This project seeks to enable the rough inside/outside-patient localization of an endoscope. At this stage the proposed feature vector is meant to be a HSV histogram of the incoming endoscope image stream.

This software is developed along the principles of test-driven development. This implies that the current abilities of the software can be deduced from the test_... methods in all tests_... .py files in the tests package.

A typical workflow at the moment consist of running several of the scripts in the top directory. I recommend the following (assuming you labeled your videos with the Aegisub subtitle editor):

• Convert your video(s) to a feature XML file by calling python convert_video_to_xml.py [path_to_video] for each video.
• Convert your labels file(s) (.ass) to a labels list file (.csv) by calling python convert_labels_to_label_list.py [path_to_labels_file] [path_to_corresponding_feature_file].
• Take a directory of feature XMLs and their corresponding label list CSVs and call python get_evaluation.py [--dir_eval] [--subsampling] [dir_train or path_to_classifier]. If [dir_eval] is omitted, cross validation is performed. The evaluation output of the learned classifier will be printed to standard output.
• Learn and export a classifier from a directory of feature XMLs and their corresponding label list CSVs with python get_classifier.py [dir_train] [dir_eval] [path_to_classifier] [--C_value] [--gamma_value]. Grid search can be skipped by supplying C and gamma values.
• Visualize your classifier by calling python display_class_live.py [path_to_video or camera_index or camera_IP_address] [path_to_classifier] [--skip_frames].