Added a mini-tutorial to Chapter 6 README

Chapter6_NonRigidFaceTracking/README.txt

@@ -37,8 +37,123 @@ Windows (MS Visual Studio):
 - 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 
+