This project implements a skin detection system using various image processing techniques and machine learning models. It aims to classify image pixels as skin or non-skin, which has numerous applications, including medical diagnosis, augmented reality, and image editing.

• Implement a comprehensive pipeline for skin detection.
• Evaluate the performance of different algorithms in classifying skin areas.
• Visualize the results of each stage to better understand the image processing workflow.

The project is organized into the following files:

• image_acquisition.py: Handles loading and resizing images from specified directories.
• preprocessing.py: Contains functions for image enhancement and restoration.
• segmentation.py: Implements algorithms for segmenting skin areas from images.
• ml_models.py: Includes machine learning models for feature extraction and classification.
• main.py: The main script that runs the entire pipeline and executes all steps.

Make sure you have the following Python libraries installed:

• OpenCV
• NumPy
• scikit-learn
• Matplotlib

You can install them using pip:

pip install opencv-python numpy scikit-learn matplotlib

To run the project, simply execute the main.py script:

python main.py

This will load the images, apply preprocessing, perform segmentation, extract features, and then train the machine learning models to classify skin regions.

Images are acquired from two directories: one containing skin images and the other containing non-skin images. The load_images_from_folder function is used to load and resize images to 800x600 pixels. The function also ensures that grayscale images are converted to RGB format.

• Gaussian Blur: Reduces image noise to enhance skin detection accuracy.
• Gamma Correction: Enhances image brightness and contrast for better skin detection.

• Color Space Conversion: Converts the image from BGR to HSV color space, which is more effective for identifying skin tones.
• Skin Tone Range: Defines a range for typical skin tones in HSV values.
• Morphological Operations: Removes noise and fills small holes in the detected skin areas.
• Mask Application: A binary mask is created where skin areas are white, and non-skin areas are black.

Features are extracted from the segmented images, including:

• Color Space Conversion: Converts images to HSV color space.
• Histogram Calculation: Computes histograms for each HSV channel.
• Mean Intensity: Measures overall brightness.
• Canny Edge Detection: Detects edges in the image.

The models (Logistic Regression and Random Forest) are trained using extracted features:

• Data Splitting: The dataset is split into 70% training and 30% testing.
• Model Training: Both models are trained using the training set.
• Model Evaluation: Performance is assessed using a confusion matrix, ROC curve, and classification report.

The models are evaluated using test images. Each test image undergoes the following steps:

1. Preprocessing
2. Segmentation
3. Feature Extraction
4. Classification

The results of the classification are visualized for both models (Random Forest and Logistic Regression).

• Random Forest Classifier: Shows how well the Random Forest model performed on the test set.
• Logistic Regression: Displays the performance of the Logistic Regression model on the same test set..