opencv小项目练习，一共九个

****************************************************************************** * Non-Rigid Face Tracking ****************************************************************************** * by Jason Saragih, 5th Dec 2012 * http://jsaragih.org/ ****************************************************************************** * Ch6 of the book "Mastering OpenCV with Practical Computer Vision Projects" * Copyright Packt Publishing 2012. * http://www.packtpub.com/cool-projects-with-opencv/book ****************************************************************************** ---------------------------------------------------------- Building the project using CMake from the command-line: ---------------------------------------------------------- Linux: export OpenCV_DIR="~/OpenCV/build" mkdir build cd build cmake -D OpenCV_DIR=$OpenCV_DIR ../src make MacOSX (Xcode): export OpenCV_DIR="~/OpenCV/build" mkdir build cd build cmake -G Xcode -D OpenCV_DIR=$OpenCV_DIR ../src open OPENCV_HOTSHOTS.xcodeproj Windows (MS Visual Studio): set OpenCV_DIR="C:\OpenCV\build" mkdir build cd build cmake -G "Visual Studio 9 2008" -D OpenCV_DIR=%OpenCV_DIR% ../src start OPENCV_HOTSHOTS.sln - A static library will be written to the "lib" directory. - The execuables can be found in the "bin" directory. ---------------------------------------------------------- Running the various programs: ---------------------------------------------------------- * On Linux or Mac: ./bin/program_name --help * On Windows: bin\Debug\program_name --help ---------------------------------------------------------- Mini tutorial: ---------------------------------------------------------- (Follow these steps in the order 1 to 10) 1. Create "annotations.yaml": --------- Usage: > ./annotate [-v video] [-m muct_dir] [-d output_dir] - video: video containing frames to annotate. - muct_dir: directory containing "muct-landmarks/muct76-opencv.csv", the pre-annotated MUCT dataset (http://www.milbo.org/muct/). - output_dir: contains the annotation file and annotated images (if using -v) Example: > mkdir muct > ./annotate -m ${MY_MUCT_DIR}/ -d muct/ 2. Visualise annotations: ---------- Usage: > ./visualise_annotation annotation_file Example: > ./visualize_annotations muct/annotations.yaml Keys: - 'p': show next image and annotations - 'o': show previous image and annotations - 'f': show flipped image and annotations 3. Train shape model: ---------- Usage: > ./train_shape_model annotation_file shape_model_file [-f fraction_of_variation] [-k maximum_modes] [--mirror] - annotation_file: generated by "annotate" - shape_model_file: output YAML file containing trained shape model - fraction_of_variation: A fraction between 0 and 1 specifying ammount of variation to retain - maximum_modes: A cap on the number of modes the shape model can have - mirror: Use mirrored images as samples (only use if symmety points were specified in "annotate") Example: > ./train_shape_model muct/annotations.yaml muct/shape_model.yaml 4. Visualise shape model: ----------- Usage: > ./visualise_shape_model shape_model - shape_model: generated using "train_shape_model" Example: > ./visualize_shape_model muct/shape_model.yaml 5. Train patch detectors: -------------- Usage: > ./train_patch_model annotation_file shape_model_file patch_model_file[-w face_width] [-p patch_size] [-s search_window_size] [--mirror] - annotation_file: generated by "annotate" - shape_model_file: generated by "train_shape_model" - patch_model_file: output YAML file containing trained patch model - face_width: How many pixels-wide the reference face is - patch_size: How many pixels-wide the patches are in the reference face image - search_window_Size: How many pixels-wide the search region is - mirror: Use mirrored images as samples (only use if symmety points were specified in "annotate") Example: > ./train_patch_model muct/annotations.yaml muct/shape_model.yaml muct/patch_model.yaml 6. Visualise patch detectors: ------------ Usage: > ./visualise_patch_model patch_model [-w face_width] - patch_model: generated using "train_patch_model" - face_width: Width of face to visualise patches on Example: > ./visualize_patch_model muct/patch_model.yaml 7. Build face detector: ------------ Usage: > ./train_face_detector detector_file annotation_file shape_model_file detector_model_file [-f min_frac_of_pts_in_det_rect] [--mirror] - detector_file: pre-trained OpenCV cascade face detector (look in the data directory of the OpenCV package) - annotation_file: generated using "annotate" - shape_model_file: generated using "train_shape_model" - detector_model_file: output YAML file containing face detector model - min_frac_of_pts_in_det_rect: Minimum fraction of points inside detection window for sample to be considered and inlier for training - mirror: Use mirrored images as samples (only use if symmety points were specified in "annotate") Example: > ./train_face_detector ${MY_OPENCV_DIR}/data/lbpcascades/lbpcascade_frontalface.xml muct/annotations.yaml muct/shape_model.yaml muct/detector.yaml 8. Visualise face detector: ------------ Usage: > ./visualise_face detector [video_file] - detector: generated using "train_face_detector" - video_file: Optional video to test results on. Default is to use webcam Example: > ./visualize_face_detector muct/detector.yaml 9. Train face tracker: ----------- Usage: > ./train_face_tracker shape_model_file patch_models_file face_detector_file face_tracker_file - shape_model_file: generated using "train_shape_model" - patch_model_file: generated using "train_patch_model" - face_detector_file: generated using "train_face_detector" - face_tracker_file: output YAML file containing face tracker model Example: > ./train_face_tracker muct/shape_model.yaml muct/patch_model.yaml muct/detector.yaml muct/tracker.yaml 10. Test face tracker: ---------- Usage: > ./visualise_face_tracker tracker [video_file] - tracker: generated using "train_face_tracker" - video_file: Optional video to test tracker on. Default is to use webcam Example: ./visualize_face_tracker muct/tracker.yaml