机器学习语义分割-随机森林，支持向量机，GBC Machine learning semantic segmentation

# Machine Learning - Image Segmentation Per pixel image segmentation using machine learning algorithms. Programmed using the following libraries: Scikit-Learn, Scikit-Image OpenCV, and Mahotas and ProgressBar. Compatible with Python 2.7+ and 3.X. ### Feature vector Spectral: * Red * Green * Blue Texture: * Local binary pattern Haralick (Co-occurance matrix) features (Also texture): * Angular second moment * Contrast * Correlation * Sum of Square: variance * Inverse difference moment * Sum average * Sum variance * Sum entropy * Entropy ### Supported Learners * Support Vector Machine * Random Forest * Gradient Boosting Classifier ### Example Usage python train.py -i <path_to_image_folder> -l <path/to/label/folder> -c <SVM, RF, GBC> -o <path/to/model.p> python inference.py -i <path_to_image_folder> -m <path/to/model.p> -o <path/to/output/folder> python evaluation.py -i <path/to/test/images> -g <path/to/ground/truth/images> [-m] ### Example Output