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].