MasteringOpenCV · DarshanRamakant · Mar 7, 2013 · Aug 20, 2013 · Mar 22, 2014 · Jun 2, 2014
diff --git a/Chapter2_iPhoneAR/Example_MarkerBasedAR/Example_MarkerBasedAR/VideoSource.mm b/Chapter2_iPhoneAR/Example_MarkerBasedAR/Example_MarkerBasedAR/VideoSource.mm
@@ -146,7 +146,7 @@ - (bool) startWithDevicePosition:(AVCaptureDevicePosition)devicePosition
         AVCaptureDeviceInput *videoIn = [AVCaptureDeviceInput deviceInputWithDevice:videoDevice error:&error];
         self.deviceInput = videoIn;
 
-        if (!error)
+        if (nil != videoIn)
         {
             if ([[self captureSession] canAddInput:videoIn])
             {
@@ -160,7 +160,7 @@ - (bool) startWithDevicePosition:(AVCaptureDevicePosition)devicePosition
         }
         else
         {
-            NSLog(@"Couldn't create video input");
+    		NSLog(@"Couldn't create video input: %@", [error localizedDescription]);
             return FALSE;
         }
     }

diff --git a/Chapter4_StructureFromMotion/GPUSURFFeatureMatcher.cpp b/Chapter4_StructureFromMotion/GPUSURFFeatureMatcher.cpp
@@ -20,15 +20,15 @@
 
 using namespace std;
 using namespace cv;
-using namespace cv::core;
 using namespace cv::gpu;
 
 //c'tor
 GPUSURFFeatureMatcher::GPUSURFFeatureMatcher(vector<cv::Mat>& imgs_, 
 									   vector<std::vector<cv::KeyPoint> >& imgpts_) :
 	imgpts(imgpts_),use_ratio_test(true)
 {
-	printShortCudaDeviceInfo(cv::gpu::getDevice());
+	// The helper function printShortCudaDeviceInfo() moved between OpenCV v2.3 and v2.4, so might not compile.
+	//printShortCudaDeviceInfo(cv::gpu::getDevice());
 
 	extractor = new gpu::SURF_GPU();
 
@@ -103,4 +103,4 @@ void GPUSURFFeatureMatcher::MatchFeatures(int idx_i, int idx_j, vector<DMatch>*
 			CV_PROFILE("match",matcher.match( descriptors_1, descriptors_2, *matches );)
 		}
 	}
-}
+}
diff --git a/Chapter4_StructureFromMotion/Triangulation.cpp b/Chapter4_StructureFromMotion/Triangulation.cpp
@@ -73,9 +73,11 @@ Mat_<double> IterativeLinearLSTriangulation(Point3d u,	//homogenous image point
 											) {
 	double wi = 1, wi1 = 1;
 	Mat_<double> X(4,1); 
-	for (int i=0; i<10; i++) { //Hartley suggests 10 iterations at most
-		Mat_<double> X_ = LinearLSTriangulation(u,P,u1,P1);
-		X(0) = X_(0); X(1) = X_(1); X(2) = X_(2); X(3) = 1.0;
+
+	Mat_<double> X_ = LinearLSTriangulation(u,P,u1,P1);
+	X(0) = X_(0); X(1) = X_(1); X(2) = X_(2); X(3) = 1.0;
+
+	for (int i=0; i<10; i++) { //Hartley suggests 10 iterations at most		
 
 		//recalculate weights
 		double p2x = Mat_<double>(Mat_<double>(P).row(2)*X)(0);

diff --git a/Chapter6_NonRigidFaceTracking/README.txt b/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 
+
diff --git a/README.md b/README.md
@@ -12,8 +12,8 @@ Full source-code for the book.
 --------------------------------------------------------------------------------
 To build & run the projects for the book:
 --------------------------------------------------------------------------------
-- Install OpenCV v2.4.2 or later. eg: go to "http://opencv.org/", click on
-  Downloads, download the latest OpenCV (including prebuilt library), and extract
+- Install OpenCV (versions between 2.4.2 to 2.4.11 are supported, whereas OpenCV 3.0 is not yet supported). eg: go to "http://opencv.org/", click on
+  Downloads, download the latest OpenCV 2.4 version (including prebuilt library), and extract
   it to "C:\OpenCV" for Windows or "~/OpenCV" for Linux. In OpenCV v2.4.3, the
   prebuilt OpenCV library is in "C:\OpenCV\build" or "~/OpenCV/build", such as
   "C:\OpenCV\build\x64\vc9" for MS Visual Studio 2008 (or "vs10" folder for MS