255)), (lo,)*3, (hi,)*3, flags)
+
+        #选定基准点用红色圆点标出
+        cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
+        print("send_pt=", seed_pt)
+
+        #显示图像
+        cv2.imshow('floodfill', flooded)
+
+    #鼠标响应函数
+    def onmouse(event, x, y, flags, param):
+        global seed_pt #基准点
+
+        #鼠标左键响应选择漫水填充基准点
+        if flags & cv2.EVENT_FLAG_LBUTTON:
+            seed_pt = x, y
+            update()
+
+    #执行图像漫水填充分割更新操作
+    update()
+    
+    #鼠标更新操作
+    cv2.setMouseCallback('floodfill', onmouse)
+
+    #设置进度条
+    cv2.createTrackbar('lo', 'floodfill', 20, 255, update)
+    cv2.createTrackbar('hi', 'floodfill', 20, 255, update)
+
+    #按键响应操作
+    while True:
+        ch = 0xFF & cv2.waitKey()
+        #退出
+        if ch == 27:
+            break
+        #选定时flags的高位比特位0
+        #此时邻域的选定为当前像素与相邻像素的差, 联通区域会很大
+        if ch == ord('f'):
+            fixed_range = not fixed_range 
+            print('using %s range' % ('floating', 'fixed')[fixed_range])
+            update()
+        #选择4方向或则8方向种子扩散
+        if ch == ord('c'):
+            connectivity = 12-connectivity 
+            print('connectivity =', connectivity)
+            update()
+    cv2.destroyAllWindows()             
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diff --git a/blog40-ImageSegmentation/word.png b/blog40-ImageSegmentation/word.png
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diff --git a/blog41-smoth/blog41-01-blur.py b/blog41-smoth/blog41-01-blur.py
new file mode 100644
index 0000000..712202c
--- /dev/null
+++ b/blog41-smoth/blog41-01-blur.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-06-07
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#均值滤波
+result = cv2.blur(source, (10,10))
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = ['原始图像', '均值滤波']  
+images = [source, result]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog41-smoth/blog41-02-boxFilter.py b/blog41-smoth/blog41-02-boxFilter.py
new file mode 100644
index 0000000..942391d
--- /dev/null
+++ b/blog41-smoth/blog41-02-boxFilter.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-06-07
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#方框滤波
+result = cv2.boxFilter(source, -1, (3,3), normalize=0)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = ['原始图像', '方框滤波']  
+images = [source, result]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog41-smoth/blog41-03-boxFilter.py b/blog41-smoth/blog41-03-boxFilter.py
new file mode 100644
index 0000000..893e469
--- /dev/null
+++ b/blog41-smoth/blog41-03-boxFilter.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-06-07
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#方框滤波
+result = cv2.boxFilter(source, -1, (3,3), normalize=1)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = ['原始图像', '方框滤波']  
+images = [source, result]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog41-smoth/blog41-04-gauss.py b/blog41-smoth/blog41-04-gauss.py
new file mode 100644
index 0000000..f01eaea
--- /dev/null
+++ b/blog41-smoth/blog41-04-gauss.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-06-07
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#高斯滤波
+result = cv2.GaussianBlur(source, (7,7), 1.8)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = ['原始图像', '高斯滤波']  
+images = [source, result]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog41-smoth/blog41-05-median.py b/blog41-smoth/blog41-05-median.py
new file mode 100644
index 0000000..4a57151
--- /dev/null
+++ b/blog41-smoth/blog41-05-median.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-06-07
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#中值滤波
+result = cv2.medianBlur(source, 3)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = ['原始图像', '中值滤波']  
+images = [source, result]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog41-smoth/blog41-06-bilateral.py b/blog41-smoth/blog41-06-bilateral.py
new file mode 100644
index 0000000..d931158
--- /dev/null
+++ b/blog41-smoth/blog41-06-bilateral.py
@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-06-07
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('te.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#双边滤波
+result = cv2.bilateralFilter(source, 15, 150, 150)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = ['原始图像', '双边滤波']  
+images = [source, result]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog41-smoth/blog41-07-all.py b/blog41-smoth/blog41-07-all.py
new file mode 100644
index 0000000..57119f6
--- /dev/null
+++ b/blog41-smoth/blog41-07-all.py
@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-06-07
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('te.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#均值滤波
+result1 = cv2.blur(source, (5,5))
+result2 = cv2.blur(source, (10,10))
+
+#方框滤波
+result3 = cv2.boxFilter(source, -1, (5,5), normalize=1)
+result4 = cv2.boxFilter(source, -1, (2,2), normalize=0)
+
+#高斯滤波
+result5 = cv2.GaussianBlur(source, (3,3), 0)
+result6 = cv2.GaussianBlur(source, (15,15), 0)
+
+#中值滤波
+result7 = cv2.medianBlur(source, 3)
+
+#高斯双边滤波
+result8 =cv2.bilateralFilter(source, 15, 150, 150)
+
+#显示图形
+titles = ['Source', 'Blur 5*5', 'Blur 10*10', 'BoxFilter 5*5',
+          'BoxFilter 2*2', 'GaussianBlur 3*3', 'GaussianBlur 15*15',
+          'medianBlur', 'bilateralFilter']  
+images = [source, result1, result2, result3,
+          result4, result5, result6, result7, result8]  
+for i in range(9):  
+   plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog41-smoth/blog41-08-all2.py b/blog41-smoth/blog41-08-all2.py
new file mode 100644
index 0000000..25f61d9
--- /dev/null
+++ b/blog41-smoth/blog41-08-all2.py
@@ -0,0 +1,30 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-06-07
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('test01_yn.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#中值滤波
+result1 = cv2.medianBlur(source, 3)
+
+#高斯双边滤波
+result2 =cv2.bilateralFilter(source, 15, 150, 150)
+
+#均值迁移
+result3 = cv2.pyrMeanShiftFiltering(source, 20, 50)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = ['原始图像',  '中值滤波', '双边滤波', '均值迁移']  
+images = [source, result1, result2, result3]  
+for i in range(4):  
+   plt.subplot(2,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog41-smoth/blog41-09-noise.py b/blog41-smoth/blog41-09-noise.py
new file mode 100644
index 0000000..3cf618e
--- /dev/null
+++ b/blog41-smoth/blog41-09-noise.py
@@ -0,0 +1,19 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#读取图片
+img = cv2.imread("yn.png", cv2.IMREAD_UNCHANGED)
+rows, cols, chn = img.shape
+
+#加噪声
+for i in range(5000):    
+    x = np.random.randint(0, rows) 
+    y = np.random.randint(0, cols)    
+    img[x,y,:] = 255
+
+cv2.imshow("noise", img)
+           
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog41-smoth/lena-yt.png b/blog41-smoth/lena-yt.png
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diff --git a/blog41-smoth/testyxz.jpg b/blog41-smoth/testyxz.jpg
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diff --git a/blog42-ruihua/Lena.png b/blog42-ruihua/Lena.png
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diff --git a/blog42-ruihua/blog42-01-roberts.py b/blog42-ruihua/blog42-01-roberts.py
new file mode 100644
index 0000000..fb43ed2
--- /dev/null
+++ b/blog42-ruihua/blog42-01-roberts.py
@@ -0,0 +1,34 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#Roberts算子
+kernelx = np.array([[-1,0],[0,1]], dtype=int)
+kernely = np.array([[0,-1],[1,0]], dtype=int)
+x = cv2.filter2D(grayImage, cv2.CV_16S, kernelx)
+y = cv2.filter2D(grayImage, cv2.CV_16S, kernely)
+#转uint8 
+absX = cv2.convertScaleAbs(x)      
+absY = cv2.convertScaleAbs(y)    
+Roberts = cv2.addWeighted(absX,0.5,absY,0.5,0)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'Roberts算子']  
+images = [lenna_img, Roberts]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog42-ruihua/blog42-02-prewitt.py b/blog42-ruihua/blog42-02-prewitt.py
new file mode 100644
index 0000000..7c4a357
--- /dev/null
+++ b/blog42-ruihua/blog42-02-prewitt.py
@@ -0,0 +1,34 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#Prewitt算子
+kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]], dtype=int)
+kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]], dtype=int)
+x = cv2.filter2D(grayImage, cv2.CV_16S, kernelx)
+y = cv2.filter2D(grayImage, cv2.CV_16S, kernely)
+#转uint8
+absX = cv2.convertScaleAbs(x)       
+absY = cv2.convertScaleAbs(y)    
+Prewitt = cv2.addWeighted(absX,0.5,absY,0.5,0)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'Prewitt算子']  
+images = [lenna_img, Prewitt]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog42-ruihua/blog42-03-sobel.py b/blog42-ruihua/blog42-03-sobel.py
new file mode 100644
index 0000000..15d02cf
--- /dev/null
+++ b/blog42-ruihua/blog42-03-sobel.py
@@ -0,0 +1,31 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#Sobel算子
+x = cv2.Sobel(grayImage, cv2.CV_16S, 1, 0) #对x求一阶导
+y = cv2.Sobel(grayImage, cv2.CV_16S, 0, 1) #对y求一阶导
+absX = cv2.convertScaleAbs(x)      
+absY = cv2.convertScaleAbs(y)    
+Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'Sobel算子']  
+images = [lenna_img, Sobel]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog42-ruihua/blog42-04-laplacian.py b/blog42-ruihua/blog42-04-laplacian.py
new file mode 100644
index 0000000..097d71c
--- /dev/null
+++ b/blog42-ruihua/blog42-04-laplacian.py
@@ -0,0 +1,28 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#拉普拉斯算法
+dst = cv2.Laplacian(grayImage, cv2.CV_16S, ksize = 3)
+Laplacian = cv2.convertScaleAbs(dst) 
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'Laplacian算子']  
+images = [lenna_img, Laplacian]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog42-ruihua/blog42-05-laplacian.py b/blog42-ruihua/blog42-05-laplacian.py
new file mode 100644
index 0000000..fe687f2
--- /dev/null
+++ b/blog42-ruihua/blog42-05-laplacian.py
@@ -0,0 +1,57 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#高斯滤波
+gaussianBlur = cv2.GaussianBlur(grayImage, (3,3), 0)
+
+#阈值处理
+ret, binary = cv2.threshold(gaussianBlur, 127, 255, cv2.THRESH_BINARY)
+
+#Roberts算子
+kernelx = np.array([[-1,0],[0,1]], dtype=int)
+kernely = np.array([[0,-1],[1,0]], dtype=int)
+x = cv2.filter2D(binary, cv2.CV_16S, kernelx)
+y = cv2.filter2D(binary, cv2.CV_16S, kernely)
+absX = cv2.convertScaleAbs(x)     
+absY = cv2.convertScaleAbs(y)    
+Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#Prewitt算子
+kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]], dtype=int)
+kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]], dtype=int)
+x = cv2.filter2D(binary, cv2.CV_16S, kernelx)
+y = cv2.filter2D(binary, cv2.CV_16S, kernely)
+absX = cv2.convertScaleAbs(x)  
+absY = cv2.convertScaleAbs(y)    
+Prewitt = cv2.addWeighted(absX,0.5,absY,0.5,0)
+
+#Sobel算子
+x = cv2.Sobel(binary, cv2.CV_16S, 1, 0)
+y = cv2.Sobel(binary, cv2.CV_16S, 0, 1)    
+absX = cv2.convertScaleAbs(x)   
+absY = cv2.convertScaleAbs(y)    
+Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#拉普拉斯算法
+dst = cv2.Laplacian(binary, cv2.CV_16S, ksize = 3)
+Laplacian = cv2.convertScaleAbs(dst) 
+
+#效果图
+titles = ['Source Image', 'Binary Image', 'Roberts Image',
+          'Prewitt Image','Sobel Image', 'Laplacian Image']  
+images = [lenna_img, binary, Roberts, Prewitt, Sobel, Laplacian]  
+for i in np.arange(6):  
+   plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog42-ruihua/blog42-06-scharr.py b/blog42-ruihua/blog42-06-scharr.py
new file mode 100644
index 0000000..77c2889
--- /dev/null
+++ b/blog42-ruihua/blog42-06-scharr.py
@@ -0,0 +1,31 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+# Scharr算子
+x = cv2.Scharr(grayImage, cv2.CV_32F, 1, 0) #X方向
+y = cv2.Scharr(grayImage, cv2.CV_32F, 0, 1) #Y方向
+absX = cv2.convertScaleAbs(x)       
+absY = cv2.convertScaleAbs(y)
+Scharr = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'Scharr算子']  
+images = [lenna_img, Scharr]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog42-ruihua/blog42-07-canny.py b/blog42-ruihua/blog42-07-canny.py
new file mode 100644
index 0000000..d5405c8
--- /dev/null
+++ b/blog42-ruihua/blog42-07-canny.py
@@ -0,0 +1,30 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#高斯滤波降噪
+gaussian = cv2.GaussianBlur(grayImage, (3,3), 0)
+ 
+#Canny算子
+Canny = cv2.Canny(gaussian, 50, 150) 
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'Canny算子']  
+images = [lenna_img, Canny]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog42-ruihua/blog42-08-LOG.py b/blog42-ruihua/blog42-08-LOG.py
new file mode 100644
index 0000000..b2e8ae3
--- /dev/null
+++ b/blog42-ruihua/blog42-08-LOG.py
@@ -0,0 +1,31 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图像
+img = cv2.imread('lena.png')
+lenna_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#先通过高斯滤波降噪
+gaussian = cv2.GaussianBlur(grayImage, (3,3), 0)
+ 
+#再通过拉普拉斯算子做边缘检测
+dst = cv2.Laplacian(gaussian, cv2.CV_16S, ksize = 3)
+LOG = cv2.convertScaleAbs(dst)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图形
+titles = [u'原始图像', u'LOG算子']  
+images = [lenna_img, LOG]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog42-ruihua/blog42-09-all.py b/blog42-ruihua/blog42-09-all.py
new file mode 100644
index 0000000..e221e1d
--- /dev/null
+++ b/blog42-ruihua/blog42-09-all.py
@@ -0,0 +1,79 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('nv.png')
+lenna_img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#高斯滤波
+gaussianBlur = cv2.GaussianBlur(grayImage, (3,3), 0)
+
+#阈值处理
+ret, binary = cv2.threshold(gaussianBlur, 127, 255, cv2.THRESH_BINARY)
+
+#Roberts算子
+kernelx = np.array([[-1,0],[0,1]], dtype=int)
+kernely = np.array([[0,-1],[1,0]], dtype=int)
+x = cv2.filter2D(binary, cv2.CV_16S, kernelx)
+y = cv2.filter2D(binary, cv2.CV_16S, kernely)
+absX = cv2.convertScaleAbs(x)     
+absY = cv2.convertScaleAbs(y)    
+Roberts = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#Prewitt算子
+kernelx = np.array([[1,1,1],[0,0,0],[-1,-1,-1]], dtype=int)
+kernely = np.array([[-1,0,1],[-1,0,1],[-1,0,1]], dtype=int)
+x = cv2.filter2D(binary, cv2.CV_16S, kernelx)
+y = cv2.filter2D(binary, cv2.CV_16S, kernely)
+absX = cv2.convertScaleAbs(x)  
+absY = cv2.convertScaleAbs(y)    
+Prewitt = cv2.addWeighted(absX,0.5,absY,0.5,0)
+
+#Sobel算子
+x = cv2.Sobel(binary, cv2.CV_16S, 1, 0)
+y = cv2.Sobel(binary, cv2.CV_16S, 0, 1)    
+absX = cv2.convertScaleAbs(x)   
+absY = cv2.convertScaleAbs(y)    
+Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#拉普拉斯算法
+dst = cv2.Laplacian(binary, cv2.CV_16S, ksize = 3)
+Laplacian = cv2.convertScaleAbs(dst)
+
+
+# Scharr算子
+x = cv2.Scharr(gaussianBlur, cv2.CV_32F, 1, 0) #X方向
+y = cv2.Scharr(gaussianBlur, cv2.CV_32F, 0, 1) #Y方向
+absX = cv2.convertScaleAbs(x)       
+absY = cv2.convertScaleAbs(y)
+Scharr = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+
+#Canny算子
+Canny = cv2.Canny(gaussianBlur, 50, 150)
+
+#先通过高斯滤波降噪
+gaussian = cv2.GaussianBlur(grayImage, (3,3), 0)
+ 
+#再通过拉普拉斯算子做边缘检测
+dst = cv2.Laplacian(gaussian, cv2.CV_16S, ksize = 3)
+LOG = cv2.convertScaleAbs(dst)
+
+
+#效果图
+titles = ['Source Image', 'Binary Image', 'Roberts Image',
+          'Prewitt Image','Sobel Image', 'Laplacian Image',
+          'Scharr Image', 'Canny Image', 'LOG Image']  
+images = [lenna_img, binary, Roberts,
+          Prewitt, Sobel, Laplacian,
+          Scharr, Canny, LOG]  
+for i in np.arange(9):  
+   plt.subplot(3,3,i+1),plt.imshow(images[i],'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
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diff --git a/blog43-morphology/blog43_01_erode.py b/blog43-morphology/blog43_01_erode.py
new file mode 100644
index 0000000..d6c2953
--- /dev/null
+++ b/blog43-morphology/blog43_01_erode.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-30
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('test01.jpg', cv2.IMREAD_UNCHANGED)
+
+#设置卷积核
+kernel = np.ones((5,5), np.uint8)
+
+#图像腐蚀处理
+erosion = cv2.erode(src, kernel, iterations=9)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", erosion)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog43-morphology/blog43_02_dilate.py b/blog43-morphology/blog43_02_dilate.py
new file mode 100644
index 0000000..fb18e3d
--- /dev/null
+++ b/blog43-morphology/blog43_02_dilate.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-30
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('test02.png', cv2.IMREAD_UNCHANGED)
+
+#设置卷积核
+kernel = np.ones((5,5), np.uint8)
+
+#图像膨胀处理
+erosion = cv2.dilate(src, kernel)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", erosion)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog43-morphology/blog43_03_open.py b/blog43-morphology/blog43_03_open.py
new file mode 100644
index 0000000..3b67cab
--- /dev/null
+++ b/blog43-morphology/blog43_03_open.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-30
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('test01.png', cv2.IMREAD_UNCHANGED)
+
+#设置卷积核
+kernel = np.ones((5,5), np.uint8)
+
+#图像开运算
+result = cv2.morphologyEx(src, cv2.MORPH_OPEN, kernel)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog43-morphology/blog43_04_open.py b/blog43-morphology/blog43_04_open.py
new file mode 100644
index 0000000..e9a95c4
--- /dev/null
+++ b/blog43-morphology/blog43_04_open.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-30
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('test01.png', cv2.IMREAD_UNCHANGED)
+
+#设置卷积核
+kernel = np.ones((10,10), np.uint8) 
+
+#图像开运算
+result = cv2.morphologyEx(src, cv2.MORPH_OPEN, kernel)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog43-morphology/blog43_05_close.py b/blog43-morphology/blog43_05_close.py
new file mode 100644
index 0000000..799e88b
--- /dev/null
+++ b/blog43-morphology/blog43_05_close.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-30
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('test03.png', cv2.IMREAD_UNCHANGED)
+
+#设置卷积核
+kernel = np.ones((10,10), np.uint8)
+
+#图像闭运算
+result = cv2.morphologyEx(src, cv2.MORPH_CLOSE, kernel)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog43-morphology/blog43_06_gradient.py b/blog43-morphology/blog43_06_gradient.py
new file mode 100644
index 0000000..d2d66fc
--- /dev/null
+++ b/blog43-morphology/blog43_06_gradient.py
@@ -0,0 +1,22 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-30
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('test04.png', cv2.IMREAD_UNCHANGED)
+
+#设置卷积核
+kernel = np.ones((10,10), np.uint8)
+
+#图像梯度运算
+result = cv2.morphologyEx(src, cv2.MORPH_GRADIENT, kernel)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
diff --git a/blog43-morphology/blog43_07_tophat.py b/blog43-morphology/blog43_07_tophat.py
new file mode 100644
index 0000000..7a84f34
--- /dev/null
+++ b/blog43-morphology/blog43_07_tophat.py
@@ -0,0 +1,22 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-30
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('test06.png', cv2.IMREAD_UNCHANGED)
+
+#设置卷积核
+kernel = np.ones((10,10), np.uint8)
+
+#图像顶帽运算
+result = cv2.morphologyEx(src, cv2.MORPH_TOPHAT, kernel)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
diff --git a/blog43-morphology/blog43_08_3D.py b/blog43-morphology/blog43_08_3D.py
new file mode 100644
index 0000000..cc49f99
--- /dev/null
+++ b/blog43-morphology/blog43_08_3D.py
@@ -0,0 +1,50 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2021-07-30
+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+from matplotlib import cm
+from matplotlib.ticker import LinearLocator, FormatStrFormatter
+
+#读取图像
+img = cv.imread("test06.png")
+img = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
+
+#图像黑帽运算
+kernel = np.ones((10,10), np.uint8)
+result = cv.morphologyEx(img, cv.MORPH_BLACKHAT, kernel)
+
+#image类转numpy
+imgd = np.array(result)   #img 原图  
+
+#准备数据
+sp = result.shape
+h = int(sp[0])        #图像高度(rows)
+w = int(sp[1])       #图像宽度(colums) of image
+
+#绘图初始处理
+fig = plt.figure(figsize=(8,6))
+ax = fig.gca(projection="3d")
+
+x = np.arange(0, w, 1)
+y = np.arange(0, h, 1)
+x, y = np.meshgrid(x,y)
+z = imgd
+surf = ax.plot_surface(x, y, z, cmap=cm.coolwarm)  
+
+#自定义z轴
+ax.set_zlim(-10, 255)
+ax.zaxis.set_major_locator(LinearLocator(10))   #设置z轴网格线的疏密
+#将z的value字符串转为float并保留2位小数
+ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f')) 
+
+# 设置坐标轴的label和标题
+ax.set_xlabel('x', size=15)
+ax.set_ylabel('y', size=15)
+ax.set_zlabel('z', size=15)
+ax.set_title("surface plot", weight='bold', size=20)
+
+#添加右侧的色卡条
+fig.colorbar(surf, shrink=0.6, aspect=8)  
+plt.show()
diff --git a/blog43-morphology/blog43_09_blackhat.py b/blog43-morphology/blog43_09_blackhat.py
new file mode 100644
index 0000000..30c263f
--- /dev/null
+++ b/blog43-morphology/blog43_09_blackhat.py
@@ -0,0 +1,21 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-07-30
+import cv2  
+import numpy as np  
+
+#读取图片
+src = cv2.imread('test06.png', cv2.IMREAD_UNCHANGED)
+
+#设置卷积核
+kernel = np.ones((10, 10), np.uint8)
+
+#图像黑帽运算
+result = cv2.morphologyEx(src, cv2.MORPH_BLACKHAT, kernel)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", result)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog43-morphology/test01.jpg b/blog43-morphology/test01.jpg
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diff --git a/blog44-ImageProcessingSummary/2019.png b/blog44-ImageProcessingSummary/2019.png
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diff --git a/blog44-ImageProcessingSummary/ImageProcessing_01_show.py b/blog44-ImageProcessingSummary/ImageProcessing_01_show.py
new file mode 100644
index 0000000..98638b4
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_01_show.py
@@ -0,0 +1,27 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img1 = cv2.imread('lena.png')
+img1 = cv2.cvtColor(img1, cv2.COLOR_BGR2RGB)
+
+img2 = cv2.imread('people.png')
+img2 = cv2.cvtColor(img2, cv2.COLOR_BGR2RGB)
+
+img3 = cv2.imread('flower.png')
+img3 = cv2.cvtColor(img3, cv2.COLOR_BGR2RGB)
+
+img4 = cv2.imread('scenery.png')
+img4 = cv2.cvtColor(img4, cv2.COLOR_BGR2RGB)
+
+#显示四张图像
+titles = ['lena', 'people', 'flower', 'scenery']
+images = [img1, img2, img3, img4]
+for i in range(4):
+   plt.subplot(2, 2, i+1), plt.imshow(images[i], 'gray')
+   plt.title(titles[i])
+   plt.xticks([]),plt.yticks([])
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_02_JH.py b/blog44-ImageProcessingSummary/ImageProcessing_02_JH.py
new file mode 100644
index 0000000..8c75d71
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_02_JH.py
@@ -0,0 +1,40 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN
+import cv2
+import numpy as np
+
+#创建黑色图像
+img = np.zeros((512,512,3), np.uint8)
+
+#(1)绘制直线：图像、起点坐标、终点坐标、颜色、粗细
+cv2.line(img, (0,0), (512,512), (55,255,155), 5)
+
+#(2)绘制矩形：图像、左上角坐标、右下角坐标、颜色、粗细
+cv2.rectangle(img, (20,300), (350,450), (255,0,0), 2)
+
+#(3)绘制圆形：图像、圆心坐标、半径、颜色、粗细\填充
+cv2.circle(img, (400,400), 50, (255,255,0), -1)
+
+#(4)绘制椭圆：图像、圆心坐标、长轴和短轴、偏转角度20
+# 圆弧起始角的角度、圆弧终结角的角度、颜色、线条粗细
+cv2.ellipse(img, (320, 100), (100, 50), 20, 0, 360, (255, 0, 255), 2)
+
+#(5)绘制多边形：图像、多边形曲线阵列、是否闭合、颜色、粗细
+pts = np.array([[10,80], [120,80], [120,200], [30,250]])
+cv2.polylines(img, [pts], True, (255, 255, 255), 5)
+
+#(6)绘制多边形
+pts = np.array([[50, 190], [380, 420], [255, 50], [120, 420], [450, 190]])
+cv2.polylines(img, [pts], True, (0, 255, 255), 1)
+
+#(7)绘制文字
+font = cv2.FONT_HERSHEY_SIMPLEX
+cv2.putText(img, 'I love Python!!! By:Eatmount CSDN',
+            (10, 500), font, 0.6, (255, 255, 0), 2)
+
+#显示图像
+cv2.imshow("img", img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_03_split.py b/blog44-ImageProcessingSummary/ImageProcessing_03_split.py
new file mode 100644
index 0000000..0e9d932
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_03_split.py
@@ -0,0 +1,32 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-08-19
+import cv2
+import numpy as np
+
+#读取图片
+img = cv2.imread("Lena.png")
+rows, cols, chn = img.shape
+print(rows, cols, chn)
+cv2.imshow("img", img)
+
+#(1)拆分通道
+b, g, r = cv2.split(img)
+cv2.imshow("B", b)
+cv2.imshow("G", g)
+cv2.imshow("R", r)
+
+#(2)合并通道
+m = cv2.merge([b, g, r])
+cv2.imshow("Merge", m)
+
+#(3)显示蓝色通道
+b_ = cv2.split(img)[0]
+g_ = np.zeros((rows,cols), dtype=img.dtype) #设置g、r通道为0
+r_ = np.zeros((rows,cols), dtype=img.dtype)
+m = cv2.merge([b_, g_, r_])
+cv2.imshow("MergeBlue", m)
+cv2.imwrite("MergeBlue.png", m)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_04_YS.py b/blog44-ImageProcessingSummary/ImageProcessing_04_YS.py
new file mode 100644
index 0000000..66172f5
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_04_YS.py
@@ -0,0 +1,55 @@
+# -*- coding:utf-8 -*-
+# By: Eastmount CSDN 2021-08-19
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+ 
+#读取图片
+src = cv2.imread("Lena.png")
+
+#获取图像宽和高
+rows, cols = src.shape[:2]
+
+#BGR转换为RGB
+img = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
+
+#(1)OpenCV加法运算
+m = np.ones(img.shape, dtype="uint8")*100 #图像各像素加100
+result1 = cv2.add(img, m)
+
+#(2)OpenCV减法运算
+m = np.ones(img.shape, dtype="uint8")*50  #图像各像素减50
+result2 = cv2.subtract(img, m)
+
+#(3)画圆形
+circle = np.zeros((rows, cols, 3), dtype="uint8")
+cv2.circle(circle, (int(rows/2.0), int(cols/2)), 100, (255,0,0), -1)
+print(circle.shape,img.size, circle.size)
+
+#(4)OpenCV图像与运算
+result4 = cv2.bitwise_and(img, circle)
+
+#(5)OpenCV图像或运算
+result5 = cv2.bitwise_or(img, circle)
+
+#(6)OpenCV图像异或运算
+result6 = cv2.bitwise_xor(img, circle)
+
+#(7)OpenCV图像非运算
+result7 = cv2.bitwise_not(img)
+
+#解决中文显示问题
+plt.rcParams['font.sans-serif'] = ['KaiTi']  #指定默认字体
+plt.rcParams['axes.unicode_minus'] = False   #解决保存图像是负号
+
+#显示九张图像
+titles = ['原图', 'RGB', '加法', '减法', '圆形', '与运算', '或运算', '异或运算', '非运算']
+images = [src, img, result1, result2, circle, result4, result5, result6, result7]
+for i in range(9):
+   plt.subplot(3, 3, i+1), plt.imshow(images[i], 'gray')
+   plt.title(titles[i])
+   plt.xticks([]),plt.yticks([])
+plt.show()
+
+
+
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_05_ROI.py b/blog44-ImageProcessingSummary/ImageProcessing_05_ROI.py
new file mode 100644
index 0000000..3e95431
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_05_ROI.py
@@ -0,0 +1,34 @@
+#encoding:utf-8
+# By: Eastmount CSDN 2021-08-19
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+src1 = cv2.imread('lena-hd.png')
+src2 = cv2.imread('na.png')
+src3 = cv2.imread("Lena.png")
+print(src3.shape)
+
+#图像融合
+res1 = cv2.addWeighted(src1, 0.6, src2, 0.8, 10)
+
+#显示ROI区域
+face = np.ones((100, 100, 3)) #定义150×150矩阵 3对应BGR
+face = src3[200:300, 180:280]
+
+#显示ROI区域
+res3 = src2.copy()
+res3[150:250, 150:250] = face
+
+#显示图像
+cv2.imshow("src1", src1)
+cv2.imshow("src2", src2)
+cv2.imshow("src3", src3)
+cv2.imshow("res1", res1)
+cv2.imshow("res2", face)
+cv2.imshow("res3", res3)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_06_cvtColor.py b/blog44-ImageProcessingSummary/ImageProcessing_06_cvtColor.py
new file mode 100644
index 0000000..0be473d
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_06_cvtColor.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+# By: Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img_BGR = cv2.imread('na.png')
+
+#BGR转换为RGB
+img_RGB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2RGB)
+
+#灰度化处理
+img_GRAY = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2GRAY)
+
+#BGR转HSV
+img_HSV = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2HSV)
+
+#BGR转YCrCb
+img_YCrCb = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YCrCb)
+
+#BGR转HLS
+img_HLS = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2HLS)
+
+#BGR转XYZ
+img_XYZ = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2XYZ)
+
+#BGR转LAB
+img_LAB = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2LAB)
+
+#BGR转YUV
+img_YUV = cv2.cvtColor(img_BGR, cv2.COLOR_BGR2YUV)
+
+#调用matplotlib显示处理结果
+titles = ['BGR', 'RGB', 'GRAY', 'HSV', 'YCrCb', 'HLS', 'XYZ', 'LAB', 'YUV']  
+images = [img_BGR, img_RGB, img_GRAY, img_HSV, img_YCrCb,
+          img_HLS, img_XYZ, img_LAB, img_YUV]  
+for i in range(9):  
+   plt.subplot(3, 3, i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_07_JHBH.py b/blog44-ImageProcessingSummary/ImageProcessing_07_JHBH.py
new file mode 100644
index 0000000..0811446
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_07_JHBH.py
@@ -0,0 +1,54 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-02-01
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('test.bmp')
+image = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+
+#图像平移矩阵
+M = np.float32([[1, 0, 100], [0, 1, 80]])
+rows, cols = image.shape[:2]
+img1 = cv2.warpAffine(image, M, (cols, rows))
+
+#图像缩小
+img2 = cv2.resize(image, (200,100))
+
+#图像放大
+img3 = cv2.resize(image, None, fx=1.1, fy=1.1)
+
+#绕图像的中心旋转
+#源图像的高、宽 以及通道数
+rows, cols, channel = image.shape
+#函数参数：旋转中心 旋转度数 scale
+M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1) 
+#函数参数：原始图像 旋转参数 元素图像宽高
+img4 = cv2.warpAffine(image, M, (cols, rows))
+
+#图像翻转
+img5 = cv2.flip(image, 0)   #参数=0以X轴为对称轴翻转 
+img6 = cv2.flip(image, 1)   #参数>0以Y轴为对称轴翻转
+
+#图像的仿射
+pts1 = np.float32([[50,50],[200,50],[50,200]])
+pts2 = np.float32([[10,100],[200,50],[100,250]])
+M = cv2.getAffineTransform(pts1,pts2)
+img7 = cv2.warpAffine(image, M, (rows,cols))
+
+#图像的透射
+pts1 = np.float32([[56,65],[238,52],[28,237],[239,240]])
+pts2 = np.float32([[0,0],[200,0],[0,200],[200,200]])
+M = cv2.getPerspectiveTransform(pts1,pts2)
+img8 = cv2.warpPerspective(image,M,(200,200))
+
+
+#循环显示图形
+titles = [ 'source', 'shift', 'reduction', 'enlarge', 'rotation', 'flipX', 'flipY', 'affine', 'transmission']  
+images = [image, img1, img2, img3, img4, img5, img6, img7, img8]  
+for i in range(9):  
+   plt.subplot(3, 3, i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_08_LH.py b/blog44-ImageProcessingSummary/ImageProcessing_08_LH.py
new file mode 100644
index 0000000..35ac898
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_08_LH.py
@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+# BY:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('lena-hd.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#创建一幅图像
+new_img1 = np.zeros((height, width, 3), np.uint8)
+new_img2 = np.zeros((height, width, 3), np.uint8)
+new_img3 = np.zeros((height, width, 3), np.uint8)
+
+#图像量化等级为2的量化处理
+for i in range(height):
+    for j in range(width):
+        for k in range(3): #对应BGR三分量
+            if img[i, j][k] < 128:
+                gray = 0
+            else:
+                gray = 128
+            new_img1[i, j][k] = np.uint8(gray)
+
+#图像量化等级为4的量化处理
+for i in range(height):
+    for j in range(width):
+        for k in range(3): #对应BGR三分量
+            if img[i, j][k] < 64:
+                gray = 0
+            elif img[i, j][k] < 128:
+                gray = 64
+            elif img[i, j][k] < 192:
+                gray = 128
+            else:
+                gray = 192
+            new_img2[i, j][k] = np.uint8(gray)
+
+#图像量化等级为8的量化处理
+for i in range(height):
+    for j in range(width):
+        for k in range(3): #对应BGR三分量
+            if img[i, j][k] < 32:
+                gray = 0
+            elif img[i, j][k] < 64:
+                gray = 32
+            elif img[i, j][k] < 96:
+                gray = 64
+            elif img[i, j][k] < 128:
+                gray = 96
+            elif img[i, j][k] < 160:
+                gray = 128
+            elif img[i, j][k] < 192:
+                gray = 160
+            elif img[i, j][k] < 224:
+                gray = 192
+            else:
+                gray = 224
+            new_img3[i, j][k] = np.uint8(gray)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = ['(a) 原始图像', '(b) 量化-L2', '(c) 量化-L4', '(d) 量化-L8']  
+images = [img, new_img1, new_img2, new_img3]  
+for i in range(4):  
+   plt.subplot(2,2,i+1), plt.imshow(images[i], 'gray'), 
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_09_LH_KMeans.py b/blog44-ImageProcessingSummary/ImageProcessing_09_LH_KMeans.py
new file mode 100644
index 0000000..b762697
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_09_LH_KMeans.py
@@ -0,0 +1,44 @@
+# coding: utf-8
+# BY:Eastmount CSDN 2021-08-20
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('nv.png') 
+
+#图像二维像素转换为一维
+data = img.reshape((-1,3))
+data = np.float32(data)
+
+#定义中心 (type,max_iter,epsilon)
+criteria = (cv2.TERM_CRITERIA_EPS +
+            cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
+
+#设置标签
+flags = cv2.KMEANS_RANDOM_CENTERS
+
+#K-Means聚类 聚集成4类
+compactness, labels, centers = cv2.kmeans(data, 4, None, criteria, 10, flags)
+
+
+#图像转换回uint8二维类型
+centers = np.uint8(centers)
+res = centers[labels.flatten()]
+dst = res.reshape((img.shape))
+
+#图像转换为RGB显示
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+dst = cv2.cvtColor(dst, cv2.COLOR_BGR2RGB)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+titles = ['原始图像', '聚类量化 K=4']  
+images = [img, dst]  
+for i in range(2):  
+   plt.subplot(1,2,i+1), plt.imshow(images[i], 'gray'), 
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_10_CY.py b/blog44-ImageProcessingSummary/ImageProcessing_10_CY.py
new file mode 100644
index 0000000..ddf9bb7
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_10_CY.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+# BY:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取原始图像
+img = cv2.imread('scenery.png')
+
+#获取图像高度和宽度
+height = img.shape[0]
+width = img.shape[1]
+
+#采样转换成8*8区域
+numHeight = int(height/8)
+numwidth = int(width/8)
+
+#创建一幅图像
+new_img = np.zeros((height, width, 3), np.uint8)
+
+#图像循环采样8*8区域
+for i in range(8):
+    #获取Y坐标
+    y = i*numHeight
+    for j in range(8):
+        #获取X坐标
+        x = j*numwidth
+        #获取填充颜色 左上角像素点
+        b = img[y, x][0]
+        g = img[y, x][1]
+        r = img[y, x][2]
+        
+        #循环设置小区域采样
+        for n in range(numHeight):
+            for m in range(numwidth):
+                new_img[y+n, x+m][0] = np.uint8(b)
+                new_img[y+n, x+m][1] = np.uint8(g)
+                new_img[y+n, x+m][2] = np.uint8(r)
+        
+#显示图像
+cv2.imshow("src", img)
+cv2.imshow("Sampling", new_img)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_11_Binarization.py b/blog44-ImageProcessingSummary/ImageProcessing_11_Binarization.py
new file mode 100644
index 0000000..c9da481
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_11_Binarization.py
@@ -0,0 +1,25 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img=cv2.imread('miao.png')
+grayImage=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  
+
+#阈值化处理
+ret,thresh1=cv2.threshold(grayImage,127,255,cv2.THRESH_BINARY)  
+ret,thresh2=cv2.threshold(grayImage,127,255,cv2.THRESH_BINARY_INV)  
+ret,thresh3=cv2.threshold(grayImage,127,255,cv2.THRESH_TRUNC)  
+ret,thresh4=cv2.threshold(grayImage,127,255,cv2.THRESH_TOZERO)  
+ret,thresh5=cv2.threshold(grayImage,127,255,cv2.THRESH_TOZERO_INV)
+
+#显示结果
+titles = ['Gray Image','BINARY','BINARY_INV','TRUNC','TOZERO','TOZERO_INV']  
+images = [grayImage, thresh1, thresh2, thresh3, thresh4, thresh5]  
+for i in range(6):  
+   plt.subplot(2,3,i+1),plt.imshow(images[i],'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_12_HD.py b/blog44-ImageProcessingSummary/ImageProcessing_12_HD.py
new file mode 100644
index 0000000..8b161db
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_12_HD.py
@@ -0,0 +1,89 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2
+import math
+import numpy as np  
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('miao.png')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+
+#图像灰度上移变换 DB=DA+50
+result1 = np.zeros((height, width), np.uint8)
+for i in range(height):
+    for j in range(width):
+        if (int(grayImage[i,j]+50) > 255):
+            gray = 255
+        else:
+            gray = int(grayImage[i,j]+50)
+        result1[i,j] = np.uint8(gray)
+
+#图像对比度增强变换 DB=DA*1.5
+result2 = np.zeros((height, width), np.uint8)
+for i in range(height):
+    for j in range(width):
+        if (int(grayImage[i,j]*1.5) > 255):
+            gray = 255
+        else:
+            gray = int(grayImage[i,j]*1.5)
+        result2[i,j] = np.uint8(gray)
+
+#图像对比度增强变换 DB=DA*0.8
+result3 = np.zeros((height, width), np.uint8)
+for i in range(height):
+    for j in range(width):
+        if (int(grayImage[i,j]*0.8) > 255):
+            gray = 255
+        else:
+            gray = int(grayImage[i,j]*0.8)
+        result3[i,j] = np.uint8(gray)
+
+#图像灰度反色变换 DB=255-DA
+result4 = np.zeros((height, width), np.uint8)
+for i in range(height):
+    for j in range(width):
+        gray = 255 - grayImage[i,j]
+        result4[i,j] = np.uint8(gray)
+
+#图像灰度非线性变换：DB=DA×DA/255
+result5 = np.zeros((height, width), np.uint8)
+for i in range(height):
+    for j in range(width):
+        gray = int(grayImage[i,j])*int(grayImage[i,j]) / 255
+        result5[i,j] = np.uint8(gray)   
+
+#图像灰度对数变换
+result6 = np.zeros((height, width), np.uint8)
+for i in range(height):
+    for j in range(width):
+        gray = 10* math.log(1 + grayImage[i, j])
+        result6[i,j] = np.uint8(gray)
+
+#图像灰度伽玛变换
+result7 = np.zeros((height, width), np.uint8)
+for i in range(height):
+    for j in range(width):
+        gray = 3*pow(grayImage[i, j], 0.8)
+        if gray>255:
+            gray = 255
+        result7[i,j] = np.uint8(gray)
+
+#用来正常显示中文标签
+plt.rcParams['font.sans-serif']=['SimHei']
+
+#显示结果
+titles = [u'原始图像', u'灰度图像', u'图像拉伸', u'对比度增强', u'对比度减弱',
+          u'图像反色', u'非线性变换', u'对数变换', u'伽马变换']  
+images = [img, grayImage, result1, result2, result3, result4, result5, result6, result7]  
+for i in range(9):  
+   plt.subplot(3,3,i+1),plt.imshow(images[i],'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_12_gama.py b/blog44-ImageProcessingSummary/ImageProcessing_12_gama.py
new file mode 100644
index 0000000..986acca
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_12_gama.py
@@ -0,0 +1,39 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import numpy as np
+import matplotlib.pyplot as plt
+import cv2
+
+#绘制曲线
+def gamma_plot(c, v):
+    x = np.arange(0, 256, 0.01)
+    y = c*x**v
+    plt.plot(x, y, 'r', linewidth=1)
+    plt.rcParams['font.sans-serif']=['SimHei'] #正常显示中文标签
+    plt.title('伽马变换函数')
+    plt.xlim([0, 255]), plt.ylim([0, 255])
+    plt.show()
+
+#伽玛变换
+def gamma(img, c, v):
+    lut = np.zeros(256, dtype=np.float32)
+    for i in range(256):
+        lut[i] = c * i ** v
+    output_img = cv2.LUT(img, lut) #像素灰度值的映射
+    output_img = np.uint8(output_img+0.5)  
+    return output_img
+
+#读取原始图像
+img = cv2.imread('2019.png')
+
+#绘制伽玛变换曲线
+gamma_plot(0.00000005, 4.0)
+
+#图像灰度伽玛变换
+output = gamma(img, 0.00000005, 4.0)
+
+#显示图像
+cv2.imshow('Imput', img)
+cv2.imshow('Output', output)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_13_morphology.py b/blog44-ImageProcessingSummary/ImageProcessing_13_morphology.py
new file mode 100644
index 0000000..86b8cfc
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_13_morphology.py
@@ -0,0 +1,68 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图片
+src = cv2.imread('na.png', cv2.IMREAD_UNCHANGED)
+img = cv2.cvtColor(src,cv2.COLOR_BGR2RGB)
+
+# 转化为灰度图
+Grayimg = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+
+# 1、消除椒盐噪声：
+# 中值滤波器
+median = cv2.medianBlur(Grayimg, 5)
+# 消除噪声图
+cv2.imshow("median-image", median)
+
+# 2、直方图均衡化：
+equalize = cv2.equalizeHist(median)
+cv2.imshow('hist', equalize)
+
+# 3、二值化处理：
+# 阈值为140
+ret, binary = cv2.threshold(equalize, 127, 255,cv2.THRESH_BINARY)
+cv2.imshow("binary-image",binary)
+cv2.waitKey(0)
+
+#设置卷积核
+kernel = np.ones((10,10), np.uint8)
+close = cv2.morphologyEx(binary, cv2.MORPH_CLOSE, kernel)
+
+#图像开运算
+kernel = np.ones((10,10), np.uint8) 
+open1 = cv2.morphologyEx(binary, cv2.MORPH_OPEN, kernel)
+
+#显示图像
+cv2.imshow("src", src)
+cv2.imshow("result", close)
+
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+#图像开运算
+kernel = np.ones((10,10), np.uint8) 
+gradient = cv2.morphologyEx(binary, cv2.MORPH_GRADIENT, kernel)
+
+# Sobel算子 XY方向求梯度 cv2.CV_8U
+x = cv2.Sobel(close, cv2.CV_32F, 1, 0, ksize = 3) #X方向
+y = cv2.Sobel(close, cv2.CV_32F, 0, 1, ksize = 3) #Y方向
+#absX = cv2.convertScaleAbs(x)   # 转回uint8    
+#absY = cv2.convertScaleAbs(y)
+#Sobel = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
+gradient = cv2.subtract(x, y)
+sobel = cv2.convertScaleAbs(gradient)
+cv2.imshow('Sobel', sobel)
+cv2.waitKey(0)
+
+#循环显示图形
+titles = [ 'source', 'gray', 'median', 'equalize', 'binary', 'close', 'open', 'gradient', 'sobel']  
+images = [img, Grayimg, median, equalize, binary, close, open1, gradient, sobel]  
+for i in range(9):  
+   plt.subplot(3, 3, i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_14_hist.py b/blog44-ImageProcessingSummary/ImageProcessing_14_hist.py
new file mode 100644
index 0000000..5e64d8e
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_14_hist.py
@@ -0,0 +1,49 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+#读取图像
+src = cv2.imread('Lena.png')
+
+#转换为RGB图像
+img_rgb = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
+
+#获取BGR三个通道的像素值
+b, g, r = cv2.split(src)
+print(r,g,b)
+
+plt.figure(figsize=(8, 6))
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#原始图像
+plt.subplot(221)
+plt.imshow(img_rgb)
+plt.axis('off')
+plt.title("(a)原图像")
+
+#绘制蓝色分量直方图
+plt.subplot(222)
+plt.hist(b.ravel(), bins=256, density=1, facecolor='b', edgecolor='b', alpha=0.75)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(b)蓝色分量直方图")
+
+#绘制绿色分量直方图
+plt.subplot(223)
+plt.hist(g.ravel(), bins=256, density=1, facecolor='g', edgecolor='g', alpha=0.75)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(c)绿色分量直方图")
+
+#绘制红色分量直方图
+plt.subplot(224)
+plt.hist(r.ravel(), bins=256, density=1, facecolor='r', edgecolor='r', alpha=0.75)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(d)红色分量直方图")
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_15_hist.py b/blog44-ImageProcessingSummary/ImageProcessing_15_hist.py
new file mode 100644
index 0000000..b94e7c8
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_15_hist.py
@@ -0,0 +1,35 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib
+
+#读取图像
+src = cv2.imread('Lena.png')
+
+#转换为RGB图像
+img_rgb = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)
+
+#计算直方图
+histb = cv2.calcHist([src], [0], None, [256], [0,255])
+histg = cv2.calcHist([src], [1], None, [256], [0,255])
+histr = cv2.calcHist([src], [2], None, [256], [0,255])
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示原始图像和绘制的直方图
+plt.subplot(121)
+plt.imshow(img_rgb, 'gray')
+plt.axis('off')
+plt.title("(a)Lena原始图像")
+
+plt.subplot(122)
+plt.plot(histb, color='b')
+plt.plot(histg, color='g')
+plt.plot(histr, color='r')
+plt.xlabel("x")
+plt.ylabel("y")
+plt.title("(b)直方图曲线")
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_16_hist.py b/blog44-ImageProcessingSummary/ImageProcessing_16_hist.py
new file mode 100644
index 0000000..692bb5a
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_16_hist.py
@@ -0,0 +1,43 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取图像
+img = cv2.imread('lena-hd.png')
+
+#图像灰度转换
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#获取图像高度和宽度
+height = grayImage.shape[0]
+width = grayImage.shape[1]
+result = np.zeros((height, width), np.uint8)
+
+#图像灰度对数变换
+for i in range(height):
+    for j in range(width):
+        gray = 42 * np.log(1.0 + grayImage[i,j])
+        result[i,j] = np.uint8(gray)
+
+#计算原图的直方图
+hist = cv2.calcHist([img], [0], None, [256], [0,255])
+
+#计算灰度变换的直方图
+hist_res = cv2.calcHist([result], [0], None, [256], [0,255])
+
+#原始图像
+plt.figure(figsize=(8, 6))
+plt.subplot(221), plt.imshow(img, 'gray'), plt.title("(a)"), plt.axis('off')
+
+#绘制原始图像直方图
+plt.subplot(222), plt.plot(hist), plt.title("(b)"), plt.xlabel("x"), plt.ylabel("y")
+
+#灰度变换后的图像
+plt.subplot(223), plt.imshow(result, 'gray'), plt.title("(c)"), plt.axis('off')
+
+#灰度变换图像的直方图
+plt.subplot(224), plt.plot(hist_res), plt.title("(d)"), plt.xlabel("x"), plt.ylabel("y")
+plt.show()
+
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_17_enhancement.py b/blog44-ImageProcessingSummary/ImageProcessing_17_enhancement.py
new file mode 100644
index 0000000..430266a
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_17_enhancement.py
@@ -0,0 +1,25 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena-hd.png')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#直方图均衡化处理
+result = cv2.equalizeHist(gray)
+
+#显示图像
+plt.subplot(221)
+plt.imshow(gray, cmap=plt.cm.gray), plt.axis("off"), plt.title('(a)') 
+plt.subplot(222)
+plt.imshow(result, cmap=plt.cm.gray), plt.axis("off"), plt.title('(b)') 
+plt.subplot(223)
+plt.hist(img.ravel(), 256), plt.title('(c)') 
+plt.subplot(224)
+plt.hist(result.ravel(), 256), plt.title('(d)') 
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_18_enhancement.py b/blog44-ImageProcessingSummary/ImageProcessing_18_enhancement.py
new file mode 100644
index 0000000..7efe95c
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_18_enhancement.py
@@ -0,0 +1,28 @@
+#encoding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('lena.png')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+ 
+#局部直方图均衡化处理
+clahe = cv2.createCLAHE(clipLimit=2, tileGridSize=(10,10))
+
+#将灰度图像和局部直方图相关联, 把直方图均衡化应用到灰度图 
+result = clahe.apply(gray)
+
+#显示图像
+plt.subplot(221)
+plt.imshow(gray, cmap=plt.cm.gray), plt.axis("off"), plt.title('(a)') 
+plt.subplot(222)
+plt.imshow(result, cmap=plt.cm.gray), plt.axis("off"), plt.title('(b)') 
+plt.subplot(223)
+plt.hist(img.ravel(), 256), plt.title('(c)') 
+plt.subplot(224)
+plt.hist(result.ravel(), 256), plt.title('(d)') 
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_19_filter.py b/blog44-ImageProcessingSummary/ImageProcessing_19_filter.py
new file mode 100644
index 0000000..58dff23
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_19_filter.py
@@ -0,0 +1,39 @@
+# -*- coding: utf-8 -*-
+# By：Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+ 
+#读取图片
+img = cv2.imread('te.png')
+source = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+ 
+#均值滤波
+result1 = cv2.blur(source, (5,5))
+result2 = cv2.blur(source, (10,10))
+
+#方框滤波
+result3 = cv2.boxFilter(source, -1, (5,5), normalize=1)
+result4 = cv2.boxFilter(source, -1, (2,2), normalize=0)
+
+#高斯滤波
+result5 = cv2.GaussianBlur(source, (3,3), 0)
+result6 = cv2.GaussianBlur(source, (15,15), 0)
+
+#中值滤波
+result7 = cv2.medianBlur(source, 3)
+
+#高斯双边滤波
+result8 =cv2.bilateralFilter(source, 15, 150, 150)
+
+#显示图形
+titles = ['Source', 'Blur 5*5', 'Blur 10*10', 'BoxFilter 5*5',
+          'BoxFilter 2*2', 'GaussianBlur 3*3', 'GaussianBlur 15*15',
+          'medianBlur', 'bilateralFilter']  
+images = [source, result1, result2, result3,
+          result4, result5, result6, result7, result8]  
+for i in range(9):  
+   plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')  
+   plt.title(titles[i])  
+   plt.xticks([]),plt.yticks([])  
+plt.show()  
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_20_TX.py b/blog44-ImageProcessingSummary/ImageProcessing_20_TX.py
new file mode 100644
index 0000000..cde918c
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_20_TX.py
@@ -0,0 +1,32 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2
+import math
+import numpy as np
+
+#读取原始图像
+img = cv2.imread('nv.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#新建目标图像
+dst = np.zeros((rows, cols, 3), dtype="uint8")
+
+#图像流年特效
+for i in range(rows):
+    for j in range(cols):
+        #B通道的数值开平方乘以参数12
+        B = math.sqrt(img[i,j][0]) * 12
+        G =  img[i,j][1]
+        R =  img[i,j][2]
+        if B>255:
+            B = 255
+        dst[i,j] = np.uint8((B, G, R))
+        
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_21_TX.py b/blog44-ImageProcessingSummary/ImageProcessing_21_TX.py
new file mode 100644
index 0000000..aa1e6c7
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_21_TX.py
@@ -0,0 +1,37 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2
+import math
+import numpy as np
+
+#获取滤镜颜色
+def getBGR(img, table, i, j):
+    #获取图像颜色
+    b, g, r = img[i][j]
+    #计算标准颜色表中颜色的位置坐标
+    x = int(g/4 + int(b/32) * 64)
+    y = int(r/4 + int((b%32) / 4) * 64)
+    #返回滤镜颜色表中对应的颜色
+    return lj_map[x][y]
+
+#读取原始图像
+img = cv2.imread('nv.png')
+lj_map = cv2.imread('table.png')
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#新建目标图像
+dst = np.zeros((rows, cols, 3), dtype="uint8")
+
+#循环设置滤镜颜色
+for i in range(rows):
+    for j in range(cols):
+        dst[i][j] = getBGR(img, lj_map, i, j)
+        
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_22_TX.py b/blog44-ImageProcessingSummary/ImageProcessing_22_TX.py
new file mode 100644
index 0000000..94d366a
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_22_TX.py
@@ -0,0 +1,58 @@
+#coding:utf-8
+#By:Eastmount CSDN 2021-08-20
+import cv2
+import numpy as np
+ 
+def dodgeNaive(image, mask):
+    # determine the shape of the input image
+    width, height = image.shape[:2]
+ 
+    # prepare output argument with same size as image
+    blend = np.zeros((width, height), np.uint8)
+ 
+    for col in range(width):
+        for row in range(height):
+            # do for every pixel
+            if mask[col, row] == 255:
+                # avoid division by zero
+                blend[col, row] = 255
+            else:
+                # shift image pixel value by 8 bits
+                # divide by the inverse of the mask
+                tmp = (image[col, row] << 8) / (255 - mask)
+                # print('tmp={}'.format(tmp.shape))
+                # make sure resulting value stays within bounds
+                if tmp.any() > 255:
+                    tmp = 255
+                    blend[col, row] = tmp
+    return blend
+ 
+def dodgeV2(image, mask):
+    return cv2.divide(image, 255 - mask, scale=256)
+ 
+def burnV2(image, mask):
+    return 255 - cv2.divide(255 - image, 255 - mask, scale=256)
+ 
+def rgb_to_sketch(src_image_name, dst_image_name):
+    img_rgb = cv2.imread(src_image_name)
+    img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
+ 
+    img_gray_inv = 255 - img_gray
+    img_blur = cv2.GaussianBlur(img_gray_inv, ksize=(21, 21),
+                                sigmaX=0, sigmaY=0)
+    img_blend = dodgeV2(img_gray, img_blur)
+ 
+    cv2.imshow('original', img_rgb)
+    cv2.imshow('gray', img_gray)
+    cv2.imshow('gray_inv', img_gray_inv)
+    cv2.imshow('gray_blur', img_blur)
+    cv2.imshow("pencil sketch", img_blend)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    cv2.imwrite(dst_image_name, img_blend)
+ 
+if __name__ == '__main__':
+    src_image_name = 'nv.png'
+    dst_image_name = 'sketch_example.jpg'
+    rgb_to_sketch(src_image_name, dst_image_name)
+
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_23_FG.py b/blog44-ImageProcessingSummary/ImageProcessing_23_FG.py
new file mode 100644
index 0000000..e30b2b0
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_23_FG.py
@@ -0,0 +1,46 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2021-08-20
+import cv2  
+import numpy as np  
+import matplotlib.pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv2.imread('nv.png')
+
+#灰度化处理图像
+grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#设置掩码、fgbModel、bgModel
+mask = np.zeros(img.shape[:2], np.uint8)
+bgdModel = np.zeros((1,65), np.float64)
+fgdModel = np.zeros((1,65), np.float64)
+
+#矩形坐标
+rect = (100, 100, 500, 800)
+
+#图像分割
+cv2.grabCut(img, mask, rect, bgdModel, fgdModel, 5,
+            cv2.GC_INIT_WITH_RECT)
+
+#设置新掩码：0和2做背景
+mask2 = np.where((mask==2)|(mask==0), 0, 1).astype('uint8')
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示原图
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+plt.subplot(1,2,1)
+plt.imshow(img)
+plt.title(u'(a)原始图像')
+plt.xticks([]), plt.yticks([])
+
+#使用蒙板来获取前景区域
+img = img*mask2[:, :, np.newaxis]
+plt.subplot(1,2,2)
+plt.imshow(img)
+plt.title(u'(b)目标图像')
+plt.colorbar()
+plt.xticks([]), plt.yticks([])
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_24_FG.py b/blog44-ImageProcessingSummary/ImageProcessing_24_FG.py
new file mode 100644
index 0000000..49c18e0
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_24_FG.py
@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2021-08-20
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#读取原始图像灰度颜色
+img = cv2.imread('scenery.png') 
+
+#获取图像行和列
+rows, cols = img.shape[:2]
+
+#mask必须行和列都加2且必须为uint8单通道阵列
+mask = np.zeros([rows+2, cols+2], np.uint8) 
+
+spatialRad = 100 #空间窗口大小
+colorRad = 100   #色彩窗口大小
+maxPyrLevel = 2  #金字塔层数
+
+#图像均值漂移分割
+dst = cv2.pyrMeanShiftFiltering( img, spatialRad, colorRad, maxPyrLevel)
+
+#图像漫水填充处理
+cv2.floodFill(dst, mask, (30, 30), (0, 255, 255),
+              (100, 100, 100), (50, 50, 50),
+              cv2.FLOODFILL_FIXED_RANGE)
+
+#显示图像
+cv2.imshow('src', img)
+cv2.imshow('dst', dst)
+cv2.waitKey()
+cv2.destroyAllWindows()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_25_FG.py b/blog44-ImageProcessingSummary/ImageProcessing_25_FG.py
new file mode 100644
index 0000000..a4f3475
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_25_FG.py
@@ -0,0 +1,105 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2021-08-20
+import cv2
+import random
+import sys
+import numpy as np
+
+#使用说明 点击鼠标选择种子点
+help_message = '''USAGE: floodfill.py [<image>]
+Click on the image to set seed point
+Keys:
+  f - toggle floating range
+  c - toggle 4/8 connectivity
+  ESC - exit
+'''
+ 
+if __name__ == '__main__':
+
+    #输出提示文本
+    print(help_message)
+
+    #读取原始图像
+    img = cv2.imread('scenery.png')
+
+    #获取图像高和宽
+    h, w = img.shape[:2]
+
+    #设置掩码 长和宽都比输入图像多两个像素点 
+    mask = np.zeros((h+2, w+2), np.uint8)
+
+    #设置种子节点和4邻接
+    seed_pt = None
+    fixed_range = True
+    connectivity = 4 
+
+    #图像漫水填充分割更新函数
+    def update(dummy=None):
+        if seed_pt is None:
+            cv2.imshow('floodfill', img)
+            return
+        
+        #建立图像副本并漫水填充
+        flooded = img.copy()
+        mask[:] = 0 #掩码初始为全0
+        lo = cv2.getTrackbarPos('lo', 'floodfill') #观察点像素邻域负差最大值
+        hi = cv2.getTrackbarPos('hi', 'floodfill') #观察点像素邻域正差最大值
+        print('lo=', lo, 'hi=', hi)
+
+        #低位比特包含连通值 4 (缺省) 或 8
+        flags = connectivity
+        
+        #考虑当前象素与种子象素之间的差（高比特也可以为0）
+        if fixed_range:
+            flags |= cv2.FLOODFILL_FIXED_RANGE
+            
+        #以白色进行漫水填充
+        cv2.floodFill(flooded, mask, seed_pt,
+                      (random.randint(0,255), random.randint(0,255),
+                       random.randint(0,255)), (lo,)*3, (hi,)*3, flags)
+
+        #选定基准点用红色圆点标出
+        cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
+        print("send_pt=", seed_pt)
+
+        #显示图像
+        cv2.imshow('floodfill', flooded)
+
+    #鼠标响应函数
+    def onmouse(event, x, y, flags, param):
+        global seed_pt #基准点
+
+        #鼠标左键响应选择漫水填充基准点
+        if flags & cv2.EVENT_FLAG_LBUTTON:
+            seed_pt = x, y
+            update()
+
+    #执行图像漫水填充分割更新操作
+    update()
+    
+    #鼠标更新操作
+    cv2.setMouseCallback('floodfill', onmouse)
+
+    #设置进度条
+    cv2.createTrackbar('lo', 'floodfill', 20, 255, update)
+    cv2.createTrackbar('hi', 'floodfill', 20, 255, update)
+
+    #按键响应操作
+    while True:
+        ch = 0xFF & cv2.waitKey()
+        #退出
+        if ch == 27:
+            break
+        #选定时flags的高位比特位0
+        #此时邻域的选定为当前像素与相邻像素的差, 联通区域会很大
+        if ch == ord('f'):
+            fixed_range = not fixed_range 
+            print('using %s range' % ('floating', 'fixed')[fixed_range])
+            update()
+        #选择4方向或则8方向种子扩散
+        if ch == ord('c'):
+            connectivity = 12-connectivity 
+            print('connectivity =', connectivity)
+            update()
+    cv2.destroyAllWindows()             
+
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_26_fly.py b/blog44-ImageProcessingSummary/ImageProcessing_26_fly.py
new file mode 100644
index 0000000..2b124fb
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_26_fly.py
@@ -0,0 +1,31 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2021-08-20
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+import matplotlib
+
+#读取图像
+img = cv2.imread('Lena.png', 0)
+
+#傅里叶变换
+dft = cv2.dft(np.float32(img), flags = cv2.DFT_COMPLEX_OUTPUT)
+dftshift = np.fft.fftshift(dft)
+res1= 20*np.log(cv2.magnitude(dftshift[:,:,0], dftshift[:,:,1]))
+
+#傅里叶逆变换
+ishift = np.fft.ifftshift(dftshift)
+iimg = cv2.idft(ishift)
+res2 = cv2.magnitude(iimg[:,:,0], iimg[:,:,1])
+
+#设置字体
+matplotlib.rcParams['font.sans-serif']=['SimHei']
+
+#显示图像
+plt.subplot(131), plt.imshow(img, 'gray'), plt.title(u'(a)原始图像')
+plt.axis('off')
+plt.subplot(132), plt.imshow(res1, 'gray'), plt.title(u'(b)傅里叶变换处理')
+plt.axis('off')
+plt.subplot(133), plt.imshow(res2, 'gray'), plt.title(u'(b)傅里叶变换逆处理')
+plt.axis('off')
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_27_hf.py b/blog44-ImageProcessingSummary/ImageProcessing_27_hf.py
new file mode 100644
index 0000000..9361c46
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_27_hf.py
@@ -0,0 +1,37 @@
+# -*- coding: utf-8 -*-
+#By:Eastmount CSDN 2021-08-20
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+#读取图像
+img = cv2.imread('eyes.png')
+
+#灰度转换
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+#显示原始图像
+plt.subplot(121), plt.imshow(gray, 'gray'), plt.title('Input Image')
+plt.axis('off')
+
+#霍夫变换检测圆
+circles1 = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20,
+                            param1=100, param2=30,
+                            minRadius=160, maxRadius=200)
+print(circles1)
+
+#提取为二维
+circles = circles1[0, :, :]
+
+#四舍五入取整
+circles = np.uint16(np.around(circles))
+
+#绘制圆
+for i in circles[:]: 
+    cv2.circle(img, (i[0],i[1]), i[2], (255,0,0), 5) #画圆
+    cv2.circle(img, (i[0],i[1]), 2, (255,0,255), 8) #画圆心
+
+#显示处理图像
+plt.subplot(122), plt.imshow(img), plt.title('Result Image')
+plt.axis('off')
+plt.show()
diff --git a/blog44-ImageProcessingSummary/ImageProcessing_28_classifer.py b/blog44-ImageProcessingSummary/ImageProcessing_28_classifer.py
new file mode 100644
index 0000000..b2d0053
--- /dev/null
+++ b/blog44-ImageProcessingSummary/ImageProcessing_28_classifer.py
@@ -0,0 +1,97 @@
+# -*- coding: utf-8 -*-
+# By: Eastmount CSDN 2021-04-01
+import os
+import cv2
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#----------------------------------------------------------------------------------
+# 第一步 切分训练集和测试集
+#----------------------------------------------------------------------------------
+
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+for i in range(0, 10):
+    #遍历文件夹，读取图片
+    for f in os.listdir("photo/%s" % i):
+        #获取图像名称
+        X.append("photo//" +str(i) + "//" + str(f))
+        #获取图像类标即为文件夹名称
+        Y.append(i)
+
+X = np.array(X)
+Y = np.array(Y)
+
+#随机率为100% 选取其中的30%作为测试集
+X_train, X_test, y_train, y_test = train_test_split(X, Y,                                                   
+test_size=0.3, random_state=1)
+
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+
+#----------------------------------------------------------------------------------
+# 第二步 图像读取及转换为像素直方图
+#----------------------------------------------------------------------------------
+
+#训练集
+XX_train = []
+for i in X_train:
+    #读取图像
+    #print i
+    image = cv2.imread(i)
+    
+    #图像像素大小一致
+    img = cv2.resize(image, (256,256),
+                     interpolation=cv2.INTER_CUBIC)
+
+    #计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    #读取图像
+    #print i
+    image = cv2.imread(i)
+    
+    #图像像素大小一致
+    img = cv2.resize(image, (256,256),
+                     interpolation=cv2.INTER_CUBIC)
+
+    #计算图像直方图并存储至X数组
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+
+    XX_test.append(((hist/255).flatten()))
+
+#----------------------------------------------------------------------------------
+# 第三步 基于朴素贝叶斯的图像分类处理
+#----------------------------------------------------------------------------------
+
+from sklearn.naive_bayes import BernoulliNB
+clf = BernoulliNB().fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+
+print('预测结果:')
+print(predictions_labels)
+
+print('算法评价:')
+print(classification_report(y_test, predictions_labels))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    #读取图像
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    #显示图像
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
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diff --git a/blog45-ImageAI/Object-detection/models/haarcascade_frontalface_default.xml b/blog45-ImageAI/Object-detection/models/haarcascade_frontalface_default.xml
new file mode 100644
index 0000000..bc2aa3c
--- /dev/null
+++ b/blog45-ImageAI/Object-detection/models/haarcascade_frontalface_default.xml
@@ -0,0 +1,33314 @@
+<?xml version="1.0"?>
+<!--
+    Stump-based 24x24 discrete(?) adaboost frontal face detector.
+    Created by Rainer Lienhart.
+
+////////////////////////////////////////////////////////////////////////////////////////
+
+  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+
+  By downloading, copying, installing or using the software you agree to this license.
+  If you do not agree to this license, do not download, install,
+  copy or use the software.
+
+
+                        Intel License Agreement
+                For Open Source Computer Vision Library
+
+ Copyright (C) 2000, Intel Corporation, all rights reserved.
+ Third party copyrights are property of their respective owners.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+   * Redistribution's of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+
+   * Redistribution's in binary form must reproduce the above copyright notice,
+     this list of conditions and the following disclaimer in the documentation
+     and/or other materials provided with the distribution.
+
+   * The name of Intel Corporation may not be used to endorse or promote products
+     derived from this software without specific prior written permission.
+
+ This software is provided by the copyright holders and contributors "as is" and
+ any express or implied warranties, including, but not limited to, the implied
+ warranties of merchantability and fitness for a particular purpose are disclaimed.
+ In no event shall the Intel Corporation or contributors be liable for any direct,
+ indirect, incidental, special, exemplary, or consequential damages
+ (including, but not limited to, procurement of substitute goods or services;
+ loss of use, data, or profits; or business interruption) however caused
+ and on any theory of liability, whether in contract, strict liability,
+ or tort (including negligence or otherwise) arising in any way out of
+ the use of this software, even if advised of the possibility of such damage.
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>24</height>
+  <width>24</width>
+  <stageParams>
+    <maxWeakCount>211</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount></featureParams>
+  <stageNum>25</stageNum>
+  <stages>
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-5.0425500869750977e+00</stageThreshold>
+      <weakClassifiers>
+        <_>
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+</opencv_storage>
\ No newline at end of file
diff --git a/blog45-ImageAI/Object-detection/models/haarcascade_smile.xml b/blog45-ImageAI/Object-detection/models/haarcascade_smile.xml
new file mode 100644
index 0000000..bbdd896
--- /dev/null
+++ b/blog45-ImageAI/Object-detection/models/haarcascade_smile.xml
@@ -0,0 +1,6729 @@
+<?xml version="1.0"?>
+<!--
+  Smile detector
+  Contributed by Oscar Deniz Suarez
+  More information can be found at http://visilab.etsii.uclm.es/personas/oscar/oscar.html
+
+//////////////////////////////////////////////////////////////////////////
+| Contributors License Agreement
+| IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
+|   By downloading, copying, installing or using the software you agree
+|   to this license.
+|   If you do not agree to this license, do not download, install,
+|   copy or use the software.
+|
+| Copyright (c) 2011, Modesto Castrillon-Santana (IUSIANI, Universidad de
+| Las Palmas de Gran Canaria, Spain).
+|  All rights reserved.
+|
+| Redistribution and use in source and binary forms, with or without
+| modification, are permitted provided that the following conditions are
+| met:
+|
+|    * Redistributions of source code must retain the above copyright
+|       notice, this list of conditions and the following disclaimer.
+|    * Redistributions in binary form must reproduce the above
+|      copyright notice, this list of conditions and the following
+|      disclaimer in the documentation and/or other materials provided
+|      with the distribution.
+|    * The name of Contributor may not used to endorse or promote products
+|      derived from this software without specific prior written permission.
+|
+| THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+| "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+| LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+| A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
+| CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+| EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+| PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+| PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+| LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+| NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+| SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.  Back to
+| Top
+//////////////////////////////////////////////////////////////////////////
+
+-->
+<opencv_storage>
+<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>18</height>
+  <width>36</width>
+  <stageParams>
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+    <_>
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diff --git a/blog45-ImageAI/Object-detection/models/yolo-tiny.h5 b/blog45-ImageAI/Object-detection/models/yolo-tiny.h5
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diff --git a/blog45-ImageAI/Object-detection/output/people-01.png b/blog45-ImageAI/Object-detection/output/people-01.png
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diff --git a/blog45-ImageAI/Object-detection/output/pre_car-01.png b/blog45-ImageAI/Object-detection/output/pre_car-01.png
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diff --git a/blog45-ImageAI/Object-detection/output/pre_car-02.png b/blog45-ImageAI/Object-detection/output/pre_car-02.png
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diff --git a/blog45-ImageAI/Object-detection/test_detector-01.py b/blog45-ImageAI/Object-detection/test_detector-01.py
new file mode 100644
index 0000000..93d22a2
--- /dev/null
+++ b/blog45-ImageAI/Object-detection/test_detector-01.py
@@ -0,0 +1,34 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Jul 26 14:26:27 2021
+@author: xiuzhang
+"""
+
+from imageai.Detection import ObjectDetection
+
+#实例化
+detector = ObjectDetection()
+
+#路径定义
+model_path = "./models/yolo-tiny.h5"
+input_path = "./input/person-02.png"
+output_path = "./output/person-02.png"
+
+#预训练模式
+detector.setModelTypeAsTinyYOLOv3()
+
+#设置预训练模型路径
+detector.setModelPath(model_path)
+
+#加载模型
+detector.loadModel()
+
+#创建对象
+detection = detector.detectObjectsFromImage(input_image=input_path, 
+                                            output_image_path=output_path)
+
+#检测结果
+for eachItem in detection:
+    print(eachItem["name"] , " : ", eachItem["percentage_probability"])
+    
+    
diff --git a/blog45-ImageAI/Object-detection/test_detector-02.py b/blog45-ImageAI/Object-detection/test_detector-02.py
new file mode 100644
index 0000000..c762baf
--- /dev/null
+++ b/blog45-ImageAI/Object-detection/test_detector-02.py
@@ -0,0 +1,35 @@
+#coding:utf-8
+import cv2
+
+# 读取原始图像
+img = cv2.imread("./input/person-01.png")
+
+# 调用熟悉的人脸分类器 识别特征类型
+# 人脸 - haarcascade_frontalface_default.xml
+# 人眼 - haarcascade_eye.xm
+# 微笑 - haarcascade_smile.xml
+face_detect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+# 灰度处理
+gray = cv2.cvtColor(img, code=cv2.COLOR_BGR2GRAY)
+
+# 检查人脸 按照1.1倍放到 周围最小像素为5
+face_zone = face_detect.detectMultiScale(gray, scaleFactor = 1.1, minNeighbors = 4) # maxSize = (55,55)
+print ('识别人脸的信息：\n',face_zone)
+
+# 绘制矩形和圆形检测人脸
+for x, y, w, h in face_zone:
+    # 绘制矩形人脸区域
+    cv2.rectangle(img, pt1 = (x, y), pt2 = (x+w, y+h), color = [0,0,255], thickness=2)
+    # 绘制圆形人脸区域 radius表示半径
+    cv2.circle(img, center = (x + w//2, y + h//2), radius = w//2, color = [0,255,0], thickness = 2)
+
+# 设置图片可以手动调节大小
+cv2.namedWindow("Easmount-CSDN", 0)
+
+# 显示图片
+cv2.imshow("Easmount-CSDN", img)
+
+# 等待显示 设置任意键退出程序
+cv2.waitKey(0)
+cv2.destroyAllWindows()
diff --git "a/blog45-ImageAI/\347\233\256\346\240\207\346\243\200\346\265\213\347\273\274\350\277\260\346\246\202\346\213\254.pptx" "b/blog45-ImageAI/\347\233\256\346\240\207\346\243\200\346\265\213\347\273\274\350\277\260\346\246\202\346\213\254.pptx"
new file mode 100644
index 0000000..809af1e
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diff --git a/blog46-FasterRCNN-Wheat/2005.02162.pdf b/blog46-FasterRCNN-Wheat/2005.02162.pdf
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diff --git a/blog46-FasterRCNN-Wheat/data/2fd875eaa.jpg b/blog46-FasterRCNN-Wheat/data/2fd875eaa.jpg
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diff --git a/blog46-FasterRCNN-Wheat/data/51b3e36ab.jpg b/blog46-FasterRCNN-Wheat/data/51b3e36ab.jpg
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diff --git a/blog46-FasterRCNN-Wheat/data/51f1be19e.jpg b/blog46-FasterRCNN-Wheat/data/51f1be19e.jpg
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index 0000000..67fa3f0
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diff --git a/blog46-FasterRCNN-Wheat/data/53f253011.jpg b/blog46-FasterRCNN-Wheat/data/53f253011.jpg
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diff --git a/blog46-FasterRCNN-Wheat/data/796707dd7.jpg b/blog46-FasterRCNN-Wheat/data/796707dd7.jpg
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index 0000000..3cd570f
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diff --git a/blog46-FasterRCNN-Wheat/data/aac893a91.jpg b/blog46-FasterRCNN-Wheat/data/aac893a91.jpg
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index 0000000..fadedc2
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diff --git a/blog46-FasterRCNN-Wheat/data/cb8d261a3.jpg b/blog46-FasterRCNN-Wheat/data/cb8d261a3.jpg
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diff --git a/blog46-FasterRCNN-Wheat/data/cc3532ff6.jpg b/blog46-FasterRCNN-Wheat/data/cc3532ff6.jpg
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diff --git a/blog46-FasterRCNN-Wheat/data/f5a1f0358.jpg b/blog46-FasterRCNN-Wheat/data/f5a1f0358.jpg
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index 0000000..d2c585f
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diff --git a/blog46-FasterRCNN-Wheat/data/readme.md b/blog46-FasterRCNN-Wheat/data/readme.md
new file mode 100644
index 0000000..4a12ab1
--- /dev/null
+++ b/blog46-FasterRCNN-Wheat/data/readme.md
@@ -0,0 +1,4 @@
+
+下载地址：
+- [https://www.kaggle.com/c/global-wheat-detection](https://www.kaggle.com/c/global-wheat-detection)
+- [https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-train](https://www.kaggle.com/pestipeti/pytorch-starter-fasterrcnn-train)
diff --git a/blog46-FasterRCNN-Wheat/data/sample_submission.csv b/blog46-FasterRCNN-Wheat/data/sample_submission.csv
new file mode 100644
index 0000000..6a8e1ff
--- /dev/null
+++ b/blog46-FasterRCNN-Wheat/data/sample_submission.csv
@@ -0,0 +1,11 @@
+image_id,PredictionString
+aac893a91,1.0 0 0 50 50
+51f1be19e,1.0 0 0 50 50
+f5a1f0358,1.0 0 0 50 50
+796707dd7,1.0 0 0 50 50
+51b3e36ab,1.0 0 0 50 50
+348a992bb,1.0 0 0 50 50
+cc3532ff6,1.0 0 0 50 50
+2fd875eaa,1.0 0 0 50 50
+cb8d261a3,1.0 0 0 50 50
+53f253011,1.0 0 0 50 50
diff --git a/blog46-FasterRCNN-Wheat/data/train.csv b/blog46-FasterRCNN-Wheat/data/train.csv
new file mode 100644
index 0000000..2a2f55a
--- /dev/null
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+91c9d9c38,1024,1024,"[283.0, 819.0, 49.0, 35.0]",usask_1
+91c9d9c38,1024,1024,"[688.0, 784.0, 88.0, 58.0]",usask_1
+91c9d9c38,1024,1024,"[1.0, 0.0, 75.0, 44.0]",usask_1
+41c0123cc,1024,1024,"[0.0, 669.0, 73.0, 111.0]",usask_1
+41c0123cc,1024,1024,"[572.0, 757.0, 110.0, 74.0]",usask_1
+41c0123cc,1024,1024,"[47.0, 481.0, 94.0, 86.0]",usask_1
+41c0123cc,1024,1024,"[106.0, 60.0, 74.0, 162.0]",usask_1
+41c0123cc,1024,1024,"[0.0, 245.0, 116.0, 117.0]",usask_1
+41c0123cc,1024,1024,"[158.0, 943.0, 110.0, 81.0]",usask_1
+41c0123cc,1024,1024,"[0.0, 15.0, 368.0, 395.0]",usask_1
+41c0123cc,1024,1024,"[101.0, 0.0, 126.0, 75.0]",usask_1
+41c0123cc,1024,1024,"[858.0, 182.0, 75.0, 96.0]",usask_1
+41c0123cc,1024,1024,"[904.0, 257.0, 77.0, 71.0]",usask_1
+41c0123cc,1024,1024,"[612.0, 321.0, 185.0, 68.0]",usask_1
+41c0123cc,1024,1024,"[4.0, 904.0, 99.0, 67.0]",usask_1
+41c0123cc,1024,1024,"[715.0, 366.0, 98.0, 91.0]",usask_1
+41c0123cc,1024,1024,"[603.0, 591.0, 174.0, 97.0]",usask_1
+41c0123cc,1024,1024,"[664.0, 280.0, 95.0, 169.0]",usask_1
+41c0123cc,1024,1024,"[860.0, 456.0, 57.0, 72.0]",usask_1
+41c0123cc,1024,1024,"[642.0, 613.0, 172.0, 164.0]",usask_1
+41c0123cc,1024,1024,"[737.0, 567.0, 65.0, 95.0]",usask_1
+41c0123cc,1024,1024,"[740.0, 834.0, 61.0, 185.0]",usask_1
+41c0123cc,1024,1024,"[619.0, 498.0, 104.0, 99.0]",usask_1
+41c0123cc,1024,1024,"[964.0, 160.0, 46.0, 105.0]",usask_1
+41c0123cc,1024,1024,"[917.0, 105.0, 66.0, 60.0]",usask_1
+41c0123cc,1024,1024,"[793.0, 152.0, 100.0, 73.0]",usask_1
+41c0123cc,1024,1024,"[240.0, 67.0, 61.0, 62.0]",usask_1
+41c0123cc,1024,1024,"[59.0, 185.0, 65.0, 46.0]",usask_1
+41c0123cc,1024,1024,"[132.0, 215.0, 46.0, 71.0]",usask_1
+41c0123cc,1024,1024,"[158.0, 191.0, 68.0, 70.0]",usask_1
+41c0123cc,1024,1024,"[311.0, 164.0, 27.0, 44.0]",usask_1
+41c0123cc,1024,1024,"[417.0, 638.0, 47.0, 96.0]",usask_1
+41c0123cc,1024,1024,"[901.0, 413.0, 65.0, 59.0]",usask_1
+41c0123cc,1024,1024,"[850.0, 420.0, 49.0, 50.0]",usask_1
+41c0123cc,1024,1024,"[738.0, 284.0, 100.0, 43.0]",usask_1
+41c0123cc,1024,1024,"[764.0, 318.0, 79.0, 66.0]",usask_1
+41c0123cc,1024,1024,"[494.0, 968.0, 117.0, 55.0]",usask_1
+c682d8c66,1024,1024,"[0.0, 80.0, 92.0, 93.0]",usask_1
+c682d8c66,1024,1024,"[0.0, 151.0, 80.0, 150.0]",usask_1
+c682d8c66,1024,1024,"[0.0, 511.0, 58.0, 72.0]",usask_1
+c682d8c66,1024,1024,"[973.0, 47.0, 51.0, 76.0]",usask_1
+c682d8c66,1024,1024,"[811.0, 286.0, 93.0, 96.0]",usask_1
+c682d8c66,1024,1024,"[67.0, 519.0, 147.0, 89.0]",usask_1
+c682d8c66,1024,1024,"[54.0, 916.0, 102.0, 89.0]",usask_1
+c682d8c66,1024,1024,"[87.0, 384.0, 90.0, 82.0]",usask_1
+c682d8c66,1024,1024,"[295.0, 665.0, 69.0, 81.0]",usask_1
+c682d8c66,1024,1024,"[134.0, 114.0, 137.0, 208.0]",usask_1
+c682d8c66,1024,1024,"[242.0, 278.0, 64.0, 111.0]",usask_1
+c682d8c66,1024,1024,"[47.0, 259.0, 143.0, 75.0]",usask_1
+c682d8c66,1024,1024,"[150.0, 614.0, 94.0, 177.0]",usask_1
+c682d8c66,1024,1024,"[252.0, 747.0, 90.0, 113.0]",usask_1
+c682d8c66,1024,1024,"[835.0, 50.0, 151.0, 234.0]",usask_1
+c682d8c66,1024,1024,"[320.0, 782.0, 91.0, 153.0]",usask_1
+c682d8c66,1024,1024,"[567.0, 876.0, 119.0, 69.0]",usask_1
+c682d8c66,1024,1024,"[169.0, 911.0, 132.0, 75.0]",usask_1
+c682d8c66,1024,1024,"[512.0, 208.0, 130.0, 167.0]",usask_1
+c682d8c66,1024,1024,"[677.0, 596.0, 163.0, 130.0]",usask_1
+c682d8c66,1024,1024,"[141.0, 315.0, 95.0, 100.0]",usask_1
+c682d8c66,1024,1024,"[272.0, 319.0, 83.0, 95.0]",usask_1
+c682d8c66,1024,1024,"[0.0, 650.0, 29.0, 63.0]",usask_1
+c682d8c66,1024,1024,"[253.0, 571.0, 58.0, 65.0]",usask_1
+c682d8c66,1024,1024,"[166.0, 643.0, 148.0, 100.0]",usask_1
+c682d8c66,1024,1024,"[400.0, 421.0, 24.0, 30.0]",usask_1
+e46378032,1024,1024,"[556.0, 526.0, 70.0, 56.0]",usask_1
+e46378032,1024,1024,"[602.0, 0.0, 93.0, 45.0]",usask_1
+e46378032,1024,1024,"[640.0, 875.0, 62.0, 76.0]",usask_1
+e46378032,1024,1024,"[36.0, 7.0, 85.0, 195.0]",usask_1
+e46378032,1024,1024,"[751.0, 0.0, 72.0, 106.0]",usask_1
+e46378032,1024,1024,"[809.0, 535.0, 87.0, 71.0]",usask_1
+e46378032,1024,1024,"[252.0, 774.0, 191.0, 113.0]",usask_1
+e46378032,1024,1024,"[633.0, 667.0, 200.0, 107.0]",usask_1
+e46378032,1024,1024,"[235.0, 623.0, 241.0, 147.0]",usask_1
+e46378032,1024,1024,"[522.0, 354.0, 122.0, 185.0]",usask_1
+e46378032,1024,1024,"[939.0, 798.0, 85.0, 120.0]",usask_1
+e46378032,1024,1024,"[727.0, 404.0, 93.0, 96.0]",usask_1
+e46378032,1024,1024,"[901.0, 231.0, 114.0, 61.0]",usask_1
+e46378032,1024,1024,"[173.0, 250.0, 78.0, 140.0]",usask_1
+e46378032,1024,1024,"[126.0, 626.0, 242.0, 51.0]",usask_1
+e46378032,1024,1024,"[24.0, 724.0, 52.0, 82.0]",usask_1
+e46378032,1024,1024,"[0.0, 538.0, 113.0, 84.0]",usask_1
+e46378032,1024,1024,"[190.0, 573.0, 224.0, 68.0]",usask_1
+e46378032,1024,1024,"[43.0, 604.0, 71.0, 65.0]",usask_1
+e46378032,1024,1024,"[772.0, 839.0, 80.0, 70.0]",usask_1
+e46378032,1024,1024,"[280.0, 7.0, 96.0, 100.0]",usask_1
+e46378032,1024,1024,"[45.0, 44.0, 58.0, 123.0]",usask_1
+e46378032,1024,1024,"[3.0, 85.0, 51.0, 85.0]",usask_1
+e46378032,1024,1024,"[0.0, 310.0, 51.0, 79.0]",usask_1
+e46378032,1024,1024,"[133.0, 195.0, 54.0, 45.0]",usask_1
+e46378032,1024,1024,"[118.0, 344.0, 72.0, 183.0]",usask_1
+e46378032,1024,1024,"[106.0, 307.0, 47.0, 69.0]",usask_1
+e46378032,1024,1024,"[66.0, 259.0, 231.0, 64.0]",usask_1
+e46378032,1024,1024,"[233.0, 393.0, 150.0, 106.0]",usask_1
+e46378032,1024,1024,"[89.0, 484.0, 68.0, 55.0]",usask_1
+e46378032,1024,1024,"[0.0, 890.0, 114.0, 109.0]",usask_1
+e46378032,1024,1024,"[86.0, 887.0, 81.0, 137.0]",usask_1
+e46378032,1024,1024,"[860.0, 849.0, 70.0, 50.0]",usask_1
+71fd03d51,1024,1024,"[896.0, 978.0, 76.0, 46.0]",usask_1
+71fd03d51,1024,1024,"[751.0, 271.0, 92.0, 76.0]",usask_1
+71fd03d51,1024,1024,"[963.0, 745.0, 58.0, 76.0]",usask_1
+71fd03d51,1024,1024,"[629.0, 285.0, 97.0, 67.0]",usask_1
+71fd03d51,1024,1024,"[592.0, 224.0, 103.0, 75.0]",usask_1
+71fd03d51,1024,1024,"[286.0, 821.0, 76.0, 58.0]",usask_1
+71fd03d51,1024,1024,"[884.0, 126.0, 61.0, 178.0]",usask_1
+71fd03d51,1024,1024,"[320.0, 628.0, 159.0, 92.0]",usask_1
+71fd03d51,1024,1024,"[198.0, 601.0, 119.0, 135.0]",usask_1
+71fd03d51,1024,1024,"[932.0, 0.0, 81.0, 287.0]",usask_1
+71fd03d51,1024,1024,"[399.0, 0.0, 89.0, 47.0]",usask_1
+71fd03d51,1024,1024,"[280.0, 0.0, 105.0, 154.0]",usask_1
+71fd03d51,1024,1024,"[174.0, 35.0, 69.0, 48.0]",usask_1
+71fd03d51,1024,1024,"[99.0, 0.0, 74.0, 35.0]",usask_1
+71fd03d51,1024,1024,"[247.0, 452.0, 99.0, 62.0]",usask_1
+71fd03d51,1024,1024,"[120.0, 957.0, 94.0, 67.0]",usask_1
+71fd03d51,1024,1024,"[0.0, 947.0, 97.0, 77.0]",usask_1
+71fd03d51,1024,1024,"[763.0, 470.0, 96.0, 106.0]",usask_1
+71fd03d51,1024,1024,"[771.0, 555.0, 81.0, 94.0]",usask_1
+71fd03d51,1024,1024,"[871.0, 513.0, 68.0, 59.0]",usask_1
+71fd03d51,1024,1024,"[915.0, 625.0, 83.0, 77.0]",usask_1
+71fd03d51,1024,1024,"[739.0, 686.0, 61.0, 51.0]",usask_1
+71fd03d51,1024,1024,"[869.0, 711.0, 76.0, 87.0]",usask_1
+71fd03d51,1024,1024,"[752.0, 822.0, 94.0, 103.0]",usask_1
+71fd03d51,1024,1024,"[852.0, 846.0, 72.0, 71.0]",usask_1
+71fd03d51,1024,1024,"[813.0, 930.0, 70.0, 94.0]",usask_1
+71fd03d51,1024,1024,"[853.0, 141.0, 139.0, 172.0]",usask_1
+71fd03d51,1024,1024,"[811.0, 314.0, 87.0, 76.0]",usask_1
+71fd03d51,1024,1024,"[16.0, 776.0, 48.0, 33.0]",usask_1
+71fd03d51,1024,1024,"[39.0, 775.0, 38.0, 80.0]",usask_1
+83a5d8ac8,1024,1024,"[101.0, 0.0, 158.0, 51.0]",usask_1
+83a5d8ac8,1024,1024,"[231.0, 718.0, 98.0, 119.0]",usask_1
+83a5d8ac8,1024,1024,"[48.0, 686.0, 156.0, 92.0]",usask_1
+83a5d8ac8,1024,1024,"[244.0, 569.0, 101.0, 66.0]",usask_1
+83a5d8ac8,1024,1024,"[256.0, 334.0, 114.0, 95.0]",usask_1
+83a5d8ac8,1024,1024,"[386.0, 369.0, 116.0, 101.0]",usask_1
+83a5d8ac8,1024,1024,"[151.0, 199.0, 124.0, 53.0]",usask_1
+83a5d8ac8,1024,1024,"[324.0, 201.0, 48.0, 61.0]",usask_1
+83a5d8ac8,1024,1024,"[333.0, 117.0, 85.0, 67.0]",usask_1
+83a5d8ac8,1024,1024,"[242.0, 141.0, 76.0, 107.0]",usask_1
+83a5d8ac8,1024,1024,"[111.0, 72.0, 184.0, 124.0]",usask_1
+83a5d8ac8,1024,1024,"[397.0, 10.0, 60.0, 59.0]",usask_1
+83a5d8ac8,1024,1024,"[290.0, 20.0, 109.0, 74.0]",usask_1
+83a5d8ac8,1024,1024,"[820.0, 511.0, 77.0, 58.0]",usask_1
+83a5d8ac8,1024,1024,"[965.0, 884.0, 59.0, 60.0]",usask_1
+83a5d8ac8,1024,1024,"[60.0, 308.0, 89.0, 66.0]",usask_1
+83a5d8ac8,1024,1024,"[1.0, 266.0, 99.0, 65.0]",usask_1
+83a5d8ac8,1024,1024,"[145.0, 714.0, 101.0, 132.0]",usask_1
+83a5d8ac8,1024,1024,"[40.0, 129.0, 149.0, 65.0]",usask_1
+83a5d8ac8,1024,1024,"[62.0, 572.0, 198.0, 89.0]",usask_1
+83a5d8ac8,1024,1024,"[931.0, 429.0, 93.0, 96.0]",usask_1
+83a5d8ac8,1024,1024,"[698.0, 629.0, 117.0, 73.0]",usask_1
+83a5d8ac8,1024,1024,"[199.0, 493.0, 70.0, 25.0]",usask_1
+02b6f199c,1024,1024,"[832.0, 727.0, 90.0, 159.0]",usask_1
+02b6f199c,1024,1024,"[286.0, 724.0, 79.0, 106.0]",usask_1
+02b6f199c,1024,1024,"[955.0, 869.0, 69.0, 74.0]",usask_1
+02b6f199c,1024,1024,"[138.0, 297.0, 76.0, 89.0]",usask_1
+02b6f199c,1024,1024,"[848.0, 0.0, 77.0, 100.0]",usask_1
+02b6f199c,1024,1024,"[696.0, 413.0, 76.0, 74.0]",usask_1
+02b6f199c,1024,1024,"[224.0, 842.0, 99.0, 145.0]",usask_1
+02b6f199c,1024,1024,"[534.0, 536.0, 115.0, 57.0]",usask_1
+02b6f199c,1024,1024,"[677.0, 970.0, 96.0, 54.0]",usask_1
+02b6f199c,1024,1024,"[619.0, 128.0, 204.0, 96.0]",usask_1
+02b6f199c,1024,1024,"[18.0, 519.0, 95.0, 80.0]",usask_1
+02b6f199c,1024,1024,"[67.0, 946.0, 141.0, 78.0]",usask_1
+02b6f199c,1024,1024,"[838.0, 318.0, 75.0, 81.0]",usask_1
+02b6f199c,1024,1024,"[6.0, 336.0, 161.0, 127.0]",usask_1
+02b6f199c,1024,1024,"[1.0, 30.0, 82.0, 125.0]",usask_1
+02b6f199c,1024,1024,"[761.0, 453.0, 92.0, 72.0]",usask_1
+02b6f199c,1024,1024,"[673.0, 807.0, 87.0, 101.0]",usask_1
+02b6f199c,1024,1024,"[390.0, 154.0, 140.0, 184.0]",usask_1
+02b6f199c,1024,1024,"[512.0, 424.0, 123.0, 106.0]",usask_1
+02b6f199c,1024,1024,"[730.0, 205.0, 104.0, 157.0]",usask_1
+02b6f199c,1024,1024,"[114.0, 481.0, 120.0, 90.0]",usask_1
+02b6f199c,1024,1024,"[133.0, 134.0, 141.0, 80.0]",usask_1
+02b6f199c,1024,1024,"[435.0, 620.0, 115.0, 116.0]",usask_1
+02b6f199c,1024,1024,"[908.0, 950.0, 94.0, 73.0]",usask_1
+02b6f199c,1024,1024,"[54.0, 580.0, 111.0, 95.0]",usask_1
+02b6f199c,1024,1024,"[545.0, 865.0, 137.0, 86.0]",usask_1
+02b6f199c,1024,1024,"[828.0, 131.0, 117.0, 116.0]",usask_1
+02b6f199c,1024,1024,"[0.0, 134.0, 63.0, 62.0]",usask_1
+02b6f199c,1024,1024,"[278.0, 319.0, 94.0, 85.0]",usask_1
+02b6f199c,1024,1024,"[219.0, 249.0, 72.0, 75.0]",usask_1
+02b6f199c,1024,1024,"[92.0, 239.0, 123.0, 82.0]",usask_1
+02b6f199c,1024,1024,"[0.0, 720.0, 66.0, 124.0]",usask_1
+02b6f199c,1024,1024,"[0.0, 864.0, 95.0, 94.0]",usask_1
+02b6f199c,1024,1024,"[196.0, 855.0, 93.0, 83.0]",usask_1
+02b6f199c,1024,1024,"[161.0, 687.0, 93.0, 81.0]",usask_1
+02b6f199c,1024,1024,"[264.0, 662.0, 71.0, 74.0]",usask_1
+02b6f199c,1024,1024,"[965.0, 772.0, 59.0, 58.0]",usask_1
+02b6f199c,1024,1024,"[471.0, 602.0, 77.0, 144.0]",usask_1
+02b6f199c,1024,1024,"[910.0, 301.0, 56.0, 57.0]",usask_1
+02b6f199c,1024,1024,"[895.0, 0.0, 70.0, 75.0]",usask_1
+02b6f199c,1024,1024,"[621.0, 530.0, 110.0, 121.0]",usask_1
+02b6f199c,1024,1024,"[919.0, 260.0, 49.0, 37.0]",usask_1
+1bda74f14,1024,1024,"[0.0, 302.0, 125.0, 119.0]",usask_1
+1bda74f14,1024,1024,"[982.0, 591.0, 42.0, 59.0]",usask_1
+1bda74f14,1024,1024,"[103.0, 988.0, 134.0, 36.0]",usask_1
+1bda74f14,1024,1024,"[0.0, 435.0, 139.0, 89.0]",usask_1
+1bda74f14,1024,1024,"[690.0, 687.0, 184.0, 86.0]",usask_1
+1bda74f14,1024,1024,"[37.0, 0.0, 254.0, 72.0]",usask_1
+1bda74f14,1024,1024,"[891.0, 955.0, 79.0, 69.0]",usask_1
+1bda74f14,1024,1024,"[1.0, 636.0, 197.0, 84.0]",usask_1
+1bda74f14,1024,1024,"[838.0, 536.0, 123.0, 112.0]",usask_1
+1bda74f14,1024,1024,"[236.0, 680.0, 141.0, 78.0]",usask_1
+1bda74f14,1024,1024,"[318.0, 27.0, 189.0, 59.0]",usask_1
+1bda74f14,1024,1024,"[947.0, 33.0, 77.0, 82.0]",usask_1
+1bda74f14,1024,1024,"[569.0, 262.0, 189.0, 63.0]",usask_1
+1bda74f14,1024,1024,"[101.0, 173.0, 253.0, 73.0]",usask_1
+1bda74f14,1024,1024,"[758.0, 805.0, 98.0, 130.0]",usask_1
+1bda74f14,1024,1024,"[318.0, 814.0, 129.0, 108.0]",usask_1
+1bda74f14,1024,1024,"[0.0, 93.0, 134.0, 60.0]",usask_1
+1bda74f14,1024,1024,"[568.0, 268.0, 188.0, 125.0]",usask_1
+1bda74f14,1024,1024,"[940.0, 344.0, 84.0, 95.0]",usask_1
+1bda74f14,1024,1024,"[0.0, 732.0, 128.0, 85.0]",usask_1
+1bda74f14,1024,1024,"[23.0, 949.0, 119.0, 74.0]",usask_1
+1bda74f14,1024,1024,"[892.0, 889.0, 132.0, 75.0]",usask_1
+1bda74f14,1024,1024,"[962.0, 945.0, 62.0, 55.0]",usask_1
+1bda74f14,1024,1024,"[651.0, 606.0, 161.0, 107.0]",usask_1
+1bda74f14,1024,1024,"[0.0, 494.0, 112.0, 71.0]",usask_1
+1bda74f14,1024,1024,"[424.0, 122.0, 59.0, 26.0]",usask_1
+1bda74f14,1024,1024,"[915.0, 813.0, 65.0, 93.0]",usask_1
+1bda74f14,1024,1024,"[74.0, 851.0, 88.0, 37.0]",usask_1
+3e1e66c76,1024,1024,"[0.0, 346.0, 109.0, 98.0]",usask_1
+3e1e66c76,1024,1024,"[236.0, 953.0, 99.0, 71.0]",usask_1
+3e1e66c76,1024,1024,"[356.0, 251.0, 86.0, 136.0]",usask_1
+3e1e66c76,1024,1024,"[87.0, 756.0, 109.0, 71.0]",usask_1
+3e1e66c76,1024,1024,"[545.0, 794.0, 107.0, 107.0]",usask_1
+3e1e66c76,1024,1024,"[328.0, 916.0, 68.0, 108.0]",usask_1
+3e1e66c76,1024,1024,"[179.0, 449.0, 60.0, 108.0]",usask_1
+3e1e66c76,1024,1024,"[850.0, 835.0, 83.0, 86.0]",usask_1
+3e1e66c76,1024,1024,"[464.0, 720.0, 90.0, 184.0]",usask_1
+3e1e66c76,1024,1024,"[0.0, 276.0, 103.0, 56.0]",usask_1
+3e1e66c76,1024,1024,"[280.0, 428.0, 77.0, 119.0]",usask_1
+3e1e66c76,1024,1024,"[40.0, 36.0, 216.0, 73.0]",usask_1
+3e1e66c76,1024,1024,"[190.0, 797.0, 97.0, 125.0]",usask_1
+3e1e66c76,1024,1024,"[745.0, 883.0, 151.0, 93.0]",usask_1
+3e1e66c76,1024,1024,"[0.0, 464.0, 126.0, 109.0]",usask_1
+3e1e66c76,1024,1024,"[400.0, 0.0, 70.0, 59.0]",usask_1
+3e1e66c76,1024,1024,"[554.0, 409.0, 80.0, 107.0]",usask_1
+3e1e66c76,1024,1024,"[784.0, 40.0, 215.0, 123.0]",usask_1
+3e1e66c76,1024,1024,"[11.0, 25.0, 117.0, 159.0]",usask_1
+3e1e66c76,1024,1024,"[538.0, 666.0, 114.0, 117.0]",usask_1
+3e1e66c76,1024,1024,"[582.0, 579.0, 137.0, 110.0]",usask_1
+3e1e66c76,1024,1024,"[359.0, 620.0, 60.0, 72.0]",usask_1
+3e1e66c76,1024,1024,"[194.0, 947.0, 60.0, 76.0]",usask_1
+3e1e66c76,1024,1024,"[731.0, 970.0, 153.0, 54.0]",usask_1
+3e1e66c76,1024,1024,"[490.0, 31.0, 76.0, 89.0]",usask_1
+3e1e66c76,1024,1024,"[67.0, 310.0, 111.0, 79.0]",usask_1
+3e1e66c76,1024,1024,"[768.0, 231.0, 189.0, 168.0]",usask_1
+3e1e66c76,1024,1024,"[690.0, 724.0, 130.0, 110.0]",usask_1
+3e1e66c76,1024,1024,"[902.0, 636.0, 63.0, 72.0]",usask_1
+3e1e66c76,1024,1024,"[0.0, 134.0, 71.0, 60.0]",usask_1
+3e1e66c76,1024,1024,"[348.0, 373.0, 81.0, 137.0]",usask_1
+3e1e66c76,1024,1024,"[376.0, 489.0, 114.0, 143.0]",usask_1
+3e1e66c76,1024,1024,"[181.0, 9.0, 61.0, 42.0]",usask_1
+3e1e66c76,1024,1024,"[586.0, 80.0, 170.0, 94.0]",usask_1
+3e1e66c76,1024,1024,"[650.0, 143.0, 107.0, 88.0]",usask_1
+3e1e66c76,1024,1024,"[500.0, 133.0, 43.0, 37.0]",usask_1
+3e1e66c76,1024,1024,"[332.0, 143.0, 45.0, 50.0]",usask_1
+3e1e66c76,1024,1024,"[305.0, 207.0, 43.0, 81.0]",usask_1
+4a3f67d2e,1024,1024,"[794.0, 68.0, 66.0, 61.0]",usask_1
+4a3f67d2e,1024,1024,"[885.0, 0.0, 54.0, 71.0]",usask_1
+4a3f67d2e,1024,1024,"[70.0, 954.0, 74.0, 70.0]",usask_1
+4a3f67d2e,1024,1024,"[0.0, 725.0, 84.0, 75.0]",usask_1
+4a3f67d2e,1024,1024,"[733.0, 54.0, 67.0, 61.0]",usask_1
+4a3f67d2e,1024,1024,"[931.0, 68.0, 66.0, 73.0]",usask_1
+4a3f67d2e,1024,1024,"[0.0, 424.0, 116.0, 147.0]",usask_1
+4a3f67d2e,1024,1024,"[651.0, 782.0, 148.0, 78.0]",usask_1
+4a3f67d2e,1024,1024,"[9.0, 806.0, 99.0, 123.0]",usask_1
+4a3f67d2e,1024,1024,"[711.0, 107.0, 75.0, 101.0]",usask_1
+4a3f67d2e,1024,1024,"[0.0, 565.0, 116.0, 87.0]",usask_1
+4a3f67d2e,1024,1024,"[51.0, 282.0, 130.0, 124.0]",usask_1
+4a3f67d2e,1024,1024,"[982.0, 32.0, 42.0, 59.0]",usask_1
+4a3f67d2e,1024,1024,"[258.0, 728.0, 70.0, 95.0]",usask_1
+4a3f67d2e,1024,1024,"[203.0, 650.0, 50.0, 74.0]",usask_1
+4a3f67d2e,1024,1024,"[0.0, 153.0, 64.0, 47.0]",usask_1
+4a3f67d2e,1024,1024,"[0.0, 918.0, 128.0, 74.0]",usask_1
+4a3f67d2e,1024,1024,"[637.0, 146.0, 129.0, 102.0]",usask_1
+4a3f67d2e,1024,1024,"[1.0, 128.0, 59.0, 45.0]",usask_1
+4a3f67d2e,1024,1024,"[0.0, 842.0, 55.0, 100.0]",usask_1
+4a3f67d2e,1024,1024,"[58.0, 653.0, 105.0, 66.0]",usask_1
+4a3f67d2e,1024,1024,"[796.0, 124.0, 69.0, 61.0]",usask_1
+4a3f67d2e,1024,1024,"[287.0, 905.0, 67.0, 43.0]",usask_1
+4a3f67d2e,1024,1024,"[848.0, 41.0, 52.0, 43.0]",usask_1
+4a3f67d2e,1024,1024,"[730.0, 0.0, 69.0, 41.0]",usask_1
+4a3f67d2e,1024,1024,"[124.0, 580.0, 54.0, 58.0]",usask_1
+4a3f67d2e,1024,1024,"[169.0, 601.0, 48.0, 80.0]",usask_1
+2ccec6dcc,1024,1024,"[334.0, 8.0, 150.0, 210.0]",usask_1
+2ccec6dcc,1024,1024,"[411.0, 128.0, 152.0, 229.0]",usask_1
+2ccec6dcc,1024,1024,"[808.0, 249.0, 96.0, 86.0]",usask_1
+2ccec6dcc,1024,1024,"[508.0, 862.0, 107.0, 113.0]",usask_1
+2ccec6dcc,1024,1024,"[464.0, 668.0, 168.0, 72.0]",usask_1
+2ccec6dcc,1024,1024,"[586.0, 501.0, 157.0, 90.0]",usask_1
+2ccec6dcc,1024,1024,"[72.0, 106.0, 198.0, 98.0]",usask_1
+2ccec6dcc,1024,1024,"[218.0, 333.0, 147.0, 110.0]",usask_1
+2ccec6dcc,1024,1024,"[111.0, 3.0, 200.0, 140.0]",usask_1
+2ccec6dcc,1024,1024,"[160.0, 834.0, 98.0, 84.0]",usask_1
+2ccec6dcc,1024,1024,"[101.0, 171.0, 95.0, 156.0]",usask_1
+2ccec6dcc,1024,1024,"[247.0, 922.0, 176.0, 83.0]",usask_1
+2ccec6dcc,1024,1024,"[0.0, 810.0, 182.0, 74.0]",usask_1
+2ccec6dcc,1024,1024,"[330.0, 249.0, 177.0, 160.0]",usask_1
+2ccec6dcc,1024,1024,"[679.0, 648.0, 117.0, 105.0]",usask_1
+2ccec6dcc,1024,1024,"[662.0, 809.0, 144.0, 180.0]",usask_1
+2ccec6dcc,1024,1024,"[877.0, 889.0, 147.0, 89.0]",usask_1
+2ccec6dcc,1024,1024,"[264.0, 913.0, 165.0, 51.0]",usask_1
+2ccec6dcc,1024,1024,"[0.0, 845.0, 88.0, 95.0]",usask_1
+2ccec6dcc,1024,1024,"[318.0, 734.0, 168.0, 70.0]",usask_1
+2ccec6dcc,1024,1024,"[299.0, 817.0, 154.0, 79.0]",usask_1
+2ccec6dcc,1024,1024,"[0.0, 0.0, 88.0, 103.0]",usask_1
+2ccec6dcc,1024,1024,"[694.0, 335.0, 201.0, 131.0]",usask_1
+2ccec6dcc,1024,1024,"[396.0, 864.0, 92.0, 75.0]",usask_1
+2ccec6dcc,1024,1024,"[407.0, 590.0, 165.0, 114.0]",usask_1
+2ccec6dcc,1024,1024,"[480.0, 582.0, 141.0, 101.0]",usask_1
+2ccec6dcc,1024,1024,"[408.0, 519.0, 118.0, 71.0]",usask_1
+2ccec6dcc,1024,1024,"[467.0, 501.0, 140.0, 81.0]",usask_1
+2ccec6dcc,1024,1024,"[469.0, 424.0, 117.0, 78.0]",usask_1
+2ccec6dcc,1024,1024,"[391.0, 412.0, 125.0, 106.0]",usask_1
+2ccec6dcc,1024,1024,"[348.0, 500.0, 102.0, 84.0]",usask_1
+2ccec6dcc,1024,1024,"[0.0, 410.0, 156.0, 103.0]",usask_1
+2ccec6dcc,1024,1024,"[1.0, 564.0, 117.0, 114.0]",usask_1
+2ccec6dcc,1024,1024,"[515.0, 336.0, 73.0, 115.0]",usask_1
+0435654cd,1024,1024,"[828.0, 371.0, 196.0, 146.0]",usask_1
+0435654cd,1024,1024,"[870.0, 0.0, 131.0, 226.0]",usask_1
+0435654cd,1024,1024,"[116.0, 363.0, 166.0, 97.0]",usask_1
+0435654cd,1024,1024,"[683.0, 541.0, 101.0, 87.0]",usask_1
+0435654cd,1024,1024,"[236.0, 11.0, 338.0, 85.0]",usask_1
+0435654cd,1024,1024,"[222.0, 74.0, 254.0, 106.0]",usask_1
+0435654cd,1024,1024,"[687.0, 410.0, 149.0, 87.0]",usask_1
+0435654cd,1024,1024,"[380.0, 165.0, 107.0, 96.0]",usask_1
+0435654cd,1024,1024,"[602.0, 0.0, 205.0, 45.0]",usask_1
+0435654cd,1024,1024,"[324.0, 892.0, 283.0, 98.0]",usask_1
+0435654cd,1024,1024,"[451.0, 835.0, 220.0, 100.0]",usask_1
+0435654cd,1024,1024,"[718.0, 848.0, 148.0, 120.0]",usask_1
+0435654cd,1024,1024,"[639.0, 692.0, 153.0, 99.0]",usask_1
+0435654cd,1024,1024,"[752.0, 764.0, 156.0, 51.0]",usask_1
+0435654cd,1024,1024,"[186.0, 486.0, 101.0, 127.0]",usask_1
+0435654cd,1024,1024,"[1.0, 313.0, 111.0, 89.0]",usask_1
+0435654cd,1024,1024,"[0.0, 390.0, 96.0, 65.0]",usask_1
+0435654cd,1024,1024,"[4.0, 192.0, 116.0, 69.0]",usask_1
+0435654cd,1024,1024,"[0.0, 49.0, 91.0, 74.0]",usask_1
+0435654cd,1024,1024,"[599.0, 431.0, 60.0, 132.0]",usask_1
+0435654cd,1024,1024,"[715.0, 84.0, 304.0, 174.0]",usask_1
+0435654cd,1024,1024,"[627.0, 96.0, 218.0, 84.0]",usask_1
+0435654cd,1024,1024,"[676.0, 151.0, 123.0, 62.0]",usask_1
+0435654cd,1024,1024,"[452.0, 947.0, 69.0, 77.0]",usask_1
+372d12c55,1024,1024,"[0.0, 0.0, 218.0, 29.0]",usask_1
+372d12c55,1024,1024,"[636.0, 357.0, 142.0, 143.0]",usask_1
+372d12c55,1024,1024,"[47.0, 325.0, 328.0, 164.0]",usask_1
+372d12c55,1024,1024,"[202.0, 0.0, 305.0, 101.0]",usask_1
+372d12c55,1024,1024,"[0.0, 284.0, 289.0, 105.0]",usask_1
+372d12c55,1024,1024,"[710.0, 92.0, 314.0, 135.0]",usask_1
+372d12c55,1024,1024,"[828.0, 562.0, 196.0, 170.0]",usask_1
+372d12c55,1024,1024,"[762.0, 0.0, 158.0, 54.0]",usask_1
+372d12c55,1024,1024,"[974.0, 772.0, 50.0, 163.0]",usask_1
+372d12c55,1024,1024,"[725.0, 751.0, 106.0, 115.0]",usask_1
+372d12c55,1024,1024,"[239.0, 640.0, 389.0, 116.0]",usask_1
+372d12c55,1024,1024,"[518.0, 17.0, 156.0, 144.0]",usask_1
+372d12c55,1024,1024,"[785.0, 758.0, 78.0, 125.0]",usask_1
+372d12c55,1024,1024,"[808.0, 772.0, 85.0, 123.0]",usask_1
+372d12c55,1024,1024,"[948.0, 747.0, 48.0, 250.0]",usask_1
+372d12c55,1024,1024,"[0.0, 99.0, 139.0, 82.0]",usask_1
+372d12c55,1024,1024,"[121.0, 0.0, 238.0, 70.0]",usask_1
+372d12c55,1024,1024,"[234.0, 0.0, 257.0, 111.0]",usask_1
+f5093f57d,1024,1024,"[741.0, 899.0, 116.0, 73.0]",usask_1
+f5093f57d,1024,1024,"[635.0, 964.0, 98.0, 60.0]",usask_1
+f5093f57d,1024,1024,"[614.0, 909.0, 119.0, 86.0]",usask_1
+f5093f57d,1024,1024,"[0.0, 11.0, 116.0, 74.0]",usask_1
+f5093f57d,1024,1024,"[592.0, 468.0, 127.0, 83.0]",usask_1
+f5093f57d,1024,1024,"[532.0, 488.0, 106.0, 76.0]",usask_1
+f5093f57d,1024,1024,"[186.0, 0.0, 83.0, 70.0]",usask_1
+f5093f57d,1024,1024,"[405.0, 918.0, 200.0, 77.0]",usask_1
+f5093f57d,1024,1024,"[397.0, 575.0, 174.0, 151.0]",usask_1
+f5093f57d,1024,1024,"[448.0, 727.0, 167.0, 96.0]",usask_1
+f5093f57d,1024,1024,"[301.0, 40.0, 91.0, 96.0]",usask_1
+f5093f57d,1024,1024,"[207.0, 342.0, 205.0, 159.0]",usask_1
+f5093f57d,1024,1024,"[247.0, 859.0, 327.0, 92.0]",usask_1
+f5093f57d,1024,1024,"[482.0, 320.0, 164.0, 123.0]",usask_1
+f5093f57d,1024,1024,"[616.0, 588.0, 133.0, 235.0]",usask_1
+f5093f57d,1024,1024,"[326.0, 323.0, 201.0, 96.0]",usask_1
+f5093f57d,1024,1024,"[751.0, 966.0, 93.0, 58.0]",usask_1
+f5093f57d,1024,1024,"[0.0, 576.0, 59.0, 82.0]",usask_1
+f5093f57d,1024,1024,"[0.0, 525.0, 105.0, 56.0]",usask_1
+f5093f57d,1024,1024,"[89.0, 316.0, 90.0, 99.0]",usask_1
+f5093f57d,1024,1024,"[126.0, 395.0, 90.0, 82.0]",usask_1
+f5093f57d,1024,1024,"[120.0, 37.0, 83.0, 57.0]",usask_1
+f5093f57d,1024,1024,"[65.0, 222.0, 59.0, 48.0]",usask_1
+f5093f57d,1024,1024,"[139.0, 0.0, 59.0, 34.0]",usask_1
+f5093f57d,1024,1024,"[914.0, 875.0, 50.0, 61.0]",usask_1
+c6b4933f7,1024,1024,"[0.0, 74.0, 39.0, 59.0]",usask_1
+c6b4933f7,1024,1024,"[811.0, 167.0, 64.0, 76.0]",usask_1
+c6b4933f7,1024,1024,"[851.0, 599.0, 70.0, 73.0]",usask_1
+c6b4933f7,1024,1024,"[881.0, 494.0, 72.0, 60.0]",usask_1
+c6b4933f7,1024,1024,"[0.0, 963.0, 169.0, 61.0]",usask_1
+c6b4933f7,1024,1024,"[53.0, 715.0, 102.0, 92.0]",usask_1
+c6b4933f7,1024,1024,"[0.0, 424.0, 49.0, 106.0]",usask_1
+c6b4933f7,1024,1024,"[754.0, 898.0, 86.0, 70.0]",usask_1
+c6b4933f7,1024,1024,"[69.0, 566.0, 121.0, 130.0]",usask_1
+c6b4933f7,1024,1024,"[882.0, 0.0, 73.0, 62.0]",usask_1
+c6b4933f7,1024,1024,"[208.0, 363.0, 83.0, 85.0]",usask_1
+c6b4933f7,1024,1024,"[195.0, 296.0, 83.0, 70.0]",usask_1
+c6b4933f7,1024,1024,"[867.0, 676.0, 59.0, 80.0]",usask_1
+c6b4933f7,1024,1024,"[779.0, 659.0, 103.0, 114.0]",usask_1
+c6b4933f7,1024,1024,"[0.0, 201.0, 36.0, 77.0]",usask_1
+c6b4933f7,1024,1024,"[421.0, 953.0, 79.0, 71.0]",usask_1
+c6b4933f7,1024,1024,"[817.0, 350.0, 115.0, 76.0]",usask_1
+c6b4933f7,1024,1024,"[858.0, 891.0, 97.0, 71.0]",usask_1
+c6b4933f7,1024,1024,"[92.0, 485.0, 104.0, 100.0]",usask_1
+c6b4933f7,1024,1024,"[660.0, 650.0, 99.0, 57.0]",usask_1
+c6b4933f7,1024,1024,"[915.0, 317.0, 109.0, 70.0]",usask_1
+c6b4933f7,1024,1024,"[495.0, 255.0, 113.0, 85.0]",usask_1
+c6b4933f7,1024,1024,"[525.0, 0.0, 157.0, 39.0]",usask_1
+c6b4933f7,1024,1024,"[243.0, 968.0, 125.0, 56.0]",usask_1
+c6b4933f7,1024,1024,"[697.0, 690.0, 111.0, 60.0]",usask_1
+c6b4933f7,1024,1024,"[660.0, 1.0, 103.0, 62.0]",usask_1
+c6b4933f7,1024,1024,"[239.0, 231.0, 91.0, 77.0]",usask_1
+c6b4933f7,1024,1024,"[372.0, 205.0, 66.0, 104.0]",usask_1
+c6b4933f7,1024,1024,"[107.0, 53.0, 101.0, 116.0]",usask_1
+c6b4933f7,1024,1024,"[203.0, 116.0, 83.0, 61.0]",usask_1
+c6b4933f7,1024,1024,"[27.0, 850.0, 102.0, 72.0]",usask_1
+c6b4933f7,1024,1024,"[100.0, 900.0, 207.0, 99.0]",usask_1
+c6b4933f7,1024,1024,"[44.0, 505.0, 76.0, 72.0]",usask_1
+c6b4933f7,1024,1024,"[187.0, 683.0, 51.0, 37.0]",usask_1
+c6b4933f7,1024,1024,"[819.0, 481.0, 78.0, 54.0]",usask_1
+895420d9f,1024,1024,"[0.0, 906.0, 88.0, 76.0]",usask_1
+895420d9f,1024,1024,"[802.0, 335.0, 83.0, 190.0]",usask_1
+895420d9f,1024,1024,"[143.0, 684.0, 156.0, 63.0]",usask_1
+895420d9f,1024,1024,"[101.0, 972.0, 91.0, 52.0]",usask_1
+895420d9f,1024,1024,"[862.0, 835.0, 79.0, 133.0]",usask_1
+895420d9f,1024,1024,"[791.0, 559.0, 90.0, 86.0]",usask_1
+895420d9f,1024,1024,"[0.0, 774.0, 151.0, 91.0]",usask_1
+895420d9f,1024,1024,"[549.0, 495.0, 128.0, 79.0]",usask_1
+895420d9f,1024,1024,"[0.0, 359.0, 88.0, 138.0]",usask_1
+895420d9f,1024,1024,"[97.0, 146.0, 187.0, 91.0]",usask_1
+895420d9f,1024,1024,"[0.0, 660.0, 46.0, 97.0]",usask_1
+895420d9f,1024,1024,"[956.0, 801.0, 68.0, 82.0]",usask_1
+895420d9f,1024,1024,"[905.0, 26.0, 76.0, 69.0]",usask_1
+895420d9f,1024,1024,"[864.0, 432.0, 88.0, 93.0]",usask_1
+895420d9f,1024,1024,"[752.0, 425.0, 98.0, 97.0]",usask_1
+895420d9f,1024,1024,"[863.0, 736.0, 66.0, 81.0]",usask_1
+895420d9f,1024,1024,"[938.0, 549.0, 62.0, 61.0]",usask_1
+895420d9f,1024,1024,"[83.0, 257.0, 98.0, 67.0]",usask_1
+895420d9f,1024,1024,"[97.0, 871.0, 73.0, 76.0]",usask_1
+895420d9f,1024,1024,"[292.0, 162.0, 70.0, 73.0]",usask_1
+895420d9f,1024,1024,"[984.0, 949.0, 40.0, 45.0]",usask_1
+8d6bb70e5,1024,1024,"[441.0, 931.0, 41.0, 52.0]",usask_1
+8d6bb70e5,1024,1024,"[893.0, 213.0, 62.0, 125.0]",usask_1
+8d6bb70e5,1024,1024,"[687.0, 448.0, 115.0, 57.0]",usask_1
+8d6bb70e5,1024,1024,"[811.0, 64.0, 86.0, 77.0]",usask_1
+8d6bb70e5,1024,1024,"[708.0, 508.0, 138.0, 59.0]",usask_1
+8d6bb70e5,1024,1024,"[879.0, 727.0, 77.0, 66.0]",usask_1
+8d6bb70e5,1024,1024,"[973.0, 284.0, 51.0, 94.0]",usask_1
+8d6bb70e5,1024,1024,"[199.0, 52.0, 87.0, 156.0]",usask_1
+8d6bb70e5,1024,1024,"[397.0, 118.0, 60.0, 63.0]",usask_1
+8d6bb70e5,1024,1024,"[933.0, 0.0, 64.0, 33.0]",usask_1
+8d6bb70e5,1024,1024,"[923.0, 521.0, 94.0, 85.0]",usask_1
+8d6bb70e5,1024,1024,"[741.0, 585.0, 89.0, 131.0]",usask_1
+8d6bb70e5,1024,1024,"[741.0, 111.0, 94.0, 80.0]",usask_1
+8d6bb70e5,1024,1024,"[951.0, 180.0, 57.0, 86.0]",usask_1
+8d6bb70e5,1024,1024,"[852.0, 0.0, 87.0, 83.0]",usask_1
+8d6bb70e5,1024,1024,"[490.0, 27.0, 66.0, 57.0]",usask_1
+8d6bb70e5,1024,1024,"[91.0, 729.0, 116.0, 142.0]",usask_1
+8d6bb70e5,1024,1024,"[6.0, 764.0, 169.0, 164.0]",usask_1
+8d6bb70e5,1024,1024,"[184.0, 282.0, 98.0, 134.0]",usask_1
+8d6bb70e5,1024,1024,"[960.0, 473.0, 62.0, 60.0]",usask_1
+8d6bb70e5,1024,1024,"[287.0, 52.0, 60.0, 66.0]",usask_1
+8d6bb70e5,1024,1024,"[450.0, 0.0, 90.0, 59.0]",usask_1
+8d6bb70e5,1024,1024,"[809.0, 208.0, 61.0, 59.0]",usask_1
+8d6bb70e5,1024,1024,"[884.0, 129.0, 73.0, 56.0]",usask_1
+8d6bb70e5,1024,1024,"[981.0, 71.0, 43.0, 97.0]",usask_1
+8d6bb70e5,1024,1024,"[1.0, 8.0, 64.0, 30.0]",usask_1
+531acb360,1024,1024,"[0.0, 701.0, 93.0, 99.0]",usask_1
+531acb360,1024,1024,"[806.0, 863.0, 102.0, 74.0]",usask_1
+531acb360,1024,1024,"[932.0, 951.0, 79.0, 73.0]",usask_1
+531acb360,1024,1024,"[809.0, 941.0, 97.0, 74.0]",usask_1
+531acb360,1024,1024,"[974.0, 859.0, 50.0, 76.0]",usask_1
+531acb360,1024,1024,"[0.0, 72.0, 128.0, 153.0]",usask_1
+531acb360,1024,1024,"[680.0, 953.0, 69.0, 71.0]",usask_1
+531acb360,1024,1024,"[148.0, 710.0, 106.0, 103.0]",usask_1
+531acb360,1024,1024,"[877.0, 20.0, 76.0, 96.0]",usask_1
+531acb360,1024,1024,"[913.0, 324.0, 109.0, 141.0]",usask_1
+531acb360,1024,1024,"[879.0, 512.0, 89.0, 92.0]",usask_1
+531acb360,1024,1024,"[525.0, 360.0, 179.0, 116.0]",usask_1
+531acb360,1024,1024,"[114.0, 835.0, 127.0, 120.0]",usask_1
+531acb360,1024,1024,"[338.0, 436.0, 149.0, 86.0]",usask_1
+531acb360,1024,1024,"[562.0, 791.0, 144.0, 113.0]",usask_1
+531acb360,1024,1024,"[16.0, 0.0, 138.0, 163.0]",usask_1
+531acb360,1024,1024,"[40.0, 929.0, 245.0, 84.0]",usask_1
+531acb360,1024,1024,"[792.0, 568.0, 96.0, 153.0]",usask_1
+531acb360,1024,1024,"[219.0, 287.0, 85.0, 107.0]",usask_1
+531acb360,1024,1024,"[76.0, 333.0, 117.0, 120.0]",usask_1
+531acb360,1024,1024,"[636.0, 495.0, 110.0, 103.0]",usask_1
+531acb360,1024,1024,"[232.0, 572.0, 127.0, 107.0]",usask_1
+531acb360,1024,1024,"[430.0, 566.0, 80.0, 133.0]",usask_1
+531acb360,1024,1024,"[265.0, 474.0, 111.0, 73.0]",usask_1
+531acb360,1024,1024,"[935.0, 813.0, 46.0, 70.0]",usask_1
+531acb360,1024,1024,"[723.0, 641.0, 96.0, 55.0]",usask_1
+531acb360,1024,1024,"[692.0, 679.0, 80.0, 54.0]",usask_1
+531acb360,1024,1024,"[775.0, 719.0, 37.0, 47.0]",usask_1
+531acb360,1024,1024,"[882.0, 724.0, 48.0, 48.0]",usask_1
+531acb360,1024,1024,"[888.0, 782.0, 37.0, 63.0]",usask_1
+531acb360,1024,1024,"[943.0, 760.0, 79.0, 61.0]",usask_1
+4aeb37788,1024,1024,"[210.0, 40.0, 69.0, 48.0]",usask_1
+4aeb37788,1024,1024,"[724.0, 242.0, 99.0, 83.0]",usask_1
+4aeb37788,1024,1024,"[85.0, 136.0, 96.0, 69.0]",usask_1
+4aeb37788,1024,1024,"[274.0, 135.0, 80.0, 54.0]",usask_1
+4aeb37788,1024,1024,"[125.0, 47.0, 86.0, 71.0]",usask_1
+4aeb37788,1024,1024,"[848.0, 0.0, 81.0, 83.0]",usask_1
+4aeb37788,1024,1024,"[771.0, 122.0, 83.0, 120.0]",usask_1
+4aeb37788,1024,1024,"[4.0, 706.0, 179.0, 81.0]",usask_1
+4aeb37788,1024,1024,"[291.0, 725.0, 185.0, 74.0]",usask_1
+4aeb37788,1024,1024,"[370.0, 33.0, 137.0, 86.0]",usask_1
+4aeb37788,1024,1024,"[370.0, 178.0, 86.0, 76.0]",usask_1
+4aeb37788,1024,1024,"[190.0, 124.0, 97.0, 74.0]",usask_1
+4aeb37788,1024,1024,"[0.0, 329.0, 58.0, 72.0]",usask_1
+4aeb37788,1024,1024,"[56.0, 9.0, 141.0, 68.0]",usask_1
+4aeb37788,1024,1024,"[397.0, 785.0, 127.0, 129.0]",usask_1
+4aeb37788,1024,1024,"[933.0, 832.0, 79.0, 119.0]",usask_1
+4aeb37788,1024,1024,"[110.0, 313.0, 130.0, 97.0]",usask_1
+4aeb37788,1024,1024,"[3.0, 404.0, 157.0, 110.0]",usask_1
+4aeb37788,1024,1024,"[451.0, 0.0, 153.0, 123.0]",usask_1
+4aeb37788,1024,1024,"[43.0, 518.0, 104.0, 83.0]",usask_1
+4aeb37788,1024,1024,"[287.0, 411.0, 90.0, 112.0]",usask_1
+4aeb37788,1024,1024,"[466.0, 141.0, 139.0, 109.0]",usask_1
+4aeb37788,1024,1024,"[0.0, 120.0, 98.0, 59.0]",usask_1
+4aeb37788,1024,1024,"[8.0, 795.0, 66.0, 48.0]",usask_1
+4aeb37788,1024,1024,"[346.0, 922.0, 97.0, 83.0]",usask_1
+4aeb37788,1024,1024,"[590.0, 694.0, 123.0, 87.0]",usask_1
+4aeb37788,1024,1024,"[54.0, 222.0, 140.0, 74.0]",usask_1
+4aeb37788,1024,1024,"[850.0, 94.0, 76.0, 75.0]",usask_1
+4aeb37788,1024,1024,"[826.0, 29.0, 66.0, 99.0]",usask_1
+4aeb37788,1024,1024,"[365.0, 824.0, 82.0, 93.0]",usask_1
+4aeb37788,1024,1024,"[121.0, 945.0, 87.0, 72.0]",usask_1
+4aeb37788,1024,1024,"[290.0, 951.0, 85.0, 72.0]",usask_1
+4aeb37788,1024,1024,"[158.0, 774.0, 103.0, 74.0]",usask_1
+4aeb37788,1024,1024,"[240.0, 579.0, 91.0, 76.0]",usask_1
+4aeb37788,1024,1024,"[0.0, 616.0, 51.0, 88.0]",usask_1
+4aeb37788,1024,1024,"[761.0, 426.0, 72.0, 70.0]",usask_1
+4aeb37788,1024,1024,"[815.0, 448.0, 65.0, 46.0]",usask_1
+4aeb37788,1024,1024,"[262.0, 202.0, 34.0, 46.0]",usask_1
+4aeb37788,1024,1024,"[51.0, 785.0, 61.0, 98.0]",usask_1
+afb7671e5,1024,1024,"[664.0, 597.0, 140.0, 87.0]",usask_1
+afb7671e5,1024,1024,"[117.0, 340.0, 77.0, 86.0]",usask_1
+afb7671e5,1024,1024,"[0.0, 648.0, 101.0, 71.0]",usask_1
+afb7671e5,1024,1024,"[346.0, 677.0, 84.0, 100.0]",usask_1
+afb7671e5,1024,1024,"[249.0, 735.0, 89.0, 99.0]",usask_1
+afb7671e5,1024,1024,"[0.0, 180.0, 107.0, 71.0]",usask_1
+afb7671e5,1024,1024,"[254.0, 320.0, 100.0, 102.0]",usask_1
+afb7671e5,1024,1024,"[105.0, 779.0, 179.0, 61.0]",usask_1
+afb7671e5,1024,1024,"[232.0, 490.0, 98.0, 81.0]",usask_1
+afb7671e5,1024,1024,"[533.0, 889.0, 152.0, 135.0]",usask_1
+afb7671e5,1024,1024,"[84.0, 628.0, 100.0, 78.0]",usask_1
+afb7671e5,1024,1024,"[890.0, 935.0, 78.0, 65.0]",usask_1
+afb7671e5,1024,1024,"[531.0, 199.0, 120.0, 80.0]",usask_1
+afb7671e5,1024,1024,"[341.0, 490.0, 116.0, 83.0]",usask_1
+afb7671e5,1024,1024,"[58.0, 112.0, 109.0, 117.0]",usask_1
+afb7671e5,1024,1024,"[178.0, 954.0, 70.0, 70.0]",usask_1
+afb7671e5,1024,1024,"[737.0, 320.0, 161.0, 156.0]",usask_1
+afb7671e5,1024,1024,"[85.0, 460.0, 102.0, 134.0]",usask_1
+afb7671e5,1024,1024,"[324.0, 308.0, 96.0, 142.0]",usask_1
+afb7671e5,1024,1024,"[620.0, 80.0, 58.0, 92.0]",usask_1
+afb7671e5,1024,1024,"[415.0, 599.0, 109.0, 74.0]",usask_1
+afb7671e5,1024,1024,"[0.0, 522.0, 79.0, 79.0]",usask_1
+afb7671e5,1024,1024,"[968.0, 272.0, 56.0, 93.0]",usask_1
+afb7671e5,1024,1024,"[755.0, 629.0, 180.0, 97.0]",usask_1
+afb7671e5,1024,1024,"[768.0, 0.0, 95.0, 149.0]",usask_1
+afb7671e5,1024,1024,"[490.0, 846.0, 144.0, 52.0]",usask_1
+afb7671e5,1024,1024,"[421.0, 863.0, 133.0, 113.0]",usask_1
+afb7671e5,1024,1024,"[134.0, 824.0, 155.0, 94.0]",usask_1
+afb7671e5,1024,1024,"[253.0, 854.0, 83.0, 99.0]",usask_1
+afb7671e5,1024,1024,"[108.0, 892.0, 139.0, 86.0]",usask_1
+afb7671e5,1024,1024,"[17.0, 850.0, 117.0, 67.0]",usask_1
+afb7671e5,1024,1024,"[199.0, 715.0, 48.0, 42.0]",usask_1
+afb7671e5,1024,1024,"[921.0, 588.0, 56.0, 56.0]",usask_1
+cc9092474,1024,1024,"[491.0, 970.0, 94.0, 30.0]",usask_1
+cc9092474,1024,1024,"[660.0, 784.0, 94.0, 59.0]",usask_1
+cc9092474,1024,1024,"[421.0, 0.0, 112.0, 90.0]",usask_1
+cc9092474,1024,1024,"[595.0, 192.0, 76.0, 57.0]",usask_1
+cc9092474,1024,1024,"[0.0, 849.0, 99.0, 82.0]",usask_1
+cc9092474,1024,1024,"[466.0, 94.0, 100.0, 77.0]",usask_1
+cc9092474,1024,1024,"[538.0, 52.0, 100.0, 82.0]",usask_1
+cc9092474,1024,1024,"[599.0, 95.0, 86.0, 109.0]",usask_1
+cc9092474,1024,1024,"[496.0, 986.0, 95.0, 38.0]",usask_1
+cc9092474,1024,1024,"[0.0, 136.0, 93.0, 57.0]",usask_1
+cc9092474,1024,1024,"[437.0, 385.0, 87.0, 66.0]",usask_1
+cc9092474,1024,1024,"[806.0, 128.0, 72.0, 89.0]",usask_1
+cc9092474,1024,1024,"[72.0, 369.0, 149.0, 154.0]",usask_1
+cc9092474,1024,1024,"[689.0, 546.0, 83.0, 101.0]",usask_1
+cc9092474,1024,1024,"[781.0, 0.0, 71.0, 79.0]",usask_1
+cc9092474,1024,1024,"[861.0, 258.0, 72.0, 96.0]",usask_1
+cc9092474,1024,1024,"[705.0, 844.0, 106.0, 68.0]",usask_1
+cc9092474,1024,1024,"[499.0, 230.0, 99.0, 79.0]",usask_1
+cc9092474,1024,1024,"[665.0, 255.0, 133.0, 54.0]",usask_1
+cc9092474,1024,1024,"[284.0, 108.0, 76.0, 74.0]",usask_1
+cc9092474,1024,1024,"[697.0, 0.0, 83.0, 66.0]",usask_1
+cc9092474,1024,1024,"[597.0, 435.0, 89.0, 72.0]",usask_1
+cc9092474,1024,1024,"[675.0, 912.0, 58.0, 60.0]",usask_1
+cc9092474,1024,1024,"[751.0, 464.0, 83.0, 80.0]",usask_1
+cc9092474,1024,1024,"[960.0, 818.0, 64.0, 80.0]",usask_1
+cc9092474,1024,1024,"[324.0, 986.0, 126.0, 38.0]",usask_1
+cc9092474,1024,1024,"[367.0, 309.0, 136.0, 69.0]",usask_1
+cc9092474,1024,1024,"[738.0, 206.0, 92.0, 56.0]",usask_1
+cc9092474,1024,1024,"[898.0, 819.0, 76.0, 95.0]",usask_1
+cc9092474,1024,1024,"[195.0, 0.0, 120.0, 41.0]",usask_1
+cc9092474,1024,1024,"[905.0, 307.0, 59.0, 109.0]",usask_1
+cc9092474,1024,1024,"[408.0, 603.0, 103.0, 74.0]",usask_1
+cc9092474,1024,1024,"[562.0, 527.0, 105.0, 73.0]",usask_1
+cc9092474,1024,1024,"[407.0, 457.0, 106.0, 67.0]",usask_1
+cc9092474,1024,1024,"[448.0, 892.0, 138.0, 76.0]",usask_1
+cc9092474,1024,1024,"[222.0, 633.0, 147.0, 79.0]",usask_1
+cc9092474,1024,1024,"[441.0, 798.0, 145.0, 87.0]",usask_1
+cc9092474,1024,1024,"[599.0, 934.0, 62.0, 63.0]",usask_1
+cc9092474,1024,1024,"[725.0, 776.0, 59.0, 68.0]",usask_1
+cc9092474,1024,1024,"[495.0, 344.0, 69.0, 66.0]",usask_1
+cc9092474,1024,1024,"[1.0, 781.0, 47.0, 55.0]",usask_1
+cc9092474,1024,1024,"[9.0, 82.0, 64.0, 61.0]",usask_1
+cc9092474,1024,1024,"[81.0, 12.0, 89.0, 81.0]",usask_1
+cc9092474,1024,1024,"[0.0, 13.0, 109.0, 64.0]",usask_1
+cc9092474,1024,1024,"[499.0, 0.0, 66.0, 44.0]",usask_1
+cc9092474,1024,1024,"[0.0, 253.0, 60.0, 61.0]",usask_1
+c3c03e0b7,1024,1024,"[933.0, 333.0, 89.0, 64.0]",usask_1
+c3c03e0b7,1024,1024,"[138.0, 708.0, 315.0, 137.0]",usask_1
+c3c03e0b7,1024,1024,"[142.0, 183.0, 297.0, 105.0]",usask_1
+c3c03e0b7,1024,1024,"[22.0, 135.0, 229.0, 126.0]",usask_1
+c3c03e0b7,1024,1024,"[161.0, 888.0, 252.0, 63.0]",usask_1
+c3c03e0b7,1024,1024,"[227.0, 613.0, 429.0, 185.0]",usask_1
+c3c03e0b7,1024,1024,"[0.0, 654.0, 217.0, 128.0]",usask_1
+c3c03e0b7,1024,1024,"[1.0, 40.0, 298.0, 83.0]",usask_1
+42e247347,1024,1024,"[485.0, 909.0, 384.0, 103.0]",usask_1
+42e247347,1024,1024,"[844.0, 0.0, 139.0, 126.0]",usask_1
+42e247347,1024,1024,"[411.0, 785.0, 364.0, 154.0]",usask_1
+42e247347,1024,1024,"[280.0, 840.0, 206.0, 68.0]",usask_1
+42e247347,1024,1024,"[0.0, 700.0, 223.0, 190.0]",usask_1
+42e247347,1024,1024,"[0.0, 134.0, 120.0, 194.0]",usask_1
+42e247347,1024,1024,"[94.0, 701.0, 196.0, 78.0]",usask_1
+42e247347,1024,1024,"[657.0, 312.0, 149.0, 200.0]",usask_1
+42e247347,1024,1024,"[0.0, 585.0, 169.0, 130.0]",usask_1
+42e247347,1024,1024,"[0.0, 300.0, 243.0, 151.0]",usask_1
+42e247347,1024,1024,"[924.0, 165.0, 97.0, 78.0]",usask_1
+42e247347,1024,1024,"[260.0, 926.0, 285.0, 98.0]",usask_1
+42e247347,1024,1024,"[272.0, 0.0, 292.0, 62.0]",usask_1
+42e247347,1024,1024,"[692.0, 3.0, 199.0, 62.0]",usask_1
+42e247347,1024,1024,"[119.0, 123.0, 226.0, 51.0]",usask_1
+42e247347,1024,1024,"[0.0, 0.0, 229.0, 114.0]",usask_1
+42e247347,1024,1024,"[751.0, 168.0, 229.0, 158.0]",usask_1
+42e247347,1024,1024,"[798.0, 400.0, 140.0, 82.0]",usask_1
+42e247347,1024,1024,"[307.0, 27.0, 146.0, 141.0]",usask_1
+42e247347,1024,1024,"[736.0, 407.0, 133.0, 104.0]",usask_1
+5738fcf14,1024,1024,"[559.0, 367.0, 140.0, 86.0]",usask_1
+5738fcf14,1024,1024,"[616.0, 154.0, 123.0, 133.0]",usask_1
+5738fcf14,1024,1024,"[317.0, 90.0, 103.0, 93.0]",usask_1
+5738fcf14,1024,1024,"[0.0, 239.0, 40.0, 70.0]",usask_1
+5738fcf14,1024,1024,"[869.0, 0.0, 86.0, 99.0]",usask_1
+5738fcf14,1024,1024,"[130.0, 236.0, 86.0, 181.0]",usask_1
+5738fcf14,1024,1024,"[58.0, 664.0, 84.0, 110.0]",usask_1
+5738fcf14,1024,1024,"[473.0, 85.0, 176.0, 87.0]",usask_1
+5738fcf14,1024,1024,"[498.0, 768.0, 169.0, 123.0]",usask_1
+5738fcf14,1024,1024,"[949.0, 742.0, 75.0, 80.0]",usask_1
+5738fcf14,1024,1024,"[869.0, 626.0, 142.0, 147.0]",usask_1
+5738fcf14,1024,1024,"[808.0, 43.0, 110.0, 147.0]",usask_1
+5738fcf14,1024,1024,"[555.0, 431.0, 154.0, 103.0]",usask_1
+5738fcf14,1024,1024,"[264.0, 613.0, 216.0, 128.0]",usask_1
+5738fcf14,1024,1024,"[561.0, 105.0, 147.0, 70.0]",usask_1
+5738fcf14,1024,1024,"[279.0, 194.0, 100.0, 184.0]",usask_1
+5738fcf14,1024,1024,"[787.0, 440.0, 99.0, 131.0]",usask_1
+5738fcf14,1024,1024,"[721.0, 513.0, 86.0, 70.0]",usask_1
+5738fcf14,1024,1024,"[0.0, 379.0, 92.0, 78.0]",usask_1
+5738fcf14,1024,1024,"[21.0, 324.0, 97.0, 104.0]",usask_1
+5738fcf14,1024,1024,"[0.0, 136.0, 109.0, 111.0]",usask_1
+5738fcf14,1024,1024,"[0.0, 70.0, 92.0, 81.0]",usask_1
+5738fcf14,1024,1024,"[719.0, 384.0, 74.0, 118.0]",usask_1
+5738fcf14,1024,1024,"[607.0, 259.0, 151.0, 131.0]",usask_1
+5738fcf14,1024,1024,"[450.0, 301.0, 114.0, 103.0]",usask_1
+5738fcf14,1024,1024,"[398.0, 159.0, 176.0, 154.0]",usask_1
+5738fcf14,1024,1024,"[274.0, 249.0, 98.0, 179.0]",usask_1
+5738fcf14,1024,1024,"[375.0, 260.0, 187.0, 125.0]",usask_1
+5738fcf14,1024,1024,"[960.0, 908.0, 64.0, 110.0]",usask_1
+5738fcf14,1024,1024,"[786.0, 681.0, 56.0, 44.0]",usask_1
+5738fcf14,1024,1024,"[883.0, 746.0, 66.0, 74.0]",usask_1
+5738fcf14,1024,1024,"[135.0, 232.0, 67.0, 109.0]",usask_1
+31a6e55f2,1024,1024,"[42.0, 84.0, 96.0, 130.0]",usask_1
+31a6e55f2,1024,1024,"[0.0, 0.0, 67.0, 127.0]",usask_1
+31a6e55f2,1024,1024,"[868.0, 20.0, 156.0, 77.0]",usask_1
+31a6e55f2,1024,1024,"[119.0, 806.0, 59.0, 175.0]",usask_1
+31a6e55f2,1024,1024,"[914.0, 52.0, 110.0, 107.0]",usask_1
+31a6e55f2,1024,1024,"[0.0, 145.0, 105.0, 86.0]",usask_1
+31a6e55f2,1024,1024,"[275.0, 688.0, 176.0, 123.0]",usask_1
+31a6e55f2,1024,1024,"[444.0, 803.0, 161.0, 221.0]",usask_1
+31a6e55f2,1024,1024,"[460.0, 407.0, 131.0, 79.0]",usask_1
+31a6e55f2,1024,1024,"[26.0, 865.0, 55.0, 72.0]",usask_1
+31a6e55f2,1024,1024,"[741.0, 57.0, 95.0, 153.0]",usask_1
+31a6e55f2,1024,1024,"[792.0, 180.0, 62.0, 112.0]",usask_1
+31a6e55f2,1024,1024,"[747.0, 185.0, 62.0, 79.0]",usask_1
+ca4cb597b,1024,1024,"[503.0, 312.0, 73.0, 76.0]",usask_1
+ca4cb597b,1024,1024,"[563.0, 543.0, 109.0, 67.0]",usask_1
+ca4cb597b,1024,1024,"[598.0, 443.0, 147.0, 128.0]",usask_1
+ca4cb597b,1024,1024,"[401.0, 630.0, 51.0, 59.0]",usask_1
+ca4cb597b,1024,1024,"[960.0, 107.0, 64.0, 80.0]",usask_1
+ca4cb597b,1024,1024,"[448.0, 415.0, 87.0, 70.0]",usask_1
+ca4cb597b,1024,1024,"[708.0, 666.0, 93.0, 66.0]",usask_1
+ca4cb597b,1024,1024,"[736.0, 724.0, 88.0, 67.0]",usask_1
+ca4cb597b,1024,1024,"[396.0, 537.0, 96.0, 67.0]",usask_1
+ca4cb597b,1024,1024,"[966.0, 656.0, 58.0, 80.0]",usask_1
+ca4cb597b,1024,1024,"[836.0, 321.0, 113.0, 78.0]",usask_1
+ca4cb597b,1024,1024,"[37.0, 943.0, 91.0, 66.0]",usask_1
+ca4cb597b,1024,1024,"[0.0, 616.0, 92.0, 66.0]",usask_1
+ca4cb597b,1024,1024,"[0.0, 0.0, 59.0, 52.0]",usask_1
+ca4cb597b,1024,1024,"[336.0, 483.0, 89.0, 69.0]",usask_1
+ca4cb597b,1024,1024,"[388.0, 365.0, 100.0, 84.0]",usask_1
+ca4cb597b,1024,1024,"[511.0, 538.0, 75.0, 57.0]",usask_1
+4e5c1078d,1024,1024,"[0.0, 527.0, 58.0, 77.0]",usask_1
+4e5c1078d,1024,1024,"[45.0, 369.0, 113.0, 96.0]",usask_1
+4e5c1078d,1024,1024,"[3.0, 941.0, 157.0, 67.0]",usask_1
+4e5c1078d,1024,1024,"[345.0, 59.0, 75.0, 89.0]",usask_1
+4e5c1078d,1024,1024,"[726.0, 0.0, 72.0, 40.0]",usask_1
+4e5c1078d,1024,1024,"[57.0, 491.0, 112.0, 86.0]",usask_1
+4e5c1078d,1024,1024,"[1017.0, 77.0, 7.0, 69.0]",usask_1
+4e5c1078d,1024,1024,"[768.0, 997.0, 103.0, 27.0]",usask_1
+4e5c1078d,1024,1024,"[751.0, 160.0, 70.0, 66.0]",usask_1
+4e5c1078d,1024,1024,"[210.0, 344.0, 71.0, 113.0]",usask_1
+4e5c1078d,1024,1024,"[471.0, 60.0, 141.0, 104.0]",usask_1
+4e5c1078d,1024,1024,"[620.0, 931.0, 98.0, 59.0]",usask_1
+4e5c1078d,1024,1024,"[468.0, 592.0, 184.0, 93.0]",usask_1
+4e5c1078d,1024,1024,"[498.0, 535.0, 123.0, 83.0]",usask_1
+4e5c1078d,1024,1024,"[764.0, 535.0, 113.0, 100.0]",usask_1
+4e5c1078d,1024,1024,"[269.0, 374.0, 83.0, 122.0]",usask_1
+4e5c1078d,1024,1024,"[661.0, 18.0, 105.0, 64.0]",usask_1
+4e5c1078d,1024,1024,"[711.0, 91.0, 88.0, 66.0]",usask_1
+4e5c1078d,1024,1024,"[828.0, 202.0, 90.0, 93.0]",usask_1
+4e5c1078d,1024,1024,"[619.0, 151.0, 90.0, 83.0]",usask_1
+4e5c1078d,1024,1024,"[639.0, 243.0, 156.0, 80.0]",usask_1
+4e5c1078d,1024,1024,"[559.0, 333.0, 123.0, 90.0]",usask_1
+4e5c1078d,1024,1024,"[512.0, 677.0, 119.0, 93.0]",usask_1
+4e5c1078d,1024,1024,"[930.0, 812.0, 71.0, 64.0]",usask_1
+4e5c1078d,1024,1024,"[715.0, 892.0, 110.0, 72.0]",usask_1
+4e5c1078d,1024,1024,"[834.0, 894.0, 70.0, 97.0]",usask_1
+4e5c1078d,1024,1024,"[820.0, 852.0, 79.0, 60.0]",usask_1
+4e5c1078d,1024,1024,"[0.0, 629.0, 70.0, 62.0]",usask_1
+4e5c1078d,1024,1024,"[0.0, 168.0, 96.0, 94.0]",usask_1
+4e5c1078d,1024,1024,"[120.0, 90.0, 92.0, 70.0]",usask_1
+4e5c1078d,1024,1024,"[43.0, 17.0, 124.0, 86.0]",usask_1
+4e5c1078d,1024,1024,"[0.0, 0.0, 67.0, 66.0]",usask_1
+4e5c1078d,1024,1024,"[710.0, 300.0, 60.0, 87.0]",usask_1
+4e5c1078d,1024,1024,"[490.0, 116.0, 148.0, 111.0]",usask_1
+4e5c1078d,1024,1024,"[171.0, 382.0, 53.0, 83.0]",usask_1
+4e5c1078d,1024,1024,"[0.0, 762.0, 75.0, 78.0]",usask_1
+4e5c1078d,1024,1024,"[841.0, 100.0, 40.0, 52.0]",usask_1
+29e44e305,1024,1024,"[940.0, 384.0, 65.0, 86.0]",usask_1
+29e44e305,1024,1024,"[100.0, 271.0, 115.0, 79.0]",usask_1
+29e44e305,1024,1024,"[901.0, 470.0, 83.0, 96.0]",usask_1
+29e44e305,1024,1024,"[872.0, 346.0, 93.0, 104.0]",usask_1
+29e44e305,1024,1024,"[155.0, 695.0, 103.0, 79.0]",usask_1
+29e44e305,1024,1024,"[194.0, 526.0, 125.0, 69.0]",usask_1
+29e44e305,1024,1024,"[964.0, 598.0, 60.0, 65.0]",usask_1
+29e44e305,1024,1024,"[55.0, 107.0, 97.0, 90.0]",usask_1
+29e44e305,1024,1024,"[754.0, 768.0, 142.0, 103.0]",usask_1
+29e44e305,1024,1024,"[220.0, 292.0, 62.0, 87.0]",usask_1
+29e44e305,1024,1024,"[953.0, 229.0, 69.0, 119.0]",usask_1
+29e44e305,1024,1024,"[0.0, 935.0, 180.0, 77.0]",usask_1
+29e44e305,1024,1024,"[3.0, 341.0, 125.0, 82.0]",usask_1
+29e44e305,1024,1024,"[989.0, 696.0, 35.0, 114.0]",usask_1
+29e44e305,1024,1024,"[359.0, 987.0, 202.0, 37.0]",usask_1
+29e44e305,1024,1024,"[670.0, 478.0, 123.0, 86.0]",usask_1
+29e44e305,1024,1024,"[378.0, 331.0, 45.0, 85.0]",usask_1
+29e44e305,1024,1024,"[50.0, 431.0, 108.0, 65.0]",usask_1
+29e44e305,1024,1024,"[44.0, 503.0, 122.0, 80.0]",usask_1
+29e44e305,1024,1024,"[194.0, 463.0, 95.0, 58.0]",usask_1
+29e44e305,1024,1024,"[191.0, 391.0, 90.0, 58.0]",usask_1
+29e44e305,1024,1024,"[9.0, 891.0, 194.0, 101.0]",usask_1
+29e44e305,1024,1024,"[0.0, 817.0, 85.0, 80.0]",usask_1
+29e44e305,1024,1024,"[198.0, 803.0, 91.0, 74.0]",usask_1
+29e44e305,1024,1024,"[224.0, 910.0, 78.0, 77.0]",usask_1
+29e44e305,1024,1024,"[323.0, 896.0, 60.0, 92.0]",usask_1
+29e44e305,1024,1024,"[949.0, 737.0, 57.0, 119.0]",usask_1
+29e44e305,1024,1024,"[355.0, 393.0, 58.0, 92.0]",usask_1
+29e44e305,1024,1024,"[863.0, 902.0, 68.0, 70.0]",usask_1
+43e3ccd92,1024,1024,"[331.0, 472.0, 122.0, 156.0]",usask_1
+43e3ccd92,1024,1024,"[196.0, 346.0, 81.0, 90.0]",usask_1
+43e3ccd92,1024,1024,"[767.0, 496.0, 100.0, 81.0]",usask_1
+43e3ccd92,1024,1024,"[623.0, 905.0, 128.0, 74.0]",usask_1
+43e3ccd92,1024,1024,"[205.0, 727.0, 123.0, 113.0]",usask_1
+43e3ccd92,1024,1024,"[140.0, 997.0, 55.0, 26.0]",usask_1
+43e3ccd92,1024,1024,"[462.0, 14.0, 106.0, 83.0]",usask_1
+43e3ccd92,1024,1024,"[282.0, 37.0, 75.0, 102.0]",usask_1
+43e3ccd92,1024,1024,"[964.0, 630.0, 60.0, 77.0]",usask_1
+43e3ccd92,1024,1024,"[0.0, 660.0, 113.0, 93.0]",usask_1
+43e3ccd92,1024,1024,"[6.0, 764.0, 86.0, 96.0]",usask_1
+43e3ccd92,1024,1024,"[909.0, 350.0, 96.0, 120.0]",usask_1
+43e3ccd92,1024,1024,"[630.0, 699.0, 104.0, 90.0]",usask_1
+43e3ccd92,1024,1024,"[1017.0, 205.0, 7.0, 150.0]",usask_1
+43e3ccd92,1024,1024,"[960.0, 185.0, 64.0, 170.0]",usask_1
+43e3ccd92,1024,1024,"[978.0, 883.0, 46.0, 76.0]",usask_1
+43e3ccd92,1024,1024,"[328.0, 680.0, 148.0, 90.0]",usask_1
+43e3ccd92,1024,1024,"[684.0, 867.0, 92.0, 60.0]",usask_1
+43e3ccd92,1024,1024,"[525.0, 764.0, 114.0, 108.0]",usask_1
+43e3ccd92,1024,1024,"[729.0, 704.0, 99.0, 104.0]",usask_1
+43e3ccd92,1024,1024,"[889.0, 880.0, 62.0, 58.0]",usask_1
+43e3ccd92,1024,1024,"[566.0, 299.0, 93.0, 80.0]",usask_1
+43e3ccd92,1024,1024,"[759.0, 428.0, 60.0, 56.0]",usask_1
+43e3ccd92,1024,1024,"[46.0, 953.0, 94.0, 54.0]",usask_1
+43e3ccd92,1024,1024,"[203.0, 867.0, 88.0, 63.0]",usask_1
+43e3ccd92,1024,1024,"[348.0, 634.0, 68.0, 42.0]",usask_1
+43e3ccd92,1024,1024,"[314.0, 740.0, 45.0, 104.0]",usask_1
+43e3ccd92,1024,1024,"[276.0, 423.0, 54.0, 83.0]",usask_1
+43e3ccd92,1024,1024,"[323.0, 420.0, 75.0, 95.0]",usask_1
+43e3ccd92,1024,1024,"[183.0, 192.0, 77.0, 95.0]",usask_1
+4c7fc04f6,1024,1024,"[384.0, 813.0, 156.0, 81.0]",usask_1
+4c7fc04f6,1024,1024,"[366.0, 213.0, 84.0, 80.0]",usask_1
+4c7fc04f6,1024,1024,"[319.0, 518.0, 65.0, 123.0]",usask_1
+4c7fc04f6,1024,1024,"[417.0, 580.0, 256.0, 74.0]",usask_1
+4c7fc04f6,1024,1024,"[0.0, 93.0, 44.0, 86.0]",usask_1
+4c7fc04f6,1024,1024,"[202.0, 349.0, 52.0, 78.0]",usask_1
+4c7fc04f6,1024,1024,"[479.0, 0.0, 69.0, 79.0]",usask_1
+4c7fc04f6,1024,1024,"[53.0, 72.0, 129.0, 88.0]",usask_1
+4c7fc04f6,1024,1024,"[892.0, 311.0, 114.0, 115.0]",usask_1
+4c7fc04f6,1024,1024,"[419.0, 638.0, 205.0, 82.0]",usask_1
+4c7fc04f6,1024,1024,"[949.0, 788.0, 73.0, 86.0]",usask_1
+4c7fc04f6,1024,1024,"[88.0, 912.0, 107.0, 101.0]",usask_1
+4c7fc04f6,1024,1024,"[40.0, 458.0, 137.0, 113.0]",usask_1
+4c7fc04f6,1024,1024,"[236.0, 388.0, 60.0, 53.0]",usask_1
+4c7fc04f6,1024,1024,"[882.0, 663.0, 110.0, 147.0]",usask_1
+4c7fc04f6,1024,1024,"[0.0, 276.0, 150.0, 89.0]",usask_1
+4c7fc04f6,1024,1024,"[373.0, 175.0, 86.0, 74.0]",usask_1
+4c7fc04f6,1024,1024,"[367.0, 126.0, 96.0, 59.0]",usask_1
+4c7fc04f6,1024,1024,"[730.0, 707.0, 124.0, 164.0]",usask_1
+4c7fc04f6,1024,1024,"[528.0, 809.0, 141.0, 82.0]",usask_1
+4c7fc04f6,1024,1024,"[858.0, 788.0, 147.0, 153.0]",usask_1
+4c7fc04f6,1024,1024,"[376.0, 0.0, 148.0, 194.0]",usask_1
+4c7fc04f6,1024,1024,"[224.0, 246.0, 126.0, 95.0]",usask_1
+4c7fc04f6,1024,1024,"[653.0, 600.0, 98.0, 87.0]",usask_1
+4c7fc04f6,1024,1024,"[494.0, 748.0, 50.0, 61.0]",usask_1
+4c7fc04f6,1024,1024,"[379.0, 535.0, 58.0, 68.0]",usask_1
+4c7fc04f6,1024,1024,"[836.0, 580.0, 166.0, 144.0]",usask_1
+4c7fc04f6,1024,1024,"[826.0, 480.0, 146.0, 179.0]",usask_1
+4c7fc04f6,1024,1024,"[865.0, 440.0, 104.0, 67.0]",usask_1
+4c7fc04f6,1024,1024,"[952.0, 420.0, 61.0, 68.0]",usask_1
+4c7fc04f6,1024,1024,"[278.0, 217.0, 79.0, 91.0]",usask_1
+4c7fc04f6,1024,1024,"[295.0, 0.0, 85.0, 75.0]",usask_1
+4c7fc04f6,1024,1024,"[532.0, 99.0, 60.0, 54.0]",usask_1
+4c7fc04f6,1024,1024,"[541.0, 155.0, 60.0, 54.0]",usask_1
+4c7fc04f6,1024,1024,"[282.0, 460.0, 48.0, 34.0]",usask_1
+4c7fc04f6,1024,1024,"[337.0, 432.0, 57.0, 30.0]",usask_1
+4c7fc04f6,1024,1024,"[179.0, 1.0, 53.0, 38.0]",usask_1
+4c7fc04f6,1024,1024,"[178.0, 267.0, 98.0, 121.0]",usask_1
+4c7fc04f6,1024,1024,"[168.0, 298.0, 74.0, 66.0]",usask_1
+a22cdd5eb,1024,1024,"[386.0, 451.0, 58.0, 79.0]",usask_1
+a22cdd5eb,1024,1024,"[645.0, 0.0, 49.0, 45.0]",usask_1
+a22cdd5eb,1024,1024,"[292.0, 492.0, 78.0, 118.0]",usask_1
+a22cdd5eb,1024,1024,"[0.0, 951.0, 80.0, 73.0]",usask_1
+a22cdd5eb,1024,1024,"[581.0, 0.0, 66.0, 72.0]",usask_1
+a22cdd5eb,1024,1024,"[48.0, 479.0, 123.0, 61.0]",usask_1
+a22cdd5eb,1024,1024,"[687.0, 13.0, 106.0, 110.0]",usask_1
+a22cdd5eb,1024,1024,"[767.0, 729.0, 71.0, 101.0]",usask_1
+a22cdd5eb,1024,1024,"[31.0, 780.0, 82.0, 129.0]",usask_1
+a22cdd5eb,1024,1024,"[626.0, 217.0, 82.0, 84.0]",usask_1
+a22cdd5eb,1024,1024,"[125.0, 416.0, 98.0, 53.0]",usask_1
+a22cdd5eb,1024,1024,"[681.0, 380.0, 99.0, 64.0]",usask_1
+a22cdd5eb,1024,1024,"[671.0, 706.0, 96.0, 100.0]",usask_1
+a22cdd5eb,1024,1024,"[350.0, 3.0, 137.0, 113.0]",usask_1
+a22cdd5eb,1024,1024,"[993.0, 450.0, 31.0, 131.0]",usask_1
+a22cdd5eb,1024,1024,"[697.0, 111.0, 93.0, 86.0]",usask_1
+a22cdd5eb,1024,1024,"[723.0, 354.0, 56.0, 87.0]",usask_1
+a22cdd5eb,1024,1024,"[171.0, 698.0, 95.0, 76.0]",usask_1
+a22cdd5eb,1024,1024,"[101.0, 720.0, 82.0, 67.0]",usask_1
+a22cdd5eb,1024,1024,"[87.0, 791.0, 76.0, 61.0]",usask_1
+a22cdd5eb,1024,1024,"[88.0, 960.0, 61.0, 64.0]",usask_1
+a22cdd5eb,1024,1024,"[335.0, 820.0, 43.0, 49.0]",usask_1
+a22cdd5eb,1024,1024,"[745.0, 840.0, 87.0, 70.0]",usask_1
+a22cdd5eb,1024,1024,"[115.0, 449.0, 103.0, 44.0]",usask_1
+a22cdd5eb,1024,1024,"[71.0, 371.0, 100.0, 76.0]",usask_1
+a22cdd5eb,1024,1024,"[67.0, 251.0, 120.0, 90.0]",usask_1
+a22cdd5eb,1024,1024,"[155.0, 173.0, 84.0, 101.0]",usask_1
+a22cdd5eb,1024,1024,"[125.0, 49.0, 66.0, 79.0]",usask_1
+a22cdd5eb,1024,1024,"[0.0, 407.0, 62.0, 74.0]",usask_1
+a22cdd5eb,1024,1024,"[0.0, 268.0, 83.0, 72.0]",usask_1
+a22cdd5eb,1024,1024,"[870.0, 270.0, 77.0, 119.0]",usask_1
+a22cdd5eb,1024,1024,"[389.0, 979.0, 115.0, 45.0]",usask_1
+a22cdd5eb,1024,1024,"[627.0, 887.0, 45.0, 35.0]",usask_1
+a22cdd5eb,1024,1024,"[11.0, 79.0, 58.0, 57.0]",usask_1
+a22cdd5eb,1024,1024,"[84.0, 133.0, 64.0, 50.0]",usask_1
+480c01244,1024,1024,"[186.0, 870.0, 87.0, 68.0]",usask_1
+480c01244,1024,1024,"[0.0, 606.0, 76.0, 113.0]",usask_1
+480c01244,1024,1024,"[451.0, 479.0, 100.0, 109.0]",usask_1
+480c01244,1024,1024,"[924.0, 463.0, 55.0, 93.0]",usask_1
+480c01244,1024,1024,"[659.0, 265.0, 44.0, 56.0]",usask_1
+480c01244,1024,1024,"[681.0, 570.0, 67.0, 91.0]",usask_1
+480c01244,1024,1024,"[133.0, 663.0, 49.0, 49.0]",usask_1
+480c01244,1024,1024,"[106.0, 834.0, 70.0, 61.0]",usask_1
+480c01244,1024,1024,"[909.0, 0.0, 84.0, 122.0]",usask_1
+480c01244,1024,1024,"[704.0, 973.0, 86.0, 51.0]",usask_1
+480c01244,1024,1024,"[24.0, 906.0, 130.0, 96.0]",usask_1
+480c01244,1024,1024,"[870.0, 487.0, 103.0, 134.0]",usask_1
+480c01244,1024,1024,"[1.0, 391.0, 47.0, 78.0]",usask_1
+480c01244,1024,1024,"[0.0, 106.0, 118.0, 133.0]",usask_1
+480c01244,1024,1024,"[303.0, 439.0, 68.0, 91.0]",usask_1
+480c01244,1024,1024,"[785.0, 845.0, 67.0, 75.0]",usask_1
+480c01244,1024,1024,"[965.0, 68.0, 59.0, 134.0]",usask_1
+480c01244,1024,1024,"[263.0, 103.0, 58.0, 132.0]",usask_1
+480c01244,1024,1024,"[458.0, 710.0, 124.0, 103.0]",usask_1
+480c01244,1024,1024,"[935.0, 885.0, 73.0, 100.0]",usask_1
+480c01244,1024,1024,"[559.0, 9.0, 88.0, 131.0]",usask_1
+480c01244,1024,1024,"[923.0, 295.0, 91.0, 140.0]",usask_1
+480c01244,1024,1024,"[435.0, 158.0, 95.0, 62.0]",usask_1
+480c01244,1024,1024,"[307.0, 608.0, 115.0, 75.0]",usask_1
+480c01244,1024,1024,"[965.0, 239.0, 56.0, 95.0]",usask_1
+480c01244,1024,1024,"[19.0, 414.0, 55.0, 67.0]",usask_1
+480c01244,1024,1024,"[889.0, 664.0, 84.0, 65.0]",usask_1
+480c01244,1024,1024,"[970.0, 695.0, 54.0, 86.0]",usask_1
+480c01244,1024,1024,"[605.0, 513.0, 68.0, 72.0]",usask_1
+480c01244,1024,1024,"[673.0, 497.0, 60.0, 69.0]",usask_1
+480c01244,1024,1024,"[771.0, 574.0, 78.0, 79.0]",usask_1
+480c01244,1024,1024,"[719.0, 691.0, 73.0, 82.0]",usask_1
+480c01244,1024,1024,"[752.0, 751.0, 84.0, 80.0]",usask_1
+480c01244,1024,1024,"[660.0, 710.0, 76.0, 112.0]",usask_1
+480c01244,1024,1024,"[217.0, 944.0, 83.0, 62.0]",usask_1
+480c01244,1024,1024,"[383.0, 350.0, 65.0, 60.0]",usask_1
+480c01244,1024,1024,"[736.0, 502.0, 54.0, 53.0]",usask_1
+480c01244,1024,1024,"[779.0, 475.0, 67.0, 65.0]",usask_1
+480c01244,1024,1024,"[967.0, 489.0, 57.0, 57.0]",usask_1
+480c01244,1024,1024,"[685.0, 667.0, 55.0, 38.0]",usask_1
+8b558e9e3,1024,1024,"[450.0, 296.0, 121.0, 61.0]",usask_1
+8b558e9e3,1024,1024,"[920.0, 936.0, 104.0, 64.0]",usask_1
+8b558e9e3,1024,1024,"[902.0, 758.0, 122.0, 68.0]",usask_1
+8b558e9e3,1024,1024,"[70.0, 154.0, 113.0, 97.0]",usask_1
+8b558e9e3,1024,1024,"[762.0, 710.0, 109.0, 117.0]",usask_1
+8b558e9e3,1024,1024,"[0.0, 713.0, 179.0, 107.0]",usask_1
+8b558e9e3,1024,1024,"[962.0, 832.0, 62.0, 60.0]",usask_1
+8b558e9e3,1024,1024,"[0.0, 645.0, 239.0, 84.0]",usask_1
+8b558e9e3,1024,1024,"[13.0, 847.0, 222.0, 123.0]",usask_1
+8b558e9e3,1024,1024,"[576.0, 121.0, 126.0, 83.0]",usask_1
+8b558e9e3,1024,1024,"[915.0, 173.0, 86.0, 65.0]",usask_1
+8b558e9e3,1024,1024,"[749.0, 870.0, 140.0, 99.0]",usask_1
+8b558e9e3,1024,1024,"[340.0, 111.0, 174.0, 121.0]",usask_1
+8b558e9e3,1024,1024,"[508.0, 0.0, 153.0, 122.0]",usask_1
+8b558e9e3,1024,1024,"[252.0, 785.0, 137.0, 78.0]",usask_1
+8b558e9e3,1024,1024,"[768.0, 0.0, 140.0, 2.0]",usask_1
+8b558e9e3,1024,1024,"[55.0, 286.0, 124.0, 93.0]",usask_1
+8b558e9e3,1024,1024,"[684.0, 142.0, 153.0, 56.0]",usask_1
+8b558e9e3,1024,1024,"[744.0, 0.0, 147.0, 55.0]",usask_1
+8b558e9e3,1024,1024,"[250.0, 683.0, 55.0, 135.0]",usask_1
+8b558e9e3,1024,1024,"[208.0, 735.0, 205.0, 56.0]",usask_1
+8b558e9e3,1024,1024,"[0.0, 971.0, 79.0, 45.0]",usask_1
+8b558e9e3,1024,1024,"[476.0, 212.0, 135.0, 137.0]",usask_1
+8b558e9e3,1024,1024,"[167.0, 20.0, 131.0, 85.0]",usask_1
+8b558e9e3,1024,1024,"[256.0, 86.0, 142.0, 81.0]",usask_1
+8b558e9e3,1024,1024,"[676.0, 107.0, 136.0, 62.0]",usask_1
+8b558e9e3,1024,1024,"[727.0, 340.0, 58.0, 100.0]",usask_1
+8b558e9e3,1024,1024,"[101.0, 609.0, 164.0, 66.0]",usask_1
+8b558e9e3,1024,1024,"[0.0, 470.0, 50.0, 70.0]",usask_1
+8b558e9e3,1024,1024,"[978.0, 898.0, 46.0, 39.0]",usask_1
+8b558e9e3,1024,1024,"[974.0, 1004.0, 50.0, 19.0]",usask_1
+029c79463,1024,1024,"[775.0, 109.0, 104.0, 35.0]",usask_1
+029c79463,1024,1024,"[272.0, 73.0, 130.0, 66.0]",usask_1
+029c79463,1024,1024,"[353.0, 424.0, 174.0, 113.0]",usask_1
+029c79463,1024,1024,"[270.0, 627.0, 65.0, 57.0]",usask_1
+029c79463,1024,1024,"[317.0, 123.0, 128.0, 106.0]",usask_1
+029c79463,1024,1024,"[385.0, 682.0, 67.0, 63.0]",usask_1
+029c79463,1024,1024,"[0.0, 984.0, 89.0, 40.0]",usask_1
+029c79463,1024,1024,"[47.0, 0.0, 153.0, 66.0]",usask_1
+029c79463,1024,1024,"[528.0, 16.0, 107.0, 110.0]",usask_1
+029c79463,1024,1024,"[916.0, 831.0, 81.0, 98.0]",usask_1
+029c79463,1024,1024,"[0.0, 142.0, 91.0, 80.0]",usask_1
+029c79463,1024,1024,"[22.0, 673.0, 108.0, 125.0]",usask_1
+029c79463,1024,1024,"[445.0, 597.0, 112.0, 74.0]",usask_1
+029c79463,1024,1024,"[878.0, 784.0, 62.0, 84.0]",usask_1
+029c79463,1024,1024,"[676.0, 419.0, 148.0, 62.0]",usask_1
+029c79463,1024,1024,"[481.0, 77.0, 150.0, 86.0]",usask_1
+029c79463,1024,1024,"[787.0, 572.0, 78.0, 81.0]",usask_1
+029c79463,1024,1024,"[649.0, 0.0, 91.0, 54.0]",usask_1
+029c79463,1024,1024,"[442.0, 911.0, 59.0, 69.0]",usask_1
+029c79463,1024,1024,"[983.0, 434.0, 41.0, 127.0]",usask_1
+029c79463,1024,1024,"[465.0, 170.0, 138.0, 59.0]",usask_1
+029c79463,1024,1024,"[390.0, 47.0, 67.0, 112.0]",usask_1
+029c79463,1024,1024,"[48.0, 275.0, 174.0, 48.0]",usask_1
+029c79463,1024,1024,"[223.0, 837.0, 95.0, 72.0]",usask_1
+029c79463,1024,1024,"[19.0, 843.0, 130.0, 92.0]",usask_1
+029c79463,1024,1024,"[869.0, 0.0, 101.0, 58.0]",usask_1
+029c79463,1024,1024,"[0.0, 765.0, 63.0, 54.0]",usask_1
+029c79463,1024,1024,"[5.0, 799.0, 113.0, 76.0]",usask_1
+029c79463,1024,1024,"[260.0, 979.0, 95.0, 45.0]",usask_1
+029c79463,1024,1024,"[467.0, 876.0, 101.0, 111.0]",usask_1
+029c79463,1024,1024,"[500.0, 851.0, 77.0, 62.0]",usask_1
+029c79463,1024,1024,"[0.0, 193.0, 56.0, 91.0]",usask_1
+029c79463,1024,1024,"[0.0, 308.0, 56.0, 53.0]",usask_1
+029c79463,1024,1024,"[820.0, 721.0, 106.0, 88.0]",usask_1
+029c79463,1024,1024,"[762.0, 753.0, 111.0, 93.0]",usask_1
+029c79463,1024,1024,"[758.0, 795.0, 66.0, 84.0]",usask_1
+029c79463,1024,1024,"[678.0, 719.0, 130.0, 67.0]",usask_1
+029c79463,1024,1024,"[611.0, 779.0, 153.0, 62.0]",usask_1
+029c79463,1024,1024,"[672.0, 773.0, 90.0, 45.0]",usask_1
+029c79463,1024,1024,"[788.0, 0.0, 110.0, 48.0]",usask_1
+029c79463,1024,1024,"[760.0, 36.0, 123.0, 49.0]",usask_1
+029c79463,1024,1024,"[680.0, 172.0, 92.0, 61.0]",usask_1
+029c79463,1024,1024,"[810.0, 475.0, 114.0, 43.0]",usask_1
+029c79463,1024,1024,"[781.0, 442.0, 123.0, 46.0]",usask_1
+029c79463,1024,1024,"[665.0, 501.0, 224.0, 139.0]",usask_1
+029c79463,1024,1024,"[686.0, 550.0, 138.0, 148.0]",usask_1
+029c79463,1024,1024,"[643.0, 826.0, 72.0, 65.0]",usask_1
+029c79463,1024,1024,"[108.0, 799.0, 58.0, 47.0]",usask_1
+14fd84198,1024,1024,"[316.0, 694.0, 207.0, 191.0]",usask_1
+14fd84198,1024,1024,"[422.0, 818.0, 175.0, 99.0]",usask_1
+14fd84198,1024,1024,"[599.0, 946.0, 115.0, 78.0]",usask_1
+14fd84198,1024,1024,"[578.0, 538.0, 87.0, 48.0]",usask_1
+14fd84198,1024,1024,"[394.0, 485.0, 134.0, 75.0]",usask_1
+14fd84198,1024,1024,"[940.0, 0.0, 84.0, 153.0]",usask_1
+14fd84198,1024,1024,"[129.0, 454.0, 94.0, 114.0]",usask_1
+14fd84198,1024,1024,"[808.0, 613.0, 204.0, 76.0]",usask_1
+14fd84198,1024,1024,"[767.0, 455.0, 136.0, 134.0]",usask_1
+14fd84198,1024,1024,"[272.0, 544.0, 264.0, 143.0]",usask_1
+14fd84198,1024,1024,"[39.0, 868.0, 120.0, 156.0]",usask_1
+14fd84198,1024,1024,"[0.0, 0.0, 95.0, 118.0]",usask_1
+14fd84198,1024,1024,"[643.0, 226.0, 263.0, 157.0]",usask_1
+14fd84198,1024,1024,"[462.0, 0.0, 349.0, 140.0]",usask_1
+14fd84198,1024,1024,"[0.0, 107.0, 86.0, 285.0]",usask_1
+14fd84198,1024,1024,"[247.0, 509.0, 155.0, 63.0]",usask_1
+14fd84198,1024,1024,"[741.0, 247.0, 221.0, 77.0]",usask_1
+14fd84198,1024,1024,"[968.0, 163.0, 56.0, 89.0]",usask_1
+14fd84198,1024,1024,"[0.0, 362.0, 141.0, 89.0]",usask_1
+14fd84198,1024,1024,"[0.0, 404.0, 134.0, 110.0]",usask_1
+14fd84198,1024,1024,"[0.0, 464.0, 93.0, 90.0]",usask_1
+14fd84198,1024,1024,"[839.0, 584.0, 42.0, 30.0]",usask_1
+5e0a7fb4d,1024,1024,"[26.0, 35.0, 110.0, 121.0]",usask_1
+5e0a7fb4d,1024,1024,"[857.0, 175.0, 62.0, 61.0]",usask_1
+5e0a7fb4d,1024,1024,"[723.0, 140.0, 57.0, 66.0]",usask_1
+5e0a7fb4d,1024,1024,"[254.0, 238.0, 210.0, 248.0]",usask_1
+5e0a7fb4d,1024,1024,"[68.0, 16.0, 119.0, 119.0]",usask_1
+5e0a7fb4d,1024,1024,"[176.0, 33.0, 106.0, 151.0]",usask_1
+5e0a7fb4d,1024,1024,"[69.0, 1.0, 123.0, 76.0]",usask_1
+5e0a7fb4d,1024,1024,"[294.0, 140.0, 168.0, 78.0]",usask_1
+5e0a7fb4d,1024,1024,"[0.0, 658.0, 267.0, 105.0]",usask_1
+5e0a7fb4d,1024,1024,"[811.0, 3.0, 120.0, 174.0]",usask_1
+5e0a7fb4d,1024,1024,"[568.0, 97.0, 183.0, 165.0]",usask_1
+5e0a7fb4d,1024,1024,"[83.0, 428.0, 112.0, 234.0]",usask_1
+5e0a7fb4d,1024,1024,"[306.0, 721.0, 201.0, 90.0]",usask_1
+5e0a7fb4d,1024,1024,"[552.0, 590.0, 140.0, 90.0]",usask_1
+5e0a7fb4d,1024,1024,"[249.0, 512.0, 258.0, 177.0]",usask_1
+5e0a7fb4d,1024,1024,"[761.0, 839.0, 63.0, 56.0]",usask_1
+5e0a7fb4d,1024,1024,"[537.0, 557.0, 157.0, 100.0]",usask_1
+5e0a7fb4d,1024,1024,"[430.0, 127.0, 127.0, 87.0]",usask_1
+5e0a7fb4d,1024,1024,"[349.0, 371.0, 200.0, 114.0]",usask_1
+5e0a7fb4d,1024,1024,"[674.0, 667.0, 106.0, 46.0]",usask_1
+798a261f0,1024,1024,"[502.0, 494.0, 144.0, 40.0]",usask_1
+798a261f0,1024,1024,"[229.0, 857.0, 117.0, 92.0]",usask_1
+798a261f0,1024,1024,"[309.0, 208.0, 173.0, 112.0]",usask_1
+798a261f0,1024,1024,"[30.0, 886.0, 93.0, 101.0]",usask_1
+798a261f0,1024,1024,"[235.0, 0.0, 188.0, 86.0]",usask_1
+798a261f0,1024,1024,"[43.0, 279.0, 289.0, 127.0]",usask_1
+798a261f0,1024,1024,"[848.0, 720.0, 176.0, 114.0]",usask_1
+798a261f0,1024,1024,"[177.0, 44.0, 166.0, 94.0]",usask_1
+798a261f0,1024,1024,"[36.0, 5.0, 133.0, 64.0]",usask_1
+798a261f0,1024,1024,"[984.0, 380.0, 37.0, 40.0]",usask_1
+798a261f0,1024,1024,"[794.0, 281.0, 75.0, 40.0]",usask_1
+798a261f0,1024,1024,"[189.0, 959.0, 85.0, 42.0]",usask_1
+b13f38e6f,1024,1024,"[574.0, 97.0, 143.0, 51.0]",usask_1
+b13f38e6f,1024,1024,"[288.0, 343.0, 126.0, 94.0]",usask_1
+b13f38e6f,1024,1024,"[93.0, 347.0, 109.0, 112.0]",usask_1
+b13f38e6f,1024,1024,"[698.0, 655.0, 113.0, 81.0]",usask_1
+b13f38e6f,1024,1024,"[363.0, 980.0, 231.0, 44.0]",usask_1
+b13f38e6f,1024,1024,"[696.0, 945.0, 107.0, 79.0]",usask_1
+b13f38e6f,1024,1024,"[764.0, 597.0, 89.0, 76.0]",usask_1
+b13f38e6f,1024,1024,"[351.0, 927.0, 296.0, 86.0]",usask_1
+b13f38e6f,1024,1024,"[1.0, 573.0, 115.0, 81.0]",usask_1
+b13f38e6f,1024,1024,"[0.0, 489.0, 50.0, 75.0]",usask_1
+b13f38e6f,1024,1024,"[39.0, 262.0, 65.0, 43.0]",usask_1
+b13f38e6f,1024,1024,"[666.0, 79.0, 60.0, 30.0]",usask_1
+b13f38e6f,1024,1024,"[239.0, 503.0, 64.0, 65.0]",usask_1
+b13f38e6f,1024,1024,"[73.0, 462.0, 58.0, 34.0]",usask_1
+c73f0a714,1024,1024,"[465.0, 587.0, 52.0, 45.0]",usask_1
+c73f0a714,1024,1024,"[125.0, 334.0, 87.0, 131.0]",usask_1
+c73f0a714,1024,1024,"[178.0, 531.0, 78.0, 105.0]",usask_1
+c73f0a714,1024,1024,"[903.0, 173.0, 87.0, 59.0]",usask_1
+c73f0a714,1024,1024,"[0.0, 333.0, 146.0, 99.0]",usask_1
+c73f0a714,1024,1024,"[913.0, 700.0, 77.0, 38.0]",usask_1
+c73f0a714,1024,1024,"[428.0, 785.0, 95.0, 92.0]",usask_1
+c73f0a714,1024,1024,"[129.0, 95.0, 115.0, 102.0]",usask_1
+c73f0a714,1024,1024,"[11.0, 148.0, 97.0, 110.0]",usask_1
+c73f0a714,1024,1024,"[23.0, 447.0, 99.0, 68.0]",usask_1
+c73f0a714,1024,1024,"[142.0, 0.0, 140.0, 74.0]",usask_1
+c73f0a714,1024,1024,"[224.0, 272.0, 98.0, 168.0]",usask_1
+c73f0a714,1024,1024,"[888.0, 0.0, 86.0, 56.0]",usask_1
+c73f0a714,1024,1024,"[557.0, 550.0, 137.0, 231.0]",usask_1
+c73f0a714,1024,1024,"[498.0, 707.0, 117.0, 214.0]",usask_1
+c73f0a714,1024,1024,"[697.0, 0.0, 120.0, 39.0]",usask_1
+c73f0a714,1024,1024,"[934.0, 725.0, 63.0, 43.0]",usask_1
+c73f0a714,1024,1024,"[157.0, 589.0, 105.0, 95.0]",usask_1
+c73f0a714,1024,1024,"[1.0, 628.0, 61.0, 63.0]",usask_1
+c73f0a714,1024,1024,"[73.0, 629.0, 84.0, 70.0]",usask_1
+c73f0a714,1024,1024,"[141.0, 663.0, 70.0, 52.0]",usask_1
+c73f0a714,1024,1024,"[256.0, 790.0, 117.0, 75.0]",usask_1
+c73f0a714,1024,1024,"[154.0, 978.0, 102.0, 46.0]",usask_1
+c73f0a714,1024,1024,"[192.0, 822.0, 94.0, 125.0]",usask_1
+c73f0a714,1024,1024,"[91.0, 871.0, 103.0, 105.0]",usask_1
+c73f0a714,1024,1024,"[0.0, 201.0, 108.0, 78.0]",usask_1
+c73f0a714,1024,1024,"[94.0, 245.0, 99.0, 45.0]",usask_1
+c73f0a714,1024,1024,"[212.0, 667.0, 49.0, 174.0]",usask_1
+c73f0a714,1024,1024,"[274.0, 738.0, 51.0, 83.0]",usask_1
+c73f0a714,1024,1024,"[489.0, 573.0, 54.0, 67.0]",usask_1
+c73f0a714,1024,1024,"[520.0, 52.0, 59.0, 71.0]",usask_1
+69fc3d3ff,1024,1024,"[266.0, 588.0, 205.0, 128.0]",usask_1
+69fc3d3ff,1024,1024,"[0.0, 609.0, 346.0, 415.0]",usask_1
+69fc3d3ff,1024,1024,"[689.0, 896.0, 110.0, 105.0]",usask_1
+69fc3d3ff,1024,1024,"[797.0, 437.0, 76.0, 74.0]",usask_1
+69fc3d3ff,1024,1024,"[686.0, 810.0, 140.0, 106.0]",usask_1
+69fc3d3ff,1024,1024,"[559.0, 0.0, 110.0, 222.0]",usask_1
+69fc3d3ff,1024,1024,"[848.0, 90.0, 176.0, 125.0]",usask_1
+69fc3d3ff,1024,1024,"[667.0, 497.0, 153.0, 145.0]",usask_1
+69fc3d3ff,1024,1024,"[605.0, 642.0, 170.0, 82.0]",usask_1
+69fc3d3ff,1024,1024,"[871.0, 0.0, 91.0, 85.0]",usask_1
+69fc3d3ff,1024,1024,"[841.0, 914.0, 88.0, 110.0]",usask_1
+69fc3d3ff,1024,1024,"[38.0, 370.0, 50.0, 73.0]",usask_1
+69fc3d3ff,1024,1024,"[0.0, 182.0, 47.0, 90.0]",usask_1
+69fc3d3ff,1024,1024,"[595.0, 898.0, 109.0, 85.0]",usask_1
+69fc3d3ff,1024,1024,"[648.0, 617.0, 111.0, 60.0]",usask_1
+69fc3d3ff,1024,1024,"[924.0, 824.0, 40.0, 50.0]",usask_1
+e99cca2a3,1024,1024,"[714.0, 559.0, 151.0, 247.0]",usask_1
+e99cca2a3,1024,1024,"[603.0, 648.0, 103.0, 136.0]",usask_1
+e99cca2a3,1024,1024,"[67.0, 935.0, 195.0, 80.0]",usask_1
+e99cca2a3,1024,1024,"[468.0, 522.0, 258.0, 188.0]",usask_1
+e99cca2a3,1024,1024,"[871.0, 573.0, 128.0, 89.0]",usask_1
+e99cca2a3,1024,1024,"[54.0, 838.0, 161.0, 79.0]",usask_1
+e99cca2a3,1024,1024,"[502.0, 156.0, 103.0, 119.0]",usask_1
+e99cca2a3,1024,1024,"[799.0, 237.0, 124.0, 92.0]",usask_1
+e99cca2a3,1024,1024,"[906.0, 660.0, 118.0, 207.0]",usask_1
+e99cca2a3,1024,1024,"[717.0, 54.0, 123.0, 143.0]",usask_1
+e99cca2a3,1024,1024,"[134.0, 54.0, 175.0, 101.0]",usask_1
+e99cca2a3,1024,1024,"[685.0, 396.0, 221.0, 86.0]",usask_1
+e99cca2a3,1024,1024,"[94.0, 218.0, 207.0, 100.0]",usask_1
+e99cca2a3,1024,1024,"[217.0, 935.0, 133.0, 70.0]",usask_1
+e99cca2a3,1024,1024,"[0.0, 871.0, 163.0, 82.0]",usask_1
+e99cca2a3,1024,1024,"[220.0, 155.0, 103.0, 42.0]",usask_1
+e99cca2a3,1024,1024,"[294.0, 107.0, 77.0, 95.0]",usask_1
+e99cca2a3,1024,1024,"[840.0, 312.0, 105.0, 71.0]",usask_1
+e99cca2a3,1024,1024,"[944.0, 283.0, 77.0, 87.0]",usask_1
+e99cca2a3,1024,1024,"[857.0, 509.0, 103.0, 80.0]",usask_1
+e99cca2a3,1024,1024,"[729.0, 690.0, 82.0, 129.0]",usask_1
+e99cca2a3,1024,1024,"[519.0, 886.0, 48.0, 47.0]",usask_1
+b815af10b,1024,1024,"[133.0, 752.0, 103.0, 83.0]",usask_1
+b815af10b,1024,1024,"[202.0, 387.0, 116.0, 93.0]",usask_1
+b815af10b,1024,1024,"[0.0, 485.0, 92.0, 99.0]",usask_1
+b815af10b,1024,1024,"[316.0, 545.0, 93.0, 103.0]",usask_1
+b815af10b,1024,1024,"[679.0, 162.0, 68.0, 87.0]",usask_1
+b815af10b,1024,1024,"[525.0, 889.0, 93.0, 83.0]",usask_1
+b815af10b,1024,1024,"[67.0, 488.0, 120.0, 106.0]",usask_1
+b815af10b,1024,1024,"[816.0, 405.0, 94.0, 72.0]",usask_1
+b815af10b,1024,1024,"[37.0, 757.0, 130.0, 97.0]",usask_1
+b815af10b,1024,1024,"[147.0, 235.0, 133.0, 87.0]",usask_1
+b815af10b,1024,1024,"[579.0, 115.0, 98.0, 143.0]",usask_1
+b815af10b,1024,1024,"[154.0, 464.0, 100.0, 137.0]",usask_1
+b815af10b,1024,1024,"[636.0, 953.0, 137.0, 71.0]",usask_1
+b815af10b,1024,1024,"[788.0, 225.0, 79.0, 164.0]",usask_1
+b815af10b,1024,1024,"[500.0, 41.0, 120.0, 75.0]",usask_1
+b815af10b,1024,1024,"[811.0, 707.0, 109.0, 98.0]",usask_1
+b815af10b,1024,1024,"[0.0, 368.0, 92.0, 77.0]",usask_1
+b815af10b,1024,1024,"[896.0, 753.0, 99.0, 103.0]",usask_1
+b815af10b,1024,1024,"[995.0, 325.0, 29.0, 83.0]",usask_1
+b815af10b,1024,1024,"[208.0, 646.0, 126.0, 94.0]",usask_1
+b815af10b,1024,1024,"[303.0, 808.0, 109.0, 110.0]",usask_1
+b815af10b,1024,1024,"[404.0, 579.0, 106.0, 153.0]",usask_1
+b815af10b,1024,1024,"[613.0, 477.0, 110.0, 75.0]",usask_1
+b815af10b,1024,1024,"[529.0, 229.0, 106.0, 76.0]",usask_1
+b815af10b,1024,1024,"[601.0, 309.0, 130.0, 74.0]",usask_1
+b815af10b,1024,1024,"[644.0, 386.0, 127.0, 87.0]",usask_1
+b815af10b,1024,1024,"[733.0, 354.0, 60.0, 95.0]",usask_1
+b815af10b,1024,1024,"[277.0, 634.0, 88.0, 69.0]",usask_1
+b815af10b,1024,1024,"[121.0, 344.0, 78.0, 66.0]",usask_1
+b815af10b,1024,1024,"[19.0, 293.0, 110.0, 118.0]",usask_1
+b815af10b,1024,1024,"[885.0, 659.0, 60.0, 68.0]",usask_1
+b815af10b,1024,1024,"[513.0, 0.0, 59.0, 35.0]",usask_1
+999d9d27d,1024,1024,"[611.0, 676.0, 103.0, 98.0]",usask_1
+999d9d27d,1024,1024,"[0.0, 848.0, 96.0, 68.0]",usask_1
+999d9d27d,1024,1024,"[394.0, 259.0, 177.0, 74.0]",usask_1
+999d9d27d,1024,1024,"[763.0, 676.0, 104.0, 73.0]",usask_1
+999d9d27d,1024,1024,"[626.0, 877.0, 166.0, 73.0]",usask_1
+999d9d27d,1024,1024,"[125.0, 299.0, 89.0, 129.0]",usask_1
+999d9d27d,1024,1024,"[437.0, 181.0, 133.0, 110.0]",usask_1
+999d9d27d,1024,1024,"[727.0, 556.0, 102.0, 123.0]",usask_1
+999d9d27d,1024,1024,"[513.0, 94.0, 112.0, 116.0]",usask_1
+999d9d27d,1024,1024,"[599.0, 505.0, 107.0, 123.0]",usask_1
+999d9d27d,1024,1024,"[848.0, 155.0, 128.0, 119.0]",usask_1
+999d9d27d,1024,1024,"[626.0, 252.0, 137.0, 187.0]",usask_1
+999d9d27d,1024,1024,"[794.0, 296.0, 120.0, 86.0]",usask_1
+999d9d27d,1024,1024,"[535.0, 879.0, 171.0, 96.0]",usask_1
+999d9d27d,1024,1024,"[762.0, 419.0, 177.0, 127.0]",usask_1
+999d9d27d,1024,1024,"[73.0, 192.0, 143.0, 94.0]",usask_1
+999d9d27d,1024,1024,"[88.0, 268.0, 139.0, 70.0]",usask_1
+999d9d27d,1024,1024,"[364.0, 720.0, 194.0, 140.0]",usask_1
+999d9d27d,1024,1024,"[454.0, 640.0, 144.0, 99.0]",usask_1
+999d9d27d,1024,1024,"[401.0, 350.0, 124.0, 82.0]",usask_1
+999d9d27d,1024,1024,"[0.0, 914.0, 84.0, 47.0]",usask_1
+999d9d27d,1024,1024,"[83.0, 629.0, 88.0, 94.0]",usask_1
+999d9d27d,1024,1024,"[758.0, 819.0, 100.0, 112.0]",usask_1
+999d9d27d,1024,1024,"[839.0, 847.0, 76.0, 58.0]",usask_1
+999d9d27d,1024,1024,"[664.0, 443.0, 94.0, 63.0]",usask_1
+999d9d27d,1024,1024,"[8.0, 651.0, 50.0, 39.0]",usask_1
+999d9d27d,1024,1024,"[0.0, 702.0, 48.0, 28.0]",usask_1
+999d9d27d,1024,1024,"[752.0, 255.0, 37.0, 46.0]",usask_1
+d13e3ffce,1024,1024,"[628.0, 68.0, 123.0, 160.0]",usask_1
+d13e3ffce,1024,1024,"[730.0, 37.0, 102.0, 108.0]",usask_1
+d13e3ffce,1024,1024,"[693.0, 648.0, 108.0, 73.0]",usask_1
+d13e3ffce,1024,1024,"[495.0, 61.0, 150.0, 143.0]",usask_1
+d13e3ffce,1024,1024,"[857.0, 834.0, 136.0, 93.0]",usask_1
+d13e3ffce,1024,1024,"[760.0, 188.0, 132.0, 133.0]",usask_1
+d13e3ffce,1024,1024,"[566.0, 0.0, 183.0, 189.0]",usask_1
+d13e3ffce,1024,1024,"[0.0, 61.0, 178.0, 143.0]",usask_1
+d13e3ffce,1024,1024,"[882.0, 119.0, 141.0, 267.0]",usask_1
+d13e3ffce,1024,1024,"[598.0, 0.0, 62.0, 53.0]",usask_1
+d13e3ffce,1024,1024,"[148.0, 277.0, 107.0, 81.0]",usask_1
+d13e3ffce,1024,1024,"[660.0, 367.0, 160.0, 79.0]",usask_1
+d13e3ffce,1024,1024,"[0.0, 423.0, 302.0, 121.0]",usask_1
+d13e3ffce,1024,1024,"[229.0, 64.0, 210.0, 92.0]",usask_1
+d13e3ffce,1024,1024,"[799.0, 966.0, 188.0, 58.0]",usask_1
+d13e3ffce,1024,1024,"[825.0, 414.0, 199.0, 153.0]",usask_1
+d13e3ffce,1024,1024,"[690.0, 808.0, 147.0, 110.0]",usask_1
+d13e3ffce,1024,1024,"[933.0, 0.0, 91.0, 36.0]",usask_1
+d13e3ffce,1024,1024,"[162.0, 211.0, 164.0, 141.0]",usask_1
+d13e3ffce,1024,1024,"[306.0, 563.0, 88.0, 176.0]",usask_1
+d13e3ffce,1024,1024,"[121.0, 640.0, 235.0, 134.0]",usask_1
+d13e3ffce,1024,1024,"[94.0, 0.0, 147.0, 183.0]",usask_1
+d13e3ffce,1024,1024,"[336.0, 188.0, 198.0, 90.0]",usask_1
+d13e3ffce,1024,1024,"[370.0, 350.0, 253.0, 143.0]",usask_1
+d13e3ffce,1024,1024,"[816.0, 827.0, 187.0, 131.0]",usask_1
+d13e3ffce,1024,1024,"[0.0, 871.0, 79.0, 84.0]",usask_1
+d13e3ffce,1024,1024,"[737.0, 606.0, 122.0, 49.0]",usask_1
+d13e3ffce,1024,1024,"[643.0, 547.0, 128.0, 115.0]",usask_1
+d13e3ffce,1024,1024,"[813.0, 320.0, 122.0, 146.0]",usask_1
+fd5624913,1024,1024,"[129.0, 0.0, 153.0, 91.0]",usask_1
+fd5624913,1024,1024,"[0.0, 0.0, 123.0, 92.0]",usask_1
+fd5624913,1024,1024,"[963.0, 848.0, 61.0, 61.0]",usask_1
+fd5624913,1024,1024,"[555.0, 924.0, 149.0, 92.0]",usask_1
+fd5624913,1024,1024,"[390.0, 710.0, 249.0, 159.0]",usask_1
+fd5624913,1024,1024,"[840.0, 382.0, 122.0, 91.0]",usask_1
+fd5624913,1024,1024,"[928.0, 0.0, 96.0, 120.0]",usask_1
+fd5624913,1024,1024,"[919.0, 903.0, 105.0, 81.0]",usask_1
+fd5624913,1024,1024,"[253.0, 950.0, 116.0, 74.0]",usask_1
+fd5624913,1024,1024,"[787.0, 0.0, 112.0, 148.0]",usask_1
+fd5624913,1024,1024,"[666.0, 986.0, 145.0, 38.0]",usask_1
+fd5624913,1024,1024,"[815.0, 311.0, 96.0, 101.0]",usask_1
+fd5624913,1024,1024,"[540.0, 979.0, 156.0, 45.0]",usask_1
+fd5624913,1024,1024,"[656.0, 756.0, 86.0, 150.0]",usask_1
+fd5624913,1024,1024,"[718.0, 741.0, 115.0, 76.0]",usask_1
+fd5624913,1024,1024,"[859.0, 53.0, 90.0, 78.0]",usask_1
+fd5624913,1024,1024,"[631.0, 342.0, 133.0, 56.0]",usask_1
+fd5624913,1024,1024,"[0.0, 723.0, 92.0, 83.0]",usask_1
+fd5624913,1024,1024,"[338.0, 350.0, 79.0, 317.0]",usask_1
+fd5624913,1024,1024,"[49.0, 39.0, 50.0, 83.0]",usask_1
+fd5624913,1024,1024,"[933.0, 119.0, 49.0, 54.0]",usask_1
+005b0d8bb,1024,1024,"[765.0, 879.0, 116.0, 79.0]",usask_1
+005b0d8bb,1024,1024,"[84.0, 539.0, 153.0, 96.0]",usask_1
+005b0d8bb,1024,1024,"[855.0, 781.0, 96.0, 76.0]",usask_1
+005b0d8bb,1024,1024,"[798.0, 934.0, 133.0, 88.0]",usask_1
+005b0d8bb,1024,1024,"[330.0, 395.0, 145.0, 215.0]",usask_1
+005b0d8bb,1024,1024,"[0.0, 0.0, 72.0, 106.0]",usask_1
+005b0d8bb,1024,1024,"[711.0, 60.0, 138.0, 97.0]",usask_1
+005b0d8bb,1024,1024,"[157.0, 393.0, 220.0, 108.0]",usask_1
+005b0d8bb,1024,1024,"[967.0, 545.0, 57.0, 139.0]",usask_1
+005b0d8bb,1024,1024,"[305.0, 577.0, 220.0, 103.0]",usask_1
+005b0d8bb,1024,1024,"[562.0, 518.0, 194.0, 110.0]",usask_1
+005b0d8bb,1024,1024,"[737.0, 555.0, 123.0, 181.0]",usask_1
+005b0d8bb,1024,1024,"[212.0, 151.0, 184.0, 116.0]",usask_1
+005b0d8bb,1024,1024,"[606.0, 229.0, 167.0, 150.0]",usask_1
+005b0d8bb,1024,1024,"[322.0, 860.0, 84.0, 164.0]",usask_1
+005b0d8bb,1024,1024,"[933.0, 43.0, 89.0, 232.0]",usask_1
+005b0d8bb,1024,1024,"[192.0, 289.0, 262.0, 98.0]",usask_1
+005b0d8bb,1024,1024,"[1.0, 643.0, 80.0, 88.0]",usask_1
+005b0d8bb,1024,1024,"[97.0, 477.0, 126.0, 56.0]",usask_1
+005b0d8bb,1024,1024,"[1.0, 399.0, 80.0, 108.0]",usask_1
+b7c97a57b,1024,1024,"[83.0, 757.0, 215.0, 115.0]",usask_1
+b7c97a57b,1024,1024,"[817.0, 0.0, 175.0, 119.0]",usask_1
+b7c97a57b,1024,1024,"[944.0, 0.0, 80.0, 61.0]",usask_1
+b7c97a57b,1024,1024,"[897.0, 258.0, 127.0, 123.0]",usask_1
+b7c97a57b,1024,1024,"[817.0, 636.0, 207.0, 151.0]",usask_1
+b7c97a57b,1024,1024,"[200.0, 492.0, 365.0, 151.0]",usask_1
+b7c97a57b,1024,1024,"[612.0, 0.0, 198.0, 95.0]",usask_1
+b7c97a57b,1024,1024,"[57.0, 202.0, 306.0, 164.0]",usask_1
+b7c97a57b,1024,1024,"[40.0, 906.0, 222.0, 118.0]",usask_1
+b7c97a57b,1024,1024,"[257.0, 773.0, 185.0, 118.0]",usask_1
+b7c97a57b,1024,1024,"[0.0, 418.0, 77.0, 170.0]",usask_1
+b7c97a57b,1024,1024,"[9.0, 250.0, 305.0, 139.0]",usask_1
+b7c97a57b,1024,1024,"[273.0, 851.0, 213.0, 69.0]",usask_1
+b7c97a57b,1024,1024,"[387.0, 432.0, 116.0, 60.0]",usask_1
+826d92f06,1024,1024,"[6.0, 542.0, 90.0, 89.0]",usask_1
+826d92f06,1024,1024,"[67.0, 677.0, 129.0, 125.0]",usask_1
+826d92f06,1024,1024,"[743.0, 293.0, 65.0, 48.0]",usask_1
+826d92f06,1024,1024,"[13.0, 326.0, 90.0, 201.0]",usask_1
+826d92f06,1024,1024,"[141.0, 552.0, 90.0, 86.0]",usask_1
+826d92f06,1024,1024,"[168.0, 172.0, 205.0, 168.0]",usask_1
+826d92f06,1024,1024,"[3.0, 481.0, 127.0, 107.0]",usask_1
+826d92f06,1024,1024,"[421.0, 23.0, 217.0, 163.0]",usask_1
+826d92f06,1024,1024,"[444.0, 6.0, 245.0, 106.0]",usask_1
+826d92f06,1024,1024,"[2.0, 714.0, 110.0, 137.0]",usask_1
+826d92f06,1024,1024,"[338.0, 268.0, 151.0, 106.0]",usask_1
+826d92f06,1024,1024,"[872.0, 933.0, 90.0, 75.0]",usask_1
+826d92f06,1024,1024,"[256.0, 949.0, 199.0, 75.0]",usask_1
+826d92f06,1024,1024,"[214.0, 510.0, 159.0, 119.0]",usask_1
+826d92f06,1024,1024,"[0.0, 30.0, 109.0, 89.0]",usask_1
+826d92f06,1024,1024,"[602.0, 879.0, 104.0, 89.0]",usask_1
+826d92f06,1024,1024,"[148.0, 835.0, 89.0, 189.0]",usask_1
+826d92f06,1024,1024,"[450.0, 441.0, 202.0, 52.0]",usask_1
+826d92f06,1024,1024,"[672.0, 720.0, 103.0, 46.0]",usask_1
+826d92f06,1024,1024,"[453.0, 617.0, 108.0, 44.0]",usask_1
+826d92f06,1024,1024,"[5.0, 636.0, 71.0, 46.0]",usask_1
+92c92d40d,1024,1024,"[860.0, 389.0, 81.0, 58.0]",usask_1
+92c92d40d,1024,1024,"[377.0, 569.0, 86.0, 66.0]",usask_1
+92c92d40d,1024,1024,"[257.0, 303.0, 81.0, 51.0]",usask_1
+92c92d40d,1024,1024,"[724.0, 481.0, 93.0, 107.0]",usask_1
+92c92d40d,1024,1024,"[90.0, 373.0, 80.0, 47.0]",usask_1
+92c92d40d,1024,1024,"[48.0, 419.0, 123.0, 81.0]",usask_1
+92c92d40d,1024,1024,"[903.0, 307.0, 59.0, 75.0]",usask_1
+92c92d40d,1024,1024,"[209.0, 935.0, 80.0, 86.0]",usask_1
+92c92d40d,1024,1024,"[822.0, 294.0, 89.0, 75.0]",usask_1
+92c92d40d,1024,1024,"[145.0, 274.0, 76.0, 97.0]",usask_1
+92c92d40d,1024,1024,"[261.0, 346.0, 63.0, 47.0]",usask_1
+92c92d40d,1024,1024,"[872.0, 707.0, 69.0, 69.0]",usask_1
+92c92d40d,1024,1024,"[974.0, 494.0, 50.0, 73.0]",usask_1
+92c92d40d,1024,1024,"[64.0, 107.0, 114.0, 122.0]",usask_1
+92c92d40d,1024,1024,"[304.0, 958.0, 92.0, 66.0]",usask_1
+92c92d40d,1024,1024,"[400.0, 117.0, 93.0, 90.0]",usask_1
+92c92d40d,1024,1024,"[889.0, 478.0, 83.0, 73.0]",usask_1
+92c92d40d,1024,1024,"[838.0, 565.0, 77.0, 154.0]",usask_1
+92c92d40d,1024,1024,"[724.0, 690.0, 79.0, 117.0]",usask_1
+92c92d40d,1024,1024,"[666.0, 811.0, 98.0, 114.0]",usask_1
+92c92d40d,1024,1024,"[764.0, 16.0, 80.0, 151.0]",usask_1
+92c92d40d,1024,1024,"[896.0, 23.0, 99.0, 144.0]",usask_1
+92c92d40d,1024,1024,"[313.0, 138.0, 106.0, 160.0]",usask_1
+92c92d40d,1024,1024,"[32.0, 5.0, 150.0, 82.0]",usask_1
+92c92d40d,1024,1024,"[272.0, 0.0, 103.0, 89.0]",usask_1
+92c92d40d,1024,1024,"[289.0, 407.0, 113.0, 177.0]",usask_1
+92c92d40d,1024,1024,"[754.0, 407.0, 90.0, 63.0]",usask_1
+92c92d40d,1024,1024,"[215.0, 850.0, 85.0, 87.0]",usask_1
+92c92d40d,1024,1024,"[762.0, 834.0, 92.0, 71.0]",usask_1
+92c92d40d,1024,1024,"[877.0, 239.0, 82.0, 74.0]",usask_1
+92c92d40d,1024,1024,"[214.0, 75.0, 105.0, 121.0]",usask_1
+92c92d40d,1024,1024,"[194.0, 184.0, 90.0, 109.0]",usask_1
+92c92d40d,1024,1024,"[262.0, 218.0, 41.0, 64.0]",usask_1
+92c92d40d,1024,1024,"[621.0, 377.0, 98.0, 56.0]",usask_1
+92c92d40d,1024,1024,"[306.0, 87.0, 61.0, 93.0]",usask_1
+c86621747,1024,1024,"[159.0, 672.0, 61.0, 52.0]",usask_1
+c86621747,1024,1024,"[0.0, 483.0, 46.0, 72.0]",usask_1
+c86621747,1024,1024,"[683.0, 288.0, 52.0, 89.0]",usask_1
+c86621747,1024,1024,"[524.0, 447.0, 99.0, 52.0]",usask_1
+c86621747,1024,1024,"[615.0, 83.0, 63.0, 33.0]",usask_1
+c86621747,1024,1024,"[532.0, 502.0, 87.0, 50.0]",usask_1
+c86621747,1024,1024,"[489.0, 183.0, 28.0, 83.0]",usask_1
+c86621747,1024,1024,"[669.0, 393.0, 62.0, 83.0]",usask_1
+c86621747,1024,1024,"[558.0, 881.0, 57.0, 57.0]",usask_1
+c86621747,1024,1024,"[436.0, 626.0, 72.0, 80.0]",usask_1
+c86621747,1024,1024,"[774.0, 40.0, 65.0, 64.0]",usask_1
+c86621747,1024,1024,"[586.0, 282.0, 89.0, 114.0]",usask_1
+c86621747,1024,1024,"[110.0, 0.0, 78.0, 58.0]",usask_1
+c86621747,1024,1024,"[387.0, 678.0, 93.0, 66.0]",usask_1
+c86621747,1024,1024,"[606.0, 643.0, 89.0, 79.0]",usask_1
+c86621747,1024,1024,"[449.0, 863.0, 70.0, 113.0]",usask_1
+c86621747,1024,1024,"[488.0, 943.0, 110.0, 81.0]",usask_1
+c86621747,1024,1024,"[226.0, 90.0, 79.0, 78.0]",usask_1
+c86621747,1024,1024,"[572.0, 129.0, 74.0, 100.0]",usask_1
+c86621747,1024,1024,"[858.0, 538.0, 79.0, 132.0]",usask_1
+c86621747,1024,1024,"[856.0, 22.0, 100.0, 74.0]",usask_1
+c86621747,1024,1024,"[582.0, 757.0, 89.0, 137.0]",usask_1
+c86621747,1024,1024,"[666.0, 680.0, 93.0, 103.0]",usask_1
+c86621747,1024,1024,"[469.0, 180.0, 34.0, 85.0]",usask_1
+c86621747,1024,1024,"[32.0, 69.0, 88.0, 140.0]",usask_1
+c86621747,1024,1024,"[673.0, 830.0, 56.0, 91.0]",usask_1
+c86621747,1024,1024,"[302.0, 749.0, 122.0, 65.0]",usask_1
+c86621747,1024,1024,"[709.0, 977.0, 96.0, 47.0]",usask_1
+c86621747,1024,1024,"[0.0, 934.0, 46.0, 58.0]",usask_1
+c86621747,1024,1024,"[62.0, 790.0, 79.0, 73.0]",usask_1
+c86621747,1024,1024,"[5.0, 784.0, 68.0, 56.0]",usask_1
+c86621747,1024,1024,"[338.0, 908.0, 96.0, 75.0]",usask_1
+c86621747,1024,1024,"[72.0, 372.0, 79.0, 57.0]",usask_1
+c86621747,1024,1024,"[113.0, 395.0, 70.0, 68.0]",usask_1
+c86621747,1024,1024,"[96.0, 263.0, 88.0, 96.0]",usask_1
+c86621747,1024,1024,"[398.0, 13.0, 89.0, 55.0]",usask_1
+c86621747,1024,1024,"[0.0, 896.0, 48.0, 45.0]",usask_1
+c86621747,1024,1024,"[505.0, 425.0, 78.0, 37.0]",usask_1
+c86621747,1024,1024,"[448.0, 309.0, 58.0, 57.0]",usask_1
+c86621747,1024,1024,"[518.0, 319.0, 85.0, 81.0]",usask_1
+c86621747,1024,1024,"[528.0, 198.0, 59.0, 71.0]",usask_1
+c86621747,1024,1024,"[575.0, 45.0, 68.0, 43.0]",usask_1
+c86621747,1024,1024,"[480.0, 97.0, 63.0, 67.0]",usask_1
+c86621747,1024,1024,"[678.0, 226.0, 44.0, 30.0]",usask_1
+c86621747,1024,1024,"[630.0, 529.0, 67.0, 31.0]",usask_1
+863e0253e,1024,1024,"[195.0, 552.0, 106.0, 100.0]",usask_1
+863e0253e,1024,1024,"[684.0, 715.0, 58.0, 94.0]",usask_1
+863e0253e,1024,1024,"[0.0, 212.0, 71.0, 67.0]",usask_1
+863e0253e,1024,1024,"[0.0, 432.0, 137.0, 82.0]",usask_1
+863e0253e,1024,1024,"[357.0, 151.0, 123.0, 137.0]",usask_1
+863e0253e,1024,1024,"[215.0, 205.0, 164.0, 100.0]",usask_1
+863e0253e,1024,1024,"[244.0, 700.0, 77.0, 180.0]",usask_1
+863e0253e,1024,1024,"[407.0, 464.0, 140.0, 113.0]",usask_1
+863e0253e,1024,1024,"[517.0, 234.0, 98.0, 94.0]",usask_1
+863e0253e,1024,1024,"[424.0, 823.0, 166.0, 120.0]",usask_1
+863e0253e,1024,1024,"[832.0, 0.0, 88.0, 249.0]",usask_1
+863e0253e,1024,1024,"[508.0, 0.0, 90.0, 106.0]",usask_1
+863e0253e,1024,1024,"[348.0, 593.0, 176.0, 91.0]",usask_1
+863e0253e,1024,1024,"[0.0, 741.0, 64.0, 89.0]",usask_1
+863e0253e,1024,1024,"[571.0, 477.0, 119.0, 58.0]",usask_1
+863e0253e,1024,1024,"[550.0, 481.0, 180.0, 254.0]",usask_1
+863e0253e,1024,1024,"[0.0, 91.0, 70.0, 68.0]",usask_1
+863e0253e,1024,1024,"[191.0, 89.0, 82.0, 67.0]",usask_1
+863e0253e,1024,1024,"[685.0, 241.0, 51.0, 33.0]",usask_1
+863e0253e,1024,1024,"[745.0, 110.0, 105.0, 125.0]",usask_1
+863e0253e,1024,1024,"[650.0, 340.0, 47.0, 30.0]",usask_1
+33d37a437,1024,1024,"[488.0, 123.0, 83.0, 243.0]",usask_1
+33d37a437,1024,1024,"[94.0, 161.0, 359.0, 156.0]",usask_1
+33d37a437,1024,1024,"[768.0, 423.0, 238.0, 125.0]",usask_1
+33d37a437,1024,1024,"[400.0, 316.0, 211.0, 157.0]",usask_1
+33d37a437,1024,1024,"[465.0, 830.0, 190.0, 127.0]",usask_1
+33d37a437,1024,1024,"[756.0, 505.0, 240.0, 80.0]",usask_1
+33d37a437,1024,1024,"[154.0, 534.0, 118.0, 137.0]",usask_1
+33d37a437,1024,1024,"[644.0, 468.0, 89.0, 133.0]",usask_1
+33d37a437,1024,1024,"[303.0, 961.0, 173.0, 63.0]",usask_1
+33d37a437,1024,1024,"[217.0, 4.0, 139.0, 77.0]",usask_1
+33d37a437,1024,1024,"[828.0, 6.0, 196.0, 201.0]",usask_1
+33d37a437,1024,1024,"[269.0, 347.0, 202.0, 82.0]",usask_1
+33d37a437,1024,1024,"[629.0, 10.0, 167.0, 109.0]",usask_1
+33d37a437,1024,1024,"[574.0, 407.0, 211.0, 143.0]",usask_1
+33d37a437,1024,1024,"[225.0, 476.0, 70.0, 198.0]",usask_1
+33d37a437,1024,1024,"[923.0, 564.0, 91.0, 115.0]",usask_1
+33d37a437,1024,1024,"[882.0, 919.0, 142.0, 100.0]",usask_1
+33d37a437,1024,1024,"[536.0, 281.0, 264.0, 155.0]",usask_1
+33d37a437,1024,1024,"[0.0, 720.0, 112.0, 209.0]",usask_1
+33d37a437,1024,1024,"[128.0, 406.0, 210.0, 119.0]",usask_1
+33d37a437,1024,1024,"[307.0, 901.0, 233.0, 123.0]",usask_1
+33d37a437,1024,1024,"[597.0, 552.0, 145.0, 136.0]",usask_1
+33d37a437,1024,1024,"[435.0, 519.0, 251.0, 109.0]",usask_1
+33d37a437,1024,1024,"[607.0, 633.0, 74.0, 70.0]",usask_1
+33d37a437,1024,1024,"[331.0, 341.0, 131.0, 140.0]",usask_1
+33d37a437,1024,1024,"[43.0, 175.0, 295.0, 155.0]",usask_1
+33d37a437,1024,1024,"[257.0, 288.0, 155.0, 79.0]",usask_1
+33d37a437,1024,1024,"[243.0, 0.0, 141.0, 41.0]",usask_1
+33d37a437,1024,1024,"[462.0, 924.0, 68.0, 98.0]",usask_1
+556c18af4,1024,1024,"[558.0, 466.0, 131.0, 144.0]",usask_1
+556c18af4,1024,1024,"[37.0, 584.0, 136.0, 91.0]",usask_1
+556c18af4,1024,1024,"[787.0, 219.0, 131.0, 186.0]",usask_1
+556c18af4,1024,1024,"[289.0, 597.0, 170.0, 193.0]",usask_1
+556c18af4,1024,1024,"[249.0, 266.0, 190.0, 76.0]",usask_1
+556c18af4,1024,1024,"[680.0, 580.0, 187.0, 70.0]",usask_1
+556c18af4,1024,1024,"[166.0, 205.0, 123.0, 117.0]",usask_1
+556c18af4,1024,1024,"[508.0, 0.0, 178.0, 56.0]",usask_1
+556c18af4,1024,1024,"[468.0, 609.0, 154.0, 78.0]",usask_1
+556c18af4,1024,1024,"[306.0, 471.0, 167.0, 171.0]",usask_1
+556c18af4,1024,1024,"[511.0, 85.0, 113.0, 167.0]",usask_1
+556c18af4,1024,1024,"[791.0, 822.0, 137.0, 99.0]",usask_1
+556c18af4,1024,1024,"[132.0, 332.0, 154.0, 134.0]",usask_1
+556c18af4,1024,1024,"[166.0, 566.0, 209.0, 120.0]",usask_1
+556c18af4,1024,1024,"[748.0, 420.0, 67.0, 135.0]",usask_1
+556c18af4,1024,1024,"[485.0, 283.0, 166.0, 137.0]",usask_1
+556c18af4,1024,1024,"[339.0, 988.0, 159.0, 36.0]",usask_1
+556c18af4,1024,1024,"[3.0, 82.0, 162.0, 103.0]",usask_1
+556c18af4,1024,1024,"[549.0, 747.0, 186.0, 122.0]",usask_1
+556c18af4,1024,1024,"[652.0, 479.0, 143.0, 84.0]",usask_1
+556c18af4,1024,1024,"[190.0, 519.0, 91.0, 86.0]",usask_1
+556c18af4,1024,1024,"[88.0, 523.0, 77.0, 54.0]",usask_1
+43650fd9c,1024,1024,"[13.0, 320.0, 93.0, 106.0]",usask_1
+43650fd9c,1024,1024,"[633.0, 346.0, 96.0, 73.0]",usask_1
+43650fd9c,1024,1024,"[0.0, 259.0, 59.0, 113.0]",usask_1
+43650fd9c,1024,1024,"[157.0, 799.0, 88.0, 69.0]",usask_1
+43650fd9c,1024,1024,"[406.0, 116.0, 165.0, 131.0]",usask_1
+43650fd9c,1024,1024,"[72.0, 0.0, 56.0, 44.0]",usask_1
+43650fd9c,1024,1024,"[666.0, 272.0, 97.0, 103.0]",usask_1
+43650fd9c,1024,1024,"[594.0, 524.0, 136.0, 92.0]",usask_1
+43650fd9c,1024,1024,"[316.0, 552.0, 107.0, 106.0]",usask_1
+43650fd9c,1024,1024,"[347.0, 901.0, 131.0, 44.0]",usask_1
+43650fd9c,1024,1024,"[104.0, 53.0, 81.0, 106.0]",usask_1
+43650fd9c,1024,1024,"[0.0, 862.0, 52.0, 120.0]",usask_1
+43650fd9c,1024,1024,"[601.0, 984.0, 67.0, 40.0]",usask_1
+43650fd9c,1024,1024,"[90.0, 485.0, 117.0, 82.0]",usask_1
+43650fd9c,1024,1024,"[456.0, 762.0, 81.0, 52.0]",usask_1
+43650fd9c,1024,1024,"[756.0, 440.0, 81.0, 132.0]",usask_1
+43650fd9c,1024,1024,"[165.0, 707.0, 93.0, 86.0]",usask_1
+43650fd9c,1024,1024,"[343.0, 861.0, 118.0, 55.0]",usask_1
+43650fd9c,1024,1024,"[25.0, 786.0, 76.0, 70.0]",usask_1
+43650fd9c,1024,1024,"[1.0, 527.0, 70.0, 82.0]",usask_1
+43650fd9c,1024,1024,"[0.0, 489.0, 64.0, 62.0]",usask_1
+43650fd9c,1024,1024,"[612.0, 153.0, 60.0, 61.0]",usask_1
+43650fd9c,1024,1024,"[2.0, 2.0, 52.0, 54.0]",usask_1
+43650fd9c,1024,1024,"[554.0, 428.0, 150.0, 82.0]",usask_1
+43650fd9c,1024,1024,"[688.0, 457.0, 64.0, 72.0]",usask_1
+43650fd9c,1024,1024,"[433.0, 698.0, 68.0, 76.0]",usask_1
+43a765c3e,1024,1024,"[0.0, 511.0, 129.0, 124.0]",usask_1
+43a765c3e,1024,1024,"[6.0, 588.0, 141.0, 95.0]",usask_1
+43a765c3e,1024,1024,"[0.0, 60.0, 162.0, 89.0]",usask_1
+43a765c3e,1024,1024,"[139.0, 0.0, 69.0, 48.0]",usask_1
+43a765c3e,1024,1024,"[541.0, 223.0, 194.0, 86.0]",usask_1
+43a765c3e,1024,1024,"[0.0, 853.0, 131.0, 127.0]",usask_1
+43a765c3e,1024,1024,"[31.0, 408.0, 97.0, 62.0]",usask_1
+43a765c3e,1024,1024,"[217.0, 87.0, 125.0, 121.0]",usask_1
+43a765c3e,1024,1024,"[350.0, 525.0, 130.0, 120.0]",usask_1
+43a765c3e,1024,1024,"[140.0, 223.0, 88.0, 121.0]",usask_1
+43a765c3e,1024,1024,"[277.0, 293.0, 77.0, 117.0]",usask_1
+43a765c3e,1024,1024,"[601.0, 382.0, 179.0, 75.0]",usask_1
+43a765c3e,1024,1024,"[131.0, 387.0, 140.0, 113.0]",usask_1
+43a765c3e,1024,1024,"[425.0, 179.0, 172.0, 63.0]",usask_1
+43a765c3e,1024,1024,"[0.0, 662.0, 127.0, 153.0]",usask_1
+43a765c3e,1024,1024,"[757.0, 596.0, 93.0, 93.0]",usask_1
+43a765c3e,1024,1024,"[454.0, 301.0, 185.0, 81.0]",usask_1
+43a765c3e,1024,1024,"[289.0, 163.0, 54.0, 130.0]",usask_1
+43a765c3e,1024,1024,"[804.0, 351.0, 149.0, 104.0]",usask_1
+43a765c3e,1024,1024,"[299.0, 856.0, 125.0, 168.0]",usask_1
+43a765c3e,1024,1024,"[100.0, 732.0, 139.0, 145.0]",usask_1
+43a765c3e,1024,1024,"[148.0, 861.0, 72.0, 154.0]",usask_1
+43a765c3e,1024,1024,"[442.0, 418.0, 205.0, 62.0]",usask_1
+43a765c3e,1024,1024,"[557.0, 473.0, 202.0, 74.0]",usask_1
+43a765c3e,1024,1024,"[1.0, 723.0, 146.0, 142.0]",usask_1
+43a765c3e,1024,1024,"[372.0, 784.0, 103.0, 237.0]",usask_1
+43a765c3e,1024,1024,"[664.0, 547.0, 87.0, 54.0]",usask_1
+43a765c3e,1024,1024,"[471.0, 152.0, 97.0, 56.0]",usask_1
+43a765c3e,1024,1024,"[169.0, 181.0, 78.0, 71.0]",usask_1
+43a765c3e,1024,1024,"[867.0, 7.0, 64.0, 103.0]",usask_1
+43a765c3e,1024,1024,"[819.0, 419.0, 83.0, 174.0]",usask_1
+43a765c3e,1024,1024,"[357.0, 120.0, 63.0, 80.0]",usask_1
+4d90cdad1,1024,1024,"[667.0, 324.0, 105.0, 79.0]",usask_1
+4d90cdad1,1024,1024,"[981.0, 696.0, 43.0, 91.0]",usask_1
+4d90cdad1,1024,1024,"[167.0, 231.0, 94.0, 126.0]",usask_1
+4d90cdad1,1024,1024,"[609.0, 866.0, 87.0, 67.0]",usask_1
+4d90cdad1,1024,1024,"[114.0, 656.0, 90.0, 183.0]",usask_1
+4d90cdad1,1024,1024,"[561.0, 235.0, 67.0, 87.0]",usask_1
+4d90cdad1,1024,1024,"[725.0, 254.0, 93.0, 93.0]",usask_1
+4d90cdad1,1024,1024,"[0.0, 190.0, 40.0, 93.0]",usask_1
+4d90cdad1,1024,1024,"[66.0, 550.0, 117.0, 119.0]",usask_1
+4d90cdad1,1024,1024,"[949.0, 864.0, 75.0, 79.0]",usask_1
+4d90cdad1,1024,1024,"[437.0, 3.0, 87.0, 66.0]",usask_1
+4d90cdad1,1024,1024,"[791.0, 272.0, 93.0, 117.0]",usask_1
+4d90cdad1,1024,1024,"[424.0, 245.0, 127.0, 120.0]",usask_1
+4d90cdad1,1024,1024,"[432.0, 918.0, 278.0, 95.0]",usask_1
+4d90cdad1,1024,1024,"[651.0, 106.0, 119.0, 120.0]",usask_1
+4d90cdad1,1024,1024,"[525.0, 0.0, 90.0, 82.0]",usask_1
+4d90cdad1,1024,1024,"[150.0, 643.0, 114.0, 202.0]",usask_1
+4d90cdad1,1024,1024,"[468.0, 80.0, 106.0, 90.0]",usask_1
+4d90cdad1,1024,1024,"[899.0, 229.0, 103.0, 82.0]",usask_1
+4d90cdad1,1024,1024,"[367.0, 210.0, 87.0, 68.0]",usask_1
+4d90cdad1,1024,1024,"[341.0, 281.0, 77.0, 92.0]",usask_1
+4d90cdad1,1024,1024,"[266.0, 282.0, 106.0, 116.0]",usask_1
+4d90cdad1,1024,1024,"[219.0, 357.0, 119.0, 69.0]",usask_1
+4d90cdad1,1024,1024,"[445.0, 399.0, 77.0, 76.0]",usask_1
+4d90cdad1,1024,1024,"[562.0, 348.0, 75.0, 92.0]",usask_1
+4d90cdad1,1024,1024,"[459.0, 496.0, 83.0, 87.0]",usask_1
+4d90cdad1,1024,1024,"[512.0, 623.0, 72.0, 86.0]",usask_1
+4d90cdad1,1024,1024,"[546.0, 553.0, 54.0, 76.0]",usask_1
+4d90cdad1,1024,1024,"[590.0, 530.0, 114.0, 130.0]",usask_1
+4d90cdad1,1024,1024,"[590.0, 481.0, 58.0, 46.0]",usask_1
+4d90cdad1,1024,1024,"[524.0, 437.0, 117.0, 66.0]",usask_1
+220488d01,1024,1024,"[869.0, 417.0, 48.0, 40.0]",usask_1
+220488d01,1024,1024,"[848.0, 148.0, 113.0, 78.0]",usask_1
+220488d01,1024,1024,"[535.0, 14.0, 191.0, 114.0]",usask_1
+220488d01,1024,1024,"[799.0, 196.0, 67.0, 203.0]",usask_1
+220488d01,1024,1024,"[83.0, 963.0, 191.0, 61.0]",usask_1
+220488d01,1024,1024,"[0.0, 25.0, 186.0, 106.0]",usask_1
+220488d01,1024,1024,"[630.0, 0.0, 214.0, 65.0]",usask_1
+220488d01,1024,1024,"[370.0, 177.0, 76.0, 99.0]",usask_1
+220488d01,1024,1024,"[872.0, 646.0, 152.0, 136.0]",usask_1
+220488d01,1024,1024,"[796.0, 780.0, 94.0, 89.0]",usask_1
+220488d01,1024,1024,"[0.0, 443.0, 63.0, 70.0]",usask_1
+220488d01,1024,1024,"[0.0, 233.0, 130.0, 111.0]",usask_1
+220488d01,1024,1024,"[0.0, 87.0, 150.0, 143.0]",usask_1
+220488d01,1024,1024,"[640.0, 196.0, 173.0, 121.0]",usask_1
+220488d01,1024,1024,"[544.0, 131.0, 154.0, 83.0]",usask_1
+220488d01,1024,1024,"[546.0, 244.0, 177.0, 127.0]",usask_1
+220488d01,1024,1024,"[671.0, 289.0, 171.0, 74.0]",usask_1
+220488d01,1024,1024,"[923.0, 227.0, 81.0, 90.0]",usask_1
+48c72c991,1024,1024,"[377.0, 727.0, 96.0, 100.0]",usask_1
+48c72c991,1024,1024,"[457.0, 296.0, 85.0, 144.0]",usask_1
+48c72c991,1024,1024,"[459.0, 191.0, 57.0, 74.0]",usask_1
+48c72c991,1024,1024,"[407.0, 821.0, 123.0, 124.0]",usask_1
+48c72c991,1024,1024,"[548.0, 471.0, 152.0, 80.0]",usask_1
+48c72c991,1024,1024,"[0.0, 111.0, 55.0, 86.0]",usask_1
+48c72c991,1024,1024,"[975.0, 124.0, 49.0, 118.0]",usask_1
+48c72c991,1024,1024,"[997.0, 243.0, 27.0, 60.0]",usask_1
+48c72c991,1024,1024,"[277.0, 923.0, 88.0, 81.0]",usask_1
+48c72c991,1024,1024,"[727.0, 0.0, 50.0, 60.0]",usask_1
+48c72c991,1024,1024,"[627.0, 375.0, 73.0, 81.0]",usask_1
+48c72c991,1024,1024,"[604.0, 469.0, 162.0, 80.0]",usask_1
+48c72c991,1024,1024,"[391.0, 180.0, 72.0, 111.0]",usask_1
+48c72c991,1024,1024,"[852.0, 0.0, 79.0, 54.0]",usask_1
+48c72c991,1024,1024,"[197.0, 476.0, 172.0, 75.0]",usask_1
+48c72c991,1024,1024,"[566.0, 0.0, 70.0, 25.0]",usask_1
+48c72c991,1024,1024,"[556.0, 371.0, 98.0, 69.0]",usask_1
+48c72c991,1024,1024,"[982.0, 813.0, 42.0, 59.0]",usask_1
+48c72c991,1024,1024,"[301.0, 169.0, 60.0, 139.0]",usask_1
+48c72c991,1024,1024,"[883.0, 367.0, 109.0, 104.0]",usask_1
+48c72c991,1024,1024,"[944.0, 234.0, 59.0, 76.0]",usask_1
+48c72c991,1024,1024,"[840.0, 663.0, 182.0, 103.0]",usask_1
+48c72c991,1024,1024,"[879.0, 540.0, 90.0, 140.0]",usask_1
+48c72c991,1024,1024,"[0.0, 939.0, 85.0, 70.0]",usask_1
+48c72c991,1024,1024,"[894.0, 868.0, 100.0, 78.0]",usask_1
+48c72c991,1024,1024,"[765.0, 122.0, 106.0, 53.0]",usask_1
+48c72c991,1024,1024,"[108.0, 755.0, 112.0, 63.0]",usask_1
+48c72c991,1024,1024,"[98.0, 771.0, 72.0, 87.0]",usask_1
+48c72c991,1024,1024,"[511.0, 660.0, 73.0, 101.0]",usask_1
+48c72c991,1024,1024,"[581.0, 685.0, 85.0, 84.0]",usask_1
+48c72c991,1024,1024,"[537.0, 734.0, 66.0, 131.0]",usask_1
+48c72c991,1024,1024,"[641.0, 679.0, 79.0, 133.0]",usask_1
+48c72c991,1024,1024,"[439.0, 444.0, 43.0, 41.0]",usask_1
+48c72c991,1024,1024,"[735.0, 819.0, 150.0, 50.0]",usask_1
+48c72c991,1024,1024,"[720.0, 865.0, 170.0, 120.0]",usask_1
+48c72c991,1024,1024,"[739.0, 898.0, 142.0, 114.0]",usask_1
+48c72c991,1024,1024,"[633.0, 797.0, 133.0, 124.0]",usask_1
+48c72c991,1024,1024,"[790.0, 0.0, 48.0, 22.0]",usask_1
+49dcb7dd3,1024,1024,"[427.0, 0.0, 90.0, 55.0]",usask_1
+49dcb7dd3,1024,1024,"[282.0, 811.0, 90.0, 90.0]",usask_1
+49dcb7dd3,1024,1024,"[756.0, 875.0, 145.0, 120.0]",usask_1
+49dcb7dd3,1024,1024,"[626.0, 613.0, 162.0, 108.0]",usask_1
+49dcb7dd3,1024,1024,"[236.0, 728.0, 160.0, 63.0]",usask_1
+49dcb7dd3,1024,1024,"[575.0, 840.0, 44.0, 152.0]",usask_1
+49dcb7dd3,1024,1024,"[256.0, 320.0, 123.0, 103.0]",usask_1
+49dcb7dd3,1024,1024,"[671.0, 415.0, 146.0, 85.0]",usask_1
+49dcb7dd3,1024,1024,"[390.0, 263.0, 137.0, 93.0]",usask_1
+49dcb7dd3,1024,1024,"[13.0, 801.0, 93.0, 117.0]",usask_1
+49dcb7dd3,1024,1024,"[0.0, 94.0, 93.0, 79.0]",usask_1
+49dcb7dd3,1024,1024,"[27.0, 445.0, 165.0, 111.0]",usask_1
+49dcb7dd3,1024,1024,"[365.0, 794.0, 154.0, 110.0]",usask_1
+49dcb7dd3,1024,1024,"[870.0, 920.0, 95.0, 104.0]",usask_1
+49dcb7dd3,1024,1024,"[542.0, 899.0, 48.0, 124.0]",usask_1
+49dcb7dd3,1024,1024,"[480.0, 920.0, 60.0, 96.0]",usask_1
+49dcb7dd3,1024,1024,"[519.0, 826.0, 138.0, 129.0]",usask_1
+49dcb7dd3,1024,1024,"[293.0, 675.0, 143.0, 71.0]",usask_1
+49dcb7dd3,1024,1024,"[330.0, 624.0, 214.0, 81.0]",usask_1
+49dcb7dd3,1024,1024,"[319.0, 596.0, 148.0, 92.0]",usask_1
+49dcb7dd3,1024,1024,"[381.0, 524.0, 130.0, 63.0]",usask_1
+49dcb7dd3,1024,1024,"[380.0, 446.0, 106.0, 61.0]",usask_1
+49dcb7dd3,1024,1024,"[497.0, 471.0, 84.0, 59.0]",usask_1
+49dcb7dd3,1024,1024,"[552.0, 439.0, 60.0, 97.0]",usask_1
+49dcb7dd3,1024,1024,"[494.0, 386.0, 72.0, 46.0]",usask_1
+49dcb7dd3,1024,1024,"[414.0, 385.0, 89.0, 67.0]",usask_1
+4c02b2f7f,1024,1024,"[471.0, 795.0, 119.0, 65.0]",usask_1
+4c02b2f7f,1024,1024,"[330.0, 974.0, 169.0, 50.0]",usask_1
+4c02b2f7f,1024,1024,"[124.0, 613.0, 77.0, 80.0]",usask_1
+4c02b2f7f,1024,1024,"[515.0, 215.0, 145.0, 74.0]",usask_1
+4c02b2f7f,1024,1024,"[3.0, 414.0, 116.0, 123.0]",usask_1
+4c02b2f7f,1024,1024,"[245.0, 164.0, 169.0, 92.0]",usask_1
+4c02b2f7f,1024,1024,"[450.0, 0.0, 141.0, 52.0]",usask_1
+4c02b2f7f,1024,1024,"[840.0, 919.0, 107.0, 105.0]",usask_1
+4c02b2f7f,1024,1024,"[465.0, 272.0, 194.0, 91.0]",usask_1
+4c02b2f7f,1024,1024,"[632.0, 914.0, 111.0, 52.0]",usask_1
+4c02b2f7f,1024,1024,"[572.0, 367.0, 120.0, 96.0]",usask_1
+4c02b2f7f,1024,1024,"[0.0, 77.0, 126.0, 103.0]",usask_1
+4c02b2f7f,1024,1024,"[690.0, 904.0, 143.0, 120.0]",usask_1
+4c02b2f7f,1024,1024,"[195.0, 758.0, 223.0, 82.0]",usask_1
+4c02b2f7f,1024,1024,"[915.0, 486.0, 109.0, 97.0]",usask_1
+4c02b2f7f,1024,1024,"[391.0, 67.0, 227.0, 116.0]",usask_1
+4c02b2f7f,1024,1024,"[955.0, 704.0, 69.0, 81.0]",usask_1
+4c02b2f7f,1024,1024,"[178.0, 431.0, 211.0, 200.0]",usask_1
+4c02b2f7f,1024,1024,"[767.0, 267.0, 197.0, 173.0]",usask_1
+4c02b2f7f,1024,1024,"[828.0, 0.0, 111.0, 107.0]",usask_1
+4c02b2f7f,1024,1024,"[697.0, 121.0, 110.0, 99.0]",usask_1
+4c02b2f7f,1024,1024,"[832.0, 757.0, 160.0, 103.0]",usask_1
+4c02b2f7f,1024,1024,"[184.0, 37.0, 175.0, 98.0]",usask_1
+4c02b2f7f,1024,1024,"[240.0, 707.0, 82.0, 79.0]",usask_1
+4c02b2f7f,1024,1024,"[877.0, 543.0, 147.0, 75.0]",usask_1
+4c02b2f7f,1024,1024,"[276.0, 406.0, 181.0, 63.0]",usask_1
+4c02b2f7f,1024,1024,"[354.0, 410.0, 186.0, 93.0]",usask_1
+4c02b2f7f,1024,1024,"[346.0, 333.0, 162.0, 85.0]",usask_1
+4c02b2f7f,1024,1024,"[188.0, 280.0, 197.0, 98.0]",usask_1
+f251e40f2,1024,1024,"[0.0, 906.0, 85.0, 82.0]",usask_1
+f251e40f2,1024,1024,"[361.0, 698.0, 65.0, 56.0]",usask_1
+f251e40f2,1024,1024,"[540.0, 664.0, 250.0, 64.0]",usask_1
+f251e40f2,1024,1024,"[521.0, 777.0, 172.0, 103.0]",usask_1
+f251e40f2,1024,1024,"[128.0, 168.0, 153.0, 133.0]",usask_1
+f251e40f2,1024,1024,"[970.0, 69.0, 54.0, 84.0]",usask_1
+f251e40f2,1024,1024,"[782.0, 979.0, 99.0, 45.0]",usask_1
+f251e40f2,1024,1024,"[24.0, 945.0, 76.0, 63.0]",usask_1
+f251e40f2,1024,1024,"[203.0, 663.0, 71.0, 69.0]",usask_1
+f251e40f2,1024,1024,"[80.0, 795.0, 101.0, 103.0]",usask_1
+f251e40f2,1024,1024,"[0.0, 161.0, 68.0, 70.0]",usask_1
+f251e40f2,1024,1024,"[16.0, 749.0, 123.0, 64.0]",usask_1
+f251e40f2,1024,1024,"[867.0, 658.0, 141.0, 104.0]",usask_1
+f251e40f2,1024,1024,"[657.0, 793.0, 118.0, 72.0]",usask_1
+f251e40f2,1024,1024,"[874.0, 0.0, 95.0, 69.0]",usask_1
+f251e40f2,1024,1024,"[571.0, 735.0, 126.0, 93.0]",usask_1
+f251e40f2,1024,1024,"[787.0, 429.0, 62.0, 55.0]",usask_1
+f251e40f2,1024,1024,"[151.0, 0.0, 64.0, 72.0]",usask_1
+f251e40f2,1024,1024,"[857.0, 404.0, 58.0, 51.0]",usask_1
+f251e40f2,1024,1024,"[995.0, 638.0, 29.0, 72.0]",usask_1
+01f37f1d3,1024,1024,"[909.0, 289.0, 100.0, 86.0]",usask_1
+01f37f1d3,1024,1024,"[500.0, 839.0, 91.0, 70.0]",usask_1
+01f37f1d3,1024,1024,"[774.0, 852.0, 73.0, 110.0]",usask_1
+01f37f1d3,1024,1024,"[793.0, 569.0, 64.0, 159.0]",usask_1
+01f37f1d3,1024,1024,"[417.0, 602.0, 73.0, 90.0]",usask_1
+01f37f1d3,1024,1024,"[992.0, 708.0, 32.0, 120.0]",usask_1
+01f37f1d3,1024,1024,"[725.0, 336.0, 80.0, 57.0]",usask_1
+01f37f1d3,1024,1024,"[755.0, 43.0, 81.0, 158.0]",usask_1
+01f37f1d3,1024,1024,"[853.0, 599.0, 136.0, 128.0]",usask_1
+01f37f1d3,1024,1024,"[805.0, 973.0, 103.0, 51.0]",usask_1
+01f37f1d3,1024,1024,"[85.0, 387.0, 72.0, 109.0]",usask_1
+01f37f1d3,1024,1024,"[989.0, 838.0, 35.0, 101.0]",usask_1
+01f37f1d3,1024,1024,"[640.0, 214.0, 87.0, 119.0]",usask_1
+01f37f1d3,1024,1024,"[528.0, 581.0, 79.0, 184.0]",usask_1
+01f37f1d3,1024,1024,"[858.0, 3.0, 120.0, 193.0]",usask_1
+01f37f1d3,1024,1024,"[609.0, 682.0, 120.0, 88.0]",usask_1
+01f37f1d3,1024,1024,"[841.0, 913.0, 86.0, 69.0]",usask_1
+01f37f1d3,1024,1024,"[343.0, 845.0, 154.0, 90.0]",usask_1
+01f37f1d3,1024,1024,"[313.0, 0.0, 155.0, 40.0]",usask_1
+01f37f1d3,1024,1024,"[879.0, 468.0, 109.0, 83.0]",usask_1
+01f37f1d3,1024,1024,"[935.0, 753.0, 66.0, 67.0]",usask_1
+01f37f1d3,1024,1024,"[520.0, 747.0, 86.0, 122.0]",usask_1
+01f37f1d3,1024,1024,"[511.0, 526.0, 74.0, 75.0]",usask_1
+01f37f1d3,1024,1024,"[671.0, 562.0, 66.0, 132.0]",usask_1
+01f37f1d3,1024,1024,"[735.0, 502.0, 82.0, 143.0]",usask_1
+01f37f1d3,1024,1024,"[735.0, 601.0, 81.0, 95.0]",usask_1
+01f37f1d3,1024,1024,"[778.0, 351.0, 67.0, 77.0]",usask_1
+01f37f1d3,1024,1024,"[735.0, 218.0, 220.0, 139.0]",usask_1
+01f37f1d3,1024,1024,"[993.0, 0.0, 31.0, 72.0]",usask_1
+01f37f1d3,1024,1024,"[683.0, 474.0, 42.0, 36.0]",usask_1
+d7d200c0b,1024,1024,"[470.0, 528.0, 110.0, 63.0]",usask_1
+d7d200c0b,1024,1024,"[700.0, 656.0, 100.0, 107.0]",usask_1
+d7d200c0b,1024,1024,"[505.0, 0.0, 103.0, 42.0]",usask_1
+d7d200c0b,1024,1024,"[264.0, 0.0, 105.0, 58.0]",usask_1
+d7d200c0b,1024,1024,"[754.0, 111.0, 80.0, 69.0]",usask_1
+d7d200c0b,1024,1024,"[0.0, 852.0, 112.0, 65.0]",usask_1
+d7d200c0b,1024,1024,"[399.0, 682.0, 97.0, 103.0]",usask_1
+d7d200c0b,1024,1024,"[555.0, 0.0, 100.0, 89.0]",usask_1
+d7d200c0b,1024,1024,"[700.0, 619.0, 221.0, 117.0]",usask_1
+d7d200c0b,1024,1024,"[257.0, 583.0, 167.0, 90.0]",usask_1
+d7d200c0b,1024,1024,"[97.0, 880.0, 159.0, 66.0]",usask_1
+d7d200c0b,1024,1024,"[879.0, 239.0, 76.0, 109.0]",usask_1
+d7d200c0b,1024,1024,"[1020.0, 781.0, 4.0, 86.0]",usask_1
+d7d200c0b,1024,1024,"[472.0, 965.0, 98.0, 59.0]",usask_1
+d7d200c0b,1024,1024,"[855.0, 171.0, 169.0, 111.0]",usask_1
+d7d200c0b,1024,1024,"[185.0, 60.0, 103.0, 113.0]",usask_1
+d7d200c0b,1024,1024,"[484.0, 459.0, 134.0, 110.0]",usask_1
+d7d200c0b,1024,1024,"[337.0, 0.0, 100.0, 81.0]",usask_1
+d7d200c0b,1024,1024,"[67.0, 946.0, 172.0, 67.0]",usask_1
+d7d200c0b,1024,1024,"[394.0, 794.0, 200.0, 110.0]",usask_1
+d7d200c0b,1024,1024,"[729.0, 405.0, 45.0, 34.0]",usask_1
+d7d200c0b,1024,1024,"[949.0, 418.0, 74.0, 50.0]",usask_1
+d7d200c0b,1024,1024,"[0.0, 145.0, 233.0, 78.0]",usask_1
+d7d200c0b,1024,1024,"[84.0, 1020.0, 221.0, 4.0]",usask_1
+d7d200c0b,1024,1024,"[872.0, 582.0, 152.0, 73.0]",usask_1
+d7d200c0b,1024,1024,"[229.0, 791.0, 142.0, 70.0]",usask_1
+d7d200c0b,1024,1024,"[158.0, 679.0, 127.0, 73.0]",usask_1
+d7d200c0b,1024,1024,"[370.0, 317.0, 120.0, 170.0]",usask_1
+d7d200c0b,1024,1024,"[892.0, 636.0, 132.0, 76.0]",usask_1
+d7d200c0b,1024,1024,"[555.0, 650.0, 120.0, 72.0]",usask_1
+d7d200c0b,1024,1024,"[461.0, 613.0, 103.0, 72.0]",usask_1
+d7d200c0b,1024,1024,"[293.0, 678.0, 117.0, 80.0]",usask_1
+d7d200c0b,1024,1024,"[342.0, 525.0, 103.0, 95.0]",usask_1
+d7d200c0b,1024,1024,"[579.0, 536.0, 43.0, 62.0]",usask_1
+d7d200c0b,1024,1024,"[363.0, 44.0, 43.0, 64.0]",usask_1
+695ae4a12,1024,1024,"[238.0, 170.0, 72.0, 87.0]",usask_1
+695ae4a12,1024,1024,"[159.0, 301.0, 62.0, 68.0]",usask_1
+695ae4a12,1024,1024,"[228.0, 63.0, 94.0, 98.0]",usask_1
+695ae4a12,1024,1024,"[95.0, 92.0, 73.0, 106.0]",usask_1
+695ae4a12,1024,1024,"[266.0, 0.0, 72.0, 66.0]",usask_1
+695ae4a12,1024,1024,"[221.0, 977.0, 77.0, 47.0]",usask_1
+695ae4a12,1024,1024,"[14.0, 33.0, 74.0, 95.0]",usask_1
+695ae4a12,1024,1024,"[159.0, 215.0, 76.0, 69.0]",usask_1
+695ae4a12,1024,1024,"[138.0, 529.0, 89.0, 74.0]",usask_1
+695ae4a12,1024,1024,"[262.0, 830.0, 71.0, 58.0]",usask_1
+695ae4a12,1024,1024,"[831.0, 280.0, 122.0, 97.0]",usask_1
+695ae4a12,1024,1024,"[97.0, 219.0, 66.0, 65.0]",usask_1
+695ae4a12,1024,1024,"[105.0, 875.0, 65.0, 58.0]",usask_1
+695ae4a12,1024,1024,"[886.0, 683.0, 62.0, 64.0]",usask_1
+695ae4a12,1024,1024,"[286.0, 562.0, 89.0, 93.0]",usask_1
+695ae4a12,1024,1024,"[38.0, 181.0, 81.0, 72.0]",usask_1
+695ae4a12,1024,1024,"[343.0, 56.0, 45.0, 74.0]",usask_1
+695ae4a12,1024,1024,"[77.0, 343.0, 100.0, 117.0]",usask_1
+695ae4a12,1024,1024,"[784.0, 47.0, 105.0, 108.0]",usask_1
+695ae4a12,1024,1024,"[17.0, 769.0, 69.0, 59.0]",usask_1
+695ae4a12,1024,1024,"[40.0, 495.0, 110.0, 150.0]",usask_1
+695ae4a12,1024,1024,"[683.0, 80.0, 93.0, 83.0]",usask_1
+695ae4a12,1024,1024,"[281.0, 224.0, 53.0, 83.0]",usask_1
+695ae4a12,1024,1024,"[858.0, 444.0, 90.0, 83.0]",usask_1
+695ae4a12,1024,1024,"[899.0, 525.0, 120.0, 83.0]",usask_1
+695ae4a12,1024,1024,"[254.0, 312.0, 94.0, 83.0]",usask_1
+695ae4a12,1024,1024,"[337.0, 242.0, 65.0, 66.0]",usask_1
+695ae4a12,1024,1024,"[263.0, 473.0, 120.0, 84.0]",usask_1
+695ae4a12,1024,1024,"[179.0, 843.0, 59.0, 68.0]",usask_1
+695ae4a12,1024,1024,"[1.0, 937.0, 110.0, 66.0]",usask_1
+695ae4a12,1024,1024,"[1.0, 989.0, 68.0, 33.0]",usask_1
+695ae4a12,1024,1024,"[883.0, 890.0, 71.0, 59.0]",usask_1
+695ae4a12,1024,1024,"[648.0, 174.0, 74.0, 85.0]",usask_1
+695ae4a12,1024,1024,"[758.0, 156.0, 70.0, 93.0]",usask_1
+695ae4a12,1024,1024,"[932.0, 112.0, 87.0, 110.0]",usask_1
+695ae4a12,1024,1024,"[926.0, 45.0, 91.0, 79.0]",usask_1
+695ae4a12,1024,1024,"[910.0, 0.0, 80.0, 86.0]",usask_1
+695ae4a12,1024,1024,"[380.0, 203.0, 52.0, 73.0]",usask_1
+695ae4a12,1024,1024,"[176.0, 131.0, 45.0, 64.0]",usask_1
+695ae4a12,1024,1024,"[178.0, 52.0, 51.0, 83.0]",usask_1
+150cabe75,1024,1024,"[559.0, 743.0, 96.0, 75.0]",usask_1
+150cabe75,1024,1024,"[829.0, 803.0, 79.0, 94.0]",usask_1
+150cabe75,1024,1024,"[441.0, 40.0, 62.0, 116.0]",usask_1
+150cabe75,1024,1024,"[892.0, 360.0, 127.0, 83.0]",usask_1
+150cabe75,1024,1024,"[158.0, 235.0, 69.0, 103.0]",usask_1
+150cabe75,1024,1024,"[847.0, 123.0, 177.0, 205.0]",usask_1
+150cabe75,1024,1024,"[724.0, 690.0, 140.0, 113.0]",usask_1
+150cabe75,1024,1024,"[410.0, 404.0, 90.0, 96.0]",usask_1
+150cabe75,1024,1024,"[767.0, 941.0, 151.0, 68.0]",usask_1
+150cabe75,1024,1024,"[493.0, 871.0, 199.0, 90.0]",usask_1
+150cabe75,1024,1024,"[586.0, 802.0, 103.0, 66.0]",usask_1
+150cabe75,1024,1024,"[562.0, 67.0, 130.0, 228.0]",usask_1
+150cabe75,1024,1024,"[616.0, 464.0, 180.0, 137.0]",usask_1
+150cabe75,1024,1024,"[762.0, 762.0, 161.0, 120.0]",usask_1
+150cabe75,1024,1024,"[125.0, 946.0, 120.0, 78.0]",usask_1
+150cabe75,1024,1024,"[600.0, 326.0, 99.0, 137.0]",usask_1
+150cabe75,1024,1024,"[232.0, 693.0, 157.0, 97.0]",usask_1
+150cabe75,1024,1024,"[714.0, 434.0, 160.0, 164.0]",usask_1
+150cabe75,1024,1024,"[629.0, 646.0, 120.0, 117.0]",usask_1
+150cabe75,1024,1024,"[89.0, 633.0, 104.0, 196.0]",usask_1
+150cabe75,1024,1024,"[472.0, 783.0, 74.0, 81.0]",usask_1
+150cabe75,1024,1024,"[175.0, 0.0, 74.0, 38.0]",usask_1
+150cabe75,1024,1024,"[111.0, 360.0, 143.0, 93.0]",usask_1
+150cabe75,1024,1024,"[798.0, 0.0, 150.0, 72.0]",usask_1
+150cabe75,1024,1024,"[877.0, 418.0, 92.0, 134.0]",usask_1
+150cabe75,1024,1024,"[497.0, 687.0, 104.0, 79.0]",usask_1
+150cabe75,1024,1024,"[53.0, 50.0, 103.0, 113.0]",usask_1
+150cabe75,1024,1024,"[1.0, 265.0, 56.0, 87.0]",usask_1
+150cabe75,1024,1024,"[160.0, 685.0, 61.0, 136.0]",usask_1
+150cabe75,1024,1024,"[356.0, 58.0, 108.0, 44.0]",usask_1
+150cabe75,1024,1024,"[545.0, 327.0, 73.0, 89.0]",usask_1
+150cabe75,1024,1024,"[618.0, 557.0, 86.0, 83.0]",usask_1
+150cabe75,1024,1024,"[814.0, 497.0, 106.0, 83.0]",usask_1
+150cabe75,1024,1024,"[740.0, 383.0, 127.0, 109.0]",usask_1
+150cabe75,1024,1024,"[807.0, 318.0, 147.0, 72.0]",usask_1
+150cabe75,1024,1024,"[752.0, 875.0, 115.0, 72.0]",usask_1
+150cabe75,1024,1024,"[694.0, 921.0, 85.0, 82.0]",usask_1
+150cabe75,1024,1024,"[932.0, 252.0, 69.0, 100.0]",usask_1
+150cabe75,1024,1024,"[986.0, 274.0, 38.0, 78.0]",usask_1
+150cabe75,1024,1024,"[421.0, 644.0, 50.0, 45.0]",usask_1
+68fedffd4,1024,1024,"[659.0, 0.0, 54.0, 33.0]",usask_1
+68fedffd4,1024,1024,"[328.0, 561.0, 144.0, 77.0]",usask_1
+68fedffd4,1024,1024,"[573.0, 194.0, 205.0, 356.0]",usask_1
+68fedffd4,1024,1024,"[945.0, 554.0, 79.0, 87.0]",usask_1
+68fedffd4,1024,1024,"[963.0, 235.0, 59.0, 188.0]",usask_1
+68fedffd4,1024,1024,"[498.0, 846.0, 144.0, 97.0]",usask_1
+68fedffd4,1024,1024,"[978.0, 454.0, 46.0, 82.0]",usask_1
+68fedffd4,1024,1024,"[47.0, 912.0, 93.0, 112.0]",usask_1
+68fedffd4,1024,1024,"[147.0, 189.0, 123.0, 198.0]",usask_1
+68fedffd4,1024,1024,"[420.0, 812.0, 225.0, 86.0]",usask_1
+68fedffd4,1024,1024,"[578.0, 56.0, 106.0, 150.0]",usask_1
+68fedffd4,1024,1024,"[595.0, 705.0, 110.0, 113.0]",usask_1
+68fedffd4,1024,1024,"[647.0, 725.0, 107.0, 90.0]",usask_1
+68fedffd4,1024,1024,"[884.0, 687.0, 70.0, 150.0]",usask_1
+68fedffd4,1024,1024,"[812.0, 622.0, 100.0, 133.0]",usask_1
+68fedffd4,1024,1024,"[637.0, 641.0, 224.0, 133.0]",usask_1
+68fedffd4,1024,1024,"[761.0, 273.0, 101.0, 79.0]",usask_1
+68fedffd4,1024,1024,"[816.0, 245.0, 54.0, 285.0]",usask_1
+68fedffd4,1024,1024,"[652.0, 183.0, 221.0, 386.0]",usask_1
+3c6f520ee,1024,1024,"[542.0, 326.0, 86.0, 93.0]",usask_1
+3c6f520ee,1024,1024,"[545.0, 927.0, 86.0, 76.0]",usask_1
+3c6f520ee,1024,1024,"[360.0, 0.0, 60.0, 62.0]",usask_1
+3c6f520ee,1024,1024,"[414.0, 0.0, 76.0, 74.0]",usask_1
+3c6f520ee,1024,1024,"[965.0, 463.0, 59.0, 116.0]",usask_1
+3c6f520ee,1024,1024,"[681.0, 400.0, 86.0, 55.0]",usask_1
+3c6f520ee,1024,1024,"[0.0, 26.0, 38.0, 63.0]",usask_1
+3c6f520ee,1024,1024,"[432.0, 618.0, 48.0, 56.0]",usask_1
+3c6f520ee,1024,1024,"[481.0, 424.0, 100.0, 93.0]",usask_1
+3c6f520ee,1024,1024,"[559.0, 0.0, 52.0, 69.0]",usask_1
+3c6f520ee,1024,1024,"[545.0, 134.0, 76.0, 157.0]",usask_1
+3c6f520ee,1024,1024,"[473.0, 686.0, 60.0, 57.0]",usask_1
+3c6f520ee,1024,1024,"[380.0, 960.0, 86.0, 64.0]",usask_1
+3c6f520ee,1024,1024,"[396.0, 511.0, 66.0, 61.0]",usask_1
+3c6f520ee,1024,1024,"[544.0, 835.0, 59.0, 80.0]",usask_1
+3c6f520ee,1024,1024,"[437.0, 118.0, 74.0, 75.0]",usask_1
+3c6f520ee,1024,1024,"[487.0, 195.0, 67.0, 106.0]",usask_1
+3c6f520ee,1024,1024,"[313.0, 49.0, 88.0, 56.0]",usask_1
+3c6f520ee,1024,1024,"[46.0, 0.0, 76.0, 54.0]",usask_1
+3c6f520ee,1024,1024,"[170.0, 21.0, 115.0, 74.0]",usask_1
+3c6f520ee,1024,1024,"[690.0, 613.0, 96.0, 96.0]",usask_1
+3c6f520ee,1024,1024,"[631.0, 216.0, 55.0, 52.0]",usask_1
+3c6f520ee,1024,1024,"[613.0, 629.0, 99.0, 110.0]",usask_1
+3c6f520ee,1024,1024,"[592.0, 827.0, 56.0, 69.0]",usask_1
+3c6f520ee,1024,1024,"[28.0, 195.0, 65.0, 67.0]",usask_1
+3c6f520ee,1024,1024,"[817.0, 0.0, 149.0, 77.0]",usask_1
+3c6f520ee,1024,1024,"[0.0, 765.0, 72.0, 86.0]",usask_1
+3c6f520ee,1024,1024,"[188.0, 986.0, 103.0, 38.0]",usask_1
+3c6f520ee,1024,1024,"[656.0, 67.0, 97.0, 140.0]",usask_1
+3c6f520ee,1024,1024,"[0.0, 449.0, 54.0, 101.0]",usask_1
+3c6f520ee,1024,1024,"[594.0, 476.0, 61.0, 72.0]",usask_1
+3c6f520ee,1024,1024,"[449.0, 289.0, 75.0, 49.0]",usask_1
+3c6f520ee,1024,1024,"[648.0, 13.0, 64.0, 59.0]",usask_1
+3c6f520ee,1024,1024,"[474.0, 541.0, 41.0, 42.0]",usask_1
+3c6f520ee,1024,1024,"[479.0, 795.0, 38.0, 45.0]",usask_1
+7099cf18a,1024,1024,"[829.0, 653.0, 74.0, 83.0]",usask_1
+7099cf18a,1024,1024,"[902.0, 630.0, 66.0, 106.0]",usask_1
+7099cf18a,1024,1024,"[602.0, 431.0, 117.0, 106.0]",usask_1
+7099cf18a,1024,1024,"[335.0, 551.0, 90.0, 59.0]",usask_1
+7099cf18a,1024,1024,"[313.0, 508.0, 93.0, 56.0]",usask_1
+7099cf18a,1024,1024,"[720.0, 803.0, 104.0, 98.0]",usask_1
+7099cf18a,1024,1024,"[474.0, 185.0, 120.0, 120.0]",usask_1
+7099cf18a,1024,1024,"[919.0, 131.0, 93.0, 96.0]",usask_1
+7099cf18a,1024,1024,"[64.0, 571.0, 94.0, 72.0]",usask_1
+7099cf18a,1024,1024,"[640.0, 306.0, 106.0, 113.0]",usask_1
+7099cf18a,1024,1024,"[0.0, 321.0, 117.0, 74.0]",usask_1
+7099cf18a,1024,1024,"[322.0, 0.0, 59.0, 50.0]",usask_1
+7099cf18a,1024,1024,"[646.0, 10.0, 157.0, 133.0]",usask_1
+7099cf18a,1024,1024,"[0.0, 885.0, 106.0, 117.0]",usask_1
+7099cf18a,1024,1024,"[178.0, 5.0, 129.0, 70.0]",usask_1
+7099cf18a,1024,1024,"[232.0, 741.0, 160.0, 183.0]",usask_1
+7099cf18a,1024,1024,"[262.0, 0.0, 72.0, 57.0]",usask_1
+7099cf18a,1024,1024,"[935.0, 612.0, 76.0, 99.0]",usask_1
+7099cf18a,1024,1024,"[964.0, 682.0, 55.0, 90.0]",usask_1
+7099cf18a,1024,1024,"[915.0, 338.0, 58.0, 82.0]",usask_1
+7099cf18a,1024,1024,"[62.0, 538.0, 83.0, 71.0]",usask_1
+7099cf18a,1024,1024,"[224.0, 525.0, 62.0, 54.0]",usask_1
+7099cf18a,1024,1024,"[204.0, 398.0, 99.0, 117.0]",usask_1
+7099cf18a,1024,1024,"[314.0, 432.0, 75.0, 54.0]",usask_1
+7099cf18a,1024,1024,"[380.0, 402.0, 107.0, 153.0]",usask_1
+7099cf18a,1024,1024,"[162.0, 4.0, 115.0, 131.0]",usask_1
+58adb478f,1024,1024,"[606.0, 446.0, 98.0, 75.0]",usask_1
+58adb478f,1024,1024,"[510.0, 322.0, 86.0, 98.0]",usask_1
+58adb478f,1024,1024,"[389.0, 816.0, 70.0, 60.0]",usask_1
+58adb478f,1024,1024,"[54.0, 444.0, 50.0, 126.0]",usask_1
+58adb478f,1024,1024,"[744.0, 277.0, 121.0, 96.0]",usask_1
+58adb478f,1024,1024,"[630.0, 946.0, 79.0, 77.0]",usask_1
+58adb478f,1024,1024,"[6.0, 586.0, 81.0, 164.0]",usask_1
+58adb478f,1024,1024,"[791.0, 355.0, 110.0, 103.0]",usask_1
+58adb478f,1024,1024,"[524.0, 469.0, 150.0, 171.0]",usask_1
+58adb478f,1024,1024,"[460.0, 398.0, 80.0, 83.0]",usask_1
+58adb478f,1024,1024,"[700.0, 219.0, 101.0, 77.0]",usask_1
+58adb478f,1024,1024,"[797.0, 574.0, 87.0, 121.0]",usask_1
+58adb478f,1024,1024,"[837.0, 547.0, 169.0, 165.0]",usask_1
+58adb478f,1024,1024,"[74.0, 431.0, 113.0, 227.0]",usask_1
+58adb478f,1024,1024,"[733.0, 52.0, 101.0, 119.0]",usask_1
+58adb478f,1024,1024,"[629.0, 88.0, 91.0, 72.0]",usask_1
+58adb478f,1024,1024,"[550.0, 23.0, 74.0, 102.0]",usask_1
+58adb478f,1024,1024,"[530.0, 219.0, 77.0, 65.0]",usask_1
+58adb478f,1024,1024,"[376.0, 243.0, 69.0, 111.0]",usask_1
+58adb478f,1024,1024,"[264.0, 217.0, 136.0, 112.0]",usask_1
+58adb478f,1024,1024,"[350.0, 126.0, 138.0, 116.0]",usask_1
+58adb478f,1024,1024,"[198.0, 91.0, 178.0, 103.0]",usask_1
+58adb478f,1024,1024,"[7.0, 119.0, 118.0, 65.0]",usask_1
+58adb478f,1024,1024,"[746.0, 0.0, 127.0, 90.0]",usask_1
+58adb478f,1024,1024,"[575.0, 52.0, 56.0, 82.0]",usask_1
+58adb478f,1024,1024,"[606.0, 0.0, 78.0, 85.0]",usask_1
+58adb478f,1024,1024,"[493.0, 880.0, 54.0, 45.0]",usask_1
+58adb478f,1024,1024,"[579.0, 882.0, 51.0, 58.0]",usask_1
+58adb478f,1024,1024,"[5.0, 232.0, 140.0, 82.0]",usask_1
+58adb478f,1024,1024,"[47.0, 202.0, 86.0, 123.0]",usask_1
+e91d7c41b,1024,1024,"[943.0, 501.0, 66.0, 151.0]",usask_1
+e91d7c41b,1024,1024,"[766.0, 977.0, 97.0, 47.0]",usask_1
+e91d7c41b,1024,1024,"[800.0, 621.0, 53.0, 67.0]",usask_1
+e91d7c41b,1024,1024,"[858.0, 0.0, 100.0, 82.0]",usask_1
+e91d7c41b,1024,1024,"[340.0, 0.0, 72.0, 124.0]",usask_1
+e91d7c41b,1024,1024,"[295.0, 357.0, 93.0, 94.0]",usask_1
+e91d7c41b,1024,1024,"[983.0, 124.0, 41.0, 91.0]",usask_1
+e91d7c41b,1024,1024,"[281.0, 448.0, 103.0, 77.0]",usask_1
+e91d7c41b,1024,1024,"[801.0, 508.0, 134.0, 96.0]",usask_1
+e91d7c41b,1024,1024,"[16.0, 943.0, 97.0, 81.0]",usask_1
+e91d7c41b,1024,1024,"[515.0, 434.0, 115.0, 81.0]",usask_1
+e91d7c41b,1024,1024,"[898.0, 869.0, 92.0, 65.0]",usask_1
+e91d7c41b,1024,1024,"[0.0, 98.0, 84.0, 151.0]",usask_1
+e91d7c41b,1024,1024,"[336.0, 875.0, 103.0, 137.0]",usask_1
+e91d7c41b,1024,1024,"[192.0, 21.0, 172.0, 121.0]",usask_1
+e91d7c41b,1024,1024,"[0.0, 245.0, 103.0, 100.0]",usask_1
+e91d7c41b,1024,1024,"[505.0, 16.0, 120.0, 103.0]",usask_1
+e91d7c41b,1024,1024,"[923.0, 813.0, 101.0, 91.0]",usask_1
+e91d7c41b,1024,1024,"[258.0, 727.0, 77.0, 75.0]",usask_1
+e91d7c41b,1024,1024,"[44.0, 651.0, 57.0, 89.0]",usask_1
+e91d7c41b,1024,1024,"[42.0, 575.0, 86.0, 67.0]",usask_1
+e91d7c41b,1024,1024,"[93.0, 740.0, 91.0, 61.0]",usask_1
+e91d7c41b,1024,1024,"[176.0, 728.0, 66.0, 56.0]",usask_1
+4c5dc3f2b,1024,1024,"[721.0, 812.0, 92.0, 87.0]",usask_1
+4c5dc3f2b,1024,1024,"[525.0, 569.0, 132.0, 143.0]",usask_1
+4c5dc3f2b,1024,1024,"[664.0, 759.0, 82.0, 81.0]",usask_1
+4c5dc3f2b,1024,1024,"[565.0, 882.0, 144.0, 100.0]",usask_1
+4c5dc3f2b,1024,1024,"[65.0, 674.0, 58.0, 165.0]",usask_1
+4c5dc3f2b,1024,1024,"[610.0, 652.0, 96.0, 101.0]",usask_1
+4c5dc3f2b,1024,1024,"[0.0, 384.0, 96.0, 103.0]",usask_1
+4c5dc3f2b,1024,1024,"[481.0, 677.0, 84.0, 119.0]",usask_1
+4c5dc3f2b,1024,1024,"[394.0, 799.0, 118.0, 139.0]",usask_1
+4c5dc3f2b,1024,1024,"[437.0, 343.0, 87.0, 160.0]",usask_1
+4c5dc3f2b,1024,1024,"[869.0, 586.0, 117.0, 125.0]",usask_1
+4c5dc3f2b,1024,1024,"[0.0, 458.0, 96.0, 99.0]",usask_1
+4c5dc3f2b,1024,1024,"[410.0, 660.0, 114.0, 120.0]",usask_1
+4c5dc3f2b,1024,1024,"[666.0, 0.0, 97.0, 76.0]",usask_1
+4c5dc3f2b,1024,1024,"[764.0, 144.0, 96.0, 140.0]",usask_1
+4c5dc3f2b,1024,1024,"[53.0, 316.0, 83.0, 144.0]",usask_1
+4c5dc3f2b,1024,1024,"[825.0, 30.0, 135.0, 73.0]",usask_1
+4c5dc3f2b,1024,1024,"[724.0, 504.0, 141.0, 144.0]",usask_1
+4c5dc3f2b,1024,1024,"[637.0, 492.0, 109.0, 153.0]",usask_1
+4c5dc3f2b,1024,1024,"[427.0, 55.0, 78.0, 66.0]",usask_1
+4c5dc3f2b,1024,1024,"[454.0, 916.0, 120.0, 108.0]",usask_1
+4c5dc3f2b,1024,1024,"[316.0, 0.0, 110.0, 36.0]",usask_1
+4c5dc3f2b,1024,1024,"[104.0, 316.0, 137.0, 212.0]",usask_1
+4c5dc3f2b,1024,1024,"[43.0, 114.0, 124.0, 120.0]",usask_1
+4c5dc3f2b,1024,1024,"[53.0, 862.0, 100.0, 113.0]",usask_1
+4c5dc3f2b,1024,1024,"[0.0, 922.0, 28.0, 102.0]",usask_1
+4c5dc3f2b,1024,1024,"[303.0, 394.0, 160.0, 146.0]",usask_1
+4c5dc3f2b,1024,1024,"[218.0, 515.0, 134.0, 69.0]",usask_1
+4c5dc3f2b,1024,1024,"[592.0, 282.0, 100.0, 161.0]",usask_1
+4c5dc3f2b,1024,1024,"[370.0, 525.0, 130.0, 83.0]",usask_1
+4c5dc3f2b,1024,1024,"[10.0, 679.0, 90.0, 163.0]",usask_1
+4c5dc3f2b,1024,1024,"[742.0, 505.0, 165.0, 90.0]",usask_1
+4c5dc3f2b,1024,1024,"[0.0, 772.0, 43.0, 68.0]",usask_1
+4c5dc3f2b,1024,1024,"[896.0, 305.0, 61.0, 73.0]",usask_1
+4c5dc3f2b,1024,1024,"[939.0, 292.0, 80.0, 56.0]",usask_1
+a1321ca95,1024,1024,"[360.0, 131.0, 187.0, 83.0]",usask_1
+a1321ca95,1024,1024,"[66.0, 799.0, 72.0, 225.0]",usask_1
+a1321ca95,1024,1024,"[421.0, 670.0, 116.0, 153.0]",usask_1
+a1321ca95,1024,1024,"[911.0, 920.0, 79.0, 104.0]",usask_1
+a1321ca95,1024,1024,"[144.0, 848.0, 206.0, 130.0]",usask_1
+a1321ca95,1024,1024,"[889.0, 0.0, 133.0, 2.0]",usask_1
+a1321ca95,1024,1024,"[118.0, 816.0, 110.0, 208.0]",usask_1
+a1321ca95,1024,1024,"[218.0, 0.0, 225.0, 69.0]",usask_1
+a1321ca95,1024,1024,"[0.0, 151.0, 118.0, 91.0]",usask_1
+a1321ca95,1024,1024,"[0.0, 651.0, 81.0, 85.0]",usask_1
+a1321ca95,1024,1024,"[0.0, 206.0, 182.0, 139.0]",usask_1
+a1321ca95,1024,1024,"[0.0, 782.0, 34.0, 128.0]",usask_1
+a1321ca95,1024,1024,"[19.0, 1.0, 176.0, 166.0]",usask_1
+a1321ca95,1024,1024,"[57.0, 129.0, 698.0, 492.0]",usask_1
+a1321ca95,1024,1024,"[834.0, 0.0, 190.0, 78.0]",usask_1
+a1321ca95,1024,1024,"[346.0, 168.0, 232.0, 103.0]",usask_1
+a1321ca95,1024,1024,"[394.0, 0.0, 237.0, 156.0]",usask_1
+a1321ca95,1024,1024,"[737.0, 249.0, 120.0, 248.0]",usask_1
+a1321ca95,1024,1024,"[626.0, 458.0, 258.0, 200.0]",usask_1
+a1321ca95,1024,1024,"[215.0, 329.0, 304.0, 118.0]",usask_1
+a1321ca95,1024,1024,"[708.0, 894.0, 231.0, 115.0]",usask_1
+a1321ca95,1024,1024,"[602.0, 357.0, 228.0, 125.0]",usask_1
+a1321ca95,1024,1024,"[0.0, 434.0, 335.0, 103.0]",usask_1
+a1321ca95,1024,1024,"[137.0, 431.0, 382.0, 392.0]",usask_1
+a1321ca95,1024,1024,"[466.0, 955.0, 162.0, 69.0]",usask_1
+a1321ca95,1024,1024,"[840.0, 330.0, 148.0, 98.0]",usask_1
+a1321ca95,1024,1024,"[810.0, 172.0, 214.0, 97.0]",usask_1
+a1321ca95,1024,1024,"[21.0, 52.0, 266.0, 212.0]",usask_1
+a1321ca95,1024,1024,"[296.0, 296.0, 213.0, 64.0]",usask_1
+a1321ca95,1024,1024,"[190.0, 940.0, 91.0, 54.0]",usask_1
+8302fe8b8,1024,1024,"[659.0, 499.0, 57.0, 71.0]",usask_1
+8302fe8b8,1024,1024,"[0.0, 56.0, 56.0, 99.0]",usask_1
+8302fe8b8,1024,1024,"[22.0, 415.0, 61.0, 70.0]",usask_1
+8302fe8b8,1024,1024,"[863.0, 121.0, 69.0, 66.0]",usask_1
+8302fe8b8,1024,1024,"[107.0, 182.0, 63.0, 98.0]",usask_1
+8302fe8b8,1024,1024,"[606.0, 811.0, 89.0, 93.0]",usask_1
+8302fe8b8,1024,1024,"[603.0, 344.0, 44.0, 57.0]",usask_1
+8302fe8b8,1024,1024,"[532.0, 183.0, 56.0, 64.0]",usask_1
+8302fe8b8,1024,1024,"[436.0, 919.0, 125.0, 86.0]",usask_1
+8302fe8b8,1024,1024,"[34.0, 98.0, 83.0, 111.0]",usask_1
+8302fe8b8,1024,1024,"[694.0, 612.0, 76.0, 75.0]",usask_1
+8302fe8b8,1024,1024,"[265.0, 470.0, 68.0, 87.0]",usask_1
+8302fe8b8,1024,1024,"[805.0, 195.0, 60.0, 60.0]",usask_1
+8302fe8b8,1024,1024,"[811.0, 87.0, 103.0, 81.0]",usask_1
+8302fe8b8,1024,1024,"[334.0, 87.0, 111.0, 70.0]",usask_1
+8302fe8b8,1024,1024,"[646.0, 334.0, 59.0, 69.0]",usask_1
+8302fe8b8,1024,1024,"[353.0, 517.0, 134.0, 86.0]",usask_1
+8302fe8b8,1024,1024,"[668.0, 225.0, 78.0, 68.0]",usask_1
+8302fe8b8,1024,1024,"[769.0, 590.0, 65.0, 48.0]",usask_1
+8302fe8b8,1024,1024,"[853.0, 584.0, 99.0, 72.0]",usask_1
+8302fe8b8,1024,1024,"[879.0, 657.0, 120.0, 75.0]",usask_1
+8302fe8b8,1024,1024,"[725.0, 704.0, 104.0, 59.0]",usask_1
+8302fe8b8,1024,1024,"[717.0, 755.0, 97.0, 42.0]",usask_1
+8302fe8b8,1024,1024,"[611.0, 926.0, 66.0, 98.0]",usask_1
+8302fe8b8,1024,1024,"[662.0, 973.0, 85.0, 51.0]",usask_1
+8302fe8b8,1024,1024,"[8.0, 814.0, 64.0, 98.0]",usask_1
+8302fe8b8,1024,1024,"[17.0, 899.0, 71.0, 64.0]",usask_1
+8302fe8b8,1024,1024,"[76.0, 989.0, 87.0, 35.0]",usask_1
+8302fe8b8,1024,1024,"[92.0, 891.0, 61.0, 79.0]",usask_1
+8302fe8b8,1024,1024,"[147.0, 907.0, 84.0, 108.0]",usask_1
+8302fe8b8,1024,1024,"[220.0, 841.0, 46.0, 90.0]",usask_1
+8302fe8b8,1024,1024,"[745.0, 5.0, 95.0, 73.0]",usask_1
+8302fe8b8,1024,1024,"[770.0, 162.0, 75.0, 69.0]",usask_1
+41761631b,1024,1024,"[328.0, 535.0, 110.0, 108.0]",usask_1
+41761631b,1024,1024,"[99.0, 704.0, 91.0, 93.0]",usask_1
+41761631b,1024,1024,"[555.0, 63.0, 109.0, 186.0]",usask_1
+41761631b,1024,1024,"[943.0, 852.0, 81.0, 102.0]",usask_1
+41761631b,1024,1024,"[324.0, 823.0, 91.0, 91.0]",usask_1
+41761631b,1024,1024,"[61.0, 630.0, 114.0, 72.0]",usask_1
+41761631b,1024,1024,"[0.0, 3.0, 52.0, 74.0]",usask_1
+41761631b,1024,1024,"[513.0, 909.0, 157.0, 88.0]",usask_1
+41761631b,1024,1024,"[264.0, 0.0, 109.0, 62.0]",usask_1
+41761631b,1024,1024,"[146.0, 720.0, 106.0, 67.0]",usask_1
+41761631b,1024,1024,"[0.0, 370.0, 50.0, 87.0]",usask_1
+41761631b,1024,1024,"[18.0, 454.0, 88.0, 78.0]",usask_1
+41761631b,1024,1024,"[725.0, 680.0, 92.0, 92.0]",usask_1
+41761631b,1024,1024,"[9.0, 734.0, 64.0, 92.0]",usask_1
+41761631b,1024,1024,"[925.0, 713.0, 77.0, 111.0]",usask_1
+41761631b,1024,1024,"[223.0, 472.0, 125.0, 124.0]",usask_1
+41761631b,1024,1024,"[370.0, 57.0, 141.0, 69.0]",usask_1
+41761631b,1024,1024,"[311.0, 462.0, 109.0, 60.0]",usask_1
+41761631b,1024,1024,"[293.0, 67.0, 112.0, 89.0]",usask_1
+41761631b,1024,1024,"[768.0, 170.0, 256.0, 96.0]",usask_1
+41761631b,1024,1024,"[303.0, 626.0, 142.0, 91.0]",usask_1
+41761631b,1024,1024,"[834.0, 0.0, 85.0, 45.0]",usask_1
+41761631b,1024,1024,"[834.0, 915.0, 112.0, 57.0]",usask_1
+41761631b,1024,1024,"[378.0, 902.0, 90.0, 89.0]",usask_1
+41761631b,1024,1024,"[889.0, 484.0, 135.0, 83.0]",usask_1
+41761631b,1024,1024,"[138.0, 934.0, 61.0, 90.0]",usask_1
+41761631b,1024,1024,"[278.0, 885.0, 70.0, 139.0]",usask_1
+41761631b,1024,1024,"[827.0, 952.0, 75.0, 72.0]",usask_1
+41761631b,1024,1024,"[515.0, 1001.0, 99.0, 23.0]",usask_1
+41761631b,1024,1024,"[723.0, 621.0, 134.0, 62.0]",usask_1
+41761631b,1024,1024,"[959.0, 543.0, 65.0, 63.0]",usask_1
+41761631b,1024,1024,"[957.0, 725.0, 66.0, 110.0]",usask_1
+41761631b,1024,1024,"[850.0, 599.0, 149.0, 59.0]",usask_1
+41761631b,1024,1024,"[748.0, 4.0, 76.0, 71.0]",usask_1
+41761631b,1024,1024,"[690.0, 82.0, 107.0, 60.0]",usask_1
+41761631b,1024,1024,"[240.0, 89.0, 106.0, 63.0]",usask_1
+41761631b,1024,1024,"[11.0, 906.0, 96.0, 76.0]",usask_1
+41761631b,1024,1024,"[34.0, 980.0, 86.0, 44.0]",usask_1
+41761631b,1024,1024,"[269.0, 692.0, 83.0, 92.0]",usask_1
+41761631b,1024,1024,"[228.0, 608.0, 73.0, 92.0]",usask_1
+41761631b,1024,1024,"[142.0, 511.0, 106.0, 111.0]",usask_1
+41761631b,1024,1024,"[0.0, 785.0, 43.0, 66.0]",usask_1
+41761631b,1024,1024,"[892.0, 980.0, 73.0, 44.0]",usask_1
+ab8fa9772,1024,1024,"[437.0, 269.0, 103.0, 96.0]",usask_1
+ab8fa9772,1024,1024,"[90.0, 865.0, 116.0, 125.0]",usask_1
+ab8fa9772,1024,1024,"[769.0, 0.0, 68.0, 134.0]",usask_1
+ab8fa9772,1024,1024,"[670.0, 958.0, 68.0, 66.0]",usask_1
+ab8fa9772,1024,1024,"[835.0, 838.0, 133.0, 186.0]",usask_1
+ab8fa9772,1024,1024,"[682.0, 816.0, 294.0, 168.0]",usask_1
+ab8fa9772,1024,1024,"[603.0, 0.0, 105.0, 54.0]",usask_1
+ab8fa9772,1024,1024,"[573.0, 905.0, 111.0, 86.0]",usask_1
+ab8fa9772,1024,1024,"[146.0, 289.0, 102.0, 119.0]",usask_1
+ab8fa9772,1024,1024,"[229.0, 277.0, 92.0, 152.0]",usask_1
+ab8fa9772,1024,1024,"[424.0, 377.0, 93.0, 93.0]",usask_1
+ab8fa9772,1024,1024,"[0.0, 357.0, 149.0, 222.0]",usask_1
+ab8fa9772,1024,1024,"[710.0, 165.0, 77.0, 138.0]",usask_1
+ab8fa9772,1024,1024,"[448.0, 616.0, 119.0, 123.0]",usask_1
+ab8fa9772,1024,1024,"[823.0, 58.0, 96.0, 128.0]",usask_1
+ab8fa9772,1024,1024,"[697.0, 0.0, 59.0, 57.0]",usask_1
+ab8fa9772,1024,1024,"[490.0, 431.0, 106.0, 109.0]",usask_1
+ab8fa9772,1024,1024,"[0.0, 205.0, 122.0, 129.0]",usask_1
+ab8fa9772,1024,1024,"[0.0, 640.0, 210.0, 99.0]",usask_1
+ab8fa9772,1024,1024,"[286.0, 111.0, 150.0, 103.0]",usask_1
+ab8fa9772,1024,1024,"[964.0, 609.0, 60.0, 114.0]",usask_1
+ab8fa9772,1024,1024,"[842.0, 683.0, 144.0, 116.0]",usask_1
+ab8fa9772,1024,1024,"[966.0, 750.0, 58.0, 56.0]",usask_1
+ab8fa9772,1024,1024,"[857.0, 757.0, 108.0, 83.0]",usask_1
+ab8fa9772,1024,1024,"[597.0, 514.0, 140.0, 267.0]",usask_1
+ab8fa9772,1024,1024,"[569.0, 332.0, 91.0, 90.0]",usask_1
+4f9b8d33e,1024,1024,"[434.0, 279.0, 80.0, 100.0]",usask_1
+4f9b8d33e,1024,1024,"[540.0, 522.0, 112.0, 66.0]",usask_1
+4f9b8d33e,1024,1024,"[515.0, 445.0, 103.0, 121.0]",usask_1
+4f9b8d33e,1024,1024,"[274.0, 249.0, 175.0, 73.0]",usask_1
+4f9b8d33e,1024,1024,"[347.0, 721.0, 61.0, 72.0]",usask_1
+4f9b8d33e,1024,1024,"[821.0, 885.0, 124.0, 90.0]",usask_1
+4f9b8d33e,1024,1024,"[0.0, 638.0, 177.0, 103.0]",usask_1
+4f9b8d33e,1024,1024,"[496.0, 479.0, 57.0, 60.0]",usask_1
+4f9b8d33e,1024,1024,"[347.0, 302.0, 74.0, 81.0]",usask_1
+4f9b8d33e,1024,1024,"[417.0, 394.0, 115.0, 102.0]",usask_1
+4f9b8d33e,1024,1024,"[12.0, 217.0, 97.0, 123.0]",usask_1
+4f9b8d33e,1024,1024,"[219.0, 487.0, 47.0, 39.0]",usask_1
+4f9b8d33e,1024,1024,"[3.0, 888.0, 54.0, 57.0]",usask_1
+4f9b8d33e,1024,1024,"[205.0, 655.0, 180.0, 105.0]",usask_1
+4f9b8d33e,1024,1024,"[902.0, 323.0, 122.0, 105.0]",usask_1
+4f9b8d33e,1024,1024,"[537.0, 1.0, 93.0, 106.0]",usask_1
+4f9b8d33e,1024,1024,"[522.0, 352.0, 101.0, 81.0]",usask_1
+4f9b8d33e,1024,1024,"[450.0, 134.0, 62.0, 78.0]",usask_1
+4f9b8d33e,1024,1024,"[433.0, 208.0, 64.0, 50.0]",usask_1
+4f9b8d33e,1024,1024,"[474.0, 172.0, 87.0, 68.0]",usask_1
+4f9b8d33e,1024,1024,"[532.0, 192.0, 110.0, 96.0]",usask_1
+4f9b8d33e,1024,1024,"[610.0, 221.0, 96.0, 80.0]",usask_1
+4f9b8d33e,1024,1024,"[542.0, 244.0, 107.0, 95.0]",usask_1
+4f9b8d33e,1024,1024,"[437.0, 831.0, 47.0, 33.0]",usask_1
+4f9b8d33e,1024,1024,"[845.0, 802.0, 32.0, 41.0]",usask_1
+88dc94b76,1024,1024,"[935.0, 306.0, 73.0, 73.0]",usask_1
+88dc94b76,1024,1024,"[859.0, 561.0, 80.0, 83.0]",usask_1
+88dc94b76,1024,1024,"[523.0, 493.0, 52.0, 73.0]",usask_1
+88dc94b76,1024,1024,"[471.0, 612.0, 70.0, 63.0]",usask_1
+88dc94b76,1024,1024,"[357.0, 514.0, 135.0, 85.0]",usask_1
+88dc94b76,1024,1024,"[370.0, 3.0, 114.0, 172.0]",usask_1
+88dc94b76,1024,1024,"[508.0, 154.0, 126.0, 104.0]",usask_1
+88dc94b76,1024,1024,"[128.0, 741.0, 167.0, 82.0]",usask_1
+88dc94b76,1024,1024,"[594.0, 271.0, 103.0, 175.0]",usask_1
+88dc94b76,1024,1024,"[0.0, 720.0, 126.0, 114.0]",usask_1
+88dc94b76,1024,1024,"[413.0, 320.0, 88.0, 90.0]",usask_1
+88dc94b76,1024,1024,"[294.0, 282.0, 104.0, 113.0]",usask_1
+88dc94b76,1024,1024,"[274.0, 350.0, 152.0, 109.0]",usask_1
+88dc94b76,1024,1024,"[430.0, 434.0, 29.0, 57.0]",usask_1
+42f5d5a5e,1024,1024,"[939.0, 23.0, 83.0, 73.0]",usask_1
+42f5d5a5e,1024,1024,"[869.0, 407.0, 72.0, 103.0]",usask_1
+42f5d5a5e,1024,1024,"[859.0, 208.0, 94.0, 40.0]",usask_1
+42f5d5a5e,1024,1024,"[0.0, 794.0, 108.0, 85.0]",usask_1
+42f5d5a5e,1024,1024,"[858.0, 20.0, 80.0, 99.0]",usask_1
+42f5d5a5e,1024,1024,"[872.0, 130.0, 89.0, 77.0]",usask_1
+42f5d5a5e,1024,1024,"[205.0, 690.0, 120.0, 96.0]",usask_1
+42f5d5a5e,1024,1024,"[930.0, 443.0, 94.0, 162.0]",usask_1
+42f5d5a5e,1024,1024,"[278.0, 705.0, 134.0, 94.0]",usask_1
+42f5d5a5e,1024,1024,"[990.0, 0.0, 34.0, 50.0]",usask_1
+42f5d5a5e,1024,1024,"[667.0, 903.0, 114.0, 68.0]",usask_1
+42f5d5a5e,1024,1024,"[181.0, 521.0, 151.0, 76.0]",usask_1
+42f5d5a5e,1024,1024,"[437.0, 637.0, 123.0, 95.0]",usask_1
+42f5d5a5e,1024,1024,"[40.0, 439.0, 192.0, 66.0]",usask_1
+42f5d5a5e,1024,1024,"[246.0, 21.0, 99.0, 70.0]",usask_1
+42f5d5a5e,1024,1024,"[778.0, 2.0, 67.0, 52.0]",usask_1
+42f5d5a5e,1024,1024,"[374.0, 67.0, 114.0, 97.0]",usask_1
+42f5d5a5e,1024,1024,"[676.0, 354.0, 74.0, 72.0]",usask_1
+42f5d5a5e,1024,1024,"[84.0, 604.0, 150.0, 86.0]",usask_1
+42f5d5a5e,1024,1024,"[0.0, 913.0, 128.0, 84.0]",usask_1
+42f5d5a5e,1024,1024,"[1.0, 1000.0, 109.0, 24.0]",usask_1
+42f5d5a5e,1024,1024,"[96.0, 883.0, 168.0, 88.0]",usask_1
+42f5d5a5e,1024,1024,"[883.0, 773.0, 60.0, 69.0]",usask_1
+42f5d5a5e,1024,1024,"[863.0, 785.0, 153.0, 127.0]",usask_1
+42f5d5a5e,1024,1024,"[953.0, 793.0, 71.0, 125.0]",usask_1
+42f5d5a5e,1024,1024,"[935.0, 940.0, 47.0, 84.0]",usask_1
+42f5d5a5e,1024,1024,"[842.0, 944.0, 132.0, 80.0]",usask_1
+42f5d5a5e,1024,1024,"[136.0, 794.0, 117.0, 104.0]",usask_1
+42f5d5a5e,1024,1024,"[0.0, 863.0, 140.0, 72.0]",usask_1
+684a2c62a,1024,1024,"[201.0, 326.0, 77.0, 156.0]",usask_1
+684a2c62a,1024,1024,"[711.0, 69.0, 86.0, 103.0]",usask_1
+684a2c62a,1024,1024,"[218.0, 615.0, 73.0, 122.0]",usask_1
+684a2c62a,1024,1024,"[347.0, 0.0, 125.0, 155.0]",usask_1
+684a2c62a,1024,1024,"[0.0, 825.0, 48.0, 147.0]",usask_1
+684a2c62a,1024,1024,"[188.0, 131.0, 96.0, 187.0]",usask_1
+684a2c62a,1024,1024,"[728.0, 715.0, 147.0, 82.0]",usask_1
+684a2c62a,1024,1024,"[855.0, 207.0, 94.0, 80.0]",usask_1
+684a2c62a,1024,1024,"[727.0, 916.0, 209.0, 108.0]",usask_1
+684a2c62a,1024,1024,"[500.0, 23.0, 123.0, 107.0]",usask_1
+684a2c62a,1024,1024,"[43.0, 967.0, 93.0, 57.0]",usask_1
+684a2c62a,1024,1024,"[685.0, 659.0, 112.0, 80.0]",usask_1
+684a2c62a,1024,1024,"[405.0, 62.0, 108.0, 122.0]",usask_1
+684a2c62a,1024,1024,"[287.0, 687.0, 57.0, 115.0]",usask_1
+684a2c62a,1024,1024,"[46.0, 238.0, 112.0, 177.0]",usask_1
+684a2c62a,1024,1024,"[943.0, 720.0, 81.0, 112.0]",usask_1
+684a2c62a,1024,1024,"[0.0, 399.0, 146.0, 231.0]",usask_1
+684a2c62a,1024,1024,"[503.0, 393.0, 90.0, 199.0]",usask_1
+684a2c62a,1024,1024,"[417.0, 373.0, 75.0, 171.0]",usask_1
+684a2c62a,1024,1024,"[630.0, 233.0, 73.0, 75.0]",usask_1
+684a2c62a,1024,1024,"[312.0, 225.0, 67.0, 131.0]",usask_1
+684a2c62a,1024,1024,"[619.0, 73.0, 87.0, 178.0]",usask_1
+684a2c62a,1024,1024,"[751.0, 234.0, 86.0, 135.0]",usask_1
+684a2c62a,1024,1024,"[0.0, 575.0, 95.0, 115.0]",usask_1
+684a2c62a,1024,1024,"[737.0, 772.0, 190.0, 121.0]",usask_1
+684a2c62a,1024,1024,"[971.0, 289.0, 53.0, 86.0]",usask_1
+684a2c62a,1024,1024,"[22.0, 1.0, 87.0, 113.0]",usask_1
+684a2c62a,1024,1024,"[57.0, 873.0, 126.0, 92.0]",usask_1
+684a2c62a,1024,1024,"[273.0, 777.0, 73.0, 68.0]",usask_1
+684a2c62a,1024,1024,"[295.0, 630.0, 59.0, 72.0]",usask_1
+684a2c62a,1024,1024,"[0.0, 127.0, 70.0, 211.0]",usask_1
+684a2c62a,1024,1024,"[381.0, 267.0, 76.0, 108.0]",usask_1
+684a2c62a,1024,1024,"[298.0, 369.0, 77.0, 79.0]",usask_1
+684a2c62a,1024,1024,"[525.0, 65.0, 56.0, 131.0]",usask_1
+684a2c62a,1024,1024,"[541.0, 152.0, 70.0, 115.0]",usask_1
+bc226ebfd,1024,1024,"[946.0, 361.0, 78.0, 308.0]",usask_1
+bc226ebfd,1024,1024,"[731.0, 832.0, 143.0, 169.0]",usask_1
+bc226ebfd,1024,1024,"[734.0, 488.0, 98.0, 109.0]",usask_1
+bc226ebfd,1024,1024,"[0.0, 110.0, 91.0, 90.0]",usask_1
+bc226ebfd,1024,1024,"[0.0, 579.0, 69.0, 140.0]",usask_1
+bc226ebfd,1024,1024,"[588.0, 919.0, 136.0, 101.0]",usask_1
+bc226ebfd,1024,1024,"[43.0, 956.0, 139.0, 68.0]",usask_1
+bc226ebfd,1024,1024,"[428.0, 278.0, 137.0, 197.0]",usask_1
+bc226ebfd,1024,1024,"[278.0, 883.0, 93.0, 85.0]",usask_1
+bc226ebfd,1024,1024,"[97.0, 892.0, 128.0, 70.0]",usask_1
+bc226ebfd,1024,1024,"[95.0, 691.0, 64.0, 157.0]",usask_1
+bc226ebfd,1024,1024,"[161.0, 761.0, 69.0, 88.0]",usask_1
+bc226ebfd,1024,1024,"[798.0, 311.0, 178.0, 97.0]",usask_1
+bc226ebfd,1024,1024,"[554.0, 319.0, 143.0, 96.0]",usask_1
+bc226ebfd,1024,1024,"[519.0, 235.0, 103.0, 73.0]",usask_1
+bc226ebfd,1024,1024,"[408.0, 210.0, 166.0, 106.0]",usask_1
+efda9b4e7,1024,1024,"[205.0, 229.0, 96.0, 93.0]",usask_1
+efda9b4e7,1024,1024,"[417.0, 234.0, 83.0, 120.0]",usask_1
+efda9b4e7,1024,1024,"[21.0, 977.0, 50.0, 47.0]",usask_1
+efda9b4e7,1024,1024,"[293.0, 485.0, 86.0, 119.0]",usask_1
+efda9b4e7,1024,1024,"[564.0, 379.0, 96.0, 89.0]",usask_1
+efda9b4e7,1024,1024,"[0.0, 856.0, 46.0, 102.0]",usask_1
+efda9b4e7,1024,1024,"[56.0, 490.0, 88.0, 115.0]",usask_1
+efda9b4e7,1024,1024,"[641.0, 259.0, 88.0, 82.0]",usask_1
+efda9b4e7,1024,1024,"[371.0, 39.0, 85.0, 75.0]",usask_1
+efda9b4e7,1024,1024,"[696.0, 636.0, 130.0, 86.0]",usask_1
+efda9b4e7,1024,1024,"[383.0, 414.0, 91.0, 70.0]",usask_1
+efda9b4e7,1024,1024,"[748.0, 714.0, 139.0, 116.0]",usask_1
+efda9b4e7,1024,1024,"[68.0, 127.0, 124.0, 77.0]",usask_1
+efda9b4e7,1024,1024,"[505.0, 26.0, 103.0, 87.0]",usask_1
+efda9b4e7,1024,1024,"[215.0, 419.0, 86.0, 87.0]",usask_1
+efda9b4e7,1024,1024,"[496.0, 962.0, 66.0, 62.0]",usask_1
+efda9b4e7,1024,1024,"[689.0, 222.0, 69.0, 81.0]",usask_1
+efda9b4e7,1024,1024,"[357.0, 286.0, 74.0, 64.0]",usask_1
+efda9b4e7,1024,1024,"[286.0, 160.0, 95.0, 86.0]",usask_1
+efda9b4e7,1024,1024,"[142.0, 182.0, 101.0, 63.0]",usask_1
+f1d20d58a,1024,1024,"[459.0, 917.0, 153.0, 103.0]",usask_1
+f1d20d58a,1024,1024,"[684.0, 5.0, 60.0, 56.0]",usask_1
+f1d20d58a,1024,1024,"[698.0, 231.0, 110.0, 197.0]",usask_1
+f1d20d58a,1024,1024,"[835.0, 332.0, 57.0, 86.0]",usask_1
+f1d20d58a,1024,1024,"[543.0, 444.0, 76.0, 75.0]",usask_1
+f1d20d58a,1024,1024,"[799.0, 242.0, 133.0, 110.0]",usask_1
+f1d20d58a,1024,1024,"[291.0, 182.0, 76.0, 72.0]",usask_1
+f1d20d58a,1024,1024,"[385.0, 973.0, 79.0, 51.0]",usask_1
+f1d20d58a,1024,1024,"[261.0, 241.0, 85.0, 131.0]",usask_1
+f1d20d58a,1024,1024,"[173.0, 51.0, 141.0, 96.0]",usask_1
+f1d20d58a,1024,1024,"[794.0, 0.0, 117.0, 160.0]",usask_1
+f1d20d58a,1024,1024,"[643.0, 158.0, 100.0, 110.0]",usask_1
+f1d20d58a,1024,1024,"[588.0, 611.0, 243.0, 115.0]",usask_1
+f1d20d58a,1024,1024,"[164.0, 541.0, 139.0, 116.0]",usask_1
+f1d20d58a,1024,1024,"[155.0, 435.0, 83.0, 263.0]",usask_1
+f1d20d58a,1024,1024,"[50.0, 607.0, 118.0, 69.0]",usask_1
+f1d20d58a,1024,1024,"[490.0, 0.0, 171.0, 310.0]",usask_1
+6c1af9100,1024,1024,"[896.0, 0.0, 103.0, 74.0]",usask_1
+6c1af9100,1024,1024,"[552.0, 505.0, 127.0, 130.0]",usask_1
+6c1af9100,1024,1024,"[282.0, 962.0, 134.0, 62.0]",usask_1
+6c1af9100,1024,1024,"[387.0, 586.0, 103.0, 106.0]",usask_1
+6c1af9100,1024,1024,"[388.0, 667.0, 182.0, 78.0]",usask_1
+6c1af9100,1024,1024,"[696.0, 188.0, 164.0, 198.0]",usask_1
+6c1af9100,1024,1024,"[610.0, 899.0, 79.0, 125.0]",usask_1
+6c1af9100,1024,1024,"[690.0, 571.0, 120.0, 110.0]",usask_1
+6c1af9100,1024,1024,"[272.0, 0.0, 167.0, 45.0]",usask_1
+6c1af9100,1024,1024,"[429.0, 468.0, 81.0, 150.0]",usask_1
+6c1af9100,1024,1024,"[60.0, 262.0, 169.0, 189.0]",usask_1
+6c1af9100,1024,1024,"[0.0, 196.0, 145.0, 102.0]",usask_1
+6c1af9100,1024,1024,"[147.0, 587.0, 287.0, 100.0]",usask_1
+6c1af9100,1024,1024,"[780.0, 363.0, 165.0, 183.0]",usask_1
+6c1af9100,1024,1024,"[547.0, 626.0, 74.0, 131.0]",usask_1
+6c1af9100,1024,1024,"[572.0, 139.0, 124.0, 195.0]",usask_1
+6c1af9100,1024,1024,"[226.0, 44.0, 211.0, 198.0]",usask_1
+6c1af9100,1024,1024,"[83.0, 579.0, 209.0, 163.0]",usask_1
+6c1af9100,1024,1024,"[164.0, 556.0, 193.0, 114.0]",usask_1
+6c1af9100,1024,1024,"[523.0, 471.0, 107.0, 67.0]",usask_1
+6c1af9100,1024,1024,"[509.0, 361.0, 104.0, 154.0]",usask_1
+6c1af9100,1024,1024,"[978.0, 5.0, 46.0, 179.0]",usask_1
+cff6e4b78,1024,1024,"[771.0, 239.0, 120.0, 66.0]",usask_1
+cff6e4b78,1024,1024,"[397.0, 925.0, 81.0, 97.0]",usask_1
+cff6e4b78,1024,1024,"[577.0, 516.0, 231.0, 262.0]",usask_1
+cff6e4b78,1024,1024,"[0.0, 892.0, 60.0, 71.0]",usask_1
+cff6e4b78,1024,1024,"[350.0, 664.0, 163.0, 294.0]",usask_1
+cff6e4b78,1024,1024,"[698.0, 732.0, 201.0, 132.0]",usask_1
+cff6e4b78,1024,1024,"[562.0, 262.0, 98.0, 270.0]",usask_1
+cff6e4b78,1024,1024,"[90.0, 0.0, 77.0, 123.0]",usask_1
+cff6e4b78,1024,1024,"[780.0, 163.0, 126.0, 76.0]",usask_1
+cff6e4b78,1024,1024,"[933.0, 43.0, 66.0, 83.0]",usask_1
+cff6e4b78,1024,1024,"[636.0, 0.0, 93.0, 2.0]",usask_1
+cff6e4b78,1024,1024,"[369.0, 688.0, 78.0, 139.0]",usask_1
+cff6e4b78,1024,1024,"[133.0, 690.0, 81.0, 73.0]",usask_1
+cff6e4b78,1024,1024,"[211.0, 683.0, 127.0, 124.0]",usask_1
+cff6e4b78,1024,1024,"[808.0, 67.0, 132.0, 93.0]",usask_1
+cff6e4b78,1024,1024,"[0.0, 513.0, 43.0, 54.0]",usask_1
+cff6e4b78,1024,1024,"[641.0, 593.0, 174.0, 117.0]",usask_1
+cff6e4b78,1024,1024,"[0.0, 0.0, 54.0, 35.0]",usask_1
+cff6e4b78,1024,1024,"[866.0, 901.0, 158.0, 70.0]",usask_1
+cff6e4b78,1024,1024,"[975.0, 739.0, 49.0, 101.0]",usask_1
+cff6e4b78,1024,1024,"[165.0, 862.0, 232.0, 109.0]",usask_1
+cff6e4b78,1024,1024,"[921.0, 720.0, 98.0, 67.0]",usask_1
+cff6e4b78,1024,1024,"[53.0, 788.0, 214.0, 111.0]",usask_1
+cff6e4b78,1024,1024,"[741.0, 259.0, 123.0, 174.0]",usask_1
+cff6e4b78,1024,1024,"[340.0, 222.0, 174.0, 177.0]",usask_1
+cff6e4b78,1024,1024,"[614.0, 132.0, 90.0, 92.0]",usask_1
+cff6e4b78,1024,1024,"[297.0, 577.0, 126.0, 70.0]",usask_1
+cff6e4b78,1024,1024,"[236.0, 516.0, 74.0, 76.0]",usask_1
+cff6e4b78,1024,1024,"[258.0, 486.0, 194.0, 74.0]",usask_1
+cff6e4b78,1024,1024,"[561.0, 7.0, 109.0, 68.0]",usask_1
+cff6e4b78,1024,1024,"[162.0, 0.0, 63.0, 72.0]",usask_1
+9181010cc,1024,1024,"[663.0, 386.0, 116.0, 89.0]",usask_1
+9181010cc,1024,1024,"[791.0, 262.0, 75.0, 101.0]",usask_1
+9181010cc,1024,1024,"[109.0, 237.0, 116.0, 83.0]",usask_1
+9181010cc,1024,1024,"[60.0, 50.0, 63.0, 144.0]",usask_1
+9181010cc,1024,1024,"[811.0, 883.0, 65.0, 67.0]",usask_1
+9181010cc,1024,1024,"[930.0, 219.0, 74.0, 145.0]",usask_1
+9181010cc,1024,1024,"[332.0, 954.0, 98.0, 70.0]",usask_1
+9181010cc,1024,1024,"[731.0, 655.0, 118.0, 100.0]",usask_1
+9181010cc,1024,1024,"[566.0, 190.0, 89.0, 86.0]",usask_1
+9181010cc,1024,1024,"[70.0, 587.0, 169.0, 78.0]",usask_1
+9181010cc,1024,1024,"[0.0, 182.0, 66.0, 68.0]",usask_1
+9181010cc,1024,1024,"[495.0, 0.0, 121.0, 83.0]",usask_1
+9181010cc,1024,1024,"[0.0, 897.0, 29.0, 79.0]",usask_1
+9181010cc,1024,1024,"[417.0, 812.0, 82.0, 183.0]",usask_1
+9181010cc,1024,1024,"[868.0, 117.0, 81.0, 178.0]",usask_1
+9181010cc,1024,1024,"[964.0, 897.0, 60.0, 115.0]",usask_1
+9181010cc,1024,1024,"[282.0, 842.0, 114.0, 146.0]",usask_1
+9181010cc,1024,1024,"[0.0, 94.0, 109.0, 116.0]",usask_1
+9181010cc,1024,1024,"[754.0, 0.0, 83.0, 25.0]",usask_1
+9181010cc,1024,1024,"[211.0, 60.0, 56.0, 133.0]",usask_1
+9181010cc,1024,1024,"[0.0, 315.0, 29.0, 89.0]",usask_1
+9181010cc,1024,1024,"[385.0, 108.0, 116.0, 59.0]",usask_1
+9181010cc,1024,1024,"[40.0, 0.0, 162.0, 57.0]",usask_1
+9181010cc,1024,1024,"[257.0, 651.0, 149.0, 88.0]",usask_1
+9181010cc,1024,1024,"[473.0, 797.0, 93.0, 60.0]",usask_1
+9181010cc,1024,1024,"[400.0, 943.0, 113.0, 80.0]",usask_1
+9181010cc,1024,1024,"[789.0, 700.0, 156.0, 173.0]",usask_1
+9181010cc,1024,1024,"[274.0, 617.0, 95.0, 56.0]",usask_1
+9181010cc,1024,1024,"[263.0, 271.0, 70.0, 66.0]",usask_1
+9181010cc,1024,1024,"[371.0, 70.0, 119.0, 119.0]",usask_1
+9181010cc,1024,1024,"[426.0, 7.0, 65.0, 65.0]",usask_1
+9181010cc,1024,1024,"[387.0, 56.0, 81.0, 45.0]",usask_1
+9181010cc,1024,1024,"[99.0, 316.0, 52.0, 37.0]",usask_1
+9181010cc,1024,1024,"[655.0, 81.0, 46.0, 119.0]",usask_1
+9181010cc,1024,1024,"[461.0, 822.0, 64.0, 95.0]",usask_1
+9181010cc,1024,1024,"[246.0, 190.0, 39.0, 42.0]",usask_1
+9181010cc,1024,1024,"[979.0, 467.0, 45.0, 47.0]",usask_1
+1def04f70,1024,1024,"[858.0, 195.0, 132.0, 50.0]",usask_1
+1def04f70,1024,1024,"[737.0, 0.0, 97.0, 79.0]",usask_1
+1def04f70,1024,1024,"[260.0, 146.0, 83.0, 74.0]",usask_1
+1def04f70,1024,1024,"[467.0, 174.0, 74.0, 89.0]",usask_1
+1def04f70,1024,1024,"[854.0, 453.0, 57.0, 79.0]",usask_1
+1def04f70,1024,1024,"[653.0, 417.0, 76.0, 110.0]",usask_1
+1def04f70,1024,1024,"[151.0, 986.0, 100.0, 38.0]",usask_1
+1def04f70,1024,1024,"[191.0, 36.0, 93.0, 59.0]",usask_1
+1def04f70,1024,1024,"[700.0, 94.0, 60.0, 139.0]",usask_1
+1def04f70,1024,1024,"[29.0, 893.0, 87.0, 102.0]",usask_1
+1def04f70,1024,1024,"[154.0, 832.0, 104.0, 146.0]",usask_1
+1def04f70,1024,1024,"[0.0, 0.0, 86.0, 86.0]",usask_1
+1def04f70,1024,1024,"[0.0, 842.0, 32.0, 120.0]",usask_1
+1def04f70,1024,1024,"[0.0, 419.0, 24.0, 83.0]",usask_1
+1def04f70,1024,1024,"[417.0, 244.0, 115.0, 60.0]",usask_1
+1def04f70,1024,1024,"[713.0, 325.0, 74.0, 67.0]",usask_1
+1def04f70,1024,1024,"[659.0, 203.0, 76.0, 93.0]",usask_1
+1def04f70,1024,1024,"[394.0, 600.0, 66.0, 121.0]",usask_1
+1def04f70,1024,1024,"[546.0, 476.0, 88.0, 218.0]",usask_1
+1def04f70,1024,1024,"[100.0, 797.0, 66.0, 95.0]",usask_1
+1def04f70,1024,1024,"[73.0, 653.0, 77.0, 120.0]",usask_1
+0d5525a7b,1024,1024,"[259.0, 374.0, 117.0, 127.0]",usask_1
+0d5525a7b,1024,1024,"[343.0, 394.0, 90.0, 261.0]",usask_1
+0d5525a7b,1024,1024,"[710.0, 222.0, 110.0, 113.0]",usask_1
+0d5525a7b,1024,1024,"[437.0, 155.0, 75.0, 307.0]",usask_1
+0d5525a7b,1024,1024,"[13.0, 518.0, 117.0, 130.0]",usask_1
+0d5525a7b,1024,1024,"[785.0, 194.0, 110.0, 160.0]",usask_1
+0d5525a7b,1024,1024,"[10.0, 607.0, 105.0, 171.0]",usask_1
+0d5525a7b,1024,1024,"[643.0, 0.0, 130.0, 2.0]",usask_1
+0d5525a7b,1024,1024,"[822.0, 370.0, 159.0, 86.0]",usask_1
+0d5525a7b,1024,1024,"[178.0, 583.0, 69.0, 66.0]",usask_1
+0d5525a7b,1024,1024,"[486.0, 339.0, 68.0, 200.0]",usask_1
+0d5525a7b,1024,1024,"[424.0, 549.0, 39.0, 43.0]",usask_1
+0d5525a7b,1024,1024,"[172.0, 660.0, 49.0, 40.0]",usask_1
+0d5525a7b,1024,1024,"[380.0, 132.0, 57.0, 66.0]",usask_1
+0d5525a7b,1024,1024,"[517.0, 60.0, 89.0, 69.0]",usask_1
+713c56d36,1024,1024,"[671.0, 10.0, 112.0, 145.0]",usask_1
+713c56d36,1024,1024,"[828.0, 945.0, 191.0, 71.0]",usask_1
+713c56d36,1024,1024,"[683.0, 390.0, 84.0, 120.0]",usask_1
+713c56d36,1024,1024,"[962.0, 390.0, 62.0, 165.0]",usask_1
+713c56d36,1024,1024,"[467.0, 0.0, 108.0, 38.0]",usask_1
+713c56d36,1024,1024,"[529.0, 831.0, 180.0, 193.0]",usask_1
+713c56d36,1024,1024,"[365.0, 777.0, 201.0, 201.0]",usask_1
+713c56d36,1024,1024,"[457.0, 974.0, 77.0, 50.0]",usask_1
+713c56d36,1024,1024,"[559.0, 138.0, 126.0, 268.0]",usask_1
+713c56d36,1024,1024,"[939.0, 934.0, 74.0, 90.0]",usask_1
+713c56d36,1024,1024,"[57.0, 0.0, 133.0, 35.0]",usask_1
+713c56d36,1024,1024,"[803.0, 625.0, 95.0, 178.0]",usask_1
+713c56d36,1024,1024,"[738.0, 353.0, 96.0, 220.0]",usask_1
+713c56d36,1024,1024,"[212.0, 673.0, 120.0, 86.0]",usask_1
+713c56d36,1024,1024,"[419.0, 496.0, 112.0, 171.0]",usask_1
+713c56d36,1024,1024,"[524.0, 787.0, 87.0, 82.0]",usask_1
+713c56d36,1024,1024,"[660.0, 879.0, 63.0, 97.0]",usask_1
+713c56d36,1024,1024,"[774.0, 538.0, 87.0, 71.0]",usask_1
+a332a2aeb,1024,1024,"[210.0, 360.0, 143.0, 110.0]",usask_1
+a332a2aeb,1024,1024,"[926.0, 16.0, 93.0, 110.0]",usask_1
+a332a2aeb,1024,1024,"[276.0, 616.0, 110.0, 93.0]",usask_1
+a332a2aeb,1024,1024,"[653.0, 889.0, 133.0, 113.0]",usask_1
+a332a2aeb,1024,1024,"[564.0, 148.0, 188.0, 149.0]",usask_1
+a332a2aeb,1024,1024,"[438.0, 888.0, 278.0, 94.0]",usask_1
+a332a2aeb,1024,1024,"[806.0, 394.0, 161.0, 65.0]",usask_1
+a332a2aeb,1024,1024,"[494.0, 572.0, 113.0, 191.0]",usask_1
+a332a2aeb,1024,1024,"[1.0, 258.0, 91.0, 99.0]",usask_1
+a332a2aeb,1024,1024,"[751.0, 0.0, 157.0, 59.0]",usask_1
+a332a2aeb,1024,1024,"[413.0, 971.0, 160.0, 53.0]",usask_1
+a332a2aeb,1024,1024,"[50.0, 906.0, 90.0, 79.0]",usask_1
+a332a2aeb,1024,1024,"[892.0, 241.0, 97.0, 114.0]",usask_1
+a332a2aeb,1024,1024,"[134.0, 46.0, 139.0, 89.0]",usask_1
+a332a2aeb,1024,1024,"[195.0, 717.0, 230.0, 143.0]",usask_1
+a332a2aeb,1024,1024,"[887.0, 135.0, 109.0, 140.0]",usask_1
+a332a2aeb,1024,1024,"[984.0, 288.0, 39.0, 70.0]",usask_1
+a332a2aeb,1024,1024,"[801.0, 138.0, 113.0, 76.0]",usask_1
+a332a2aeb,1024,1024,"[794.0, 443.0, 179.0, 61.0]",usask_1
+a332a2aeb,1024,1024,"[762.0, 337.0, 83.0, 87.0]",usask_1
+a332a2aeb,1024,1024,"[803.0, 289.0, 81.0, 72.0]",usask_1
+a332a2aeb,1024,1024,"[106.0, 725.0, 87.0, 131.0]",usask_1
+a332a2aeb,1024,1024,"[146.0, 398.0, 68.0, 45.0]",usask_1
+a332a2aeb,1024,1024,"[198.0, 301.0, 50.0, 204.0]",usask_1
+a332a2aeb,1024,1024,"[68.0, 398.0, 76.0, 175.0]",usask_1
+a332a2aeb,1024,1024,"[0.0, 190.0, 35.0, 77.0]",usask_1
+a332a2aeb,1024,1024,"[102.0, 214.0, 61.0, 51.0]",usask_1
+a332a2aeb,1024,1024,"[456.0, 136.0, 63.0, 64.0]",usask_1
+a332a2aeb,1024,1024,"[486.0, 319.0, 74.0, 98.0]",usask_1
+a332a2aeb,1024,1024,"[508.0, 243.0, 76.0, 91.0]",usask_1
+a332a2aeb,1024,1024,"[727.0, 138.0, 62.0, 71.0]",usask_1
+a332a2aeb,1024,1024,"[602.0, 211.0, 115.0, 119.0]",usask_1
+a332a2aeb,1024,1024,"[89.0, 543.0, 98.0, 192.0]",usask_1
+a332a2aeb,1024,1024,"[196.0, 506.0, 49.0, 109.0]",usask_1
+a332a2aeb,1024,1024,"[285.0, 481.0, 202.0, 129.0]",usask_1
+a332a2aeb,1024,1024,"[286.0, 455.0, 120.0, 109.0]",usask_1
+a332a2aeb,1024,1024,"[335.0, 212.0, 98.0, 239.0]",usask_1
+a332a2aeb,1024,1024,"[380.0, 251.0, 91.0, 140.0]",usask_1
+a332a2aeb,1024,1024,"[3.0, 383.0, 88.0, 139.0]",usask_1
+a332a2aeb,1024,1024,"[0.0, 496.0, 80.0, 117.0]",usask_1
+a332a2aeb,1024,1024,"[11.0, 743.0, 131.0, 195.0]",usask_1
+a332a2aeb,1024,1024,"[451.0, 0.0, 122.0, 151.0]",usask_1
+a332a2aeb,1024,1024,"[188.0, 626.0, 72.0, 128.0]",usask_1
+a332a2aeb,1024,1024,"[521.0, 704.0, 105.0, 85.0]",usask_1
+a332a2aeb,1024,1024,"[783.0, 516.0, 134.0, 221.0]",usask_1
+311a6b1e4,1024,1024,"[747.0, 613.0, 87.0, 79.0]",usask_1
+311a6b1e4,1024,1024,"[825.0, 67.0, 99.0, 76.0]",usask_1
+311a6b1e4,1024,1024,"[857.0, 93.0, 114.0, 100.0]",usask_1
+311a6b1e4,1024,1024,"[325.0, 69.0, 76.0, 87.0]",usask_1
+311a6b1e4,1024,1024,"[183.0, 586.0, 65.0, 79.0]",usask_1
+311a6b1e4,1024,1024,"[468.0, 94.0, 101.0, 94.0]",usask_1
+311a6b1e4,1024,1024,"[250.0, 967.0, 89.0, 57.0]",usask_1
+311a6b1e4,1024,1024,"[627.0, 683.0, 111.0, 79.0]",usask_1
+311a6b1e4,1024,1024,"[125.0, 666.0, 68.0, 59.0]",usask_1
+311a6b1e4,1024,1024,"[909.0, 159.0, 107.0, 83.0]",usask_1
+311a6b1e4,1024,1024,"[196.0, 944.0, 59.0, 53.0]",usask_1
+311a6b1e4,1024,1024,"[838.0, 0.0, 120.0, 69.0]",usask_1
+311a6b1e4,1024,1024,"[980.0, 68.0, 44.0, 107.0]",usask_1
+311a6b1e4,1024,1024,"[234.0, 541.0, 56.0, 66.0]",usask_1
+311a6b1e4,1024,1024,"[40.0, 584.0, 67.0, 76.0]",usask_1
+311a6b1e4,1024,1024,"[341.0, 182.0, 64.0, 53.0]",usask_1
+311a6b1e4,1024,1024,"[421.0, 609.0, 94.0, 96.0]",usask_1
+311a6b1e4,1024,1024,"[259.0, 734.0, 89.0, 69.0]",usask_1
+311a6b1e4,1024,1024,"[426.0, 741.0, 89.0, 83.0]",usask_1
+311a6b1e4,1024,1024,"[721.0, 903.0, 106.0, 75.0]",usask_1
+311a6b1e4,1024,1024,"[262.0, 156.0, 73.0, 75.0]",usask_1
+311a6b1e4,1024,1024,"[253.0, 412.0, 89.0, 85.0]",usask_1
+311a6b1e4,1024,1024,"[490.0, 833.0, 99.0, 91.0]",usask_1
+311a6b1e4,1024,1024,"[946.0, 0.0, 69.0, 69.0]",usask_1
+311a6b1e4,1024,1024,"[83.0, 862.0, 65.0, 60.0]",usask_1
+311a6b1e4,1024,1024,"[372.0, 944.0, 98.0, 80.0]",usask_1
+311a6b1e4,1024,1024,"[97.0, 964.0, 85.0, 56.0]",usask_1
+311a6b1e4,1024,1024,"[64.0, 771.0, 96.0, 76.0]",usask_1
+311a6b1e4,1024,1024,"[162.0, 869.0, 73.0, 68.0]",usask_1
+311a6b1e4,1024,1024,"[553.0, 138.0, 129.0, 76.0]",usask_1
+311a6b1e4,1024,1024,"[71.0, 0.0, 103.0, 48.0]",usask_1
+311a6b1e4,1024,1024,"[897.0, 988.0, 72.0, 36.0]",usask_1
+311a6b1e4,1024,1024,"[940.0, 782.0, 69.0, 85.0]",usask_1
+311a6b1e4,1024,1024,"[954.0, 753.0, 70.0, 68.0]",usask_1
+311a6b1e4,1024,1024,"[875.0, 632.0, 64.0, 65.0]",usask_1
+311a6b1e4,1024,1024,"[912.0, 615.0, 78.0, 78.0]",usask_1
+311a6b1e4,1024,1024,"[941.0, 615.0, 83.0, 87.0]",usask_1
+311a6b1e4,1024,1024,"[884.0, 519.0, 86.0, 81.0]",usask_1
+311a6b1e4,1024,1024,"[972.0, 301.0, 52.0, 67.0]",usask_1
+311a6b1e4,1024,1024,"[837.0, 336.0, 82.0, 69.0]",usask_1
+311a6b1e4,1024,1024,"[693.0, 345.0, 76.0, 76.0]",usask_1
+311a6b1e4,1024,1024,"[671.0, 441.0, 77.0, 63.0]",usask_1
+311a6b1e4,1024,1024,"[686.0, 516.0, 77.0, 68.0]",usask_1
+311a6b1e4,1024,1024,"[804.0, 432.0, 61.0, 77.0]",usask_1
+311a6b1e4,1024,1024,"[118.0, 533.0, 94.0, 83.0]",usask_1
+311a6b1e4,1024,1024,"[857.0, 426.0, 94.0, 103.0]",usask_1
+19ecc4446,1024,1024,"[971.0, 385.0, 53.0, 72.0]",usask_1
+19ecc4446,1024,1024,"[0.0, 585.0, 130.0, 101.0]",usask_1
+19ecc4446,1024,1024,"[761.0, 0.0, 73.0, 113.0]",usask_1
+19ecc4446,1024,1024,"[747.0, 875.0, 219.0, 101.0]",usask_1
+19ecc4446,1024,1024,"[0.0, 448.0, 76.0, 89.0]",usask_1
+19ecc4446,1024,1024,"[646.0, 592.0, 113.0, 83.0]",usask_1
+19ecc4446,1024,1024,"[734.0, 704.0, 74.0, 115.0]",usask_1
+19ecc4446,1024,1024,"[778.0, 202.0, 138.0, 110.0]",usask_1
+19ecc4446,1024,1024,"[444.0, 427.0, 107.0, 87.0]",usask_1
+19ecc4446,1024,1024,"[252.0, 990.0, 72.0, 34.0]",usask_1
+19ecc4446,1024,1024,"[800.0, 909.0, 74.0, 91.0]",usask_1
+19ecc4446,1024,1024,"[809.0, 595.0, 129.0, 74.0]",usask_1
+19ecc4446,1024,1024,"[290.0, 649.0, 112.0, 150.0]",usask_1
+19ecc4446,1024,1024,"[384.0, 713.0, 141.0, 78.0]",usask_1
+19ecc4446,1024,1024,"[512.0, 296.0, 149.0, 107.0]",usask_1
+19ecc4446,1024,1024,"[554.0, 859.0, 159.0, 124.0]",usask_1
+19ecc4446,1024,1024,"[0.0, 951.0, 38.0, 53.0]",usask_1
+19ecc4446,1024,1024,"[635.0, 959.0, 62.0, 65.0]",usask_1
+19ecc4446,1024,1024,"[72.0, 448.0, 70.0, 283.0]",usask_1
+19ecc4446,1024,1024,"[277.0, 77.0, 116.0, 107.0]",usask_1
+19ecc4446,1024,1024,"[878.0, 822.0, 93.0, 110.0]",usask_1
+19ecc4446,1024,1024,"[431.0, 0.0, 109.0, 62.0]",usask_1
+19ecc4446,1024,1024,"[639.0, 490.0, 71.0, 68.0]",usask_1
+19ecc4446,1024,1024,"[8.0, 191.0, 83.0, 100.0]",usask_1
+19ecc4446,1024,1024,"[161.0, 259.0, 110.0, 96.0]",usask_1
+19ecc4446,1024,1024,"[557.0, 493.0, 74.0, 71.0]",usask_1
+19ecc4446,1024,1024,"[680.0, 695.0, 75.0, 119.0]",usask_1
+19ecc4446,1024,1024,"[664.0, 394.0, 94.0, 74.0]",usask_1
+19ecc4446,1024,1024,"[574.0, 411.0, 118.0, 55.0]",usask_1
+19ecc4446,1024,1024,"[109.0, 657.0, 147.0, 71.0]",usask_1
+19ecc4446,1024,1024,"[343.0, 144.0, 102.0, 81.0]",usask_1
+19ecc4446,1024,1024,"[560.0, 449.0, 63.0, 68.0]",usask_1
+19ecc4446,1024,1024,"[101.0, 393.0, 78.0, 70.0]",usask_1
+19ecc4446,1024,1024,"[187.0, 462.0, 94.0, 91.0]",usask_1
+19ecc4446,1024,1024,"[865.0, 91.0, 69.0, 45.0]",usask_1
+f8e590769,1024,1024,"[0.0, 7.0, 152.0, 99.0]",usask_1
+f8e590769,1024,1024,"[699.0, 759.0, 253.0, 88.0]",usask_1
+f8e590769,1024,1024,"[801.0, 357.0, 137.0, 82.0]",usask_1
+f8e590769,1024,1024,"[943.0, 906.0, 81.0, 84.0]",usask_1
+f8e590769,1024,1024,"[360.0, 483.0, 151.0, 72.0]",usask_1
+f8e590769,1024,1024,"[92.0, 155.0, 141.0, 74.0]",usask_1
+f8e590769,1024,1024,"[410.0, 778.0, 242.0, 109.0]",usask_1
+f8e590769,1024,1024,"[86.0, 50.0, 121.0, 65.0]",usask_1
+f8e590769,1024,1024,"[630.0, 686.0, 285.0, 84.0]",usask_1
+f8e590769,1024,1024,"[0.0, 488.0, 192.0, 111.0]",usask_1
+f8e590769,1024,1024,"[316.0, 395.0, 159.0, 95.0]",usask_1
+f8e590769,1024,1024,"[32.0, 716.0, 201.0, 101.0]",usask_1
+f8e590769,1024,1024,"[828.0, 526.0, 196.0, 132.0]",usask_1
+f8e590769,1024,1024,"[260.0, 318.0, 112.0, 74.0]",usask_1
+f8e590769,1024,1024,"[898.0, 161.0, 126.0, 74.0]",usask_1
+f8e590769,1024,1024,"[376.0, 691.0, 279.0, 93.0]",usask_1
+f8e590769,1024,1024,"[596.0, 133.0, 222.0, 141.0]",usask_1
+f8e590769,1024,1024,"[434.0, 37.0, 137.0, 89.0]",usask_1
+f8e590769,1024,1024,"[801.0, 284.0, 123.0, 75.0]",usask_1
+f8e590769,1024,1024,"[11.0, 768.0, 275.0, 92.0]",usask_1
+f8e590769,1024,1024,"[0.0, 653.0, 93.0, 101.0]",usask_1
+f8e590769,1024,1024,"[829.0, 260.0, 106.0, 80.0]",usask_1
+f8e590769,1024,1024,"[961.0, 287.0, 63.0, 107.0]",usask_1
+f8e590769,1024,1024,"[189.0, 645.0, 114.0, 80.0]",usask_1
+f8e590769,1024,1024,"[178.0, 506.0, 134.0, 103.0]",usask_1
+f8e590769,1024,1024,"[0.0, 326.0, 217.0, 88.0]",usask_1
+f8e590769,1024,1024,"[97.0, 435.0, 153.0, 59.0]",usask_1
+f8e590769,1024,1024,"[99.0, 269.0, 106.0, 81.0]",usask_1
+f8e590769,1024,1024,"[0.0, 264.0, 81.0, 77.0]",usask_1
+f8e590769,1024,1024,"[1.0, 410.0, 107.0, 69.0]",usask_1
+f8e590769,1024,1024,"[0.0, 143.0, 108.0, 109.0]",usask_1
+f8e590769,1024,1024,"[67.0, 212.0, 90.0, 72.0]",usask_1
+f8e590769,1024,1024,"[0.0, 99.0, 145.0, 65.0]",usask_1
+f8e590769,1024,1024,"[359.0, 133.0, 101.0, 42.0]",usask_1
+f8e590769,1024,1024,"[783.0, 662.0, 170.0, 120.0]",usask_1
+7727eeebc,1024,1024,"[181.0, 51.0, 100.0, 93.0]",usask_1
+7727eeebc,1024,1024,"[183.0, 298.0, 94.0, 48.0]",usask_1
+7727eeebc,1024,1024,"[0.0, 892.0, 120.0, 84.0]",usask_1
+7727eeebc,1024,1024,"[486.0, 956.0, 111.0, 59.0]",usask_1
+7727eeebc,1024,1024,"[330.0, 192.0, 93.0, 92.0]",usask_1
+7727eeebc,1024,1024,"[700.0, 64.0, 93.0, 79.0]",usask_1
+7727eeebc,1024,1024,"[759.0, 370.0, 145.0, 65.0]",usask_1
+7727eeebc,1024,1024,"[377.0, 232.0, 120.0, 120.0]",usask_1
+7727eeebc,1024,1024,"[253.0, 88.0, 98.0, 153.0]",usask_1
+7727eeebc,1024,1024,"[825.0, 561.0, 100.0, 111.0]",usask_1
+7727eeebc,1024,1024,"[0.0, 749.0, 61.0, 69.0]",usask_1
+7727eeebc,1024,1024,"[727.0, 862.0, 150.0, 162.0]",usask_1
+7727eeebc,1024,1024,"[0.0, 581.0, 112.0, 109.0]",usask_1
+7727eeebc,1024,1024,"[873.0, 927.0, 120.0, 97.0]",usask_1
+7727eeebc,1024,1024,"[124.0, 911.0, 92.0, 92.0]",usask_1
+7727eeebc,1024,1024,"[232.0, 0.0, 79.0, 53.0]",usask_1
+7727eeebc,1024,1024,"[243.0, 973.0, 79.0, 50.0]",usask_1
+7727eeebc,1024,1024,"[289.0, 785.0, 72.0, 70.0]",usask_1
+7727eeebc,1024,1024,"[211.0, 764.0, 73.0, 63.0]",usask_1
+7727eeebc,1024,1024,"[108.0, 793.0, 65.0, 62.0]",usask_1
+7727eeebc,1024,1024,"[83.0, 843.0, 105.0, 57.0]",usask_1
+7727eeebc,1024,1024,"[178.0, 811.0, 55.0, 70.0]",usask_1
+7727eeebc,1024,1024,"[217.0, 815.0, 74.0, 64.0]",usask_1
+7727eeebc,1024,1024,"[897.0, 771.0, 73.0, 87.0]",usask_1
+7727eeebc,1024,1024,"[948.0, 793.0, 44.0, 67.0]",usask_1
+7727eeebc,1024,1024,"[900.0, 690.0, 90.0, 78.0]",usask_1
+7727eeebc,1024,1024,"[965.0, 626.0, 59.0, 105.0]",usask_1
+7727eeebc,1024,1024,"[857.0, 303.0, 92.0, 88.0]",usask_1
+7727eeebc,1024,1024,"[922.0, 408.0, 79.0, 135.0]",usask_1
+7727eeebc,1024,1024,"[872.0, 367.0, 84.0, 93.0]",usask_1
+7727eeebc,1024,1024,"[470.0, 103.0, 94.0, 51.0]",usask_1
+7727eeebc,1024,1024,"[884.0, 443.0, 56.0, 56.0]",usask_1
+bbdc7e299,1024,1024,"[77.0, 366.0, 109.0, 127.0]",usask_1
+bbdc7e299,1024,1024,"[471.0, 439.0, 80.0, 71.0]",usask_1
+bbdc7e299,1024,1024,"[121.0, 425.0, 144.0, 89.0]",usask_1
+bbdc7e299,1024,1024,"[637.0, 819.0, 69.0, 136.0]",usask_1
+bbdc7e299,1024,1024,"[153.0, 690.0, 91.0, 111.0]",usask_1
+bbdc7e299,1024,1024,"[25.0, 268.0, 122.0, 91.0]",usask_1
+bbdc7e299,1024,1024,"[297.0, 736.0, 115.0, 94.0]",usask_1
+bbdc7e299,1024,1024,"[719.0, 542.0, 192.0, 88.0]",usask_1
+bbdc7e299,1024,1024,"[478.0, 599.0, 61.0, 60.0]",usask_1
+bbdc7e299,1024,1024,"[615.0, 415.0, 110.0, 91.0]",usask_1
+bbdc7e299,1024,1024,"[272.0, 505.0, 226.0, 81.0]",usask_1
+bbdc7e299,1024,1024,"[496.0, 0.0, 80.0, 115.0]",usask_1
+bbdc7e299,1024,1024,"[781.0, 439.0, 96.0, 67.0]",usask_1
+bbdc7e299,1024,1024,"[745.0, 405.0, 136.0, 75.0]",usask_1
+bbdc7e299,1024,1024,"[0.0, 943.0, 89.0, 81.0]",usask_1
+bbdc7e299,1024,1024,"[577.0, 590.0, 76.0, 76.0]",usask_1
+bbdc7e299,1024,1024,"[1.0, 448.0, 55.0, 82.0]",usask_1
+bbdc7e299,1024,1024,"[13.0, 572.0, 53.0, 75.0]",usask_1
+bbdc7e299,1024,1024,"[522.0, 243.0, 62.0, 73.0]",usask_1
+bbdc7e299,1024,1024,"[274.0, 707.0, 212.0, 59.0]",usask_1
+bbdc7e299,1024,1024,"[91.0, 618.0, 47.0, 40.0]",usask_1
+bbdc7e299,1024,1024,"[446.0, 600.0, 194.0, 135.0]",usask_1
+36d1cc44b,1024,1024,"[778.0, 855.0, 129.0, 95.0]",usask_1
+36d1cc44b,1024,1024,"[58.0, 463.0, 153.0, 145.0]",usask_1
+36d1cc44b,1024,1024,"[905.0, 646.0, 118.0, 97.0]",usask_1
+36d1cc44b,1024,1024,"[179.0, 511.0, 88.0, 143.0]",usask_1
+36d1cc44b,1024,1024,"[833.0, 825.0, 145.0, 99.0]",usask_1
+36d1cc44b,1024,1024,"[435.0, 455.0, 246.0, 146.0]",usask_1
+36d1cc44b,1024,1024,"[538.0, 282.0, 251.0, 304.0]",usask_1
+36d1cc44b,1024,1024,"[371.0, 632.0, 201.0, 235.0]",usask_1
+36d1cc44b,1024,1024,"[343.0, 249.0, 238.0, 126.0]",usask_1
+36d1cc44b,1024,1024,"[23.0, 0.0, 124.0, 130.0]",usask_1
+36d1cc44b,1024,1024,"[0.0, 241.0, 38.0, 70.0]",usask_1
+36d1cc44b,1024,1024,"[771.0, 0.0, 39.0, 21.0]",usask_1
+be11c4e40,1024,1024,"[28.0, 0.0, 184.0, 77.0]",usask_1
+be11c4e40,1024,1024,"[965.0, 0.0, 59.0, 152.0]",usask_1
+be11c4e40,1024,1024,"[477.0, 408.0, 168.0, 84.0]",usask_1
+be11c4e40,1024,1024,"[378.0, 1.0, 197.0, 203.0]",usask_1
+be11c4e40,1024,1024,"[131.0, 50.0, 112.0, 139.0]",usask_1
+be11c4e40,1024,1024,"[635.0, 0.0, 113.0, 72.0]",usask_1
+be11c4e40,1024,1024,"[0.0, 698.0, 161.0, 134.0]",usask_1
+be11c4e40,1024,1024,"[58.0, 327.0, 112.0, 99.0]",usask_1
+be11c4e40,1024,1024,"[205.0, 791.0, 332.0, 126.0]",usask_1
+be11c4e40,1024,1024,"[480.0, 169.0, 191.0, 225.0]",usask_1
+be11c4e40,1024,1024,"[589.0, 633.0, 224.0, 214.0]",usask_1
+be11c4e40,1024,1024,"[806.0, 783.0, 120.0, 197.0]",usask_1
+be11c4e40,1024,1024,"[157.0, 905.0, 364.0, 115.0]",usask_1
+be11c4e40,1024,1024,"[705.0, 964.0, 122.0, 60.0]",usask_1
+be11c4e40,1024,1024,"[451.0, 0.0, 142.0, 103.0]",usask_1
+be11c4e40,1024,1024,"[516.0, 462.0, 133.0, 79.0]",usask_1
+be11c4e40,1024,1024,"[855.0, 249.0, 103.0, 201.0]",usask_1
+be11c4e40,1024,1024,"[224.0, 285.0, 225.0, 139.0]",usask_1
+be11c4e40,1024,1024,"[323.0, 240.0, 147.0, 180.0]",usask_1
+be11c4e40,1024,1024,"[344.0, 171.0, 172.0, 228.0]",usask_1
+be11c4e40,1024,1024,"[891.0, 52.0, 133.0, 79.0]",usask_1
+c64a4006c,1024,1024,"[175.0, 750.0, 81.0, 85.0]",usask_1
+c64a4006c,1024,1024,"[892.0, 0.0, 88.0, 54.0]",usask_1
+c64a4006c,1024,1024,"[206.0, 101.0, 138.0, 101.0]",usask_1
+c64a4006c,1024,1024,"[0.0, 579.0, 49.0, 100.0]",usask_1
+c64a4006c,1024,1024,"[732.0, 154.0, 93.0, 93.0]",usask_1
+c64a4006c,1024,1024,"[715.0, 782.0, 81.0, 92.0]",usask_1
+c64a4006c,1024,1024,"[592.0, 959.0, 124.0, 65.0]",usask_1
+c64a4006c,1024,1024,"[529.0, 414.0, 87.0, 146.0]",usask_1
+c64a4006c,1024,1024,"[779.0, 103.0, 82.0, 84.0]",usask_1
+c64a4006c,1024,1024,"[46.0, 96.0, 79.0, 86.0]",usask_1
+c64a4006c,1024,1024,"[561.0, 4.0, 103.0, 75.0]",usask_1
+c64a4006c,1024,1024,"[902.0, 860.0, 88.0, 77.0]",usask_1
+c64a4006c,1024,1024,"[510.0, 84.0, 72.0, 63.0]",usask_1
+c64a4006c,1024,1024,"[0.0, 111.0, 51.0, 87.0]",usask_1
+c64a4006c,1024,1024,"[391.0, 97.0, 95.0, 147.0]",usask_1
+c64a4006c,1024,1024,"[282.0, 0.0, 119.0, 284.0]",usask_1
+c64a4006c,1024,1024,"[791.0, 194.0, 180.0, 71.0]",usask_1
+c64a4006c,1024,1024,"[796.0, 388.0, 102.0, 188.0]",usask_1
+c64a4006c,1024,1024,"[27.0, 694.0, 133.0, 142.0]",usask_1
+c64a4006c,1024,1024,"[606.0, 479.0, 114.0, 99.0]",usask_1
+c64a4006c,1024,1024,"[607.0, 82.0, 75.0, 67.0]",usask_1
+c64a4006c,1024,1024,"[445.0, 795.0, 260.0, 104.0]",usask_1
+c64a4006c,1024,1024,"[686.0, 713.0, 122.0, 81.0]",usask_1
+c64a4006c,1024,1024,"[805.0, 345.0, 86.0, 93.0]",usask_1
+c64a4006c,1024,1024,"[719.0, 982.0, 60.0, 42.0]",usask_1
+c64a4006c,1024,1024,"[828.0, 251.0, 196.0, 54.0]",usask_1
+c64a4006c,1024,1024,"[850.0, 32.0, 114.0, 95.0]",usask_1
+c64a4006c,1024,1024,"[194.0, 175.0, 56.0, 213.0]",usask_1
+c64a4006c,1024,1024,"[72.0, 240.0, 114.0, 52.0]",usask_1
+c64a4006c,1024,1024,"[0.0, 258.0, 34.0, 133.0]",usask_1
+c64a4006c,1024,1024,"[0.0, 402.0, 32.0, 115.0]",usask_1
+c64a4006c,1024,1024,"[24.0, 306.0, 71.0, 176.0]",usask_1
+c64a4006c,1024,1024,"[79.0, 554.0, 106.0, 136.0]",usask_1
+c64a4006c,1024,1024,"[238.0, 619.0, 267.0, 105.0]",usask_1
+c64a4006c,1024,1024,"[208.0, 622.0, 49.0, 83.0]",usask_1
+c64a4006c,1024,1024,"[196.0, 483.0, 124.0, 152.0]",usask_1
+c64a4006c,1024,1024,"[295.0, 509.0, 99.0, 68.0]",usask_1
+c64a4006c,1024,1024,"[144.0, 394.0, 83.0, 118.0]",usask_1
+c64a4006c,1024,1024,"[307.0, 375.0, 161.0, 113.0]",usask_1
+c64a4006c,1024,1024,"[275.0, 323.0, 158.0, 135.0]",usask_1
+c64a4006c,1024,1024,"[948.0, 116.0, 76.0, 75.0]",usask_1
+c64a4006c,1024,1024,"[968.0, 68.0, 56.0, 50.0]",usask_1
+c64a4006c,1024,1024,"[482.0, 12.0, 72.0, 64.0]",usask_1
+c64a4006c,1024,1024,"[665.0, 0.0, 51.0, 25.0]",usask_1
+c64a4006c,1024,1024,"[731.0, 1.0, 57.0, 32.0]",usask_1
+c64a4006c,1024,1024,"[802.0, 1010.0, 51.0, 14.0]",usask_1
+c64a4006c,1024,1024,"[153.0, 505.0, 54.0, 93.0]",usask_1
+4d60c73db,1024,1024,"[896.0, 0.0, 97.0, 47.0]",usask_1
+4d60c73db,1024,1024,"[0.0, 851.0, 87.0, 103.0]",usask_1
+4d60c73db,1024,1024,"[228.0, 167.0, 113.0, 133.0]",usask_1
+4d60c73db,1024,1024,"[579.0, 837.0, 90.0, 64.0]",usask_1
+4d60c73db,1024,1024,"[672.0, 475.0, 124.0, 118.0]",usask_1
+4d60c73db,1024,1024,"[87.0, 691.0, 102.0, 112.0]",usask_1
+4d60c73db,1024,1024,"[222.0, 361.0, 137.0, 76.0]",usask_1
+4d60c73db,1024,1024,"[828.0, 204.0, 109.0, 151.0]",usask_1
+4d60c73db,1024,1024,"[347.0, 0.0, 214.0, 101.0]",usask_1
+4d60c73db,1024,1024,"[513.0, 559.0, 105.0, 106.0]",usask_1
+4d60c73db,1024,1024,"[525.0, 336.0, 103.0, 161.0]",usask_1
+4d60c73db,1024,1024,"[35.0, 204.0, 82.0, 106.0]",usask_1
+4d60c73db,1024,1024,"[740.0, 368.0, 100.0, 129.0]",usask_1
+4d60c73db,1024,1024,"[291.0, 117.0, 171.0, 181.0]",usask_1
+4d60c73db,1024,1024,"[387.0, 259.0, 144.0, 100.0]",usask_1
+4d60c73db,1024,1024,"[727.0, 178.0, 96.0, 147.0]",usask_1
+4d60c73db,1024,1024,"[463.0, 950.0, 172.0, 61.0]",usask_1
+4d60c73db,1024,1024,"[535.0, 904.0, 101.0, 78.0]",usask_1
+d89b34cb1,1024,1024,"[911.0, 806.0, 70.0, 128.0]",usask_1
+d89b34cb1,1024,1024,"[266.0, 641.0, 99.0, 88.0]",usask_1
+d89b34cb1,1024,1024,"[0.0, 365.0, 42.0, 67.0]",usask_1
+d89b34cb1,1024,1024,"[848.0, 761.0, 73.0, 86.0]",usask_1
+d89b34cb1,1024,1024,"[341.0, 675.0, 123.0, 82.0]",usask_1
+d89b34cb1,1024,1024,"[357.0, 778.0, 102.0, 99.0]",usask_1
+d89b34cb1,1024,1024,"[463.0, 635.0, 89.0, 117.0]",usask_1
+d89b34cb1,1024,1024,"[916.0, 609.0, 70.0, 112.0]",usask_1
+d89b34cb1,1024,1024,"[473.0, 485.0, 147.0, 91.0]",usask_1
+d89b34cb1,1024,1024,"[207.0, 245.0, 75.0, 89.0]",usask_1
+d89b34cb1,1024,1024,"[167.0, 104.0, 97.0, 61.0]",usask_1
+d89b34cb1,1024,1024,"[821.0, 690.0, 79.0, 64.0]",usask_1
+d89b34cb1,1024,1024,"[976.0, 512.0, 48.0, 161.0]",usask_1
+d89b34cb1,1024,1024,"[923.0, 366.0, 101.0, 104.0]",usask_1
+d89b34cb1,1024,1024,"[181.0, 709.0, 136.0, 241.0]",usask_1
+d89b34cb1,1024,1024,"[727.0, 22.0, 106.0, 57.0]",usask_1
+d89b34cb1,1024,1024,"[309.0, 303.0, 125.0, 163.0]",usask_1
+d89b34cb1,1024,1024,"[114.0, 12.0, 103.0, 81.0]",usask_1
+d89b34cb1,1024,1024,"[319.0, 0.0, 89.0, 66.0]",usask_1
+d89b34cb1,1024,1024,"[193.0, 0.0, 98.0, 90.0]",usask_1
+d89b34cb1,1024,1024,"[702.0, 54.0, 208.0, 88.0]",usask_1
+d89b34cb1,1024,1024,"[67.0, 431.0, 98.0, 169.0]",usask_1
+d89b34cb1,1024,1024,"[969.0, 805.0, 42.0, 99.0]",usask_1
+d89b34cb1,1024,1024,"[922.0, 745.0, 77.0, 64.0]",usask_1
+d89b34cb1,1024,1024,"[829.0, 587.0, 86.0, 100.0]",usask_1
+d89b34cb1,1024,1024,"[854.0, 385.0, 86.0, 74.0]",usask_1
+d89b34cb1,1024,1024,"[461.0, 546.0, 125.0, 82.0]",usask_1
+d89b34cb1,1024,1024,"[0.0, 293.0, 104.0, 63.0]",usask_1
+d89b34cb1,1024,1024,"[124.0, 245.0, 70.0, 79.0]",usask_1
+d89b34cb1,1024,1024,"[129.0, 157.0, 79.0, 74.0]",usask_1
+d89b34cb1,1024,1024,"[192.0, 177.0, 92.0, 81.0]",usask_1
+3edfa2df0,1024,1024,"[384.0, 724.0, 86.0, 234.0]",usask_1
+3edfa2df0,1024,1024,"[387.0, 954.0, 120.0, 69.0]",usask_1
+3edfa2df0,1024,1024,"[286.0, 535.0, 153.0, 110.0]",usask_1
+3edfa2df0,1024,1024,"[574.0, 954.0, 66.0, 69.0]",usask_1
+3edfa2df0,1024,1024,"[513.0, 888.0, 83.0, 93.0]",usask_1
+3edfa2df0,1024,1024,"[353.0, 424.0, 134.0, 265.0]",usask_1
+3edfa2df0,1024,1024,"[448.0, 101.0, 126.0, 146.0]",usask_1
+3edfa2df0,1024,1024,"[784.0, 0.0, 157.0, 106.0]",usask_1
+3edfa2df0,1024,1024,"[0.0, 734.0, 90.0, 75.0]",usask_1
+3edfa2df0,1024,1024,"[935.0, 687.0, 89.0, 79.0]",usask_1
+3edfa2df0,1024,1024,"[734.0, 97.0, 119.0, 167.0]",usask_1
+3edfa2df0,1024,1024,"[351.0, 192.0, 139.0, 95.0]",usask_1
+3edfa2df0,1024,1024,"[0.0, 0.0, 86.0, 8.0]",usask_1
+3edfa2df0,1024,1024,"[558.0, 138.0, 179.0, 153.0]",usask_1
+3edfa2df0,1024,1024,"[192.0, 306.0, 183.0, 100.0]",usask_1
+3edfa2df0,1024,1024,"[522.0, 40.0, 96.0, 86.0]",usask_1
+3edfa2df0,1024,1024,"[285.0, 281.0, 109.0, 75.0]",usask_1
+3edfa2df0,1024,1024,"[599.0, 856.0, 120.0, 145.0]",usask_1
+3edfa2df0,1024,1024,"[579.0, 846.0, 97.0, 161.0]",usask_1
+3edfa2df0,1024,1024,"[535.0, 766.0, 72.0, 79.0]",usask_1
+3edfa2df0,1024,1024,"[478.0, 666.0, 53.0, 171.0]",usask_1
+3edfa2df0,1024,1024,"[467.0, 691.0, 91.0, 71.0]",usask_1
+3edfa2df0,1024,1024,"[576.0, 687.0, 74.0, 71.0]",usask_1
+3edfa2df0,1024,1024,"[671.0, 745.0, 118.0, 89.0]",usask_1
+3edfa2df0,1024,1024,"[565.0, 593.0, 86.0, 79.0]",usask_1
+3edfa2df0,1024,1024,"[598.0, 502.0, 64.0, 69.0]",usask_1
+3edfa2df0,1024,1024,"[647.0, 546.0, 103.0, 48.0]",usask_1
+3edfa2df0,1024,1024,"[656.0, 576.0, 144.0, 80.0]",usask_1
+3edfa2df0,1024,1024,"[661.0, 653.0, 91.0, 84.0]",usask_1
+1172e0f0a,1024,1024,"[309.0, 531.0, 84.0, 75.0]",usask_1
+1172e0f0a,1024,1024,"[382.0, 623.0, 89.0, 78.0]",usask_1
+1172e0f0a,1024,1024,"[49.0, 972.0, 97.0, 49.0]",usask_1
+1172e0f0a,1024,1024,"[818.0, 256.0, 170.0, 204.0]",usask_1
+1172e0f0a,1024,1024,"[329.0, 710.0, 59.0, 81.0]",usask_1
+1172e0f0a,1024,1024,"[277.0, 704.0, 72.0, 96.0]",usask_1
+1172e0f0a,1024,1024,"[0.0, 77.0, 130.0, 69.0]",usask_1
+1172e0f0a,1024,1024,"[893.0, 606.0, 81.0, 82.0]",usask_1
+1172e0f0a,1024,1024,"[947.0, 380.0, 76.0, 140.0]",usask_1
+1172e0f0a,1024,1024,"[653.0, 57.0, 187.0, 162.0]",usask_1
+1172e0f0a,1024,1024,"[323.0, 961.0, 70.0, 61.0]",usask_1
+1172e0f0a,1024,1024,"[333.0, 862.0, 74.0, 73.0]",usask_1
+1172e0f0a,1024,1024,"[242.0, 814.0, 135.0, 65.0]",usask_1
+1172e0f0a,1024,1024,"[495.0, 536.0, 123.0, 101.0]",usask_1
+1172e0f0a,1024,1024,"[509.0, 600.0, 135.0, 69.0]",usask_1
+1172e0f0a,1024,1024,"[394.0, 704.0, 54.0, 55.0]",usask_1
+1172e0f0a,1024,1024,"[439.0, 715.0, 49.0, 55.0]",usask_1
+1172e0f0a,1024,1024,"[391.0, 780.0, 57.0, 61.0]",usask_1
+1172e0f0a,1024,1024,"[510.0, 671.0, 102.0, 54.0]",usask_1
+1172e0f0a,1024,1024,"[628.0, 789.0, 173.0, 60.0]",usask_1
+1172e0f0a,1024,1024,"[517.0, 808.0, 62.0, 76.0]",usask_1
+1172e0f0a,1024,1024,"[416.0, 848.0, 40.0, 46.0]",usask_1
+1172e0f0a,1024,1024,"[451.0, 820.0, 86.0, 75.0]",usask_1
+1172e0f0a,1024,1024,"[454.0, 910.0, 87.0, 68.0]",usask_1
+1172e0f0a,1024,1024,"[613.0, 850.0, 134.0, 60.0]",usask_1
+1172e0f0a,1024,1024,"[564.0, 987.0, 102.0, 35.0]",usask_1
+1172e0f0a,1024,1024,"[656.0, 951.0, 112.0, 68.0]",usask_1
+1172e0f0a,1024,1024,"[706.0, 853.0, 113.0, 82.0]",usask_1
+1172e0f0a,1024,1024,"[687.0, 880.0, 158.0, 87.0]",usask_1
+1172e0f0a,1024,1024,"[555.0, 871.0, 59.0, 81.0]",usask_1
+1a13195b8,1024,1024,"[505.0, 758.0, 91.0, 140.0]",usask_1
+1a13195b8,1024,1024,"[675.0, 821.0, 85.0, 114.0]",usask_1
+1a13195b8,1024,1024,"[262.0, 868.0, 82.0, 82.0]",usask_1
+1a13195b8,1024,1024,"[542.0, 972.0, 142.0, 52.0]",usask_1
+1a13195b8,1024,1024,"[411.0, 12.0, 69.0, 62.0]",usask_1
+1a13195b8,1024,1024,"[498.0, 110.0, 66.0, 49.0]",usask_1
+1a13195b8,1024,1024,"[541.0, 905.0, 61.0, 62.0]",usask_1
+1a13195b8,1024,1024,"[288.0, 644.0, 103.0, 73.0]",usask_1
+1a13195b8,1024,1024,"[1.0, 489.0, 57.0, 71.0]",usask_1
+1a13195b8,1024,1024,"[177.0, 105.0, 126.0, 147.0]",usask_1
+1a13195b8,1024,1024,"[119.0, 143.0, 69.0, 99.0]",usask_1
+1a13195b8,1024,1024,"[287.0, 703.0, 111.0, 66.0]",usask_1
+1a13195b8,1024,1024,"[927.0, 810.0, 94.0, 103.0]",usask_1
+1a13195b8,1024,1024,"[0.0, 1020.0, 55.0, 4.0]",usask_1
+1a13195b8,1024,1024,"[428.0, 694.0, 87.0, 81.0]",usask_1
+1a13195b8,1024,1024,"[141.0, 449.0, 79.0, 121.0]",usask_1
+1a13195b8,1024,1024,"[796.0, 854.0, 90.0, 67.0]",usask_1
+1a13195b8,1024,1024,"[414.0, 901.0, 68.0, 116.0]",usask_1
+1a13195b8,1024,1024,"[347.0, 885.0, 55.0, 85.0]",usask_1
+1a13195b8,1024,1024,"[293.0, 931.0, 100.0, 93.0]",usask_1
+1a13195b8,1024,1024,"[314.0, 159.0, 108.0, 109.0]",usask_1
+1a13195b8,1024,1024,"[289.0, 243.0, 121.0, 58.0]",usask_1
+1a13195b8,1024,1024,"[311.0, 81.0, 115.0, 98.0]",usask_1
+1a13195b8,1024,1024,"[191.0, 64.0, 94.0, 88.0]",usask_1
+e734652d6,1024,1024,"[48.0, 692.0, 180.0, 73.0]",usask_1
+e734652d6,1024,1024,"[346.0, 97.0, 90.0, 93.0]",usask_1
+e734652d6,1024,1024,"[596.0, 218.0, 93.0, 87.0]",usask_1
+e734652d6,1024,1024,"[501.0, 394.0, 77.0, 123.0]",usask_1
+e734652d6,1024,1024,"[58.0, 380.0, 116.0, 265.0]",usask_1
+e734652d6,1024,1024,"[505.0, 933.0, 89.0, 79.0]",usask_1
+e734652d6,1024,1024,"[607.0, 973.0, 144.0, 51.0]",usask_1
+e734652d6,1024,1024,"[189.0, 440.0, 134.0, 78.0]",usask_1
+e734652d6,1024,1024,"[194.0, 987.0, 70.0, 36.0]",usask_1
+e734652d6,1024,1024,"[165.0, 858.0, 122.0, 80.0]",usask_1
+e734652d6,1024,1024,"[357.0, 700.0, 177.0, 137.0]",usask_1
+e734652d6,1024,1024,"[964.0, 934.0, 60.0, 78.0]",usask_1
+e734652d6,1024,1024,"[0.0, 600.0, 215.0, 77.0]",usask_1
+e734652d6,1024,1024,"[363.0, 175.0, 90.0, 143.0]",usask_1
+e734652d6,1024,1024,"[215.0, 0.0, 92.0, 29.0]",usask_1
+e734652d6,1024,1024,"[513.0, 8.0, 129.0, 91.0]",usask_1
+e734652d6,1024,1024,"[960.0, 24.0, 64.0, 106.0]",usask_1
+e734652d6,1024,1024,"[604.0, 789.0, 155.0, 121.0]",usask_1
+e734652d6,1024,1024,"[606.0, 424.0, 93.0, 154.0]",usask_1
+e734652d6,1024,1024,"[52.0, 225.0, 76.0, 148.0]",usask_1
+e734652d6,1024,1024,"[680.0, 171.0, 141.0, 89.0]",usask_1
+e734652d6,1024,1024,"[451.0, 525.0, 93.0, 238.0]",usask_1
+e734652d6,1024,1024,"[656.0, 77.0, 124.0, 80.0]",usask_1
+e734652d6,1024,1024,"[657.0, 913.0, 175.0, 84.0]",usask_1
+e734652d6,1024,1024,"[133.0, 460.0, 115.0, 79.0]",usask_1
+e734652d6,1024,1024,"[113.0, 225.0, 127.0, 153.0]",usask_1
+e734652d6,1024,1024,"[475.0, 377.0, 69.0, 54.0]",usask_1
+e734652d6,1024,1024,"[0.0, 464.0, 32.0, 135.0]",usask_1
+dc9ac3f45,1024,1024,"[196.0, 496.0, 113.0, 59.0]",usask_1
+dc9ac3f45,1024,1024,"[852.0, 75.0, 110.0, 94.0]",usask_1
+dc9ac3f45,1024,1024,"[872.0, 634.0, 95.0, 85.0]",usask_1
+dc9ac3f45,1024,1024,"[555.0, 279.0, 107.0, 157.0]",usask_1
+dc9ac3f45,1024,1024,"[818.0, 147.0, 109.0, 82.0]",usask_1
+dc9ac3f45,1024,1024,"[491.0, 888.0, 59.0, 98.0]",usask_1
+dc9ac3f45,1024,1024,"[239.0, 430.0, 142.0, 85.0]",usask_1
+dc9ac3f45,1024,1024,"[646.0, 377.0, 134.0, 120.0]",usask_1
+dc9ac3f45,1024,1024,"[887.0, 924.0, 90.0, 100.0]",usask_1
+dc9ac3f45,1024,1024,"[70.0, 623.0, 147.0, 106.0]",usask_1
+dc9ac3f45,1024,1024,"[0.0, 805.0, 2.0, 103.0]",usask_1
+dc9ac3f45,1024,1024,"[316.0, 160.0, 124.0, 80.0]",usask_1
+dc9ac3f45,1024,1024,"[428.0, 2.0, 68.0, 133.0]",usask_1
+dc9ac3f45,1024,1024,"[518.0, 141.0, 90.0, 100.0]",usask_1
+dc9ac3f45,1024,1024,"[290.0, 486.0, 123.0, 120.0]",usask_1
+dc9ac3f45,1024,1024,"[842.0, 497.0, 182.0, 171.0]",usask_1
+dc9ac3f45,1024,1024,"[155.0, 1.0, 146.0, 51.0]",usask_1
+dc9ac3f45,1024,1024,"[333.0, 683.0, 106.0, 221.0]",usask_1
+dc9ac3f45,1024,1024,"[754.0, 350.0, 221.0, 86.0]",usask_1
+dc9ac3f45,1024,1024,"[0.0, 0.0, 69.0, 22.0]",usask_1
+dc9ac3f45,1024,1024,"[689.0, 826.0, 102.0, 64.0]",usask_1
+dc9ac3f45,1024,1024,"[852.0, 824.0, 81.0, 93.0]",usask_1
+dc9ac3f45,1024,1024,"[891.0, 758.0, 128.0, 73.0]",usask_1
+dc9ac3f45,1024,1024,"[445.0, 83.0, 55.0, 74.0]",usask_1
+dc9ac3f45,1024,1024,"[342.0, 951.0, 50.0, 40.0]",usask_1
+dc9ac3f45,1024,1024,"[254.0, 134.0, 56.0, 83.0]",usask_1
+dc9ac3f45,1024,1024,"[907.0, 0.0, 68.0, 43.0]",usask_1
+dc9ac3f45,1024,1024,"[275.0, 0.0, 69.0, 22.0]",usask_1
+e081e431a,1024,1024,"[602.0, 365.0, 87.0, 135.0]",usask_1
+e081e431a,1024,1024,"[802.0, 20.0, 78.0, 65.0]",usask_1
+e081e431a,1024,1024,"[550.0, 807.0, 84.0, 103.0]",usask_1
+e081e431a,1024,1024,"[719.0, 961.0, 94.0, 62.0]",usask_1
+e081e431a,1024,1024,"[124.0, 278.0, 76.0, 74.0]",usask_1
+e081e431a,1024,1024,"[590.0, 944.0, 91.0, 73.0]",usask_1
+e081e431a,1024,1024,"[687.0, 756.0, 87.0, 116.0]",usask_1
+e081e431a,1024,1024,"[789.0, 385.0, 80.0, 137.0]",usask_1
+e081e431a,1024,1024,"[566.0, 475.0, 81.0, 81.0]",usask_1
+e081e431a,1024,1024,"[610.0, 213.0, 117.0, 63.0]",usask_1
+e081e431a,1024,1024,"[609.0, 104.0, 130.0, 103.0]",usask_1
+e081e431a,1024,1024,"[202.0, 0.0, 140.0, 72.0]",usask_1
+e081e431a,1024,1024,"[28.0, 65.0, 62.0, 94.0]",usask_1
+e081e431a,1024,1024,"[0.0, 145.0, 63.0, 76.0]",usask_1
+e081e431a,1024,1024,"[732.0, 471.0, 84.0, 154.0]",usask_1
+e081e431a,1024,1024,"[350.0, 791.0, 186.0, 136.0]",usask_1
+e081e431a,1024,1024,"[266.0, 948.0, 89.0, 76.0]",usask_1
+e081e431a,1024,1024,"[674.0, 873.0, 89.0, 88.0]",usask_1
+e081e431a,1024,1024,"[64.0, 522.0, 115.0, 102.0]",usask_1
+e081e431a,1024,1024,"[118.0, 344.0, 109.0, 125.0]",usask_1
+e081e431a,1024,1024,"[47.0, 309.0, 76.0, 137.0]",usask_1
+e081e431a,1024,1024,"[729.0, 11.0, 87.0, 70.0]",usask_1
+e081e431a,1024,1024,"[572.0, 26.0, 130.0, 114.0]",usask_1
+e081e431a,1024,1024,"[0.0, 929.0, 79.0, 95.0]",usask_1
+e081e431a,1024,1024,"[851.0, 706.0, 146.0, 114.0]",usask_1
+e081e431a,1024,1024,"[306.0, 30.0, 103.0, 207.0]",usask_1
+e081e431a,1024,1024,"[236.0, 461.0, 110.0, 210.0]",usask_1
+e081e431a,1024,1024,"[858.0, 721.0, 110.0, 136.0]",usask_1
+e081e431a,1024,1024,"[817.0, 800.0, 175.0, 144.0]",usask_1
+e081e431a,1024,1024,"[821.0, 852.0, 165.0, 172.0]",usask_1
+e081e431a,1024,1024,"[118.0, 840.0, 60.0, 99.0]",usask_1
+e081e431a,1024,1024,"[700.0, 370.0, 54.0, 96.0]",usask_1
+e081e431a,1024,1024,"[820.0, 196.0, 82.0, 162.0]",usask_1
+e081e431a,1024,1024,"[845.0, 176.0, 112.0, 192.0]",usask_1
+e081e431a,1024,1024,"[897.0, 102.0, 127.0, 75.0]",usask_1
+e081e431a,1024,1024,"[839.0, 83.0, 74.0, 108.0]",usask_1
+e081e431a,1024,1024,"[785.0, 169.0, 77.0, 77.0]",usask_1
+e081e431a,1024,1024,"[704.0, 316.0, 80.0, 61.0]",usask_1
+e081e431a,1024,1024,"[983.0, 337.0, 41.0, 71.0]",usask_1
+e081e431a,1024,1024,"[0.0, 75.0, 47.0, 61.0]",usask_1
+e081e431a,1024,1024,"[735.0, 158.0, 55.0, 65.0]",usask_1
+5343c02b0,1024,1024,"[0.0, 794.0, 79.0, 107.0]",usask_1
+5343c02b0,1024,1024,"[118.0, 88.0, 77.0, 55.0]",usask_1
+5343c02b0,1024,1024,"[935.0, 75.0, 79.0, 59.0]",usask_1
+5343c02b0,1024,1024,"[84.0, 646.0, 87.0, 131.0]",usask_1
+5343c02b0,1024,1024,"[528.0, 0.0, 73.0, 58.0]",usask_1
+5343c02b0,1024,1024,"[60.0, 0.0, 85.0, 75.0]",usask_1
+5343c02b0,1024,1024,"[904.0, 102.0, 80.0, 59.0]",usask_1
+5343c02b0,1024,1024,"[813.0, 0.0, 79.0, 93.0]",usask_1
+5343c02b0,1024,1024,"[539.0, 942.0, 128.0, 79.0]",usask_1
+5343c02b0,1024,1024,"[829.0, 879.0, 115.0, 72.0]",usask_1
+5343c02b0,1024,1024,"[931.0, 36.0, 65.0, 66.0]",usask_1
+5343c02b0,1024,1024,"[221.0, 947.0, 63.0, 77.0]",usask_1
+5343c02b0,1024,1024,"[357.0, 972.0, 146.0, 52.0]",usask_1
+5343c02b0,1024,1024,"[370.0, 789.0, 178.0, 124.0]",usask_1
+5343c02b0,1024,1024,"[159.0, 932.0, 57.0, 84.0]",usask_1
+5343c02b0,1024,1024,"[872.0, 942.0, 152.0, 80.0]",usask_1
+5343c02b0,1024,1024,"[188.0, 404.0, 191.0, 211.0]",usask_1
+5343c02b0,1024,1024,"[729.0, 115.0, 86.0, 79.0]",usask_1
+5343c02b0,1024,1024,"[107.0, 811.0, 114.0, 95.0]",usask_1
+5343c02b0,1024,1024,"[64.0, 844.0, 82.0, 105.0]",usask_1
+5343c02b0,1024,1024,"[649.0, 935.0, 120.0, 89.0]",usask_1
+5343c02b0,1024,1024,"[71.0, 918.0, 66.0, 95.0]",usask_1
+5343c02b0,1024,1024,"[97.0, 332.0, 85.0, 58.0]",usask_1
+5343c02b0,1024,1024,"[126.0, 624.0, 74.0, 76.0]",usask_1
+5343c02b0,1024,1024,"[607.0, 195.0, 75.0, 48.0]",usask_1
+5343c02b0,1024,1024,"[985.0, 127.0, 39.0, 67.0]",usask_1
+5343c02b0,1024,1024,"[129.0, 111.0, 84.0, 64.0]",usask_1
+5343c02b0,1024,1024,"[902.0, 23.0, 62.0, 64.0]",usask_1
+5343c02b0,1024,1024,"[467.0, 0.0, 83.0, 29.0]",usask_1
+ad0d224d6,1024,1024,"[747.0, 612.0, 103.0, 109.0]",usask_1
+ad0d224d6,1024,1024,"[626.0, 755.0, 86.0, 89.0]",usask_1
+ad0d224d6,1024,1024,"[868.0, 733.0, 67.0, 56.0]",usask_1
+ad0d224d6,1024,1024,"[726.0, 765.0, 93.0, 129.0]",usask_1
+ad0d224d6,1024,1024,"[523.0, 856.0, 115.0, 143.0]",usask_1
+ad0d224d6,1024,1024,"[628.0, 928.0, 94.0, 67.0]",usask_1
+ad0d224d6,1024,1024,"[665.0, 274.0, 87.0, 109.0]",usask_1
+ad0d224d6,1024,1024,"[508.0, 352.0, 78.0, 163.0]",usask_1
+ad0d224d6,1024,1024,"[755.0, 946.0, 140.0, 66.0]",usask_1
+ad0d224d6,1024,1024,"[537.0, 969.0, 133.0, 55.0]",usask_1
+ad0d224d6,1024,1024,"[487.0, 881.0, 69.0, 82.0]",usask_1
+ad0d224d6,1024,1024,"[816.0, 690.0, 54.0, 113.0]",usask_1
+ad0d224d6,1024,1024,"[674.0, 673.0, 77.0, 93.0]",usask_1
+ad0d224d6,1024,1024,"[525.0, 564.0, 113.0, 82.0]",usask_1
+ad0d224d6,1024,1024,"[316.0, 809.0, 106.0, 95.0]",usask_1
+ad0d224d6,1024,1024,"[579.0, 0.0, 89.0, 45.0]",usask_1
+ad0d224d6,1024,1024,"[363.0, 483.0, 88.0, 125.0]",usask_1
+ad0d224d6,1024,1024,"[375.0, 780.0, 94.0, 101.0]",usask_1
+ad0d224d6,1024,1024,"[169.0, 36.0, 171.0, 90.0]",usask_1
+ad0d224d6,1024,1024,"[0.0, 458.0, 89.0, 187.0]",usask_1
+ad0d224d6,1024,1024,"[121.0, 735.0, 126.0, 85.0]",usask_1
+ad0d224d6,1024,1024,"[941.0, 175.0, 83.0, 93.0]",usask_1
+ad0d224d6,1024,1024,"[567.0, 427.0, 109.0, 83.0]",usask_1
+ad0d224d6,1024,1024,"[121.0, 192.0, 227.0, 109.0]",usask_1
+ad0d224d6,1024,1024,"[472.0, 509.0, 110.0, 94.0]",usask_1
+ad0d224d6,1024,1024,"[305.0, 398.0, 86.0, 76.0]",usask_1
+ad0d224d6,1024,1024,"[73.0, 634.0, 127.0, 87.0]",usask_1
+ad0d224d6,1024,1024,"[649.0, 224.0, 60.0, 67.0]",usask_1
+ad0d224d6,1024,1024,"[584.0, 231.0, 67.0, 102.0]",usask_1
+ad0d224d6,1024,1024,"[531.0, 213.0, 48.0, 82.0]",usask_1
+ad0d224d6,1024,1024,"[459.0, 162.0, 82.0, 105.0]",usask_1
+ad0d224d6,1024,1024,"[668.0, 19.0, 86.0, 75.0]",usask_1
+ad0d224d6,1024,1024,"[5.0, 5.0, 88.0, 72.0]",usask_1
+ad0d224d6,1024,1024,"[174.0, 4.0, 98.0, 79.0]",usask_1
+ad0d224d6,1024,1024,"[260.0, 389.0, 66.0, 62.0]",usask_1
+91fefb8e4,1024,1024,"[983.0, 771.0, 41.0, 88.0]",usask_1
+91fefb8e4,1024,1024,"[930.0, 976.0, 90.0, 48.0]",usask_1
+91fefb8e4,1024,1024,"[506.0, 595.0, 99.0, 107.0]",usask_1
+91fefb8e4,1024,1024,"[245.0, 958.0, 135.0, 66.0]",usask_1
+91fefb8e4,1024,1024,"[980.0, 0.0, 44.0, 69.0]",usask_1
+91fefb8e4,1024,1024,"[329.0, 816.0, 105.0, 71.0]",usask_1
+91fefb8e4,1024,1024,"[274.0, 617.0, 161.0, 100.0]",usask_1
+91fefb8e4,1024,1024,"[47.0, 885.0, 161.0, 116.0]",usask_1
+91fefb8e4,1024,1024,"[252.0, 493.0, 77.0, 68.0]",usask_1
+91fefb8e4,1024,1024,"[867.0, 372.0, 79.0, 155.0]",usask_1
+91fefb8e4,1024,1024,"[550.0, 952.0, 175.0, 64.0]",usask_1
+91fefb8e4,1024,1024,"[410.0, 802.0, 85.0, 69.0]",usask_1
+91fefb8e4,1024,1024,"[352.0, 929.0, 120.0, 79.0]",usask_1
+91fefb8e4,1024,1024,"[589.0, 1007.0, 180.0, 17.0]",usask_1
+91fefb8e4,1024,1024,"[385.0, 267.0, 114.0, 190.0]",usask_1
+91fefb8e4,1024,1024,"[537.0, 363.0, 71.0, 80.0]",usask_1
+57393e4e9,1024,1024,"[913.0, 317.0, 111.0, 77.0]",usask_1
+57393e4e9,1024,1024,"[320.0, 828.0, 82.0, 121.0]",usask_1
+57393e4e9,1024,1024,"[384.0, 431.0, 160.0, 113.0]",usask_1
+57393e4e9,1024,1024,"[400.0, 705.0, 151.0, 146.0]",usask_1
+57393e4e9,1024,1024,"[956.0, 223.0, 68.0, 76.0]",usask_1
+57393e4e9,1024,1024,"[863.0, 18.0, 161.0, 134.0]",usask_1
+57393e4e9,1024,1024,"[0.0, 212.0, 133.0, 110.0]",usask_1
+57393e4e9,1024,1024,"[273.0, 0.0, 242.0, 65.0]",usask_1
+57393e4e9,1024,1024,"[643.0, 818.0, 87.0, 175.0]",usask_1
+57393e4e9,1024,1024,"[929.0, 0.0, 95.0, 59.0]",usask_1
+57393e4e9,1024,1024,"[571.0, 582.0, 118.0, 93.0]",usask_1
+57393e4e9,1024,1024,"[937.0, 430.0, 87.0, 159.0]",usask_1
+57393e4e9,1024,1024,"[212.0, 959.0, 124.0, 65.0]",usask_1
+57393e4e9,1024,1024,"[112.0, 615.0, 156.0, 148.0]",usask_1
+57393e4e9,1024,1024,"[573.0, 694.0, 120.0, 50.0]",usask_1
+b7fc05af8,1024,1024,"[242.0, 202.0, 243.0, 85.0]",usask_1
+b7fc05af8,1024,1024,"[378.0, 14.0, 231.0, 95.0]",usask_1
+b7fc05af8,1024,1024,"[865.0, 397.0, 159.0, 113.0]",usask_1
+b7fc05af8,1024,1024,"[402.0, 467.0, 183.0, 87.0]",usask_1
+b7fc05af8,1024,1024,"[225.0, 830.0, 189.0, 91.0]",usask_1
+b7fc05af8,1024,1024,"[13.0, 318.0, 187.0, 102.0]",usask_1
+b7fc05af8,1024,1024,"[250.0, 0.0, 155.0, 61.0]",usask_1
+b7fc05af8,1024,1024,"[686.0, 251.0, 299.0, 90.0]",usask_1
+b7fc05af8,1024,1024,"[656.0, 441.0, 251.0, 99.0]",usask_1
+b7fc05af8,1024,1024,"[970.0, 225.0, 54.0, 84.0]",usask_1
+b7fc05af8,1024,1024,"[117.0, 179.0, 295.0, 67.0]",usask_1
+b7fc05af8,1024,1024,"[0.0, 606.0, 99.0, 106.0]",usask_1
+b7fc05af8,1024,1024,"[37.0, 680.0, 106.0, 96.0]",usask_1
+b7fc05af8,1024,1024,"[515.0, 832.0, 127.0, 192.0]",usask_1
+b7fc05af8,1024,1024,"[98.0, 219.0, 234.0, 94.0]",usask_1
+b7fc05af8,1024,1024,"[445.0, 362.0, 115.0, 53.0]",usask_1
+b7fc05af8,1024,1024,"[709.0, 278.0, 300.0, 157.0]",usask_1
+c8851847c,1024,1024,"[845.0, 683.0, 133.0, 131.0]",usask_1
+c8851847c,1024,1024,"[87.0, 297.0, 66.0, 62.0]",usask_1
+c8851847c,1024,1024,"[313.0, 99.0, 92.0, 73.0]",usask_1
+c8851847c,1024,1024,"[59.0, 914.0, 69.0, 62.0]",usask_1
+c8851847c,1024,1024,"[184.0, 733.0, 100.0, 83.0]",usask_1
+c8851847c,1024,1024,"[488.0, 622.0, 144.0, 84.0]",usask_1
+c8851847c,1024,1024,"[370.0, 485.0, 221.0, 143.0]",usask_1
+c8851847c,1024,1024,"[522.0, 330.0, 174.0, 170.0]",usask_1
+c8851847c,1024,1024,"[0.0, 231.0, 75.0, 111.0]",usask_1
+c8851847c,1024,1024,"[163.0, 142.0, 172.0, 87.0]",usask_1
+c8851847c,1024,1024,"[0.0, 464.0, 55.0, 103.0]",usask_1
+c8851847c,1024,1024,"[829.0, 886.0, 114.0, 90.0]",usask_1
+c8851847c,1024,1024,"[319.0, 188.0, 160.0, 122.0]",usask_1
+c8851847c,1024,1024,"[649.0, 964.0, 130.0, 60.0]",usask_1
+c8851847c,1024,1024,"[279.0, 762.0, 131.0, 72.0]",usask_1
+c8851847c,1024,1024,"[346.0, 983.0, 99.0, 41.0]",usask_1
+c8851847c,1024,1024,"[154.0, 342.0, 183.0, 87.0]",usask_1
+c8851847c,1024,1024,"[70.0, 724.0, 120.0, 120.0]",usask_1
+c8851847c,1024,1024,"[234.0, 306.0, 90.0, 59.0]",usask_1
+c8851847c,1024,1024,"[307.0, 732.0, 175.0, 63.0]",usask_1
+c8851847c,1024,1024,"[0.0, 867.0, 57.0, 77.0]",usask_1
+c8851847c,1024,1024,"[950.0, 902.0, 74.0, 65.0]",usask_1
+c8851847c,1024,1024,"[992.0, 859.0, 32.0, 42.0]",usask_1
+c8851847c,1024,1024,"[892.0, 888.0, 131.0, 135.0]",usask_1
+c8851847c,1024,1024,"[997.0, 676.0, 27.0, 90.0]",usask_1
+c8851847c,1024,1024,"[590.0, 750.0, 62.0, 92.0]",usask_1
+c8851847c,1024,1024,"[644.0, 715.0, 79.0, 61.0]",usask_1
+c8851847c,1024,1024,"[76.0, 959.0, 71.0, 65.0]",usask_1
+c8851847c,1024,1024,"[77.0, 247.0, 74.0, 62.0]",usask_1
+04b7f3a15,1024,1024,"[138.0, 119.0, 153.0, 101.0]",usask_1
+04b7f3a15,1024,1024,"[492.0, 238.0, 92.0, 82.0]",usask_1
+04b7f3a15,1024,1024,"[373.0, 835.0, 123.0, 110.0]",usask_1
+04b7f3a15,1024,1024,"[872.0, 94.0, 100.0, 93.0]",usask_1
+04b7f3a15,1024,1024,"[68.0, 552.0, 159.0, 191.0]",usask_1
+04b7f3a15,1024,1024,"[259.0, 875.0, 123.0, 113.0]",usask_1
+04b7f3a15,1024,1024,"[385.0, 764.0, 143.0, 99.0]",usask_1
+04b7f3a15,1024,1024,"[474.0, 896.0, 97.0, 103.0]",usask_1
+04b7f3a15,1024,1024,"[903.0, 315.0, 115.0, 209.0]",usask_1
+04b7f3a15,1024,1024,"[0.0, 26.0, 97.0, 238.0]",usask_1
+04b7f3a15,1024,1024,"[10.0, 370.0, 237.0, 106.0]",usask_1
+04b7f3a15,1024,1024,"[0.0, 209.0, 64.0, 88.0]",usask_1
+04b7f3a15,1024,1024,"[353.0, 996.0, 122.0, 28.0]",usask_1
+04b7f3a15,1024,1024,"[81.0, 448.0, 156.0, 174.0]",usask_1
+04b7f3a15,1024,1024,"[589.0, 939.0, 127.0, 85.0]",usask_1
+04b7f3a15,1024,1024,"[264.0, 966.0, 101.0, 58.0]",usask_1
+04b7f3a15,1024,1024,"[791.0, 485.0, 191.0, 114.0]",usask_1
+04b7f3a15,1024,1024,"[704.0, 719.0, 141.0, 188.0]",usask_1
+04b7f3a15,1024,1024,"[451.0, 125.0, 122.0, 137.0]",usask_1
+04b7f3a15,1024,1024,"[778.0, 257.0, 172.0, 150.0]",usask_1
+04b7f3a15,1024,1024,"[441.0, 582.0, 108.0, 157.0]",usask_1
+04b7f3a15,1024,1024,"[58.0, 61.0, 194.0, 81.0]",usask_1
+04b7f3a15,1024,1024,"[989.0, 262.0, 35.0, 185.0]",usask_1
+04b7f3a15,1024,1024,"[576.0, 562.0, 133.0, 187.0]",usask_1
+04b7f3a15,1024,1024,"[176.0, 997.0, 108.0, 27.0]",usask_1
+04b7f3a15,1024,1024,"[28.0, 955.0, 204.0, 69.0]",usask_1
+04b7f3a15,1024,1024,"[89.0, 187.0, 204.0, 146.0]",usask_1
+04b7f3a15,1024,1024,"[104.0, 250.0, 218.0, 88.0]",usask_1
+04b7f3a15,1024,1024,"[154.0, 305.0, 173.0, 106.0]",usask_1
+e596e6bb2,1024,1024,"[395.0, 490.0, 163.0, 118.0]",usask_1
+e596e6bb2,1024,1024,"[300.0, 685.0, 145.0, 91.0]",usask_1
+e596e6bb2,1024,1024,"[983.0, 175.0, 41.0, 109.0]",usask_1
+e596e6bb2,1024,1024,"[140.0, 468.0, 277.0, 109.0]",usask_1
+e596e6bb2,1024,1024,"[476.0, 984.0, 183.0, 40.0]",usask_1
+e596e6bb2,1024,1024,"[414.0, 175.0, 126.0, 118.0]",usask_1
+e596e6bb2,1024,1024,"[0.0, 867.0, 234.0, 156.0]",usask_1
+e596e6bb2,1024,1024,"[0.0, 0.0, 225.0, 97.0]",usask_1
+e596e6bb2,1024,1024,"[0.0, 208.0, 306.0, 265.0]",usask_1
+e596e6bb2,1024,1024,"[301.0, 785.0, 247.0, 168.0]",usask_1
+e596e6bb2,1024,1024,"[940.0, 366.0, 84.0, 114.0]",usask_1
+e596e6bb2,1024,1024,"[696.0, 610.0, 123.0, 84.0]",usask_1
+e596e6bb2,1024,1024,"[0.0, 976.0, 127.0, 48.0]",usask_1
+e596e6bb2,1024,1024,"[266.0, 678.0, 214.0, 72.0]",usask_1
+e596e6bb2,1024,1024,"[0.0, 348.0, 253.0, 141.0]",usask_1
+e596e6bb2,1024,1024,"[158.0, 242.0, 266.0, 210.0]",usask_1
+e596e6bb2,1024,1024,"[500.0, 156.0, 177.0, 299.0]",usask_1
+e596e6bb2,1024,1024,"[619.0, 835.0, 169.0, 162.0]",usask_1
+e596e6bb2,1024,1024,"[88.0, 820.0, 223.0, 199.0]",usask_1
+e596e6bb2,1024,1024,"[516.0, 929.0, 155.0, 79.0]",usask_1
+0a4408b37,1024,1024,"[955.0, 43.0, 69.0, 80.0]",usask_1
+0a4408b37,1024,1024,"[80.0, 0.0, 93.0, 59.0]",usask_1
+0a4408b37,1024,1024,"[516.0, 551.0, 70.0, 56.0]",usask_1
+0a4408b37,1024,1024,"[370.0, 741.0, 86.0, 86.0]",usask_1
+0a4408b37,1024,1024,"[811.0, 90.0, 73.0, 117.0]",usask_1
+0a4408b37,1024,1024,"[670.0, 407.0, 97.0, 73.0]",usask_1
+0a4408b37,1024,1024,"[286.0, 541.0, 91.0, 81.0]",usask_1
+0a4408b37,1024,1024,"[286.0, 181.0, 93.0, 114.0]",usask_1
+0a4408b37,1024,1024,"[324.0, 467.0, 86.0, 76.0]",usask_1
+0a4408b37,1024,1024,"[519.0, 36.0, 132.0, 104.0]",usask_1
+0a4408b37,1024,1024,"[0.0, 607.0, 87.0, 67.0]",usask_1
+0a4408b37,1024,1024,"[136.0, 215.0, 72.0, 67.0]",usask_1
+0a4408b37,1024,1024,"[436.0, 669.0, 84.0, 93.0]",usask_1
+0a4408b37,1024,1024,"[569.0, 458.0, 99.0, 123.0]",usask_1
+0a4408b37,1024,1024,"[269.0, 262.0, 90.0, 93.0]",usask_1
+0a4408b37,1024,1024,"[101.0, 175.0, 128.0, 92.0]",usask_1
+0a4408b37,1024,1024,"[154.0, 0.0, 99.0, 72.0]",usask_1
+0a4408b37,1024,1024,"[346.0, 118.0, 66.0, 68.0]",usask_1
+0a4408b37,1024,1024,"[292.0, 637.0, 93.0, 80.0]",usask_1
+0a4408b37,1024,1024,"[358.0, 0.0, 100.0, 87.0]",usask_1
+0a4408b37,1024,1024,"[410.0, 119.0, 78.0, 83.0]",usask_1
+0a4408b37,1024,1024,"[214.0, 787.0, 117.0, 81.0]",usask_1
+0a4408b37,1024,1024,"[276.0, 922.0, 89.0, 102.0]",usask_1
+0a4408b37,1024,1024,"[0.0, 738.0, 52.0, 93.0]",usask_1
+0a4408b37,1024,1024,"[77.0, 768.0, 106.0, 96.0]",usask_1
+0a4408b37,1024,1024,"[1.0, 579.0, 100.0, 76.0]",usask_1
+0a4408b37,1024,1024,"[772.0, 358.0, 86.0, 102.0]",usask_1
+0a4408b37,1024,1024,"[940.0, 171.0, 83.0, 70.0]",usask_1
+0a4408b37,1024,1024,"[990.0, 358.0, 34.0, 98.0]",usask_1
+0a4408b37,1024,1024,"[536.0, 116.0, 132.0, 64.0]",usask_1
+0a4408b37,1024,1024,"[46.0, 289.0, 138.0, 82.0]",usask_1
+0a4408b37,1024,1024,"[191.0, 340.0, 62.0, 97.0]",usask_1
+0a4408b37,1024,1024,"[103.0, 352.0, 99.0, 74.0]",usask_1
+0a4408b37,1024,1024,"[293.0, 107.0, 72.0, 73.0]",usask_1
+0a4408b37,1024,1024,"[229.0, 75.0, 98.0, 80.0]",usask_1
+0594af5a7,1024,1024,"[831.0, 0.0, 76.0, 97.0]",usask_1
+0594af5a7,1024,1024,"[0.0, 407.0, 76.0, 70.0]",usask_1
+0594af5a7,1024,1024,"[456.0, 785.0, 72.0, 168.0]",usask_1
+0594af5a7,1024,1024,"[769.0, 281.0, 108.0, 121.0]",usask_1
+0594af5a7,1024,1024,"[84.0, 862.0, 140.0, 103.0]",usask_1
+0594af5a7,1024,1024,"[307.0, 642.0, 117.0, 87.0]",usask_1
+0594af5a7,1024,1024,"[420.0, 759.0, 54.0, 81.0]",usask_1
+0594af5a7,1024,1024,"[169.0, 646.0, 140.0, 93.0]",usask_1
+0594af5a7,1024,1024,"[178.0, 569.0, 106.0, 84.0]",usask_1
+0594af5a7,1024,1024,"[445.0, 219.0, 65.0, 152.0]",usask_1
+0594af5a7,1024,1024,"[185.0, 464.0, 137.0, 114.0]",usask_1
+0594af5a7,1024,1024,"[446.0, 118.0, 63.0, 111.0]",usask_1
+0594af5a7,1024,1024,"[325.0, 538.0, 83.0, 63.0]",usask_1
+0594af5a7,1024,1024,"[880.0, 397.0, 108.0, 90.0]",usask_1
+0594af5a7,1024,1024,"[252.0, 924.0, 171.0, 100.0]",usask_1
+0594af5a7,1024,1024,"[136.0, 126.0, 142.0, 45.0]",usask_1
+0594af5a7,1024,1024,"[961.0, 184.0, 63.0, 75.0]",usask_1
+0594af5a7,1024,1024,"[299.0, 421.0, 103.0, 177.0]",usask_1
+0594af5a7,1024,1024,"[267.0, 248.0, 105.0, 97.0]",usask_1
+0594af5a7,1024,1024,"[243.0, 764.0, 170.0, 51.0]",usask_1
+0594af5a7,1024,1024,"[342.0, 741.0, 73.0, 69.0]",usask_1
+0594af5a7,1024,1024,"[262.0, 299.0, 135.0, 49.0]",usask_1
+0594af5a7,1024,1024,"[400.0, 400.0, 80.0, 43.0]",usask_1
+0594af5a7,1024,1024,"[384.0, 379.0, 80.0, 35.0]",usask_1
+0594af5a7,1024,1024,"[486.0, 198.0, 78.0, 66.0]",usask_1
+0594af5a7,1024,1024,"[167.0, 108.0, 125.0, 41.0]",usask_1
+0594af5a7,1024,1024,"[516.0, 172.0, 133.0, 103.0]",usask_1
+a90446700,1024,1024,"[552.0, 535.0, 99.0, 144.0]",usask_1
+a90446700,1024,1024,"[94.0, 235.0, 89.0, 164.0]",usask_1
+a90446700,1024,1024,"[527.0, 427.0, 162.0, 126.0]",usask_1
+a90446700,1024,1024,"[109.0, 583.0, 97.0, 56.0]",usask_1
+a90446700,1024,1024,"[498.0, 224.0, 149.0, 64.0]",usask_1
+a90446700,1024,1024,"[687.0, 936.0, 79.0, 88.0]",usask_1
+a90446700,1024,1024,"[700.0, 507.0, 67.0, 94.0]",usask_1
+a90446700,1024,1024,"[0.0, 0.0, 89.0, 92.0]",usask_1
+a90446700,1024,1024,"[370.0, 882.0, 96.0, 76.0]",usask_1
+a90446700,1024,1024,"[740.0, 206.0, 81.0, 72.0]",usask_1
+a90446700,1024,1024,"[435.0, 131.0, 148.0, 83.0]",usask_1
+a90446700,1024,1024,"[674.0, 345.0, 73.0, 147.0]",usask_1
+a90446700,1024,1024,"[629.0, 440.0, 85.0, 114.0]",usask_1
+a90446700,1024,1024,"[518.0, 900.0, 98.0, 82.0]",usask_1
+a90446700,1024,1024,"[686.0, 575.0, 73.0, 79.0]",usask_1
+a90446700,1024,1024,"[768.0, 990.0, 89.0, 34.0]",usask_1
+a90446700,1024,1024,"[659.0, 879.0, 75.0, 71.0]",usask_1
+a90446700,1024,1024,"[607.0, 517.0, 94.0, 58.0]",usask_1
+a90446700,1024,1024,"[698.0, 670.0, 87.0, 111.0]",usask_1
+a90446700,1024,1024,"[871.0, 868.0, 152.0, 127.0]",usask_1
+a90446700,1024,1024,"[46.0, 499.0, 115.0, 63.0]",usask_1
+a90446700,1024,1024,"[316.0, 935.0, 125.0, 89.0]",usask_1
+a90446700,1024,1024,"[647.0, 729.0, 107.0, 74.0]",usask_1
+a90446700,1024,1024,"[403.0, 791.0, 194.0, 148.0]",usask_1
+a90446700,1024,1024,"[721.0, 300.0, 71.0, 55.0]",usask_1
+a90446700,1024,1024,"[711.0, 337.0, 63.0, 77.0]",usask_1
+a90446700,1024,1024,"[191.0, 335.0, 86.0, 103.0]",usask_1
+a90446700,1024,1024,"[309.0, 501.0, 154.0, 134.0]",usask_1
+a90446700,1024,1024,"[142.0, 439.0, 89.0, 84.0]",usask_1
+a90446700,1024,1024,"[778.0, 851.0, 73.0, 131.0]",usask_1
+a90446700,1024,1024,"[826.0, 587.0, 74.0, 90.0]",usask_1
+a90446700,1024,1024,"[902.0, 528.0, 105.0, 115.0]",usask_1
+a90446700,1024,1024,"[586.0, 665.0, 106.0, 76.0]",usask_1
+a90446700,1024,1024,"[802.0, 726.0, 70.0, 113.0]",usask_1
+a90446700,1024,1024,"[855.0, 805.0, 96.0, 111.0]",usask_1
+a90446700,1024,1024,"[763.0, 618.0, 78.0, 149.0]",usask_1
+a90446700,1024,1024,"[882.0, 747.0, 105.0, 76.0]",usask_1
+a90446700,1024,1024,"[648.0, 787.0, 93.0, 102.0]",usask_1
+a90446700,1024,1024,"[540.0, 726.0, 140.0, 142.0]",usask_1
+a90446700,1024,1024,"[863.0, 684.0, 69.0, 52.0]",usask_1
+a90446700,1024,1024,"[441.0, 213.0, 145.0, 90.0]",usask_1
+a90446700,1024,1024,"[668.0, 204.0, 111.0, 133.0]",usask_1
+a90446700,1024,1024,"[595.0, 157.0, 89.0, 82.0]",usask_1
+a90446700,1024,1024,"[626.0, 132.0, 86.0, 87.0]",usask_1
+a90446700,1024,1024,"[675.0, 0.0, 80.0, 127.0]",usask_1
+a90446700,1024,1024,"[554.0, 41.0, 165.0, 104.0]",usask_1
+a90446700,1024,1024,"[851.0, 0.0, 58.0, 37.0]",usask_1
+a90446700,1024,1024,"[952.0, 740.0, 53.0, 42.0]",usask_1
+a90446700,1024,1024,"[925.0, 689.0, 79.0, 72.0]",usask_1
+a90446700,1024,1024,"[742.0, 780.0, 59.0, 88.0]",usask_1
+286af845e,1024,1024,"[297.0, 307.0, 257.0, 75.0]",usask_1
+286af845e,1024,1024,"[838.0, 69.0, 146.0, 120.0]",usask_1
+286af845e,1024,1024,"[0.0, 367.0, 66.0, 109.0]",usask_1
+286af845e,1024,1024,"[520.0, 106.0, 261.0, 129.0]",usask_1
+286af845e,1024,1024,"[469.0, 351.0, 196.0, 94.0]",usask_1
+286af845e,1024,1024,"[119.0, 492.0, 138.0, 83.0]",usask_1
+286af845e,1024,1024,"[695.0, 53.0, 88.0, 75.0]",usask_1
+286af845e,1024,1024,"[76.0, 937.0, 123.0, 87.0]",usask_1
+286af845e,1024,1024,"[7.0, 806.0, 171.0, 95.0]",usask_1
+286af845e,1024,1024,"[972.0, 406.0, 52.0, 79.0]",usask_1
+286af845e,1024,1024,"[89.0, 233.0, 94.0, 139.0]",usask_1
+286af845e,1024,1024,"[501.0, 576.0, 74.0, 85.0]",usask_1
+286af845e,1024,1024,"[883.0, 29.0, 134.0, 113.0]",usask_1
+286af845e,1024,1024,"[767.0, 32.0, 142.0, 75.0]",usask_1
+286af845e,1024,1024,"[694.0, 261.0, 109.0, 161.0]",usask_1
+286af845e,1024,1024,"[0.0, 720.0, 240.0, 61.0]",usask_1
+286af845e,1024,1024,"[570.0, 501.0, 234.0, 141.0]",usask_1
+286af845e,1024,1024,"[218.0, 358.0, 67.0, 123.0]",usask_1
+286af845e,1024,1024,"[705.0, 405.0, 69.0, 76.0]",usask_1
+286af845e,1024,1024,"[428.0, 132.0, 154.0, 62.0]",usask_1
+286af845e,1024,1024,"[590.0, 12.0, 95.0, 118.0]",usask_1
+286af845e,1024,1024,"[474.0, 0.0, 101.0, 103.0]",usask_1
+2d07aba59,1024,1024,"[257.0, 970.0, 131.0, 53.0]",usask_1
+2d07aba59,1024,1024,"[391.0, 982.0, 66.0, 42.0]",usask_1
+2d07aba59,1024,1024,"[556.0, 291.0, 66.0, 72.0]",usask_1
+2d07aba59,1024,1024,"[478.0, 259.0, 76.0, 100.0]",usask_1
+2d07aba59,1024,1024,"[91.0, 955.0, 45.0, 68.0]",usask_1
+2d07aba59,1024,1024,"[24.0, 364.0, 117.0, 61.0]",usask_1
+2d07aba59,1024,1024,"[511.0, 464.0, 73.0, 99.0]",usask_1
+2d07aba59,1024,1024,"[476.0, 984.0, 55.0, 40.0]",usask_1
+2d07aba59,1024,1024,"[907.0, 54.0, 117.0, 60.0]",usask_1
+2d07aba59,1024,1024,"[0.0, 476.0, 84.0, 56.0]",usask_1
+2d07aba59,1024,1024,"[612.0, 174.0, 106.0, 136.0]",usask_1
+2d07aba59,1024,1024,"[250.0, 200.0, 106.0, 53.0]",usask_1
+2d07aba59,1024,1024,"[388.0, 129.0, 79.0, 70.0]",usask_1
+2d07aba59,1024,1024,"[613.0, 283.0, 133.0, 99.0]",usask_1
+2d07aba59,1024,1024,"[22.0, 415.0, 107.0, 88.0]",usask_1
+2d07aba59,1024,1024,"[426.0, 226.0, 71.0, 122.0]",usask_1
+2d07aba59,1024,1024,"[633.0, 629.0, 102.0, 46.0]",usask_1
+2d07aba59,1024,1024,"[342.0, 549.0, 53.0, 58.0]",usask_1
+2d07aba59,1024,1024,"[609.0, 760.0, 138.0, 96.0]",usask_1
+2d07aba59,1024,1024,"[768.0, 235.0, 184.0, 65.0]",usask_1
+2d07aba59,1024,1024,"[395.0, 738.0, 31.0, 65.0]",usask_1
+2d07aba59,1024,1024,"[591.0, 476.0, 69.0, 90.0]",usask_1
+2d07aba59,1024,1024,"[398.0, 669.0, 87.0, 98.0]",usask_1
+2d07aba59,1024,1024,"[159.0, 463.0, 143.0, 50.0]",usask_1
+2d07aba59,1024,1024,"[116.0, 0.0, 91.0, 57.0]",usask_1
+2d07aba59,1024,1024,"[663.0, 75.0, 59.0, 48.0]",usask_1
+2d07aba59,1024,1024,"[553.0, 79.0, 85.0, 57.0]",usask_1
+2d07aba59,1024,1024,"[527.0, 122.0, 61.0, 92.0]",usask_1
+2d07aba59,1024,1024,"[474.0, 57.0, 70.0, 121.0]",usask_1
+2d07aba59,1024,1024,"[657.0, 16.0, 121.0, 61.0]",usask_1
+2d07aba59,1024,1024,"[540.0, 3.0, 95.0, 74.0]",usask_1
+1e9ff110c,1024,1024,"[0.0, 352.0, 70.0, 95.0]",usask_1
+1e9ff110c,1024,1024,"[173.0, 0.0, 159.0, 39.0]",usask_1
+1e9ff110c,1024,1024,"[104.0, 293.0, 219.0, 217.0]",usask_1
+1e9ff110c,1024,1024,"[677.0, 122.0, 168.0, 186.0]",usask_1
+1e9ff110c,1024,1024,"[390.0, 340.0, 120.0, 86.0]",usask_1
+1e9ff110c,1024,1024,"[171.0, 170.0, 202.0, 63.0]",usask_1
+1e9ff110c,1024,1024,"[865.0, 502.0, 80.0, 53.0]",usask_1
+1e9ff110c,1024,1024,"[640.0, 293.0, 160.0, 136.0]",usask_1
+1e9ff110c,1024,1024,"[373.0, 0.0, 147.0, 2.0]",usask_1
+1e9ff110c,1024,1024,"[114.0, 0.0, 255.0, 2.0]",usask_1
+1e9ff110c,1024,1024,"[84.0, 835.0, 231.0, 116.0]",usask_1
+1e9ff110c,1024,1024,"[532.0, 76.0, 219.0, 144.0]",usask_1
+1e9ff110c,1024,1024,"[697.0, 0.0, 174.0, 72.0]",usask_1
+1e9ff110c,1024,1024,"[274.0, 122.0, 128.0, 67.0]",usask_1
+1e9ff110c,1024,1024,"[301.0, 240.0, 94.0, 41.0]",usask_1
+1e9ff110c,1024,1024,"[186.0, 267.0, 115.0, 62.0]",usask_1
+1e9ff110c,1024,1024,"[62.0, 376.0, 98.0, 114.0]",usask_1
+1e9ff110c,1024,1024,"[333.0, 797.0, 126.0, 85.0]",usask_1
+1e9ff110c,1024,1024,"[289.0, 717.0, 86.0, 63.0]",usask_1
+1e9ff110c,1024,1024,"[345.0, 464.0, 114.0, 191.0]",usask_1
+1e9ff110c,1024,1024,"[531.0, 595.0, 73.0, 57.0]",usask_1
+1e9ff110c,1024,1024,"[761.0, 500.0, 74.0, 73.0]",usask_1
+1e9ff110c,1024,1024,"[840.0, 433.0, 65.0, 63.0]",usask_1
+1e9ff110c,1024,1024,"[459.0, 212.0, 99.0, 95.0]",usask_1
+1e9ff110c,1024,1024,"[492.0, 236.0, 141.0, 123.0]",usask_1
+1e9ff110c,1024,1024,"[921.0, 486.0, 77.0, 86.0]",usask_1
+1e9ff110c,1024,1024,"[873.0, 534.0, 66.0, 57.0]",usask_1
+a93b2e119,1024,1024,"[0.0, 521.0, 245.0, 70.0]",usask_1
+a93b2e119,1024,1024,"[272.0, 100.0, 78.0, 125.0]",usask_1
+a93b2e119,1024,1024,"[705.0, 596.0, 165.0, 160.0]",usask_1
+a93b2e119,1024,1024,"[235.0, 262.0, 80.0, 134.0]",usask_1
+a93b2e119,1024,1024,"[418.0, 681.0, 245.0, 79.0]",usask_1
+a93b2e119,1024,1024,"[371.0, 312.0, 73.0, 245.0]",usask_1
+a93b2e119,1024,1024,"[306.0, 770.0, 77.0, 114.0]",usask_1
+a93b2e119,1024,1024,"[471.0, 67.0, 157.0, 96.0]",usask_1
+a93b2e119,1024,1024,"[635.0, 5.0, 96.0, 109.0]",usask_1
+a93b2e119,1024,1024,"[663.0, 653.0, 107.0, 160.0]",usask_1
+a93b2e119,1024,1024,"[41.0, 932.0, 85.0, 71.0]",usask_1
+a93b2e119,1024,1024,"[963.0, 504.0, 61.0, 88.0]",usask_1
+a93b2e119,1024,1024,"[734.0, 451.0, 137.0, 79.0]",usask_1
+a93b2e119,1024,1024,"[786.0, 611.0, 127.0, 328.0]",usask_1
+a93b2e119,1024,1024,"[347.0, 764.0, 104.0, 186.0]",usask_1
+a93b2e119,1024,1024,"[377.0, 151.0, 106.0, 116.0]",usask_1
+a93b2e119,1024,1024,"[515.0, 228.0, 107.0, 111.0]",usask_1
+a93b2e119,1024,1024,"[804.0, 334.0, 86.0, 121.0]",usask_1
+a93b2e119,1024,1024,"[681.0, 283.0, 225.0, 124.0]",usask_1
+a93b2e119,1024,1024,"[887.0, 136.0, 137.0, 182.0]",usask_1
+6a739d41b,1024,1024,"[524.0, 173.0, 62.0, 50.0]",usask_1
+6a739d41b,1024,1024,"[546.0, 98.0, 54.0, 58.0]",usask_1
+6a739d41b,1024,1024,"[910.0, 258.0, 114.0, 72.0]",usask_1
+6a739d41b,1024,1024,"[592.0, 365.0, 54.0, 37.0]",usask_1
+6a739d41b,1024,1024,"[627.0, 267.0, 52.0, 87.0]",usask_1
+6a739d41b,1024,1024,"[608.0, 111.0, 86.0, 73.0]",usask_1
+6a739d41b,1024,1024,"[975.0, 622.0, 49.0, 87.0]",usask_1
+6a739d41b,1024,1024,"[475.0, 204.0, 55.0, 96.0]",usask_1
+6a739d41b,1024,1024,"[382.0, 497.0, 48.0, 52.0]",usask_1
+6a739d41b,1024,1024,"[0.0, 15.0, 39.0, 81.0]",usask_1
+6a739d41b,1024,1024,"[30.0, 23.0, 86.0, 137.0]",usask_1
+6a739d41b,1024,1024,"[124.0, 77.0, 93.0, 103.0]",usask_1
+6a739d41b,1024,1024,"[515.0, 333.0, 73.0, 68.0]",usask_1
+6a739d41b,1024,1024,"[537.0, 225.0, 85.0, 67.0]",usask_1
+6a739d41b,1024,1024,"[361.0, 228.0, 72.0, 77.0]",usask_1
+6a739d41b,1024,1024,"[51.0, 357.0, 130.0, 83.0]",usask_1
+6a739d41b,1024,1024,"[437.0, 93.0, 63.0, 63.0]",usask_1
+6a739d41b,1024,1024,"[232.0, 119.0, 88.0, 88.0]",usask_1
+6a739d41b,1024,1024,"[492.0, 0.0, 65.0, 95.0]",usask_1
+6a739d41b,1024,1024,"[970.0, 310.0, 54.0, 89.0]",usask_1
+6a739d41b,1024,1024,"[924.0, 356.0, 43.0, 72.0]",usask_1
+6a739d41b,1024,1024,"[357.0, 655.0, 64.0, 55.0]",usask_1
+6a739d41b,1024,1024,"[405.0, 622.0, 43.0, 37.0]",usask_1
+6a739d41b,1024,1024,"[392.0, 585.0, 81.0, 48.0]",usask_1
+6a739d41b,1024,1024,"[569.0, 401.0, 161.0, 112.0]",usask_1
+6a739d41b,1024,1024,"[617.0, 503.0, 58.0, 114.0]",usask_1
+6a739d41b,1024,1024,"[602.0, 626.0, 70.0, 64.0]",usask_1
+6a739d41b,1024,1024,"[463.0, 655.0, 60.0, 62.0]",usask_1
+6a739d41b,1024,1024,"[467.0, 603.0, 68.0, 56.0]",usask_1
+6a739d41b,1024,1024,"[503.0, 634.0, 88.0, 83.0]",usask_1
+6a739d41b,1024,1024,"[407.0, 833.0, 60.0, 61.0]",usask_1
+6a739d41b,1024,1024,"[451.0, 868.0, 54.0, 71.0]",usask_1
+6a739d41b,1024,1024,"[515.0, 859.0, 129.0, 61.0]",usask_1
+6a739d41b,1024,1024,"[573.0, 925.0, 128.0, 75.0]",usask_1
+6a739d41b,1024,1024,"[663.0, 996.0, 101.0, 28.0]",usask_1
+6a739d41b,1024,1024,"[712.0, 937.0, 162.0, 87.0]",usask_1
+6a739d41b,1024,1024,"[387.0, 965.0, 56.0, 39.0]",usask_1
+6a739d41b,1024,1024,"[328.0, 278.0, 55.0, 64.0]",usask_1
+6a739d41b,1024,1024,"[422.0, 412.0, 78.0, 73.0]",usask_1
+6a739d41b,1024,1024,"[482.0, 476.0, 62.0, 72.0]",usask_1
+0874422e6,1024,1024,"[697.0, 611.0, 79.0, 92.0]",usask_1
+0874422e6,1024,1024,"[141.0, 0.0, 100.0, 49.0]",usask_1
+0874422e6,1024,1024,"[879.0, 236.0, 68.0, 109.0]",usask_1
+0874422e6,1024,1024,"[0.0, 323.0, 140.0, 84.0]",usask_1
+0874422e6,1024,1024,"[259.0, 860.0, 77.0, 49.0]",usask_1
+0874422e6,1024,1024,"[778.0, 623.0, 74.0, 53.0]",usask_1
+0874422e6,1024,1024,"[916.0, 78.0, 82.0, 73.0]",usask_1
+0874422e6,1024,1024,"[337.0, 30.0, 72.0, 73.0]",usask_1
+0874422e6,1024,1024,"[819.0, 956.0, 130.0, 68.0]",usask_1
+0874422e6,1024,1024,"[50.0, 402.0, 100.0, 59.0]",usask_1
+0874422e6,1024,1024,"[794.0, 584.0, 75.0, 55.0]",usask_1
+0874422e6,1024,1024,"[0.0, 591.0, 105.0, 79.0]",usask_1
+0874422e6,1024,1024,"[77.0, 1000.0, 127.0, 24.0]",usask_1
+0874422e6,1024,1024,"[799.0, 52.0, 104.0, 67.0]",usask_1
+0874422e6,1024,1024,"[51.0, 915.0, 136.0, 64.0]",usask_1
+0874422e6,1024,1024,"[75.0, 669.0, 150.0, 83.0]",usask_1
+0874422e6,1024,1024,"[0.0, 765.0, 130.0, 119.0]",usask_1
+0874422e6,1024,1024,"[298.0, 1.0, 98.0, 72.0]",usask_1
+0874422e6,1024,1024,"[751.0, 4.0, 97.0, 71.0]",usask_1
+0874422e6,1024,1024,"[831.0, 724.0, 114.0, 85.0]",usask_1
+0874422e6,1024,1024,"[309.0, 515.0, 87.0, 97.0]",usask_1
+0874422e6,1024,1024,"[184.0, 284.0, 135.0, 93.0]",usask_1
+0874422e6,1024,1024,"[804.0, 530.0, 63.0, 55.0]",usask_1
+0874422e6,1024,1024,"[673.0, 26.0, 103.0, 104.0]",usask_1
+0874422e6,1024,1024,"[132.0, 433.0, 128.0, 123.0]",usask_1
+0874422e6,1024,1024,"[789.0, 457.0, 58.0, 58.0]",usask_1
+0874422e6,1024,1024,"[890.0, 539.0, 57.0, 89.0]",usask_1
+0874422e6,1024,1024,"[981.0, 456.0, 43.0, 77.0]",usask_1
+0874422e6,1024,1024,"[820.0, 217.0, 78.0, 54.0]",usask_1
+0874422e6,1024,1024,"[191.0, 781.0, 103.0, 86.0]",usask_1
+0874422e6,1024,1024,"[154.0, 857.0, 94.0, 61.0]",usask_1
+0874422e6,1024,1024,"[0.0, 904.0, 85.0, 59.0]",usask_1
+0874422e6,1024,1024,"[170.0, 519.0, 99.0, 73.0]",usask_1
+0874422e6,1024,1024,"[25.0, 449.0, 72.0, 76.0]",usask_1
+0874422e6,1024,1024,"[0.0, 494.0, 55.0, 76.0]",usask_1
+0874422e6,1024,1024,"[276.0, 469.0, 60.0, 91.0]",usask_1
+0874422e6,1024,1024,"[171.0, 234.0, 111.0, 83.0]",usask_1
+0874422e6,1024,1024,"[7.0, 241.0, 141.0, 78.0]",usask_1
+0874422e6,1024,1024,"[30.0, 720.0, 132.0, 53.0]",usask_1
+0874422e6,1024,1024,"[992.0, 30.0, 31.0, 50.0]",usask_1
+c7637f1dd,1024,1024,"[714.0, 410.0, 96.0, 104.0]",usask_1
+c7637f1dd,1024,1024,"[0.0, 863.0, 55.0, 123.0]",usask_1
+c7637f1dd,1024,1024,"[274.0, 368.0, 138.0, 83.0]",usask_1
+c7637f1dd,1024,1024,"[944.0, 412.0, 61.0, 70.0]",usask_1
+c7637f1dd,1024,1024,"[165.0, 459.0, 126.0, 111.0]",usask_1
+c7637f1dd,1024,1024,"[828.0, 512.0, 76.0, 113.0]",usask_1
+c7637f1dd,1024,1024,"[832.0, 609.0, 113.0, 110.0]",usask_1
+c7637f1dd,1024,1024,"[828.0, 343.0, 103.0, 90.0]",usask_1
+c7637f1dd,1024,1024,"[875.0, 768.0, 99.0, 144.0]",usask_1
+c7637f1dd,1024,1024,"[992.0, 606.0, 32.0, 90.0]",usask_1
+c7637f1dd,1024,1024,"[429.0, 581.0, 76.0, 118.0]",usask_1
+c7637f1dd,1024,1024,"[265.0, 472.0, 78.0, 92.0]",usask_1
+c7637f1dd,1024,1024,"[33.0, 905.0, 127.0, 119.0]",usask_1
+c7637f1dd,1024,1024,"[231.0, 866.0, 111.0, 76.0]",usask_1
+c7637f1dd,1024,1024,"[152.0, 95.0, 59.0, 105.0]",usask_1
+c7637f1dd,1024,1024,"[670.0, 444.0, 130.0, 103.0]",usask_1
+c7637f1dd,1024,1024,"[0.0, 0.0, 130.0, 66.0]",usask_1
+c7637f1dd,1024,1024,"[370.0, 814.0, 191.0, 131.0]",usask_1
+c7637f1dd,1024,1024,"[175.0, 663.0, 69.0, 65.0]",usask_1
+c7637f1dd,1024,1024,"[361.0, 0.0, 81.0, 138.0]",usask_1
+c7637f1dd,1024,1024,"[629.0, 902.0, 225.0, 122.0]",usask_1
+c7637f1dd,1024,1024,"[223.0, 962.0, 104.0, 62.0]",usask_1
+c7637f1dd,1024,1024,"[421.0, 80.0, 190.0, 147.0]",usask_1
+c7637f1dd,1024,1024,"[593.0, 761.0, 96.0, 99.0]",usask_1
+c7637f1dd,1024,1024,"[38.0, 355.0, 98.0, 118.0]",usask_1
+c7637f1dd,1024,1024,"[924.0, 813.0, 100.0, 169.0]",usask_1
+c7637f1dd,1024,1024,"[2.0, 516.0, 152.0, 77.0]",usask_1
+c7637f1dd,1024,1024,"[146.0, 811.0, 93.0, 58.0]",usask_1
+c7637f1dd,1024,1024,"[249.0, 780.0, 53.0, 81.0]",usask_1
+c7637f1dd,1024,1024,"[340.0, 776.0, 143.0, 50.0]",usask_1
+c7637f1dd,1024,1024,"[266.0, 566.0, 74.0, 35.0]",usask_1
+c7637f1dd,1024,1024,"[775.0, 929.0, 91.0, 54.0]",usask_1
+c7637f1dd,1024,1024,"[926.0, 276.0, 62.0, 68.0]",usask_1
+bff5440bc,1024,1024,"[805.0, 815.0, 218.0, 134.0]",usask_1
+bff5440bc,1024,1024,"[94.0, 0.0, 110.0, 8.0]",usask_1
+bff5440bc,1024,1024,"[569.0, 798.0, 207.0, 214.0]",usask_1
+bff5440bc,1024,1024,"[0.0, 857.0, 97.0, 158.0]",usask_1
+bff5440bc,1024,1024,"[906.0, 309.0, 118.0, 140.0]",usask_1
+bff5440bc,1024,1024,"[24.0, 530.0, 193.0, 90.0]",usask_1
+bff5440bc,1024,1024,"[313.0, 972.0, 96.0, 52.0]",usask_1
+bff5440bc,1024,1024,"[394.0, 0.0, 123.0, 52.0]",usask_1
+bff5440bc,1024,1024,"[118.0, 394.0, 239.0, 135.0]",usask_1
+bff5440bc,1024,1024,"[94.0, 0.0, 113.0, 119.0]",usask_1
+e2d034e7d,1024,1024,"[619.0, 231.0, 81.0, 136.0]",usask_1
+e2d034e7d,1024,1024,"[151.0, 559.0, 144.0, 69.0]",usask_1
+e2d034e7d,1024,1024,"[0.0, 935.0, 169.0, 70.0]",usask_1
+e2d034e7d,1024,1024,"[163.0, 208.0, 311.0, 77.0]",usask_1
+e2d034e7d,1024,1024,"[383.0, 0.0, 80.0, 168.0]",usask_1
+e2d034e7d,1024,1024,"[170.0, 660.0, 112.0, 262.0]",usask_1
+e2d034e7d,1024,1024,"[135.0, 811.0, 192.0, 168.0]",usask_1
+e2d034e7d,1024,1024,"[360.0, 252.0, 231.0, 83.0]",usask_1
+e2d034e7d,1024,1024,"[233.0, 0.0, 85.0, 103.0]",usask_1
+e2d034e7d,1024,1024,"[729.0, 262.0, 99.0, 147.0]",usask_1
+e2d034e7d,1024,1024,"[879.0, 758.0, 107.0, 94.0]",usask_1
+e2d034e7d,1024,1024,"[515.0, 963.0, 71.0, 61.0]",usask_1
+e2d034e7d,1024,1024,"[266.0, 284.0, 114.0, 180.0]",usask_1
+e2d034e7d,1024,1024,"[760.0, 513.0, 123.0, 151.0]",usask_1
+e2d034e7d,1024,1024,"[40.0, 384.0, 113.0, 153.0]",usask_1
+e2d034e7d,1024,1024,"[715.0, 210.0, 129.0, 68.0]",usask_1
+e2d034e7d,1024,1024,"[213.0, 273.0, 86.0, 107.0]",usask_1
+e2d034e7d,1024,1024,"[857.0, 701.0, 121.0, 83.0]",usask_1
+e2d034e7d,1024,1024,"[708.0, 100.0, 71.0, 100.0]",usask_1
+e2d034e7d,1024,1024,"[0.0, 8.0, 200.0, 125.0]",usask_1
+e2d034e7d,1024,1024,"[280.0, 935.0, 95.0, 89.0]",usask_1
+e2d034e7d,1024,1024,"[74.0, 350.0, 184.0, 120.0]",usask_1
+e2d034e7d,1024,1024,"[485.0, 333.0, 79.0, 107.0]",usask_1
+e2d034e7d,1024,1024,"[542.0, 281.0, 74.0, 123.0]",usask_1
+e2d034e7d,1024,1024,"[586.0, 999.0, 120.0, 25.0]",usask_1
+e2d034e7d,1024,1024,"[0.0, 0.0, 146.0, 28.0]",usask_1
+e2d034e7d,1024,1024,"[201.0, 994.0, 69.0, 30.0]",usask_1
+e2d034e7d,1024,1024,"[710.0, 0.0, 76.0, 36.0]",usask_1
+e2d034e7d,1024,1024,"[451.0, 0.0, 96.0, 173.0]",usask_1
+e2d034e7d,1024,1024,"[923.0, 217.0, 87.0, 169.0]",usask_1
+e2d034e7d,1024,1024,"[658.0, 398.0, 74.0, 132.0]",usask_1
+e2d034e7d,1024,1024,"[617.0, 535.0, 99.0, 107.0]",usask_1
+e2d034e7d,1024,1024,"[486.0, 455.0, 173.0, 88.0]",usask_1
+e2d034e7d,1024,1024,"[425.0, 596.0, 98.0, 68.0]",usask_1
+e2d034e7d,1024,1024,"[354.0, 488.0, 127.0, 168.0]",usask_1
+e2d034e7d,1024,1024,"[359.0, 641.0, 129.0, 57.0]",usask_1
+e2d034e7d,1024,1024,"[383.0, 693.0, 81.0, 195.0]",usask_1
+e2d034e7d,1024,1024,"[379.0, 751.0, 247.0, 94.0]",usask_1
+e2d034e7d,1024,1024,"[590.0, 818.0, 70.0, 80.0]",usask_1
+e2d034e7d,1024,1024,"[474.0, 670.0, 211.0, 193.0]",usask_1
+e2d034e7d,1024,1024,"[618.0, 792.0, 140.0, 49.0]",usask_1
+e2d034e7d,1024,1024,"[629.0, 736.0, 189.0, 63.0]",usask_1
+e2d034e7d,1024,1024,"[477.0, 580.0, 156.0, 162.0]",usask_1
+e2d034e7d,1024,1024,"[604.0, 652.0, 227.0, 75.0]",usask_1
+e2d034e7d,1024,1024,"[104.0, 947.0, 187.0, 62.0]",usask_1
+e2d034e7d,1024,1024,"[342.0, 16.0, 156.0, 81.0]",usask_1
+a76ca8630,1024,1024,"[269.0, 178.0, 150.0, 96.0]",usask_1
+a76ca8630,1024,1024,"[0.0, 178.0, 173.0, 117.0]",usask_1
+a76ca8630,1024,1024,"[141.0, 188.0, 207.0, 199.0]",usask_1
+a76ca8630,1024,1024,"[808.0, 954.0, 157.0, 70.0]",usask_1
+a76ca8630,1024,1024,"[43.0, 852.0, 134.0, 113.0]",usask_1
+a76ca8630,1024,1024,"[135.0, 0.0, 151.0, 94.0]",usask_1
+a76ca8630,1024,1024,"[155.0, 655.0, 123.0, 160.0]",usask_1
+a76ca8630,1024,1024,"[513.0, 914.0, 146.0, 89.0]",usask_1
+a76ca8630,1024,1024,"[153.0, 33.0, 109.0, 82.0]",usask_1
+2cc75e9f5,1024,1024,"[0.0, 52.0, 19.0, 70.0]",usask_1
+2cc75e9f5,1024,1024,"[565.0, 596.0, 130.0, 123.0]",usask_1
+2cc75e9f5,1024,1024,"[640.0, 1.0, 56.0, 100.0]",usask_1
+2cc75e9f5,1024,1024,"[732.0, 18.0, 73.0, 145.0]",usask_1
+2cc75e9f5,1024,1024,"[602.0, 453.0, 86.0, 54.0]",usask_1
+2cc75e9f5,1024,1024,"[37.0, 84.0, 987.0, 255.0]",usask_1
+2cc75e9f5,1024,1024,"[593.0, 126.0, 108.0, 133.0]",usask_1
+2cc75e9f5,1024,1024,"[560.0, 0.0, 88.0, 138.0]",usask_1
+2cc75e9f5,1024,1024,"[122.0, 927.0, 102.0, 97.0]",usask_1
+2cc75e9f5,1024,1024,"[949.0, 433.0, 75.0, 100.0]",usask_1
+2cc75e9f5,1024,1024,"[488.0, 148.0, 96.0, 103.0]",usask_1
+2cc75e9f5,1024,1024,"[445.0, 27.0, 99.0, 130.0]",usask_1
+2cc75e9f5,1024,1024,"[559.0, 724.0, 93.0, 103.0]",usask_1
+2cc75e9f5,1024,1024,"[104.0, 242.0, 133.0, 117.0]",usask_1
+2cc75e9f5,1024,1024,"[437.0, 555.0, 144.0, 103.0]",usask_1
+2cc75e9f5,1024,1024,"[242.0, 629.0, 127.0, 100.0]",usask_1
+2cc75e9f5,1024,1024,"[218.0, 441.0, 178.0, 113.0]",usask_1
+2cc75e9f5,1024,1024,"[744.0, 957.0, 100.0, 67.0]",usask_1
+2cc75e9f5,1024,1024,"[875.0, 970.0, 76.0, 54.0]",usask_1
+2cc75e9f5,1024,1024,"[795.0, 114.0, 105.0, 79.0]",usask_1
+2cc75e9f5,1024,1024,"[794.0, 25.0, 61.0, 93.0]",usask_1
+2cc75e9f5,1024,1024,"[603.0, 501.0, 63.0, 44.0]",usask_1
+2cc75e9f5,1024,1024,"[457.0, 383.0, 194.0, 87.0]",usask_1
+2cc75e9f5,1024,1024,"[838.0, 48.0, 52.0, 84.0]",usask_1
+05dfa9bb1,1024,1024,"[0.0, 559.0, 116.0, 128.0]",usask_1
+05dfa9bb1,1024,1024,"[872.0, 964.0, 134.0, 60.0]",usask_1
+05dfa9bb1,1024,1024,"[588.0, 392.0, 96.0, 82.0]",usask_1
+05dfa9bb1,1024,1024,"[659.0, 489.0, 109.0, 112.0]",usask_1
+05dfa9bb1,1024,1024,"[990.0, 165.0, 34.0, 66.0]",usask_1
+05dfa9bb1,1024,1024,"[592.0, 282.0, 103.0, 100.0]",usask_1
+05dfa9bb1,1024,1024,"[469.0, 122.0, 97.0, 64.0]",usask_1
+05dfa9bb1,1024,1024,"[613.0, 97.0, 106.0, 97.0]",usask_1
+05dfa9bb1,1024,1024,"[636.0, 20.0, 117.0, 123.0]",usask_1
+05dfa9bb1,1024,1024,"[956.0, 865.0, 68.0, 120.0]",usask_1
+05dfa9bb1,1024,1024,"[859.0, 826.0, 85.0, 66.0]",usask_1
+05dfa9bb1,1024,1024,"[421.0, 879.0, 197.0, 99.0]",usask_1
+05dfa9bb1,1024,1024,"[960.0, 245.0, 64.0, 161.0]",usask_1
+05dfa9bb1,1024,1024,"[407.0, 143.0, 133.0, 103.0]",usask_1
+05dfa9bb1,1024,1024,"[628.0, 36.0, 68.0, 88.0]",usask_1
+05dfa9bb1,1024,1024,"[673.0, 188.0, 100.0, 96.0]",usask_1
+05dfa9bb1,1024,1024,"[808.0, 676.0, 114.0, 127.0]",usask_1
+05dfa9bb1,1024,1024,"[550.0, 93.0, 99.0, 63.0]",usask_1
+05dfa9bb1,1024,1024,"[545.0, 0.0, 113.0, 35.0]",usask_1
+05dfa9bb1,1024,1024,"[0.0, 609.0, 58.0, 82.0]",usask_1
+05dfa9bb1,1024,1024,"[513.0, 407.0, 193.0, 90.0]",usask_1
+05dfa9bb1,1024,1024,"[601.0, 387.0, 83.0, 163.0]",usask_1
+05dfa9bb1,1024,1024,"[737.0, 696.0, 109.0, 90.0]",usask_1
+05dfa9bb1,1024,1024,"[745.0, 653.0, 75.0, 82.0]",usask_1
+05dfa9bb1,1024,1024,"[822.0, 851.0, 147.0, 104.0]",usask_1
+05dfa9bb1,1024,1024,"[696.0, 941.0, 86.0, 82.0]",usask_1
+05dfa9bb1,1024,1024,"[729.0, 895.0, 95.0, 88.0]",usask_1
+05dfa9bb1,1024,1024,"[748.0, 871.0, 94.0, 59.0]",usask_1
+05dfa9bb1,1024,1024,"[0.0, 267.0, 52.0, 75.0]",usask_1
+05dfa9bb1,1024,1024,"[43.0, 913.0, 102.0, 108.0]",usask_1
+05dfa9bb1,1024,1024,"[0.0, 935.0, 39.0, 78.0]",usask_1
+84a6298a3,1024,1024,"[303.0, 640.0, 113.0, 86.0]",usask_1
+84a6298a3,1024,1024,"[887.0, 382.0, 83.0, 92.0]",usask_1
+84a6298a3,1024,1024,"[835.0, 30.0, 73.0, 110.0]",usask_1
+84a6298a3,1024,1024,"[0.0, 489.0, 139.0, 124.0]",usask_1
+84a6298a3,1024,1024,"[498.0, 286.0, 147.0, 96.0]",usask_1
+84a6298a3,1024,1024,"[648.0, 832.0, 159.0, 66.0]",usask_1
+84a6298a3,1024,1024,"[128.0, 427.0, 191.0, 73.0]",usask_1
+84a6298a3,1024,1024,"[491.0, 983.0, 177.0, 41.0]",usask_1
+84a6298a3,1024,1024,"[721.0, 668.0, 162.0, 113.0]",usask_1
+84a6298a3,1024,1024,"[751.0, 270.0, 73.0, 59.0]",usask_1
+84a6298a3,1024,1024,"[867.0, 810.0, 149.0, 103.0]",usask_1
+84a6298a3,1024,1024,"[299.0, 734.0, 218.0, 96.0]",usask_1
+84a6298a3,1024,1024,"[431.0, 94.0, 140.0, 116.0]",usask_1
+84a6298a3,1024,1024,"[245.0, 825.0, 218.0, 113.0]",usask_1
+84a6298a3,1024,1024,"[471.0, 464.0, 127.0, 171.0]",usask_1
+84a6298a3,1024,1024,"[648.0, 192.0, 67.0, 76.0]",usask_1
+84a6298a3,1024,1024,"[323.0, 896.0, 211.0, 96.0]",usask_1
+84a6298a3,1024,1024,"[570.0, 78.0, 81.0, 74.0]",usask_1
+84a6298a3,1024,1024,"[215.0, 877.0, 95.0, 146.0]",usask_1
+84a6298a3,1024,1024,"[298.0, 456.0, 122.0, 58.0]",usask_1
+84a6298a3,1024,1024,"[619.0, 665.0, 147.0, 159.0]",usask_1
+84a6298a3,1024,1024,"[738.0, 597.0, 106.0, 168.0]",usask_1
+84a6298a3,1024,1024,"[692.0, 478.0, 74.0, 55.0]",usask_1
+84a6298a3,1024,1024,"[717.0, 422.0, 58.0, 97.0]",usask_1
+84a6298a3,1024,1024,"[688.0, 340.0, 150.0, 91.0]",usask_1
+84a6298a3,1024,1024,"[642.0, 317.0, 105.0, 86.0]",usask_1
+84a6298a3,1024,1024,"[664.0, 337.0, 87.0, 118.0]",usask_1
+572a469e9,1024,1024,"[458.0, 619.0, 109.0, 90.0]",usask_1
+572a469e9,1024,1024,"[825.0, 604.0, 85.0, 60.0]",usask_1
+572a469e9,1024,1024,"[596.0, 683.0, 160.0, 100.0]",usask_1
+572a469e9,1024,1024,"[391.0, 733.0, 149.0, 90.0]",usask_1
+572a469e9,1024,1024,"[800.0, 374.0, 87.0, 71.0]",usask_1
+572a469e9,1024,1024,"[832.0, 801.0, 123.0, 97.0]",usask_1
+572a469e9,1024,1024,"[684.0, 618.0, 107.0, 84.0]",usask_1
+572a469e9,1024,1024,"[862.0, 1020.0, 89.0, 4.0]",usask_1
+572a469e9,1024,1024,"[943.0, 734.0, 81.0, 83.0]",usask_1
+572a469e9,1024,1024,"[903.0, 229.0, 68.0, 70.0]",usask_1
+572a469e9,1024,1024,"[389.0, 827.0, 142.0, 67.0]",usask_1
+572a469e9,1024,1024,"[913.0, 561.0, 63.0, 61.0]",usask_1
+572a469e9,1024,1024,"[734.0, 380.0, 89.0, 80.0]",usask_1
+572a469e9,1024,1024,"[54.0, 791.0, 124.0, 102.0]",usask_1
+572a469e9,1024,1024,"[747.0, 538.0, 124.0, 73.0]",usask_1
+572a469e9,1024,1024,"[579.0, 229.0, 89.0, 89.0]",usask_1
+572a469e9,1024,1024,"[320.0, 892.0, 130.0, 83.0]",usask_1
+572a469e9,1024,1024,"[583.0, 29.0, 162.0, 72.0]",usask_1
+572a469e9,1024,1024,"[0.0, 0.0, 66.0, 49.0]",usask_1
+572a469e9,1024,1024,"[559.0, 622.0, 142.0, 86.0]",usask_1
+572a469e9,1024,1024,"[959.0, 861.0, 65.0, 117.0]",usask_1
+572a469e9,1024,1024,"[643.0, 464.0, 150.0, 83.0]",usask_1
+572a469e9,1024,1024,"[679.0, 865.0, 113.0, 97.0]",usask_1
+572a469e9,1024,1024,"[793.0, 916.0, 117.0, 108.0]",usask_1
+572a469e9,1024,1024,"[0.0, 378.0, 129.0, 98.0]",usask_1
+572a469e9,1024,1024,"[192.0, 751.0, 103.0, 58.0]",usask_1
+572a469e9,1024,1024,"[808.0, 448.0, 118.0, 66.0]",usask_1
+572a469e9,1024,1024,"[262.0, 80.0, 174.0, 90.0]",usask_1
+572a469e9,1024,1024,"[774.0, 222.0, 147.0, 89.0]",usask_1
+572a469e9,1024,1024,"[468.0, 48.0, 80.0, 74.0]",usask_1
+572a469e9,1024,1024,"[96.0, 729.0, 108.0, 78.0]",usask_1
+572a469e9,1024,1024,"[0.0, 632.0, 31.0, 73.0]",usask_1
+572a469e9,1024,1024,"[45.0, 357.0, 70.0, 68.0]",usask_1
+572a469e9,1024,1024,"[657.0, 423.0, 159.0, 73.0]",usask_1
+572a469e9,1024,1024,"[565.0, 0.0, 113.0, 66.0]",usask_1
+572a469e9,1024,1024,"[510.0, 78.0, 110.0, 46.0]",usask_1
+572a469e9,1024,1024,"[0.0, 38.0, 81.0, 61.0]",usask_1
+572a469e9,1024,1024,"[702.0, 938.0, 67.0, 67.0]",usask_1
+572a469e9,1024,1024,"[680.0, 992.0, 49.0, 32.0]",usask_1
+572a469e9,1024,1024,"[0.0, 474.0, 42.0, 60.0]",usask_1
+7d6cc4b2a,1024,1024,"[945.0, 156.0, 79.0, 90.0]",usask_1
+7d6cc4b2a,1024,1024,"[86.0, 210.0, 117.0, 76.0]",usask_1
+7d6cc4b2a,1024,1024,"[621.0, 888.0, 155.0, 89.0]",usask_1
+7d6cc4b2a,1024,1024,"[961.0, 902.0, 63.0, 107.0]",usask_1
+7d6cc4b2a,1024,1024,"[721.0, 868.0, 125.0, 93.0]",usask_1
+7d6cc4b2a,1024,1024,"[821.0, 235.0, 120.0, 136.0]",usask_1
+7d6cc4b2a,1024,1024,"[0.0, 512.0, 75.0, 106.0]",usask_1
+7d6cc4b2a,1024,1024,"[0.0, 715.0, 92.0, 92.0]",usask_1
+7d6cc4b2a,1024,1024,"[863.0, 0.0, 109.0, 56.0]",usask_1
+7d6cc4b2a,1024,1024,"[0.0, 324.0, 122.0, 95.0]",usask_1
+7d6cc4b2a,1024,1024,"[322.0, 210.0, 127.0, 93.0]",usask_1
+7d6cc4b2a,1024,1024,"[626.0, 0.0, 150.0, 2.0]",usask_1
+7d6cc4b2a,1024,1024,"[254.0, 922.0, 151.0, 96.0]",usask_1
+7d6cc4b2a,1024,1024,"[186.0, 440.0, 203.0, 65.0]",usask_1
+7d6cc4b2a,1024,1024,"[208.0, 387.0, 135.0, 72.0]",usask_1
+6d1879f19,1024,1024,"[561.0, 408.0, 102.0, 86.0]",usask_1
+6d1879f19,1024,1024,"[695.0, 861.0, 93.0, 73.0]",usask_1
+6d1879f19,1024,1024,"[76.0, 861.0, 83.0, 61.0]",usask_1
+6d1879f19,1024,1024,"[0.0, 117.0, 79.0, 84.0]",usask_1
+6d1879f19,1024,1024,"[616.0, 592.0, 96.0, 87.0]",usask_1
+6d1879f19,1024,1024,"[851.0, 164.0, 99.0, 116.0]",usask_1
+6d1879f19,1024,1024,"[683.0, 518.0, 76.0, 110.0]",usask_1
+6d1879f19,1024,1024,"[535.0, 554.0, 139.0, 256.0]",usask_1
+6d1879f19,1024,1024,"[139.0, 757.0, 93.0, 91.0]",usask_1
+6d1879f19,1024,1024,"[713.0, 600.0, 114.0, 260.0]",usask_1
+6d1879f19,1024,1024,"[592.0, 358.0, 88.0, 75.0]",usask_1
+6d1879f19,1024,1024,"[35.0, 21.0, 87.0, 119.0]",usask_1
+6d1879f19,1024,1024,"[551.0, 771.0, 153.0, 134.0]",usask_1
+6d1879f19,1024,1024,"[58.0, 170.0, 80.0, 76.0]",usask_1
+6d1879f19,1024,1024,"[0.0, 571.0, 85.0, 158.0]",usask_1
+6d1879f19,1024,1024,"[442.0, 362.0, 141.0, 110.0]",usask_1
+6d1879f19,1024,1024,"[766.0, 0.0, 96.0, 149.0]",usask_1
+6d1879f19,1024,1024,"[800.0, 87.0, 85.0, 86.0]",usask_1
+6d1879f19,1024,1024,"[434.0, 161.0, 140.0, 86.0]",usask_1
+6d1879f19,1024,1024,"[799.0, 955.0, 92.0, 69.0]",usask_1
+6d1879f19,1024,1024,"[326.0, 990.0, 141.0, 34.0]",usask_1
+6d1879f19,1024,1024,"[226.0, 945.0, 108.0, 76.0]",usask_1
+6d1879f19,1024,1024,"[972.0, 800.0, 52.0, 104.0]",usask_1
+6d1879f19,1024,1024,"[373.0, 737.0, 103.0, 103.0]",usask_1
+6d1879f19,1024,1024,"[0.0, 59.0, 33.0, 72.0]",usask_1
+6d1879f19,1024,1024,"[988.0, 323.0, 36.0, 92.0]",usask_1
+6d1879f19,1024,1024,"[91.0, 713.0, 222.0, 114.0]",usask_1
+6d1879f19,1024,1024,"[541.0, 217.0, 148.0, 171.0]",usask_1
+6d1879f19,1024,1024,"[602.0, 197.0, 89.0, 102.0]",usask_1
+6d1879f19,1024,1024,"[642.0, 138.0, 62.0, 150.0]",usask_1
+6d1879f19,1024,1024,"[902.0, 103.0, 43.0, 85.0]",usask_1
+6d1879f19,1024,1024,"[9.0, 0.0, 82.0, 64.0]",usask_1
+6d1879f19,1024,1024,"[564.0, 926.0, 95.0, 87.0]",usask_1
+ae1fcb590,1024,1024,"[759.0, 840.0, 116.0, 57.0]",usask_1
+ae1fcb590,1024,1024,"[1.0, 615.0, 104.0, 65.0]",usask_1
+ae1fcb590,1024,1024,"[41.0, 888.0, 92.0, 95.0]",usask_1
+ae1fcb590,1024,1024,"[636.0, 221.0, 79.0, 91.0]",usask_1
+ae1fcb590,1024,1024,"[397.0, 265.0, 64.0, 89.0]",usask_1
+ae1fcb590,1024,1024,"[759.0, 802.0, 127.0, 55.0]",usask_1
+ae1fcb590,1024,1024,"[929.0, 420.0, 93.0, 154.0]",usask_1
+ae1fcb590,1024,1024,"[507.0, 126.0, 103.0, 72.0]",usask_1
+ae1fcb590,1024,1024,"[909.0, 951.0, 84.0, 73.0]",usask_1
+ae1fcb590,1024,1024,"[0.0, 302.0, 194.0, 186.0]",usask_1
+ae1fcb590,1024,1024,"[378.0, 88.0, 112.0, 120.0]",usask_1
+ae1fcb590,1024,1024,"[206.0, 586.0, 71.0, 108.0]",usask_1
+ae1fcb590,1024,1024,"[24.0, 544.0, 193.0, 110.0]",usask_1
+ae1fcb590,1024,1024,"[0.0, 580.0, 91.0, 71.0]",usask_1
+ae1fcb590,1024,1024,"[428.0, 609.0, 129.0, 119.0]",usask_1
+ae1fcb590,1024,1024,"[48.0, 258.0, 76.0, 99.0]",usask_1
+ae1fcb590,1024,1024,"[236.0, 404.0, 185.0, 127.0]",usask_1
+ae1fcb590,1024,1024,"[427.0, 27.0, 134.0, 120.0]",usask_1
+ae1fcb590,1024,1024,"[438.0, 0.0, 102.0, 35.0]",usask_1
+ae1fcb590,1024,1024,"[826.0, 475.0, 96.0, 133.0]",usask_1
+ae1fcb590,1024,1024,"[126.0, 826.0, 69.0, 115.0]",usask_1
+ae1fcb590,1024,1024,"[155.0, 854.0, 68.0, 89.0]",usask_1
+ae1fcb590,1024,1024,"[631.0, 798.0, 184.0, 81.0]",usask_1
+ae1fcb590,1024,1024,"[918.0, 386.0, 105.0, 222.0]",usask_1
+ae1fcb590,1024,1024,"[800.0, 222.0, 126.0, 210.0]",usask_1
+ae1fcb590,1024,1024,"[58.0, 500.0, 159.0, 141.0]",usask_1
+ae1fcb590,1024,1024,"[0.0, 713.0, 25.0, 57.0]",usask_1
+ae1fcb590,1024,1024,"[285.0, 85.0, 91.0, 109.0]",usask_1
+ae1fcb590,1024,1024,"[614.0, 173.0, 76.0, 77.0]",usask_1
+0c3d9007c,1024,1024,"[611.0, 765.0, 76.0, 80.0]",usask_1
+0c3d9007c,1024,1024,"[0.0, 688.0, 134.0, 76.0]",usask_1
+0c3d9007c,1024,1024,"[972.0, 404.0, 52.0, 143.0]",usask_1
+0c3d9007c,1024,1024,"[364.0, 816.0, 94.0, 97.0]",usask_1
+0c3d9007c,1024,1024,"[168.0, 564.0, 109.0, 107.0]",usask_1
+0c3d9007c,1024,1024,"[471.0, 869.0, 115.0, 76.0]",usask_1
+0c3d9007c,1024,1024,"[906.0, 583.0, 103.0, 107.0]",usask_1
+0c3d9007c,1024,1024,"[569.0, 76.0, 117.0, 62.0]",usask_1
+0c3d9007c,1024,1024,"[145.0, 698.0, 240.0, 112.0]",usask_1
+0c3d9007c,1024,1024,"[672.0, 797.0, 80.0, 72.0]",usask_1
+0c3d9007c,1024,1024,"[660.0, 417.0, 123.0, 248.0]",usask_1
+0c3d9007c,1024,1024,"[244.0, 90.0, 109.0, 258.0]",usask_1
+0c3d9007c,1024,1024,"[636.0, 269.0, 153.0, 184.0]",usask_1
+0c3d9007c,1024,1024,"[418.0, 573.0, 128.0, 98.0]",usask_1
+0c3d9007c,1024,1024,"[539.0, 149.0, 93.0, 155.0]",usask_1
+0c3d9007c,1024,1024,"[431.0, 299.0, 109.0, 225.0]",usask_1
+0c3d9007c,1024,1024,"[811.0, 508.0, 93.0, 120.0]",usask_1
+0c3d9007c,1024,1024,"[729.0, 837.0, 106.0, 63.0]",usask_1
+0c3d9007c,1024,1024,"[405.0, 12.0, 132.0, 74.0]",usask_1
+c1279ebc9,1024,1024,"[525.0, 80.0, 106.0, 100.0]",usask_1
+c1279ebc9,1024,1024,"[421.0, 333.0, 77.0, 165.0]",usask_1
+c1279ebc9,1024,1024,"[774.0, 643.0, 114.0, 105.0]",usask_1
+c1279ebc9,1024,1024,"[457.0, 402.0, 97.0, 142.0]",usask_1
+c1279ebc9,1024,1024,"[778.0, 865.0, 140.0, 110.0]",usask_1
+c1279ebc9,1024,1024,"[587.0, 933.0, 109.0, 64.0]",usask_1
+c1279ebc9,1024,1024,"[0.0, 677.0, 2.0, 157.0]",usask_1
+c1279ebc9,1024,1024,"[0.0, 914.0, 130.0, 110.0]",usask_1
+c1279ebc9,1024,1024,"[331.0, 69.0, 67.0, 64.0]",usask_1
+c1279ebc9,1024,1024,"[956.0, 0.0, 68.0, 36.0]",usask_1
+c1279ebc9,1024,1024,"[126.0, 992.0, 77.0, 32.0]",usask_1
+c1279ebc9,1024,1024,"[273.0, 365.0, 135.0, 119.0]",usask_1
+c1279ebc9,1024,1024,"[592.0, 0.0, 133.0, 74.0]",usask_1
+c1279ebc9,1024,1024,"[734.0, 523.0, 72.0, 108.0]",usask_1
+c1279ebc9,1024,1024,"[555.0, 559.0, 110.0, 170.0]",usask_1
+c1279ebc9,1024,1024,"[704.0, 259.0, 106.0, 249.0]",usask_1
+c1279ebc9,1024,1024,"[117.0, 771.0, 139.0, 105.0]",usask_1
+c1279ebc9,1024,1024,"[916.0, 882.0, 108.0, 85.0]",usask_1
+c1279ebc9,1024,1024,"[151.0, 317.0, 111.0, 79.0]",usask_1
+c1279ebc9,1024,1024,"[291.0, 579.0, 60.0, 67.0]",usask_1
+c1279ebc9,1024,1024,"[353.0, 641.0, 52.0, 68.0]",usask_1
+c1279ebc9,1024,1024,"[577.0, 754.0, 66.0, 57.0]",usask_1
+c1279ebc9,1024,1024,"[650.0, 928.0, 160.0, 71.0]",usask_1
+42cc125bc,1024,1024,"[394.0, 463.0, 68.0, 69.0]",usask_1
+42cc125bc,1024,1024,"[448.0, 787.0, 106.0, 51.0]",usask_1
+42cc125bc,1024,1024,"[367.0, 922.0, 83.0, 71.0]",usask_1
+42cc125bc,1024,1024,"[414.0, 578.0, 95.0, 52.0]",usask_1
+42cc125bc,1024,1024,"[412.0, 527.0, 103.0, 53.0]",usask_1
+42cc125bc,1024,1024,"[454.0, 471.0, 68.0, 57.0]",usask_1
+42cc125bc,1024,1024,"[403.0, 665.0, 56.0, 40.0]",usask_1
+42cc125bc,1024,1024,"[513.0, 449.0, 48.0, 77.0]",usask_1
+42cc125bc,1024,1024,"[125.0, 229.0, 177.0, 90.0]",usask_1
+42cc125bc,1024,1024,"[553.0, 460.0, 68.0, 89.0]",usask_1
+42cc125bc,1024,1024,"[502.0, 615.0, 48.0, 36.0]",usask_1
+42cc125bc,1024,1024,"[559.0, 786.0, 59.0, 81.0]",usask_1
+42cc125bc,1024,1024,"[609.0, 236.0, 70.0, 59.0]",usask_1
+42cc125bc,1024,1024,"[672.0, 299.0, 112.0, 74.0]",usask_1
+42cc125bc,1024,1024,"[601.0, 506.0, 71.0, 67.0]",usask_1
+42cc125bc,1024,1024,"[31.0, 586.0, 94.0, 82.0]",usask_1
+42cc125bc,1024,1024,"[299.0, 667.0, 84.0, 126.0]",usask_1
+42cc125bc,1024,1024,"[815.0, 0.0, 113.0, 68.0]",usask_1
+42cc125bc,1024,1024,"[391.0, 628.0, 103.0, 54.0]",usask_1
+42cc125bc,1024,1024,"[643.0, 64.0, 84.0, 78.0]",usask_1
+42cc125bc,1024,1024,"[555.0, 197.0, 90.0, 59.0]",usask_1
+42cc125bc,1024,1024,"[572.0, 0.0, 86.0, 66.0]",usask_1
+42cc125bc,1024,1024,"[352.0, 804.0, 75.0, 86.0]",usask_1
+42cc125bc,1024,1024,"[426.0, 859.0, 83.0, 69.0]",usask_1
+42cc125bc,1024,1024,"[488.0, 898.0, 61.0, 52.0]",usask_1
+42cc125bc,1024,1024,"[958.0, 649.0, 66.0, 70.0]",usask_1
+42cc125bc,1024,1024,"[760.0, 680.0, 102.0, 55.0]",usask_1
+42cc125bc,1024,1024,"[813.0, 689.0, 116.0, 120.0]",usask_1
+42cc125bc,1024,1024,"[935.0, 822.0, 76.0, 133.0]",usask_1
+42cc125bc,1024,1024,"[713.0, 910.0, 68.0, 65.0]",usask_1
+42cc125bc,1024,1024,"[807.0, 801.0, 87.0, 40.0]",usask_1
+42cc125bc,1024,1024,"[804.0, 834.0, 108.0, 44.0]",usask_1
+42cc125bc,1024,1024,"[801.0, 943.0, 38.0, 80.0]",usask_1
+42cc125bc,1024,1024,"[832.0, 936.0, 48.0, 87.0]",usask_1
+42cc125bc,1024,1024,"[721.0, 987.0, 35.0, 37.0]",usask_1
+42cc125bc,1024,1024,"[679.0, 985.0, 41.0, 39.0]",usask_1
+42cc125bc,1024,1024,"[251.0, 255.0, 63.0, 57.0]",usask_1
+42cc125bc,1024,1024,"[461.0, 0.0, 56.0, 49.0]",usask_1
+7fc9fe138,1024,1024,"[782.0, 357.0, 121.0, 185.0]",usask_1
+7fc9fe138,1024,1024,"[513.0, 384.0, 149.0, 158.0]",usask_1
+7fc9fe138,1024,1024,"[118.0, 7.0, 343.0, 347.0]",usask_1
+7fc9fe138,1024,1024,"[669.0, 148.0, 141.0, 99.0]",usask_1
+7fc9fe138,1024,1024,"[431.0, 282.0, 147.0, 99.0]",usask_1
+7fc9fe138,1024,1024,"[0.0, 127.0, 163.0, 98.0]",usask_1
+7fc9fe138,1024,1024,"[131.0, 218.0, 207.0, 114.0]",usask_1
+7fc9fe138,1024,1024,"[453.0, 0.0, 144.0, 235.0]",usask_1
+7fc9fe138,1024,1024,"[302.0, 876.0, 74.0, 96.0]",usask_1
+7fc9fe138,1024,1024,"[361.0, 798.0, 125.0, 226.0]",usask_1
+7fc9fe138,1024,1024,"[844.0, 0.0, 93.0, 65.0]",usask_1
+7fc9fe138,1024,1024,"[599.0, 758.0, 113.0, 251.0]",usask_1
+7fc9fe138,1024,1024,"[657.0, 263.0, 147.0, 91.0]",usask_1
+7fc9fe138,1024,1024,"[0.0, 0.0, 88.0, 90.0]",usask_1
+7fc9fe138,1024,1024,"[467.0, 911.0, 115.0, 87.0]",usask_1
+8947f1a02,1024,1024,"[417.0, 569.0, 73.0, 127.0]",usask_1
+8947f1a02,1024,1024,"[474.0, 809.0, 125.0, 50.0]",usask_1
+8947f1a02,1024,1024,"[723.0, 604.0, 56.0, 98.0]",usask_1
+8947f1a02,1024,1024,"[2.0, 0.0, 91.0, 80.0]",usask_1
+8947f1a02,1024,1024,"[746.0, 168.0, 67.0, 78.0]",usask_1
+8947f1a02,1024,1024,"[915.0, 803.0, 108.0, 77.0]",usask_1
+8947f1a02,1024,1024,"[0.0, 741.0, 136.0, 89.0]",usask_1
+8947f1a02,1024,1024,"[696.0, 190.0, 66.0, 90.0]",usask_1
+8947f1a02,1024,1024,"[529.0, 917.0, 164.0, 83.0]",usask_1
+8947f1a02,1024,1024,"[762.0, 1.0, 73.0, 22.0]",usask_1
+8947f1a02,1024,1024,"[993.0, 198.0, 31.0, 110.0]",usask_1
+8947f1a02,1024,1024,"[841.0, 48.0, 107.0, 96.0]",usask_1
+8947f1a02,1024,1024,"[605.0, 946.0, 118.0, 78.0]",usask_1
+8947f1a02,1024,1024,"[821.0, 937.0, 51.0, 82.0]",usask_1
+8947f1a02,1024,1024,"[425.0, 866.0, 92.0, 59.0]",usask_1
+8947f1a02,1024,1024,"[0.0, 884.0, 80.0, 66.0]",usask_1
+8947f1a02,1024,1024,"[1.0, 476.0, 65.0, 64.0]",usask_1
+8947f1a02,1024,1024,"[0.0, 435.0, 85.0, 61.0]",usask_1
+8947f1a02,1024,1024,"[81.0, 404.0, 86.0, 83.0]",usask_1
+8947f1a02,1024,1024,"[88.0, 340.0, 76.0, 63.0]",usask_1
+8947f1a02,1024,1024,"[1.0, 224.0, 48.0, 89.0]",usask_1
+8947f1a02,1024,1024,"[9.0, 185.0, 64.0, 67.0]",usask_1
+8947f1a02,1024,1024,"[0.0, 83.0, 71.0, 96.0]",usask_1
+8947f1a02,1024,1024,"[634.0, 301.0, 59.0, 93.0]",usask_1
+8947f1a02,1024,1024,"[782.0, 322.0, 80.0, 77.0]",usask_1
+8947f1a02,1024,1024,"[694.0, 322.0, 52.0, 84.0]",usask_1
+8947f1a02,1024,1024,"[734.0, 978.0, 74.0, 46.0]",usask_1
+8947f1a02,1024,1024,"[83.0, 0.0, 53.0, 39.0]",usask_1
+13a368f78,1024,1024,"[725.0, 51.0, 91.0, 83.0]",usask_1
+13a368f78,1024,1024,"[638.0, 0.0, 80.0, 116.0]",usask_1
+13a368f78,1024,1024,"[42.0, 200.0, 104.0, 74.0]",usask_1
+13a368f78,1024,1024,"[312.0, 674.0, 96.0, 61.0]",usask_1
+13a368f78,1024,1024,"[349.0, 539.0, 89.0, 73.0]",usask_1
+13a368f78,1024,1024,"[828.0, 645.0, 71.0, 119.0]",usask_1
+13a368f78,1024,1024,"[90.0, 74.0, 105.0, 111.0]",usask_1
+13a368f78,1024,1024,"[338.0, 450.0, 110.0, 111.0]",usask_1
+13a368f78,1024,1024,"[871.0, 712.0, 86.0, 74.0]",usask_1
+13a368f78,1024,1024,"[673.0, 158.0, 123.0, 83.0]",usask_1
+13a368f78,1024,1024,"[330.0, 709.0, 68.0, 98.0]",usask_1
+13a368f78,1024,1024,"[592.0, 528.0, 137.0, 90.0]",usask_1
+13a368f78,1024,1024,"[572.0, 53.0, 99.0, 82.0]",usask_1
+13a368f78,1024,1024,"[768.0, 838.0, 150.0, 153.0]",usask_1
+13a368f78,1024,1024,"[939.0, 488.0, 85.0, 84.0]",usask_1
+13a368f78,1024,1024,"[10.0, 894.0, 118.0, 130.0]",usask_1
+13a368f78,1024,1024,"[381.0, 14.0, 156.0, 100.0]",usask_1
+13a368f78,1024,1024,"[555.0, 734.0, 117.0, 110.0]",usask_1
+13a368f78,1024,1024,"[572.0, 175.0, 160.0, 163.0]",usask_1
+13a368f78,1024,1024,"[397.0, 697.0, 170.0, 174.0]",usask_1
+13a368f78,1024,1024,"[647.0, 997.0, 104.0, 27.0]",usask_1
+13a368f78,1024,1024,"[261.0, 144.0, 164.0, 65.0]",usask_1
+13a368f78,1024,1024,"[970.0, 131.0, 54.0, 79.0]",usask_1
+13a368f78,1024,1024,"[818.0, 144.0, 123.0, 73.0]",usask_1
+13a368f78,1024,1024,"[0.0, 986.0, 55.0, 38.0]",usask_1
+13a368f78,1024,1024,"[136.0, 978.0, 56.0, 46.0]",usask_1
+13a368f78,1024,1024,"[307.0, 642.0, 52.0, 69.0]",usask_1
+13a368f78,1024,1024,"[720.0, 504.0, 99.0, 67.0]",usask_1
+13a368f78,1024,1024,"[792.0, 581.0, 95.0, 73.0]",usask_1
+13a368f78,1024,1024,"[828.0, 526.0, 72.0, 67.0]",usask_1
+13a368f78,1024,1024,"[807.0, 473.0, 71.0, 69.0]",usask_1
+13a368f78,1024,1024,"[943.0, 412.0, 63.0, 86.0]",usask_1
+13a368f78,1024,1024,"[206.0, 484.0, 103.0, 76.0]",usask_1
+13a368f78,1024,1024,"[124.0, 497.0, 86.0, 88.0]",usask_1
+13a368f78,1024,1024,"[56.0, 478.0, 101.0, 81.0]",usask_1
+13a368f78,1024,1024,"[29.0, 469.0, 66.0, 139.0]",usask_1
+13a368f78,1024,1024,"[0.0, 527.0, 73.0, 58.0]",usask_1
+13a368f78,1024,1024,"[187.0, 330.0, 104.0, 72.0]",usask_1
+13a368f78,1024,1024,"[196.0, 160.0, 66.0, 81.0]",usask_1
+13a368f78,1024,1024,"[838.0, 242.0, 74.0, 75.0]",usask_1
+13a368f78,1024,1024,"[793.0, 241.0, 127.0, 104.0]",usask_1
+13a368f78,1024,1024,"[796.0, 193.0, 155.0, 99.0]",usask_1
+13a368f78,1024,1024,"[583.0, 93.0, 172.0, 58.0]",usask_1
+13a368f78,1024,1024,"[809.0, 13.0, 142.0, 126.0]",usask_1
+13a368f78,1024,1024,"[822.0, 78.0, 106.0, 52.0]",usask_1
+13a368f78,1024,1024,"[834.0, 2.0, 97.0, 46.0]",usask_1
+010b216d4,1024,1024,"[367.0, 293.0, 113.0, 110.0]",usask_1
+010b216d4,1024,1024,"[0.0, 556.0, 91.0, 81.0]",usask_1
+010b216d4,1024,1024,"[134.0, 219.0, 157.0, 76.0]",usask_1
+010b216d4,1024,1024,"[131.0, 751.0, 140.0, 62.0]",usask_1
+010b216d4,1024,1024,"[353.0, 0.0, 129.0, 54.0]",usask_1
+010b216d4,1024,1024,"[182.0, 801.0, 168.0, 107.0]",usask_1
+010b216d4,1024,1024,"[902.0, 346.0, 113.0, 80.0]",usask_1
+010b216d4,1024,1024,"[488.0, 493.0, 172.0, 136.0]",usask_1
+010b216d4,1024,1024,"[93.0, 345.0, 176.0, 115.0]",usask_1
+010b216d4,1024,1024,"[633.0, 852.0, 265.0, 111.0]",usask_1
+010b216d4,1024,1024,"[0.0, 966.0, 96.0, 58.0]",usask_1
+010b216d4,1024,1024,"[261.0, 271.0, 139.0, 192.0]",usask_1
+010b216d4,1024,1024,"[967.0, 578.0, 57.0, 114.0]",usask_1
+010b216d4,1024,1024,"[380.0, 499.0, 106.0, 89.0]",usask_1
+010b216d4,1024,1024,"[29.0, 762.0, 212.0, 129.0]",usask_1
+010b216d4,1024,1024,"[185.0, 148.0, 169.0, 112.0]",usask_1
+010b216d4,1024,1024,"[72.0, 591.0, 192.0, 187.0]",usask_1
+010b216d4,1024,1024,"[949.0, 778.0, 75.0, 79.0]",usask_1
+010b216d4,1024,1024,"[764.0, 232.0, 167.0, 106.0]",usask_1
+010b216d4,1024,1024,"[363.0, 65.0, 112.0, 75.0]",usask_1
+010b216d4,1024,1024,"[397.0, 673.0, 181.0, 83.0]",usask_1
+010b216d4,1024,1024,"[939.0, 0.0, 85.0, 89.0]",usask_1
+010b216d4,1024,1024,"[631.0, 986.0, 109.0, 38.0]",usask_1
+010b216d4,1024,1024,"[279.0, 0.0, 94.0, 50.0]",usask_1
+010b216d4,1024,1024,"[913.0, 421.0, 106.0, 180.0]",usask_1
+010b216d4,1024,1024,"[815.0, 23.0, 207.0, 144.0]",usask_1
+010b216d4,1024,1024,"[0.0, 872.0, 138.0, 81.0]",usask_1
+010b216d4,1024,1024,"[798.0, 309.0, 140.0, 73.0]",usask_1
+010b216d4,1024,1024,"[511.0, 904.0, 68.0, 120.0]",usask_1
+010b216d4,1024,1024,"[380.0, 568.0, 155.0, 90.0]",usask_1
+010b216d4,1024,1024,"[849.0, 505.0, 120.0, 83.0]",usask_1
+010b216d4,1024,1024,"[680.0, 542.0, 173.0, 74.0]",usask_1
+010b216d4,1024,1024,"[606.0, 510.0, 113.0, 70.0]",usask_1
+010b216d4,1024,1024,"[285.0, 435.0, 116.0, 96.0]",usask_1
+010b216d4,1024,1024,"[67.0, 161.0, 86.0, 66.0]",usask_1
+010b216d4,1024,1024,"[137.0, 97.0, 88.0, 70.0]",usask_1
+ddc160ce0,1024,1024,"[478.0, 892.0, 71.0, 72.0]",usask_1
+ddc160ce0,1024,1024,"[538.0, 937.0, 88.0, 49.0]",usask_1
+ddc160ce0,1024,1024,"[0.0, 58.0, 170.0, 121.0]",usask_1
+ddc160ce0,1024,1024,"[488.0, 817.0, 82.0, 62.0]",usask_1
+ddc160ce0,1024,1024,"[896.0, 0.0, 116.0, 2.0]",usask_1
+ddc160ce0,1024,1024,"[945.0, 367.0, 78.0, 147.0]",usask_1
+ddc160ce0,1024,1024,"[253.0, 113.0, 89.0, 157.0]",usask_1
+ddc160ce0,1024,1024,"[883.0, 645.0, 85.0, 99.0]",usask_1
+ddc160ce0,1024,1024,"[282.0, 101.0, 105.0, 136.0]",usask_1
+ddc160ce0,1024,1024,"[87.0, 205.0, 90.0, 96.0]",usask_1
+ddc160ce0,1024,1024,"[589.0, 811.0, 99.0, 95.0]",usask_1
+ddc160ce0,1024,1024,"[956.0, 280.0, 68.0, 106.0]",usask_1
+ddc160ce0,1024,1024,"[560.0, 141.0, 100.0, 103.0]",usask_1
+ddc160ce0,1024,1024,"[512.0, 57.0, 163.0, 96.0]",usask_1
+ddc160ce0,1024,1024,"[41.0, 58.0, 136.0, 117.0]",usask_1
+ddc160ce0,1024,1024,"[970.0, 718.0, 54.0, 90.0]",usask_1
+ddc160ce0,1024,1024,"[754.0, 599.0, 96.0, 130.0]",usask_1
+ddc160ce0,1024,1024,"[879.0, 134.0, 109.0, 113.0]",usask_1
+ddc160ce0,1024,1024,"[133.0, 0.0, 88.0, 45.0]",usask_1
+ddc160ce0,1024,1024,"[145.0, 633.0, 84.0, 100.0]",usask_1
+ddc160ce0,1024,1024,"[458.0, 303.0, 86.0, 83.0]",usask_1
+ddc160ce0,1024,1024,"[426.0, 946.0, 59.0, 57.0]",usask_1
+ddc160ce0,1024,1024,"[350.0, 988.0, 91.0, 36.0]",usask_1
+ddc160ce0,1024,1024,"[320.0, 902.0, 82.0, 67.0]",usask_1
+ddc160ce0,1024,1024,"[238.0, 883.0, 83.0, 87.0]",usask_1
+ddc160ce0,1024,1024,"[568.0, 432.0, 103.0, 86.0]",usask_1
+ddc160ce0,1024,1024,"[489.0, 452.0, 73.0, 108.0]",usask_1
+ddc160ce0,1024,1024,"[394.0, 478.0, 78.0, 99.0]",usask_1
+ddc160ce0,1024,1024,"[252.0, 495.0, 91.0, 76.0]",usask_1
+ddc160ce0,1024,1024,"[212.0, 28.0, 94.0, 49.0]",usask_1
+be5ba0745,1024,1024,"[533.0, 305.0, 131.0, 111.0]",usask_1
+be5ba0745,1024,1024,"[600.0, 253.0, 142.0, 104.0]",usask_1
+be5ba0745,1024,1024,"[368.0, 553.0, 120.0, 72.0]",usask_1
+be5ba0745,1024,1024,"[620.0, 430.0, 82.0, 83.0]",usask_1
+be5ba0745,1024,1024,"[585.0, 99.0, 138.0, 80.0]",usask_1
+be5ba0745,1024,1024,"[96.0, 553.0, 147.0, 95.0]",usask_1
+be5ba0745,1024,1024,"[424.0, 968.0, 133.0, 56.0]",usask_1
+be5ba0745,1024,1024,"[744.0, 400.0, 144.0, 140.0]",usask_1
+71b460a14,1024,1024,"[23.0, 418.0, 41.0, 69.0]",usask_1
+71b460a14,1024,1024,"[0.0, 815.0, 100.0, 83.0]",usask_1
+71b460a14,1024,1024,"[346.0, 628.0, 95.0, 107.0]",usask_1
+71b460a14,1024,1024,"[185.0, 879.0, 85.0, 63.0]",usask_1
+71b460a14,1024,1024,"[175.0, 0.0, 96.0, 2.0]",usask_1
+71b460a14,1024,1024,"[66.0, 884.0, 117.0, 83.0]",usask_1
+71b460a14,1024,1024,"[175.0, 94.0, 99.0, 108.0]",usask_1
+71b460a14,1024,1024,"[752.0, 756.0, 127.0, 108.0]",usask_1
+71b460a14,1024,1024,"[114.0, 105.0, 86.0, 159.0]",usask_1
+71b460a14,1024,1024,"[353.0, 357.0, 121.0, 189.0]",usask_1
+71b460a14,1024,1024,"[30.0, 663.0, 160.0, 160.0]",usask_1
+71b460a14,1024,1024,"[339.0, 479.0, 110.0, 145.0]",usask_1
+71b460a14,1024,1024,"[367.0, 158.0, 87.0, 142.0]",usask_1
+71b460a14,1024,1024,"[818.0, 0.0, 107.0, 104.0]",usask_1
+71b460a14,1024,1024,"[193.0, 614.0, 67.0, 102.0]",usask_1
+71b460a14,1024,1024,"[432.0, 259.0, 152.0, 93.0]",usask_1
+71b460a14,1024,1024,"[367.0, 929.0, 103.0, 95.0]",usask_1
+71b460a14,1024,1024,"[0.0, 164.0, 93.0, 206.0]",usask_1
+71b460a14,1024,1024,"[504.0, 41.0, 163.0, 215.0]",usask_1
+71b460a14,1024,1024,"[553.0, 171.0, 176.0, 100.0]",usask_1
+71b460a14,1024,1024,"[62.0, 842.0, 74.0, 75.0]",usask_1
+71b460a14,1024,1024,"[289.0, 830.0, 84.0, 194.0]",usask_1
+71b460a14,1024,1024,"[0.0, 943.0, 99.0, 79.0]",usask_1
+71b460a14,1024,1024,"[630.0, 0.0, 104.0, 115.0]",usask_1
+71b460a14,1024,1024,"[939.0, 325.0, 83.0, 144.0]",usask_1
+71b460a14,1024,1024,"[748.0, 316.0, 106.0, 184.0]",usask_1
+71b460a14,1024,1024,"[758.0, 154.0, 115.0, 76.0]",usask_1
+71b460a14,1024,1024,"[960.0, 0.0, 64.0, 2.0]",usask_1
+71b460a14,1024,1024,"[861.0, 826.0, 51.0, 84.0]",usask_1
+71b460a14,1024,1024,"[870.0, 893.0, 61.0, 53.0]",usask_1
+71b460a14,1024,1024,"[820.0, 925.0, 72.0, 47.0]",usask_1
+71b460a14,1024,1024,"[253.0, 621.0, 61.0, 79.0]",usask_1
+71b460a14,1024,1024,"[22.0, 519.0, 82.0, 58.0]",usask_1
+71b460a14,1024,1024,"[293.0, 502.0, 84.0, 56.0]",usask_1
+71b460a14,1024,1024,"[376.0, 482.0, 77.0, 69.0]",usask_1
+71b460a14,1024,1024,"[0.0, 422.0, 29.0, 67.0]",usask_1
+71b460a14,1024,1024,"[940.0, 281.0, 81.0, 54.0]",usask_1
+71b460a14,1024,1024,"[727.0, 66.0, 102.0, 95.0]",usask_1
+71b460a14,1024,1024,"[519.0, 11.0, 140.0, 104.0]",usask_1
+71b460a14,1024,1024,"[583.0, 0.0, 62.0, 65.0]",usask_1
+71b460a14,1024,1024,"[71.0, 0.0, 108.0, 152.0]",usask_1
+f6afe5443,1024,1024,"[9.0, 20.0, 95.0, 153.0]",usask_1
+f6afe5443,1024,1024,"[384.0, 188.0, 66.0, 70.0]",usask_1
+f6afe5443,1024,1024,"[0.0, 496.0, 30.0, 47.0]",usask_1
+f6afe5443,1024,1024,"[122.0, 429.0, 152.0, 74.0]",usask_1
+f6afe5443,1024,1024,"[0.0, 426.0, 55.0, 60.0]",usask_1
+f6afe5443,1024,1024,"[930.0, 874.0, 94.0, 120.0]",usask_1
+f6afe5443,1024,1024,"[523.0, 262.0, 37.0, 71.0]",usask_1
+f6afe5443,1024,1024,"[582.0, 825.0, 113.0, 184.0]",usask_1
+f6afe5443,1024,1024,"[332.0, 393.0, 122.0, 73.0]",usask_1
+f6afe5443,1024,1024,"[687.0, 747.0, 288.0, 144.0]",usask_1
+f6afe5443,1024,1024,"[835.0, 547.0, 189.0, 119.0]",usask_1
+f6afe5443,1024,1024,"[592.0, 301.0, 49.0, 66.0]",usask_1
+f6afe5443,1024,1024,"[3.0, 359.0, 59.0, 65.0]",usask_1
+f6afe5443,1024,1024,"[326.0, 596.0, 137.0, 103.0]",usask_1
+f6afe5443,1024,1024,"[910.0, 0.0, 100.0, 39.0]",usask_1
+f6afe5443,1024,1024,"[647.0, 300.0, 46.0, 117.0]",usask_1
+f6afe5443,1024,1024,"[660.0, 326.0, 148.0, 68.0]",usask_1
+f6afe5443,1024,1024,"[813.0, 390.0, 171.0, 111.0]",usask_1
+f6afe5443,1024,1024,"[455.0, 431.0, 60.0, 88.0]",usask_1
+f6afe5443,1024,1024,"[98.0, 470.0, 172.0, 80.0]",usask_1
+f6afe5443,1024,1024,"[157.0, 581.0, 93.0, 70.0]",usask_1
+f6afe5443,1024,1024,"[94.0, 606.0, 130.0, 77.0]",usask_1
+f6afe5443,1024,1024,"[55.0, 660.0, 79.0, 88.0]",usask_1
+f6afe5443,1024,1024,"[58.0, 750.0, 136.0, 75.0]",usask_1
+f6afe5443,1024,1024,"[0.0, 774.0, 69.0, 125.0]",usask_1
+f6afe5443,1024,1024,"[61.0, 820.0, 55.0, 92.0]",usask_1
+6202ea12d,1024,1024,"[256.0, 154.0, 95.0, 157.0]",usask_1
+6202ea12d,1024,1024,"[150.0, 378.0, 101.0, 72.0]",usask_1
+6202ea12d,1024,1024,"[418.0, 767.0, 136.0, 81.0]",usask_1
+6202ea12d,1024,1024,"[894.0, 616.0, 130.0, 145.0]",usask_1
+6202ea12d,1024,1024,"[545.0, 40.0, 90.0, 73.0]",usask_1
+6202ea12d,1024,1024,"[710.0, 447.0, 66.0, 141.0]",usask_1
+6202ea12d,1024,1024,"[403.0, 368.0, 128.0, 85.0]",usask_1
+6202ea12d,1024,1024,"[59.0, 437.0, 118.0, 84.0]",usask_1
+6202ea12d,1024,1024,"[403.0, 567.0, 195.0, 131.0]",usask_1
+6202ea12d,1024,1024,"[503.0, 385.0, 149.0, 74.0]",usask_1
+6202ea12d,1024,1024,"[124.0, 254.0, 109.0, 84.0]",usask_1
+6202ea12d,1024,1024,"[131.0, 566.0, 73.0, 137.0]",usask_1
+6202ea12d,1024,1024,"[922.0, 491.0, 102.0, 216.0]",usask_1
+6202ea12d,1024,1024,"[2.0, 1.0, 94.0, 161.0]",usask_1
+6202ea12d,1024,1024,"[892.0, 867.0, 100.0, 146.0]",usask_1
+6202ea12d,1024,1024,"[513.0, 963.0, 103.0, 61.0]",usask_1
+6202ea12d,1024,1024,"[0.0, 781.0, 110.0, 75.0]",usask_1
+6202ea12d,1024,1024,"[969.0, 151.0, 54.0, 118.0]",usask_1
+6202ea12d,1024,1024,"[211.0, 673.0, 67.0, 117.0]",usask_1
+6202ea12d,1024,1024,"[15.0, 16.0, 121.0, 59.0]",usask_1
+6202ea12d,1024,1024,"[738.0, 970.0, 85.0, 54.0]",usask_1
+6202ea12d,1024,1024,"[336.0, 195.0, 80.0, 101.0]",usask_1
+6202ea12d,1024,1024,"[232.0, 447.0, 218.0, 95.0]",usask_1
+6202ea12d,1024,1024,"[161.0, 848.0, 103.0, 106.0]",usask_1
+6202ea12d,1024,1024,"[18.0, 418.0, 74.0, 63.0]",usask_1
+6202ea12d,1024,1024,"[0.0, 594.0, 67.0, 58.0]",usask_1
+6202ea12d,1024,1024,"[869.0, 119.0, 99.0, 155.0]",usask_1
+6202ea12d,1024,1024,"[766.0, 437.0, 80.0, 171.0]",usask_1
+6202ea12d,1024,1024,"[272.0, 966.0, 82.0, 54.0]",usask_1
+6202ea12d,1024,1024,"[842.0, 995.0, 163.0, 29.0]",usask_1
+6202ea12d,1024,1024,"[844.0, 889.0, 71.0, 98.0]",usask_1
+6202ea12d,1024,1024,"[287.0, 819.0, 142.0, 108.0]",usask_1
+6202ea12d,1024,1024,"[109.0, 785.0, 93.0, 65.0]",usask_1
+6202ea12d,1024,1024,"[837.0, 456.0, 54.0, 135.0]",usask_1
+6202ea12d,1024,1024,"[824.0, 585.0, 49.0, 97.0]",usask_1
+6202ea12d,1024,1024,"[453.0, 338.0, 93.0, 58.0]",usask_1
+6202ea12d,1024,1024,"[0.0, 441.0, 68.0, 94.0]",usask_1
+6202ea12d,1024,1024,"[179.0, 485.0, 91.0, 79.0]",usask_1
+6202ea12d,1024,1024,"[186.0, 317.0, 88.0, 74.0]",usask_1
+6202ea12d,1024,1024,"[105.0, 1.0, 82.0, 91.0]",usask_1
+3b829f3b0,1024,1024,"[205.0, 128.0, 98.0, 107.0]",usask_1
+3b829f3b0,1024,1024,"[190.0, 87.0, 87.0, 70.0]",usask_1
+3b829f3b0,1024,1024,"[207.0, 758.0, 108.0, 96.0]",usask_1
+3b829f3b0,1024,1024,"[407.0, 269.0, 196.0, 109.0]",usask_1
+3b829f3b0,1024,1024,"[936.0, 488.0, 86.0, 90.0]",usask_1
+3b829f3b0,1024,1024,"[850.0, 0.0, 91.0, 85.0]",usask_1
+3b829f3b0,1024,1024,"[180.0, 609.0, 71.0, 104.0]",usask_1
+3b829f3b0,1024,1024,"[87.0, 754.0, 140.0, 122.0]",usask_1
+3b829f3b0,1024,1024,"[0.0, 778.0, 106.0, 79.0]",usask_1
+3b829f3b0,1024,1024,"[783.0, 708.0, 98.0, 116.0]",usask_1
+3b829f3b0,1024,1024,"[417.0, 724.0, 121.0, 89.0]",usask_1
+3b829f3b0,1024,1024,"[927.0, 600.0, 97.0, 103.0]",usask_1
+3b829f3b0,1024,1024,"[0.0, 0.0, 40.0, 49.0]",usask_1
+3b829f3b0,1024,1024,"[162.0, 232.0, 98.0, 174.0]",usask_1
+3b829f3b0,1024,1024,"[793.0, 404.0, 64.0, 72.0]",usask_1
+3b829f3b0,1024,1024,"[978.0, 252.0, 46.0, 115.0]",usask_1
+3b829f3b0,1024,1024,"[933.0, 4.0, 78.0, 120.0]",usask_1
+3b829f3b0,1024,1024,"[970.0, 145.0, 54.0, 61.0]",usask_1
+7803a8211,1024,1024,"[366.0, 39.0, 139.0, 52.0]",usask_1
+7803a8211,1024,1024,"[491.0, 174.0, 125.0, 147.0]",usask_1
+7803a8211,1024,1024,"[965.0, 227.0, 58.0, 41.0]",usask_1
+7803a8211,1024,1024,"[901.0, 248.0, 101.0, 131.0]",usask_1
+7803a8211,1024,1024,"[46.0, 709.0, 104.0, 68.0]",usask_1
+7803a8211,1024,1024,"[507.0, 70.0, 101.0, 116.0]",usask_1
+7803a8211,1024,1024,"[673.0, 854.0, 103.0, 117.0]",usask_1
+7803a8211,1024,1024,"[623.0, 953.0, 167.0, 71.0]",usask_1
+7803a8211,1024,1024,"[741.0, 623.0, 61.0, 75.0]",usask_1
+7803a8211,1024,1024,"[0.0, 632.0, 62.0, 63.0]",usask_1
+7803a8211,1024,1024,"[226.0, 508.0, 129.0, 116.0]",usask_1
+7803a8211,1024,1024,"[936.0, 798.0, 88.0, 86.0]",usask_1
+7803a8211,1024,1024,"[509.0, 0.0, 93.0, 40.0]",usask_1
+7803a8211,1024,1024,"[166.0, 537.0, 65.0, 64.0]",usask_1
+7803a8211,1024,1024,"[517.0, 810.0, 146.0, 139.0]",usask_1
+7803a8211,1024,1024,"[65.0, 393.0, 65.0, 175.0]",usask_1
+7803a8211,1024,1024,"[0.0, 0.0, 102.0, 76.0]",usask_1
+7803a8211,1024,1024,"[411.0, 543.0, 75.0, 116.0]",usask_1
+7803a8211,1024,1024,"[990.0, 320.0, 34.0, 130.0]",usask_1
+7803a8211,1024,1024,"[981.0, 772.0, 43.0, 63.0]",usask_1
+7803a8211,1024,1024,"[388.0, 60.0, 131.0, 125.0]",usask_1
+7803a8211,1024,1024,"[1002.0, 580.0, 21.0, 131.0]",usask_1
+7803a8211,1024,1024,"[130.0, 183.0, 70.0, 106.0]",usask_1
+7803a8211,1024,1024,"[809.0, 576.0, 85.0, 134.0]",usask_1
+7803a8211,1024,1024,"[896.0, 880.0, 57.0, 59.0]",usask_1
+7803a8211,1024,1024,"[369.0, 393.0, 78.0, 110.0]",usask_1
+7803a8211,1024,1024,"[54.0, 610.0, 82.0, 61.0]",usask_1
+7803a8211,1024,1024,"[19.0, 626.0, 68.0, 60.0]",usask_1
+7803a8211,1024,1024,"[95.0, 473.0, 42.0, 115.0]",usask_1
+7803a8211,1024,1024,"[813.0, 959.0, 66.0, 61.0]",usask_1
+7803a8211,1024,1024,"[894.0, 958.0, 90.0, 42.0]",usask_1
+7803a8211,1024,1024,"[937.0, 978.0, 59.0, 46.0]",usask_1
+7803a8211,1024,1024,"[910.0, 941.0, 89.0, 51.0]",usask_1
+7803a8211,1024,1024,"[965.0, 914.0, 58.0, 56.0]",usask_1
+7803a8211,1024,1024,"[984.0, 739.0, 40.0, 43.0]",usask_1
+7803a8211,1024,1024,"[677.0, 665.0, 99.0, 74.0]",usask_1
+7803a8211,1024,1024,"[141.0, 354.0, 94.0, 116.0]",usask_1
+7803a8211,1024,1024,"[48.0, 291.0, 62.0, 98.0]",usask_1
+7803a8211,1024,1024,"[0.0, 193.0, 79.0, 101.0]",usask_1
+7803a8211,1024,1024,"[0.0, 170.0, 45.0, 58.0]",usask_1
+7803a8211,1024,1024,"[118.0, 108.0, 76.0, 89.0]",usask_1
+7803a8211,1024,1024,"[334.0, 301.0, 80.0, 78.0]",usask_1
+7803a8211,1024,1024,"[750.0, 474.0, 95.0, 99.0]",usask_1
+7803a8211,1024,1024,"[771.0, 325.0, 236.0, 123.0]",usask_1
+7803a8211,1024,1024,"[846.0, 348.0, 57.0, 121.0]",usask_1
+7803a8211,1024,1024,"[864.0, 494.0, 45.0, 62.0]",usask_1
+7803a8211,1024,1024,"[511.0, 336.0, 201.0, 72.0]",usask_1
+7803a8211,1024,1024,"[525.0, 332.0, 194.0, 59.0]",usask_1
+7803a8211,1024,1024,"[434.0, 455.0, 107.0, 186.0]",usask_1
+7803a8211,1024,1024,"[478.0, 441.0, 123.0, 135.0]",usask_1
+7803a8211,1024,1024,"[287.0, 426.0, 77.0, 128.0]",usask_1
+7803a8211,1024,1024,"[233.0, 406.0, 77.0, 87.0]",usask_1
+7803a8211,1024,1024,"[532.0, 416.0, 103.0, 151.0]",usask_1
+7803a8211,1024,1024,"[577.0, 432.0, 100.0, 141.0]",usask_1
+7803a8211,1024,1024,"[782.0, 461.0, 60.0, 53.0]",usask_1
+5de7e4f98,1024,1024,"[454.0, 127.0, 59.0, 87.0]",usask_1
+5de7e4f98,1024,1024,"[257.0, 658.0, 81.0, 67.0]",usask_1
+5de7e4f98,1024,1024,"[306.0, 269.0, 66.0, 113.0]",usask_1
+5de7e4f98,1024,1024,"[934.0, 55.0, 82.0, 100.0]",usask_1
+5de7e4f98,1024,1024,"[229.0, 259.0, 66.0, 86.0]",usask_1
+5de7e4f98,1024,1024,"[872.0, 909.0, 116.0, 93.0]",usask_1
+5de7e4f98,1024,1024,"[862.0, 549.0, 100.0, 113.0]",usask_1
+5de7e4f98,1024,1024,"[435.0, 384.0, 106.0, 86.0]",usask_1
+5de7e4f98,1024,1024,"[818.0, 868.0, 100.0, 78.0]",usask_1
+5de7e4f98,1024,1024,"[367.0, 319.0, 76.0, 107.0]",usask_1
+5de7e4f98,1024,1024,"[720.0, 104.0, 83.0, 86.0]",usask_1
+5de7e4f98,1024,1024,"[850.0, 69.0, 76.0, 73.0]",usask_1
+5de7e4f98,1024,1024,"[458.0, 797.0, 82.0, 92.0]",usask_1
+5de7e4f98,1024,1024,"[407.0, 225.0, 96.0, 120.0]",usask_1
+5de7e4f98,1024,1024,"[278.0, 976.0, 110.0, 48.0]",usask_1
+5de7e4f98,1024,1024,"[794.0, 950.0, 84.0, 74.0]",usask_1
+5de7e4f98,1024,1024,"[121.0, 138.0, 96.0, 149.0]",usask_1
+5de7e4f98,1024,1024,"[365.0, 152.0, 82.0, 109.0]",usask_1
+5de7e4f98,1024,1024,"[14.0, 703.0, 96.0, 95.0]",usask_1
+5de7e4f98,1024,1024,"[962.0, 677.0, 62.0, 101.0]",usask_1
+5de7e4f98,1024,1024,"[877.0, 185.0, 103.0, 107.0]",usask_1
+5de7e4f98,1024,1024,"[157.0, 725.0, 92.0, 117.0]",usask_1
+5de7e4f98,1024,1024,"[499.0, 169.0, 117.0, 75.0]",usask_1
+5de7e4f98,1024,1024,"[932.0, 618.0, 72.0, 143.0]",usask_1
+5de7e4f98,1024,1024,"[0.0, 814.0, 38.0, 90.0]",usask_1
+5de7e4f98,1024,1024,"[0.0, 0.0, 103.0, 52.0]",usask_1
+5de7e4f98,1024,1024,"[417.0, 501.0, 83.0, 97.0]",usask_1
+5de7e4f98,1024,1024,"[9.0, 434.0, 85.0, 88.0]",usask_1
+5de7e4f98,1024,1024,"[556.0, 0.0, 130.0, 55.0]",usask_1
+5de7e4f98,1024,1024,"[138.0, 912.0, 103.0, 103.0]",usask_1
+5de7e4f98,1024,1024,"[165.0, 602.0, 99.0, 93.0]",usask_1
+5de7e4f98,1024,1024,"[190.0, 187.0, 69.0, 48.0]",usask_1
+5de7e4f98,1024,1024,"[259.0, 171.0, 70.0, 69.0]",usask_1
+5de7e4f98,1024,1024,"[223.0, 69.0, 74.0, 112.0]",usask_1
+5de7e4f98,1024,1024,"[174.0, 79.0, 68.0, 87.0]",usask_1
+5de7e4f98,1024,1024,"[736.0, 413.0, 49.0, 53.0]",usask_1
+5de7e4f98,1024,1024,"[795.0, 0.0, 71.0, 83.0]",usask_1
+5de7e4f98,1024,1024,"[918.0, 4.0, 79.0, 89.0]",usask_1
+93d67b171,1024,1024,"[174.0, 184.0, 165.0, 111.0]",usask_1
+93d67b171,1024,1024,"[204.0, 294.0, 73.0, 314.0]",usask_1
+93d67b171,1024,1024,"[660.0, 342.0, 107.0, 176.0]",usask_1
+93d67b171,1024,1024,"[587.0, 697.0, 121.0, 84.0]",usask_1
+93d67b171,1024,1024,"[376.0, 572.0, 214.0, 106.0]",usask_1
+93d67b171,1024,1024,"[419.0, 547.0, 150.0, 86.0]",usask_1
+93d67b171,1024,1024,"[737.0, 434.0, 100.0, 90.0]",usask_1
+93d67b171,1024,1024,"[258.0, 650.0, 87.0, 101.0]",usask_1
+93d67b171,1024,1024,"[485.0, 219.0, 160.0, 79.0]",usask_1
+93d67b171,1024,1024,"[96.0, 267.0, 121.0, 85.0]",usask_1
+93d67b171,1024,1024,"[77.0, 343.0, 290.0, 118.0]",usask_1
+93d67b171,1024,1024,"[906.0, 198.0, 118.0, 100.0]",usask_1
+93d67b171,1024,1024,"[814.0, 330.0, 167.0, 170.0]",usask_1
+93d67b171,1024,1024,"[797.0, 0.0, 123.0, 234.0]",usask_1
+93d67b171,1024,1024,"[203.0, 58.0, 82.0, 91.0]",usask_1
+93d67b171,1024,1024,"[697.0, 124.0, 190.0, 160.0]",usask_1
+93d67b171,1024,1024,"[248.0, 1.0, 70.0, 73.0]",usask_1
+93d67b171,1024,1024,"[687.0, 515.0, 211.0, 127.0]",usask_1
+93d67b171,1024,1024,"[391.0, 263.0, 188.0, 182.0]",usask_1
+93d67b171,1024,1024,"[882.0, 889.0, 121.0, 101.0]",usask_1
+93d67b171,1024,1024,"[515.0, 114.0, 120.0, 103.0]",usask_1
+93d67b171,1024,1024,"[502.0, 0.0, 109.0, 41.0]",usask_1
+93d67b171,1024,1024,"[622.0, 0.0, 88.0, 186.0]",usask_1
+93d67b171,1024,1024,"[882.0, 0.0, 67.0, 107.0]",usask_1
+93d67b171,1024,1024,"[340.0, 400.0, 188.0, 164.0]",usask_1
+93d67b171,1024,1024,"[471.0, 445.0, 151.0, 77.0]",usask_1
+93d67b171,1024,1024,"[0.0, 435.0, 186.0, 82.0]",usask_1
+93d67b171,1024,1024,"[278.0, 159.0, 140.0, 133.0]",usask_1
+93d67b171,1024,1024,"[911.0, 468.0, 95.0, 95.0]",usask_1
+5a155e612,1024,1024,"[904.0, 702.0, 112.0, 78.0]",usask_1
+5a155e612,1024,1024,"[951.0, 24.0, 73.0, 58.0]",usask_1
+5a155e612,1024,1024,"[946.0, 773.0, 78.0, 81.0]",usask_1
+5a155e612,1024,1024,"[896.0, 944.0, 113.0, 80.0]",usask_1
+5a155e612,1024,1024,"[394.0, 687.0, 74.0, 104.0]",usask_1
+5a155e612,1024,1024,"[888.0, 775.0, 90.0, 92.0]",usask_1
+5a155e612,1024,1024,"[707.0, 653.0, 155.0, 96.0]",usask_1
+5a155e612,1024,1024,"[437.0, 697.0, 99.0, 109.0]",usask_1
+5a155e612,1024,1024,"[179.0, 516.0, 137.0, 93.0]",usask_1
+5a155e612,1024,1024,"[685.0, 610.0, 152.0, 70.0]",usask_1
+5a155e612,1024,1024,"[280.0, 465.0, 87.0, 108.0]",usask_1
+5a155e612,1024,1024,"[450.0, 630.0, 96.0, 83.0]",usask_1
+5a155e612,1024,1024,"[943.0, 232.0, 75.0, 125.0]",usask_1
+5a155e612,1024,1024,"[327.0, 598.0, 104.0, 89.0]",usask_1
+5a155e612,1024,1024,"[914.0, 885.0, 110.0, 73.0]",usask_1
+5a155e612,1024,1024,"[0.0, 848.0, 140.0, 147.0]",usask_1
+5a155e612,1024,1024,"[240.0, 0.0, 137.0, 40.0]",usask_1
+5a155e612,1024,1024,"[565.0, 909.0, 167.0, 96.0]",usask_1
+5a155e612,1024,1024,"[773.0, 887.0, 132.0, 88.0]",usask_1
+5a155e612,1024,1024,"[302.0, 299.0, 112.0, 82.0]",usask_1
+5a155e612,1024,1024,"[591.0, 673.0, 126.0, 86.0]",usask_1
+5a155e612,1024,1024,"[350.0, 351.0, 79.0, 63.0]",usask_1
+5a155e612,1024,1024,"[194.0, 329.0, 97.0, 61.0]",usask_1
+5a155e612,1024,1024,"[108.0, 274.0, 140.0, 71.0]",usask_1
+5a155e612,1024,1024,"[140.0, 201.0, 119.0, 76.0]",usask_1
+5a155e612,1024,1024,"[252.0, 181.0, 91.0, 107.0]",usask_1
+5a155e612,1024,1024,"[321.0, 202.0, 115.0, 83.0]",usask_1
+5a155e612,1024,1024,"[247.0, 80.0, 98.0, 68.0]",usask_1
+5a155e612,1024,1024,"[534.0, 299.0, 81.0, 81.0]",usask_1
+5a155e612,1024,1024,"[596.0, 227.0, 70.0, 108.0]",usask_1
+5a155e612,1024,1024,"[539.0, 155.0, 59.0, 133.0]",usask_1
+5a155e612,1024,1024,"[511.0, 159.0, 68.0, 64.0]",usask_1
+5a155e612,1024,1024,"[504.0, 54.0, 53.0, 85.0]",usask_1
+5a155e612,1024,1024,"[442.0, 52.0, 65.0, 163.0]",usask_1
+169be79d1,1024,1024,"[350.0, 0.0, 84.0, 46.0]",usask_1
+169be79d1,1024,1024,"[522.0, 953.0, 96.0, 71.0]",usask_1
+169be79d1,1024,1024,"[259.0, 838.0, 89.0, 80.0]",usask_1
+169be79d1,1024,1024,"[245.0, 970.0, 77.0, 54.0]",usask_1
+169be79d1,1024,1024,"[389.0, 609.0, 91.0, 81.0]",usask_1
+169be79d1,1024,1024,"[946.0, 614.0, 76.0, 168.0]",usask_1
+169be79d1,1024,1024,"[667.0, 514.0, 155.0, 66.0]",usask_1
+169be79d1,1024,1024,"[187.0, 511.0, 413.0, 99.0]",usask_1
+169be79d1,1024,1024,"[485.0, 704.0, 115.0, 124.0]",usask_1
+169be79d1,1024,1024,"[833.0, 922.0, 189.0, 102.0]",usask_1
+169be79d1,1024,1024,"[562.0, 882.0, 66.0, 76.0]",usask_1
+169be79d1,1024,1024,"[13.0, 259.0, 110.0, 140.0]",usask_1
+169be79d1,1024,1024,"[736.0, 629.0, 215.0, 110.0]",usask_1
+169be79d1,1024,1024,"[709.0, 441.0, 143.0, 76.0]",usask_1
+169be79d1,1024,1024,"[530.0, 813.0, 70.0, 62.0]",usask_1
+169be79d1,1024,1024,"[61.0, 942.0, 135.0, 82.0]",usask_1
+169be79d1,1024,1024,"[247.0, 522.0, 75.0, 103.0]",usask_1
+169be79d1,1024,1024,"[236.0, 469.0, 64.0, 51.0]",usask_1
+169be79d1,1024,1024,"[380.0, 518.0, 79.0, 87.0]",usask_1
+169be79d1,1024,1024,"[467.0, 527.0, 64.0, 84.0]",usask_1
+169be79d1,1024,1024,"[303.0, 457.0, 76.0, 70.0]",usask_1
+169be79d1,1024,1024,"[269.0, 352.0, 66.0, 79.0]",usask_1
+169be79d1,1024,1024,"[323.0, 376.0, 71.0, 78.0]",usask_1
+169be79d1,1024,1024,"[183.0, 213.0, 184.0, 120.0]",usask_1
+169be79d1,1024,1024,"[223.0, 682.0, 72.0, 115.0]",usask_1
+169be79d1,1024,1024,"[285.0, 739.0, 62.0, 93.0]",usask_1
+169be79d1,1024,1024,"[336.0, 566.0, 53.0, 63.0]",usask_1
+169be79d1,1024,1024,"[992.0, 519.0, 32.0, 279.0]",usask_1
+51e3f1a43,1024,1024,"[724.0, 491.0, 72.0, 110.0]",usask_1
+51e3f1a43,1024,1024,"[131.0, 185.0, 101.0, 75.0]",usask_1
+51e3f1a43,1024,1024,"[2.0, 407.0, 41.0, 146.0]",usask_1
+51e3f1a43,1024,1024,"[168.0, 148.0, 120.0, 126.0]",usask_1
+51e3f1a43,1024,1024,"[205.0, 402.0, 145.0, 109.0]",usask_1
+51e3f1a43,1024,1024,"[653.0, 465.0, 121.0, 89.0]",usask_1
+51e3f1a43,1024,1024,"[0.0, 592.0, 23.0, 63.0]",usask_1
+51e3f1a43,1024,1024,"[555.0, 79.0, 79.0, 80.0]",usask_1
+51e3f1a43,1024,1024,"[19.0, 655.0, 115.0, 110.0]",usask_1
+51e3f1a43,1024,1024,"[727.0, 0.0, 102.0, 68.0]",usask_1
+51e3f1a43,1024,1024,"[648.0, 168.0, 152.0, 74.0]",usask_1
+51e3f1a43,1024,1024,"[0.0, 686.0, 35.0, 128.0]",usask_1
+51e3f1a43,1024,1024,"[576.0, 909.0, 103.0, 93.0]",usask_1
+51e3f1a43,1024,1024,"[491.0, 691.0, 107.0, 94.0]",usask_1
+51e3f1a43,1024,1024,"[332.0, 173.0, 147.0, 74.0]",usask_1
+51e3f1a43,1024,1024,"[544.0, 614.0, 87.0, 78.0]",usask_1
+51e3f1a43,1024,1024,"[346.0, 215.0, 125.0, 86.0]",usask_1
+51e3f1a43,1024,1024,"[672.0, 210.0, 100.0, 110.0]",usask_1
+51e3f1a43,1024,1024,"[727.0, 274.0, 95.0, 79.0]",usask_1
+51e3f1a43,1024,1024,"[51.0, 432.0, 85.0, 78.0]",usask_1
+51e3f1a43,1024,1024,"[105.0, 721.0, 70.0, 69.0]",usask_1
+51e3f1a43,1024,1024,"[811.0, 0.0, 109.0, 85.0]",usask_1
+51e3f1a43,1024,1024,"[0.0, 8.0, 107.0, 91.0]",usask_1
+4b4639da9,1024,1024,"[922.0, 320.0, 97.0, 181.0]",usask_1
+4b4639da9,1024,1024,"[179.0, 330.0, 99.0, 113.0]",usask_1
+4b4639da9,1024,1024,"[94.0, 880.0, 86.0, 144.0]",usask_1
+4b4639da9,1024,1024,"[0.0, 399.0, 173.0, 104.0]",usask_1
+4b4639da9,1024,1024,"[945.0, 9.0, 60.0, 49.0]",usask_1
+4b4639da9,1024,1024,"[377.0, 715.0, 104.0, 93.0]",usask_1
+4b4639da9,1024,1024,"[267.0, 636.0, 147.0, 114.0]",usask_1
+4b4639da9,1024,1024,"[579.0, 660.0, 96.0, 106.0]",usask_1
+4b4639da9,1024,1024,"[904.0, 18.0, 57.0, 73.0]",usask_1
+4b4639da9,1024,1024,"[0.0, 20.0, 2.0, 130.0]",usask_1
+4b4639da9,1024,1024,"[134.0, 223.0, 142.0, 74.0]",usask_1
+4b4639da9,1024,1024,"[122.0, 136.0, 129.0, 101.0]",usask_1
+4b4639da9,1024,1024,"[851.0, 288.0, 78.0, 148.0]",usask_1
+4b4639da9,1024,1024,"[413.0, 795.0, 103.0, 71.0]",usask_1
+4b4639da9,1024,1024,"[565.0, 165.0, 93.0, 103.0]",usask_1
+4b4639da9,1024,1024,"[355.0, 855.0, 131.0, 69.0]",usask_1
+4b4639da9,1024,1024,"[811.0, 49.0, 97.0, 77.0]",usask_1
+4b4639da9,1024,1024,"[713.0, 362.0, 106.0, 144.0]",usask_1
+4b4639da9,1024,1024,"[465.0, 111.0, 81.0, 225.0]",usask_1
+4b4639da9,1024,1024,"[188.0, 491.0, 90.0, 87.0]",usask_1
+4b4639da9,1024,1024,"[478.0, 592.0, 93.0, 154.0]",usask_1
+4b4639da9,1024,1024,"[343.0, 346.0, 129.0, 74.0]",usask_1
+4b4639da9,1024,1024,"[602.0, 387.0, 116.0, 235.0]",usask_1
+4b4639da9,1024,1024,"[53.0, 0.0, 174.0, 35.0]",usask_1
+4b4639da9,1024,1024,"[232.0, 128.0, 96.0, 200.0]",usask_1
+4b4639da9,1024,1024,"[233.0, 395.0, 74.0, 80.0]",usask_1
+4b4639da9,1024,1024,"[989.0, 448.0, 35.0, 94.0]",usask_1
+4b4639da9,1024,1024,"[965.0, 592.0, 59.0, 105.0]",usask_1
+4b4639da9,1024,1024,"[880.0, 600.0, 79.0, 104.0]",usask_1
+4b4639da9,1024,1024,"[138.0, 715.0, 138.0, 86.0]",usask_1
+4b4639da9,1024,1024,"[147.0, 971.0, 93.0, 53.0]",usask_1
+4b4639da9,1024,1024,"[258.0, 985.0, 86.0, 34.0]",usask_1
+4b4639da9,1024,1024,"[224.0, 937.0, 90.0, 60.0]",usask_1
+4b4639da9,1024,1024,"[190.0, 867.0, 99.0, 76.0]",usask_1
+4b4639da9,1024,1024,"[401.0, 949.0, 103.0, 75.0]",usask_1
+4b4639da9,1024,1024,"[220.0, 2.0, 94.0, 62.0]",usask_1
+d781b9195,1024,1024,"[0.0, 0.0, 103.0, 2.0]",usask_1
+d781b9195,1024,1024,"[605.0, 620.0, 186.0, 93.0]",usask_1
+d781b9195,1024,1024,"[94.0, 0.0, 101.0, 87.0]",usask_1
+d781b9195,1024,1024,"[0.0, 852.0, 89.0, 81.0]",usask_1
+d781b9195,1024,1024,"[0.0, 936.0, 101.0, 76.0]",usask_1
+d781b9195,1024,1024,"[343.0, 581.0, 206.0, 102.0]",usask_1
+d781b9195,1024,1024,"[358.0, 0.0, 154.0, 122.0]",usask_1
+d781b9195,1024,1024,"[217.0, 767.0, 130.0, 142.0]",usask_1
+d781b9195,1024,1024,"[556.0, 556.0, 208.0, 119.0]",usask_1
+d781b9195,1024,1024,"[701.0, 546.0, 103.0, 141.0]",usask_1
+d781b9195,1024,1024,"[787.0, 804.0, 127.0, 100.0]",usask_1
+d781b9195,1024,1024,"[194.0, 544.0, 222.0, 98.0]",usask_1
+d781b9195,1024,1024,"[942.0, 365.0, 82.0, 135.0]",usask_1
+d781b9195,1024,1024,"[313.0, 804.0, 120.0, 160.0]",usask_1
+d781b9195,1024,1024,"[818.0, 933.0, 103.0, 91.0]",usask_1
+d781b9195,1024,1024,"[621.0, 777.0, 81.0, 83.0]",usask_1
+d781b9195,1024,1024,"[486.0, 0.0, 122.0, 69.0]",usask_1
+d781b9195,1024,1024,"[698.0, 160.0, 84.0, 84.0]",usask_1
+d781b9195,1024,1024,"[976.0, 548.0, 48.0, 111.0]",usask_1
+d781b9195,1024,1024,"[69.0, 32.0, 195.0, 109.0]",usask_1
+d781b9195,1024,1024,"[385.0, 756.0, 78.0, 201.0]",usask_1
+d781b9195,1024,1024,"[406.0, 386.0, 90.0, 91.0]",usask_1
+0429c9ff6,1024,1024,"[2.0, 967.0, 59.0, 57.0]",usask_1
+0429c9ff6,1024,1024,"[0.0, 835.0, 67.0, 74.0]",usask_1
+0429c9ff6,1024,1024,"[757.0, 289.0, 80.0, 85.0]",usask_1
+0429c9ff6,1024,1024,"[461.0, 908.0, 104.0, 74.0]",usask_1
+0429c9ff6,1024,1024,"[52.0, 956.0, 67.0, 68.0]",usask_1
+0429c9ff6,1024,1024,"[597.0, 609.0, 90.0, 128.0]",usask_1
+0429c9ff6,1024,1024,"[782.0, 833.0, 122.0, 74.0]",usask_1
+0429c9ff6,1024,1024,"[769.0, 370.0, 107.0, 94.0]",usask_1
+0429c9ff6,1024,1024,"[670.0, 167.0, 110.0, 138.0]",usask_1
+0429c9ff6,1024,1024,"[520.0, 808.0, 89.0, 180.0]",usask_1
+0429c9ff6,1024,1024,"[922.0, 929.0, 102.0, 83.0]",usask_1
+0429c9ff6,1024,1024,"[791.0, 0.0, 117.0, 130.0]",usask_1
+0429c9ff6,1024,1024,"[497.0, 239.0, 130.0, 120.0]",usask_1
+0429c9ff6,1024,1024,"[673.0, 0.0, 91.0, 57.0]",usask_1
+0429c9ff6,1024,1024,"[761.0, 960.0, 38.0, 64.0]",usask_1
+0429c9ff6,1024,1024,"[196.0, 596.0, 187.0, 85.0]",usask_1
+0429c9ff6,1024,1024,"[265.0, 235.0, 93.0, 53.0]",usask_1
+0429c9ff6,1024,1024,"[890.0, 417.0, 57.0, 51.0]",usask_1
+0429c9ff6,1024,1024,"[868.0, 378.0, 47.0, 39.0]",usask_1
+5a0db55d8,1024,1024,"[902.0, 56.0, 122.0, 66.0]",usask_1
+5a0db55d8,1024,1024,"[227.0, 523.0, 113.0, 222.0]",usask_1
+5a0db55d8,1024,1024,"[590.0, 809.0, 103.0, 57.0]",usask_1
+5a0db55d8,1024,1024,"[77.0, 33.0, 120.0, 161.0]",usask_1
+5a0db55d8,1024,1024,"[0.0, 287.0, 70.0, 94.0]",usask_1
+5a0db55d8,1024,1024,"[796.0, 630.0, 79.0, 77.0]",usask_1
+5a0db55d8,1024,1024,"[915.0, 617.0, 77.0, 74.0]",usask_1
+5a0db55d8,1024,1024,"[557.0, 343.0, 153.0, 184.0]",usask_1
+5a0db55d8,1024,1024,"[75.0, 330.0, 139.0, 69.0]",usask_1
+5a0db55d8,1024,1024,"[945.0, 698.0, 74.0, 153.0]",usask_1
+5a0db55d8,1024,1024,"[908.0, 4.0, 115.0, 79.0]",usask_1
+5a0db55d8,1024,1024,"[397.0, 47.0, 107.0, 212.0]",usask_1
+5a0db55d8,1024,1024,"[19.0, 922.0, 93.0, 65.0]",usask_1
+5a0db55d8,1024,1024,"[673.0, 272.0, 148.0, 185.0]",usask_1
+5a0db55d8,1024,1024,"[673.0, 434.0, 140.0, 128.0]",usask_1
+5a0db55d8,1024,1024,"[445.0, 718.0, 59.0, 97.0]",usask_1
+5a0db55d8,1024,1024,"[388.0, 262.0, 131.0, 256.0]",usask_1
+5a0db55d8,1024,1024,"[370.0, 512.0, 93.0, 220.0]",usask_1
+5a0db55d8,1024,1024,"[107.0, 771.0, 127.0, 113.0]",usask_1
+5a0db55d8,1024,1024,"[330.0, 937.0, 83.0, 67.0]",usask_1
+5a0db55d8,1024,1024,"[243.0, 698.0, 145.0, 74.0]",usask_1
+5a0db55d8,1024,1024,"[0.0, 0.0, 146.0, 76.0]",usask_1
+5a0db55d8,1024,1024,"[0.0, 744.0, 125.0, 141.0]",usask_1
+5a0db55d8,1024,1024,"[81.0, 875.0, 74.0, 96.0]",usask_1
+5a0db55d8,1024,1024,"[579.0, 727.0, 166.0, 112.0]",usask_1
+5a0db55d8,1024,1024,"[492.0, 148.0, 185.0, 78.0]",usask_1
+5a0db55d8,1024,1024,"[0.0, 104.0, 99.0, 116.0]",usask_1
+5a0db55d8,1024,1024,"[241.0, 817.0, 93.0, 88.0]",usask_1
+5a0db55d8,1024,1024,"[858.0, 476.0, 95.0, 85.0]",usask_1
+5a0db55d8,1024,1024,"[46.0, 452.0, 154.0, 95.0]",usask_1
+5a0db55d8,1024,1024,"[48.0, 501.0, 159.0, 98.0]",usask_1
+5a0db55d8,1024,1024,"[10.0, 581.0, 173.0, 78.0]",usask_1
+5a0db55d8,1024,1024,"[0.0, 476.0, 75.0, 66.0]",usask_1
+5a0db55d8,1024,1024,"[886.0, 692.0, 61.0, 73.0]",usask_1
+5a0db55d8,1024,1024,"[722.0, 702.0, 71.0, 99.0]",usask_1
+5a0db55d8,1024,1024,"[525.0, 484.0, 120.0, 181.0]",usask_1
+5a0db55d8,1024,1024,"[780.0, 268.0, 78.0, 90.0]",usask_1
+5a0db55d8,1024,1024,"[343.0, 92.0, 73.0, 92.0]",usask_1
+d2265cb91,1024,1024,"[135.0, 782.0, 122.0, 64.0]",usask_1
+d2265cb91,1024,1024,"[320.0, 64.0, 66.0, 76.0]",usask_1
+d2265cb91,1024,1024,"[0.0, 912.0, 86.0, 87.0]",usask_1
+d2265cb91,1024,1024,"[818.0, 94.0, 73.0, 96.0]",usask_1
+d2265cb91,1024,1024,"[107.0, 300.0, 110.0, 67.0]",usask_1
+d2265cb91,1024,1024,"[0.0, 520.0, 71.0, 79.0]",usask_1
+d2265cb91,1024,1024,"[853.0, 904.0, 96.0, 84.0]",usask_1
+d2265cb91,1024,1024,"[79.0, 0.0, 103.0, 54.0]",usask_1
+d2265cb91,1024,1024,"[223.0, 144.0, 72.0, 65.0]",usask_1
+d2265cb91,1024,1024,"[764.0, 282.0, 100.0, 93.0]",usask_1
+d2265cb91,1024,1024,"[267.0, 184.0, 75.0, 62.0]",usask_1
+d2265cb91,1024,1024,"[957.0, 144.0, 47.0, 52.0]",usask_1
+d2265cb91,1024,1024,"[340.0, 364.0, 69.0, 121.0]",usask_1
+d2265cb91,1024,1024,"[110.0, 86.0, 127.0, 70.0]",usask_1
+d2265cb91,1024,1024,"[0.0, 151.0, 89.0, 117.0]",usask_1
+d2265cb91,1024,1024,"[697.0, 90.0, 106.0, 124.0]",usask_1
+d2265cb91,1024,1024,"[74.0, 589.0, 106.0, 86.0]",usask_1
+d2265cb91,1024,1024,"[967.0, 0.0, 57.0, 36.0]",usask_1
+d2265cb91,1024,1024,"[44.0, 739.0, 129.0, 60.0]",usask_1
+d2265cb91,1024,1024,"[390.0, 47.0, 113.0, 113.0]",usask_1
+d2265cb91,1024,1024,"[167.0, 557.0, 66.0, 67.0]",usask_1
+d2265cb91,1024,1024,"[136.0, 969.0, 106.0, 55.0]",usask_1
+d2265cb91,1024,1024,"[741.0, 346.0, 103.0, 97.0]",usask_1
+d2265cb91,1024,1024,"[741.0, 754.0, 109.0, 96.0]",usask_1
+d2265cb91,1024,1024,"[404.0, 462.0, 146.0, 93.0]",usask_1
+d2265cb91,1024,1024,"[211.0, 711.0, 83.0, 62.0]",usask_1
+d2265cb91,1024,1024,"[397.0, 660.0, 106.0, 110.0]",usask_1
+d2265cb91,1024,1024,"[569.0, 121.0, 177.0, 130.0]",usask_1
+d2265cb91,1024,1024,"[240.0, 330.0, 103.0, 90.0]",usask_1
+d2265cb91,1024,1024,"[875.0, 572.0, 109.0, 102.0]",usask_1
+d2265cb91,1024,1024,"[717.0, 919.0, 127.0, 93.0]",usask_1
+d2265cb91,1024,1024,"[0.0, 806.0, 101.0, 74.0]",usask_1
+d2265cb91,1024,1024,"[623.0, 468.0, 103.0, 110.0]",usask_1
+d2265cb91,1024,1024,"[880.0, 15.0, 71.0, 107.0]",usask_1
+d2265cb91,1024,1024,"[808.0, 864.0, 90.0, 69.0]",usask_1
+d2265cb91,1024,1024,"[240.0, 902.0, 70.0, 69.0]",usask_1
+d2265cb91,1024,1024,"[206.0, 803.0, 79.0, 81.0]",usask_1
+d2265cb91,1024,1024,"[144.0, 682.0, 91.0, 63.0]",usask_1
+d2265cb91,1024,1024,"[121.0, 245.0, 77.0, 63.0]",usask_1
+d2265cb91,1024,1024,"[103.0, 481.0, 118.0, 82.0]",usask_1
+d2265cb91,1024,1024,"[66.0, 445.0, 100.0, 70.0]",usask_1
+d2265cb91,1024,1024,"[9.0, 436.0, 96.0, 67.0]",usask_1
+d2265cb91,1024,1024,"[0.0, 103.0, 59.0, 64.0]",usask_1
+d2265cb91,1024,1024,"[13.0, 9.0, 80.0, 55.0]",usask_1
+d2265cb91,1024,1024,"[209.0, 429.0, 68.0, 58.0]",usask_1
+b1e31c10b,1024,1024,"[608.0, 521.0, 67.0, 131.0]",usask_1
+b1e31c10b,1024,1024,"[613.0, 371.0, 67.0, 82.0]",usask_1
+b1e31c10b,1024,1024,"[584.0, 282.0, 86.0, 74.0]",usask_1
+b1e31c10b,1024,1024,"[745.0, 308.0, 34.0, 188.0]",usask_1
+b1e31c10b,1024,1024,"[674.0, 776.0, 96.0, 80.0]",usask_1
+b1e31c10b,1024,1024,"[509.0, 643.0, 70.0, 70.0]",usask_1
+b1e31c10b,1024,1024,"[622.0, 748.0, 73.0, 168.0]",usask_1
+b1e31c10b,1024,1024,"[26.0, 397.0, 62.0, 65.0]",usask_1
+b1e31c10b,1024,1024,"[0.0, 515.0, 63.0, 159.0]",usask_1
+b1e31c10b,1024,1024,"[852.0, 394.0, 151.0, 70.0]",usask_1
+b1e31c10b,1024,1024,"[2.0, 26.0, 74.0, 85.0]",usask_1
+b1e31c10b,1024,1024,"[478.0, 346.0, 83.0, 83.0]",usask_1
+b1e31c10b,1024,1024,"[953.0, 29.0, 71.0, 70.0]",usask_1
+b1e31c10b,1024,1024,"[492.0, 53.0, 138.0, 78.0]",usask_1
+b1e31c10b,1024,1024,"[81.0, 48.0, 72.0, 56.0]",usask_1
+b1e31c10b,1024,1024,"[557.0, 585.0, 84.0, 323.0]",usask_1
+b1e31c10b,1024,1024,"[557.0, 0.0, 109.0, 62.0]",usask_1
+b1e31c10b,1024,1024,"[808.0, 581.0, 216.0, 80.0]",usask_1
+b1e31c10b,1024,1024,"[303.0, 875.0, 173.0, 87.0]",usask_1
+b1e31c10b,1024,1024,"[0.0, 781.0, 41.0, 104.0]",usask_1
+b1e31c10b,1024,1024,"[390.0, 397.0, 104.0, 73.0]",usask_1
+b1e31c10b,1024,1024,"[231.0, 377.0, 88.0, 68.0]",usask_1
+b1e31c10b,1024,1024,"[704.0, 87.0, 113.0, 100.0]",usask_1
+b1e31c10b,1024,1024,"[857.0, 678.0, 133.0, 79.0]",usask_1
+b1e31c10b,1024,1024,"[721.0, 653.0, 135.0, 75.0]",usask_1
+b1e31c10b,1024,1024,"[705.0, 717.0, 223.0, 86.0]",usask_1
+b1e31c10b,1024,1024,"[837.0, 796.0, 182.0, 73.0]",usask_1
+b1e31c10b,1024,1024,"[774.0, 856.0, 121.0, 69.0]",usask_1
+b1e31c10b,1024,1024,"[888.0, 884.0, 109.0, 57.0]",usask_1
+b1e31c10b,1024,1024,"[885.0, 961.0, 72.0, 61.0]",usask_1
+b1e31c10b,1024,1024,"[955.0, 951.0, 69.0, 70.0]",usask_1
+b1e31c10b,1024,1024,"[701.0, 382.0, 69.0, 55.0]",usask_1
+b1e31c10b,1024,1024,"[713.0, 332.0, 54.0, 42.0]",usask_1
+b1e31c10b,1024,1024,"[727.0, 346.0, 88.0, 75.0]",usask_1
+b1e31c10b,1024,1024,"[765.0, 272.0, 117.0, 55.0]",usask_1
+b1e31c10b,1024,1024,"[749.0, 191.0, 109.0, 76.0]",usask_1
+b1e31c10b,1024,1024,"[190.0, 13.0, 184.0, 86.0]",usask_1
+b1e31c10b,1024,1024,"[823.0, 0.0, 47.0, 21.0]",usask_1
+b1e31c10b,1024,1024,"[80.0, 351.0, 77.0, 65.0]",usask_1
+b1e31c10b,1024,1024,"[4.0, 327.0, 53.0, 72.0]",usask_1
+b1e31c10b,1024,1024,"[151.0, 894.0, 49.0, 32.0]",usask_1
+b1e31c10b,1024,1024,"[2.0, 1009.0, 29.0, 15.0]",usask_1
+b1e31c10b,1024,1024,"[6.0, 0.0, 70.0, 33.0]",usask_1
+3ba04140c,1024,1024,"[62.0, 764.0, 97.0, 77.0]",usask_1
+3ba04140c,1024,1024,"[501.0, 221.0, 61.0, 46.0]",usask_1
+3ba04140c,1024,1024,"[0.0, 879.0, 92.0, 100.0]",usask_1
+3ba04140c,1024,1024,"[494.0, 426.0, 48.0, 58.0]",usask_1
+3ba04140c,1024,1024,"[288.0, 694.0, 169.0, 84.0]",usask_1
+3ba04140c,1024,1024,"[734.0, 97.0, 93.0, 93.0]",usask_1
+3ba04140c,1024,1024,"[270.0, 700.0, 249.0, 187.0]",usask_1
+3ba04140c,1024,1024,"[657.0, 0.0, 61.0, 26.0]",usask_1
+3ba04140c,1024,1024,"[566.0, 391.0, 77.0, 53.0]",usask_1
+3ba04140c,1024,1024,"[2.0, 0.0, 164.0, 52.0]",usask_1
+3ba04140c,1024,1024,"[538.0, 45.0, 67.0, 66.0]",usask_1
+3ba04140c,1024,1024,"[467.0, 210.0, 68.0, 112.0]",usask_1
+3ba04140c,1024,1024,"[0.0, 961.0, 83.0, 63.0]",usask_1
+3ba04140c,1024,1024,"[379.0, 179.0, 85.0, 68.0]",usask_1
+3ba04140c,1024,1024,"[0.0, 511.0, 37.0, 154.0]",usask_1
+3ba04140c,1024,1024,"[636.0, 574.0, 71.0, 91.0]",usask_1
+3ba04140c,1024,1024,"[760.0, 885.0, 110.0, 82.0]",usask_1
+3ba04140c,1024,1024,"[0.0, 358.0, 34.0, 68.0]",usask_1
+3ba04140c,1024,1024,"[110.0, 173.0, 80.0, 85.0]",usask_1
+3ba04140c,1024,1024,"[719.0, 816.0, 129.0, 80.0]",usask_1
+3ba04140c,1024,1024,"[809.0, 916.0, 135.0, 103.0]",usask_1
+3ba04140c,1024,1024,"[896.0, 148.0, 123.0, 201.0]",usask_1
+3ba04140c,1024,1024,"[1.0, 743.0, 61.0, 70.0]",usask_1
+3ba04140c,1024,1024,"[80.0, 864.0, 62.0, 48.0]",usask_1
+3ba04140c,1024,1024,"[116.0, 873.0, 88.0, 51.0]",usask_1
+3ba04140c,1024,1024,"[0.0, 851.0, 54.0, 47.0]",usask_1
+3ba04140c,1024,1024,"[665.0, 753.0, 130.0, 126.0]",usask_1
+3ba04140c,1024,1024,"[725.0, 625.0, 149.0, 195.0]",usask_1
+3ba04140c,1024,1024,"[770.0, 579.0, 136.0, 84.0]",usask_1
+3ba04140c,1024,1024,"[542.0, 880.0, 81.0, 137.0]",usask_1
+3ba04140c,1024,1024,"[579.0, 791.0, 68.0, 68.0]",usask_1
+3ba04140c,1024,1024,"[535.0, 719.0, 76.0, 65.0]",usask_1
+3ba04140c,1024,1024,"[635.0, 711.0, 86.0, 80.0]",usask_1
+3ba04140c,1024,1024,"[554.0, 625.0, 69.0, 87.0]",usask_1
+3ba04140c,1024,1024,"[350.0, 817.0, 157.0, 142.0]",usask_1
+3ba04140c,1024,1024,"[792.0, 209.0, 108.0, 62.0]",usask_1
+3ba04140c,1024,1024,"[722.0, 204.0, 156.0, 243.0]",usask_1
+3ba04140c,1024,1024,"[788.0, 452.0, 227.0, 124.0]",usask_1
+3ba04140c,1024,1024,"[597.0, 31.0, 80.0, 72.0]",usask_1
+6a2eac670,1024,1024,"[753.0, 628.0, 97.0, 109.0]",usask_1
+6a2eac670,1024,1024,"[678.0, 245.0, 102.0, 140.0]",usask_1
+6a2eac670,1024,1024,"[879.0, 218.0, 56.0, 171.0]",usask_1
+6a2eac670,1024,1024,"[940.0, 340.0, 84.0, 87.0]",usask_1
+6a2eac670,1024,1024,"[65.0, 211.0, 84.0, 102.0]",usask_1
+6a2eac670,1024,1024,"[616.0, 252.0, 70.0, 126.0]",usask_1
+6a2eac670,1024,1024,"[768.0, 533.0, 61.0, 73.0]",usask_1
+6a2eac670,1024,1024,"[229.0, 937.0, 115.0, 87.0]",usask_1
+6a2eac670,1024,1024,"[199.0, 866.0, 133.0, 75.0]",usask_1
+6a2eac670,1024,1024,"[151.0, 15.0, 76.0, 101.0]",usask_1
+6a2eac670,1024,1024,"[828.0, 909.0, 100.0, 115.0]",usask_1
+6a2eac670,1024,1024,"[556.0, 515.0, 129.0, 68.0]",usask_1
+6a2eac670,1024,1024,"[357.0, 115.0, 190.0, 187.0]",usask_1
+6a2eac670,1024,1024,"[272.0, 706.0, 71.0, 162.0]",usask_1
+6a2eac670,1024,1024,"[163.0, 842.0, 143.0, 93.0]",usask_1
+6a2eac670,1024,1024,"[697.0, 609.0, 65.0, 103.0]",usask_1
+6a2eac670,1024,1024,"[784.0, 296.0, 95.0, 93.0]",usask_1
+6a2eac670,1024,1024,"[882.0, 103.0, 57.0, 111.0]",usask_1
+6a2eac670,1024,1024,"[685.0, 163.0, 201.0, 80.0]",usask_1
+6a2eac670,1024,1024,"[649.0, 357.0, 109.0, 103.0]",usask_1
+6a2eac670,1024,1024,"[331.0, 484.0, 108.0, 84.0]",usask_1
+6a2eac670,1024,1024,"[570.0, 429.0, 72.0, 69.0]",usask_1
+6a2eac670,1024,1024,"[593.0, 474.0, 56.0, 68.0]",usask_1
+6a2eac670,1024,1024,"[768.0, 441.0, 74.0, 57.0]",usask_1
+6a2eac670,1024,1024,"[779.0, 231.0, 78.0, 85.0]",usask_1
+6a2eac670,1024,1024,"[229.0, 305.0, 68.0, 57.0]",usask_1
+6a2eac670,1024,1024,"[675.0, 803.0, 71.0, 85.0]",usask_1
+6a2eac670,1024,1024,"[705.0, 76.0, 52.0, 43.0]",usask_1
+85e11a8bc,1024,1024,"[252.0, 221.0, 63.0, 80.0]",usask_1
+85e11a8bc,1024,1024,"[55.0, 111.0, 81.0, 79.0]",usask_1
+85e11a8bc,1024,1024,"[383.0, 415.0, 177.0, 63.0]",usask_1
+85e11a8bc,1024,1024,"[816.0, 169.0, 110.0, 187.0]",usask_1
+85e11a8bc,1024,1024,"[344.0, 859.0, 144.0, 61.0]",usask_1
+85e11a8bc,1024,1024,"[379.0, 0.0, 83.0, 52.0]",usask_1
+85e11a8bc,1024,1024,"[335.0, 191.0, 92.0, 40.0]",usask_1
+85e11a8bc,1024,1024,"[148.0, 151.0, 103.0, 66.0]",usask_1
+85e11a8bc,1024,1024,"[85.0, 217.0, 73.0, 103.0]",usask_1
+85e11a8bc,1024,1024,"[319.0, 449.0, 73.0, 85.0]",usask_1
+85e11a8bc,1024,1024,"[3.0, 125.0, 69.0, 67.0]",usask_1
+85e11a8bc,1024,1024,"[744.0, 812.0, 110.0, 98.0]",usask_1
+85e11a8bc,1024,1024,"[646.0, 312.0, 132.0, 82.0]",usask_1
+85e11a8bc,1024,1024,"[333.0, 347.0, 88.0, 83.0]",usask_1
+85e11a8bc,1024,1024,"[57.0, 0.0, 69.0, 55.0]",usask_1
+85e11a8bc,1024,1024,"[0.0, 183.0, 54.0, 112.0]",usask_1
+85e11a8bc,1024,1024,"[93.0, 710.0, 76.0, 107.0]",usask_1
+85e11a8bc,1024,1024,"[461.0, 404.0, 171.0, 89.0]",usask_1
+85e11a8bc,1024,1024,"[424.0, 552.0, 151.0, 96.0]",usask_1
+85e11a8bc,1024,1024,"[773.0, 978.0, 97.0, 46.0]",usask_1
+85e11a8bc,1024,1024,"[599.0, 603.0, 73.0, 91.0]",usask_1
+85e11a8bc,1024,1024,"[565.0, 989.0, 112.0, 35.0]",usask_1
+85e11a8bc,1024,1024,"[264.0, 520.0, 117.0, 185.0]",usask_1
+85e11a8bc,1024,1024,"[224.0, 918.0, 133.0, 106.0]",usask_1
+85e11a8bc,1024,1024,"[678.0, 630.0, 82.0, 102.0]",usask_1
+85e11a8bc,1024,1024,"[95.0, 411.0, 89.0, 112.0]",usask_1
+85e11a8bc,1024,1024,"[412.0, 140.0, 140.0, 142.0]",usask_1
+85e11a8bc,1024,1024,"[188.0, 380.0, 80.0, 121.0]",usask_1
+85e11a8bc,1024,1024,"[0.0, 881.0, 142.0, 111.0]",usask_1
+85e11a8bc,1024,1024,"[229.0, 602.0, 93.0, 138.0]",usask_1
+85e11a8bc,1024,1024,"[845.0, 0.0, 66.0, 82.0]",usask_1
+85e11a8bc,1024,1024,"[0.0, 10.0, 49.0, 106.0]",usask_1
+85e11a8bc,1024,1024,"[121.0, 914.0, 83.0, 109.0]",usask_1
+85e11a8bc,1024,1024,"[524.0, 0.0, 172.0, 65.0]",usask_1
+85e11a8bc,1024,1024,"[858.0, 48.0, 78.0, 164.0]",usask_1
+85e11a8bc,1024,1024,"[374.0, 963.0, 100.0, 61.0]",usask_1
+85e11a8bc,1024,1024,"[171.0, 542.0, 69.0, 63.0]",usask_1
+85e11a8bc,1024,1024,"[675.0, 98.0, 118.0, 61.0]",usask_1
+85e11a8bc,1024,1024,"[763.0, 19.0, 75.0, 78.0]",usask_1
+85e11a8bc,1024,1024,"[766.0, 123.0, 79.0, 119.0]",usask_1
+85e11a8bc,1024,1024,"[597.0, 293.0, 108.0, 61.0]",usask_1
+85e11a8bc,1024,1024,"[105.0, 313.0, 60.0, 81.0]",usask_1
+85e11a8bc,1024,1024,"[529.0, 356.0, 87.0, 52.0]",usask_1
+85e11a8bc,1024,1024,"[416.0, 33.0, 99.0, 97.0]",usask_1
+85e11a8bc,1024,1024,"[170.0, 0.0, 84.0, 136.0]",usask_1
+85e11a8bc,1024,1024,"[248.0, 83.0, 49.0, 90.0]",usask_1
+85e11a8bc,1024,1024,"[846.0, 193.0, 101.0, 143.0]",usask_1
+85e11a8bc,1024,1024,"[343.0, 911.0, 60.0, 41.0]",usask_1
+85e11a8bc,1024,1024,"[181.0, 713.0, 85.0, 59.0]",usask_1
+85e11a8bc,1024,1024,"[906.0, 1008.0, 114.0, 16.0]",usask_1
+85e11a8bc,1024,1024,"[262.0, 158.0, 82.0, 56.0]",usask_1
+78edb3987,1024,1024,"[994.0, 0.0, 30.0, 99.0]",usask_1
+78edb3987,1024,1024,"[542.0, 0.0, 116.0, 133.0]",usask_1
+78edb3987,1024,1024,"[724.0, 798.0, 134.0, 120.0]",usask_1
+78edb3987,1024,1024,"[494.0, 577.0, 86.0, 163.0]",usask_1
+78edb3987,1024,1024,"[680.0, 0.0, 140.0, 135.0]",usask_1
+78edb3987,1024,1024,"[354.0, 723.0, 140.0, 134.0]",usask_1
+78edb3987,1024,1024,"[833.0, 0.0, 165.0, 219.0]",usask_1
+78edb3987,1024,1024,"[226.0, 667.0, 122.0, 209.0]",usask_1
+78edb3987,1024,1024,"[352.0, 114.0, 99.0, 197.0]",usask_1
+78edb3987,1024,1024,"[467.0, 797.0, 95.0, 71.0]",usask_1
+78edb3987,1024,1024,"[497.0, 299.0, 97.0, 238.0]",usask_1
+78edb3987,1024,1024,"[172.0, 875.0, 129.0, 50.0]",usask_1
+78edb3987,1024,1024,"[569.0, 564.0, 240.0, 111.0]",usask_1
+78edb3987,1024,1024,"[431.0, 766.0, 86.0, 143.0]",usask_1
+78edb3987,1024,1024,"[410.0, 550.0, 97.0, 237.0]",usask_1
+ffc870198,1024,1024,"[447.0, 976.0, 78.0, 48.0]",usask_1
+ffc870198,1024,1024,"[18.0, 141.0, 218.0, 126.0]",usask_1
+ffc870198,1024,1024,"[704.0, 687.0, 79.0, 78.0]",usask_1
+ffc870198,1024,1024,"[943.0, 842.0, 81.0, 143.0]",usask_1
+ffc870198,1024,1024,"[361.0, 222.0, 178.0, 70.0]",usask_1
+ffc870198,1024,1024,"[373.0, 34.0, 103.0, 68.0]",usask_1
+ffc870198,1024,1024,"[1010.0, 0.0, 14.0, 42.0]",usask_1
+ffc870198,1024,1024,"[585.0, 906.0, 115.0, 45.0]",usask_1
+ffc870198,1024,1024,"[0.0, 906.0, 68.0, 88.0]",usask_1
+ffc870198,1024,1024,"[309.0, 323.0, 110.0, 103.0]",usask_1
+ffc870198,1024,1024,"[976.0, 336.0, 48.0, 90.0]",usask_1
+ffc870198,1024,1024,"[855.0, 0.0, 93.0, 55.0]",usask_1
+ffc870198,1024,1024,"[509.0, 0.0, 105.0, 124.0]",usask_1
+ffc870198,1024,1024,"[578.0, 710.0, 103.0, 129.0]",usask_1
+ffc870198,1024,1024,"[0.0, 0.0, 126.0, 42.0]",usask_1
+ffc870198,1024,1024,"[166.0, 816.0, 244.0, 77.0]",usask_1
+ffc870198,1024,1024,"[498.0, 640.0, 150.0, 92.0]",usask_1
+ffc870198,1024,1024,"[253.0, 561.0, 172.0, 68.0]",usask_1
+ffc870198,1024,1024,"[198.0, 380.0, 159.0, 98.0]",usask_1
+ffc870198,1024,1024,"[778.0, 841.0, 77.0, 63.0]",usask_1
+ffc870198,1024,1024,"[258.0, 50.0, 171.0, 109.0]",usask_1
+ffc870198,1024,1024,"[902.0, 0.0, 122.0, 99.0]",usask_1
+ffc870198,1024,1024,"[396.0, 586.0, 145.0, 69.0]",usask_1
+ffc870198,1024,1024,"[14.0, 139.0, 86.0, 161.0]",usask_1
+ffc870198,1024,1024,"[710.0, 121.0, 93.0, 143.0]",usask_1
+ffc870198,1024,1024,"[781.0, 549.0, 90.0, 93.0]",usask_1
+ffc870198,1024,1024,"[39.0, 361.0, 70.0, 182.0]",usask_1
+ffc870198,1024,1024,"[252.0, 737.0, 134.0, 90.0]",usask_1
+ffc870198,1024,1024,"[562.0, 885.0, 126.0, 49.0]",usask_1
+ffc870198,1024,1024,"[472.0, 857.0, 77.0, 60.0]",usask_1
+ffc870198,1024,1024,"[260.0, 817.0, 168.0, 60.0]",usask_1
+ffc870198,1024,1024,"[545.0, 758.0, 202.0, 77.0]",usask_1
+ffc870198,1024,1024,"[589.0, 791.0, 133.0, 63.0]",usask_1
+ffc870198,1024,1024,"[640.0, 694.0, 64.0, 72.0]",usask_1
+ffc870198,1024,1024,"[369.0, 473.0, 195.0, 110.0]",usask_1
+ffc870198,1024,1024,"[1.0, 332.0, 98.0, 216.0]",usask_1
+ffc870198,1024,1024,"[1.0, 333.0, 108.0, 71.0]",usask_1
+ffc870198,1024,1024,"[216.0, 229.0, 265.0, 139.0]",usask_1
+ffc870198,1024,1024,"[182.0, 128.0, 219.0, 134.0]",usask_1
+ffc870198,1024,1024,"[551.0, 258.0, 133.0, 47.0]",usask_1
+ffc870198,1024,1024,"[453.0, 0.0, 58.0, 72.0]",usask_1
+586a28040,1024,1024,"[174.0, 930.0, 92.0, 93.0]",usask_1
+586a28040,1024,1024,"[314.0, 534.0, 127.0, 111.0]",usask_1
+586a28040,1024,1024,"[501.0, 85.0, 94.0, 68.0]",usask_1
+586a28040,1024,1024,"[1.0, 964.0, 252.0, 60.0]",usask_1
+586a28040,1024,1024,"[232.0, 562.0, 90.0, 194.0]",usask_1
+586a28040,1024,1024,"[941.0, 138.0, 83.0, 108.0]",usask_1
+586a28040,1024,1024,"[0.0, 397.0, 37.0, 76.0]",usask_1
+586a28040,1024,1024,"[111.0, 808.0, 93.0, 147.0]",usask_1
+586a28040,1024,1024,"[275.0, 437.0, 209.0, 90.0]",usask_1
+586a28040,1024,1024,"[426.0, 504.0, 72.0, 81.0]",usask_1
+586a28040,1024,1024,"[326.0, 990.0, 157.0, 34.0]",usask_1
+586a28040,1024,1024,"[10.0, 387.0, 235.0, 94.0]",usask_1
+586a28040,1024,1024,"[877.0, 963.0, 147.0, 61.0]",usask_1
+586a28040,1024,1024,"[50.0, 282.0, 369.0, 104.0]",usask_1
+586a28040,1024,1024,"[330.0, 70.0, 261.0, 194.0]",usask_1
+586a28040,1024,1024,"[0.0, 225.0, 174.0, 127.0]",usask_1
+586a28040,1024,1024,"[845.0, 252.0, 114.0, 131.0]",usask_1
+586a28040,1024,1024,"[714.0, 417.0, 133.0, 211.0]",usask_1
+586a28040,1024,1024,"[653.0, 0.0, 212.0, 138.0]",usask_1
+586a28040,1024,1024,"[836.0, 0.0, 96.0, 153.0]",usask_1
+586a28040,1024,1024,"[43.0, 158.0, 173.0, 151.0]",usask_1
+586a28040,1024,1024,"[208.0, 64.0, 114.0, 200.0]",usask_1
+586a28040,1024,1024,"[21.0, 421.0, 70.0, 73.0]",usask_1
+586a28040,1024,1024,"[55.0, 472.0, 58.0, 90.0]",usask_1
+586a28040,1024,1024,"[239.0, 471.0, 73.0, 69.0]",usask_1
+8f05b9e06,1024,1024,"[235.0, 282.0, 164.0, 93.0]",usask_1
+8f05b9e06,1024,1024,"[541.0, 886.0, 110.0, 138.0]",usask_1
+8f05b9e06,1024,1024,"[570.0, 175.0, 64.0, 103.0]",usask_1
+8f05b9e06,1024,1024,"[661.0, 0.0, 72.0, 50.0]",usask_1
+8f05b9e06,1024,1024,"[602.0, 74.0, 88.0, 134.0]",usask_1
+8f05b9e06,1024,1024,"[0.0, 232.0, 235.0, 103.0]",usask_1
+8f05b9e06,1024,1024,"[478.0, 1.0, 108.0, 110.0]",usask_1
+8f05b9e06,1024,1024,"[620.0, 0.0, 56.0, 53.0]",usask_1
+864210505,1024,1024,"[798.0, 599.0, 66.0, 154.0]",usask_1
+864210505,1024,1024,"[848.0, 363.0, 176.0, 73.0]",usask_1
+864210505,1024,1024,"[804.0, 410.0, 220.0, 63.0]",usask_1
+864210505,1024,1024,"[269.0, 87.0, 125.0, 72.0]",usask_1
+864210505,1024,1024,"[388.0, 60.0, 144.0, 67.0]",usask_1
+864210505,1024,1024,"[206.0, 12.0, 78.0, 118.0]",usask_1
+864210505,1024,1024,"[12.0, 425.0, 58.0, 125.0]",usask_1
+864210505,1024,1024,"[0.0, 192.0, 32.0, 63.0]",usask_1
+864210505,1024,1024,"[427.0, 109.0, 69.0, 89.0]",usask_1
+864210505,1024,1024,"[702.0, 175.0, 210.0, 62.0]",usask_1
+864210505,1024,1024,"[218.0, 229.0, 130.0, 187.0]",usask_1
+864210505,1024,1024,"[397.0, 589.0, 123.0, 127.0]",usask_1
+864210505,1024,1024,"[673.0, 0.0, 133.0, 55.0]",usask_1
+864210505,1024,1024,"[882.0, 555.0, 140.0, 87.0]",usask_1
+864210505,1024,1024,"[33.0, 979.0, 104.0, 45.0]",usask_1
+864210505,1024,1024,"[296.0, 680.0, 133.0, 100.0]",usask_1
+864210505,1024,1024,"[821.0, 0.0, 120.0, 5.0]",usask_1
+864210505,1024,1024,"[144.0, 461.0, 181.0, 124.0]",usask_1
+864210505,1024,1024,"[848.0, 488.0, 144.0, 73.0]",usask_1
+864210505,1024,1024,"[406.0, 987.0, 86.0, 37.0]",usask_1
+864210505,1024,1024,"[596.0, 986.0, 64.0, 38.0]",usask_1
+864210505,1024,1024,"[680.0, 682.0, 111.0, 107.0]",usask_1
+864210505,1024,1024,"[557.0, 534.0, 60.0, 47.0]",usask_1
+864210505,1024,1024,"[496.0, 524.0, 66.0, 71.0]",usask_1
+864210505,1024,1024,"[125.0, 291.0, 64.0, 58.0]",usask_1
+864210505,1024,1024,"[568.0, 229.0, 81.0, 101.0]",usask_1
+864210505,1024,1024,"[497.0, 323.0, 177.0, 87.0]",usask_1
+864210505,1024,1024,"[644.0, 350.0, 81.0, 82.0]",usask_1
+864210505,1024,1024,"[678.0, 344.0, 152.0, 60.0]",usask_1
+864210505,1024,1024,"[766.0, 127.0, 162.0, 62.0]",usask_1
+864210505,1024,1024,"[421.0, 289.0, 53.0, 129.0]",usask_1
+864210505,1024,1024,"[405.0, 271.0, 108.0, 85.0]",usask_1
+864210505,1024,1024,"[334.0, 222.0, 60.0, 124.0]",usask_1
+864210505,1024,1024,"[381.0, 234.0, 113.0, 88.0]",usask_1
+864210505,1024,1024,"[392.0, 177.0, 84.0, 77.0]",usask_1
+864210505,1024,1024,"[339.0, 177.0, 92.0, 99.0]",usask_1
+864210505,1024,1024,"[0.0, 378.0, 31.0, 111.0]",usask_1
+864210505,1024,1024,"[981.0, 16.0, 43.0, 73.0]",usask_1
+864210505,1024,1024,"[0.0, 568.0, 34.0, 51.0]",usask_1
+2cc952ee8,1024,1024,"[525.0, 505.0, 73.0, 110.0]",usask_1
+2cc952ee8,1024,1024,"[478.0, 673.0, 69.0, 66.0]",usask_1
+2cc952ee8,1024,1024,"[34.0, 353.0, 123.0, 65.0]",usask_1
+2cc952ee8,1024,1024,"[458.0, 768.0, 89.0, 106.0]",usask_1
+2cc952ee8,1024,1024,"[717.0, 359.0, 82.0, 86.0]",usask_1
+2cc952ee8,1024,1024,"[168.0, 677.0, 154.0, 163.0]",usask_1
+2cc952ee8,1024,1024,"[138.0, 609.0, 113.0, 86.0]",usask_1
+2cc952ee8,1024,1024,"[652.0, 568.0, 72.0, 122.0]",usask_1
+2cc952ee8,1024,1024,"[0.0, 202.0, 2.0, 120.0]",usask_1
+2cc952ee8,1024,1024,"[205.0, 865.0, 170.0, 126.0]",usask_1
+2cc952ee8,1024,1024,"[56.0, 404.0, 127.0, 93.0]",usask_1
+2cc952ee8,1024,1024,"[236.0, 276.0, 121.0, 80.0]",usask_1
+2cc952ee8,1024,1024,"[320.0, 0.0, 89.0, 79.0]",usask_1
+2cc952ee8,1024,1024,"[380.0, 246.0, 123.0, 103.0]",usask_1
+2cc952ee8,1024,1024,"[710.0, 0.0, 90.0, 146.0]",usask_1
+2cc952ee8,1024,1024,"[902.0, 993.0, 101.0, 31.0]",usask_1
+2cc952ee8,1024,1024,"[672.0, 946.0, 88.0, 63.0]",usask_1
+2cc952ee8,1024,1024,"[491.0, 897.0, 82.0, 65.0]",usask_1
+2cc952ee8,1024,1024,"[968.0, 770.0, 56.0, 95.0]",usask_1
+2cc952ee8,1024,1024,"[700.0, 424.0, 91.0, 79.0]",usask_1
+2cc952ee8,1024,1024,"[373.0, 107.0, 61.0, 70.0]",usask_1
+2cc952ee8,1024,1024,"[391.0, 288.0, 88.0, 107.0]",usask_1
+2cc952ee8,1024,1024,"[311.0, 887.0, 118.0, 60.0]",usask_1
+2cc952ee8,1024,1024,"[59.0, 596.0, 66.0, 61.0]",usask_1
+2cc952ee8,1024,1024,"[11.0, 555.0, 55.0, 74.0]",usask_1
+29dc6c59a,1024,1024,"[239.0, 198.0, 66.0, 93.0]",usask_1
+29dc6c59a,1024,1024,"[286.0, 73.0, 144.0, 91.0]",usask_1
+29dc6c59a,1024,1024,"[197.0, 773.0, 117.0, 77.0]",usask_1
+29dc6c59a,1024,1024,"[272.0, 198.0, 124.0, 137.0]",usask_1
+29dc6c59a,1024,1024,"[14.0, 462.0, 87.0, 101.0]",usask_1
+29dc6c59a,1024,1024,"[827.0, 0.0, 130.0, 278.0]",usask_1
+29dc6c59a,1024,1024,"[0.0, 230.0, 121.0, 68.0]",usask_1
+29dc6c59a,1024,1024,"[928.0, 86.0, 78.0, 178.0]",usask_1
+29dc6c59a,1024,1024,"[250.0, 481.0, 192.0, 102.0]",usask_1
+29dc6c59a,1024,1024,"[100.0, 622.0, 137.0, 77.0]",usask_1
+29dc6c59a,1024,1024,"[711.0, 510.0, 121.0, 115.0]",usask_1
+29dc6c59a,1024,1024,"[881.0, 312.0, 128.0, 71.0]",usask_1
+29dc6c59a,1024,1024,"[16.0, 70.0, 208.0, 73.0]",usask_1
+29dc6c59a,1024,1024,"[147.0, 662.0, 77.0, 94.0]",usask_1
+29dc6c59a,1024,1024,"[59.0, 445.0, 94.0, 90.0]",usask_1
+29dc6c59a,1024,1024,"[947.0, 624.0, 77.0, 140.0]",usask_1
+29dc6c59a,1024,1024,"[260.0, 469.0, 74.0, 168.0]",usask_1
+29dc6c59a,1024,1024,"[392.0, 502.0, 90.0, 120.0]",usask_1
+29dc6c59a,1024,1024,"[405.0, 320.0, 61.0, 88.0]",usask_1
+29dc6c59a,1024,1024,"[466.0, 346.0, 83.0, 105.0]",usask_1
+29dc6c59a,1024,1024,"[511.0, 356.0, 71.0, 79.0]",usask_1
+29dc6c59a,1024,1024,"[184.0, 2.0, 216.0, 39.0]",usask_1
+29dc6c59a,1024,1024,"[138.0, 111.0, 171.0, 103.0]",usask_1
+1aebf1c4c,1024,1024,"[666.0, 832.0, 77.0, 116.0]",usask_1
+1aebf1c4c,1024,1024,"[94.0, 30.0, 73.0, 96.0]",usask_1
+1aebf1c4c,1024,1024,"[569.0, 882.0, 93.0, 90.0]",usask_1
+1aebf1c4c,1024,1024,"[65.0, 736.0, 75.0, 80.0]",usask_1
+1aebf1c4c,1024,1024,"[512.0, 0.0, 159.0, 73.0]",usask_1
+1aebf1c4c,1024,1024,"[397.0, 577.0, 159.0, 88.0]",usask_1
+1aebf1c4c,1024,1024,"[319.0, 138.0, 73.0, 75.0]",usask_1
+1aebf1c4c,1024,1024,"[892.0, 269.0, 113.0, 228.0]",usask_1
+1aebf1c4c,1024,1024,"[357.0, 0.0, 113.0, 28.0]",usask_1
+1aebf1c4c,1024,1024,"[488.0, 902.0, 93.0, 90.0]",usask_1
+1aebf1c4c,1024,1024,"[224.0, 3.0, 102.0, 61.0]",usask_1
+1aebf1c4c,1024,1024,"[187.0, 702.0, 82.0, 110.0]",usask_1
+1aebf1c4c,1024,1024,"[540.0, 182.0, 63.0, 103.0]",usask_1
+1aebf1c4c,1024,1024,"[994.0, 381.0, 30.0, 100.0]",usask_1
+1aebf1c4c,1024,1024,"[427.0, 160.0, 103.0, 213.0]",usask_1
+1aebf1c4c,1024,1024,"[245.0, 690.0, 110.0, 154.0]",usask_1
+1aebf1c4c,1024,1024,"[0.0, 239.0, 113.0, 123.0]",usask_1
+1aebf1c4c,1024,1024,"[14.0, 539.0, 103.0, 143.0]",usask_1
+1aebf1c4c,1024,1024,"[829.0, 728.0, 114.0, 119.0]",usask_1
+1aebf1c4c,1024,1024,"[739.0, 838.0, 105.0, 83.0]",usask_1
+1aebf1c4c,1024,1024,"[532.0, 289.0, 108.0, 96.0]",usask_1
+1aebf1c4c,1024,1024,"[522.0, 676.0, 87.0, 96.0]",usask_1
+1aebf1c4c,1024,1024,"[55.0, 102.0, 83.0, 77.0]",usask_1
+1aebf1c4c,1024,1024,"[148.0, 804.0, 88.0, 79.0]",usask_1
+1aebf1c4c,1024,1024,"[573.0, 183.0, 60.0, 86.0]",usask_1
+1aebf1c4c,1024,1024,"[638.0, 185.0, 67.0, 73.0]",usask_1
+1aebf1c4c,1024,1024,"[649.0, 137.0, 124.0, 77.0]",usask_1
+1aebf1c4c,1024,1024,"[774.0, 140.0, 76.0, 76.0]",usask_1
+1aebf1c4c,1024,1024,"[700.0, 79.0, 65.0, 49.0]",usask_1
+1aebf1c4c,1024,1024,"[781.0, 45.0, 60.0, 91.0]",usask_1
+1aebf1c4c,1024,1024,"[839.0, 65.0, 63.0, 71.0]",usask_1
+9bedf6645,1024,1024,"[798.0, 487.0, 65.0, 107.0]",usask_1
+9bedf6645,1024,1024,"[60.0, 0.0, 103.0, 66.0]",usask_1
+9bedf6645,1024,1024,"[450.0, 598.0, 97.0, 105.0]",usask_1
+9bedf6645,1024,1024,"[376.0, 667.0, 81.0, 65.0]",usask_1
+9bedf6645,1024,1024,"[367.0, 398.0, 92.0, 65.0]",usask_1
+9bedf6645,1024,1024,"[491.0, 663.0, 93.0, 83.0]",usask_1
+9bedf6645,1024,1024,"[831.0, 633.0, 100.0, 66.0]",usask_1
+9bedf6645,1024,1024,"[489.0, 260.0, 67.0, 113.0]",usask_1
+9bedf6645,1024,1024,"[269.0, 289.0, 103.0, 90.0]",usask_1
+9bedf6645,1024,1024,"[770.0, 966.0, 142.0, 58.0]",usask_1
+9bedf6645,1024,1024,"[390.0, 240.0, 110.0, 90.0]",usask_1
+9bedf6645,1024,1024,"[852.0, 908.0, 136.0, 87.0]",usask_1
+9bedf6645,1024,1024,"[578.0, 502.0, 180.0, 82.0]",usask_1
+9bedf6645,1024,1024,"[898.0, 159.0, 126.0, 178.0]",usask_1
+9bedf6645,1024,1024,"[589.0, 680.0, 86.0, 90.0]",usask_1
+9bedf6645,1024,1024,"[64.0, 155.0, 82.0, 108.0]",usask_1
+9bedf6645,1024,1024,"[394.0, 332.0, 80.0, 76.0]",usask_1
+9bedf6645,1024,1024,"[640.0, 184.0, 66.0, 117.0]",usask_1
+9bedf6645,1024,1024,"[322.0, 684.0, 117.0, 64.0]",usask_1
+9bedf6645,1024,1024,"[0.0, 485.0, 119.0, 103.0]",usask_1
+9bedf6645,1024,1024,"[137.0, 485.0, 127.0, 61.0]",usask_1
+9bedf6645,1024,1024,"[235.0, 717.0, 162.0, 81.0]",usask_1
+9bedf6645,1024,1024,"[416.0, 769.0, 224.0, 96.0]",usask_1
+9bedf6645,1024,1024,"[457.0, 814.0, 88.0, 110.0]",usask_1
+9bedf6645,1024,1024,"[422.0, 774.0, 73.0, 107.0]",usask_1
+9bedf6645,1024,1024,"[916.0, 695.0, 83.0, 31.0]",usask_1
+9bedf6645,1024,1024,"[861.0, 706.0, 116.0, 54.0]",usask_1
+9bedf6645,1024,1024,"[853.0, 992.0, 107.0, 32.0]",usask_1
+9bedf6645,1024,1024,"[552.0, 444.0, 72.0, 66.0]",usask_1
+9bedf6645,1024,1024,"[599.0, 192.0, 53.0, 132.0]",usask_1
+9bedf6645,1024,1024,"[457.0, 35.0, 166.0, 272.0]",usask_1
+9bedf6645,1024,1024,"[978.0, 460.0, 46.0, 80.0]",usask_1
+9bedf6645,1024,1024,"[351.0, 56.0, 77.0, 73.0]",usask_1
+f437363d5,1024,1024,"[595.0, 836.0, 175.0, 110.0]",usask_1
+f437363d5,1024,1024,"[466.0, 798.0, 160.0, 91.0]",usask_1
+f437363d5,1024,1024,"[424.0, 505.0, 106.0, 68.0]",usask_1
+f437363d5,1024,1024,"[478.0, 727.0, 164.0, 93.0]",usask_1
+f437363d5,1024,1024,"[618.0, 917.0, 87.0, 88.0]",usask_1
+f437363d5,1024,1024,"[407.0, 943.0, 241.0, 81.0]",usask_1
+f437363d5,1024,1024,"[45.0, 713.0, 216.0, 80.0]",usask_1
+f437363d5,1024,1024,"[380.0, 191.0, 139.0, 80.0]",usask_1
+f437363d5,1024,1024,"[817.0, 816.0, 104.0, 72.0]",usask_1
+f437363d5,1024,1024,"[285.0, 59.0, 143.0, 64.0]",usask_1
+f437363d5,1024,1024,"[133.0, 786.0, 197.0, 70.0]",usask_1
+f437363d5,1024,1024,"[0.0, 107.0, 128.0, 74.0]",usask_1
+f437363d5,1024,1024,"[357.0, 1003.0, 207.0, 21.0]",usask_1
+f437363d5,1024,1024,"[467.0, 849.0, 135.0, 98.0]",usask_1
+f437363d5,1024,1024,"[650.0, 528.0, 153.0, 147.0]",usask_1
+f437363d5,1024,1024,"[812.0, 962.0, 176.0, 62.0]",usask_1
+f437363d5,1024,1024,"[445.0, 0.0, 146.0, 83.0]",usask_1
+f437363d5,1024,1024,"[43.0, 495.0, 190.0, 73.0]",usask_1
+f437363d5,1024,1024,"[77.0, 239.0, 221.0, 144.0]",usask_1
+f437363d5,1024,1024,"[818.0, 30.0, 106.0, 133.0]",usask_1
+f437363d5,1024,1024,"[597.0, 424.0, 146.0, 82.0]",usask_1
+f437363d5,1024,1024,"[700.0, 850.0, 154.0, 89.0]",usask_1
+f437363d5,1024,1024,"[87.0, 410.0, 235.0, 109.0]",usask_1
+f437363d5,1024,1024,"[449.0, 834.0, 78.0, 55.0]",usask_1
+f437363d5,1024,1024,"[328.0, 842.0, 172.0, 71.0]",usask_1
+f437363d5,1024,1024,"[377.0, 860.0, 103.0, 90.0]",usask_1
+f437363d5,1024,1024,"[326.0, 682.0, 212.0, 105.0]",usask_1
+f437363d5,1024,1024,"[270.0, 716.0, 171.0, 71.0]",usask_1
+f437363d5,1024,1024,"[443.0, 645.0, 185.0, 98.0]",usask_1
+f437363d5,1024,1024,"[820.0, 742.0, 83.0, 53.0]",usask_1
+f437363d5,1024,1024,"[0.0, 551.0, 73.0, 61.0]",usask_1
+f437363d5,1024,1024,"[51.0, 272.0, 222.0, 131.0]",usask_1
+f437363d5,1024,1024,"[659.0, 223.0, 59.0, 84.0]",usask_1
+f437363d5,1024,1024,"[629.0, 471.0, 115.0, 66.0]",usask_1
+89fc62745,1024,1024,"[451.0, 70.0, 111.0, 87.0]",usask_1
+89fc62745,1024,1024,"[803.0, 875.0, 67.0, 71.0]",usask_1
+89fc62745,1024,1024,"[663.0, 757.0, 96.0, 97.0]",usask_1
+89fc62745,1024,1024,"[482.0, 874.0, 97.0, 65.0]",usask_1
+89fc62745,1024,1024,"[742.0, 473.0, 111.0, 77.0]",usask_1
+89fc62745,1024,1024,"[786.0, 932.0, 75.0, 79.0]",usask_1
+89fc62745,1024,1024,"[774.0, 778.0, 97.0, 69.0]",usask_1
+89fc62745,1024,1024,"[629.0, 370.0, 131.0, 63.0]",usask_1
+89fc62745,1024,1024,"[822.0, 471.0, 150.0, 106.0]",usask_1
+89fc62745,1024,1024,"[0.0, 168.0, 115.0, 142.0]",usask_1
+89fc62745,1024,1024,"[603.0, 309.0, 116.0, 81.0]",usask_1
+89fc62745,1024,1024,"[928.0, 17.0, 96.0, 119.0]",usask_1
+89fc62745,1024,1024,"[869.0, 836.0, 86.0, 97.0]",usask_1
+89fc62745,1024,1024,"[361.0, 766.0, 179.0, 102.0]",usask_1
+89fc62745,1024,1024,"[512.0, 551.0, 227.0, 123.0]",usask_1
+89fc62745,1024,1024,"[448.0, 973.0, 124.0, 51.0]",usask_1
+89fc62745,1024,1024,"[635.0, 100.0, 168.0, 109.0]",usask_1
+89fc62745,1024,1024,"[523.0, 587.0, 77.0, 104.0]",usask_1
+89fc62745,1024,1024,"[480.0, 208.0, 144.0, 83.0]",usask_1
+89fc62745,1024,1024,"[537.0, 161.0, 116.0, 98.0]",usask_1
+89fc62745,1024,1024,"[603.0, 234.0, 118.0, 75.0]",usask_1
+a4dd288c0,1024,1024,"[949.0, 574.0, 75.0, 83.0]",usask_1
+a4dd288c0,1024,1024,"[766.0, 688.0, 222.0, 143.0]",usask_1
+a4dd288c0,1024,1024,"[20.0, 544.0, 163.0, 68.0]",usask_1
+a4dd288c0,1024,1024,"[50.0, 338.0, 149.0, 68.0]",usask_1
+a4dd288c0,1024,1024,"[50.0, 111.0, 187.0, 93.0]",usask_1
+a4dd288c0,1024,1024,"[607.0, 201.0, 200.0, 94.0]",usask_1
+a4dd288c0,1024,1024,"[574.0, 602.0, 75.0, 96.0]",usask_1
+a4dd288c0,1024,1024,"[0.0, 28.0, 159.0, 99.0]",usask_1
+a4dd288c0,1024,1024,"[363.0, 90.0, 218.0, 114.0]",usask_1
+a4dd288c0,1024,1024,"[696.0, 442.0, 254.0, 90.0]",usask_1
+a4dd288c0,1024,1024,"[671.0, 676.0, 177.0, 96.0]",usask_1
+a4dd288c0,1024,1024,"[949.0, 0.0, 75.0, 72.0]",usask_1
+a4dd288c0,1024,1024,"[255.0, 854.0, 100.0, 76.0]",usask_1
+a4dd288c0,1024,1024,"[408.0, 865.0, 207.0, 156.0]",usask_1
+a4dd288c0,1024,1024,"[370.0, 0.0, 238.0, 79.0]",usask_1
+a4dd288c0,1024,1024,"[93.0, 235.0, 187.0, 123.0]",usask_1
+a4dd288c0,1024,1024,"[247.0, 918.0, 97.0, 106.0]",usask_1
+a4dd288c0,1024,1024,"[678.0, 169.0, 217.0, 82.0]",usask_1
+a4dd288c0,1024,1024,"[739.0, 922.0, 89.0, 102.0]",usask_1
+a4dd288c0,1024,1024,"[720.0, 572.0, 255.0, 90.0]",usask_1
+a4dd288c0,1024,1024,"[972.0, 248.0, 52.0, 82.0]",usask_1
+a4dd288c0,1024,1024,"[0.0, 264.0, 59.0, 139.0]",usask_1
+a4dd288c0,1024,1024,"[268.0, 929.0, 97.0, 95.0]",usask_1
+a4dd288c0,1024,1024,"[173.0, 953.0, 93.0, 55.0]",usask_1
+a4dd288c0,1024,1024,"[52.0, 930.0, 177.0, 73.0]",usask_1
+a4dd288c0,1024,1024,"[59.0, 836.0, 127.0, 126.0]",usask_1
+a4dd288c0,1024,1024,"[610.0, 789.0, 102.0, 104.0]",usask_1
+a4dd288c0,1024,1024,"[767.0, 264.0, 162.0, 103.0]",usask_1
+a4dd288c0,1024,1024,"[635.0, 309.0, 251.0, 99.0]",usask_1
+a4dd288c0,1024,1024,"[791.0, 377.0, 145.0, 68.0]",usask_1
+a4dd288c0,1024,1024,"[784.0, 215.0, 115.0, 100.0]",usask_1
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+0da996869,1024,1024,"[0.0, 642.0, 91.0, 77.0]",usask_1
+0da996869,1024,1024,"[587.0, 805.0, 98.0, 104.0]",usask_1
+0da996869,1024,1024,"[592.0, 640.0, 114.0, 126.0]",usask_1
+0da996869,1024,1024,"[5.0, 321.0, 75.0, 139.0]",usask_1
+0da996869,1024,1024,"[514.0, 715.0, 92.0, 117.0]",usask_1
+0da996869,1024,1024,"[168.0, 647.0, 81.0, 119.0]",usask_1
+0da996869,1024,1024,"[576.0, 402.0, 100.0, 115.0]",usask_1
+0da996869,1024,1024,"[841.0, 323.0, 105.0, 146.0]",usask_1
+0da996869,1024,1024,"[183.0, 797.0, 94.0, 63.0]",usask_1
+0da996869,1024,1024,"[89.0, 334.0, 81.0, 85.0]",usask_1
+0da996869,1024,1024,"[350.0, 649.0, 110.0, 188.0]",usask_1
+0da996869,1024,1024,"[867.0, 641.0, 142.0, 129.0]",usask_1
+0da996869,1024,1024,"[661.0, 880.0, 87.0, 122.0]",usask_1
+0da996869,1024,1024,"[63.0, 739.0, 102.0, 83.0]",usask_1
+0da996869,1024,1024,"[68.0, 250.0, 72.0, 130.0]",usask_1
+0da996869,1024,1024,"[74.0, 0.0, 116.0, 99.0]",usask_1
+0da996869,1024,1024,"[157.0, 848.0, 192.0, 64.0]",usask_1
+0da996869,1024,1024,"[147.0, 882.0, 213.0, 83.0]",usask_1
+2c7241d19,1024,1024,"[4.0, 154.0, 97.0, 76.0]",usask_1
+2c7241d19,1024,1024,"[400.0, 671.0, 78.0, 65.0]",usask_1
+2c7241d19,1024,1024,"[790.0, 457.0, 159.0, 107.0]",usask_1
+2c7241d19,1024,1024,"[919.0, 721.0, 92.0, 98.0]",usask_1
+2c7241d19,1024,1024,"[329.0, 893.0, 159.0, 112.0]",usask_1
+2c7241d19,1024,1024,"[761.0, 799.0, 64.0, 76.0]",usask_1
+2c7241d19,1024,1024,"[0.0, 240.0, 174.0, 129.0]",usask_1
+2c7241d19,1024,1024,"[285.0, 772.0, 108.0, 96.0]",usask_1
+2c7241d19,1024,1024,"[61.0, 691.0, 103.0, 106.0]",usask_1
+2c7241d19,1024,1024,"[207.0, 606.0, 122.0, 99.0]",usask_1
+2c7241d19,1024,1024,"[534.0, 664.0, 187.0, 120.0]",usask_1
+2c7241d19,1024,1024,"[807.0, 87.0, 120.0, 174.0]",usask_1
+2c7241d19,1024,1024,"[525.0, 185.0, 177.0, 174.0]",usask_1
+2c7241d19,1024,1024,"[935.0, 313.0, 71.0, 148.0]",usask_1
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+2c7241d19,1024,1024,"[1.0, 491.0, 30.0, 110.0]",usask_1
+2c7241d19,1024,1024,"[684.0, 161.0, 197.0, 152.0]",usask_1
+2c7241d19,1024,1024,"[367.0, 154.0, 125.0, 148.0]",usask_1
+2c7241d19,1024,1024,"[446.0, 886.0, 131.0, 138.0]",usask_1
+2c7241d19,1024,1024,"[718.0, 619.0, 172.0, 86.0]",usask_1
+2c7241d19,1024,1024,"[284.0, 721.0, 109.0, 80.0]",usask_1
+2c7241d19,1024,1024,"[658.0, 819.0, 79.0, 68.0]",usask_1
+2c7241d19,1024,1024,"[712.0, 566.0, 62.0, 52.0]",usask_1
+91a285076,1024,1024,"[763.0, 625.0, 99.0, 88.0]",usask_1
+91a285076,1024,1024,"[615.0, 407.0, 79.0, 67.0]",usask_1
+91a285076,1024,1024,"[671.0, 376.0, 75.0, 87.0]",usask_1
+91a285076,1024,1024,"[857.0, 612.0, 167.0, 90.0]",usask_1
+91a285076,1024,1024,"[129.0, 683.0, 63.0, 143.0]",usask_1
+91a285076,1024,1024,"[224.0, 652.0, 119.0, 80.0]",usask_1
+91a285076,1024,1024,"[577.0, 734.0, 80.0, 120.0]",usask_1
+91a285076,1024,1024,"[663.0, 144.0, 103.0, 116.0]",usask_1
+91a285076,1024,1024,"[35.0, 152.0, 86.0, 131.0]",usask_1
+91a285076,1024,1024,"[807.0, 692.0, 105.0, 80.0]",usask_1
+91a285076,1024,1024,"[568.0, 292.0, 67.0, 85.0]",usask_1
+91a285076,1024,1024,"[583.0, 207.0, 90.0, 96.0]",usask_1
+91a285076,1024,1024,"[899.0, 686.0, 114.0, 138.0]",usask_1
+91a285076,1024,1024,"[782.0, 228.0, 128.0, 60.0]",usask_1
+91a285076,1024,1024,"[444.0, 0.0, 90.0, 79.0]",usask_1
+91a285076,1024,1024,"[149.0, 817.0, 133.0, 85.0]",usask_1
+91a285076,1024,1024,"[683.0, 774.0, 93.0, 93.0]",usask_1
+91a285076,1024,1024,"[71.0, 941.0, 145.0, 83.0]",usask_1
+91a285076,1024,1024,"[557.0, 0.0, 79.0, 39.0]",usask_1
+91a285076,1024,1024,"[77.0, 307.0, 93.0, 200.0]",usask_1
+91a285076,1024,1024,"[489.0, 811.0, 93.0, 113.0]",usask_1
+91a285076,1024,1024,"[759.0, 289.0, 95.0, 74.0]",usask_1
+91a285076,1024,1024,"[0.0, 912.0, 42.0, 112.0]",usask_1
+91a285076,1024,1024,"[676.0, 602.0, 87.0, 62.0]",usask_1
+91a285076,1024,1024,"[583.0, 568.0, 83.0, 103.0]",usask_1
+91a285076,1024,1024,"[653.0, 492.0, 94.0, 87.0]",usask_1
+91a285076,1024,1024,"[755.0, 160.0, 126.0, 65.0]",usask_1
+91a285076,1024,1024,"[945.0, 165.0, 79.0, 86.0]",usask_1
+91a285076,1024,1024,"[898.0, 69.0, 103.0, 64.0]",usask_1
+91a285076,1024,1024,"[750.0, 64.0, 93.0, 65.0]",usask_1
+91a285076,1024,1024,"[684.0, 16.0, 86.0, 62.0]",usask_1
+91a285076,1024,1024,"[640.0, 47.0, 79.0, 89.0]",usask_1
+a0d6d5690,1024,1024,"[888.0, 576.0, 64.0, 94.0]",usask_1
+a0d6d5690,1024,1024,"[0.0, 933.0, 122.0, 91.0]",usask_1
+a0d6d5690,1024,1024,"[619.0, 174.0, 82.0, 67.0]",usask_1
+a0d6d5690,1024,1024,"[751.0, 144.0, 71.0, 82.0]",usask_1
+a0d6d5690,1024,1024,"[1004.0, 261.0, 20.0, 92.0]",usask_1
+a0d6d5690,1024,1024,"[742.0, 418.0, 80.0, 97.0]",usask_1
+a0d6d5690,1024,1024,"[573.0, 104.0, 95.0, 93.0]",usask_1
+a0d6d5690,1024,1024,"[175.0, 0.0, 72.0, 42.0]",usask_1
+a0d6d5690,1024,1024,"[973.0, 393.0, 51.0, 99.0]",usask_1
+a0d6d5690,1024,1024,"[119.0, 519.0, 127.0, 123.0]",usask_1
+a0d6d5690,1024,1024,"[820.0, 690.0, 66.0, 104.0]",usask_1
+a0d6d5690,1024,1024,"[311.0, 552.0, 161.0, 111.0]",usask_1
+a0d6d5690,1024,1024,"[224.0, 745.0, 87.0, 98.0]",usask_1
+a0d6d5690,1024,1024,"[797.0, 8.0, 155.0, 190.0]",usask_1
+a0d6d5690,1024,1024,"[0.0, 884.0, 100.0, 79.0]",usask_1
+a0d6d5690,1024,1024,"[134.0, 896.0, 64.0, 109.0]",usask_1
+a0d6d5690,1024,1024,"[836.0, 994.0, 79.0, 30.0]",usask_1
+a0d6d5690,1024,1024,"[969.0, 828.0, 55.0, 136.0]",usask_1
+a0d6d5690,1024,1024,"[915.0, 947.0, 58.0, 77.0]",usask_1
+a0d6d5690,1024,1024,"[859.0, 801.0, 85.0, 79.0]",usask_1
+a0d6d5690,1024,1024,"[841.0, 872.0, 59.0, 103.0]",usask_1
+a0d6d5690,1024,1024,"[890.0, 720.0, 66.0, 53.0]",usask_1
+a0d6d5690,1024,1024,"[976.0, 638.0, 48.0, 87.0]",usask_1
+a0d6d5690,1024,1024,"[984.0, 591.0, 40.0, 68.0]",usask_1
+a0d6d5690,1024,1024,"[875.0, 470.0, 78.0, 61.0]",usask_1
+a0d6d5690,1024,1024,"[741.0, 354.0, 92.0, 85.0]",usask_1
+a0d6d5690,1024,1024,"[854.0, 385.0, 60.0, 58.0]",usask_1
+a0d6d5690,1024,1024,"[729.0, 570.0, 71.0, 51.0]",usask_1
+a0d6d5690,1024,1024,"[0.0, 657.0, 58.0, 63.0]",usask_1
+a0d6d5690,1024,1024,"[0.0, 720.0, 39.0, 48.0]",usask_1
+a0d6d5690,1024,1024,"[0.0, 572.0, 58.0, 56.0]",usask_1
+a0d6d5690,1024,1024,"[0.0, 596.0, 52.0, 47.0]",usask_1
+a0d6d5690,1024,1024,"[890.0, 193.0, 55.0, 95.0]",usask_1
+a0d6d5690,1024,1024,"[796.0, 200.0, 82.0, 68.0]",usask_1
+a0d6d5690,1024,1024,"[896.0, 0.0, 82.0, 179.0]",usask_1
+a0d6d5690,1024,1024,"[961.0, 138.0, 63.0, 104.0]",usask_1
+a0d6d5690,1024,1024,"[217.0, 437.0, 109.0, 49.0]",usask_1
+a815bae49,1024,1024,"[311.0, 153.0, 76.0, 80.0]",usask_1
+a815bae49,1024,1024,"[471.0, 194.0, 59.0, 86.0]",usask_1
+a815bae49,1024,1024,"[516.0, 327.0, 79.0, 73.0]",usask_1
+a815bae49,1024,1024,"[520.0, 13.0, 80.0, 142.0]",usask_1
+a815bae49,1024,1024,"[375.0, 185.0, 69.0, 83.0]",usask_1
+a815bae49,1024,1024,"[354.0, 87.0, 75.0, 103.0]",usask_1
+a815bae49,1024,1024,"[62.0, 0.0, 80.0, 50.0]",usask_1
+a815bae49,1024,1024,"[288.0, 260.0, 70.0, 81.0]",usask_1
+a815bae49,1024,1024,"[576.0, 590.0, 119.0, 77.0]",usask_1
+a815bae49,1024,1024,"[0.0, 220.0, 72.0, 80.0]",usask_1
+a815bae49,1024,1024,"[606.0, 197.0, 162.0, 75.0]",usask_1
+a815bae49,1024,1024,"[301.0, 900.0, 54.0, 101.0]",usask_1
+a815bae49,1024,1024,"[446.0, 504.0, 79.0, 195.0]",usask_1
+a815bae49,1024,1024,"[476.0, 814.0, 121.0, 102.0]",usask_1
+a815bae49,1024,1024,"[347.0, 665.0, 73.0, 96.0]",usask_1
+a815bae49,1024,1024,"[440.0, 720.0, 112.0, 61.0]",usask_1
+a815bae49,1024,1024,"[100.0, 247.0, 131.0, 82.0]",usask_1
+a815bae49,1024,1024,"[529.0, 276.0, 75.0, 47.0]",usask_1
+a815bae49,1024,1024,"[357.0, 16.0, 68.0, 87.0]",usask_1
+a815bae49,1024,1024,"[422.0, 46.0, 60.0, 105.0]",usask_1
+a815bae49,1024,1024,"[140.0, 0.0, 72.0, 29.0]",usask_1
+a815bae49,1024,1024,"[331.0, 995.0, 67.0, 29.0]",usask_1
+a815bae49,1024,1024,"[986.0, 0.0, 38.0, 31.0]",usask_1
+a815bae49,1024,1024,"[418.0, 0.0, 123.0, 55.0]",usask_1
+a815bae49,1024,1024,"[1.0, 326.0, 151.0, 103.0]",usask_1
+6d9127129,1024,1024,"[724.0, 770.0, 211.0, 74.0]",usask_1
+6d9127129,1024,1024,"[811.0, 947.0, 191.0, 54.0]",usask_1
+6d9127129,1024,1024,"[885.0, 657.0, 139.0, 121.0]",usask_1
+6d9127129,1024,1024,"[70.0, 707.0, 322.0, 149.0]",usask_1
+6d9127129,1024,1024,"[853.0, 500.0, 100.0, 52.0]",usask_1
+6d9127129,1024,1024,"[936.0, 744.0, 88.0, 108.0]",usask_1
+6d9127129,1024,1024,"[643.0, 657.0, 88.0, 127.0]",usask_1
+6d9127129,1024,1024,"[82.0, 488.0, 188.0, 88.0]",usask_1
+6d9127129,1024,1024,"[0.0, 385.0, 184.0, 93.0]",usask_1
+6d9127129,1024,1024,"[628.0, 0.0, 89.0, 57.0]",usask_1
+6d9127129,1024,1024,"[241.0, 853.0, 118.0, 86.0]",usask_1
+6d9127129,1024,1024,"[662.0, 752.0, 224.0, 188.0]",usask_1
+6d9127129,1024,1024,"[842.0, 80.0, 182.0, 162.0]",usask_1
+6d9127129,1024,1024,"[386.0, 826.0, 101.0, 95.0]",usask_1
+6d9127129,1024,1024,"[0.0, 303.0, 183.0, 108.0]",usask_1
+6d9127129,1024,1024,"[212.0, 320.0, 188.0, 117.0]",usask_1
+6d9127129,1024,1024,"[323.0, 255.0, 86.0, 157.0]",usask_1
+6d9127129,1024,1024,"[77.0, 176.0, 234.0, 153.0]",usask_1
+6d9127129,1024,1024,"[355.0, 210.0, 112.0, 131.0]",usask_1
+6d9127129,1024,1024,"[567.0, 346.0, 60.0, 57.0]",usask_1
+6d9127129,1024,1024,"[759.0, 35.0, 71.0, 52.0]",usask_1
+6d9127129,1024,1024,"[831.0, 974.0, 166.0, 48.0]",usask_1
+bd5cb3bc5,1024,1024,"[202.0, 410.0, 76.0, 92.0]",usask_1
+bd5cb3bc5,1024,1024,"[343.0, 308.0, 124.0, 178.0]",usask_1
+bd5cb3bc5,1024,1024,"[563.0, 844.0, 76.0, 71.0]",usask_1
+bd5cb3bc5,1024,1024,"[751.0, 806.0, 92.0, 218.0]",usask_1
+bd5cb3bc5,1024,1024,"[678.0, 848.0, 105.0, 94.0]",usask_1
+bd5cb3bc5,1024,1024,"[445.0, 491.0, 157.0, 93.0]",usask_1
+bd5cb3bc5,1024,1024,"[67.0, 154.0, 120.0, 157.0]",usask_1
+bd5cb3bc5,1024,1024,"[910.0, 488.0, 114.0, 191.0]",usask_1
+bd5cb3bc5,1024,1024,"[527.0, 380.0, 83.0, 68.0]",usask_1
+bd5cb3bc5,1024,1024,"[616.0, 117.0, 103.0, 93.0]",usask_1
+bd5cb3bc5,1024,1024,"[40.0, 77.0, 73.0, 83.0]",usask_1
+bd5cb3bc5,1024,1024,"[185.0, 195.0, 104.0, 114.0]",usask_1
+bd5cb3bc5,1024,1024,"[707.0, 0.0, 113.0, 72.0]",usask_1
+bd5cb3bc5,1024,1024,"[97.0, 370.0, 76.0, 111.0]",usask_1
+bd5cb3bc5,1024,1024,"[626.0, 490.0, 63.0, 179.0]",usask_1
+bd5cb3bc5,1024,1024,"[633.0, 941.0, 59.0, 57.0]",usask_1
+bd5cb3bc5,1024,1024,"[121.0, 30.0, 103.0, 96.0]",usask_1
+bd5cb3bc5,1024,1024,"[963.0, 256.0, 61.0, 164.0]",usask_1
+bd5cb3bc5,1024,1024,"[424.0, 0.0, 103.0, 39.0]",usask_1
+bd5cb3bc5,1024,1024,"[899.0, 796.0, 125.0, 100.0]",usask_1
+bd5cb3bc5,1024,1024,"[284.0, 163.0, 86.0, 128.0]",usask_1
+bd5cb3bc5,1024,1024,"[106.0, 943.0, 100.0, 81.0]",usask_1
+bd5cb3bc5,1024,1024,"[744.0, 491.0, 123.0, 110.0]",usask_1
+bd5cb3bc5,1024,1024,"[772.0, 100.0, 73.0, 115.0]",usask_1
+bd5cb3bc5,1024,1024,"[0.0, 131.0, 45.0, 79.0]",usask_1
+bd5cb3bc5,1024,1024,"[840.0, 568.0, 91.0, 62.0]",usask_1
+bd5cb3bc5,1024,1024,"[70.0, 733.0, 94.0, 101.0]",usask_1
+bd5cb3bc5,1024,1024,"[647.0, 403.0, 119.0, 115.0]",usask_1
+bd5cb3bc5,1024,1024,"[571.0, 356.0, 74.0, 76.0]",usask_1
+bd5cb3bc5,1024,1024,"[578.0, 452.0, 85.0, 123.0]",usask_1
+bd5cb3bc5,1024,1024,"[545.0, 116.0, 87.0, 89.0]",usask_1
+bd5cb3bc5,1024,1024,"[446.0, 70.0, 122.0, 149.0]",usask_1
+bd5cb3bc5,1024,1024,"[473.0, 3.0, 187.0, 207.0]",usask_1
+bd5cb3bc5,1024,1024,"[620.0, 38.0, 73.0, 61.0]",usask_1
+56cb8b67d,1024,1024,"[494.0, 240.0, 87.0, 97.0]",usask_1
+56cb8b67d,1024,1024,"[178.0, 154.0, 170.0, 144.0]",usask_1
+56cb8b67d,1024,1024,"[345.0, 402.0, 102.0, 66.0]",usask_1
+56cb8b67d,1024,1024,"[303.0, 161.0, 96.0, 100.0]",usask_1
+56cb8b67d,1024,1024,"[983.0, 360.0, 41.0, 103.0]",usask_1
+56cb8b67d,1024,1024,"[427.0, 634.0, 94.0, 94.0]",usask_1
+56cb8b67d,1024,1024,"[869.0, 0.0, 103.0, 25.0]",usask_1
+56cb8b67d,1024,1024,"[324.0, 35.0, 48.0, 96.0]",usask_1
+56cb8b67d,1024,1024,"[876.0, 746.0, 148.0, 94.0]",usask_1
+56cb8b67d,1024,1024,"[289.0, 42.0, 62.0, 80.0]",usask_1
+56cb8b67d,1024,1024,"[559.0, 0.0, 123.0, 76.0]",usask_1
+56cb8b67d,1024,1024,"[111.0, 222.0, 133.0, 116.0]",usask_1
+56cb8b67d,1024,1024,"[860.0, 912.0, 118.0, 112.0]",usask_1
+56cb8b67d,1024,1024,"[199.0, 888.0, 175.0, 112.0]",usask_1
+56cb8b67d,1024,1024,"[879.0, 43.0, 113.0, 76.0]",usask_1
+56cb8b67d,1024,1024,"[984.0, 797.0, 40.0, 72.0]",usask_1
+56cb8b67d,1024,1024,"[799.0, 930.0, 70.0, 90.0]",usask_1
+56cb8b67d,1024,1024,"[117.0, 533.0, 149.0, 118.0]",usask_1
+56cb8b67d,1024,1024,"[145.0, 463.0, 179.0, 105.0]",usask_1
+56cb8b67d,1024,1024,"[241.0, 433.0, 134.0, 52.0]",usask_1
+56cb8b67d,1024,1024,"[241.0, 373.0, 95.0, 70.0]",usask_1
+56cb8b67d,1024,1024,"[307.0, 311.0, 84.0, 72.0]",usask_1
+56cb8b67d,1024,1024,"[260.0, 324.0, 67.0, 51.0]",usask_1
+56cb8b67d,1024,1024,"[643.0, 218.0, 69.0, 81.0]",usask_1
+56cb8b67d,1024,1024,"[560.0, 210.0, 80.0, 93.0]",usask_1
+56cb8b67d,1024,1024,"[385.0, 99.0, 73.0, 68.0]",usask_1
+56cb8b67d,1024,1024,"[363.0, 20.0, 46.0, 78.0]",usask_1
+5f0b34ad6,1024,1024,"[306.0, 485.0, 87.0, 87.0]",usask_1
+5f0b34ad6,1024,1024,"[259.0, 313.0, 103.0, 86.0]",usask_1
+5f0b34ad6,1024,1024,"[476.0, 882.0, 62.0, 80.0]",usask_1
+5f0b34ad6,1024,1024,"[264.0, 264.0, 76.0, 48.0]",usask_1
+5f0b34ad6,1024,1024,"[185.0, 250.0, 85.0, 79.0]",usask_1
+5f0b34ad6,1024,1024,"[0.0, 845.0, 48.0, 90.0]",usask_1
+5f0b34ad6,1024,1024,"[953.0, 287.0, 69.0, 95.0]",usask_1
+5f0b34ad6,1024,1024,"[231.0, 971.0, 137.0, 53.0]",usask_1
+5f0b34ad6,1024,1024,"[640.0, 544.0, 68.0, 61.0]",usask_1
+5f0b34ad6,1024,1024,"[534.0, 20.0, 71.0, 88.0]",usask_1
+5f0b34ad6,1024,1024,"[101.0, 547.0, 97.0, 66.0]",usask_1
+5f0b34ad6,1024,1024,"[467.0, 0.0, 82.0, 124.0]",usask_1
+5f0b34ad6,1024,1024,"[245.0, 208.0, 79.0, 58.0]",usask_1
+5f0b34ad6,1024,1024,"[245.0, 370.0, 87.0, 147.0]",usask_1
+5f0b34ad6,1024,1024,"[29.0, 298.0, 99.0, 75.0]",usask_1
+5f0b34ad6,1024,1024,"[0.0, 551.0, 58.0, 87.0]",usask_1
+5f0b34ad6,1024,1024,"[252.0, 656.0, 172.0, 152.0]",usask_1
+5f0b34ad6,1024,1024,"[434.0, 518.0, 106.0, 100.0]",usask_1
+5f0b34ad6,1024,1024,"[527.0, 914.0, 58.0, 78.0]",usask_1
+5f0b34ad6,1024,1024,"[61.0, 953.0, 150.0, 71.0]",usask_1
+5f0b34ad6,1024,1024,"[961.0, 798.0, 63.0, 74.0]",usask_1
+5f0b34ad6,1024,1024,"[977.0, 863.0, 47.0, 58.0]",usask_1
+5f0b34ad6,1024,1024,"[896.0, 243.0, 77.0, 76.0]",usask_1
+5f0b34ad6,1024,1024,"[803.0, 403.0, 91.0, 66.0]",usask_1
+5f0b34ad6,1024,1024,"[593.0, 514.0, 42.0, 67.0]",usask_1
+5f0b34ad6,1024,1024,"[628.0, 484.0, 60.0, 99.0]",usask_1
+5f0b34ad6,1024,1024,"[632.0, 387.0, 130.0, 77.0]",usask_1
+5f0b34ad6,1024,1024,"[643.0, 407.0, 59.0, 123.0]",usask_1
+5f0b34ad6,1024,1024,"[562.0, 370.0, 62.0, 74.0]",usask_1
+5f0b34ad6,1024,1024,"[420.0, 658.0, 76.0, 59.0]",usask_1
+5f0b34ad6,1024,1024,"[264.0, 140.0, 83.0, 72.0]",usask_1
+ee91a1343,1024,1024,"[387.0, 461.0, 66.0, 83.0]",usask_1
+ee91a1343,1024,1024,"[417.0, 236.0, 63.0, 98.0]",usask_1
+ee91a1343,1024,1024,"[0.0, 285.0, 110.0, 68.0]",usask_1
+ee91a1343,1024,1024,"[302.0, 925.0, 82.0, 86.0]",usask_1
+ee91a1343,1024,1024,"[172.0, 226.0, 75.0, 75.0]",usask_1
+ee91a1343,1024,1024,"[965.0, 396.0, 59.0, 78.0]",usask_1
+ee91a1343,1024,1024,"[959.0, 293.0, 65.0, 92.0]",usask_1
+ee91a1343,1024,1024,"[413.0, 819.0, 137.0, 99.0]",usask_1
+ee91a1343,1024,1024,"[205.0, 59.0, 118.0, 53.0]",usask_1
+ee91a1343,1024,1024,"[333.0, 0.0, 80.0, 79.0]",usask_1
+ee91a1343,1024,1024,"[475.0, 559.0, 103.0, 113.0]",usask_1
+ee91a1343,1024,1024,"[513.0, 898.0, 112.0, 77.0]",usask_1
+ee91a1343,1024,1024,"[828.0, 709.0, 160.0, 116.0]",usask_1
+ee91a1343,1024,1024,"[0.0, 340.0, 66.0, 86.0]",usask_1
+ee91a1343,1024,1024,"[106.0, 981.0, 126.0, 43.0]",usask_1
+ee91a1343,1024,1024,"[848.0, 538.0, 171.0, 110.0]",usask_1
+ee91a1343,1024,1024,"[198.0, 495.0, 103.0, 96.0]",usask_1
+ee91a1343,1024,1024,"[446.0, 23.0, 93.0, 96.0]",usask_1
+ee91a1343,1024,1024,"[620.0, 558.0, 184.0, 85.0]",usask_1
+ee91a1343,1024,1024,"[611.0, 905.0, 121.0, 98.0]",usask_1
+ee91a1343,1024,1024,"[581.0, 861.0, 127.0, 55.0]",usask_1
+ee91a1343,1024,1024,"[553.0, 787.0, 125.0, 97.0]",usask_1
+ee91a1343,1024,1024,"[715.0, 845.0, 95.0, 87.0]",usask_1
+ee91a1343,1024,1024,"[753.0, 829.0, 123.0, 69.0]",usask_1
+ee91a1343,1024,1024,"[633.0, 713.0, 160.0, 82.0]",usask_1
+ee91a1343,1024,1024,"[547.0, 676.0, 86.0, 66.0]",usask_1
+ee91a1343,1024,1024,"[456.0, 709.0, 124.0, 67.0]",usask_1
+ee91a1343,1024,1024,"[455.0, 748.0, 105.0, 71.0]",usask_1
+ee91a1343,1024,1024,"[955.0, 0.0, 69.0, 119.0]",usask_1
+7e75b3e43,1024,1024,"[653.0, 148.0, 140.0, 113.0]",usask_1
+7e75b3e43,1024,1024,"[0.0, 646.0, 34.0, 98.0]",usask_1
+7e75b3e43,1024,1024,"[468.0, 222.0, 56.0, 106.0]",usask_1
+7e75b3e43,1024,1024,"[449.0, 397.0, 100.0, 141.0]",usask_1
+7e75b3e43,1024,1024,"[758.0, 161.0, 130.0, 167.0]",usask_1
+7e75b3e43,1024,1024,"[0.0, 955.0, 187.0, 61.0]",usask_1
+7e75b3e43,1024,1024,"[52.0, 155.0, 151.0, 137.0]",usask_1
+7e75b3e43,1024,1024,"[229.0, 747.0, 177.0, 117.0]",usask_1
+7e75b3e43,1024,1024,"[650.0, 972.0, 83.0, 52.0]",usask_1
+7e75b3e43,1024,1024,"[640.0, 0.0, 78.0, 48.0]",usask_1
+7e75b3e43,1024,1024,"[229.0, 960.0, 194.0, 64.0]",usask_1
+7e75b3e43,1024,1024,"[421.0, 101.0, 89.0, 93.0]",usask_1
+7e75b3e43,1024,1024,"[591.0, 513.0, 198.0, 91.0]",usask_1
+7e75b3e43,1024,1024,"[0.0, 670.0, 271.0, 201.0]",usask_1
+7e75b3e43,1024,1024,"[855.0, 94.0, 113.0, 86.0]",usask_1
+7e75b3e43,1024,1024,"[323.0, 616.0, 295.0, 166.0]",usask_1
+7e75b3e43,1024,1024,"[350.0, 64.0, 99.0, 231.0]",usask_1
+7e75b3e43,1024,1024,"[833.0, 350.0, 191.0, 250.0]",usask_1
+7e75b3e43,1024,1024,"[735.0, 618.0, 209.0, 147.0]",usask_1
+7e75b3e43,1024,1024,"[178.0, 16.0, 120.0, 192.0]",usask_1
+7e75b3e43,1024,1024,"[14.0, 0.0, 66.0, 68.0]",usask_1
+7e75b3e43,1024,1024,"[929.0, 852.0, 82.0, 119.0]",usask_1
+7e75b3e43,1024,1024,"[0.0, 182.0, 37.0, 102.0]",usask_1
+7e75b3e43,1024,1024,"[142.0, 902.0, 204.0, 85.0]",usask_1
+7e75b3e43,1024,1024,"[407.0, 788.0, 204.0, 99.0]",usask_1
+7e75b3e43,1024,1024,"[80.0, 87.0, 107.0, 100.0]",usask_1
+7e75b3e43,1024,1024,"[46.0, 883.0, 173.0, 78.0]",usask_1
+7e75b3e43,1024,1024,"[870.0, 440.0, 124.0, 165.0]",usask_1
+7e75b3e43,1024,1024,"[656.0, 208.0, 54.0, 138.0]",usask_1
+0a029eead,1024,1024,"[139.0, 0.0, 60.0, 85.0]",usask_1
+0a029eead,1024,1024,"[0.0, 493.0, 72.0, 121.0]",usask_1
+0a029eead,1024,1024,"[0.0, 900.0, 72.0, 75.0]",usask_1
+0a029eead,1024,1024,"[434.0, 306.0, 178.0, 286.0]",usask_1
+0a029eead,1024,1024,"[651.0, 625.0, 294.0, 203.0]",usask_1
+0a029eead,1024,1024,"[709.0, 138.0, 178.0, 113.0]",usask_1
+0a029eead,1024,1024,"[134.0, 855.0, 124.0, 117.0]",usask_1
+0a029eead,1024,1024,"[239.0, 367.0, 225.0, 114.0]",usask_1
+0a029eead,1024,1024,"[469.0, 332.0, 227.0, 106.0]",usask_1
+0a029eead,1024,1024,"[224.0, 70.0, 93.0, 292.0]",usask_1
+0a029eead,1024,1024,"[727.0, 387.0, 90.0, 83.0]",usask_1
+0a029eead,1024,1024,"[67.0, 117.0, 133.0, 130.0]",usask_1
+0a029eead,1024,1024,"[208.0, 0.0, 101.0, 106.0]",usask_1
+0a029eead,1024,1024,"[101.0, 704.0, 136.0, 180.0]",usask_1
+0a029eead,1024,1024,"[0.0, 794.0, 116.0, 97.0]",usask_1
+0a029eead,1024,1024,"[912.0, 390.0, 112.0, 370.0]",usask_1
+0a029eead,1024,1024,"[668.0, 0.0, 78.0, 36.0]",usask_1
+0a029eead,1024,1024,"[80.0, 939.0, 120.0, 85.0]",usask_1
+0a029eead,1024,1024,"[690.0, 194.0, 105.0, 93.0]",usask_1
+0a029eead,1024,1024,"[212.0, 540.0, 239.0, 96.0]",usask_1
+0a029eead,1024,1024,"[423.0, 221.0, 162.0, 169.0]",usask_1
+0a029eead,1024,1024,"[494.0, 847.0, 134.0, 97.0]",usask_1
+0a029eead,1024,1024,"[13.0, 0.0, 137.0, 116.0]",usask_1
+0a029eead,1024,1024,"[761.0, 872.0, 96.0, 152.0]",usask_1
+0a029eead,1024,1024,"[521.0, 604.0, 216.0, 136.0]",usask_1
+0a029eead,1024,1024,"[0.0, 696.0, 64.0, 80.0]",usask_1
+0a029eead,1024,1024,"[327.0, 0.0, 111.0, 216.0]",usask_1
+0a029eead,1024,1024,"[408.0, 0.0, 153.0, 75.0]",usask_1
+0a029eead,1024,1024,"[285.0, 2.0, 94.0, 93.0]",usask_1
+0a029eead,1024,1024,"[0.0, 161.0, 46.0, 75.0]",usask_1
+4a4a1d3e0,1024,1024,"[467.0, 755.0, 171.0, 76.0]",usask_1
+4a4a1d3e0,1024,1024,"[31.0, 301.0, 140.0, 159.0]",usask_1
+4a4a1d3e0,1024,1024,"[454.0, 1000.0, 129.0, 24.0]",usask_1
+4a4a1d3e0,1024,1024,"[237.0, 393.0, 143.0, 76.0]",usask_1
+4a4a1d3e0,1024,1024,"[522.0, 421.0, 153.0, 93.0]",usask_1
+4a4a1d3e0,1024,1024,"[421.0, 371.0, 191.0, 81.0]",usask_1
+4a4a1d3e0,1024,1024,"[595.0, 146.0, 123.0, 113.0]",usask_1
+4a4a1d3e0,1024,1024,"[807.0, 449.0, 92.0, 82.0]",usask_1
+4a4a1d3e0,1024,1024,"[635.0, 70.0, 104.0, 169.0]",usask_1
+4a4a1d3e0,1024,1024,"[0.0, 831.0, 117.0, 56.0]",usask_1
+4a4a1d3e0,1024,1024,"[184.0, 418.0, 98.0, 179.0]",usask_1
+4a4a1d3e0,1024,1024,"[285.0, 666.0, 182.0, 94.0]",usask_1
+4a4a1d3e0,1024,1024,"[579.0, 0.0, 133.0, 62.0]",usask_1
+4a4a1d3e0,1024,1024,"[734.0, 289.0, 110.0, 120.0]",usask_1
+4a4a1d3e0,1024,1024,"[424.0, 582.0, 129.0, 95.0]",usask_1
+4a4a1d3e0,1024,1024,"[0.0, 796.0, 107.0, 64.0]",usask_1
+4a4a1d3e0,1024,1024,"[286.0, 991.0, 109.0, 33.0]",usask_1
+4a4a1d3e0,1024,1024,"[248.0, 463.0, 226.0, 129.0]",usask_1
+4a4a1d3e0,1024,1024,"[367.0, 0.0, 211.0, 76.0]",usask_1
+4a4a1d3e0,1024,1024,"[122.0, 819.0, 156.0, 64.0]",usask_1
+4a4a1d3e0,1024,1024,"[549.0, 916.0, 153.0, 83.0]",usask_1
+4a4a1d3e0,1024,1024,"[452.0, 776.0, 184.0, 78.0]",usask_1
+4a4a1d3e0,1024,1024,"[855.0, 687.0, 53.0, 53.0]",usask_1
+4a4a1d3e0,1024,1024,"[573.0, 226.0, 98.0, 85.0]",usask_1
+4a4a1d3e0,1024,1024,"[277.0, 633.0, 177.0, 91.0]",usask_1
+4a4a1d3e0,1024,1024,"[50.0, 649.0, 195.0, 91.0]",usask_1
+4a4a1d3e0,1024,1024,"[80.0, 595.0, 181.0, 76.0]",usask_1
+4a4a1d3e0,1024,1024,"[154.0, 626.0, 107.0, 184.0]",usask_1
+4a4a1d3e0,1024,1024,"[689.0, 461.0, 104.0, 68.0]",usask_1
+4a4a1d3e0,1024,1024,"[620.0, 382.0, 98.0, 112.0]",usask_1
+4a4a1d3e0,1024,1024,"[651.0, 402.0, 76.0, 100.0]",usask_1
+097af10ac,1024,1024,"[334.0, 653.0, 65.0, 66.0]",usask_1
+097af10ac,1024,1024,"[50.0, 462.0, 100.0, 230.0]",usask_1
+097af10ac,1024,1024,"[239.0, 0.0, 103.0, 28.0]",usask_1
+097af10ac,1024,1024,"[740.0, 318.0, 106.0, 121.0]",usask_1
+097af10ac,1024,1024,"[556.0, 240.0, 79.0, 70.0]",usask_1
+097af10ac,1024,1024,"[353.0, 0.0, 86.0, 25.0]",usask_1
+097af10ac,1024,1024,"[856.0, 561.0, 168.0, 120.0]",usask_1
+097af10ac,1024,1024,"[528.0, 764.0, 64.0, 68.0]",usask_1
+097af10ac,1024,1024,"[565.0, 663.0, 100.0, 117.0]",usask_1
+097af10ac,1024,1024,"[0.0, 877.0, 171.0, 77.0]",usask_1
+097af10ac,1024,1024,"[508.0, 27.0, 94.0, 191.0]",usask_1
+097af10ac,1024,1024,"[190.0, 6.0, 132.0, 178.0]",usask_1
+097af10ac,1024,1024,"[576.0, 77.0, 79.0, 150.0]",usask_1
+097af10ac,1024,1024,"[3.0, 20.0, 110.0, 126.0]",usask_1
+097af10ac,1024,1024,"[918.0, 889.0, 106.0, 114.0]",usask_1
+097af10ac,1024,1024,"[835.0, 101.0, 103.0, 113.0]",usask_1
+097af10ac,1024,1024,"[646.0, 471.0, 97.0, 93.0]",usask_1
+097af10ac,1024,1024,"[258.0, 925.0, 105.0, 90.0]",usask_1
+097af10ac,1024,1024,"[825.0, 397.0, 93.0, 184.0]",usask_1
+097af10ac,1024,1024,"[388.0, 213.0, 98.0, 139.0]",usask_1
+097af10ac,1024,1024,"[707.0, 0.0, 184.0, 133.0]",usask_1
+097af10ac,1024,1024,"[174.0, 953.0, 143.0, 70.0]",usask_1
+097af10ac,1024,1024,"[278.0, 877.0, 126.0, 136.0]",usask_1
+097af10ac,1024,1024,"[784.0, 806.0, 79.0, 79.0]",usask_1
+097af10ac,1024,1024,"[0.0, 348.0, 48.0, 89.0]",usask_1
+097af10ac,1024,1024,"[759.0, 366.0, 168.0, 94.0]",usask_1
+097af10ac,1024,1024,"[442.0, 24.0, 94.0, 112.0]",usask_1
+097af10ac,1024,1024,"[128.0, 4.0, 88.0, 120.0]",usask_1
+097af10ac,1024,1024,"[878.0, 20.0, 119.0, 71.0]",usask_1
+097af10ac,1024,1024,"[528.0, 0.0, 78.0, 68.0]",usask_1
+ed23785c8,1024,1024,"[210.0, 625.0, 93.0, 94.0]",usask_1
+ed23785c8,1024,1024,"[0.0, 548.0, 70.0, 85.0]",usask_1
+ed23785c8,1024,1024,"[681.0, 296.0, 103.0, 81.0]",usask_1
+ed23785c8,1024,1024,"[0.0, 700.0, 125.0, 91.0]",usask_1
+ed23785c8,1024,1024,"[900.0, 578.0, 124.0, 89.0]",usask_1
+ed23785c8,1024,1024,"[529.0, 296.0, 90.0, 110.0]",usask_1
+ed23785c8,1024,1024,"[666.0, 670.0, 90.0, 86.0]",usask_1
+ed23785c8,1024,1024,"[771.0, 613.0, 89.0, 86.0]",usask_1
+ed23785c8,1024,1024,"[121.0, 390.0, 85.0, 110.0]",usask_1
+ed23785c8,1024,1024,"[33.0, 0.0, 110.0, 79.0]",usask_1
+ed23785c8,1024,1024,"[760.0, 872.0, 82.0, 108.0]",usask_1
+ed23785c8,1024,1024,"[580.0, 0.0, 102.0, 42.0]",usask_1
+ed23785c8,1024,1024,"[670.0, 821.0, 126.0, 97.0]",usask_1
+ed23785c8,1024,1024,"[552.0, 545.0, 85.0, 81.0]",usask_1
+ed23785c8,1024,1024,"[842.0, 202.0, 85.0, 87.0]",usask_1
+ed23785c8,1024,1024,"[963.0, 747.0, 61.0, 93.0]",usask_1
+ed23785c8,1024,1024,"[939.0, 87.0, 85.0, 93.0]",usask_1
+ed23785c8,1024,1024,"[43.0, 555.0, 99.0, 75.0]",usask_1
+ed23785c8,1024,1024,"[663.0, 365.0, 113.0, 71.0]",usask_1
+ed23785c8,1024,1024,"[587.0, 436.0, 91.0, 63.0]",usask_1
+ed23785c8,1024,1024,"[439.0, 371.0, 163.0, 76.0]",usask_1
+ed23785c8,1024,1024,"[464.0, 303.0, 80.0, 69.0]",usask_1
+ed23785c8,1024,1024,"[491.0, 528.0, 88.0, 74.0]",usask_1
+65d6480b2,1024,1024,"[63.0, 784.0, 79.0, 59.0]",usask_1
+65d6480b2,1024,1024,"[0.0, 957.0, 79.0, 66.0]",usask_1
+65d6480b2,1024,1024,"[545.0, 781.0, 181.0, 83.0]",usask_1
+65d6480b2,1024,1024,"[525.0, 0.0, 223.0, 366.0]",usask_1
+65d6480b2,1024,1024,"[585.0, 0.0, 90.0, 166.0]",usask_1
+65d6480b2,1024,1024,"[778.0, 434.0, 96.0, 154.0]",usask_1
+65d6480b2,1024,1024,"[168.0, 464.0, 160.0, 151.0]",usask_1
+65d6480b2,1024,1024,"[286.0, 485.0, 106.0, 119.0]",usask_1
+65d6480b2,1024,1024,"[586.0, 646.0, 241.0, 117.0]",usask_1
+65d6480b2,1024,1024,"[98.0, 231.0, 112.0, 167.0]",usask_1
+65d6480b2,1024,1024,"[383.0, 640.0, 228.0, 138.0]",usask_1
+65d6480b2,1024,1024,"[636.0, 752.0, 128.0, 82.0]",usask_1
+65d6480b2,1024,1024,"[444.0, 20.0, 103.0, 116.0]",usask_1
+65d6480b2,1024,1024,"[944.0, 170.0, 75.0, 182.0]",usask_1
+65d6480b2,1024,1024,"[33.0, 582.0, 217.0, 119.0]",usask_1
+65d6480b2,1024,1024,"[175.0, 229.0, 365.0, 175.0]",usask_1
+65d6480b2,1024,1024,"[78.0, 525.0, 105.0, 83.0]",usask_1
+65d6480b2,1024,1024,"[143.0, 310.0, 96.0, 138.0]",usask_1
+65d6480b2,1024,1024,"[19.0, 340.0, 107.0, 158.0]",usask_1
+65d6480b2,1024,1024,"[603.0, 384.0, 216.0, 167.0]",usask_1
+65d6480b2,1024,1024,"[525.0, 970.0, 187.0, 51.0]",usask_1
+65d6480b2,1024,1024,"[0.0, 886.0, 140.0, 109.0]",usask_1
+65d6480b2,1024,1024,"[360.0, 538.0, 228.0, 145.0]",usask_1
+65d6480b2,1024,1024,"[551.0, 397.0, 215.0, 154.0]",usask_1
+65d6480b2,1024,1024,"[278.0, 288.0, 310.0, 205.0]",usask_1
+65d6480b2,1024,1024,"[511.0, 747.0, 123.0, 50.0]",usask_1
+9d137bb37,1024,1024,"[361.0, 0.0, 89.0, 69.0]",usask_1
+9d137bb37,1024,1024,"[565.0, 892.0, 84.0, 57.0]",usask_1
+9d137bb37,1024,1024,"[572.0, 444.0, 100.0, 103.0]",usask_1
+9d137bb37,1024,1024,"[0.0, 198.0, 42.0, 66.0]",usask_1
+9d137bb37,1024,1024,"[623.0, 404.0, 160.0, 140.0]",usask_1
+9d137bb37,1024,1024,"[0.0, 754.0, 43.0, 82.0]",usask_1
+9d137bb37,1024,1024,"[434.0, 390.0, 75.0, 93.0]",usask_1
+9d137bb37,1024,1024,"[912.0, 330.0, 93.0, 103.0]",usask_1
+9d137bb37,1024,1024,"[333.0, 559.0, 110.0, 103.0]",usask_1
+9d137bb37,1024,1024,"[504.0, 895.0, 70.0, 59.0]",usask_1
+9d137bb37,1024,1024,"[0.0, 117.0, 62.0, 100.0]",usask_1
+9d137bb37,1024,1024,"[527.0, 976.0, 90.0, 48.0]",usask_1
+9d137bb37,1024,1024,"[417.0, 37.0, 66.0, 72.0]",usask_1
+9d137bb37,1024,1024,"[791.0, 822.0, 109.0, 170.0]",usask_1
+9d137bb37,1024,1024,"[340.0, 264.0, 82.0, 80.0]",usask_1
+9d137bb37,1024,1024,"[262.0, 44.0, 79.0, 77.0]",usask_1
+9d137bb37,1024,1024,"[235.0, 528.0, 87.0, 90.0]",usask_1
+9d137bb37,1024,1024,"[456.0, 194.0, 57.0, 68.0]",usask_1
+9d137bb37,1024,1024,"[295.0, 359.0, 85.0, 82.0]",usask_1
+9d137bb37,1024,1024,"[362.0, 782.0, 95.0, 93.0]",usask_1
+9d137bb37,1024,1024,"[558.0, 699.0, 85.0, 77.0]",usask_1
+9d137bb37,1024,1024,"[417.0, 880.0, 84.0, 78.0]",usask_1
+9d137bb37,1024,1024,"[151.0, 0.0, 90.0, 57.0]",usask_1
+9d137bb37,1024,1024,"[651.0, 152.0, 122.0, 82.0]",usask_1
+9d137bb37,1024,1024,"[0.0, 360.0, 67.0, 98.0]",usask_1
+9d137bb37,1024,1024,"[505.0, 256.0, 78.0, 85.0]",usask_1
+9d137bb37,1024,1024,"[0.0, 901.0, 46.0, 78.0]",usask_1
+9d137bb37,1024,1024,"[957.0, 712.0, 67.0, 83.0]",usask_1
+9d137bb37,1024,1024,"[907.0, 114.0, 81.0, 66.0]",usask_1
+9d137bb37,1024,1024,"[969.0, 95.0, 55.0, 58.0]",usask_1
+9d137bb37,1024,1024,"[988.0, 0.0, 36.0, 27.0]",usask_1
+9d137bb37,1024,1024,"[360.0, 214.0, 56.0, 60.0]",usask_1
+9d137bb37,1024,1024,"[553.0, 402.0, 78.0, 75.0]",usask_1
+4bfb54f09,1024,1024,"[0.0, 555.0, 59.0, 117.0]",usask_1
+4bfb54f09,1024,1024,"[374.0, 631.0, 76.0, 72.0]",usask_1
+4bfb54f09,1024,1024,"[454.0, 908.0, 93.0, 97.0]",usask_1
+4bfb54f09,1024,1024,"[86.0, 0.0, 196.0, 95.0]",usask_1
+4bfb54f09,1024,1024,"[75.0, 863.0, 114.0, 107.0]",usask_1
+4bfb54f09,1024,1024,"[0.0, 45.0, 59.0, 88.0]",usask_1
+4bfb54f09,1024,1024,"[781.0, 498.0, 172.0, 134.0]",usask_1
+4bfb54f09,1024,1024,"[87.0, 0.0, 123.0, 59.0]",usask_1
+4bfb54f09,1024,1024,"[47.0, 663.0, 111.0, 157.0]",usask_1
+4bfb54f09,1024,1024,"[511.0, 619.0, 135.0, 155.0]",usask_1
+4bfb54f09,1024,1024,"[106.0, 755.0, 70.0, 102.0]",usask_1
+4bfb54f09,1024,1024,"[35.0, 863.0, 107.0, 109.0]",usask_1
+4bfb54f09,1024,1024,"[0.0, 931.0, 84.0, 81.0]",usask_1
+4bfb54f09,1024,1024,"[685.0, 670.0, 77.0, 83.0]",usask_1
+4bfb54f09,1024,1024,"[805.0, 659.0, 73.0, 77.0]",usask_1
+4bfb54f09,1024,1024,"[5.0, 488.0, 84.0, 87.0]",usask_1
+4bfb54f09,1024,1024,"[0.0, 382.0, 37.0, 62.0]",usask_1
+4bfb54f09,1024,1024,"[748.0, 0.0, 130.0, 77.0]",usask_1
+4bfb54f09,1024,1024,"[414.0, 0.0, 87.0, 46.0]",usask_1
+01e2c61d9,1024,1024,"[371.0, 383.0, 80.0, 68.0]",usask_1
+01e2c61d9,1024,1024,"[132.0, 948.0, 54.0, 76.0]",usask_1
+01e2c61d9,1024,1024,"[358.0, 159.0, 71.0, 84.0]",usask_1
+01e2c61d9,1024,1024,"[549.0, 0.0, 47.0, 23.0]",usask_1
+01e2c61d9,1024,1024,"[879.0, 430.0, 109.0, 198.0]",usask_1
+01e2c61d9,1024,1024,"[47.0, 811.0, 150.0, 76.0]",usask_1
+01e2c61d9,1024,1024,"[208.0, 225.0, 137.0, 181.0]",usask_1
+01e2c61d9,1024,1024,"[569.0, 333.0, 150.0, 106.0]",usask_1
+01e2c61d9,1024,1024,"[168.0, 377.0, 157.0, 96.0]",usask_1
+01e2c61d9,1024,1024,"[339.0, 331.0, 124.0, 75.0]",usask_1
+01e2c61d9,1024,1024,"[704.0, 229.0, 96.0, 116.0]",usask_1
+01e2c61d9,1024,1024,"[346.0, 388.0, 127.0, 119.0]",usask_1
+01e2c61d9,1024,1024,"[154.0, 464.0, 175.0, 66.0]",usask_1
+01e2c61d9,1024,1024,"[442.0, 481.0, 150.0, 103.0]",usask_1
+01e2c61d9,1024,1024,"[851.0, 703.0, 137.0, 114.0]",usask_1
+01e2c61d9,1024,1024,"[458.0, 367.0, 93.0, 113.0]",usask_1
+01e2c61d9,1024,1024,"[397.0, 848.0, 132.0, 120.0]",usask_1
+01e2c61d9,1024,1024,"[415.0, 190.0, 134.0, 202.0]",usask_1
+01e2c61d9,1024,1024,"[619.0, 813.0, 129.0, 172.0]",usask_1
+01e2c61d9,1024,1024,"[826.0, 784.0, 198.0, 80.0]",usask_1
+01e2c61d9,1024,1024,"[22.0, 882.0, 127.0, 141.0]",usask_1
+01e2c61d9,1024,1024,"[618.0, 455.0, 65.0, 80.0]",usask_1
+01e2c61d9,1024,1024,"[768.0, 60.0, 103.0, 96.0]",usask_1
+01e2c61d9,1024,1024,"[0.0, 30.0, 86.0, 110.0]",usask_1
+01e2c61d9,1024,1024,"[208.0, 946.0, 124.0, 78.0]",usask_1
+01e2c61d9,1024,1024,"[0.0, 0.0, 62.0, 2.0]",usask_1
+01e2c61d9,1024,1024,"[345.0, 494.0, 97.0, 91.0]",usask_1
+01e2c61d9,1024,1024,"[46.0, 776.0, 167.0, 42.0]",usask_1
+01e2c61d9,1024,1024,"[312.0, 702.0, 173.0, 117.0]",usask_1
+01e2c61d9,1024,1024,"[312.0, 689.0, 151.0, 84.0]",usask_1
+01e2c61d9,1024,1024,"[176.0, 739.0, 161.0, 109.0]",usask_1
+01e2c61d9,1024,1024,"[202.0, 649.0, 118.0, 182.0]",usask_1
+01e2c61d9,1024,1024,"[167.0, 561.0, 122.0, 224.0]",usask_1
+01e2c61d9,1024,1024,"[261.0, 613.0, 117.0, 91.0]",usask_1
+01e2c61d9,1024,1024,"[286.0, 556.0, 53.0, 159.0]",usask_1
+01e2c61d9,1024,1024,"[253.0, 503.0, 88.0, 72.0]",usask_1
+01e2c61d9,1024,1024,"[0.0, 274.0, 31.0, 82.0]",usask_1
+01e2c61d9,1024,1024,"[547.0, 684.0, 73.0, 52.0]",usask_1
+01e2c61d9,1024,1024,"[583.0, 300.0, 30.0, 52.0]",usask_1
+a6a921e82,1024,1024,"[353.0, 367.0, 80.0, 87.0]",usask_1
+a6a921e82,1024,1024,"[406.0, 297.0, 80.0, 65.0]",usask_1
+a6a921e82,1024,1024,"[754.0, 15.0, 202.0, 100.0]",usask_1
+a6a921e82,1024,1024,"[312.0, 291.0, 88.0, 71.0]",usask_1
+a6a921e82,1024,1024,"[808.0, 853.0, 83.0, 81.0]",usask_1
+a6a921e82,1024,1024,"[500.0, 567.0, 56.0, 77.0]",usask_1
+a6a921e82,1024,1024,"[338.0, 441.0, 59.0, 158.0]",usask_1
+a6a921e82,1024,1024,"[559.0, 524.0, 134.0, 289.0]",usask_1
+a6a921e82,1024,1024,"[508.0, 937.0, 139.0, 68.0]",usask_1
+a6a921e82,1024,1024,"[141.0, 266.0, 96.0, 177.0]",usask_1
+a6a921e82,1024,1024,"[633.0, 0.0, 174.0, 73.0]",usask_1
+a6a921e82,1024,1024,"[781.0, 929.0, 170.0, 86.0]",usask_1
+a6a921e82,1024,1024,"[19.0, 402.0, 168.0, 145.0]",usask_1
+a6a921e82,1024,1024,"[775.0, 266.0, 249.0, 197.0]",usask_1
+a6a921e82,1024,1024,"[178.0, 899.0, 123.0, 125.0]",usask_1
+a6a921e82,1024,1024,"[431.0, 151.0, 101.0, 87.0]",usask_1
+a6a921e82,1024,1024,"[13.0, 926.0, 133.0, 98.0]",usask_1
+a6a921e82,1024,1024,"[30.0, 158.0, 130.0, 120.0]",usask_1
+a6a921e82,1024,1024,"[741.0, 764.0, 173.0, 133.0]",usask_1
+a6a921e82,1024,1024,"[314.0, 807.0, 78.0, 151.0]",usask_1
+a6a921e82,1024,1024,"[648.0, 564.0, 240.0, 101.0]",usask_1
+a6a921e82,1024,1024,"[852.0, 171.0, 167.0, 87.0]",usask_1
+a6a921e82,1024,1024,"[1.0, 886.0, 67.0, 99.0]",usask_1
+a6a921e82,1024,1024,"[569.0, 94.0, 180.0, 130.0]",usask_1
+a6a921e82,1024,1024,"[686.0, 144.0, 152.0, 186.0]",usask_1
+a6a921e82,1024,1024,"[148.0, 710.0, 110.0, 76.0]",usask_1
+a6a921e82,1024,1024,"[138.0, 660.0, 103.0, 76.0]",usask_1
+a6a921e82,1024,1024,"[461.0, 845.0, 83.0, 150.0]",usask_1
+a6a921e82,1024,1024,"[373.0, 869.0, 100.0, 155.0]",usask_1
+a6a921e82,1024,1024,"[306.0, 980.0, 144.0, 44.0]",usask_1
+a6a921e82,1024,1024,"[18.0, 448.0, 184.0, 140.0]",usask_1
+a6a921e82,1024,1024,"[16.0, 519.0, 69.0, 152.0]",usask_1
+a6a921e82,1024,1024,"[284.0, 444.0, 72.0, 160.0]",usask_1
+a6a921e82,1024,1024,"[238.0, 459.0, 97.0, 54.0]",usask_1
+a6a921e82,1024,1024,"[183.0, 542.0, 87.0, 53.0]",usask_1
+a6a921e82,1024,1024,"[225.0, 576.0, 98.0, 69.0]",usask_1
+a6a921e82,1024,1024,"[296.0, 599.0, 76.0, 58.0]",usask_1
+a6a921e82,1024,1024,"[108.0, 508.0, 191.0, 92.0]",usask_1
+a6a921e82,1024,1024,"[122.0, 497.0, 102.0, 139.0]",usask_1
+a6a921e82,1024,1024,"[513.0, 890.0, 121.0, 61.0]",usask_1
+a6a921e82,1024,1024,"[259.0, 727.0, 104.0, 80.0]",usask_1
+a6a921e82,1024,1024,"[836.0, 861.0, 104.0, 69.0]",usask_1
+a6a921e82,1024,1024,"[922.0, 910.0, 71.0, 75.0]",usask_1
+a6a921e82,1024,1024,"[867.0, 606.0, 85.0, 57.0]",usask_1
+a6a921e82,1024,1024,"[902.0, 655.0, 72.0, 66.0]",usask_1
+a6a921e82,1024,1024,"[999.0, 307.0, 25.0, 168.0]",usask_1
+a6a921e82,1024,1024,"[759.0, 91.0, 139.0, 66.0]",usask_1
+a6a921e82,1024,1024,"[509.0, 170.0, 57.0, 77.0]",usask_1
+a6a921e82,1024,1024,"[659.0, 811.0, 237.0, 119.0]",usask_1
+69855d3de,1024,1024,"[21.0, 832.0, 267.0, 111.0]",usask_1
+69855d3de,1024,1024,"[931.0, 669.0, 93.0, 111.0]",usask_1
+69855d3de,1024,1024,"[467.0, 945.0, 165.0, 79.0]",usask_1
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+69855d3de,1024,1024,"[758.0, 894.0, 113.0, 68.0]",usask_1
+69855d3de,1024,1024,"[855.0, 673.0, 103.0, 134.0]",usask_1
+69855d3de,1024,1024,"[30.0, 245.0, 194.0, 93.0]",usask_1
+69855d3de,1024,1024,"[439.0, 912.0, 111.0, 72.0]",usask_1
+69855d3de,1024,1024,"[474.0, 571.0, 154.0, 124.0]",usask_1
+69855d3de,1024,1024,"[421.0, 485.0, 223.0, 133.0]",usask_1
+69855d3de,1024,1024,"[216.0, 909.0, 101.0, 115.0]",usask_1
+69855d3de,1024,1024,"[772.0, 93.0, 139.0, 88.0]",usask_1
+69855d3de,1024,1024,"[309.0, 734.0, 218.0, 116.0]",usask_1
+69855d3de,1024,1024,"[252.0, 246.0, 137.0, 113.0]",usask_1
+69855d3de,1024,1024,"[962.0, 104.0, 62.0, 178.0]",usask_1
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+69855d3de,1024,1024,"[586.0, 27.0, 79.0, 135.0]",usask_1
+69855d3de,1024,1024,"[95.0, 588.0, 105.0, 128.0]",usask_1
+69855d3de,1024,1024,"[632.0, 794.0, 135.0, 75.0]",usask_1
+aa171bd87,1024,1024,"[447.0, 180.0, 127.0, 88.0]",usask_1
+aa171bd87,1024,1024,"[54.0, 799.0, 148.0, 172.0]",usask_1
+aa171bd87,1024,1024,"[505.0, 448.0, 76.0, 99.0]",usask_1
+aa171bd87,1024,1024,"[263.0, 748.0, 85.0, 160.0]",usask_1
+aa171bd87,1024,1024,"[747.0, 269.0, 100.0, 113.0]",usask_1
+aa171bd87,1024,1024,"[0.0, 923.0, 98.0, 101.0]",usask_1
+aa171bd87,1024,1024,"[802.0, 786.0, 220.0, 139.0]",usask_1
+aa171bd87,1024,1024,"[693.0, 27.0, 147.0, 265.0]",usask_1
+aa171bd87,1024,1024,"[0.0, 29.0, 34.0, 104.0]",usask_1
+aa171bd87,1024,1024,"[312.0, 349.0, 98.0, 141.0]",usask_1
+aa171bd87,1024,1024,"[936.0, 358.0, 88.0, 113.0]",usask_1
+aa171bd87,1024,1024,"[948.0, 894.0, 71.0, 114.0]",usask_1
+aa171bd87,1024,1024,"[338.0, 523.0, 144.0, 92.0]",usask_1
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+aa171bd87,1024,1024,"[249.0, 556.0, 90.0, 54.0]",usask_1
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+aa171bd87,1024,1024,"[413.0, 811.0, 188.0, 171.0]",usask_1
+aa171bd87,1024,1024,"[737.0, 508.0, 107.0, 208.0]",usask_1
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+aa171bd87,1024,1024,"[568.0, 8.0, 109.0, 94.0]",usask_1
+80d651f47,1024,1024,"[358.0, 23.0, 100.0, 84.0]",usask_1
+80d651f47,1024,1024,"[515.0, 613.0, 63.0, 96.0]",usask_1
+80d651f47,1024,1024,"[53.0, 758.0, 151.0, 80.0]",usask_1
+80d651f47,1024,1024,"[566.0, 785.0, 90.0, 59.0]",usask_1
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+80d651f47,1024,1024,"[570.0, 732.0, 73.0, 58.0]",usask_1
+80d651f47,1024,1024,"[873.0, 321.0, 109.0, 78.0]",usask_1
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+80d651f47,1024,1024,"[720.0, 13.0, 100.0, 96.0]",usask_1
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+5a9be233e,1024,1024,"[487.0, 256.0, 134.0, 79.0]",usask_1
+5a9be233e,1024,1024,"[188.0, 815.0, 83.0, 69.0]",usask_1
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+a9b3451c9,1024,1024,"[265.0, 25.0, 49.0, 48.0]",arvalis_1
+a9b3451c9,1024,1024,"[475.0, 129.0, 39.0, 34.0]",arvalis_1
+a9b3451c9,1024,1024,"[803.0, 123.0, 61.0, 44.0]",arvalis_1
+a9b3451c9,1024,1024,"[722.0, 18.0, 46.0, 40.0]",arvalis_1
+a9b3451c9,1024,1024,"[418.0, 895.0, 76.0, 46.0]",arvalis_1
+a9b3451c9,1024,1024,"[406.0, 860.0, 62.0, 56.0]",arvalis_1
+a9b3451c9,1024,1024,"[15.0, 879.0, 55.0, 60.0]",arvalis_1
+a9b3451c9,1024,1024,"[64.0, 990.0, 56.0, 34.0]",arvalis_1
+a9b3451c9,1024,1024,"[345.0, 947.0, 60.0, 44.0]",arvalis_1
+e5e1f2c65,1024,1024,"[116.0, 253.0, 92.0, 65.0]",arvalis_1
+e5e1f2c65,1024,1024,"[18.0, 272.0, 94.0, 79.0]",arvalis_1
+e5e1f2c65,1024,1024,"[0.0, 358.0, 101.0, 65.0]",arvalis_1
+e5e1f2c65,1024,1024,"[109.0, 396.0, 97.0, 64.0]",arvalis_1
+e5e1f2c65,1024,1024,"[179.0, 466.0, 97.0, 65.0]",arvalis_1
+e5e1f2c65,1024,1024,"[309.0, 447.0, 84.0, 73.0]",arvalis_1
+e5e1f2c65,1024,1024,"[324.0, 366.0, 61.0, 56.0]",arvalis_1
+e5e1f2c65,1024,1024,"[324.0, 301.0, 69.0, 62.0]",arvalis_1
+e5e1f2c65,1024,1024,"[291.0, 234.0, 61.0, 61.0]",arvalis_1
+e5e1f2c65,1024,1024,"[332.0, 170.0, 138.0, 62.0]",arvalis_1
+e5e1f2c65,1024,1024,"[206.0, 144.0, 65.0, 57.0]",arvalis_1
+e5e1f2c65,1024,1024,"[227.0, 58.0, 80.0, 102.0]",arvalis_1
+e5e1f2c65,1024,1024,"[82.0, 7.0, 73.0, 67.0]",arvalis_1
+e5e1f2c65,1024,1024,"[44.0, 488.0, 68.0, 124.0]",arvalis_1
+e5e1f2c65,1024,1024,"[27.0, 564.0, 153.0, 87.0]",arvalis_1
+e5e1f2c65,1024,1024,"[440.0, 526.0, 57.0, 69.0]",arvalis_1
+e5e1f2c65,1024,1024,"[144.0, 676.0, 95.0, 63.0]",arvalis_1
+e5e1f2c65,1024,1024,"[115.0, 758.0, 70.0, 46.0]",arvalis_1
+e5e1f2c65,1024,1024,"[73.0, 930.0, 91.0, 81.0]",arvalis_1
+e5e1f2c65,1024,1024,"[182.0, 893.0, 87.0, 75.0]",arvalis_1
+e5e1f2c65,1024,1024,"[499.0, 955.0, 107.0, 51.0]",arvalis_1
+e5e1f2c65,1024,1024,"[768.0, 867.0, 155.0, 126.0]",arvalis_1
+e5e1f2c65,1024,1024,"[546.0, 847.0, 170.0, 72.0]",arvalis_1
+e5e1f2c65,1024,1024,"[404.0, 837.0, 104.0, 64.0]",arvalis_1
+e5e1f2c65,1024,1024,"[404.0, 772.0, 67.0, 54.0]",arvalis_1
+e5e1f2c65,1024,1024,"[479.0, 761.0, 66.0, 65.0]",arvalis_1
+e5e1f2c65,1024,1024,"[240.0, 727.0, 62.0, 50.0]",arvalis_1
+e5e1f2c65,1024,1024,"[200.0, 778.0, 73.0, 48.0]",arvalis_1
+e5e1f2c65,1024,1024,"[539.0, 499.0, 95.0, 58.0]",arvalis_1
+e5e1f2c65,1024,1024,"[499.0, 630.0, 158.0, 97.0]",arvalis_1
+e5e1f2c65,1024,1024,"[512.0, 706.0, 64.0, 44.0]",arvalis_1
+e5e1f2c65,1024,1024,"[557.0, 710.0, 109.0, 126.0]",arvalis_1
+e5e1f2c65,1024,1024,"[684.0, 720.0, 71.0, 91.0]",arvalis_1
+e5e1f2c65,1024,1024,"[752.0, 684.0, 140.0, 64.0]",arvalis_1
+e5e1f2c65,1024,1024,"[817.0, 587.0, 76.0, 54.0]",arvalis_1
+e5e1f2c65,1024,1024,"[795.0, 538.0, 127.0, 64.0]",arvalis_1
+e5e1f2c65,1024,1024,"[912.0, 818.0, 112.0, 107.0]",arvalis_1
+e5e1f2c65,1024,1024,"[911.0, 402.0, 113.0, 77.0]",arvalis_1
+e5e1f2c65,1024,1024,"[897.0, 320.0, 127.0, 79.0]",arvalis_1
+e5e1f2c65,1024,1024,"[402.0, 359.0, 68.0, 62.0]",arvalis_1
+e5e1f2c65,1024,1024,"[492.0, 266.0, 63.0, 63.0]",arvalis_1
+e5e1f2c65,1024,1024,"[572.0, 268.0, 73.0, 53.0]",arvalis_1
+e5e1f2c65,1024,1024,"[598.0, 318.0, 99.0, 78.0]",arvalis_1
+e5e1f2c65,1024,1024,"[550.0, 333.0, 66.0, 73.0]",arvalis_1
+e5e1f2c65,1024,1024,"[565.0, 428.0, 76.0, 66.0]",arvalis_1
+e5e1f2c65,1024,1024,"[645.0, 381.0, 93.0, 70.0]",arvalis_1
+e5e1f2c65,1024,1024,"[538.0, 0.0, 88.0, 56.0]",arvalis_1
+e5e1f2c65,1024,1024,"[523.0, 84.0, 66.0, 60.0]",arvalis_1
+e5e1f2c65,1024,1024,"[602.0, 49.0, 58.0, 58.0]",arvalis_1
+e5e1f2c65,1024,1024,"[799.0, 15.0, 95.0, 64.0]",arvalis_1
+e5e1f2c65,1024,1024,"[673.0, 52.0, 101.0, 71.0]",arvalis_1
+e5e1f2c65,1024,1024,"[679.0, 118.0, 112.0, 50.0]",arvalis_1
+e5e1f2c65,1024,1024,"[907.0, 114.0, 102.0, 54.0]",arvalis_1
+e5e1f2c65,1024,1024,"[363.0, 223.0, 66.0, 78.0]",arvalis_1
+e5e1f2c65,1024,1024,"[908.0, 606.0, 53.0, 62.0]",arvalis_1
+e5e1f2c65,1024,1024,"[870.0, 56.0, 78.0, 78.0]",arvalis_1
+e5e1f2c65,1024,1024,"[886.0, 707.0, 48.0, 44.0]",arvalis_1
+e5e1f2c65,1024,1024,"[829.0, 762.0, 48.0, 44.0]",arvalis_1
+e5e1f2c65,1024,1024,"[857.0, 720.0, 64.0, 45.0]",arvalis_1
+e5e1f2c65,1024,1024,"[411.0, 453.0, 103.0, 67.0]",arvalis_1
+e5e1f2c65,1024,1024,"[842.0, 885.0, 78.0, 47.0]",arvalis_1
+dc11d49a0,1024,1024,"[0.0, 115.0, 66.0, 95.0]",arvalis_1
+dc11d49a0,1024,1024,"[27.0, 246.0, 111.0, 72.0]",arvalis_1
+dc11d49a0,1024,1024,"[77.0, 169.0, 86.0, 87.0]",arvalis_1
+dc11d49a0,1024,1024,"[163.0, 277.0, 69.0, 52.0]",arvalis_1
+dc11d49a0,1024,1024,"[307.0, 309.0, 65.0, 60.0]",arvalis_1
+dc11d49a0,1024,1024,"[344.0, 250.0, 47.0, 112.0]",arvalis_1
+dc11d49a0,1024,1024,"[366.0, 268.0, 101.0, 94.0]",arvalis_1
+dc11d49a0,1024,1024,"[423.0, 159.0, 67.0, 95.0]",arvalis_1
+dc11d49a0,1024,1024,"[324.0, 10.0, 127.0, 138.0]",arvalis_1
+dc11d49a0,1024,1024,"[202.0, 94.0, 74.0, 89.0]",arvalis_1
+dc11d49a0,1024,1024,"[48.0, 70.0, 49.0, 71.0]",arvalis_1
+dc11d49a0,1024,1024,"[135.0, 112.0, 92.0, 77.0]",arvalis_1
+dc11d49a0,1024,1024,"[0.0, 467.0, 63.0, 150.0]",arvalis_1
+dc11d49a0,1024,1024,"[798.0, 970.0, 60.0, 54.0]",arvalis_1
+dc11d49a0,1024,1024,"[856.0, 955.0, 56.0, 69.0]",arvalis_1
+dc11d49a0,1024,1024,"[904.0, 901.0, 49.0, 95.0]",arvalis_1
+dc11d49a0,1024,1024,"[916.0, 874.0, 103.0, 105.0]",arvalis_1
+dc11d49a0,1024,1024,"[332.0, 795.0, 83.0, 110.0]",arvalis_1
+dc11d49a0,1024,1024,"[231.0, 847.0, 89.0, 86.0]",arvalis_1
+dc11d49a0,1024,1024,"[279.0, 736.0, 71.0, 71.0]",arvalis_1
+dc11d49a0,1024,1024,"[276.0, 676.0, 64.0, 63.0]",arvalis_1
+dc11d49a0,1024,1024,"[176.0, 642.0, 107.0, 80.0]",arvalis_1
+dc11d49a0,1024,1024,"[428.0, 706.0, 137.0, 96.0]",arvalis_1
+dc11d49a0,1024,1024,"[538.0, 695.0, 96.0, 77.0]",arvalis_1
+dc11d49a0,1024,1024,"[579.0, 825.0, 127.0, 79.0]",arvalis_1
+dc11d49a0,1024,1024,"[874.0, 511.0, 70.0, 131.0]",arvalis_1
+dc11d49a0,1024,1024,"[952.0, 662.0, 56.0, 121.0]",arvalis_1
+dc11d49a0,1024,1024,"[793.0, 610.0, 63.0, 85.0]",arvalis_1
+dc11d49a0,1024,1024,"[624.0, 720.0, 85.0, 83.0]",arvalis_1
+dc11d49a0,1024,1024,"[686.0, 482.0, 76.0, 201.0]",arvalis_1
+dc11d49a0,1024,1024,"[755.0, 535.0, 133.0, 85.0]",arvalis_1
+dc11d49a0,1024,1024,"[709.0, 344.0, 94.0, 81.0]",arvalis_1
+dc11d49a0,1024,1024,"[684.0, 404.0, 55.0, 74.0]",arvalis_1
+dc11d49a0,1024,1024,"[466.0, 377.0, 71.0, 82.0]",arvalis_1
+dc11d49a0,1024,1024,"[437.0, 489.0, 56.0, 83.0]",arvalis_1
+dc11d49a0,1024,1024,"[410.0, 530.0, 41.0, 97.0]",arvalis_1
+dc11d49a0,1024,1024,"[563.0, 574.0, 60.0, 71.0]",arvalis_1
+dc11d49a0,1024,1024,"[531.0, 167.0, 63.0, 124.0]",arvalis_1
+dc11d49a0,1024,1024,"[580.0, 154.0, 84.0, 70.0]",arvalis_1
+dc11d49a0,1024,1024,"[561.0, 194.0, 88.0, 85.0]",arvalis_1
+dc11d49a0,1024,1024,"[690.0, 55.0, 79.0, 146.0]",arvalis_1
+dc11d49a0,1024,1024,"[781.0, 23.0, 74.0, 65.0]",arvalis_1
+dc11d49a0,1024,1024,"[953.0, 275.0, 58.0, 71.0]",arvalis_1
+dc11d49a0,1024,1024,"[619.0, 358.0, 38.0, 45.0]",arvalis_1
+dc11d49a0,1024,1024,"[818.0, 316.0, 87.0, 43.0]",arvalis_1
+dc11d49a0,1024,1024,"[369.0, 183.0, 45.0, 83.0]",arvalis_1
+dc11d49a0,1024,1024,"[403.0, 356.0, 44.0, 38.0]",arvalis_1
+dc11d49a0,1024,1024,"[679.0, 497.0, 51.0, 154.0]",arvalis_1
+dc11d49a0,1024,1024,"[79.0, 296.0, 43.0, 60.0]",arvalis_1
+0d3bd5eac,1024,1024,"[32.0, 0.0, 109.0, 70.0]",arvalis_1
+0d3bd5eac,1024,1024,"[0.0, 81.0, 35.0, 110.0]",arvalis_1
+0d3bd5eac,1024,1024,"[153.0, 252.0, 137.0, 93.0]",arvalis_1
+0d3bd5eac,1024,1024,"[122.0, 357.0, 164.0, 90.0]",arvalis_1
+0d3bd5eac,1024,1024,"[0.0, 262.0, 95.0, 65.0]",arvalis_1
+0d3bd5eac,1024,1024,"[363.0, 5.0, 160.0, 241.0]",arvalis_1
+0d3bd5eac,1024,1024,"[538.0, 130.0, 153.0, 116.0]",arvalis_1
+0d3bd5eac,1024,1024,"[632.0, 18.0, 206.0, 106.0]",arvalis_1
+0d3bd5eac,1024,1024,"[946.0, 54.0, 78.0, 165.0]",arvalis_1
+0d3bd5eac,1024,1024,"[916.0, 560.0, 79.0, 154.0]",arvalis_1
+0d3bd5eac,1024,1024,"[809.0, 534.0, 78.0, 77.0]",arvalis_1
+0d3bd5eac,1024,1024,"[716.0, 496.0, 123.0, 190.0]",arvalis_1
+0d3bd5eac,1024,1024,"[545.0, 480.0, 98.0, 172.0]",arvalis_1
+0d3bd5eac,1024,1024,"[547.0, 597.0, 75.0, 111.0]",arvalis_1
+0d3bd5eac,1024,1024,"[425.0, 551.0, 87.0, 102.0]",arvalis_1
+0d3bd5eac,1024,1024,"[489.0, 675.0, 83.0, 101.0]",arvalis_1
+0d3bd5eac,1024,1024,"[688.0, 716.0, 113.0, 121.0]",arvalis_1
+0d3bd5eac,1024,1024,"[779.0, 751.0, 76.0, 57.0]",arvalis_1
+0d3bd5eac,1024,1024,"[424.0, 812.0, 104.0, 68.0]",arvalis_1
+0d3bd5eac,1024,1024,"[677.0, 884.0, 199.0, 131.0]",arvalis_1
+0d3bd5eac,1024,1024,"[269.0, 841.0, 99.0, 81.0]",arvalis_1
+0d3bd5eac,1024,1024,"[49.0, 939.0, 252.0, 85.0]",arvalis_1
+0d3bd5eac,1024,1024,"[116.0, 953.0, 227.0, 71.0]",arvalis_1
+0d3bd5eac,1024,1024,"[56.0, 827.0, 177.0, 103.0]",arvalis_1
+0d3bd5eac,1024,1024,"[9.0, 734.0, 48.0, 124.0]",arvalis_1
+0d3bd5eac,1024,1024,"[573.0, 322.0, 96.0, 207.0]",arvalis_1
+0d3bd5eac,1024,1024,"[287.0, 318.0, 262.0, 200.0]",arvalis_1
+0d3bd5eac,1024,1024,"[854.0, 212.0, 67.0, 62.0]",arvalis_1
+0d3bd5eac,1024,1024,"[629.0, 874.0, 72.0, 123.0]",arvalis_1
+0d3bd5eac,1024,1024,"[955.0, 454.0, 40.0, 68.0]",arvalis_1
+0d3bd5eac,1024,1024,"[981.0, 445.0, 43.0, 43.0]",arvalis_1
+dc1406483,1024,1024,"[291.0, 272.0, 90.0, 173.0]",arvalis_1
+dc1406483,1024,1024,"[119.0, 328.0, 71.0, 201.0]",arvalis_1
+dc1406483,1024,1024,"[12.0, 408.0, 76.0, 74.0]",arvalis_1
+dc1406483,1024,1024,"[473.0, 665.0, 139.0, 109.0]",arvalis_1
+dc1406483,1024,1024,"[389.0, 628.0, 114.0, 96.0]",arvalis_1
+dc1406483,1024,1024,"[616.0, 718.0, 134.0, 152.0]",arvalis_1
+dc1406483,1024,1024,"[755.0, 724.0, 68.0, 155.0]",arvalis_1
+dc1406483,1024,1024,"[811.0, 796.0, 79.0, 205.0]",arvalis_1
+dc1406483,1024,1024,"[609.0, 833.0, 81.0, 186.0]",arvalis_1
+dc1406483,1024,1024,"[679.0, 909.0, 89.0, 87.0]",arvalis_1
+dc1406483,1024,1024,"[265.0, 802.0, 145.0, 94.0]",arvalis_1
+dc1406483,1024,1024,"[273.0, 736.0, 73.0, 81.0]",arvalis_1
+dc1406483,1024,1024,"[49.0, 641.0, 116.0, 152.0]",arvalis_1
+dc1406483,1024,1024,"[0.0, 661.0, 36.0, 98.0]",arvalis_1
+dc1406483,1024,1024,"[0.0, 952.0, 55.0, 71.0]",arvalis_1
+dc1406483,1024,1024,"[45.0, 923.0, 58.0, 75.0]",arvalis_1
+dc1406483,1024,1024,"[0.0, 875.0, 88.0, 62.0]",arvalis_1
+dc1406483,1024,1024,"[22.0, 814.0, 60.0, 55.0]",arvalis_1
+dc1406483,1024,1024,"[63.0, 21.0, 81.0, 64.0]",arvalis_1
+dc1406483,1024,1024,"[126.0, 114.0, 50.0, 76.0]",arvalis_1
+dc1406483,1024,1024,"[232.0, 81.0, 108.0, 112.0]",arvalis_1
+dc1406483,1024,1024,"[348.0, 116.0, 110.0, 182.0]",arvalis_1
+dc1406483,1024,1024,"[421.0, 0.0, 94.0, 62.0]",arvalis_1
+dc1406483,1024,1024,"[686.0, 0.0, 168.0, 67.0]",arvalis_1
+dc1406483,1024,1024,"[650.0, 32.0, 146.0, 58.0]",arvalis_1
+dc1406483,1024,1024,"[619.0, 67.0, 99.0, 66.0]",arvalis_1
+dc1406483,1024,1024,"[897.0, 23.0, 96.0, 114.0]",arvalis_1
+dc1406483,1024,1024,"[507.0, 418.0, 99.0, 57.0]",arvalis_1
+dc1406483,1024,1024,"[623.0, 523.0, 71.0, 83.0]",arvalis_1
+dc1406483,1024,1024,"[739.0, 583.0, 127.0, 53.0]",arvalis_1
+dc1406483,1024,1024,"[785.0, 645.0, 129.0, 140.0]",arvalis_1
+dc1406483,1024,1024,"[739.0, 675.0, 104.0, 42.0]",arvalis_1
+dc1406483,1024,1024,"[968.0, 641.0, 47.0, 105.0]",arvalis_1
+dc1406483,1024,1024,"[766.0, 337.0, 128.0, 106.0]",arvalis_1
+dc1406483,1024,1024,"[681.0, 425.0, 133.0, 61.0]",arvalis_1
+dc1406483,1024,1024,"[804.0, 240.0, 78.0, 123.0]",arvalis_1
+dc1406483,1024,1024,"[923.0, 296.0, 101.0, 81.0]",arvalis_1
+dc1406483,1024,1024,"[273.0, 970.0, 160.0, 54.0]",arvalis_1
+83521e96e,1024,1024,"[0.0, 755.0, 101.0, 63.0]",arvalis_1
+83521e96e,1024,1024,"[5.0, 856.0, 81.0, 90.0]",arvalis_1
+83521e96e,1024,1024,"[151.0, 791.0, 55.0, 149.0]",arvalis_1
+83521e96e,1024,1024,"[109.0, 754.0, 81.0, 59.0]",arvalis_1
+83521e96e,1024,1024,"[180.0, 486.0, 65.0, 123.0]",arvalis_1
+83521e96e,1024,1024,"[81.0, 313.0, 83.0, 94.0]",arvalis_1
+83521e96e,1024,1024,"[67.0, 231.0, 102.0, 59.0]",arvalis_1
+83521e96e,1024,1024,"[51.0, 147.0, 76.0, 91.0]",arvalis_1
+83521e96e,1024,1024,"[160.0, 707.0, 70.0, 61.0]",arvalis_1
+83521e96e,1024,1024,"[255.0, 890.0, 67.0, 131.0]",arvalis_1
+83521e96e,1024,1024,"[361.0, 860.0, 128.0, 86.0]",arvalis_1
+83521e96e,1024,1024,"[512.0, 774.0, 86.0, 103.0]",arvalis_1
+83521e96e,1024,1024,"[473.0, 854.0, 143.0, 91.0]",arvalis_1
+83521e96e,1024,1024,"[946.0, 975.0, 74.0, 49.0]",arvalis_1
+83521e96e,1024,1024,"[714.0, 658.0, 148.0, 85.0]",arvalis_1
+83521e96e,1024,1024,"[598.0, 665.0, 118.0, 70.0]",arvalis_1
+83521e96e,1024,1024,"[548.0, 681.0, 86.0, 66.0]",arvalis_1
+83521e96e,1024,1024,"[399.0, 707.0, 119.0, 76.0]",arvalis_1
+83521e96e,1024,1024,"[276.0, 646.0, 46.0, 61.0]",arvalis_1
+83521e96e,1024,1024,"[318.0, 647.0, 62.0, 55.0]",arvalis_1
+83521e96e,1024,1024,"[276.0, 731.0, 72.0, 47.0]",arvalis_1
+83521e96e,1024,1024,"[297.0, 799.0, 80.0, 64.0]",arvalis_1
+83521e96e,1024,1024,"[546.0, 501.0, 81.0, 70.0]",arvalis_1
+83521e96e,1024,1024,"[608.0, 470.0, 97.0, 74.0]",arvalis_1
+83521e96e,1024,1024,"[773.0, 586.0, 68.0, 67.0]",arvalis_1
+83521e96e,1024,1024,"[649.0, 597.0, 97.0, 57.0]",arvalis_1
+83521e96e,1024,1024,"[963.0, 477.0, 61.0, 114.0]",arvalis_1
+83521e96e,1024,1024,"[277.0, 176.0, 67.0, 64.0]",arvalis_1
+83521e96e,1024,1024,"[382.0, 111.0, 62.0, 78.0]",arvalis_1
+83521e96e,1024,1024,"[511.0, 90.0, 72.0, 107.0]",arvalis_1
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+ec3d2ced1,1024,1024,"[578.0, 245.0, 88.0, 82.0]",rres_1
+ec3d2ced1,1024,1024,"[293.0, 31.0, 70.0, 82.0]",rres_1
+ec3d2ced1,1024,1024,"[0.0, 123.0, 61.0, 77.0]",rres_1
+ec3d2ced1,1024,1024,"[564.0, 950.0, 49.0, 61.0]",rres_1
+de4e0eecf,1024,1024,"[0.0, 23.0, 40.0, 97.0]",rres_1
+de4e0eecf,1024,1024,"[0.0, 293.0, 40.0, 99.0]",rres_1
+de4e0eecf,1024,1024,"[2.0, 218.0, 58.0, 76.0]",rres_1
+de4e0eecf,1024,1024,"[0.0, 790.0, 50.0, 103.0]",rres_1
+de4e0eecf,1024,1024,"[0.0, 996.0, 42.0, 28.0]",rres_1
+de4e0eecf,1024,1024,"[236.0, 822.0, 70.0, 133.0]",rres_1
+de4e0eecf,1024,1024,"[346.0, 709.0, 128.0, 234.0]",rres_1
+de4e0eecf,1024,1024,"[233.0, 637.0, 72.0, 97.0]",rres_1
+de4e0eecf,1024,1024,"[438.0, 577.0, 101.0, 154.0]",rres_1
+de4e0eecf,1024,1024,"[511.0, 599.0, 104.0, 62.0]",rres_1
+de4e0eecf,1024,1024,"[501.0, 530.0, 209.0, 86.0]",rres_1
+de4e0eecf,1024,1024,"[322.0, 380.0, 64.0, 126.0]",rres_1
+de4e0eecf,1024,1024,"[233.0, 395.0, 113.0, 157.0]",rres_1
+de4e0eecf,1024,1024,"[166.0, 479.0, 75.0, 121.0]",rres_1
+de4e0eecf,1024,1024,"[227.0, 567.0, 81.0, 62.0]",rres_1
+de4e0eecf,1024,1024,"[42.0, 365.0, 151.0, 70.0]",rres_1
+de4e0eecf,1024,1024,"[31.0, 389.0, 86.0, 148.0]",rres_1
+de4e0eecf,1024,1024,"[494.0, 180.0, 61.0, 114.0]",rres_1
+de4e0eecf,1024,1024,"[243.0, 130.0, 137.0, 69.0]",rres_1
+de4e0eecf,1024,1024,"[158.0, 116.0, 66.0, 57.0]",rres_1
+de4e0eecf,1024,1024,"[44.0, 29.0, 86.0, 80.0]",rres_1
+de4e0eecf,1024,1024,"[336.0, 110.0, 127.0, 63.0]",rres_1
+de4e0eecf,1024,1024,"[560.0, 106.0, 81.0, 117.0]",rres_1
+de4e0eecf,1024,1024,"[704.0, 195.0, 107.0, 114.0]",rres_1
+de4e0eecf,1024,1024,"[623.0, 431.0, 109.0, 70.0]",rres_1
+de4e0eecf,1024,1024,"[720.0, 431.0, 99.0, 94.0]",rres_1
+de4e0eecf,1024,1024,"[683.0, 518.0, 94.0, 88.0]",rres_1
+de4e0eecf,1024,1024,"[567.0, 36.0, 219.0, 100.0]",rres_1
+de4e0eecf,1024,1024,"[745.0, 0.0, 66.0, 79.0]",rres_1
+de4e0eecf,1024,1024,"[303.0, 0.0, 72.0, 63.0]",rres_1
+de4e0eecf,1024,1024,"[778.0, 0.0, 124.0, 21.0]",rres_1
+de4e0eecf,1024,1024,"[938.0, 0.0, 86.0, 32.0]",rres_1
+de4e0eecf,1024,1024,"[953.0, 173.0, 71.0, 78.0]",rres_1
+de4e0eecf,1024,1024,"[980.0, 133.0, 44.0, 41.0]",rres_1
+de4e0eecf,1024,1024,"[914.0, 29.0, 81.0, 62.0]",rres_1
+de4e0eecf,1024,1024,"[794.0, 509.0, 100.0, 83.0]",rres_1
+de4e0eecf,1024,1024,"[856.0, 413.0, 149.0, 118.0]",rres_1
+de4e0eecf,1024,1024,"[981.0, 522.0, 43.0, 85.0]",rres_1
+de4e0eecf,1024,1024,"[928.0, 576.0, 62.0, 63.0]",rres_1
+de4e0eecf,1024,1024,"[907.0, 626.0, 105.0, 92.0]",rres_1
+de4e0eecf,1024,1024,"[841.0, 676.0, 116.0, 87.0]",rres_1
+de4e0eecf,1024,1024,"[506.0, 783.0, 74.0, 77.0]",rres_1
+de4e0eecf,1024,1024,"[674.0, 814.0, 91.0, 67.0]",rres_1
+de4e0eecf,1024,1024,"[836.0, 775.0, 67.0, 69.0]",rres_1
+de4e0eecf,1024,1024,"[971.0, 775.0, 53.0, 89.0]",rres_1
+de4e0eecf,1024,1024,"[844.0, 881.0, 141.0, 140.0]",rres_1
+de4e0eecf,1024,1024,"[658.0, 937.0, 113.0, 77.0]",rres_1
+de4e0eecf,1024,1024,"[461.0, 973.0, 81.0, 51.0]",rres_1
+de4e0eecf,1024,1024,"[530.0, 938.0, 78.0, 76.0]",rres_1
+de4e0eecf,1024,1024,"[544.0, 994.0, 87.0, 30.0]",rres_1
+de4e0eecf,1024,1024,"[253.0, 533.0, 55.0, 56.0]",rres_1
+de4e0eecf,1024,1024,"[109.0, 448.0, 60.0, 73.0]",rres_1
+de4e0eecf,1024,1024,"[293.0, 177.0, 67.0, 64.0]",rres_1
+de4e0eecf,1024,1024,"[476.0, 61.0, 54.0, 59.0]",rres_1
+de4e0eecf,1024,1024,"[480.0, 5.0, 41.0, 41.0]",rres_1
+de4e0eecf,1024,1024,"[961.0, 104.0, 63.0, 49.0]",rres_1
+de4e0eecf,1024,1024,"[941.0, 62.0, 75.0, 77.0]",rres_1
+de4e0eecf,1024,1024,"[17.0, 391.0, 46.0, 64.0]",rres_1
+de4e0eecf,1024,1024,"[717.0, 313.0, 66.0, 78.0]",rres_1
+3b4f7ba0a,1024,1024,"[953.0, 820.0, 71.0, 72.0]",arvalis_2
+3b4f7ba0a,1024,1024,"[583.0, 573.0, 110.0, 120.0]",arvalis_2
+3b4f7ba0a,1024,1024,"[938.0, 790.0, 86.0, 68.0]",arvalis_2
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diff --git a/blog46-FasterRCNN-Wheat/dataset.py b/blog46-FasterRCNN-Wheat/dataset.py
new file mode 100644
index 0000000..1bbf70e
--- /dev/null
+++ b/blog46-FasterRCNN-Wheat/dataset.py
@@ -0,0 +1,66 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 29 13:42:38 2021
+@author: xiuzhang
+"""
+import numpy as np
+import cv2
+import torch
+from torch.utils.data import Dataset
+
+
+class WheatDataset(Dataset):
+
+    def __init__(self, dataframe, image_dir, transforms=None):
+        super().__init__()
+
+        self.image_ids = dataframe['image_id'].unique()
+        self.df = dataframe
+        self.image_dir = image_dir
+        self.transforms = transforms
+
+    def __getitem__(self, index: int):
+        image_id = self.image_ids[index]
+        records = self.df[self.df['image_id'] == image_id]
+
+        image = cv2.imread(f'{self.image_dir}/{image_id}.jpg', cv2.IMREAD_COLOR)
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB).astype(np.float32)
+        image /= 255.0
+
+        boxes = records[['x', 'y', 'w', 'h']].values
+        boxes[:, 2] = boxes[:, 0] + boxes[:, 2]
+        boxes[:, 3] = boxes[:, 1] + boxes[:, 3]
+
+        area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
+        area = torch.as_tensor(area, dtype=torch.float32)
+
+        # there is only one class
+        labels = torch.ones((records.shape[0],), dtype=torch.int64)
+
+        # suppose all instances are not crowd
+        iscrowd = torch.zeros((records.shape[0],), dtype=torch.int64)
+
+        target = {}
+        target['boxes'] = boxes
+        target['labels'] = labels
+        # target['masks'] = None
+        target['image_id'] = torch.tensor([index])
+        target['area'] = area
+        target['iscrowd'] = iscrowd
+
+        if self.transforms:
+            sample = {
+                'image': image,
+                'bboxes': target['boxes'],
+                'labels': labels
+            }
+            sample = self.transforms(**sample)
+            image = sample['image']
+
+            target['boxes'] = torch.stack(tuple(map(torch.tensor, zip(*sample['bboxes'])))).permute(1, 0)
+
+        return image, target, image_id
+
+    def __len__(self) -> int:
+        return self.image_ids.shape[0]
+
diff --git a/blog46-FasterRCNN-Wheat/pytorch-starter-fasterrcnn-train.py b/blog46-FasterRCNN-Wheat/pytorch-starter-fasterrcnn-train.py
new file mode 100644
index 0000000..bd27f4d
--- /dev/null
+++ b/blog46-FasterRCNN-Wheat/pytorch-starter-fasterrcnn-train.py
@@ -0,0 +1,252 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Oct 29 13:42:38 2021
+@author: xiuzhang
+"""
+import os
+import re
+import cv2
+import pandas as pd
+import numpy as np
+from PIL import Image
+import albumentations as A
+from matplotlib import pyplot as plt
+from albumentations.pytorch.transforms import ToTensorV2
+
+import torch
+import torchvision
+from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
+from torchvision.models.detection import FasterRCNN
+from torchvision.models.detection.rpn import AnchorGenerator
+from torch.utils.data import DataLoader, Dataset
+from torch.utils.data.sampler import SequentialSampler
+
+from dataset import WheatDataset
+
+#-----------------------------------------------------------------------------
+#第一步 函数定义
+#----------------------------------------------------------------------------
+#提取box的四个坐标
+def expand_bbox(x):
+    r = np.array(re.findall("([0-9]+[.]?[0-9]*)", x))
+    if len(r) == 0:
+        r = [-1, -1, -1, -1]
+    return r
+
+#训练图像增强 Albumentations
+def get_train_transform():
+    return A.Compose([
+        A.Flip(0.5),
+        ToTensorV2(p=1.0)
+    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})
+
+#验证图像增强
+def get_valid_transform():
+    return A.Compose([
+        ToTensorV2(p=1.0)
+    ], bbox_params={'format': 'pascal_voc', 'label_fields': ['labels']})
+
+def collate_fn(batch):
+    return tuple(zip(*batch))
+
+#-----------------------------------------------------------------------------
+#第二步 定义变量并读取数据
+#-----------------------------------------------------------------------------
+DIR_INPUT = 'data'
+DIR_TRAIN = f'{DIR_INPUT}/train'
+DIR_TEST = f'{DIR_INPUT}/test'
+train_df = pd.read_csv(f'{DIR_INPUT}/train.csv')
+print(train_df.shape)
+
+train_df['x'] = -1
+train_df['y'] = -1
+train_df['w'] = -1
+train_df['h'] = -1
+
+#读取box四个坐标
+train_df[['x', 'y', 'w', 'h']] = np.stack(train_df['bbox'].apply(lambda x: expand_bbox(x)))
+train_df.drop(columns=['bbox'], inplace=True)
+train_df['x'] = train_df['x'].astype(np.float)
+train_df['y'] = train_df['y'].astype(np.float)
+train_df['w'] = train_df['w'].astype(np.float)
+train_df['h'] = train_df['h'].astype(np.float)
+
+#获取图像id
+image_ids = train_df['image_id'].unique()
+valid_ids = image_ids[-665:]
+train_ids = image_ids[:-665]
+valid_df = train_df[train_df['image_id'].isin(valid_ids)]
+train_df = train_df[train_df['image_id'].isin(train_ids)]
+print(valid_df.shape, train_df.shape)
+print(train_df.head())
+
+"""
+(147793, 5)
+(25006, 8) (122787, 8)
+    image_id  width  height   source      x      y      w      h
+0  b6ab77fd7   1024    1024  usask_1  834.0  222.0   56.0   36.0
+1  b6ab77fd7   1024    1024  usask_1  226.0  548.0  130.0   58.0
+2  b6ab77fd7   1024    1024  usask_1  377.0  504.0   74.0  160.0
+3  b6ab77fd7   1024    1024  usask_1  834.0   95.0  109.0  107.0
+4  b6ab77fd7   1024    1024  usask_1   26.0  144.0  124.0  117.0
+"""
+
+#-----------------------------------------------------------------------------
+#第三步 加载数据
+#-----------------------------------------------------------------------------
+train_dataset = WheatDataset(train_df, DIR_TRAIN, get_train_transform())
+valid_dataset = WheatDataset(valid_df, DIR_TRAIN, get_valid_transform())
+
+train_data_loader = DataLoader(
+    train_dataset,
+    batch_size=2,
+    shuffle=False,
+    num_workers=0,
+    collate_fn=collate_fn
+)
+
+valid_data_loader = DataLoader(
+    valid_dataset,
+    batch_size=2,
+    shuffle=False,
+    num_workers=0,
+    collate_fn=collate_fn
+)
+
+#-----------------------------------------------------------------------------
+#第四步 数据可视化
+#-----------------------------------------------------------------------------
+#提取训练数据和类别
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+images, targets, image_ids = next(iter(train_data_loader))
+images = list(image.to(device) for image in images)
+targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
+boxes = targets[0]['boxes'].cpu().numpy().astype(np.int32)
+sample = images[0].permute(1, 2, 0).cpu().numpy()
+
+fig, ax = plt.subplots(1, 1, figsize=(10, 8))
+
+#绘制小麦目标识别box
+for box in boxes:
+    cv2.rectangle(sample,
+                  (box[0], box[1]),
+                  (box[2], box[3]),
+                  (255, 0, 0), 3)
+
+    ax.text(box[0], 
+            box[1] - 2, 
+            '{:s}'.format('wheat'), 
+            bbox=dict(facecolor='blue', alpha=0.5),
+            fontsize=12, 
+            color='white')
+
+ax.set_axis_off()
+ax.imshow(sample)
+#plt.show()
+
+#-----------------------------------------------------------------------------
+#第五步 模型构建
+#-----------------------------------------------------------------------------
+num_classes = 2  #1 class (wheat) + background
+lr_scheduler = None
+num_epochs = 1
+itr = 1
+
+class Averager:
+    def __init__(self):
+        self.current_total = 0.0
+        self.iterations = 0.0
+
+    def send(self, value):
+        self.current_total += value
+        self.iterations += 1
+
+    @property
+    def value(self):
+        if self.iterations == 0:
+            return 0
+        else:
+            return 1.0 * self.current_total / self.iterations
+
+    def reset(self):
+        self.current_total = 0.0
+        self.iterations = 0.0
+
+#load a model pre-trained on COCO
+model = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
+
+#获取分类器输入特征数量
+in_features = model.roi_heads.box_predictor.cls_score.in_features
+
+#replace the pre-trained head with a new one
+model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
+
+#参数设置
+model.to(device)
+params = [p for p in model.parameters() if p.requires_grad]
+optimizer = torch.optim.SGD(params, lr=0.005, momentum=0.9, weight_decay=0.0005)
+#lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.1)
+
+loss_hist = Averager()
+print("Start training....")
+
+# 迭代训练
+for epoch in range(num_epochs):
+    loss_hist.reset()
+
+    for images, targets, image_ids in train_data_loader:
+
+        images = list(image.to(device) for image in images)
+        targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
+        for t in targets:
+            t['boxes'] = t['boxes'].float()
+        
+        loss_dict = model(images, targets)
+        losses = sum(loss for loss in loss_dict.values())
+        loss_value = losses.item()
+        loss_hist.send(loss_value)
+        print("loss is :",loss_value)
+
+        optimizer.zero_grad()
+        losses.backward()
+        optimizer.step()
+        if itr % 50 == 0:
+            print(f"Iteration #{itr}/{len(train_data_loader)} loss: {loss_value}")
+        itr += 1
+
+    #更新学习率
+    if lr_scheduler is not None:
+        lr_scheduler.step()
+    print(f"Epoch #{epoch} loss: {loss_hist.value}")
+
+torch.save(model.state_dict(), 'fasterrcnn_resnet50_fpn.pth')
+print("Next Test....")
+
+#-----------------------------------------------------------------------------
+#第六步 模型测试
+#-----------------------------------------------------------------------------
+images, targets, image_ids = next(iter(valid_data_loader))
+images = list(img.to(device) for img in images)
+targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
+boxes = targets[0]['boxes'].cpu().numpy().astype(np.int32)
+sample = images[0].permute(1, 2, 0).cpu().numpy()
+
+model.eval()
+cpu_device = torch.device("cpu")
+
+outputs = model(images)
+outputs = [{k: v.to(cpu_device) for k, v in t.items()} for t in outputs]
+fig, ax = plt.subplots(1, 1, figsize=(16, 8))
+for box in boxes:
+    cv2.rectangle(sample,
+                  (box[0], box[1]),
+                  (box[2], box[3]),
+                  (220, 0, 0), 3)
+
+ax.set_axis_off()
+ax.imshow(sample)
+plt.show()
+
+
+
+    
\ No newline at end of file
diff --git a/blog47-quwu/Paper-A new algorithm for unsupervised global and localcolor correction.pdf b/blog47-quwu/Paper-A new algorithm for unsupervised global and localcolor correction.pdf
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diff --git a/blog47-quwu/Paper-Single_Image_Haze_Removal_Using_Dark_Channel_Prior.pdf b/blog47-quwu/Paper-Single_Image_Haze_Removal_Using_Dark_Channel_Prior.pdf
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diff --git a/blog47-quwu/blog47_quwu_01_ACE.py b/blog47-quwu/blog47_quwu_01_ACE.py
new file mode 100644
index 0000000..be565f4
--- /dev/null
+++ b/blog47-quwu/blog47_quwu_01_ACE.py
@@ -0,0 +1,99 @@
+# -*- coding: utf-8 -*-
+# By:Eastmount CSDN 2021-03-12
+# 惨zmshy2128老师文章并修改成Python3代码
+import cv2
+import numpy as np
+import math
+import matplotlib.pyplot as plt
+
+#线性拉伸处理
+#去掉最大最小0.5%的像素值 线性拉伸至[0,1]
+def stretchImage(data, s=0.005, bins = 2000):   
+    ht = np.histogram(data, bins);
+    d = np.cumsum(ht[0])/float(data.size)
+    lmin = 0; lmax=bins-1
+    while lmin<bins:
+        if d[lmin]>=s:
+            break
+        lmin+=1
+    while lmax>=0:
+        if d[lmax]<=1-s:
+            break
+        lmax-=1
+    return np.clip((data-ht[1][lmin])/(ht[1][lmax]-ht[1][lmin]), 0,1)
+
+#根据半径计算权重参数矩阵
+g_para = {}
+def getPara(radius = 5):                        
+    global g_para
+    m = g_para.get(radius, None)
+    if m is not None:
+        return m
+    size = radius*2+1
+    m = np.zeros((size, size))
+    for h in range(-radius, radius+1):
+        for w in range(-radius, radius+1):
+            if h==0 and w==0:
+                continue
+            m[radius+h, radius+w] = 1.0/math.sqrt(h**2+w**2)
+    m /= m.sum()
+    g_para[radius] = m
+    return m
+
+#常规的ACE实现
+def zmIce(I, ratio=4, radius=300):                     
+    para = getPara(radius)
+    height,width = I.shape
+    zh = []
+    zw = []
+    n = 0
+    while n < radius:
+        zh.append(0)
+        zw.append(0)
+        n += 1
+    for n in range(height):
+        zh.append(n)
+    for n in range(width):
+        zw.append(n)
+    n = 0
+    while n < radius:
+        zh.append(height-1)
+        zw.append(width-1)
+        n += 1
+    #print(zh)
+    #print(zw)
+    
+    Z = I[np.ix_(zh, zw)]
+    res = np.zeros(I.shape)
+    for h in range(radius*2+1):
+        for w in range(radius*2+1):
+            if para[h][w] == 0:
+                continue
+            res += (para[h][w] * np.clip((I-Z[h:h+height, w:w+width])*ratio, -1, 1))
+    return res
+
+#单通道ACE快速增强实现
+def zmIceFast(I, ratio, radius):
+    print(I)
+    height, width = I.shape[:2]
+    if min(height, width) <=2:
+        return np.zeros(I.shape)+0.5
+    Rs = cv2.resize(I, (int((width+1)/2), int((height+1)/2)))
+    Rf = zmIceFast(Rs, ratio, radius)             #递归调用
+    Rf = cv2.resize(Rf, (width, height))
+    Rs = cv2.resize(Rs, (width, height))
+ 
+    return Rf+zmIce(I,ratio, radius)-zmIce(Rs,ratio,radius)   
+
+#rgb三通道分别增强 ratio是对比度增强因子 radius是卷积模板半径          
+def zmIceColor(I, ratio=4, radius=3):               
+    res = np.zeros(I.shape)
+    for k in range(3):
+        res[:,:,k] = stretchImage(zmIceFast(I[:,:,k], ratio, radius))
+    return res
+
+#主函数
+if __name__ == '__main__':
+    img = cv2.imread('car-01.png')
+    res = zmIceColor(img/255.0)*255
+    cv2.imwrite('car-02-Ice.jpg', res)
diff --git a/blog47-quwu/blog47_quwu_02.py b/blog47-quwu/blog47_quwu_02.py
new file mode 100644
index 0000000..77c56aa
--- /dev/null
+++ b/blog47-quwu/blog47_quwu_02.py
@@ -0,0 +1,102 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Sep 11 00:16:07 2021
+@author: xiuzhang
+
+参考资料：
+https://blog.csdn.net/leviopku/article/details/83898619
+"""
+
+import sys
+import cv2
+import math
+import numpy as np
+ 
+def DarkChannel(im,sz):
+    b,g,r = cv2.split(im)
+    dc = cv2.min(cv2.min(r,g),b)
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(sz,sz))
+    dark = cv2.erode(dc,kernel)
+    return dark
+ 
+def AtmLight(im,dark):
+    [h,w] = im.shape[:2]
+    imsz = h*w
+    numpx = int(max(math.floor(imsz/1000),1))
+    darkvec = dark.reshape(imsz,1)
+    imvec = im.reshape(imsz,3)
+ 
+    indices = darkvec.argsort()
+    indices = indices[imsz-numpx::]
+ 
+    atmsum = np.zeros([1,3])
+    for ind in range(1,numpx):
+       atmsum = atmsum + imvec[indices[ind]]
+ 
+    A = atmsum / numpx;
+    return A
+ 
+def TransmissionEstimate(im,A,sz):
+    omega = 0.95
+    im3 = np.empty(im.shape,im.dtype)
+ 
+    for ind in range(0,3):
+        im3[:,:,ind] = im[:,:,ind]/A[0,ind]
+
+    transmission = 1 - omega*DarkChannel(im3,sz)
+    return transmission
+ 
+def Guidedfilter(im,p,r,eps):
+    mean_I = cv2.boxFilter(im,cv2.CV_64F,(r,r))
+    mean_p = cv2.boxFilter(p, cv2.CV_64F,(r,r))
+    mean_Ip = cv2.boxFilter(im*p,cv2.CV_64F,(r,r))
+    cov_Ip = mean_Ip - mean_I*mean_p
+ 
+    mean_II = cv2.boxFilter(im*im,cv2.CV_64F,(r,r))
+    var_I   = mean_II - mean_I*mean_I
+ 
+    a = cov_Ip/(var_I + eps)
+    b = mean_p - a*mean_I
+ 
+    mean_a = cv2.boxFilter(a,cv2.CV_64F,(r,r))
+    mean_b = cv2.boxFilter(b,cv2.CV_64F,(r,r))
+ 
+    q = mean_a*im + mean_b
+    return q
+ 
+def TransmissionRefine(im,et):
+    gray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
+    gray = np.float64(gray)/255
+    r = 60
+    eps = 0.0001
+    t = Guidedfilter(gray,et,r,eps)
+ 
+    return t
+ 
+def Recover(im,t,A,tx = 0.1):
+    res = np.empty(im.shape,im.dtype)
+    t = cv2.max(t,tx)
+ 
+    for ind in range(0,3):
+        res[:,:,ind] = (im[:,:,ind]-A[0,ind])/t + A[0,ind]
+ 
+    return res
+ 
+if __name__ == '__main__':
+    
+    fn = 'car-02.png'
+    src = cv2.imread(fn)
+    I = src.astype('float64')/255
+    
+    dark = DarkChannel(I,15)
+    A = AtmLight(I,dark)
+    te = TransmissionEstimate(I,A,15)
+    t = TransmissionRefine(src,te)
+    J = Recover(I,t,A,0.1)
+    
+    arr = np.hstack((I, J))
+    cv2.imshow("contrast", arr)
+    cv2.imwrite("car-02-dehaze.png", J*255 )
+    cv2.imwrite("car-02-contrast.png", arr*255)
+    cv2.waitKey();
+    
diff --git a/blog47-quwu/blog47_quwu_03_add.py b/blog47-quwu/blog47_quwu_03_add.py
new file mode 100644
index 0000000..9f0b2cf
--- /dev/null
+++ b/blog47-quwu/blog47_quwu_03_add.py
@@ -0,0 +1,20 @@
+# -*- coding:utf-8 -*-
+import cv2
+import numpy as np
+
+#读取图片
+img = cv2.imread("fj.png", cv2.IMREAD_UNCHANGED)
+rows, cols, chn = img.shape
+
+#加噪声
+for i in range(50000):    
+    x = np.random.randint(0, rows) 
+    y = np.random.randint(0, cols)    
+    img[x,y,:] = 210
+
+cv2.imshow("noise", img)
+           
+#等待显示
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+cv2.imwrite('fj-res.png',img)
diff --git a/blog47-quwu/blog47_quwu_04_add.py b/blog47-quwu/blog47_quwu_04_add.py
new file mode 100644
index 0000000..778578f
--- /dev/null
+++ b/blog47-quwu/blog47_quwu_04_add.py
@@ -0,0 +1,23 @@
+import numpy as np
+import cv2 as cv
+import os
+import random
+ 
+file = ['fj-02.png']
+output = 'fj-02-wu.png'
+
+for file_img in file:
+    #打开图像
+    img = cv.imread(file_img)
+    mask_img = cv.imread(file_img)
+    
+    #雾的颜色
+    mask_img[:, :] = (166, 178, 180) 
+    
+    #里面参数可调，主要调整雾的浓度
+    image = cv.addWeighted(img,
+                           round(random.uniform(0.03, 0.28), 2),
+                           mask_img, 1, 0) 
+
+    #保存的文件夹
+    cv.imwrite(output, image) 
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diff --git a/blog48-ArabImage/blog48-01-cnn-dataset.py b/blog48-ArabImage/blog48-01-cnn-dataset.py
new file mode 100644
index 0000000..b697ad2
--- /dev/null
+++ b/blog48-ArabImage/blog48-01-cnn-dataset.py
@@ -0,0 +1,197 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul  7 18:54:36 2021
+@author: xiuzhang CSDN
+参考：刘润森老师博客 推荐大家关注 很厉害的一位CV大佬
+     https://maoli.blog.csdn.net/article/details/117688738
+"""
+import numpy as np
+import pandas as pd
+from IPython.display import display
+import csv
+from PIL import Image
+from scipy.ndimage import rotate
+
+#----------------------------------------------------------------
+#                      第一步 读取数据
+#----------------------------------------------------------------
+#训练数据images和labels
+letters_training_images_file_path = "dataset/csvTrainImages 13440x1024.csv"
+letters_training_labels_file_path = "dataset/csvTrainLabel 13440x1.csv"
+#测试数据images和labels
+letters_testing_images_file_path = "dataset/csvTestImages 3360x1024.csv"
+letters_testing_labels_file_path = "dataset/csvTestLabel 3360x1.csv"
+
+#加载数据
+training_letters_images = pd.read_csv(letters_training_images_file_path, header=None)
+training_letters_labels = pd.read_csv(letters_training_labels_file_path, header=None)
+testing_letters_images = pd.read_csv(letters_testing_images_file_path, header=None)
+testing_letters_labels = pd.read_csv(letters_testing_labels_file_path, header=None)
+print("%d个32x32像素的训练阿拉伯字母图像" % training_letters_images.shape[0])
+print("%d个32x32像素的测试阿拉伯字母图像" % testing_letters_images.shape[0])
+print(training_letters_images.head())
+print(np.unique(training_letters_labels))
+
+
+#----------------------------------------------------------------
+#                      第二步 数值转换为图像特征
+#----------------------------------------------------------------
+#原始数据集被反射使用np.flip翻转它 通过rotate旋转从而获得更好的图像
+def convert_values_to_image(image_values, display=False):
+    #转换成32x32
+    image_array = np.asarray(image_values)
+    image_array = image_array.reshape(32,32).astype('uint8')
+    #翻转+旋转
+    image_array = np.flip(image_array, 0)
+    image_array = rotate(image_array, -90)
+    #图像显示
+    new_image = Image.fromarray(image_array)
+    if display == True:
+        new_image.show()
+    return new_image
+
+convert_values_to_image(training_letters_images.loc[0], True)
+
+
+#----------------------------------------------------------------
+#                      第三步 图像标准化处理
+#----------------------------------------------------------------
+training_letters_images_scaled = training_letters_images.values.astype('float32')/255
+training_letters_labels = training_letters_labels.values.astype('int32')
+testing_letters_images_scaled = testing_letters_images.values.astype('float32')/255
+testing_letters_labels = testing_letters_labels.values.astype('int32')
+print("Training images of letters after scaling")
+print(training_letters_images_scaled.shape)
+print(training_letters_images_scaled[0:5])
+
+
+#----------------------------------------------------------------
+#                      第四步 输出One-hot编码转换
+#----------------------------------------------------------------
+import keras
+from keras.utils import to_categorical
+number_of_classes = 28
+training_letters_labels_encoded = to_categorical(training_letters_labels-1, 
+                                                 num_classes=number_of_classes)
+testing_letters_labels_encoded = to_categorical(testing_letters_labels-1, 
+                                                num_classes=number_of_classes)
+print(training_letters_labels)
+print(training_letters_labels_encoded)
+print(training_letters_images_scaled.shape)
+# (13440, 1024)
+
+
+#----------------------------------------------------------------
+#                         第五步 形状修改
+#----------------------------------------------------------------
+#输入形状 32x32x1
+training_letters_images_scaled = training_letters_images_scaled.reshape([-1, 32, 32, 1])
+testing_letters_images_scaled = testing_letters_images_scaled.reshape([-1, 32, 32, 1])
+print(training_letters_images_scaled.shape, 
+      training_letters_labels_encoded.shape, 
+      testing_letters_images_scaled.shape, 
+      testing_letters_labels_encoded.shape)
+# (13440, 32, 32, 1) (13440, 28) (3360, 32, 32, 1) (3360, 28)
+
+
+#----------------------------------------------------------------
+#                         第六步 CNN模型设计
+#----------------------------------------------------------------
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, Dropout, Dense
+
+#定义模型
+def create_model(optimizer='adam', kernel_initializer='he_normal', activation='relu'):
+    #第一个卷积层
+    model = Sequential()
+    model.add(Conv2D(filters=16, kernel_size=3, padding='same', input_shape=(32, 32, 1), kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第二个卷积层
+    model.add(Conv2D(filters=32, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第三个卷积层
+    model.add(Conv2D(filters=64, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第四个卷积层
+    model.add(Conv2D(filters=128, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    model.add(GlobalAveragePooling2D())
+
+    #全连接层输出28类结果
+    model.add(Dense(28, activation='softmax'))
+
+    #损失函数定义
+    model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=optimizer)
+    return model
+
+#创建模型
+model = create_model(optimizer='Adam', kernel_initializer='uniform', activation='relu')
+model.summary()
+
+
+#----------------------------------------------------------------
+#                         第七步 模型绘制
+#----------------------------------------------------------------
+from keras.utils.vis_utils import plot_model
+from IPython.display import Image as IPythonImage
+
+plot_model(model, to_file="model.png", show_shapes=True)
+display(IPythonImage('model.png'))
+
+
+#----------------------------------------------------------------
+#                         第八步 模型训练
+#----------------------------------------------------------------
+from keras.callbacks import ModelCheckpoint
+
+checkpointer = ModelCheckpoint(filepath='weights.hdf5', 
+                               verbose=1, 
+                               save_best_only=True)
+history = model.fit(training_letters_images_scaled, 
+                    training_letters_labels_encoded,
+                    validation_data=(testing_letters_images_scaled,
+                                     testing_letters_labels_encoded),
+                    epochs=15, 
+                    batch_size=20, 
+                    verbose=1, 
+                    callbacks=[checkpointer])
+print(history)
+
+#----------------------------------------------------------------
+#                         第九步 绘制图形
+#----------------------------------------------------------------
+import matplotlib.pyplot as plt
+
+def plot_loss_accuracy(history):
+    # Loss 
+    plt.figure(figsize=[8,6])
+    plt.plot(history.history['loss'],'r',linewidth=3.0)
+    plt.plot(history.history['val_loss'],'b',linewidth=3.0)
+    plt.legend(['Training loss', 'Validation Loss'],fontsize=18)
+    plt.xlabel('Epochs ',fontsize=16)
+    plt.ylabel('Loss',fontsize=16)
+    plt.title('Loss Curves',fontsize=16)
+
+    # Accuracy 
+    plt.figure(figsize=[8,6])
+    plt.plot(history.history['accuracy'],'r',linewidth=3.0)
+    plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)
+    plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)
+    plt.xlabel('Epochs ',fontsize=16)
+    plt.ylabel('Accuracy',fontsize=16)
+    plt.title('Accuracy Curves',fontsize=16) 
+
+plot_loss_accuracy(history)
+
+
diff --git a/blog48-ArabImage/blog48-02-cnn-dataset.py b/blog48-ArabImage/blog48-02-cnn-dataset.py
new file mode 100644
index 0000000..75140ea
--- /dev/null
+++ b/blog48-ArabImage/blog48-02-cnn-dataset.py
@@ -0,0 +1,243 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul  7 18:54:36 2021
+@author: xiuzhang CSDN
+参考：刘润森老师博客 推荐大家关注 很厉害的一位CV大佬
+     https://maoli.blog.csdn.net/article/details/117688738
+"""
+import numpy as np
+import pandas as pd
+from IPython.display import display
+import csv
+from PIL import Image
+from scipy.ndimage import rotate
+
+#----------------------------------------------------------------
+#                      第一步 读取数据
+#----------------------------------------------------------------
+#训练数据images和labels
+letters_training_images_file_path = "dataset/csvTrainImages 13440x1024.csv"
+letters_training_labels_file_path = "dataset/csvTrainLabel 13440x1.csv"
+#测试数据images和labels
+letters_testing_images_file_path = "dataset/csvTestImages 3360x1024.csv"
+letters_testing_labels_file_path = "dataset/csvTestLabel 3360x1.csv"
+
+#加载数据
+training_letters_images = pd.read_csv(letters_training_images_file_path, header=None)
+training_letters_labels = pd.read_csv(letters_training_labels_file_path, header=None)
+testing_letters_images = pd.read_csv(letters_testing_images_file_path, header=None)
+testing_letters_labels = pd.read_csv(letters_testing_labels_file_path, header=None)
+print("%d个32x32像素的训练阿拉伯字母图像" % training_letters_images.shape[0])
+print("%d个32x32像素的测试阿拉伯字母图像" % testing_letters_images.shape[0])
+print(training_letters_images.head())
+print(np.unique(training_letters_labels))
+
+
+#----------------------------------------------------------------
+#                      第二步 数值转换为图像特征
+#----------------------------------------------------------------
+#原始数据集被反射使用np.flip翻转它 通过rotate旋转从而获得更好的图像
+def convert_values_to_image(image_values, display=False):
+    #转换成32x32
+    image_array = np.asarray(image_values)
+    image_array = image_array.reshape(32,32).astype('uint8')
+    #翻转+旋转
+    image_array = np.flip(image_array, 0)
+    image_array = rotate(image_array, -90)
+    #图像显示
+    new_image = Image.fromarray(image_array)
+    if display == True:
+        new_image.show()
+    return new_image
+
+convert_values_to_image(training_letters_images.loc[0], True)
+
+
+#----------------------------------------------------------------
+#                      第三步 图像标准化处理
+#----------------------------------------------------------------
+training_letters_images_scaled = training_letters_images.values.astype('float32')/255
+training_letters_labels = training_letters_labels.values.astype('int32')
+testing_letters_images_scaled = testing_letters_images.values.astype('float32')/255
+testing_letters_labels = testing_letters_labels.values.astype('int32')
+print("Training images of letters after scaling")
+print(training_letters_images_scaled.shape)
+print(training_letters_images_scaled[0:5])
+
+
+#----------------------------------------------------------------
+#                      第四步 输出One-hot编码转换
+#----------------------------------------------------------------
+import keras
+from keras.utils import to_categorical
+number_of_classes = 28
+training_letters_labels_encoded = to_categorical(training_letters_labels-1, 
+                                                 num_classes=number_of_classes)
+testing_letters_labels_encoded = to_categorical(testing_letters_labels-1, 
+                                                num_classes=number_of_classes)
+print(training_letters_labels)
+print(training_letters_labels_encoded)
+print(training_letters_images_scaled.shape)
+# (13440, 1024)
+
+
+#----------------------------------------------------------------
+#                         第五步 形状修改
+#----------------------------------------------------------------
+#输入形状 32x32x1
+training_letters_images_scaled = training_letters_images_scaled.reshape([-1, 32, 32, 1])
+testing_letters_images_scaled = testing_letters_images_scaled.reshape([-1, 32, 32, 1])
+print(training_letters_images_scaled.shape, 
+      training_letters_labels_encoded.shape, 
+      testing_letters_images_scaled.shape, 
+      testing_letters_labels_encoded.shape)
+# (13440, 32, 32, 1) (13440, 28) (3360, 32, 32, 1) (3360, 28)
+
+
+#----------------------------------------------------------------
+#                         第六步 CNN模型设计
+#----------------------------------------------------------------
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, Dropout, Dense
+
+#定义模型
+def create_model(optimizer='adam', kernel_initializer='he_normal', activation='relu'):
+    #第一个卷积层
+    model = Sequential()
+    model.add(Conv2D(filters=16, kernel_size=3, padding='same', input_shape=(32, 32, 1), kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第二个卷积层
+    model.add(Conv2D(filters=32, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第三个卷积层
+    model.add(Conv2D(filters=64, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第四个卷积层
+    model.add(Conv2D(filters=128, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    model.add(GlobalAveragePooling2D())
+
+    #全连接层输出28类结果
+    model.add(Dense(28, activation='softmax'))
+
+    #损失函数定义
+    model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=optimizer)
+    return model
+
+#创建模型
+model = create_model(optimizer='Adam', kernel_initializer='uniform', activation='relu')
+model.summary()
+
+
+#----------------------------------------------------------------
+#                         第七步 模型绘制
+#----------------------------------------------------------------
+from keras.utils.vis_utils import plot_model
+from IPython.display import Image as IPythonImage
+
+plot_model(model, to_file="model.png", show_shapes=True)
+display(IPythonImage('model.png'))
+
+
+#----------------------------------------------------------------
+#                         第八步 模型训练+输出结果
+#----------------------------------------------------------------
+from keras.callbacks import ModelCheckpoint
+from sklearn.metrics import classification_report
+import matplotlib.pyplot as plt
+
+#绘制图形
+def plot_loss_accuracy(history):
+    # Loss 
+    plt.figure(figsize=[8,6])
+    plt.plot(history.history['loss'],'r',linewidth=3.0)
+    plt.plot(history.history['val_loss'],'b',linewidth=3.0)
+    plt.legend(['Training loss', 'Validation Loss'],fontsize=18)
+    plt.xlabel('Epochs ',fontsize=16)
+    plt.ylabel('Loss',fontsize=16)
+    plt.title('Loss Curves',fontsize=16)
+
+    # Accuracy 
+    plt.figure(figsize=[8,6])
+    plt.plot(history.history['accuracy'],'r',linewidth=3.0)
+    plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)
+    plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)
+    plt.xlabel('Epochs ',fontsize=16)
+    plt.ylabel('Accuracy',fontsize=16)
+    plt.title('Accuracy Curves',fontsize=16) 
+
+#混淆矩阵
+def get_predicted_classes(model, data, labels=None):
+    image_predictions = model.predict(data)
+    predicted_classes = np.argmax(image_predictions, axis=1)
+    true_classes = np.argmax(labels, axis=1)
+    return predicted_classes, true_classes, image_predictions
+
+def get_classification_report(y_true, y_pred):
+    print(classification_report(y_true, y_pred, digits=4)) #小数点4位
+
+checkpointer = ModelCheckpoint(filepath='weights.hdf5', 
+                               verbose=1, 
+                               save_best_only=True)
+flag = "test" 
+if flag=="train":
+    history = model.fit(training_letters_images_scaled, 
+                        training_letters_labels_encoded,
+                        validation_data=(testing_letters_images_scaled,
+                                         testing_letters_labels_encoded),
+                        epochs=15, 
+                        batch_size=20, 
+                        verbose=1, 
+                        callbacks=[checkpointer])
+    print(history)
+    plot_loss_accuracy(history)
+else:
+    #加载具有最佳验证损失的模型
+    model.load_weights('weights.hdf5')
+    metrics = model.evaluate(testing_letters_images_scaled, 
+                             testing_letters_labels_encoded, 
+                             verbose=1)
+    print("Test Accuracy: {}".format(metrics[1]))
+    print("Test Loss: {}".format(metrics[0]))
+    
+    y_pred, y_true, image_predictions = get_predicted_classes(model, 
+                                                              testing_letters_images_scaled, 
+                                                              testing_letters_labels_encoded)
+    get_classification_report(y_true, y_pred)
+
+
+    #----------------------------------------------------------------
+    #                         第九步 绘制测试图像
+    #----------------------------------------------------------------
+    fig = plt.figure(0, figsize=(14,14))
+    indices = np.random.randint(0, testing_letters_labels.shape[0], size=49)
+    y_pred = np.argmax(model.predict(training_letters_images_scaled), axis=1)
+
+    for i, idx in enumerate(indices):
+        plt.subplot(7,7,i+1)
+            
+        image_array = training_letters_images_scaled[idx][:,:,0]
+        image_array = np.flip(image_array, 0)
+        image_array = rotate(image_array, -90)
+           
+        plt.imshow(image_array, cmap='gray')
+        plt.title("Pred: {} - Label: {}".format(y_pred[idx], 
+                  (training_letters_labels[idx] -1)))
+        plt.xticks([])
+        plt.yticks([])
+    plt.show()
+    plt.savefig("resutl.png")
+
+
+
diff --git a/blog48-ArabImage/blog48-03-best-cnn-dataset.py b/blog48-ArabImage/blog48-03-best-cnn-dataset.py
new file mode 100644
index 0000000..565a97a
--- /dev/null
+++ b/blog48-ArabImage/blog48-03-best-cnn-dataset.py
@@ -0,0 +1,275 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul  7 18:54:36 2021
+@author: xiuzhang CSDN
+参考：刘润森老师博客 推荐大家关注 很厉害的一位CV大佬
+     https://maoli.blog.csdn.net/article/details/117688738
+"""
+import numpy as np
+import pandas as pd
+from IPython.display import display
+import csv
+from PIL import Image
+from scipy.ndimage import rotate
+
+#----------------------------------------------------------------
+#                      第一步 读取数据
+#----------------------------------------------------------------
+#训练数据images和labels
+letters_training_images_file_path = "dataset/csvTrainImages 13440x1024.csv"
+letters_training_labels_file_path = "dataset/csvTrainLabel 13440x1.csv"
+#测试数据images和labels
+letters_testing_images_file_path = "dataset/csvTestImages 3360x1024.csv"
+letters_testing_labels_file_path = "dataset/csvTestLabel 3360x1.csv"
+
+#加载数据
+training_letters_images = pd.read_csv(letters_training_images_file_path, header=None)
+training_letters_labels = pd.read_csv(letters_training_labels_file_path, header=None)
+testing_letters_images = pd.read_csv(letters_testing_images_file_path, header=None)
+testing_letters_labels = pd.read_csv(letters_testing_labels_file_path, header=None)
+print("%d个32x32像素的训练阿拉伯字母图像" % training_letters_images.shape[0])
+print("%d个32x32像素的测试阿拉伯字母图像" % testing_letters_images.shape[0])
+print(training_letters_images.head())
+print(np.unique(training_letters_labels))
+
+
+#----------------------------------------------------------------
+#                      第二步 数值转换为图像特征
+#----------------------------------------------------------------
+#原始数据集被反射使用np.flip翻转它 通过rotate旋转从而获得更好的图像
+def convert_values_to_image(image_values, display=False):
+    #转换成32x32
+    image_array = np.asarray(image_values)
+    image_array = image_array.reshape(32,32).astype('uint8')
+    #翻转+旋转
+    image_array = np.flip(image_array, 0)
+    image_array = rotate(image_array, -90)
+    #图像显示
+    new_image = Image.fromarray(image_array)
+    if display == True:
+        new_image.show()
+    return new_image
+
+#convert_values_to_image(training_letters_images.loc[0], True)
+
+
+#----------------------------------------------------------------
+#                      第三步 图像标准化处理
+#----------------------------------------------------------------
+training_letters_images_scaled = training_letters_images.values.astype('float32')/255
+training_letters_labels = training_letters_labels.values.astype('int32')
+testing_letters_images_scaled = testing_letters_images.values.astype('float32')/255
+testing_letters_labels = testing_letters_labels.values.astype('int32')
+print("Training images of letters after scaling")
+print(training_letters_images_scaled.shape)
+print(training_letters_images_scaled[0:5])
+
+
+#----------------------------------------------------------------
+#                      第四步 输出One-hot编码转换
+#----------------------------------------------------------------
+import keras
+from keras.utils import to_categorical
+number_of_classes = 28
+training_letters_labels_encoded = to_categorical(training_letters_labels-1, 
+                                                 num_classes=number_of_classes)
+testing_letters_labels_encoded = to_categorical(testing_letters_labels-1, 
+                                                num_classes=number_of_classes)
+print(training_letters_labels)
+print(training_letters_labels_encoded)
+print(training_letters_images_scaled.shape)
+# (13440, 1024)
+
+
+#----------------------------------------------------------------
+#                         第五步 形状修改
+#----------------------------------------------------------------
+#输入形状 32x32x1
+training_letters_images_scaled = training_letters_images_scaled.reshape([-1, 32, 32, 1])
+testing_letters_images_scaled = testing_letters_images_scaled.reshape([-1, 32, 32, 1])
+print(training_letters_images_scaled.shape, 
+      training_letters_labels_encoded.shape, 
+      testing_letters_images_scaled.shape, 
+      testing_letters_labels_encoded.shape)
+# (13440, 32, 32, 1) (13440, 28) (3360, 32, 32, 1) (3360, 28)
+
+
+#----------------------------------------------------------------
+#                         第六步 CNN模型设计
+#----------------------------------------------------------------
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, Dropout, Dense
+
+#定义模型
+def create_model(optimizer='adam', kernel_initializer='he_normal', activation='relu'):
+    #第一个卷积层
+    model = Sequential()
+    model.add(Conv2D(filters=16, kernel_size=3, padding='same', input_shape=(32, 32, 1), kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第二个卷积层
+    model.add(Conv2D(filters=32, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第三个卷积层
+    model.add(Conv2D(filters=64, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    
+    #第四个卷积层
+    model.add(Conv2D(filters=128, kernel_size=3, padding='same', kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))
+    model.add(GlobalAveragePooling2D())
+
+    #全连接层输出28类结果
+    model.add(Dense(28, activation='softmax'))
+
+    #损失函数定义
+    model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=optimizer)
+    return model
+
+#创建模型
+model = create_model(optimizer='Adam', kernel_initializer='uniform', activation='relu')
+model.summary()
+
+
+#----------------------------------------------------------------
+#                         第七步 模型绘制
+#----------------------------------------------------------------
+from keras.utils.vis_utils import plot_model
+from IPython.display import Image as IPythonImage
+
+plot_model(model, to_file="model.png", show_shapes=True)
+display(IPythonImage('model.png'))
+
+
+#----------------------------------------------------------------
+#                         第八步 模型训练+输出结果
+#----------------------------------------------------------------
+from keras.callbacks import ModelCheckpoint
+from sklearn.metrics import classification_report
+import matplotlib.pyplot as plt
+
+#绘制图形
+def plot_loss_accuracy(history):
+    # Loss 
+    plt.figure(figsize=[8,6])
+    plt.plot(history.history['loss'],'r',linewidth=3.0)
+    plt.plot(history.history['val_loss'],'b',linewidth=3.0)
+    plt.legend(['Training loss', 'Validation Loss'],fontsize=18)
+    plt.xlabel('Epochs ',fontsize=16)
+    plt.ylabel('Loss',fontsize=16)
+    plt.title('Loss Curves',fontsize=16)
+
+    # Accuracy 
+    plt.figure(figsize=[8,6])
+    plt.plot(history.history['accuracy'],'r',linewidth=3.0)
+    plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)
+    plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)
+    plt.xlabel('Epochs ',fontsize=16)
+    plt.ylabel('Accuracy',fontsize=16)
+    plt.title('Accuracy Curves',fontsize=16) 
+
+#混淆矩阵
+def get_predicted_classes(model, data, labels=None):
+    image_predictions = model.predict(data)
+    predicted_classes = np.argmax(image_predictions, axis=1)
+    true_classes = np.argmax(labels, axis=1)
+    return predicted_classes, true_classes, image_predictions
+
+def get_classification_report(y_true, y_pred):
+    print(classification_report(y_true, y_pred, digits=4)) #小数点4位
+
+checkpointer = ModelCheckpoint(filepath='weights.hdf5', 
+                               verbose=1, 
+                               save_best_only=True)
+flag = "test" 
+if flag=="train":
+    history = model.fit(training_letters_images_scaled, 
+                        training_letters_labels_encoded,
+                        validation_data=(testing_letters_images_scaled,
+                                         testing_letters_labels_encoded),
+                        epochs=20, 
+                        batch_size=128, 
+                        verbose=1, 
+                        callbacks=[checkpointer])
+    print(history)
+    plot_loss_accuracy(history)
+else:
+    #加载具有最佳验证损失的模型
+    model.load_weights('weights.hdf5')
+    metrics_ = model.evaluate(testing_letters_images_scaled, 
+                             testing_letters_labels_encoded, 
+                             verbose=1)
+    print("Test Accuracy: {}".format(metrics_[1]))
+    print("Test Loss: {}".format(metrics_[0]))
+    
+    y_pre_test, y_true, image_predictions = get_predicted_classes(model, 
+                                                              testing_letters_images_scaled, 
+                                                              testing_letters_labels_encoded)
+    get_classification_report(y_true, y_pre_test)
+
+    #----------------------------------------------------------------
+    #                         第九步 绘制测试图像
+    #----------------------------------------------------------------
+    
+    
+    plt.rcParams['font.sans-serif']=['SimHei']  #用来正常显示中文标签
+    plt.rcParams['axes.unicode_minus']=False    #用来正常显示负号
+
+
+    fig = plt.figure(0, figsize=(14,14))
+    indices = np.random.randint(0, testing_letters_labels.shape[0], size=42)
+    y_pred = np.argmax(model.predict(training_letters_images_scaled), axis=1)
+    
+    for i, idx in enumerate(indices):
+        plt.subplot(7,6,i+1)
+            
+        image_array = training_letters_images_scaled[idx][:,:,0]
+        image_array = np.flip(image_array, 0)
+        image_array = rotate(image_array, -90)
+           
+        plt.imshow(image_array, cmap='gray')
+        plt.title("预测:{} 真实:{}".format(y_pred[idx], 
+                  (training_letters_labels[idx] -1)))
+        plt.xticks([])
+        plt.yticks([])
+    plt.show()
+    plt.savefig("resutl.png", dpi=300)
+    
+    ## 评价预测效果，计算混淆矩阵
+    import seaborn as sns
+    from sklearn import metrics
+    
+    Labname = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,
+               15,16,17,18,19,20,21,22,23,24,25,26,27,28]
+    print(y_pre_test)
+    y_pre_test = [num+1 for num in y_pre_test]
+    print(np.argmax(testing_letters_labels,axis=1))
+    confm = metrics.confusion_matrix(testing_letters_labels,
+                                     y_pre_test)
+    print(confm.T)
+    
+    plt.figure(figsize=(10,10))
+    heatmap = sns.heatmap(confm.T, square=True, annot=True,
+                fmt='d', cbar=True, linewidths=.6,
+                cmap="YlGnBu")
+    bottom, top = heatmap.get_ylim()
+    heatmap.set_ylim(bottom + 0.5, top - 0.5)
+    plt.xlabel('True label',size = 12)
+    plt.ylabel('Predicted label', size = 12)
+    #plt.xticks(np.arange(28)+0.5, Labname, size = 10)
+    #plt.yticks(np.arange(28)+0.5, Labname, size = 10)
+    plt.savefig('headmap.png', dpi=300)
+    plt.show()
+    
+
+
+
diff --git a/blog48-ArabImage/blog48-04-basic-cnn-dataset.py b/blog48-ArabImage/blog48-04-basic-cnn-dataset.py
new file mode 100644
index 0000000..35e71ba
--- /dev/null
+++ b/blog48-ArabImage/blog48-04-basic-cnn-dataset.py
@@ -0,0 +1,225 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul  7 18:54:36 2021
+@author: xiuzhang CSDN
+参考：刘润森老师博客 推荐大家关注 很厉害的一位CV大佬
+     https://maoli.blog.csdn.net/article/details/117688738
+"""
+import numpy as np
+import pandas as pd
+from IPython.display import display
+import csv
+from PIL import Image
+from scipy.ndimage import rotate
+
+#----------------------------------------------------------------
+#                      第一步 读取数据
+#----------------------------------------------------------------
+#训练数据images和labels
+letters_training_images_file_path = "dataset/csvTrainImages 13440x1024.csv"
+letters_training_labels_file_path = "dataset/csvTrainLabel 13440x1.csv"
+#测试数据images和labels
+letters_testing_images_file_path = "dataset/csvTestImages 3360x1024.csv"
+letters_testing_labels_file_path = "dataset/csvTestLabel 3360x1.csv"
+
+#加载数据
+training_letters_images = pd.read_csv(letters_training_images_file_path, header=None)
+training_letters_labels = pd.read_csv(letters_training_labels_file_path, header=None)
+testing_letters_images = pd.read_csv(letters_testing_images_file_path, header=None)
+testing_letters_labels = pd.read_csv(letters_testing_labels_file_path, header=None)
+print("%d个32x32像素的训练阿拉伯字母图像" % training_letters_images.shape[0])
+print("%d个32x32像素的测试阿拉伯字母图像" % testing_letters_images.shape[0])
+print(training_letters_images.head())
+print(np.unique(training_letters_labels))
+
+
+#----------------------------------------------------------------
+#                      第二步 数值转换为图像特征
+#----------------------------------------------------------------
+#原始数据集被反射使用np.flip翻转它 通过rotate旋转从而获得更好的图像
+def convert_values_to_image(image_values, display=False):
+    #转换成32x32
+    image_array = np.asarray(image_values)
+    image_array = image_array.reshape(32,32).astype('uint8')
+    #翻转+旋转
+    image_array = np.flip(image_array, 0)
+    image_array = rotate(image_array, -90)
+    #图像显示
+    new_image = Image.fromarray(image_array)
+    if display == True:
+        new_image.show()
+    return new_image
+
+convert_values_to_image(training_letters_images.loc[0], True)
+
+
+#----------------------------------------------------------------
+#                      第三步 图像标准化处理
+#----------------------------------------------------------------
+training_letters_images_scaled = training_letters_images.values.astype('float32')/255
+training_letters_labels = training_letters_labels.values.astype('int32')
+testing_letters_images_scaled = testing_letters_images.values.astype('float32')/255
+testing_letters_labels = testing_letters_labels.values.astype('int32')
+print("Training images of letters after scaling")
+print(training_letters_images_scaled.shape)
+print(training_letters_images_scaled[0:5])
+
+
+#----------------------------------------------------------------
+#                      第四步 输出One-hot编码转换
+#----------------------------------------------------------------
+import keras
+from keras.utils import to_categorical
+number_of_classes = 28
+training_letters_labels_encoded = to_categorical(training_letters_labels-1, 
+                                                 num_classes=number_of_classes)
+testing_letters_labels_encoded = to_categorical(testing_letters_labels-1, 
+                                                num_classes=number_of_classes)
+print(training_letters_labels)
+print(training_letters_labels_encoded)
+print(training_letters_images_scaled.shape)
+# (13440, 1024)
+
+
+#----------------------------------------------------------------
+#                         第五步 形状修改
+#----------------------------------------------------------------
+#输入形状 32x32x1
+training_letters_images_scaled = training_letters_images_scaled.reshape([-1, 32, 32, 1])
+testing_letters_images_scaled = testing_letters_images_scaled.reshape([-1, 32, 32, 1])
+print(training_letters_images_scaled.shape, 
+      training_letters_labels_encoded.shape, 
+      testing_letters_images_scaled.shape, 
+      testing_letters_labels_encoded.shape)
+# (13440, 32, 32, 1) (13440, 28) (3360, 32, 32, 1) (3360, 28)
+
+
+#----------------------------------------------------------------
+#                         第六步 CNN模型设计
+#----------------------------------------------------------------
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D, BatchNormalization, Dropout, Dense
+
+#定义模型
+def create_model(optimizer='adam', kernel_initializer='he_normal', activation='relu'):
+    #第一个卷积层
+    model = Sequential()
+    model.add(Conv2D(filters=64, kernel_size=3, padding='same', input_shape=(32, 32, 1), kernel_initializer=kernel_initializer, activation=activation))
+    model.add(BatchNormalization())
+    model.add(MaxPooling2D(pool_size=2))
+    model.add(Dropout(0.2))  
+    model.add(GlobalAveragePooling2D())
+    
+    #全连接层输出28类结果
+    model.add(Dense(28, activation='softmax'))
+
+    #损失函数定义
+    model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=optimizer)
+    return model
+
+#创建模型
+model = create_model(optimizer='Adam', kernel_initializer='uniform', activation='relu')
+model.summary()
+
+
+#----------------------------------------------------------------
+#                         第七步 模型绘制
+#----------------------------------------------------------------
+from keras.utils.vis_utils import plot_model
+from IPython.display import Image as IPythonImage
+
+plot_model(model, to_file="model.png", show_shapes=True)
+display(IPythonImage('model.png'))
+
+
+#----------------------------------------------------------------
+#                         第八步 模型训练+输出结果
+#----------------------------------------------------------------
+from keras.callbacks import ModelCheckpoint
+from sklearn.metrics import classification_report
+import matplotlib.pyplot as plt
+
+#绘制图形
+def plot_loss_accuracy(history):
+    # Loss 
+    plt.figure(figsize=[8,6])
+    plt.plot(history.history['loss'],'r',linewidth=3.0)
+    plt.plot(history.history['val_loss'],'b',linewidth=3.0)
+    plt.legend(['Training loss', 'Validation Loss'],fontsize=18)
+    plt.xlabel('Epochs ',fontsize=16)
+    plt.ylabel('Loss',fontsize=16)
+    plt.title('Loss Curves',fontsize=16)
+
+    # Accuracy 
+    plt.figure(figsize=[8,6])
+    plt.plot(history.history['accuracy'],'r',linewidth=3.0)
+    plt.plot(history.history['val_accuracy'],'b',linewidth=3.0)
+    plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)
+    plt.xlabel('Epochs ',fontsize=16)
+    plt.ylabel('Accuracy',fontsize=16)
+    plt.title('Accuracy Curves',fontsize=16) 
+
+#混淆矩阵
+def get_predicted_classes(model, data, labels=None):
+    image_predictions = model.predict(data)
+    predicted_classes = np.argmax(image_predictions, axis=1)
+    true_classes = np.argmax(labels, axis=1)
+    return predicted_classes, true_classes, image_predictions
+
+def get_classification_report(y_true, y_pred):
+    print(classification_report(y_true, y_pred, digits=4)) #小数点4位
+
+checkpointer = ModelCheckpoint(filepath='weights-cnn.hdf5', 
+                               verbose=1, 
+                               save_best_only=True)
+flag = "train" 
+if flag=="train":
+    history = model.fit(training_letters_images_scaled, 
+                        training_letters_labels_encoded,
+                        validation_data=(testing_letters_images_scaled,
+                                         testing_letters_labels_encoded),
+                        epochs=20, 
+                        batch_size=128, 
+                        verbose=1, 
+                        callbacks=[checkpointer])
+    print(history)
+    plot_loss_accuracy(history)
+else:
+    #加载具有最佳验证损失的模型
+    model.load_weights('weights-cnn.hdf5')
+    metrics = model.evaluate(testing_letters_images_scaled, 
+                             testing_letters_labels_encoded, 
+                             verbose=1)
+    print("Test Accuracy: {}".format(metrics[1]))
+    print("Test Loss: {}".format(metrics[0]))
+    
+    y_pred, y_true, image_predictions = get_predicted_classes(model, 
+                                                              testing_letters_images_scaled, 
+                                                              testing_letters_labels_encoded)
+    get_classification_report(y_true, y_pred)
+
+
+    #----------------------------------------------------------------
+    #                         第九步 绘制测试图像
+    #----------------------------------------------------------------
+    fig = plt.figure(0, figsize=(14,14))
+    indices = np.random.randint(0, testing_letters_labels.shape[0], size=49)
+    y_pred = np.argmax(model.predict(training_letters_images_scaled), axis=1)
+
+    for i, idx in enumerate(indices):
+        plt.subplot(7,7,i+1)
+            
+        image_array = training_letters_images_scaled[idx][:,:,0]
+        image_array = np.flip(image_array, 0)
+        image_array = rotate(image_array, -90)
+           
+        plt.imshow(image_array, cmap='gray')
+        plt.title("Pred: {} - Label: {}".format(y_pred[idx], 
+                  (training_letters_labels[idx] -1)))
+        plt.xticks([])
+        plt.yticks([])
+    plt.show()
+    plt.savefig("resutl.png")
+
+
+
diff --git a/blog48-ArabImage/blog48-05-knn-dataset.py b/blog48-ArabImage/blog48-05-knn-dataset.py
new file mode 100644
index 0000000..2b34ab5
--- /dev/null
+++ b/blog48-ArabImage/blog48-05-knn-dataset.py
@@ -0,0 +1,142 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul  7 18:54:36 2021
+@author: xiuzhang CSDN
+参考：刘润森老师博客 推荐大家关注 很厉害的一位CV大佬
+     https://maoli.blog.csdn.net/article/details/117688738
+"""
+import numpy as np
+import pandas as pd
+from IPython.display import display
+import csv
+from PIL import Image
+from scipy.ndimage import rotate
+
+#----------------------------------------------------------------
+#                      第一步 读取数据
+#----------------------------------------------------------------
+#训练数据images和labels
+letters_training_images_file_path = "dataset/csvTrainImages 13440x1024.csv"
+letters_training_labels_file_path = "dataset/csvTrainLabel 13440x1.csv"
+#测试数据images和labels
+letters_testing_images_file_path = "dataset/csvTestImages 3360x1024.csv"
+letters_testing_labels_file_path = "dataset/csvTestLabel 3360x1.csv"
+
+#加载数据
+training_letters_images = pd.read_csv(letters_training_images_file_path, header=None)
+training_letters_labels = pd.read_csv(letters_training_labels_file_path, header=None)
+testing_letters_images = pd.read_csv(letters_testing_images_file_path, header=None)
+testing_letters_labels = pd.read_csv(letters_testing_labels_file_path, header=None)
+print("%d个32x32像素的训练阿拉伯字母图像" % training_letters_images.shape[0])
+print("%d个32x32像素的测试阿拉伯字母图像" % testing_letters_images.shape[0])
+print(training_letters_images.head())
+print(np.unique(training_letters_labels))
+
+
+#----------------------------------------------------------------
+#                      第二步 数值转换为图像特征
+#----------------------------------------------------------------
+#原始数据集被反射使用np.flip翻转它 通过rotate旋转从而获得更好的图像
+def convert_values_to_image(image_values, display=False):
+    #转换成32x32
+    image_array = np.asarray(image_values)
+    image_array = image_array.reshape(32,32).astype('uint8')
+    #翻转+旋转
+    image_array = np.flip(image_array, 0)
+    image_array = rotate(image_array, -90)
+    #图像显示
+    new_image = Image.fromarray(image_array)
+    if display == True:
+        new_image.show()
+    return new_image
+
+#convert_values_to_image(training_letters_images.loc[0], True)
+
+
+#----------------------------------------------------------------
+#                      第三步 图像标准化处理
+#----------------------------------------------------------------
+training_letters_images_scaled = training_letters_images.values.astype('float32')/255
+training_letters_labels = training_letters_labels.values.astype('int32')
+testing_letters_images_scaled = testing_letters_images.values.astype('float32')/255
+testing_letters_labels = testing_letters_labels.values.astype('int32')
+print("Training images of letters after scaling")
+print(training_letters_images_scaled.shape)
+print(training_letters_images_scaled[0:5])
+
+
+#----------------------------------------------------------------
+#                      第四步 输出One-hot编码转换
+#----------------------------------------------------------------
+import keras
+from numpy import *
+from keras.utils import to_categorical
+number_of_classes = 28
+training_letters_labels_encoded = to_categorical(training_letters_labels-1, 
+                                                 num_classes=number_of_classes)
+testing_letters_labels_encoded = to_categorical(testing_letters_labels-1, 
+                                                num_classes=number_of_classes)
+print(training_letters_labels)
+print(training_letters_labels_encoded)
+print(training_letters_images_scaled.shape)
+# (13440, 1024)
+
+#机器学习不使用One-hot 每个值减1处理
+training_letters_labels_list = training_letters_labels.tolist()
+testing_letters_labels_list = testing_letters_labels.tolist()
+print(training_letters_labels_list[:10])
+#[[1], [1], [1], [1], [1], [1], [1], [1], [2], [2]]
+
+training_letters_labels_res = [i-1 for num in training_letters_labels_list for i in num]
+testing_letters_labels_res = [i-1 for num in testing_letters_labels_list for i in num]
+print(training_letters_labels_res[:10])
+print(len(training_letters_labels_res), len(testing_letters_labels_res))
+#[0, 0, 0, 0, 0, 0, 0, 0, 1, 1]
+
+
+#----------------------------------------------------------------
+#                         第五步 形状修改
+#----------------------------------------------------------------
+#输入形状 32x32x1 注意sklearn.fit接收两维否则报错
+#ValueError: Found array with dim 4. Estimator expected <= 2.
+training_letters_images_scaled = training_letters_images_scaled.reshape([-1, 32*32*1])
+testing_letters_images_scaled = testing_letters_images_scaled.reshape([-1, 32*32*1])
+print(training_letters_images_scaled.shape, 
+      training_letters_labels_encoded.shape, 
+      testing_letters_images_scaled.shape, 
+      testing_letters_labels_encoded.shape)
+# (13440, 32, 32, 1) (13440, 28) (3360, 32, 32, 1) (3360, 28)
+# (13440, 1024) (13440, 28) (3360, 1024) (3360, 28)
+
+
+#----------------------------------------------------------------
+#                         第六步 KNN模型设计
+#----------------------------------------------------------------
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.naive_bayes import BernoulliNB
+from sklearn.svm import SVC  
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.metrics import confusion_matrix, classification_report
+
+#clf = KNeighborsClassifier(n_neighbors=11)
+#clf = BernoulliNB()
+#clf = SVC()
+#clf = DecisionTreeClassifier(criterion='gini',max_depth=4,random_state=1)
+#clf = LogisticRegression(C=100.0,random_state=1)
+clf = RandomForestClassifier(n_estimators=25,random_state=1,n_jobs=2,verbose=1)
+#clf = AdaBoostClassifier()
+
+clf.fit(training_letters_images_scaled, training_letters_labels_res)
+predictions_labels = clf.predict(testing_letters_images_scaled)
+
+print('预测结果:')
+print(predictions_labels)
+print(testing_letters_labels_res)
+
+print('算法评价:')
+print((classification_report(testing_letters_labels_res, 
+                             predictions_labels,
+                             digits=4)))
diff --git a/blog48-ArabImage/csvTestImages 3360x1024.csv b/blog48-ArabImage/csvTestImages 3360x1024.csv
new file mode 100644
index 0000000..8280cfe
--- /dev/null
+++ b/blog48-ArabImage/csvTestImages 3360x1024.csv	
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diff --git a/blog48-ArabImage/id_1_label_1.png b/blog48-ArabImage/id_1_label_1.png
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diff --git "a/blog51-SuiAnalysis/add_dataset-01 (\345\207\240\344\275\225-break).py" "b/blog51-SuiAnalysis/add_dataset-01 (\345\207\240\344\275\225-break).py"
new file mode 100644
index 0000000..afe6f11
--- /dev/null
+++ "b/blog51-SuiAnalysis/add_dataset-01 (\345\207\240\344\275\225-break).py"	
@@ -0,0 +1,80 @@
+# -*- coding: utf-8 -*-
+"""
+2022-12-24
+By: Eastmount CSDN xiuzhang
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#-----------------------------------------------------------------------
+#几何变换-镜像、翻转、旋转、缩放
+def JH_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    #print(rows,cols,channel)
+    
+    #镜像处理
+    jx = cv2.flip(img, 1)
+
+    #旋转30度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1)
+    rotated30 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转45度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 45, 1)
+    rotated45 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转90度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 90, 1)
+    rotated90 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转330度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 330, 1)
+    rotated330 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转180度
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 180, 1)
+    rotated180 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #缩小
+    sx = cv2.resize(img, (int(cols*0.8), int(rows*0.8)))
+    
+    #放大
+    fd = cv2.resize(img, (int(cols*1.2), int(rows*1.2)))
+    
+    #显示图像
+    titles = ['Source', 'JX', 'XZ30', 'XZ45', 'XZ90', 'XZ330', 'XZ180', 'SX', 'FD']
+    images = [img, jx, rotated30, rotated45, rotated90, rotated330, rotated180, sx, fd]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_jx.png', jx)
+    cv2.imwrite(img_path[0:-4] + '_rotated30.png', rotated30)
+    cv2.imwrite(img_path[0:-4] + '_rotated45.png', rotated45)
+    cv2.imwrite(img_path[0:-4] + '_rotated90.png', rotated90)
+    cv2.imwrite(img_path[0:-4] + '_rotated330.png', rotated330)
+    cv2.imwrite(img_path[0:-4] + '_rotated180.png', rotated180)
+    cv2.imwrite(img_path[0:-4] + '_sx.png', sx)
+    cv2.imwrite(img_path[0:-4] + '_fd.png', fd)
+    
+#-----------------------------------------------------------------------
+#读取指定文件夹下的所有图像
+def main():
+    file_path = "data"
+    for img_name in os.listdir(file_path):
+        img_path = file_path + "\\" + img_name
+        img = cv2.imread(img_path)
+
+        #1.几何变换-镜像、翻转、旋转、缩放
+        JH_Transformation(img,img_path)
+
+        break
+        
+if __name__ == "__main__":
+    main()
+
diff --git "a/blog51-SuiAnalysis/add_dataset-02 (\345\207\240\344\275\225\343\200\201\344\272\256\345\272\246\343\200\201\351\200\232\351\201\223-break).py" "b/blog51-SuiAnalysis/add_dataset-02 (\345\207\240\344\275\225\343\200\201\344\272\256\345\272\246\343\200\201\351\200\232\351\201\223-break).py"
new file mode 100644
index 0000000..3fdd69c
--- /dev/null
+++ "b/blog51-SuiAnalysis/add_dataset-02 (\345\207\240\344\275\225\343\200\201\344\272\256\345\272\246\343\200\201\351\200\232\351\201\223-break).py"	
@@ -0,0 +1,219 @@
+# -*- coding: utf-8 -*-
+"""
+2022-12-24
+By: Eastmount CSDN xiuzhang
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+#-----------------------------------------------------------------------
+#几何变换-镜像、翻转、旋转、缩放
+def JH_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    #print(rows,cols,channel)
+    
+    #镜像处理
+    jx = cv2.flip(img, 1)
+
+    #旋转30度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1)
+    rotated30 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转45度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 45, 1)
+    rotated45 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转90度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 90, 1)
+    rotated90 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转330度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 330, 1)
+    rotated330 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转180度
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 180, 1)
+    rotated180 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #缩小
+    sx = cv2.resize(img, (int(cols*0.8), int(rows*0.8)))
+    
+    #放大
+    fd = cv2.resize(img, (int(cols*1.2), int(rows*1.2)))
+    
+    #显示图像
+    titles = ['Source', 'JX', 'XZ30', 'XZ45', 'XZ90', 'XZ330', 'XZ180', 'SX', 'FD']
+    images = [img, jx, rotated30, rotated45, rotated90, rotated330, rotated180, sx, fd]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_jx.png', jx)
+    cv2.imwrite(img_path[0:-4] + '_rotated30.png', rotated30)
+    cv2.imwrite(img_path[0:-4] + '_rotated45.png', rotated45)
+    cv2.imwrite(img_path[0:-4] + '_rotated90.png', rotated90)
+    cv2.imwrite(img_path[0:-4] + '_rotated330.png', rotated330)
+    cv2.imwrite(img_path[0:-4] + '_rotated180.png', rotated180)
+    cv2.imwrite(img_path[0:-4] + '_sx.png', sx)
+    cv2.imwrite(img_path[0:-4] + '_fd.png', fd)
+
+#-----------------------------------------------------------------------
+def Brighter_Transformation(img, percetage):
+    img_copy = img.copy()
+    w = img.shape[1]
+    h = img.shape[0]
+    for xi in range(0, w):
+        for xj in range(0, h):
+            img_copy[xj, xi, 0] = np.clip(int(img[xj, xi, 0] * percetage), a_max=255, a_min=0)
+            img_copy[xj, xi, 1] = np.clip(int(img[xj, xi, 1] * percetage), a_max=255, a_min=0)
+            img_copy[xj, xi, 2] = np.clip(int(img[xj, xi, 2] * percetage), a_max=255, a_min=0)
+    return img_copy
+
+def Darker_Transformation(img, percetage):
+    img_copy = img.copy()
+    w = img.shape[1]
+    h = img.shape[0]
+    for xi in range(0, w):
+        for xj in range(0, h):
+            img_copy[xj, xi, 0] = int(img[xj, xi, 0] * percetage)
+            img_copy[xj, xi, 1] = int(img[xj, xi, 1] * percetage)
+            img_copy[xj, xi, 2] = int(img[xj, xi, 2] * percetage)
+    return img_copy
+
+#亮度变换-增强、减弱
+def LD_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    
+    #增强1.1、1.5、2.0、2.5、3.0
+    zq_11 = Brighter_Transformation(img,1.1)
+    zq_15 = Brighter_Transformation(img,1.5)
+    zq_20 = Brighter_Transformation(img,2.0)
+    zq_25 = Brighter_Transformation(img,2.5)
+    zq_30 = Brighter_Transformation(img,3.0)
+
+    #减弱0.9、0.8、0.5
+    jr_09 = Darker_Transformation(img,0.9)
+    jr_08 = Darker_Transformation(img,0.8)
+    jr_07 = Darker_Transformation(img,0.7)
+
+    #显示图像
+    titles = ['Source', 'ZQ1.1', 'ZQ1.5', 'ZQ2.0', 'ZQ2.5', 'ZQ3.0', 'JR0.9', 'JR0.8', 'JR0.7']
+    images = [img, zq_11, zq_15, zq_20, zq_25, zq_30, jr_09, jr_08, jr_07]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_zq_11.png', zq_11)
+    cv2.imwrite(img_path[0:-4] + '_zq_15.png', zq_15)
+    cv2.imwrite(img_path[0:-4] + '_zq_20.png', zq_20)
+    cv2.imwrite(img_path[0:-4] + '_zq_25.png', zq_25)
+    cv2.imwrite(img_path[0:-4] + '_zq_30.png', zq_30)
+    cv2.imwrite(img_path[0:-4] + '_jr_09.png', jr_09)
+    cv2.imwrite(img_path[0:-4] + '_jr_08.png', jr_08)
+    cv2.imwrite(img_path[0:-4] + '_jr_07.png', jr_07)
+    
+#-----------------------------------------------------------------------
+#颜色通道变换
+def Channel__Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    img = Brighter_Transformation(img,1.5)  #边缘增强
+    color_map = [
+        cv2.COLORMAP_AUTUMN,
+        cv2.COLORMAP_BONE,
+        cv2.COLORMAP_JET,
+        cv2.COLORMAP_WINTER,
+        cv2.COLORMAP_PARULA,
+        cv2.COLORMAP_OCEAN,
+        cv2.COLORMAP_SUMMER,
+        cv2.COLORMAP_SPRING,
+        cv2.COLORMAP_COOL,
+        cv2.COLORMAP_PINK,
+        cv2.COLORMAP_HOT,
+        cv2.COLORMAP_PARULA,
+        cv2.COLORMAP_MAGMA,
+        cv2.COLORMAP_INFERNO,
+        cv2.COLORMAP_PLASMA,
+        cv2.COLORMAP_TWILIGHT,
+        cv2.COLORMAP_TWILIGHT_SHIFTED
+    ]
+
+    rgb_img_01 = cv2.applyColorMap(img, color_map[0])
+    rgb_img_02 = cv2.applyColorMap(img, color_map[1])
+    rgb_img_03 = cv2.applyColorMap(img, color_map[9])
+    rgb_img_04 = cv2.applyColorMap(img, color_map[3])
+    rgb_img_05 = cv2.applyColorMap(img, color_map[13])
+    rgb_img_06 = cv2.applyColorMap(img, color_map[5])
+    rgb_img_07 = cv2.applyColorMap(img, color_map[12])
+    rgb_img_08 = cv2.applyColorMap(img, color_map[10])
+
+    # 通道分离
+    b = cv2.split(img)[0]
+    g = np.zeros((rows,cols), dtype=img.dtype)
+    r = np.zeros((rows,cols), dtype=img.dtype)
+    mb = cv2.merge([b, g, r])
+
+    g = cv2.split(img)[1]
+    b = np.zeros((rows,cols), dtype=img.dtype)
+    r = np.zeros((rows,cols), dtype=img.dtype)
+    mg = cv2.merge([b, g, r])
+
+    r = cv2.split(img)[2]
+    b = np.zeros((rows,cols), dtype=img.dtype)
+    g = np.zeros((rows,cols), dtype=img.dtype)
+    mr = cv2.merge([b, g, r])
+    
+    #显示图像
+    titles = ['Source', 'RGB-1', 'RGB-2', 'RGB-3', 'RGB-4',
+              'RGB-5', 'RGB-6', 'RGB-7', 'RGB-8', 'B', 'G', 'R']
+    images = [img, rgb_img_01, rgb_img_02, rgb_img_03, rgb_img_04,
+              rgb_img_05, rgb_img_06, rgb_img_07, rgb_img_08, mb, mg, mr]
+    for i in range(12):
+       plt.subplot(4,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_01.png', rgb_img_01)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_02.png', rgb_img_02)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_03.png', rgb_img_03)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_04.png', rgb_img_04)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_05.png', rgb_img_05)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_06.png', rgb_img_06)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_07.png', rgb_img_07)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_08.png', rgb_img_08)
+    cv2.imwrite(img_path[0:-4] + '_mb.png', mb)
+    cv2.imwrite(img_path[0:-4] + '_mg.png', mg)
+    cv2.imwrite(img_path[0:-4] + '_mr.png', mr)
+
+#-----------------------------------------------------------------------
+#读取指定文件夹下的所有图像
+def main():
+    file_path = "data"
+    for img_name in os.listdir(file_path):
+        img_path = file_path + "\\" + img_name
+        img = cv2.imread(img_path)
+
+        #1.几何变换-镜像、翻转、旋转、缩放
+        JH_Transformation(img,img_path)
+
+        #2.亮度变换-增强、减弱
+        LD_Transformation(img,img_path)
+
+        #3.颜色通道变换
+        Channel__Transformation(img,img_path)
+        
+
+        break
+        
+if __name__ == "__main__":
+    main()
+
diff --git a/blog51-SuiAnalysis/add_dataset-03.py b/blog51-SuiAnalysis/add_dataset-03.py
new file mode 100644
index 0000000..1c92bf7
--- /dev/null
+++ b/blog51-SuiAnalysis/add_dataset-03.py
@@ -0,0 +1,351 @@
+# -*- coding: utf-8 -*-
+"""
+2022-12-24
+By: Eastmount CSDN xiuzhang
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import random
+
+#-----------------------------------------------------------------------
+#几何变换-镜像、翻转、旋转、缩放
+def JH_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    #print(rows,cols,channel)
+    
+    #镜像处理
+    jx = cv2.flip(img, 1)
+
+    #旋转30度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1)
+    rotated30 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转45度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 45, 1)
+    rotated45 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转90度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 90, 1)
+    rotated90 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转330度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 330, 1)
+    rotated330 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转180度
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 180, 1)
+    rotated180 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #缩小
+    sx = cv2.resize(img, (int(cols*0.8), int(rows*0.8)))
+    
+    #放大
+    fd = cv2.resize(img, (int(cols*1.2), int(rows*1.2)))
+
+    """
+    #显示图像
+    titles = ['Source', 'JX', 'XZ30', 'XZ45', 'XZ90', 'XZ330', 'XZ180', 'SX', 'FD']
+    images = [img, jx, rotated30, rotated45, rotated90, rotated330, rotated180, sx, fd]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+    """
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_jx.png', jx)
+    cv2.imwrite(img_path[0:-4] + '_rotated30.png', rotated30)
+    cv2.imwrite(img_path[0:-4] + '_rotated45.png', rotated45)
+    cv2.imwrite(img_path[0:-4] + '_rotated90.png', rotated90)
+    cv2.imwrite(img_path[0:-4] + '_rotated330.png', rotated330)
+    cv2.imwrite(img_path[0:-4] + '_rotated180.png', rotated180)
+    cv2.imwrite(img_path[0:-4] + '_sx.png', sx)
+    cv2.imwrite(img_path[0:-4] + '_fd.png', fd)
+
+#-----------------------------------------------------------------------
+#亮度变换-增强、减弱
+def Brighter_Transformation(img, percetage):
+    img_copy = img.copy()
+    w = img.shape[1]
+    h = img.shape[0]
+    for xi in range(0, w):
+        for xj in range(0, h):
+            img_copy[xj, xi, 0] = np.clip(int(img[xj, xi, 0] * percetage), a_max=255, a_min=0)
+            img_copy[xj, xi, 1] = np.clip(int(img[xj, xi, 1] * percetage), a_max=255, a_min=0)
+            img_copy[xj, xi, 2] = np.clip(int(img[xj, xi, 2] * percetage), a_max=255, a_min=0)
+    return img_copy
+
+def Darker_Transformation(img, percetage):
+    img_copy = img.copy()
+    w = img.shape[1]
+    h = img.shape[0]
+    for xi in range(0, w):
+        for xj in range(0, h):
+            img_copy[xj, xi, 0] = int(img[xj, xi, 0] * percetage)
+            img_copy[xj, xi, 1] = int(img[xj, xi, 1] * percetage)
+            img_copy[xj, xi, 2] = int(img[xj, xi, 2] * percetage)
+    return img_copy
+
+def LD_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    
+    #增强1.1、1.5、2.0、2.5、3.0
+    zq_11 = Brighter_Transformation(img,1.1)
+    zq_15 = Brighter_Transformation(img,1.5)
+    zq_18 = Brighter_Transformation(img,1.8)
+    zq_20 = Brighter_Transformation(img,2.0)
+    zq_25 = Brighter_Transformation(img,2.5)
+
+    #减弱0.9、0.8、0.5
+    jr_09 = Darker_Transformation(img,0.9)
+    jr_08 = Darker_Transformation(img,0.8)
+    jr_07 = Darker_Transformation(img,0.7)
+
+    """
+    #显示图像
+    titles = ['Source', 'ZQ1.1', 'ZQ1.5', 'ZQ1.8', 'ZQ2.0', 'ZQ2.5', 'JR0.9', 'JR0.8', 'JR0.7']
+    images = [img, zq_11, zq_15, zq_18, zq_20, zq_25, jr_09, jr_08, jr_07]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+    """
+    
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_zq_11.png', zq_11)
+    cv2.imwrite(img_path[0:-4] + '_zq_15.png', zq_15)
+    cv2.imwrite(img_path[0:-4] + '_zq_18.png', zq_18)
+    cv2.imwrite(img_path[0:-4] + '_zq_20.png', zq_20)
+    cv2.imwrite(img_path[0:-4] + '_zq_25.png', zq_25)
+    cv2.imwrite(img_path[0:-4] + '_jr_09.png', jr_09)
+    cv2.imwrite(img_path[0:-4] + '_jr_08.png', jr_08)
+    cv2.imwrite(img_path[0:-4] + '_jr_07.png', jr_07)
+    
+#-----------------------------------------------------------------------
+#颜色通道变换
+def Channel_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    img = Brighter_Transformation(img,1.5)  #边缘增强
+    color_map = [
+        cv2.COLORMAP_AUTUMN,
+        cv2.COLORMAP_BONE,
+        cv2.COLORMAP_JET,
+        cv2.COLORMAP_WINTER,
+        cv2.COLORMAP_PARULA,
+        cv2.COLORMAP_OCEAN,
+        cv2.COLORMAP_SUMMER,
+        cv2.COLORMAP_SPRING,
+        cv2.COLORMAP_COOL,
+        cv2.COLORMAP_PINK,
+        cv2.COLORMAP_HOT,
+        cv2.COLORMAP_PARULA,
+        cv2.COLORMAP_MAGMA,
+        cv2.COLORMAP_INFERNO,
+        cv2.COLORMAP_PLASMA,
+        cv2.COLORMAP_TWILIGHT,
+        cv2.COLORMAP_TWILIGHT_SHIFTED
+    ]
+
+    rgb_img_01 = cv2.applyColorMap(img, color_map[0])
+    rgb_img_02 = cv2.applyColorMap(img, color_map[1])
+    rgb_img_03 = cv2.applyColorMap(img, color_map[9])
+    rgb_img_04 = cv2.applyColorMap(img, color_map[3])
+    rgb_img_05 = cv2.applyColorMap(img, color_map[13])
+    rgb_img_06 = cv2.applyColorMap(img, color_map[5])
+    rgb_img_07 = cv2.applyColorMap(img, color_map[12])
+    rgb_img_08 = cv2.applyColorMap(img, color_map[10])
+
+    # 通道分离
+    b = cv2.split(img)[0]
+    g = np.zeros((rows,cols), dtype=img.dtype)
+    r = np.zeros((rows,cols), dtype=img.dtype)
+    mb = cv2.merge([b, g, r])
+
+    g = cv2.split(img)[1]
+    b = np.zeros((rows,cols), dtype=img.dtype)
+    r = np.zeros((rows,cols), dtype=img.dtype)
+    mg = cv2.merge([b, g, r])
+
+    r = cv2.split(img)[2]
+    b = np.zeros((rows,cols), dtype=img.dtype)
+    g = np.zeros((rows,cols), dtype=img.dtype)
+    mr = cv2.merge([b, g, r])
+    
+    """
+    #显示图像
+    titles = ['Source', 'RGB-1', 'RGB-2', 'RGB-3', 'RGB-4',
+              'RGB-5', 'RGB-6', 'RGB-7', 'RGB-8', 'B', 'G', 'R']
+    images = [img, rgb_img_01, rgb_img_02, rgb_img_03, rgb_img_04,
+              rgb_img_05, rgb_img_06, rgb_img_07, rgb_img_08, mb, mg, mr]
+    for i in range(12):
+       plt.subplot(4,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+    """
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_01.png', rgb_img_01)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_02.png', rgb_img_02)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_03.png', rgb_img_03)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_04.png', rgb_img_04)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_05.png', rgb_img_05)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_06.png', rgb_img_06)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_07.png', rgb_img_07)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_08.png', rgb_img_08)
+    cv2.imwrite(img_path[0:-4] + '_mb.png', mb)
+    cv2.imwrite(img_path[0:-4] + '_mg.png', mg)
+    cv2.imwrite(img_path[0:-4] + '_mr.png', mr)
+
+#-----------------------------------------------------------------------
+#添加高斯噪声和椒盐噪声
+def GaussianNoise(img, percetage):
+    gn = img.copy()
+    w = img.shape[1]
+    h = img.shape[0]
+    G_NoiseNum = int(percetage * img.shape[0] * img.shape[1])
+    for i in range(G_NoiseNum):
+        lx = np.random.randint(0, h)
+        ly = np.random.randint(0, w)
+        gn[lx][ly][np.random.randint(3)] = np.random.randn(1)[0]
+    return gn
+
+def SaltNoise(img, percetage):
+    sn = img.copy()
+    SP_NoiseNum = int(percetage * img.shape[0] * img.shape[1])
+    for i in range(SP_NoiseNum):
+        randR = np.random.randint(0, img.shape[0] - 1)
+        randG = np.random.randint(0, img.shape[1] - 1)
+        randB = np.random.randint(0, 3)
+        if np.random.randint(0, 1) == 0:
+            sn[randR, randG, randB] = 0
+        else:
+            sn[randR, randG, randB] = 255
+    return sn
+
+def Gaussian_Salt_Noise(img,img_path):
+    rows, cols, channel = img.shape
+    img = Brighter_Transformation(img,1.5)  #边缘增强
+
+    #高斯噪声
+    gn_005 = GaussianNoise(img, 0.05)
+    gn_010 = GaussianNoise(img, 0.10)
+    gn_015 = GaussianNoise(img, 0.15)
+    gn_020 = GaussianNoise(img, 0.20)
+
+    #椒盐噪声
+    sn_005 = SaltNoise(img, 0.05)
+    sn_010 = SaltNoise(img, 0.10)
+    sn_015 = SaltNoise(img, 0.15)
+    sn_020 = SaltNoise(img, 0.20)
+    
+    #显示图像
+    titles = ['Source', 'Gaussian-0.05', 'Gaussian-0.10', 'Gaussian-0.15', 'Gaussian-0.20',
+              'Salt-0.05', 'Salt-0.10', 'Salt-0.15', 'Salt-0.20']
+    images = [img, gn_005, gn_010, gn_015, gn_020, sn_005, sn_010, sn_015, sn_020]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_gn_005.png', gn_005)
+    cv2.imwrite(img_path[0:-4] + '_gn_010.png', gn_010)
+    cv2.imwrite(img_path[0:-4] + '_gn_015.png', gn_015)
+    cv2.imwrite(img_path[0:-4] + '_gn_020.png', gn_020)
+    cv2.imwrite(img_path[0:-4] + '_sn_005.png', sn_005)
+    cv2.imwrite(img_path[0:-4] + '_sn_010.png', sn_010)
+    cv2.imwrite(img_path[0:-4] + '_sn_015.png', sn_015)
+    cv2.imwrite(img_path[0:-4] + '_sn_020.png', sn_020)
+
+#-----------------------------------------------------------------------
+#模拟怀旧和噪声添加
+def Salt(img, num):
+    wn = img.copy()
+    rows, cols, chn = wn.shape
+    for i in range(num):
+        x = np.random.randint(0, rows)
+        y = np.random.randint(0, cols)    
+        wn[x,y,:] = 210
+    return wn
+
+def Fog_Noise(img, percetage):
+    mask_img = img.copy()
+    mask_img[:, :] = (166, 178, 180)  #雾的颜色 (146,182,213)
+    
+    #调整雾的浓度 round(random.uniform(0.03, 0.28), 2)
+    res = cv2.addWeighted(img, percetage, mask_img, 1-percetage, 0) 
+    return res
+
+def Fog_Salt_Noise(img,img_path):
+    rows, cols, channel = img.shape
+    img = Brighter_Transformation(img,1.5)  #边缘增强
+
+    #白点噪声
+    wn_100 = Salt(img, 100)
+    wn_150 = Salt(img, 150)
+    wn_200 = Salt(img, 200)
+    wn_250 = Salt(img, 250)
+
+    #模拟怀旧
+    fog_06 = Fog_Noise(img, 0.6)
+    fog_07 = Fog_Noise(img, 0.7)
+    fog_08 = Fog_Noise(img, 0.8)
+    fog_09 = Fog_Noise(img, 0.9)
+
+    #显示图像
+    titles = ['Source', 'wn-100', 'wn-150', 'wn-200', 'wn-250',
+              'fog_06', 'fog_07', 'fog_08', 'fog_09']
+    images = [img, wn_100, wn_150, wn_200, wn_250,
+              fog_06, fog_07, fog_08, fog_09]
+    for i in range(9):
+       plt.subplot(3,3 ,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_wn_100.png', wn_100)
+    cv2.imwrite(img_path[0:-4] + '_wn_150.png', wn_150)
+    cv2.imwrite(img_path[0:-4] + '_wn_200.png', wn_200)
+    cv2.imwrite(img_path[0:-4] + '_wn_250.png', wn_250)
+    cv2.imwrite(img_path[0:-4] + '_fog_06.png', fog_06)
+    cv2.imwrite(img_path[0:-4] + '_fog_07.png', fog_07)
+    cv2.imwrite(img_path[0:-4] + '_fog_08.png', fog_08)
+    cv2.imwrite(img_path[0:-4] + '_fog_09.png', fog_09)
+
+#-----------------------------------------------------------------------
+#读取指定文件夹下的所有图像
+def main():
+    file_path = "data"
+    for img_name in os.listdir(file_path):
+        img_path = file_path + "\\" + img_name
+        res_path = "data_add" + "\\" + img_name
+        img = cv2.imread(img_path)
+
+        #1.几何变换-镜像、翻转、旋转、缩放
+        JH_Transformation(img,res_path)
+
+        #2.亮度变换-增强、减弱
+        LD_Transformation(img,res_path)
+
+        #3.颜色通道变换
+        Channel_Transformation(img,res_path)
+
+        #4.添加高斯噪声和椒盐噪声
+        Gaussian_Salt_Noise(img,res_path)
+
+        #5.模拟怀旧和噪声添加
+        Fog_Salt_Noise(img,res_path)
+        
+
+        break
+        
+if __name__ == "__main__":
+    main()
+
diff --git a/blog51-SuiAnalysis/add_dataset-04-final.py b/blog51-SuiAnalysis/add_dataset-04-final.py
new file mode 100644
index 0000000..49fc641
--- /dev/null
+++ b/blog51-SuiAnalysis/add_dataset-04-final.py
@@ -0,0 +1,352 @@
+# -*- coding: utf-8 -*-
+"""
+2022-12-24
+By: Eastmount CSDN xiuzhang
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import random
+
+#-----------------------------------------------------------------------
+#几何变换-镜像、翻转、旋转、缩放
+def JH_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    #print(rows,cols,channel)
+    
+    #镜像处理
+    jx = cv2.flip(img, 1)
+
+    #旋转30度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 30, 1)
+    rotated30 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转45度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 45, 1)
+    rotated45 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转90度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 90, 1)
+    rotated90 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转330度 (旋转中心,旋转度数,scale)
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 330, 1)
+    rotated330 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #旋转180度
+    M = cv2.getRotationMatrix2D((cols/2, rows/2), 180, 1)
+    rotated180 = cv2.warpAffine(img, M, (cols, rows), borderValue=(255,255,255))
+
+    #缩小
+    sx = cv2.resize(img, (int(cols*0.8), int(rows*0.8)))
+    
+    #放大
+    fd = cv2.resize(img, (int(cols*1.2), int(rows*1.2)))
+
+    """
+    #显示图像
+    titles = ['Source', 'JX', 'XZ30', 'XZ45', 'XZ90', 'XZ330', 'XZ180', 'SX', 'FD']
+    images = [img, jx, rotated30, rotated45, rotated90, rotated330, rotated180, sx, fd]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+    """
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_jx.png', jx)
+    cv2.imwrite(img_path[0:-4] + '_rotated30.png', rotated30)
+    cv2.imwrite(img_path[0:-4] + '_rotated45.png', rotated45)
+    cv2.imwrite(img_path[0:-4] + '_rotated90.png', rotated90)
+    cv2.imwrite(img_path[0:-4] + '_rotated330.png', rotated330)
+    cv2.imwrite(img_path[0:-4] + '_rotated180.png', rotated180)
+    cv2.imwrite(img_path[0:-4] + '_sx.png', sx)
+    cv2.imwrite(img_path[0:-4] + '_fd.png', fd)
+
+#-----------------------------------------------------------------------
+#亮度变换-增强、减弱
+def Brighter_Transformation(img, percetage):
+    img_copy = img.copy()
+    w = img.shape[1]
+    h = img.shape[0]
+    for xi in range(0, w):
+        for xj in range(0, h):
+            img_copy[xj, xi, 0] = np.clip(int(img[xj, xi, 0] * percetage), a_max=255, a_min=0)
+            img_copy[xj, xi, 1] = np.clip(int(img[xj, xi, 1] * percetage), a_max=255, a_min=0)
+            img_copy[xj, xi, 2] = np.clip(int(img[xj, xi, 2] * percetage), a_max=255, a_min=0)
+    return img_copy
+
+def Darker_Transformation(img, percetage):
+    img_copy = img.copy()
+    w = img.shape[1]
+    h = img.shape[0]
+    for xi in range(0, w):
+        for xj in range(0, h):
+            img_copy[xj, xi, 0] = int(img[xj, xi, 0] * percetage)
+            img_copy[xj, xi, 1] = int(img[xj, xi, 1] * percetage)
+            img_copy[xj, xi, 2] = int(img[xj, xi, 2] * percetage)
+    return img_copy
+
+def LD_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    
+    #增强1.1、1.5、2.0、2.5、3.0
+    zq_11 = Brighter_Transformation(img,1.1)
+    zq_15 = Brighter_Transformation(img,1.5)
+    zq_18 = Brighter_Transformation(img,1.8)
+    zq_20 = Brighter_Transformation(img,2.0)
+    zq_25 = Brighter_Transformation(img,2.5)
+
+    #减弱0.9、0.8、0.5
+    jr_09 = Darker_Transformation(img,0.9)
+    jr_08 = Darker_Transformation(img,0.8)
+    jr_07 = Darker_Transformation(img,0.7)
+
+    """
+    #显示图像
+    titles = ['Source', 'ZQ1.1', 'ZQ1.5', 'ZQ1.8', 'ZQ2.0', 'ZQ2.5', 'JR0.9', 'JR0.8', 'JR0.7']
+    images = [img, zq_11, zq_15, zq_18, zq_20, zq_25, jr_09, jr_08, jr_07]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+    """
+    
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_zq_11.png', zq_11)
+    cv2.imwrite(img_path[0:-4] + '_zq_15.png', zq_15)
+    cv2.imwrite(img_path[0:-4] + '_zq_18.png', zq_18)
+    cv2.imwrite(img_path[0:-4] + '_zq_20.png', zq_20)
+    cv2.imwrite(img_path[0:-4] + '_zq_25.png', zq_25)
+    cv2.imwrite(img_path[0:-4] + '_jr_09.png', jr_09)
+    cv2.imwrite(img_path[0:-4] + '_jr_08.png', jr_08)
+    cv2.imwrite(img_path[0:-4] + '_jr_07.png', jr_07)
+    
+#-----------------------------------------------------------------------
+#颜色通道变换
+def Channel_Transformation(img,img_path):
+    rows, cols, channel = img.shape
+    img = Brighter_Transformation(img,1.5)  #边缘增强
+    color_map = [
+        cv2.COLORMAP_AUTUMN,
+        cv2.COLORMAP_BONE,
+        cv2.COLORMAP_JET,
+        cv2.COLORMAP_WINTER,
+        cv2.COLORMAP_PARULA,
+        cv2.COLORMAP_OCEAN,
+        cv2.COLORMAP_SUMMER,
+        cv2.COLORMAP_SPRING,
+        cv2.COLORMAP_COOL,
+        cv2.COLORMAP_PINK,
+        cv2.COLORMAP_HOT,
+        cv2.COLORMAP_PARULA,
+        cv2.COLORMAP_MAGMA,
+        cv2.COLORMAP_INFERNO,
+        cv2.COLORMAP_PLASMA,
+        cv2.COLORMAP_TWILIGHT,
+        cv2.COLORMAP_TWILIGHT_SHIFTED
+    ]
+
+    rgb_img_01 = cv2.applyColorMap(img, color_map[0])
+    rgb_img_02 = cv2.applyColorMap(img, color_map[1])
+    rgb_img_03 = cv2.applyColorMap(img, color_map[9])
+    rgb_img_04 = cv2.applyColorMap(img, color_map[3])
+    rgb_img_05 = cv2.applyColorMap(img, color_map[13])
+    rgb_img_06 = cv2.applyColorMap(img, color_map[5])
+    rgb_img_07 = cv2.applyColorMap(img, color_map[12])
+    rgb_img_08 = cv2.applyColorMap(img, color_map[10])
+
+    # 通道分离
+    b = cv2.split(img)[0]
+    g = np.zeros((rows,cols), dtype=img.dtype)
+    r = np.zeros((rows,cols), dtype=img.dtype)
+    mb = cv2.merge([b, g, r])
+
+    g = cv2.split(img)[1]
+    b = np.zeros((rows,cols), dtype=img.dtype)
+    r = np.zeros((rows,cols), dtype=img.dtype)
+    mg = cv2.merge([b, g, r])
+
+    r = cv2.split(img)[2]
+    b = np.zeros((rows,cols), dtype=img.dtype)
+    g = np.zeros((rows,cols), dtype=img.dtype)
+    mr = cv2.merge([b, g, r])
+    
+    """
+    #显示图像
+    titles = ['Source', 'RGB-1', 'RGB-2', 'RGB-3', 'RGB-4',
+              'RGB-5', 'RGB-6', 'RGB-7', 'RGB-8', 'B', 'G', 'R']
+    images = [img, rgb_img_01, rgb_img_02, rgb_img_03, rgb_img_04,
+              rgb_img_05, rgb_img_06, rgb_img_07, rgb_img_08, mb, mg, mr]
+    for i in range(12):
+       plt.subplot(4,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+    """
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_01.png', rgb_img_01)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_02.png', rgb_img_02)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_03.png', rgb_img_03)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_04.png', rgb_img_04)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_05.png', rgb_img_05)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_06.png', rgb_img_06)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_07.png', rgb_img_07)
+    cv2.imwrite(img_path[0:-4] + '_rgb_img_08.png', rgb_img_08)
+    cv2.imwrite(img_path[0:-4] + '_mb.png', mb)
+    cv2.imwrite(img_path[0:-4] + '_mg.png', mg)
+    cv2.imwrite(img_path[0:-4] + '_mr.png', mr)
+
+#-----------------------------------------------------------------------
+#添加高斯噪声和椒盐噪声
+def GaussianNoise(img, percetage):
+    gn = img.copy()
+    w = img.shape[1]
+    h = img.shape[0]
+    G_NoiseNum = int(percetage * img.shape[0] * img.shape[1])
+    for i in range(G_NoiseNum):
+        lx = np.random.randint(0, h)
+        ly = np.random.randint(0, w)
+        gn[lx][ly][np.random.randint(3)] = np.random.randn(1)[0]
+    return gn
+
+def SaltNoise(img, percetage):
+    sn = img.copy()
+    SP_NoiseNum = int(percetage * img.shape[0] * img.shape[1])
+    for i in range(SP_NoiseNum):
+        randR = np.random.randint(0, img.shape[0] - 1)
+        randG = np.random.randint(0, img.shape[1] - 1)
+        randB = np.random.randint(0, 3)
+        if np.random.randint(0, 1) == 0:
+            sn[randR, randG, randB] = 0
+        else:
+            sn[randR, randG, randB] = 255
+    return sn
+
+def Gaussian_Salt_Noise(img,img_path):
+    rows, cols, channel = img.shape
+    img = Brighter_Transformation(img,1.5)  #边缘增强
+
+    #高斯噪声
+    gn_005 = GaussianNoise(img, 0.05)
+    gn_010 = GaussianNoise(img, 0.10)
+    gn_015 = GaussianNoise(img, 0.15)
+    gn_020 = GaussianNoise(img, 0.20)
+
+    #椒盐噪声
+    sn_005 = SaltNoise(img, 0.05)
+    sn_010 = SaltNoise(img, 0.10)
+    sn_015 = SaltNoise(img, 0.15)
+    sn_020 = SaltNoise(img, 0.20)
+
+    """
+    #显示图像
+    titles = ['Source', 'Gaussian-0.05', 'Gaussian-0.10', 'Gaussian-0.15', 'Gaussian-0.20',
+              'Salt-0.05', 'Salt-0.10', 'Salt-0.15', 'Salt-0.20']
+    images = [img, gn_005, gn_010, gn_015, gn_020, sn_005, sn_010, sn_015, sn_020]
+    for i in range(9):
+       plt.subplot(3,3,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+    """
+    
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_gn_005.png', gn_005)
+    cv2.imwrite(img_path[0:-4] + '_gn_010.png', gn_010)
+    cv2.imwrite(img_path[0:-4] + '_gn_015.png', gn_015)
+    cv2.imwrite(img_path[0:-4] + '_gn_020.png', gn_020)
+    cv2.imwrite(img_path[0:-4] + '_sn_005.png', sn_005)
+    cv2.imwrite(img_path[0:-4] + '_sn_010.png', sn_010)
+    cv2.imwrite(img_path[0:-4] + '_sn_015.png', sn_015)
+    cv2.imwrite(img_path[0:-4] + '_sn_020.png', sn_020)
+
+#-----------------------------------------------------------------------
+#模拟怀旧和噪声添加
+def Salt(img, num):
+    wn = img.copy()
+    rows, cols, chn = wn.shape
+    for i in range(num):
+        x = np.random.randint(0, rows)
+        y = np.random.randint(0, cols)    
+        wn[x,y,:] = 210
+    return wn
+
+def Fog_Noise(img, percetage):
+    mask_img = img.copy()
+    mask_img[:, :] = (166, 178, 180)  #雾的颜色 (146,182,213)
+    
+    #调整雾的浓度 round(random.uniform(0.03, 0.28), 2)
+    res = cv2.addWeighted(img, percetage, mask_img, 1-percetage, 0) 
+    return res
+
+def Fog_Salt_Noise(img,img_path):
+    rows, cols, channel = img.shape
+    img = Brighter_Transformation(img,1.5)  #边缘增强
+
+    #白点噪声
+    wn_100 = Salt(img, 100)
+    wn_150 = Salt(img, 150)
+    wn_200 = Salt(img, 200)
+    wn_250 = Salt(img, 250)
+
+    #模拟怀旧
+    fog_06 = Fog_Noise(img, 0.6)
+    fog_07 = Fog_Noise(img, 0.7)
+    fog_08 = Fog_Noise(img, 0.8)
+    fog_09 = Fog_Noise(img, 0.9)
+
+    """
+    #显示图像
+    titles = ['Source', 'wn-100', 'wn-150', 'wn-200', 'wn-250',
+              'fog_06', 'fog_07', 'fog_08', 'fog_09']
+    images = [img, wn_100, wn_150, wn_200, wn_250,
+              fog_06, fog_07, fog_08, fog_09]
+    for i in range(9):
+       plt.subplot(3,3 ,i+1), plt.imshow(images[i], 'gray')
+       plt.title(titles[i])
+       plt.xticks([]),plt.yticks([])
+    plt.show()
+    """
+
+    #保存图像
+    cv2.imwrite(img_path[0:-4] + '_wn_100.png', wn_100)
+    cv2.imwrite(img_path[0:-4] + '_wn_150.png', wn_150)
+    cv2.imwrite(img_path[0:-4] + '_wn_200.png', wn_200)
+    cv2.imwrite(img_path[0:-4] + '_wn_250.png', wn_250)
+    cv2.imwrite(img_path[0:-4] + '_fog_06.png', fog_06)
+    cv2.imwrite(img_path[0:-4] + '_fog_07.png', fog_07)
+    cv2.imwrite(img_path[0:-4] + '_fog_08.png', fog_08)
+    cv2.imwrite(img_path[0:-4] + '_fog_09.png', fog_09)
+
+#-----------------------------------------------------------------------
+#读取指定文件夹下的所有图像
+def main():
+    file_path = "data"
+    for img_name in os.listdir(file_path):
+        img_path = file_path + "\\" + img_name
+        res_path = "data_add" + "\\" + img_name
+        img = cv2.imread(img_path)
+
+        #1.几何变换-镜像、翻转、旋转、缩放
+        JH_Transformation(img,res_path)
+
+        #2.亮度变换-增强、减弱
+        LD_Transformation(img,res_path)
+
+        #3.颜色通道变换
+        Channel_Transformation(img,res_path)
+
+        #4.添加高斯噪声和椒盐噪声
+        Gaussian_Salt_Noise(img,res_path)
+
+        #5.模拟怀旧和噪声添加
+        Fog_Salt_Noise(img,res_path)
+        
+if __name__ == "__main__":
+    main()
+
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diff --git a/blog51-SuiAnalysis/data/readme.txt b/blog51-SuiAnalysis/data/readme.txt
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--- /dev/null
+++ b/blog51-SuiAnalysis/data/readme.txt
@@ -0,0 +1 @@
+原始图像文件
diff --git a/blog51-SuiAnalysis/final_data.rar b/blog51-SuiAnalysis/final_data.rar
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diff --git "a/blog52-SuiAnalysis-ML/blog52_01_Image_nb\357\274\210\345\203\217\347\264\240\347\273\237\350\256\241\357\274\211.py" "b/blog52-SuiAnalysis-ML/blog52_01_Image_nb\357\274\210\345\203\217\347\264\240\347\273\237\350\256\241\357\274\211.py"
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+++ "b/blog52-SuiAnalysis-ML/blog52_01_Image_nb\357\274\210\345\203\217\347\264\240\347\273\237\350\256\241\357\274\211.py"
@@ -0,0 +1,73 @@
+# -*- coding: utf-8 -*-
+"""
+By: Eastmount CSDN xiuzhang 2024-04-12
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.naive_bayes import BernoulliNB
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#-----------------------------------------------------------------------
+#第一步 读取数据集并划分训练集
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+#遍历文件夹读取图片
+for i in range(0, 12):
+    for f in os.listdir("final_data/%s" % i):
+        X.append("final_data//" +str(i) + "//" + str(f))
+        Y.append(i)
+X = np.array(X)
+Y = np.array(Y)
+print(X[:2])
+
+X_train, X_test, y_train, y_test = train_test_split(X, Y,                                                   
+test_size=0.3, random_state=1)
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+#3696 1584 3696 1584
+
+#-----------------------------------------------------------------------
+#第二步 图像读取及转换为像素直方图
+#训练集
+XX_train = []
+for i in X_train:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (32,32),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (32,32),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_test.append(((hist/255).flatten()))
+
+#-----------------------------------------------------------------------
+#第三步 基于机器学习的图像分类处理
+clf = BernoulliNB().fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+print('预测结果:')
+print(predictions_labels)
+print('算法评价:')
+print(classification_report(y_test, predictions_labels,digits=4))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
diff --git a/blog52-SuiAnalysis-ML/blog52_02_Image_knn.py b/blog52-SuiAnalysis-ML/blog52_02_Image_knn.py
new file mode 100644
index 0000000..6965a57
--- /dev/null
+++ b/blog52-SuiAnalysis-ML/blog52_02_Image_knn.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+By: Eastmount CSDN xiuzhang 2024-04-12
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#-----------------------------------------------------------------------
+#第一步 读取数据集并划分训练集
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+#遍历文件夹读取图片
+for i in range(0, 12):
+    for f in os.listdir("final_data/%s" % i):
+        X.append("final_data//" +str(i) + "//" + str(f))
+        Y.append(i)
+X = np.array(X)
+Y = np.array(Y)
+print(X[:2])
+
+X_train, X_test, y_train, y_test = train_test_split(X, Y,                                                   
+test_size=0.3, random_state=1)
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+#3696 1584 3696 1584
+
+#-----------------------------------------------------------------------
+#第二步 图像读取及转换为像素直方图
+#训练集
+XX_train = []
+for i in X_train:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (32,32),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (32,32),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_test.append(((hist/255).flatten()))
+
+#-----------------------------------------------------------------------
+#第三步 基于机器学习的图像分类处理
+clf = KNeighborsClassifier(n_neighbors=3,algorithm='kd_tree')
+clf.fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+print('预测结果:')
+print(predictions_labels)
+print('算法评价:')
+print(classification_report(y_test, predictions_labels,digits=4))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
+
+
diff --git a/blog52-SuiAnalysis-ML/blog52_03_Image_svm.py b/blog52-SuiAnalysis-ML/blog52_03_Image_svm.py
new file mode 100644
index 0000000..1bcc025
--- /dev/null
+++ b/blog52-SuiAnalysis-ML/blog52_03_Image_svm.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+By: Eastmount CSDN xiuzhang 2024-04-12
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.svm import LinearSVC
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#-----------------------------------------------------------------------
+#第一步 读取数据集并划分训练集
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+#遍历文件夹读取图片
+for i in range(0, 12):
+    for f in os.listdir("final_data/%s" % i):
+        X.append("final_data//" +str(i) + "//" + str(f))
+        Y.append(i)
+X = np.array(X)
+Y = np.array(Y)
+print(X[:2])
+
+X_train, X_test, y_train, y_test = train_test_split(X, Y,                                                   
+test_size=0.3, random_state=1)
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+#3696 1584 3696 1584
+
+#-----------------------------------------------------------------------
+#第二步 图像读取及转换为像素直方图
+#训练集
+XX_train = []
+for i in X_train:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (64,64),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (64,64),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_test.append(((hist/255).flatten()))
+
+#-----------------------------------------------------------------------
+#第三步 基于机器学习的图像分类处理
+clf = LinearSVC()
+clf.fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+print('预测结果:')
+print(predictions_labels)
+print('算法评价:')
+print(classification_report(y_test, predictions_labels,digits=4))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
+
+
diff --git a/blog52-SuiAnalysis-ML/blog52_04_Image_lr.py b/blog52-SuiAnalysis-ML/blog52_04_Image_lr.py
new file mode 100644
index 0000000..65a171f
--- /dev/null
+++ b/blog52-SuiAnalysis-ML/blog52_04_Image_lr.py
@@ -0,0 +1,76 @@
+# -*- coding: utf-8 -*-
+"""
+By: Eastmount CSDN xiuzhang 2024-04-12
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LogisticRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#-----------------------------------------------------------------------
+#第一步 读取数据集并划分训练集
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+#遍历文件夹读取图片
+for i in range(0, 12):
+    for f in os.listdir("final_data/%s" % i):
+        X.append("final_data//" +str(i) + "//" + str(f))
+        Y.append(i)
+X = np.array(X)
+Y = np.array(Y)
+print(X[:2])
+
+X_train, X_test, y_train, y_test = train_test_split(X, Y,                                                   
+test_size=0.3, random_state=1)
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+#3696 1584 3696 1584
+
+#-----------------------------------------------------------------------
+#第二步 图像读取及转换为像素直方图
+#训练集
+XX_train = []
+for i in X_train:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (64,64),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (64,64),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_test.append(((hist/255).flatten()))
+
+#-----------------------------------------------------------------------
+#第三步 基于机器学习的图像分类处理
+clf = LogisticRegression(C=100.0,random_state=1)
+clf.fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+print('预测结果:')
+print(predictions_labels)
+print('算法评价:')
+print(classification_report(y_test, predictions_labels,digits=4))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
+
+
diff --git a/blog52-SuiAnalysis-ML/blog52_05_Image_rf.py b/blog52-SuiAnalysis-ML/blog52_05_Image_rf.py
new file mode 100644
index 0000000..074ec12
--- /dev/null
+++ b/blog52-SuiAnalysis-ML/blog52_05_Image_rf.py
@@ -0,0 +1,80 @@
+# -*- coding: utf-8 -*-
+"""
+By: Eastmount CSDN xiuzhang 2024-04-12
+"""
+import os
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix, classification_report
+
+#-----------------------------------------------------------------------
+#第一步 读取数据集并划分训练集
+X = [] #定义图像名称
+Y = [] #定义图像分类类标
+Z = [] #定义图像像素
+
+#遍历文件夹读取图片
+for i in range(0, 12):
+    for f in os.listdir("final_data/%s" % i):
+        X.append("final_data//" +str(i) + "//" + str(f))
+        Y.append(i)
+X = np.array(X)
+Y = np.array(Y)
+print(X[:2])
+
+X_train, X_test, y_train, y_test = train_test_split(X, Y,                                                   
+test_size=0.3, random_state=1)
+print(len(X_train), len(X_test), len(y_train), len(y_test))
+#3696 1584 3696 1584
+
+#-----------------------------------------------------------------------
+#第二步 图像读取及转换为像素直方图
+#训练集
+XX_train = []
+for i in X_train:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (128,128),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_train.append(((hist/255).flatten()))
+
+#测试集
+XX_test = []
+for i in X_test:
+    image = cv2.imread(i)
+    img = cv2.resize(image, (128,128),
+                     interpolation=cv2.INTER_CUBIC)
+    hist = cv2.calcHist([img], [0,1], None,
+                            [256,256], [0.0,255.0,0.0,255.0])
+    XX_test.append(((hist/255).flatten()))
+
+#-----------------------------------------------------------------------
+#第三步 基于机器学习的图像分类处理
+clf = RandomForestClassifier(criterion='gini',
+                                n_estimators=11,
+                                random_state=1,
+                                n_jobs=2,
+                                verbose=1)
+clf.fit(XX_train, y_train)
+predictions_labels = clf.predict(XX_test)
+print('预测结果:')
+print(predictions_labels)
+print('算法评价:')
+print(classification_report(y_test, predictions_labels,digits=4))
+
+#输出前10张图片及预测结果
+k = 0
+while k<10:
+    print(X_test[k])
+    image = cv2.imread(X_test[k])
+    print(predictions_labels[k])
+    cv2.imshow("img", image)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+    k = k + 1
+
+
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+原始水书数据集。