Team: Phillip Ly, Resham Rajendra Jhangiani, Anoop Jeerige, Eizat Mushtaq, Tong Zhou, Matt Corrente

Professor: Lidia Morrison

CPSC 587 Computer Vision and Deep Learning - Fall 2017 - California State University Fullerton

Link to PPT slides: https://goo.gl/BKr2zL

• Python 3.5.2

1. TensorFlow 1.3
2. TensorFlow Object Detection API
3. OpenCV 3.2
4. Flask 0.12.2

# apt-get update
# apt-get install -y protobuf-compiler python-pil python-lxml python-pip python-dev git
# pip install Flask==0.12.2 WTForms==2.1 Flask_WTF==0.14.2 Werkzeug==0.12.2

# cd /opt
# git clone https://github.com/tensorflow/models
# cd models/research
# protoc object_detection/protos/*.proto --python_out=.

# mkdir -p /opt/graph_def
# cd /tmp
# for model in \
    ssd_mobilenet_v1_coco_11_06_2017 \
    ssd_inception_v2_coco_11_06_2017 \
    rfcn_resnet101_coco_11_06_2017 \
    faster_rcnn_resnet101_coco_11_06_2017 \
    faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017
  do \
    curl -OL http://download.tensorflow.org/models/object_detection/$model.tar.gz
    tar -xzf $model.tar.gz $model/frozen_inference_graph.pb
    cp -a $model /opt/graph_def/
  done
# ln -sf /opt/graph_def/faster_rcnn_resnet101_coco_11_06_2017/frozen_inference_graph.pb /opt/graph_def/frozen_inference_graph.pb

# cd ~/realtimeCV/obj_detect_multi
# export FLASK_APP=detector.app
# flask run

You have to wait around 60secs for the application to finish loading the pretrained model graph. You'll see the message Running on http://127.0.0.1:5000/ (Press CTRL+C to quit) when it's ready.

Now you can access the instance's IP address using a web browser. When you upload an image file with a jpeg, jpg, or png extension, the application shows the result of the object detection inference. The inference may take up to 10 seconds, depending on the image.

You can check the objects by clicking labels shown to the right of the image.

You can upload an video file (preferably small < 5MB, .mp4, 3gp), for the object detection inference. The Real-time detection take the web cam as the source for the object detection.

There are five pretrained models that can be used by the application. They have diffrent characteristics in terms of accuracy and speed. You can change the model used by the application by setting the PATH_TO_CKPT to point the frozen weights of the required model.

You specify one of the following models.

• ssd_mobilenet_v1_coco_11_06_2017
• ssd_inception_v2_coco_11_06_2017
• rfcn_resnet101_coco_11_06_2017
• faster_rcnn_resnet101_coco_11_06_2017
• faster_rcnn_inception_resnet_v2_atrous_coco_11_06_2017

• https://github.com/GoogleCloudPlatform/tensorflow-object-detection-example
• https://github.com/datitran/object_detector_app