Merge branch 'master' into feature/Windows-GUI

# Conflicts:
#	.gitignore
#	exe/FaceLandmarkVid/FaceLandmarkVid.cpp
#	exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp
#	exe/FeatureExtraction/FeatureExtraction.cpp
#	lib/local/FaceAnalyser/src/FaceAnalyser.cpp
#	lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h
#	lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp
#	matlab_runners/Action Unit Experiments/results/DISFA_valid_res.txt
#	matlab_runners/Action Unit Experiments/results/FERA2011_res_class.txt
#	matlab_runners/Action Unit Experiments/results/SEMAINE_valid_res.txt
#	matlab_runners/Demos/Read_HOG_file.m
#	matlab_runners/Demos/feature_extraction_demo_img_seq.m
#	matlab_runners/Feature Point Experiments/results/fps_yt.mat
#	matlab_runners/Feature Point Experiments/results/fps_yt.txt
#	matlab_runners/Feature Point Experiments/results/in-the-wild-res-no-outline.pdf
#	matlab_runners/Feature Point Experiments/results/landmark_detections.mat
#	matlab_runners/Gaze Experiments/mpii_1500_errs.mat
#	matlab_runners/Head Pose Experiments/results/Pose_OF.mat
#	matlab_runners/Head Pose Experiments/results/Pose_OF.txt

.gitignore

@@ -1,11 +1,3 @@
-exe/Recording/x64/
-exe/FaceLandmarkVidMulti/x64/
-lib/3rdParty/dlib/x64/
-lib/local/FaceAnalyser/x64/
-lib/local/LandmarkDetector/x64/
-exe/FaceLandmarkImg/x64/
-exe/FaceLandmarkVid/x64/
-exe/FeatureExtraction/x64/
 /x64/Release/
 OpenFace.VC.opendb
 OpenFace.sdf
@@ -34,20 +26,44 @@ matlab_runners/Action Unit Experiments/out_DISFA/
 matlab_runners/Action Unit Experiments/out_fera/
 matlab_runners/Demos/output_features_seq/
 matlab_runners/Demos/output_features_vid/
+exe/FaceLandmarkImg/Release/
 exe/FaceLandmarkImg/Debug/
+exe/FaceLandmarkVid/Release/
 exe/FaceLandmarkVid/Debug/
+exe/FaceLandmarkVidMulti/Release/
 exe/FaceLandmarkVidMulti/Debug/
 exe/FeatureExtraction/Debug/
+exe/FeatureExtraction/Release/
+exe/Recording/Release/
 exe/Recording/Debug/
+exe/Recording/x64/
+exe/FaceLandmarkVidMulti/x64/
+exe/FaceLandmarkImg/x64/
+exe/FaceLandmarkVid/x64/
+exe/FeatureExtraction/x64/
 gui/OpenFaceOffline/bin/
 gui/OpenFaceOffline/obj/
+gui/OpenFaceDemo/obj/
+gui/HeadPose-live/obj/
+lib/3rdParty/dlib/Release/
 lib/3rdParty/dlib/Debug/
+lib/local/CamCom/Release/
 lib/local/CamCom/Debug/
 lib/local/CamCom/x64/
+lib/local/CppInerop/x64/
+lib/local/CppInerop/Release/
 lib/local/CppInerop/Debug/
 lib/local/CppInerop/x64/Debug/
+lib/local/FaceAnalyser/Release/
 lib/local/FaceAnalyser/Debug/
+lib/local/LandmarkDetector/Release/
 lib/local/LandmarkDetector/Debug/
+lib/3rdParty/dlib/x64/
+lib/local/FaceAnalyser/x64/
+lib/local/LandmarkDetector/x64/
+lib/local/GazeAnalyser/x64/
+lib/local/GazeAnalyser/Release/
+lib/local/GazeAnalyser/Debug/
 OpenFaceDemo/bin/
 OpenFaceDemo/obj/
 Debug/model/
@@ -58,19 +74,6 @@ lib/local/LandmarkDetector/Debug/
 matlab_runners/Head Pose Experiments/experiments/biwi_out/
 matlab_runners/Head Pose Experiments/experiments/bu_out/
 matlab_runners/Head Pose Experiments/experiments/ict_out/
-exe/FaceLandmarkImg/Release/
+OpenFace\.VC\.db
-exe/FaceLandmarkVid/Release/
+matlab_version/face_validation/vlfeat-0.9.20/
-exe/FaceLandmarkVidMulti/Release/
+matlab_version/face_validation/trained/intermediate/
-exe/FeatureExtraction/Release/
-exe/Recording/Release/
-gui/OpenFaceDemo/obj/
-lib/3rdParty/dlib/Release/
-lib/local/CamCom/Release/
-lib/local/CppInerop/Release/
-lib/local/CppInerop/x64/
-lib/local/FaceAnalyser/Release/
-lib/local/LandmarkDetector/Release/
-gui/HeadPose-live/obj/
-lib/local/GazeAnalyser/x64/
-lib/local/GazeAnalyser/Release/
-lib/local/GazeAnalyser/Debug/

.travis.yml

@@ -7,7 +7,6 @@ branches:
     - /^feature-.*$/
 compiler:
   - gcc
-  - clang
 
 os:
   - osx
@@ -45,7 +44,7 @@ before_install:
       if [ ${TRAVIS_OS_NAME} = linux ]; then  
         $CXX --version;
         sudo sed -i -e 's/^Defaults\tsecure_path.*$//' /etc/sudoers;
-        sudo cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_V4L=ON -D WITH_OPENCL=OFF -D INSTALL_C_EXAMPLES=OFF -D BUILD_EXAMPLES=OFF -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF -D INSTALL_PYTHON_EXAMPLES=OFF ..;
+        sudo cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_V4L=ON -D WITH_OPENCL=OFF -D INSTALL_C_EXAMPLES=OFF -D BUILD_EXAMPLES=OFF -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF -D INSTALL_PYTHON_EXAMPLES=OFF -D WITH_TBB=ON ..;
       fi
     fi
 
@@ -69,7 +68,7 @@ script:
   - $CXX --version
   - mkdir build
   - cd build
-  - cmake -D CMAKE_BUILD_TYPE=RELEASE .. 
+  - cmake -D CMAKE_BUILD_TYPE=RELEASE CMAKE_CXX_FLAGS="-std=c++11" -D CMAKE_EXE_LINKER_FLAGS="-std=c++11" .. 
   - make -j2
   - ../build/bin/FaceLandmarkImg -fdir "../samples/" -ofdir "./demo_img/" -oidir "./demo_img/" -wild -q
   - ../build/bin/FaceLandmarkImg -inroot ../samples -f sample1.jpg -outroot data -of sample1.txt -op sample1.3d -oi sample1.bmp -multi_view 1 -wild -q

Copyright.txt

@@ -1,12 +1,11 @@
-///////////////////////////////////////////////////////////////////////////////
+Copyright (C) 2017, University of Southern California, University of Cambridge, and Carnegie Mellon University, all rights reserved
-// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
+
-// all rights reserved.
+ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
-//
+
-// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
+THIS SOFTWARE IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-//
+
-// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  
+Notwithstanding the license granted herein, Licensee acknowledges that certain components of the Software may be covered by so-called “open source” software licenses (“Open Source Components”), which means any software licenses approved as open source licenses by the Open Source Initiative or any substantially similar licenses, including without limitation any license that, as a condition of distribution of the software licensed under such license, requires that the distributor make the software available in source code format. Carnegie Mellon shall provide a list of Open Source Components for a particular version of the Software upon Licensee’s request.  Licensee will comply with the applicable terms of such licenses and to the extent required by the licenses covering Open Source Components, the terms of such licenses will apply in lieu of the terms of this Agreement.  To the extent the terms of the licenses applicable to Open Source Components prohibit any of the restrictions in this License Agreement with respect to such Open Source Component, such restrictions will not apply to such Open Source Component.  To the extent the terms of the licenses applicable to Open Source Components require Carnegie Mellon to make an offer to provide source code or related information in connection with the Software, such offer is hereby made.  Any request for source code or related information should be directed to Tadas Baltrusaitis.  Licensee acknowledges receipt of notices for the Open Source Components for the initial delivery of the Software.
-// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
+
-//
 // License can be found in OpenFace-license.txt
 //
 //     * Any publications arising from the use of this software, including but

README.md

@@ -68,13 +68,17 @@ Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
 in *Facial Expression Recognition and Analysis Challenge*, 
 *IEEE International Conference on Automatic Face and Gesture Recognition*, 2015 
 
-# Final remarks
-
-I did my best to make sure that the code runs out of the box but there are always issues and I would be grateful for your understanding that this is research code and not a commercial level product. However, if you encounter any problems/bugs/issues please contact me on github or by emailing me at Tadas.Baltrusaitis@cl.cam.ac.uk for any bug reports/questions/suggestions. 
-
 # Copyright
 
 Copyright can be found in the Copyright.txt
 
 You have to respect boost, TBB, dlib, and OpenCV licenses.
 
+# Commercial license
+
+For inquiries about the commercial licensing of the OpenFace toolkit please contact innovation@cmu.edu
+
+# Final remarks
+
+I did my best to make sure that the code runs out of the box but there are always issues and I would be grateful for your understanding that this is research code and not full fledged product. However, if you encounter any problems/bugs/issues please contact me on github or by emailing me at Tadas.Baltrusaitis@cl.cam.ac.uk for any bug reports/questions/suggestions. 
+

exe/FaceLandmarkImg/FaceLandmarkImg.cpp

@@ -302,12 +302,12 @@ int main (int argc, char **argv)
 	boost::filesystem::path parent_path = boost::filesystem::path(arguments[0]).parent_path();
 
 	// Some initial parameters that can be overriden from command line
-	vector<string> files, depth_files, output_images, output_landmark_locations, output_pose_locations;
+	vector<string> files, output_images, output_landmark_locations, output_pose_locations;
 
 	// Bounding boxes for a face in each image (optional)
 	vector<cv::Rect_<double> > bounding_boxes;
 	
-	LandmarkDetector::get_image_input_output_params(files, depth_files, output_landmark_locations, output_pose_locations, output_images, bounding_boxes, arguments);
+	LandmarkDetector::get_image_input_output_params(files, output_landmark_locations, output_pose_locations, output_images, bounding_boxes, arguments);
 	LandmarkDetector::FaceModelParameters det_parameters(arguments);	
 	// No need to validate detections, as we're not doing tracking
 	det_parameters.validate_detections = false;
@@ -358,16 +358,6 @@ int main (int argc, char **argv)
 			return 1;
 		}
 		
-		// Loading depth file if exists (optional)
-		cv::Mat_<float> depth_image;
-
-		if(depth_files.size() > 0)
-		{
-			string dFile = depth_files.at(i);
-			cv::Mat dTemp = cv::imread(dFile, -1);
-			dTemp.convertTo(depth_image, CV_32F);
-		}
-
 		// Making sure the image is in uchar grayscale
 		cv::Mat_<uchar> grayscale_image;
 		convert_to_grayscale(read_image, grayscale_image);
@@ -413,7 +403,7 @@ int main (int argc, char **argv)
 			for(size_t face=0; face < face_detections.size(); ++face)
 			{
 				// if there are multiple detections go through them
-				bool success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, depth_image, face_detections[face], clnf_model, det_parameters);
+				bool success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, face_detections[face], clnf_model, det_parameters);
 
 				// Estimate head pose and eye gaze				
 				cv::Vec6d headPose = LandmarkDetector::GetPose(clnf_model, fx, fy, cx, cy);

exe/FaceLandmarkVid/FaceLandmarkVid.cpp

@@ -87,7 +87,7 @@ double fps_tracker = -1.0;
 int64 t0 = 0;
 
 // Visualising the results
-void visualise_tracking(cv::Mat& captured_image, cv::Mat_<float>& depth_image, const LandmarkDetector::CLNF& face_model, const LandmarkDetector::FaceModelParameters& det_parameters, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, int frame_count, double fx, double fy, double cx, double cy)
+void visualise_tracking(cv::Mat& captured_image, const LandmarkDetector::CLNF& face_model, const LandmarkDetector::FaceModelParameters& det_parameters, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, int frame_count, double fx, double fy, double cx, double cy)
 {
 
 	// Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised
@@ -142,13 +142,6 @@ void visualise_tracking(cv::Mat& captured_image, cv::Mat_<float>& depth_image, c
 	{
 		cv::namedWindow("tracking_result", 1);
 		cv::imshow("tracking_result", captured_image);
-
-		if (!depth_image.empty())
-		{
-			// Division needed for visualisation purposes
-			imshow("depth", depth_image / 2000.0);
-		}
-
 	}
 }
 
@@ -158,7 +151,7 @@ int main (int argc, char **argv)
 	vector<string> arguments = get_arguments(argc, argv);
 
 	// Some initial parameters that can be overriden from command line	
-	vector<string> files, depth_directories, output_video_files, out_dummy;
+	vector<string> files, output_video_files, out_dummy;
 	
 	// By default try webcam 0
 	int device = 0;
@@ -169,7 +162,7 @@ int main (int argc, char **argv)
 	
 	// Indicates that rotation should be with respect to world or camera coordinates
 	string output_codec;
-	LandmarkDetector::get_video_input_output_params(files, depth_directories, out_dummy, output_video_files, output_codec, arguments);
+	LandmarkDetector::get_video_input_output_params(files, out_dummy, output_video_files, output_codec, arguments);
 	
 	// The modules that are being used for tracking
 	LandmarkDetector::CLNF clnf_model(det_parameters.model_location);	
@@ -214,8 +207,6 @@ int main (int argc, char **argv)
 			f_n = 0;
 		}
 		
-		bool use_depth = !depth_directories.empty();	
-
 		// Do some grabbing
 		cv::VideoCapture video_capture;
 		if( current_file.size() > 0 )
@@ -291,7 +282,6 @@ int main (int argc, char **argv)
 		{		
 
 			// Reading the images
-			cv::Mat_<float> depth_image;
 			cv::Mat_<uchar> grayscale_image;
 
 			if(captured_image.channels() == 3)
@@ -303,30 +293,8 @@ int main (int argc, char **argv)
 				grayscale_image = captured_image.clone();				
 			}
 					
-			// Get depth image
-			if(use_depth)
-			{
-				char* dst = new char[100];
-				std::stringstream sstream;
-
-				sstream << depth_directories[f_n] << "\\depth%05d.png";
-				sprintf(dst, sstream.str().c_str(), frame_count + 1);
-				// Reading in 16-bit png image representing depth
-				cv::Mat_<short> depth_image_16_bit = cv::imread(string(dst), -1);
-
-				// Convert to a floating point depth image
-				if(!depth_image_16_bit.empty())
-				{
-					depth_image_16_bit.convertTo(depth_image, CV_32F);
-				}
-				else
-				{
-					WARN_STREAM( "Can't find depth image" );
-				}
-			}
-			
 			// The actual facial landmark detection / tracking
-			bool detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, depth_image, clnf_model, det_parameters);
+			bool detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, clnf_model, det_parameters);
 			
 			// Visualising the results
 			// Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised
@@ -342,7 +310,7 @@ int main (int argc, char **argv)
 				GazeAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false);
 			}
 
-			visualise_tracking(captured_image, depth_image, clnf_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
+			visualise_tracking(captured_image, clnf_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
 			
 			// output the tracked video
 			if (!output_video_files.empty())

exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp

@@ -107,7 +107,7 @@ int main (int argc, char **argv)
 	vector<string> arguments = get_arguments(argc, argv);
 
 	// Some initial parameters that can be overriden from command line	
-	vector<string> files, depth_directories, tracked_videos_output, dummy_out;
+	vector<string> files, tracked_videos_output, dummy_out;
 	
 	// By default try webcam 0
 	int device = 0;
@@ -127,7 +127,8 @@ int main (int argc, char **argv)
 
 	// Get the input output file parameters
 	string output_codec;
-	LandmarkDetector::get_video_input_output_params(files, depth_directories, dummy_out, tracked_videos_output, output_codec, arguments);
+	LandmarkDetector::get_video_input_output_params(files, dummy_out, tracked_videos_output, output_codec, arguments);
+
 	// Get camera parameters
 	LandmarkDetector::get_camera_params(device, fx, fy, cx, cy, arguments);
 	
@@ -176,8 +177,6 @@ int main (int argc, char **argv)
 		    current_file = files[f_n];
 		}
 
-		bool use_depth = !depth_directories.empty();	
-
 		// Do some grabbing
 		cv::VideoCapture video_capture;
 		if( current_file.size() > 0 )
@@ -253,28 +252,6 @@ int main (int argc, char **argv)
 				grayscale_image = captured_image.clone();				
 			}
 		
-			// Get depth image
-			if(use_depth)
-			{
-				char* dst = new char[100];
-				std::stringstream sstream;
-
-				sstream << depth_directories[f_n] << "\\depth%05d.png";
-				sprintf(dst, sstream.str().c_str(), frame_count + 1);
-				// Reading in 16-bit png image representing depth
-				cv::Mat_<short> depth_image_16_bit = cv::imread(string(dst), -1);
-
-				// Convert to a floating point depth image
-				if(!depth_image_16_bit.empty())
-				{
-					depth_image_16_bit.convertTo(depth_image, CV_32F);
-				}
-				else
-				{
-					WARN_STREAM( "Can't find depth image" );
-				}
-			}
-
 			vector<cv::Rect_<double> > face_detections;
 
 			bool all_models_active = true;
@@ -336,7 +313,7 @@ int main (int argc, char **argv)
 
 							// This ensures that a wider window is used for the initial landmark localisation
 							clnf_models[model].detection_success = false;
-							detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, depth_image, face_detections[detection_ind], clnf_models[model], det_parameters[model]);
+							detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_detections[detection_ind], clnf_models[model], det_parameters[model]);
 													
 							// This activates the model
 							active_models[model] = true;
@@ -350,7 +327,7 @@ int main (int argc, char **argv)
 				else
 				{
 					// The actual facial landmark detection / tracking
-					detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, depth_image, clnf_models[model], det_parameters[model]);
+					detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, clnf_models[model], det_parameters[model]);
 				}
 			});
 								

exe/FeatureExtraction/FeatureExtraction.cpp

@@ -132,8 +132,9 @@ void create_directory(string output_path)
 	}
 }
 
-void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, bool& verbose, 
+void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
-	bool &output_2D_landmarks, bool &output_3D_landmarks, bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze, vector<string> &arguments);
+	int &similarity_size, bool &grayscale, bool& visualize_track, bool& visualize_align, bool& visualize_hog, bool& dynamic, bool &output_2D_landmarks, bool &output_3D_landmarks,
+	bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze, vector<string> &arguments);
 
 void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments);
 
@@ -219,16 +220,19 @@ int main (int argc, char **argv)
 	vector<string> arguments = get_arguments(argc, argv);
 
 	// Some initial parameters that can be overriden from command line	
-	vector<string> input_files, depth_directories, output_files, tracked_videos_output;
+	vector<string> input_files, output_files, tracked_videos_output;
+	
+	LandmarkDetector::FaceModelParameters det_parameters(arguments);
+	// Always track gaze in feature extraction
+	det_parameters.track_gaze = true;
 
 	// Get the input output file parameters
 	
 	// Indicates that rotation should be with respect to camera or world coordinates
 	string output_codec; //not used but should
-	LandmarkDetector::get_video_input_output_params(input_files, depth_directories, output_files, tracked_videos_output, output_codec, arguments);
+	LandmarkDetector::get_video_input_output_params(input_files, output_files, tracked_videos_output, output_codec, arguments);
 
 	bool video_input = true;
-	bool verbose = true;
 	bool images_as_video = false;
 
 	vector<vector<string> > input_image_files;
@@ -270,6 +274,14 @@ int main (int argc, char **argv)
 
 	bool video_output = false;
 	
+	double sim_scale = -1;
+	int sim_size = 112;
+	bool grayscale = false;
+	bool video_output = false;
+	bool dynamic = true; // Indicates if a dynamic AU model should be used (dynamic is useful if the video is long enough to include neutral expressions)
+	int num_hog_rows;
+	int num_hog_cols;
+
 	// By default output all parameters, but these can be turned off to get smaller files or slightly faster processing times
 	// use -no2Dfp, -no3Dfp, -noMparams, -noPose, -noAUs, -noGaze to turn them off
 	bool output_2D_landmarks = true;
@@ -279,10 +291,12 @@ int main (int argc, char **argv)
 	bool output_AUs = true;
 	bool output_gaze = true;
 
-	get_output_feature_params(output_similarity_align, output_hog_align_files, verbose,
+	bool visualize_track = false;
+	bool visualize_align = false;
+	bool visualize_hog = false;
+	get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, visualize_track, visualize_align, visualize_hog, dynamic,
 		output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
 
-
 	// If multiple video files are tracked, use this to indicate if we are done
 	bool done = false;	
 	int f_n = -1;
@@ -292,9 +306,6 @@ int main (int argc, char **argv)
 	// Load the modules that are being used for tracking and face analysis
 
 	// Load face landmark detector
-	LandmarkDetector::FaceModelParameters det_parameters(arguments);
-	// Always track gaze in feature extraction
-	det_parameters.track_gaze = true;
 	LandmarkDetector::CLNF face_model(det_parameters.model_location);
 
 	// Load facial feature extractor and AU analyser
@@ -370,6 +381,8 @@ int main (int argc, char **argv)
 				return 1;
 			}
 
+			// If image sequence provided, assume the fps is 30
+			fps_vid_in = 30;
 		}	
 		
 		// If optical centers are not defined just use center of image
@@ -430,8 +443,6 @@ int main (int argc, char **argv)
 		// Use for timestamping if using a webcam
 		int64 t_initial = cv::getTickCount();
 
-		bool visualise_hog = verbose;
-
 		// Timestamp in seconds of current processing
 		double time_stamp = 0;
 
@@ -498,18 +509,19 @@ int main (int argc, char **argv)
 			// But only if needed in output
 			if(!output_similarity_align.empty() || hog_output_file.is_open() || output_AUs)
 			{
-				face_analyser.AddNextFrame(captured_image, face_model.detected_landmarks, face_model.detection_success, time_stamp, false, !det_parameters.quiet_mode);
+
+				face_analyser.AddNextFrame(captured_image, face_model.detected_landmarks, face_model.detection_success, time_stamp, false, !det_parameters.quiet_mode && (visualize_align || visualize_hog));
 				face_analyser.GetLatestAlignedFace(sim_warped_img);
 
-				if(!det_parameters.quiet_mode)
+				if(!det_parameters.quiet_mode && visualize_align)
 				{
 					cv::imshow("sim_warp", sim_warped_img);			
 				}
-				if(hog_output_file.is_open())
+				if(hog_output_file.is_open() || (visualize_hog && !det_parameters.quiet_mode))
 				{
 					face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
 
-					if(visualise_hog && !det_parameters.quiet_mode)
+					if(visualize_hog && !det_parameters.quiet_mode)
 					{
 						cv::Mat_<double> hog_descriptor_vis;
 						FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_vis);
@@ -549,7 +561,10 @@ int main (int argc, char **argv)
 			}
 
 			// Visualising the tracker
+			if(visualize_track && !det_parameters.quiet_mode)
+			{
 				visualise_tracking(captured_image, face_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
+			}
 
 			// Output the landmarks, pose, gaze, parameters and AUs
 			outputAllFeatures(&output_file, output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze,
@@ -910,7 +925,8 @@ void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, boo
 }
 
 
-void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, bool& verbose, 
+void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
+	int &similarity_size, bool &grayscale, bool& visualize_track, bool& visualize_align, bool& visualize_hog, bool& dynamic,
 	bool &output_2D_landmarks, bool &output_3D_landmarks, bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze,
 	vector<string> &arguments)
 {
@@ -926,6 +942,13 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
 
 	string output_root = "";
 
+	// By default the model is dynamic
+	dynamic = true;
+
+	visualize_align = false;
+	visualize_hog = false;
+	visualize_track = false;
+
 	string separator = string(1, boost::filesystem::path::preferred_separator);
 
 	// First check if there is a root argument (so that videos and outputs could be defined more easilly)
@@ -963,7 +986,24 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
 		}
 		else if (arguments[i].compare("-verbose") == 0)
 		{
-			verbose = true;
+			visualize_track = true;
+			visualize_align = true;
+			visualize_hog = true;
+		}
+		else if (arguments[i].compare("-vis-align") == 0)
+		{
+			visualize_align = true;
+			valid[i] = false;
+		}
+		else if (arguments[i].compare("-vis-hog") == 0)
+		{
+			visualize_hog = true;
+			valid[i] = false;
+		}
+		else if (arguments[i].compare("-vis-track") == 0)
+		{
+			visualize_track = true;
+			valid[i] = false;
 		}
 		else if (arguments[i].compare("-no2Dfp") == 0)
 		{

lib/local/FaceAnalyser/src/FaceAnalyser.cpp

@@ -416,11 +416,6 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const cv::Mat_<double>& de
 		UpdateRunningMedian(this->geom_desc_hist, this->geom_hist_sum, this->geom_descriptor_median, geom_descriptor_frame, update_median, this->num_bins_geom, this->min_val_geom, this->max_val_geom);
 	}
 
-	// First convert the face image to double representation as a row vector, TODO rem?
-	//cv::Mat_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
-	//cv::Mat_<double> aligned_face_cols_double;
-	//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
-	
 	// Visualising the median HOG
 	if(visualise)
 	{

lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h

@@ -1,14 +1,38 @@
 ///////////////////////////////////////////////////////////////////////////////
-// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
+// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
 // all rights reserved.
 //
-// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
+// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
-//
+// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
-// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  
+// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
+// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
-//
+// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
-// License can be found in OpenFace-license.txt
+// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
 //
+// Notwithstanding the license granted herein, Licensee acknowledges that certain components
+// of the Software may be covered by so-called “open source” software licenses (“Open Source
+// Components”), which means any software licenses approved as open source licenses by the
+// Open Source Initiative or any substantially similar licenses, including without limitation any
+// license that, as a condition of distribution of the software licensed under such license,
+// requires that the distributor make the software available in source code format. Licensor shall
+// provide a list of Open Source Components for a particular version of the Software upon
+// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
+// the extent required by the licenses covering Open Source Components, the terms of such
+// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
+// licenses applicable to Open Source Components prohibit any of the restrictions in this
+// License Agreement with respect to such Open Source Component, such restrictions will not
+// apply to such Open Source Component. To the extent the terms of the licenses applicable to
+// Open Source Components require Licensor to make an offer to provide source code or
+// related information in connection with the Software, such offer is hereby made. Any request
+// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
+// Licensee acknowledges receipt of notices for the Open Source Components for the initial
+// delivery of the Software.
+
 //     * Any publications arising from the use of this software, including but
 //       not limited to academic journal and conference publications, technical
 //       reports and manuals, must cite at least one of the following works:
@@ -59,7 +83,7 @@ class DetectionValidator
 		
 public:    
 	
-	// What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net
+	// What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net, 3 - new version of convolutional neural net
 	int validator_type;
 
 	// The orientations of each of the landmark detection validator
@@ -98,11 +122,15 @@ public:
 	vector<vector<vector<vector<pair<int, cv::Mat_<double> > > > > > cnn_convolutional_layers_dft;
 	vector<vector<vector<float > > > cnn_convolutional_layers_bias;
 	vector< vector<int> > cnn_subsampling_layers;
-	vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers;
+	vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_weights;
 	vector< vector<float > > cnn_fully_connected_layers_bias;
-	// 0 - convolutional, 1 - subsampling, 2 - fully connected
+	// OLD CNN: 0 - convolutional, 1 - subsampling, 2 - fully connected
+	// NEW CNN: 0 - convolutional, 1 - max pooling (2x2 stride 2), 2 - fully connected, 3 - relu, 4 - sigmoid
 	vector<vector<int> > cnn_layer_types;
 	
+	// Extra params for the new CNN
+	vector< vector<cv::Mat_<float>  > > cnn_fully_connected_layers_biases;
+
 	//==========================================
 
 	// Normalisation for face validation
@@ -134,9 +162,15 @@ private:
 	// Feed-forward Neural Network
 	double CheckNN(const cv::Mat_<double>& warped_img, int view_id);
 
+	// Convolutional Neural Network
+	double CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id);
+
 	// Convolutional Neural Network
 	double CheckCNN(const cv::Mat_<double>& warped_img, int view_id);
 
+	// Convolutional Neural Network
+	double CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id);
+
 	// A normalisation helper
 	void NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id);
 

lib/local/LandmarkDetector/include/LandmarkDetectorFunc.h

@@ -55,10 +55,7 @@ namespace LandmarkDetector
 	// Optionally can provide a bounding box from which to start tracking
 	//================================================================================================================
 	bool DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params);
-	bool DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, CLNF& clnf_model, FaceModelParameters& params);
-
 	bool DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params);
-	bool DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params);
 
 	//================================================================================================================
 	// Landmark detection in image, need to provide an image and optionally CLNF model together with parameters (default values work well)
@@ -68,11 +65,6 @@ namespace LandmarkDetector
 	// Providing a bounding box
 	bool DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params);
 
-	//================================================
-	// CLM-Z versions
-	bool DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, CLNF& clnf_model, FaceModelParameters& params);
-	bool DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params);
-
 	//================================================================
 	// Helper function for getting head pose from CLNF parameters
 

lib/local/LandmarkDetector/include/LandmarkDetectorModel.h

@@ -157,7 +157,7 @@ public:
 	CLNF & operator= (const CLNF&& other);
 
 	// Does the actual work - landmark detection
-	bool DetectLandmarks(const cv::Mat_<uchar> &image, const cv::Mat_<float> &depth, FaceModelParameters& params);
+	bool DetectLandmarks(const cv::Mat_<uchar> &image, FaceModelParameters& params);
 	
 	// Gets the shape of the current detected landmarks in camera space (given camera calibration)
 	// Can only be called after a call to DetectLandmarksInVideo or DetectLandmarksInImage
@@ -184,7 +184,7 @@ private:
 	map<int, cv::Mat_<float> >		kde_resp_precalc;
 
 	// The model fitting: patch response computation and optimisation steps
-    bool Fit(const cv::Mat_<uchar>& intensity_image, const cv::Mat_<float>& depth_image, const std::vector<int>& window_sizes, const FaceModelParameters& parameters);
+    bool Fit(const cv::Mat_<uchar>& intensity_image, const std::vector<int>& window_sizes, const FaceModelParameters& parameters);
 
 	// Mean shift computation that uses precalculated kernel density estimators (the one actually used)
 	void NonVectorisedMeanShift_precalc_kde(cv::Mat_<float>& out_mean_shifts, const vector<cv::Mat_<float> >& patch_expert_responses, const cv::Mat_<float> &dxs, const cv::Mat_<float> &dys, int resp_size, float a, int scale, int view_id, map<int, cv::Mat_<float> >& mean_shifts);
@@ -193,9 +193,6 @@ private:
     double NU_RLMS(cv::Vec6d& final_global, cv::Mat_<double>& final_local, const vector<cv::Mat_<float> >& patch_expert_responses, const cv::Vec6d& initial_global, const cv::Mat_<double>& initial_local,
 		          const cv::Mat_<double>& base_shape, const cv::Matx22d& sim_img_to_ref, const cv::Matx22f& sim_ref_to_img, int resp_size, int view_idx, bool rigid, int scale, cv::Mat_<double>& landmark_lhoods, const FaceModelParameters& parameters);
 
-	// Removing background image from the depth
-	bool RemoveBackground(cv::Mat_<float>& out_depth_image, const cv::Mat_<float>& depth_image);
-
 	// Generating the weight matrix for the Weighted least squares
 	void GetWeightMatrix(cv::Mat_<float>& WeightMatrix, int scale, int view_id, const FaceModelParameters& parameters);
 

lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h

@@ -52,12 +52,12 @@ namespace LandmarkDetector
 	//=============================================================================================
 	// Helper functions for parsing the inputs
 	//=============================================================================================
-	void get_video_input_output_params(vector<string> &input_video_file, vector<string> &depth_dir, vector<string> &output_files,
+	void get_video_input_output_params(vector<string> &input_video_file, vector<string> &output_files,
 		vector<string> &output_video_files, string &output_codec, vector<string> &arguments);
 
 	void get_camera_params(int &device, float &fx, float &fy, float &cx, float &cy, vector<string> &arguments);
 
-	void get_image_input_output_params(vector<string> &input_image_files, vector<string> &input_depth_files, vector<string> &output_feature_files, vector<string> &output_pose_files, vector<string> &output_image_files,
+	void get_image_input_output_params(vector<string> &input_image_files, vector<string> &output_feature_files, vector<string> &output_pose_files, vector<string> &output_image_files,
 		vector<cv::Rect_<double>> &input_bounding_boxes, vector<string> &arguments);
 
 	//===========================================================================

lib/local/LandmarkDetector/include/Patch_experts.h

@@ -57,9 +57,6 @@ public:
 	// The collection of SVR patch experts (for intensity/grayscale images), the experts are laid out scale->view->landmark
 	vector<vector<vector<Multi_SVR_patch_expert> > >	svr_expert_intensity;
 	 
-	// The collection of SVR patch experts (for depth/range images), the experts are laid out scale->view->landmark
-	vector<vector<vector<Multi_SVR_patch_expert> > >	svr_expert_depth;
-
 	// The collection of LNF (CCNF) patch experts (for intensity images), the experts are laid out scale->view->landmark
 	vector<vector<vector<CCNF_patch_expert> > >			ccnf_expert_intensity;
 
@@ -81,11 +78,11 @@ public:
 	// A copy constructor
 	Patch_experts(const Patch_experts& other);
 
-	// Returns the patch expert responses given a grayscale and an optional depth image.
+	// Returns the patch expert responses given a grayscale image.
 	// Additionally returns the transform from the image coordinates to the response coordinates (and vice versa).
 	// The computation also requires the current landmark locations to compute response around, the PDM corresponding to the desired model, and the parameters describing its instance
 	// Also need to provide the size of the area of interest and the desired scale of analysis
-	void Response(vector<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image, const cv::Mat_<float>& depth_image,
+	void Response(vector<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image, 
 							 const PDM& pdm, const cv::Vec6d& params_global, const cv::Mat_<double>& params_local, int window_size, int scale);
 
 	// Getting the best view associated with the current orientation
@@ -95,7 +92,7 @@ public:
 	inline int nViews(size_t scale = 0) const { return (int)centers[scale].size(); };
 
 	// Reading in all of the patch experts
-	void Read(vector<string> intensity_svr_expert_locations, vector<string> depth_svr_expert_locations, vector<string> intensity_ccnf_expert_locations);
+	void Read(vector<string> intensity_svr_expert_locations, vector<string> intensity_ccnf_expert_locations);
 
 
    

lib/local/LandmarkDetector/model/clm-z.txt

@@ -1,8 +0,0 @@
-PDM pdms/Multi-PIE_aligned_PDM_66.txt
-Triangulations tris_66.txt
-PatchesIntensity patch_experts/intensity_patches_0.25.txt
-PatchesIntensity patch_experts/intensity_patches_0.35.txt
-PatchesIntensity patch_experts/intensity_patches_0.5.txt
-PatchesDepth patch_experts/depth_patches_0.25.txt
-PatchesDepth patch_experts/depth_patches_0.35.txt
-PatchesDepth patch_experts/depth_patches_0.5.txt

lib/local/LandmarkDetector/model/main_clm-z.txt

@@ -1,3 +0,0 @@
-LandmarkDetector clm-z.txt
-FaceDetConversion haarAlign.txt
-DetectionValidator detection_validation/validator_general_66.txt

lib/local/LandmarkDetector/model/main_clm_general.txt

@@ -1,3 +1,3 @@
 LandmarkDetector clm_general.txt
 FaceDetConversion haarAlign.txt
-DetectionValidator detection_validation/validator_general_68.txt
+DetectionValidator detection_validation/validator_cnn.txt

lib/local/LandmarkDetector/model/main_clm_wild.txt

@@ -1,3 +1,3 @@
 LandmarkDetector clm_wild.txt
 FaceDetConversion haarAlign.txt
-DetectionValidator detection_validation/validator_general_68.txt
+DetectionValidator detection_validation/validator_cnn.txt

lib/local/LandmarkDetector/model/main_clnf_general.txt

@@ -3,4 +3,4 @@ LandmarkDetector_part model_inner/main_clnf_inner.txt inner 17 0 18 1 19 2 20 3
 LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18
 LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18
 FaceDetConversion haarAlign.txt
-DetectionValidator detection_validation/validator_general_68.txt
+DetectionValidator detection_validation/validator_cnn.txt

lib/local/LandmarkDetector/model/main_clnf_wild.txt

@@ -2,4 +2,4 @@ LandmarkDetector clnf_wild.txt
 LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18
 LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18
 FaceDetConversion haarAlign.txt
-DetectionValidator detection_validation/validator_general_68.txt
+DetectionValidator detection_validation/validator_cnn.txt

lib/local/LandmarkDetector/model/pdms/Multi-PIE_aligned_PDM_66.txt

@@ -1,409 +0,0 @@
-# The mean values of the components (in mm)
-198
-1
-6
--73.447014 
--72.593444 
--70.508936 
--66.868861 
--60.414326 
--49.631717 
--35.391108 
--18.754141 
-0.114597 
-19.016913 
-35.598673 
-49.771346 
-60.477080 
-66.851714 
-70.423155 
-72.449235 
-73.242955 
--58.203530 
--49.478500 
--38.505934 
--27.177561 
--16.300987 
-16.156900 
-27.016187 
-38.332565 
-49.304224 
-58.038529 
--0.040354 
--0.033363 
--0.017873 
-0.004084 
--12.557676 
--6.454282 
-0.021435 
-6.508846 
-12.601163 
--43.222559 
--36.097646 
--27.574181 
--20.557521 
--28.056401 
--35.953854 
-20.451629 
-27.455875 
-35.977844 
-43.113320 
-35.851506 
-27.955232 
--26.517287 
--19.122816 
--10.001914 
-0.033411 
-10.096699 
-19.222369 
-26.631040 
-19.826245 
-10.719912 
-0.073977 
--10.546541 
--19.679901 
--10.743288 
-0.054542 
-10.855802 
-11.052930 
-0.066245 
--10.914660 
--24.746512 
--5.563341 
-13.586104 
-31.935985 
-48.494172 
-62.540756 
-73.747225 
-81.753306 
-83.703222 
-81.701197 
-73.643185 
-62.391933 
-48.310331 
-31.731624 
-13.372130 
--5.780379 
--24.966474 
--55.382734 
--62.743537 
--65.767452 
--65.366721 
--62.577889 
--62.609412 
--65.431918 
--65.867839 
--62.878026 
--55.545001 
--42.794388 
--31.898600 
--21.092928 
--10.293158 
-2.090045 
-3.917313 
-4.546474 
-3.913121 
-2.066722 
--38.786133 
--43.150534 
--43.235540 
--38.961437 
--36.861866 
--36.568711 
--39.010632 
--43.306353 
--43.245963 
--38.901782 
--36.662572 
--36.932904 
-30.400416 
-23.498452 
-18.833915 
-19.206597 
-18.816801 
-23.452358 
-30.330913 
-37.487315 
-41.984978 
-42.996350 
-42.005283 
-37.535894 
-26.744930 
-26.063568 
-26.723925 
-32.163959 
-33.052706 
-32.187531 
-78.681255 
-76.697610 
-75.100729 
-67.144829 
-48.865709 
-28.603564 
-11.177305 
--4.295402 
--17.795472 
--4.239701 
-11.236558 
-28.650983 
-48.897325 
-67.163067 
-75.133633 
-76.746544 
-78.714887 
-1.731363 
--4.557739 
--9.659810 
--14.615709 
--19.199130 
--19.060969 
--14.484504 
--9.533992 
--4.439184 
-1.839139 
--17.498844 
--27.916448 
--38.687865 
--48.878729 
--23.622361 
--26.603554 
--29.386742 
--26.488498 
--23.509038 
--2.510802 
--3.222846 
--3.712807 
--2.897381 
--3.340207 
--3.258814 
--2.796041 
--3.614055 
--3.114678 
--2.391598 
--3.139280 
--3.231401 
--10.336622 
--18.125938 
--24.157414 
--30.363981 
--24.048139 
--18.017482 
--10.235142 
--15.032664 
--19.983990 
--25.047636 
--20.087424 
--15.139002 
--19.356132 
--27.503141 
--19.258370 
--16.964648 
--23.973005 
--17.050167 
-# The principal components (eigenvectors) of identity or combined identity and expression model
-198
-24
-6
--0.131273 0.061642 -0.012510 0.102280 -0.050892 0.032543 -0.075885 0.038389 -0.127712 0.121736 -0.051410 0.006800 -0.109190 0.068214 -0.191040 0.005581 -0.001793 0.030290 -0.014701 0.054678 -0.099910 0.129486 -0.039664 0.030211 
--0.130535 0.076644 0.005955 0.128992 -0.087766 0.003856 -0.062712 0.011035 -0.104311 0.121196 -0.051551 -0.031437 -0.072782 0.078546 -0.149843 0.002235 -0.009600 0.014969 -0.023432 0.076747 -0.057106 0.057651 -0.045418 0.037971 
--0.131360 0.097772 0.019892 0.147686 -0.117376 -0.003328 -0.044024 -0.011450 -0.072751 0.113932 -0.046556 -0.055102 0.000908 0.067126 -0.101982 -0.000479 -0.013325 -0.013626 -0.032762 0.091546 -0.014217 -0.020913 -0.046980 0.035342 
--0.137970 0.110929 0.030689 0.139783 -0.115369 0.026630 -0.019212 -0.029754 -0.010726 0.072704 -0.021451 -0.056577 0.082231 0.025464 -0.003511 0.005508 -0.024960 -0.025544 -0.036912 0.062564 0.039080 -0.055324 -0.046860 0.017641 
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-0.030716 -0.019142 0.015104 0.048347 0.008061 -0.057675 0.034305 0.001908 -0.032288 0.037098 -0.025976 0.008895 -0.034955 -0.015408 -0.033946 -0.012214 0.004252 -0.031221 0.011547 -0.052185 0.013885 0.023495 -0.041625 -0.018600 
-0.017217 -0.005005 -0.000825 0.038309 -0.033691 -0.049929 0.035044 0.006099 -0.026296 -0.013340 -0.050572 0.018098 0.017580 0.008349 -0.037259 -0.027207 0.030530 0.047757 -0.042520 -0.027716 0.002119 0.011201 -0.013641 -0.023775 
--0.002977 -0.004519 -0.005401 0.007102 -0.020136 0.003735 0.010466 0.031329 -0.051627 0.040202 -0.038370 -0.038383 0.005010 0.005392 -0.023086 0.029715 0.008523 0.055107 -0.010657 0.004865 -0.007244 0.004499 -0.049346 0.002795 
--0.010096 0.001042 0.000059 0.011824 -0.026881 0.025272 0.041191 0.020275 -0.012375 0.000779 -0.033145 0.002458 -0.007586 0.007785 -0.013394 0.017417 0.014159 -0.005881 -0.014046 -0.035161 0.017520 -0.016349 -0.017341 -0.009992 
-0.019776 -0.000284 -0.012557 0.024116 -0.012968 0.027447 0.037641 -0.003555 0.001294 -0.019848 -0.025716 -0.009227 0.017166 0.003187 -0.003003 -0.013572 0.026600 -0.056937 -0.027703 -0.029589 0.036149 -0.017405 -0.005140 -0.014149 
-0.003043 0.002636 -0.007479 0.021282 0.022862 0.026962 0.012532 0.017070 -0.011934 -0.027974 -0.031975 -0.014367 0.009348 0.001082 -0.006111 0.023087 0.042828 -0.015792 -0.000859 -0.010996 0.010873 0.003257 0.004459 0.021874 
-0.003741 0.016913 -0.001204 -0.017634 -0.016073 0.036588 0.022771 0.035887 0.015065 -0.020207 -0.010793 -0.000589 0.017515 -0.028759 -0.005286 0.026388 0.045892 -0.023168 0.012083 0.037597 0.001342 0.008441 -0.027373 0.056358 
-0.006235 0.002336 -0.014149 0.012474 0.020734 -0.047581 0.012805 0.019052 -0.009281 -0.032040 0.036141 -0.011251 0.009517 0.003028 -0.009339 0.025537 -0.049242 -0.015047 0.003361 0.000855 -0.014520 -0.007131 0.002813 0.033132 
-0.021965 -0.001009 -0.015741 0.019932 -0.015721 -0.035531 0.037303 -0.002551 0.003316 -0.020857 0.030647 -0.007770 0.014825 -0.001334 -0.003715 -0.011282 -0.035968 -0.059442 -0.024581 0.000602 -0.041615 -0.016669 0.018792 -0.007243 
--0.008996 -0.000549 -0.001255 0.012659 -0.029948 -0.024644 0.041247 0.020575 -0.011513 0.000978 0.039860 0.002944 -0.010132 -0.008154 -0.011641 0.019826 -0.019156 -0.010840 -0.011057 -0.014449 -0.029046 -0.005862 0.026793 -0.003840 
--0.003031 -0.007050 -0.005025 0.013159 -0.022487 -0.002090 0.009577 0.031278 -0.051872 0.041429 0.045054 -0.039255 0.000680 -0.006840 -0.019272 0.033989 -0.010335 0.047850 -0.007969 0.005201 0.010707 0.029877 0.041337 0.009335 
-0.015909 -0.008608 0.000093 0.050988 -0.032880 0.056455 0.031226 0.007102 -0.028123 -0.011172 0.054224 0.016291 0.012312 -0.014037 -0.034003 -0.018029 -0.036754 0.038949 -0.041639 -0.016244 -0.014604 0.017632 -0.000615 -0.021925 
-0.028911 -0.020564 0.020663 0.057528 0.009832 0.063863 0.029412 0.000800 -0.032018 0.041995 0.029433 0.006736 -0.041794 0.006742 -0.027211 -0.007554 -0.006106 -0.039369 0.009745 -0.029838 -0.038610 0.047533 0.015143 -0.020532 
-0.023922 -0.023664 0.013385 0.048180 0.001429 0.109536 0.018845 -0.002766 -0.046065 0.060109 0.023434 0.024933 -0.030917 0.021761 0.016546 -0.033368 -0.000156 -0.037766 -0.016409 0.003256 -0.032063 -0.016957 0.022986 -0.020118 
-0.025479 -0.016511 0.031827 0.041393 -0.008403 0.126612 0.007288 0.001233 -0.035336 0.029687 0.040922 -0.007776 -0.013868 -0.005459 -0.027831 -0.001951 0.005347 0.020879 0.007693 0.007357 0.014333 -0.017694 0.012221 -0.043917 
-0.003189 0.026538 0.038597 0.018669 0.012396 -0.003064 -0.024561 -0.002817 0.002622 -0.000148 0.017020 0.022609 -0.004868 0.005963 0.023348 -0.028816 -0.032968 0.005698 -0.055577 0.020448 0.022584 0.024768 0.047859 0.001740 
-0.000463 0.019463 0.024278 -0.029989 0.006319 -0.005217 -0.013751 0.006275 0.010675 0.049628 0.022928 0.001551 0.009671 -0.024186 0.018628 0.002273 -0.059675 0.026343 -0.063193 0.033560 0.003140 0.020410 0.001326 0.016946 
--0.000029 0.020658 0.017072 -0.027930 0.007062 -0.017970 -0.006258 -0.005185 0.004557 0.025434 0.022254 0.009542 0.011170 -0.004279 0.003404 0.005904 -0.053521 0.031271 -0.024813 0.038249 0.003914 0.022292 -0.001279 0.009964 
-0.003294 0.025279 0.021921 -0.021950 0.016281 -0.028948 -0.013909 -0.011395 -0.011052 -0.005952 0.021898 0.008144 0.008008 0.011346 -0.002342 0.008934 -0.040320 0.014749 0.007476 0.035145 0.004734 0.013044 0.014277 0.001855 
-0.008232 0.024965 0.029824 -0.004269 0.019641 -0.043536 -0.030921 -0.012568 -0.008932 -0.041767 0.012154 0.000783 -0.004622 0.026748 0.019253 0.007897 -0.032740 -0.002789 0.020610 0.019074 0.013518 -0.004310 0.037242 -0.007354 
-0.010354 0.027336 0.023251 -0.015652 0.025251 0.036485 -0.033761 -0.007033 -0.005841 -0.047403 -0.018907 0.006933 0.007124 -0.023824 0.008963 -0.000927 0.036586 0.012466 0.022380 0.013472 -0.003293 -0.035777 -0.027321 -0.012812 
-0.004813 0.027050 0.017023 -0.030956 0.020204 0.026792 -0.015645 -0.007011 -0.008335 -0.010363 -0.029818 0.012299 0.017923 -0.008348 -0.009660 -0.000164 0.045111 0.026769 0.008711 0.022239 0.011627 -0.005512 -0.015953 -0.003064 
-0.000766 0.021777 0.013939 -0.033866 0.009609 0.020486 -0.007124 -0.001779 0.006741 0.022061 -0.029365 0.012405 0.019471 0.006576 -0.002112 -0.003785 0.059780 0.041340 -0.023649 0.024858 0.014139 0.012995 -0.007512 0.005546 
-0.000800 0.019861 0.022529 -0.033132 0.007255 0.008814 -0.013329 0.008770 0.012220 0.047301 -0.028907 0.002944 0.016370 0.025151 0.014628 -0.007662 0.068549 0.034586 -0.062338 0.021464 0.011355 0.013293 -0.010281 0.012728 
-0.003226 0.025832 0.036768 0.019095 0.012590 0.005204 -0.023585 -0.000822 0.003035 -0.000961 -0.021105 0.022764 0.001173 -0.008868 0.020613 -0.035042 0.039000 0.010165 -0.056131 0.022830 -0.009516 -0.002921 -0.057879 -0.004983 
-0.001974 0.012342 0.008902 -0.001035 -0.014319 -0.014808 -0.005193 -0.002275 0.011929 -0.018206 0.019006 0.009941 0.038655 0.005308 0.018479 0.030950 -0.036894 -0.017445 0.032165 -0.091752 0.005902 -0.021467 0.001966 -0.009808 
--0.004216 0.004994 -0.016522 -0.029024 0.004502 0.007034 0.013316 0.000795 -0.012080 0.017514 0.036897 -0.007448 0.020571 -0.003682 0.025414 0.004514 -0.022096 -0.025326 0.029382 -0.047762 -0.017414 0.005327 -0.013496 0.012521 
--0.018832 -0.002126 -0.034571 -0.026174 0.029176 0.018516 0.009517 0.001888 -0.008390 0.017435 0.027928 -0.015628 0.017215 -0.007801 0.016744 -0.024043 -0.014056 0.003402 0.019569 -0.013736 -0.015438 0.013625 -0.001066 0.006400 
--0.028577 -0.003106 -0.035186 0.006865 0.039718 0.022501 -0.012257 -0.000103 0.031018 -0.027697 -0.003165 -0.006225 0.003329 0.006415 -0.021346 -0.045658 -0.008373 0.037353 0.024522 0.008467 0.007100 0.009292 0.037864 -0.028296 
--0.010439 -0.016610 -0.032891 0.026755 0.038727 0.019621 -0.001639 -0.035212 0.021531 0.043250 -0.011573 -0.047443 -0.090454 0.017415 0.029955 -0.029753 -0.016593 0.003223 0.000312 -0.050431 -0.067987 0.007906 -0.010813 0.001715 
--0.018644 -0.010357 -0.028056 -0.000468 0.032730 0.008727 -0.000374 -0.021592 0.004388 0.003520 0.005170 -0.016747 -0.034045 0.003118 0.010412 -0.016663 0.001397 -0.000975 0.017040 -0.015979 -0.012284 -0.003719 0.001706 -0.008266 
--0.024274 -0.003386 -0.017513 -0.036282 0.010578 0.005796 0.000745 -0.000204 -0.000937 -0.029211 0.016619 0.019301 0.020013 -0.001295 -0.025894 0.021730 0.004179 0.018245 0.011996 0.012299 0.036561 -0.004257 0.003014 0.001946 
--0.014891 -0.008411 -0.036029 -0.015021 0.034034 -0.016902 -0.000349 -0.017533 0.008122 -0.003290 -0.006353 -0.011299 -0.025911 0.001618 0.002149 -0.022488 -0.009485 0.009421 0.019984 -0.027222 0.008181 -0.016199 0.011061 -0.004347 
--0.007543 -0.015474 -0.040189 0.015384 0.038625 -0.027574 -0.000491 -0.031990 0.023963 0.036850 0.011293 -0.042285 -0.082698 -0.016002 0.021122 -0.035817 0.009754 0.014281 0.004730 -0.087198 0.033671 -0.000797 0.015535 0.007350 
-0.007930 0.023774 0.036743 -0.014038 -0.036477 -0.006774 0.011374 0.022541 -0.022535 0.042305 0.026335 0.036750 0.021916 0.007611 -0.005653 -0.015182 -0.047638 -0.014341 0.054505 -0.043196 0.023290 -0.056673 -0.015723 -0.007742 
--0.004915 0.015933 0.017345 -0.015680 -0.017967 -0.004915 0.011614 0.016946 0.029827 0.028125 0.011868 -0.004711 -0.005276 -0.001960 -0.009475 0.009741 -0.046956 0.011883 0.007010 -0.018339 -0.002865 -0.004312 -0.013932 -0.006942 
--0.003521 0.004654 -0.015590 -0.013889 0.004395 -0.008732 0.007700 -0.004791 0.067569 -0.003581 0.005624 -0.039265 -0.012761 -0.005771 -0.015968 0.006255 -0.033275 -0.009690 -0.041270 0.014617 -0.023760 0.048481 -0.000051 -0.022552 
-0.000458 0.005431 -0.018104 -0.000980 0.011696 -0.042760 -0.002120 -0.006394 0.021871 -0.044384 0.017191 0.023310 -0.058988 -0.023639 0.049743 -0.022378 0.011228 -0.017895 -0.021546 0.054782 -0.005029 0.079345 0.046779 0.008618 
--0.000473 -0.003483 -0.000317 -0.000032 -0.013100 -0.027729 -0.010188 -0.006816 0.024254 -0.025317 0.025138 -0.003575 -0.027818 -0.016550 0.033888 -0.020679 -0.003022 -0.026972 0.004090 0.018494 0.016316 0.039096 0.026870 -0.021700 
--0.000384 0.006795 0.002821 0.001952 -0.016537 -0.017312 -0.006126 -0.003212 0.008095 0.008878 0.020727 0.007447 -0.003471 -0.004029 0.015335 -0.019076 -0.026372 -0.019477 0.020929 -0.007807 0.020166 -0.006328 0.008143 -0.024274 
-0.002071 0.006466 -0.023492 -0.007974 0.015683 0.036241 -0.004189 -0.003038 0.024520 -0.048551 -0.020624 0.025659 -0.052563 0.020100 0.044440 -0.026317 -0.003525 -0.010101 -0.021391 0.035466 0.034123 0.038210 -0.080648 0.006556 
--0.002402 0.005398 -0.019679 -0.020452 0.005835 0.006932 0.007837 -0.002300 0.069086 -0.006995 -0.007547 -0.036643 -0.005613 0.005258 -0.021338 -0.000437 0.040201 0.000064 -0.039798 -0.010442 0.026457 0.034652 -0.023664 -0.024801 
--0.003825 0.016801 0.014153 -0.022104 -0.016761 0.006166 0.011717 0.019730 0.031672 0.024962 -0.015533 -0.002099 0.002298 0.002520 -0.014551 0.001733 0.051460 0.020880 0.007994 -0.024340 -0.011603 -0.003149 0.015724 -0.010346 
-0.009293 0.025153 0.034610 -0.021073 -0.035140 0.010154 0.010695 0.025885 -0.019886 0.039855 -0.033031 0.039020 0.029686 -0.005553 -0.011205 -0.023293 0.045845 -0.005817 0.054993 -0.027600 -0.047966 -0.047026 0.044749 -0.012053 
-0.001196 0.008104 -0.000550 -0.005319 -0.014490 0.017382 -0.007183 0.000135 0.010803 0.005711 -0.024559 0.010185 0.003984 0.004249 0.009649 -0.025892 0.026929 -0.011175 0.020838 -0.000927 -0.021532 -0.016130 -0.001564 -0.027490 
-0.001089 -0.002355 -0.004514 -0.007150 -0.010291 0.025128 -0.011639 -0.003498 0.027026 -0.029088 -0.027714 -0.001506 -0.021034 0.014769 0.028630 -0.025597 0.007130 -0.019412 0.003749 0.018448 -0.003519 0.014143 -0.041945 -0.024701 
--0.014752 -0.007321 0.013877 -0.002884 -0.024108 0.002303 -0.026512 0.020183 -0.015461 -0.030184 -0.006267 0.026068 0.044433 -0.016670 0.030474 0.011557 0.027340 0.038203 -0.005839 0.006177 0.027136 0.000316 0.019255 0.000552 
--0.016868 -0.029752 0.004473 -0.020764 -0.021435 0.027753 -0.000612 0.014380 -0.000613 0.002858 0.009706 -0.012946 0.027082 -0.001179 0.028245 0.039466 0.007036 0.048356 -0.016487 -0.020655 0.013899 0.035330 -0.037198 0.035797 
--0.020584 -0.025552 0.009208 -0.010018 -0.001434 0.030711 0.008708 -0.025079 0.018193 -0.004982 0.019040 -0.024397 0.011371 0.005362 0.014803 0.024863 0.004075 -0.004452 0.006803 -0.034515 0.013896 0.032023 -0.042288 0.024877 
--0.007172 -0.030730 -0.013156 0.002041 0.013713 0.018878 -0.002702 -0.036444 -0.004836 -0.014642 0.027953 -0.026214 0.006306 -0.001831 0.021305 -0.037591 0.021327 -0.044823 0.050411 -0.036042 0.008177 0.014946 -0.015597 -0.013038 
--0.017181 -0.023576 0.003032 -0.025203 -0.001919 -0.036232 0.009083 -0.022286 0.022601 -0.010671 -0.019965 -0.020816 0.016805 0.003686 0.009380 0.019998 -0.010796 0.002809 0.008988 -0.025250 -0.024145 0.039778 0.034154 0.028887 
--0.014072 -0.028410 -0.000822 -0.033387 -0.022436 -0.033373 -0.000370 0.016901 0.003275 -0.002184 -0.011401 -0.010105 0.031211 0.010900 0.023483 0.035466 -0.010793 0.053986 -0.014690 -0.011441 -0.019274 0.041437 0.027803 0.041183 
--0.011745 -0.006901 0.008132 -0.012160 -0.024078 -0.016750 -0.027255 0.023374 -0.012432 -0.034736 0.001738 0.028410 0.047103 0.022609 0.024876 0.009700 -0.029335 0.042447 -0.004444 0.021981 -0.024490 -0.016464 -0.009335 0.005723 
-0.013230 -0.012106 -0.008624 0.006624 -0.005346 -0.037879 -0.023831 0.001700 -0.000246 -0.040315 0.016171 -0.000787 0.040941 -0.027783 -0.034923 0.000944 -0.023770 -0.015075 0.022171 0.070943 0.004816 -0.018620 0.009349 0.002391 
-0.017298 0.003296 0.001998 -0.019318 -0.015374 -0.040235 -0.032471 -0.025113 0.013205 -0.017560 -0.001988 -0.032933 0.018524 0.000793 -0.025342 0.018806 -0.022045 -0.076625 0.010375 0.067569 0.001278 -0.039650 0.037479 0.024286 
-0.026498 0.011392 -0.000362 -0.042663 -0.029473 0.013919 -0.020835 -0.008795 -0.003774 0.015161 0.010246 -0.040784 0.002108 -0.020452 -0.003005 0.013382 0.020127 -0.090119 -0.018840 0.070445 0.011233 -0.062439 -0.022872 0.049291 
-0.012895 0.001538 0.008666 -0.004337 -0.015103 0.016876 -0.032324 -0.028284 0.009022 -0.012605 0.000615 -0.037415 0.014420 0.004265 -0.019532 0.022244 0.021863 -0.083473 0.007511 0.058851 0.031450 -0.040010 -0.012168 0.018051 
-0.009878 -0.013189 -0.002893 0.018337 -0.004429 0.018560 -0.023726 -0.000676 -0.003245 -0.036619 -0.018168 -0.004958 0.037474 0.033212 -0.029730 0.002752 0.018367 -0.021047 0.019956 0.061906 0.033994 -0.009713 0.002178 -0.002604 
--0.036455 -0.002450 -0.017922 -0.006097 0.022282 0.049421 -0.011847 -0.011101 -0.004510 -0.004388 0.004868 0.031117 0.011638 -0.001303 -0.008047 0.039302 0.009212 0.001789 0.021092 -0.019512 -0.016745 0.011997 0.019345 0.006406 
-0.000423 -0.007319 -0.057292 0.016176 0.031459 0.007973 -0.009437 -0.016403 0.000322 -0.021840 -0.027543 0.040725 0.012995 0.020647 -0.022551 -0.047601 0.009484 -0.033190 0.025752 -0.014163 -0.038431 -0.002155 0.032958 -0.015735 
--0.032717 -0.001468 -0.026128 -0.020284 0.020055 -0.061717 -0.009130 -0.007757 -0.001532 -0.011309 -0.008803 0.034833 0.018287 0.008545 -0.015543 0.035259 -0.013646 0.010114 0.025166 -0.029561 0.005239 -0.010502 -0.011794 0.015129 
--0.015427 -0.008969 -0.007325 0.001209 0.003884 -0.065420 -0.014325 0.005239 0.003813 -0.021528 0.005891 0.012697 -0.008585 -0.000428 -0.006703 0.035593 -0.008463 0.035073 0.011345 0.016756 -0.004326 -0.043418 0.005114 0.014734 
--0.016076 0.002727 -0.004804 0.030704 0.014818 0.010395 -0.028232 0.002524 0.026332 0.003282 -0.053346 0.020559 -0.065119 0.005758 -0.005391 0.059666 -0.008954 0.058529 0.015960 0.017843 -0.028979 -0.059673 -0.002070 0.043925 
--0.018698 -0.009367 0.000474 0.013333 0.006238 0.041309 -0.016795 0.002286 0.001409 -0.014775 -0.007769 0.009326 -0.013965 0.003250 0.000451 0.038211 0.013024 0.027985 0.007332 0.016022 0.004588 -0.027147 0.015319 0.006226 
-# The variances of the components (eigenvalues) of identity or combined identity and expression model
-1
-24
-6
-634.723159 232.843080 101.461353 70.892811 62.146634 61.253370 53.263370 41.886102 37.065997 30.948553 27.957356 24.039353 20.687504 19.610950 14.875372 14.281810 13.358140 11.883143 10.946700 9.934766 9.739733 7.997896 7.885247 6.790098 

lib/local/LandmarkDetector/model/tris_66.txt

@@ -1,518 +0,0 @@
-# Number of triangulations
-7
-# Triangulation 1
-# triangulation 
-91
-3
-4
-23 20 21 
-23 21 22 
-36 0 1 
-15 16 45 
-17 0 36 
-16 26 45 
-18 17 37 
-26 25 44 
-37 17 36 
-45 26 44 
-19 18 38 
-25 24 43 
-38 18 37 
-44 25 43 
-20 19 38 
-24 23 43 
-21 20 39 
-23 22 42 
-39 20 38 
-43 23 42 
-22 21 27 
-27 21 39 
-22 27 42 
-27 28 42 
-28 27 39 
-42 28 47 
-28 39 40 
-36 1 41 
-46 15 45 
-41 1 2 
-14 15 46 
-29 28 40 
-28 29 47 
-41 2 40 
-47 14 46 
-2 29 40 
-29 14 47 
-29 2 3 
-13 14 29 
-30 29 31 
-35 29 30 
-29 3 31 
-35 13 29 
-33 30 32 
-34 30 33 
-32 30 31 
-35 30 34 
-31 3 4 
-12 13 35 
-4 5 48 
-11 12 54 
-5 6 48 
-10 11 54 
-48 6 59 
-55 10 54 
-6 7 59 
-9 10 55 
-59 7 58 
-56 9 55 
-58 8 57 
-57 8 56 
-7 8 58 
-8 9 56 
-31 4 48 
-54 12 35 
-49 31 48 
-54 35 53 
-50 31 49 
-53 35 52 
-32 31 50 
-35 34 52 
-33 32 50 
-34 33 52 
-51 33 50 
-52 33 51 
-49 48 60 
-50 49 60 
-61 50 60 
-51 50 61 
-52 51 61 
-61 62 52 
-53 52 62 
-54 53 62 
-55 54 63 
-56 55 63 
-64 56 63 
-57 56 64 
-64 65 57 
-58 57 65 
-59 58 65 
-65 48 59 
-# Triangulation 2
-# triangulation 
-90
-3
-4
-23 20 21 
-23 21 22 
-36 0 1 
-15 16 45 
-17 0 36 
-16 26 45 
-18 17 37 
-26 25 44 
-37 17 36 
-45 26 44 
-19 18 38 
-25 24 43 
-38 18 37 
-44 25 43 
-20 19 38 
-24 23 43 
-21 20 39 
-23 22 42 
-39 20 38 
-43 23 42 
-22 21 27 
-27 21 39 
-22 27 42 
-27 28 42 
-28 27 39 
-42 28 47 
-28 39 40 
-36 1 41 
-46 15 45 
-41 1 2 
-14 15 46 
-29 28 40 
-28 29 47 
-41 2 40 
-47 14 46 
-2 29 40 
-29 14 47 
-29 2 3 
-13 14 29 
-30 29 31 
-29 3 31 
-35 13 29 
-33 30 32 
-34 30 33 
-32 30 31 
-35 30 34 
-31 3 4 
-12 13 35 
-4 5 48 
-11 12 54 
-5 6 48 
-10 11 54 
-48 6 59 
-55 10 54 
-6 7 59 
-9 10 55 
-59 7 58 
-56 9 55 
-58 8 57 
-57 8 56 
-7 8 58 
-8 9 56 
-31 4 48 
-54 12 35 
-49 31 48 
-54 35 53 
-50 31 49 
-53 35 52 
-32 31 50 
-35 34 52 
-33 32 50 
-34 33 52 
-51 33 50 
-52 33 51 
-49 48 60 
-50 49 60 
-61 50 60 
-51 50 61 
-52 51 61 
-61 62 52 
-53 52 62 
-54 53 62 
-55 54 63 
-56 55 63 
-64 56 63 
-57 56 64 
-64 65 57 
-58 57 65 
-59 58 65 
-65 48 59 
-# Triangulation 3
-# triangulation 
-77
-3
-4
-23 20 21 
-23 21 22 
-36 0 1 
-17 0 36 
-18 17 37 
-26 25 44 
-37 17 36 
-45 26 44 
-19 18 38 
-25 24 43 
-38 18 37 
-44 25 43 
-20 19 38 
-24 23 43 
-21 20 39 
-23 22 42 
-39 20 38 
-43 23 42 
-22 21 27 
-27 21 39 
-22 27 42 
-27 28 42 
-28 27 39 
-42 28 47 
-28 39 40 
-36 1 41 
-46 15 45 
-41 1 2 
-29 28 40 
-28 29 47 
-41 2 40 
-47 14 46 
-2 29 40 
-29 2 3 
-30 29 31 
-29 3 31 
-33 30 32 
-34 30 33 
-32 30 31 
-35 30 34 
-31 3 4 
-4 5 48 
-5 6 48 
-48 6 59 
-6 7 59 
-59 7 58 
-56 9 55 
-58 8 57 
-57 8 56 
-7 8 58 
-8 9 56 
-31 4 48 
-49 31 48 
-50 31 49 
-53 35 52 
-32 31 50 
-35 34 52 
-33 32 50 
-34 33 52 
-51 33 50 
-52 33 51 
-49 48 60 
-50 49 60 
-61 50 60 
-51 50 61 
-52 51 61 
-61 62 52 
-53 52 62 
-54 53 62 
-55 54 63 
-56 55 63 
-64 56 63 
-57 56 64 
-64 65 57 
-58 57 65 
-59 58 65 
-65 48 59 
-# Triangulation 4
-# triangulation 
-28
-3
-4
-36 0 1 
-17 0 36 
-18 17 37 
-37 17 36 
-19 18 38 
-38 18 37 
-20 19 38 
-36 1 41 
-41 1 2 
-29 28 40 
-41 2 40 
-2 29 40 
-29 2 3 
-30 29 31 
-29 3 31 
-31 3 4 
-4 5 48 
-5 6 48 
-48 6 59 
-6 7 59 
-59 7 58 
-58 8 57 
-7 8 58 
-31 4 48 
-49 31 48 
-50 31 49 
-51 33 50 
-51 50 61 
-# Triangulation 5
-# triangulation 
-90
-3
-4
-23 20 21 
-23 21 22 
-36 0 1 
-15 16 45 
-17 0 36 
-16 26 45 
-18 17 37 
-26 25 44 
-37 17 36 
-45 26 44 
-19 18 38 
-25 24 43 
-38 18 37 
-44 25 43 
-20 19 38 
-24 23 43 
-21 20 39 
-23 22 42 
-39 20 38 
-43 23 42 
-22 21 27 
-27 21 39 
-22 27 42 
-27 28 42 
-28 27 39 
-42 28 47 
-28 39 40 
-36 1 41 
-46 15 45 
-41 1 2 
-14 15 46 
-29 28 40 
-28 29 47 
-41 2 40 
-47 14 46 
-2 29 40 
-29 14 47 
-29 2 3 
-13 14 29 
-35 29 30 
-29 3 31 
-35 13 29 
-33 30 32 
-34 30 33 
-32 30 31 
-35 30 34 
-31 3 4 
-12 13 35 
-4 5 48 
-11 12 54 
-5 6 48 
-10 11 54 
-48 6 59 
-55 10 54 
-6 7 59 
-9 10 55 
-59 7 58 
-56 9 55 
-58 8 57 
-57 8 56 
-7 8 58 
-8 9 56 
-31 4 48 
-54 12 35 
-49 31 48 
-54 35 53 
-50 31 49 
-53 35 52 
-32 31 50 
-35 34 52 
-33 32 50 
-34 33 52 
-51 33 50 
-52 33 51 
-49 48 60 
-50 49 60 
-61 50 60 
-51 50 61 
-52 51 61 
-61 62 52 
-53 52 62 
-54 53 62 
-55 54 63 
-56 55 63 
-64 56 63 
-57 56 64 
-64 65 57 
-58 57 65 
-59 58 65 
-65 48 59 
-# Triangulation 6
-# triangulation 
-77
-3
-4
-23 20 21 
-23 21 22 
-15 16 45 
-16 26 45 
-18 17 37 
-26 25 44 
-37 17 36 
-45 26 44 
-19 18 38 
-25 24 43 
-38 18 37 
-44 25 43 
-20 19 38 
-24 23 43 
-21 20 39 
-23 22 42 
-39 20 38 
-43 23 42 
-22 21 27 
-27 21 39 
-22 27 42 
-27 28 42 
-28 27 39 
-42 28 47 
-28 39 40 
-36 1 41 
-46 15 45 
-14 15 46 
-29 28 40 
-28 29 47 
-41 2 40 
-47 14 46 
-29 14 47 
-13 14 29 
-35 29 30 
-35 13 29 
-33 30 32 
-34 30 33 
-32 30 31 
-35 30 34 
-12 13 35 
-11 12 54 
-10 11 54 
-55 10 54 
-9 10 55 
-59 7 58 
-56 9 55 
-58 8 57 
-57 8 56 
-7 8 58 
-8 9 56 
-54 12 35 
-54 35 53 
-50 31 49 
-53 35 52 
-32 31 50 
-35 34 52 
-33 32 50 
-34 33 52 
-51 33 50 
-52 33 51 
-49 48 60 
-50 49 60 
-61 50 60 
-51 50 61 
-52 51 61 
-61 62 52 
-53 52 62 
-54 53 62 
-55 54 63 
-56 55 63 
-64 56 63 
-57 56 64 
-64 65 57 
-58 57 65 
-59 58 65 
-65 48 59 
-# Triangulation 7
-# triangulation 
-28
-3
-4
-15 16 45 
-16 26 45 
-26 25 44 
-45 26 44 
-25 24 43 
-44 25 43 
-24 23 43 
-46 15 45 
-14 15 46 
-28 29 47 
-47 14 46 
-29 14 47 
-13 14 29 
-35 29 30 
-35 13 29 
-12 13 35 
-11 12 54 
-10 11 54 
-55 10 54 
-9 10 55 
-56 9 55 
-57 8 56 
-8 9 56 
-54 12 35 
-54 35 53 
-53 35 52 
-52 33 51 
-52 51 61 

lib/local/LandmarkDetector/src/LandmarkDetectionValidator.cpp

@@ -1,14 +1,38 @@
 ///////////////////////////////////////////////////////////////////////////////
-// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
+// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
 // all rights reserved.
 //
-// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
+// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
-//
+// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
-// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.  
+// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
-// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
+// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
-//
+// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
-// License can be found in OpenFace-license.txt
+// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
+// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
+// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
+// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+// POSSIBILITY OF SUCH DAMAGE.
 //
+// Notwithstanding the license granted herein, Licensee acknowledges that certain components
+// of the Software may be covered by so-called “open source” software licenses (“Open Source
+// Components”), which means any software licenses approved as open source licenses by the
+// Open Source Initiative or any substantially similar licenses, including without limitation any
+// license that, as a condition of distribution of the software licensed under such license,
+// requires that the distributor make the software available in source code format. Licensor shall
+// provide a list of Open Source Components for a particular version of the Software upon
+// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
+// the extent required by the licenses covering Open Source Components, the terms of such
+// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
+// licenses applicable to Open Source Components prohibit any of the restrictions in this
+// License Agreement with respect to such Open Source Component, such restrictions will not
+// apply to such Open Source Component. To the extent the terms of the licenses applicable to
+// Open Source Components require Licensor to make an offer to provide source code or
+// related information in connection with the Software, such offer is hereby made. Any request
+// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
+// Licensee acknowledges receipt of notices for the Open Source Components for the initial
+// delivery of the Software.
+
 //     * Any publications arising from the use of this software, including but
 //       not limited to academic journal and conference publications, technical
 //       reports and manuals, must cite at least one of the following works:
@@ -40,6 +64,9 @@
 #include <opencv2/core/core.hpp>
 #include <opencv2/imgproc.hpp>
 
+// TBB includes
+#include <tbb/tbb.h>
+
 // System includes
 #include <fstream>
 
@@ -108,15 +135,27 @@ cnn_convolutional_layers_bias(other.cnn_convolutional_layers_bias), cnn_convolut
 		}
 	}
 
-	this->cnn_fully_connected_layers.resize(other.cnn_fully_connected_layers.size());
+	this->cnn_fully_connected_layers_weights.resize(other.cnn_fully_connected_layers_weights.size());
-	for (size_t v = 0; v < other.cnn_fully_connected_layers.size(); ++v)
+	for (size_t v = 0; v < other.cnn_fully_connected_layers_weights.size(); ++v)
 	{
-		this->cnn_fully_connected_layers[v].resize(other.cnn_fully_connected_layers[v].size());
+		this->cnn_fully_connected_layers_weights[v].resize(other.cnn_fully_connected_layers_weights[v].size());
 
-		for (size_t l = 0; l < other.cnn_fully_connected_layers[v].size(); ++l)
+		for (size_t l = 0; l < other.cnn_fully_connected_layers_weights[v].size(); ++l)
 		{
 			// Make sure the matrix is copied.
-			this->cnn_fully_connected_layers[v][l] = other.cnn_fully_connected_layers[v][l].clone();
+			this->cnn_fully_connected_layers_weights[v][l] = other.cnn_fully_connected_layers_weights[v][l].clone();
+		}
+	}
+
+	this->cnn_fully_connected_layers_biases.resize(other.cnn_fully_connected_layers_biases.size());
+	for (size_t v = 0; v < other.cnn_fully_connected_layers_biases.size(); ++v)
+	{
+		this->cnn_fully_connected_layers_biases[v].resize(other.cnn_fully_connected_layers_biases[v].size());
+
+		for (size_t l = 0; l < other.cnn_fully_connected_layers_biases[v].size(); ++l)
+		{
+			// Make sure the matrix is copied.
+			this->cnn_fully_connected_layers_biases[v][l] = other.cnn_fully_connected_layers_biases[v][l].clone();
 		}
 	}
 
@@ -188,11 +227,20 @@ void DetectionValidator::Read(string location)
 			cnn_convolutional_layers.resize(n);
 			cnn_convolutional_layers_dft.resize(n);
 			cnn_subsampling_layers.resize(n);
-			cnn_fully_connected_layers.resize(n);
+			cnn_fully_connected_layers_weights.resize(n);
 			cnn_layer_types.resize(n);
 			cnn_fully_connected_layers_bias.resize(n);
 			cnn_convolutional_layers_bias.resize(n);
 		}			
+		else if (validator_type == 3)
+		{
+			cnn_convolutional_layers.resize(n);
+			cnn_convolutional_layers_dft.resize(n);
+			cnn_fully_connected_layers_weights.resize(n);
+			cnn_layer_types.resize(n);
+			cnn_fully_connected_layers_biases.resize(n);
+			cnn_convolutional_layers_bias.resize(n);
+		}
 
 		// Initialise the normalisation terms
 		mean_images.resize(n);
@@ -318,11 +366,82 @@ void DetectionValidator::Read(string location)
 						// Fully connected layer
 						cv::Mat_<float> weights;
 						ReadMatBin(detection_validator_stream, weights);
-						cnn_fully_connected_layers[i].push_back(weights);
+						cnn_fully_connected_layers_weights[i].push_back(weights);
 					}
 				}
 			}
+			else if (validator_type == 3)
+			{
+				int network_depth;
+				detection_validator_stream.read((char*)&network_depth, 4);
+
+				cnn_layer_types[i].resize(network_depth);
+
+				for (int layer = 0; layer < network_depth; ++layer)
+				{
+
+					int layer_type;
+					detection_validator_stream.read((char*)&layer_type, 4);
+					cnn_layer_types[i][layer] = layer_type;
+
+					// convolutional
+					if (layer_type == 0)
+					{
+
+						// Read the number of input maps
+						int num_in_maps;
+						detection_validator_stream.read((char*)&num_in_maps, 4);
+
+						// Read the number of kernels for each input map
+						int num_kernels;
+						detection_validator_stream.read((char*)&num_kernels, 4);
+
+						vector<vector<cv::Mat_<float> > > kernels;
+						vector<vector<pair<int, cv::Mat_<double> > > > kernel_dfts;
+
+						kernels.resize(num_in_maps);
+						kernel_dfts.resize(num_in_maps);
+
+						vector<float> biases;
+						for (int k = 0; k < num_kernels; ++k)
+						{
+							float bias;
+							detection_validator_stream.read((char*)&bias, 4);
+							biases.push_back(bias);
+						}
+
+						cnn_convolutional_layers_bias[i].push_back(biases);
+
+						// For every input map
+						for (int in = 0; in < num_in_maps; ++in)
+						{
+							kernels[in].resize(num_kernels);
+							kernel_dfts[in].resize(num_kernels);
+
+							// For every kernel on that input map
+							for (int k = 0; k < num_kernels; ++k)
+							{
+								ReadMatBin(detection_validator_stream, kernels[in][k]);
+
+							}
+						}
 
+						cnn_convolutional_layers[i].push_back(kernels);
+						cnn_convolutional_layers_dft[i].push_back(kernel_dfts);
+					}
+					else if (layer_type == 2)
+					{
+						cv::Mat_<float> biases;
+						ReadMatBin(detection_validator_stream, biases);
+						cnn_fully_connected_layers_biases[i].push_back(biases);
+
+						// Fully connected layer
+						cv::Mat_<float> weights;
+						ReadMatBin(detection_validator_stream, weights);
+						cnn_fully_connected_layers_weights[i].push_back(weights);
+					}
+				}
+			}
 			// Read in the piece-wise affine warps
 			paws[i].Read(detection_validator_stream);
 		}
@@ -361,7 +480,13 @@ double DetectionValidator::Check(const cv::Vec3d& orientation, const cv::Mat_<uc
 	}
 	else if(validator_type == 2)
 	{
-		dec = CheckCNN(warped, id);
+		dec = CheckCNN_old(warped, id);
+	}
+	else if (validator_type == 3)
+	{
+		// On some machines the non-TBB version may be faster
+		//dec = CheckCNN(warped, id);
+		dec = CheckCNN_tbb(warped, id);
 	}
 	return dec;
 }
@@ -433,7 +558,7 @@ double DetectionValidator::CheckSVR(const cv::Mat_<double>& warped_img, int view
 }
 
 // Convolutional Neural Network
-double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view_id)
+double DetectionValidator::CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id)
 {
 
 	cv::Mat_<double> feature_vec;
@@ -599,7 +724,7 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view
 				cv::hconcat(input_concat, add, input_concat);
 			}
 						
-			input_concat = input_concat * cnn_fully_connected_layers[view_id][fully_connected_layer].t();
+			input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer].t();
 
 			cv::exp(-input_concat - cnn_fully_connected_layers_bias[view_id][fully_connected_layer], input_concat);
 			input_concat = 1.0 /(1.0 + input_concat);		
@@ -609,6 +734,55 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view
 
 			fully_connected_layer++;
 		}
+		// Max pooling layer
+		if (layer_type == 3)
+		{
+
+			vector<cv::Mat_<float>> outputs_sub;
+
+			// Iterate over pool height and width, all the stride is 2x2 and no padding is used
+			int stride_x = 2;
+			int stride_y = 2;
+
+			int pool_x = 2;
+			int pool_y = 2;
+
+			for (size_t in = 0; in < input_maps.size(); ++in)
+			{
+				int out_x = input_maps[in].cols / stride_x;
+				int out_y = input_maps[in].rows / stride_y;
+
+				cv::Mat_<float> sub_out(out_y, out_x, 0.0);
+				cv::Mat_<float> in_map = input_maps[in];
+
+				for (int x = 0; x < input_maps[in].cols; x+= stride_x)
+				{
+					for (int y = 0; y < input_maps[in].rows; y+= stride_y) 
+					{
+						float curr_max = -FLT_MAX;
+						for (int x_in = x; x_in < x+pool_x; ++x_in) 
+						{
+							for (int y_in = y; y_in < y + pool_y; ++y_in)
+							{
+								float curr_val = in_map.at<float>(y_in, x_in);
+								if (curr_val > curr_max)
+								{
+									curr_max = curr_val;
+								}
+							}
+						}
+						int x_in_out = x / stride_x;
+						int y_in_out = y / stride_y;
+						sub_out.at<float>(y_in_out, x_in_out) = curr_max;
+					}
+				}
+
+				outputs_sub.push_back(sub_out);
+			}
+			outputs = outputs_sub;
+			subsample_layer++;
+		}
+
 		// Set the outputs of this layer to inputs of the next
 		input_maps = outputs;
 
@@ -620,6 +794,471 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view
 	return dec;
 }
 
+// Convolutional Neural Network
+double DetectionValidator::CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id)
+{
+
+	cv::Mat_<double> feature_vec;
+	NormaliseWarpedToVector(warped_img, feature_vec, view_id);
+
+	// Create a normalised image from the crop vector
+	cv::Mat_<float> img(warped_img.size(), 0.0);
+	img = img.t();
+
+	cv::Mat mask = paws[view_id].pixel_mask.t();
+	cv::MatIterator_<uchar>  mask_it = mask.begin<uchar>();
+
+	cv::MatIterator_<double> feature_it = feature_vec.begin();
+	cv::MatIterator_<float> img_it = img.begin();
+
+	int wInt = img.cols;
+	int hInt = img.rows;
+
+	for (int i = 0; i < wInt; ++i)
+	{
+		for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it)
+		{
+			// if is within mask
+			if (*mask_it)
+			{
+				// assign the feature to image if it is within the mask
+				*img_it = (float)*feature_it++;
+			}
+		}
+	}
+	img = img.t();
+
+	int cnn_layer = 0;
+	int fully_connected_layer = 0;
+
+	vector<cv::Mat_<float> > input_maps;
+	input_maps.push_back(img);
+
+	vector<cv::Mat_<float> > outputs;
+
+	for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
+	{
+		// Determine layer type
+		int layer_type = cnn_layer_types[view_id][layer];
+
+		// Convolutional layer
+		if (layer_type == 0)
+		{
+			outputs.clear();
+			// Pre-allocate the output feature maps
+			outputs.resize(cnn_convolutional_layers[view_id][cnn_layer][0].size());
+			for (size_t in = 0; in < input_maps.size(); ++in)
+			{
+				cv::Mat_<float> input_image = input_maps[in];
+
+				// Useful precomputed data placeholders for quick correlation (convolution)
+				cv::Mat_<double> input_image_dft;
+				cv::Mat integral_image;
+				cv::Mat integral_image_sq;
+
+				// To adapt for TBB, perform the first convolution in a non TBB way so that dft, and integral images are computed
+				cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][0];
+
+				// The convolution (with precomputation)
+				cv::Mat_<float> output;
+				if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second.empty()) // This will only be needed during the first pass
+				{
+					std::map<int, cv::Mat_<double> > precomputed_dft;
+
+					LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+
+					cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first = precomputed_dft.begin()->first;
+					cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second = precomputed_dft.begin()->second;
+				}
+				else
+				{
+					std::map<int, cv::Mat_<double> > precomputed_dft;
+					precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second;
+					LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+				}
+
+				// Combining the maps
+				if (in == 0)
+				{
+					outputs[0] = output;
+				}
+				else
+				{
+					outputs[0] = outputs[0] + output;
+				}
+
+
+				// TBB pass for the remaining kernels, empirically helps with layers with more kernels
+				tbb::parallel_for(1, (int)cnn_convolutional_layers[view_id][cnn_layer][in].size(), [&](int k) {
+				{
+					cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
+
+					// The convolution (with precomputation)
+					cv::Mat_<float> output;
+					if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) // This will only be needed during the first pass
+					{
+						std::map<int, cv::Mat_<double> > precomputed_dft;
+
+						LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+
+						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
+						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
+					}
+					else
+					{
+						std::map<int, cv::Mat_<double> > precomputed_dft;
+						precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
+						LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+					}
+
+					// Combining the maps
+					if (in == 0)
+					{
+						outputs[k] = output;
+					}
+					else
+					{
+						outputs[k] = outputs[k] + output;
+					}
+				}
+				});
+				
+			}
+
+			for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
+			{
+				outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k];
+			}
+			cnn_layer++;
+		}
+		if (layer_type == 1)
+		{
+			vector<cv::Mat_<float>> outputs_sub;
+
+			// Iterate over pool height and width, all the stride is 2x2 and no padding is used
+			int stride_x = 2;
+			int stride_y = 2;
+
+			int pool_x = 2;
+			int pool_y = 2;
+
+			for (size_t in = 0; in < input_maps.size(); ++in)
+			{
+				int out_x = input_maps[in].cols / stride_x;
+				int out_y = input_maps[in].rows / stride_y;
+
+				cv::Mat_<float> sub_out(out_y, out_x, 0.0);
+				cv::Mat_<float> in_map = input_maps[in];
+
+				for (int x = 0; x < input_maps[in].cols; x += stride_x)
+				{
+					for (int y = 0; y < input_maps[in].rows; y += stride_y)
+					{
+						float curr_max = -FLT_MAX;
+						for (int x_in = x; x_in < x + pool_x; ++x_in)
+						{
+							for (int y_in = y; y_in < y + pool_y; ++y_in)
+							{
+								float curr_val = in_map.at<float>(y_in, x_in);
+								if (curr_val > curr_max)
+								{
+									curr_max = curr_val;
+								}
+							}
+						}
+						int x_in_out = x / stride_x;
+						int y_in_out = y / stride_y;
+						sub_out.at<float>(y_in_out, x_in_out) = curr_max;
+					}
+				}
+
+				outputs_sub.push_back(sub_out);
+
+			}
+			outputs = outputs_sub;
+		}
+		if (layer_type == 2)
+		{
+			// Concatenate all the maps
+			cv::Mat_<float> input_concat = input_maps[0].t();
+			input_concat = input_concat.reshape(0, 1);
+
+			for (size_t in = 1; in < input_maps.size(); ++in)
+			{
+				cv::Mat_<float> add = input_maps[in].t();
+				add = add.reshape(0, 1);
+				cv::hconcat(input_concat, add, input_concat);
+			}
+
+			input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer];
+			input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t();
+
+			outputs.clear();
+			outputs.push_back(input_concat);
+
+			fully_connected_layer++;
+		}
+		if (layer_type == 3) // ReLU
+		{
+			outputs.clear();
+			for (size_t k = 0; k < input_maps.size(); ++k)
+			{
+				// Apply the ReLU
+				cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO);
+				outputs.push_back(input_maps[k]);
+
+			}
+		}
+		if (layer_type == 4)
+		{
+			outputs.clear();
+			for (size_t k = 0; k < input_maps.size(); ++k)
+			{
+				// Apply the sigmoid
+				cv::exp(-input_maps[k], input_maps[k]);
+				input_maps[k] = 1.0 / (1.0 + input_maps[k]);
+
+				outputs.push_back(input_maps[k]);
+
+			}
+		}
+		// Set the outputs of this layer to inputs of the next
+		input_maps = outputs;
+
+	}
+
+	// Convert the class label to a continuous value
+	double max_val = 0;
+	cv::Point max_loc;
+	cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc);
+	int max_idx = max_loc.y;
+	double max = 1;
+	double min = -1;
+	double bins = (double)outputs[0].cols;
+	// Unquantizing the softmax layer to continuous value
+	double step_size = (max - min) / bins; // This should be saved somewhere
+	double unquantized = min + step_size / 2.0 + max_idx * step_size;
+
+	return unquantized;
+}
+
+// Convolutional Neural Network
+double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view_id)
+{
+	
+	cv::Mat_<double> feature_vec;
+	NormaliseWarpedToVector(warped_img, feature_vec, view_id);
+
+	// Create a normalised image from the crop vector
+	cv::Mat_<float> img(warped_img.size(), 0.0);
+	img = img.t();
+
+	cv::Mat mask = paws[view_id].pixel_mask.t();
+	cv::MatIterator_<uchar>  mask_it = mask.begin<uchar>();
+
+	cv::MatIterator_<double> feature_it = feature_vec.begin();
+	cv::MatIterator_<float> img_it = img.begin();
+
+	int wInt = img.cols;
+	int hInt = img.rows;
+
+	for (int i = 0; i < wInt; ++i)
+	{
+		for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it)
+		{
+			// if is within mask
+			if (*mask_it)
+			{
+				// assign the feature to image if it is within the mask
+				*img_it = (float)*feature_it++;
+			}
+		}
+	}
+	img = img.t();
+
+	int cnn_layer = 0;
+	int fully_connected_layer = 0;
+
+	vector<cv::Mat_<float> > input_maps;
+	input_maps.push_back(img);
+
+	vector<cv::Mat_<float> > outputs;
+
+	for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer)
+	{
+		// Determine layer type
+		int layer_type = cnn_layer_types[view_id][layer];
+
+		// Convolutional layer
+		if (layer_type == 0)
+		{
+			outputs.clear();
+			for (size_t in = 0; in < input_maps.size(); ++in)
+			{
+				cv::Mat_<float> input_image = input_maps[in];
+
+				// Useful precomputed data placeholders for quick correlation (convolution)
+				cv::Mat_<double> input_image_dft;
+				cv::Mat integral_image;
+				cv::Mat integral_image_sq;
+
+				// TODO can TBB-ify this
+				for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k)
+				{
+					cv::Mat_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
+
+					// The convolution (with precomputation)
+					cv::Mat_<float> output;
+					if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty())
+					{
+						std::map<int, cv::Mat_<double> > precomputed_dft;
+
+						LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+
+						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first;
+						cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second;
+					}
+					else
+					{
+						std::map<int, cv::Mat_<double> > precomputed_dft;
+						precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second;
+						LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR);
+					}
+
+					// Combining the maps
+					if (in == 0)
+					{
+						outputs.push_back(output);
+					}
+					else
+					{
+						outputs[k] = outputs[k] + output;
+					}
+
+				}
+
+			}
+
+			for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k)
+			{
+				outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k];
+			}
+			cnn_layer++;
+		}
+		if (layer_type == 1)
+		{
+			vector<cv::Mat_<float>> outputs_sub;
+
+			// Iterate over pool height and width, all the stride is 2x2 and no padding is used
+			int stride_x = 2;
+			int stride_y = 2;
+
+			int pool_x = 2;
+			int pool_y = 2;
+
+			for (size_t in = 0; in < input_maps.size(); ++in)
+			{
+				int out_x = input_maps[in].cols / stride_x;
+				int out_y = input_maps[in].rows / stride_y;
+
+				cv::Mat_<float> sub_out(out_y, out_x, 0.0);
+				cv::Mat_<float> in_map = input_maps[in];
+
+				for (int x = 0; x < input_maps[in].cols; x += stride_x)
+				{
+					for (int y = 0; y < input_maps[in].rows; y += stride_y)
+					{
+						float curr_max = -FLT_MAX;
+						for (int x_in = x; x_in < x + pool_x; ++x_in)
+						{
+							for (int y_in = y; y_in < y + pool_y; ++y_in)
+							{
+								float curr_val = in_map.at<float>(y_in, x_in);
+								if (curr_val > curr_max)
+								{
+									curr_max = curr_val;
+								}
+							}
+						}
+						int x_in_out = x / stride_x;
+						int y_in_out = y / stride_y;
+						sub_out.at<float>(y_in_out, x_in_out) = curr_max;
+					}
+				}
+
+				outputs_sub.push_back(sub_out);
+
+			}
+			outputs = outputs_sub;
+		}
+		if (layer_type == 2)
+		{
+			// Concatenate all the maps
+			cv::Mat_<float> input_concat = input_maps[0].t();
+			input_concat = input_concat.reshape(0, 1);
+
+			for (size_t in = 1; in < input_maps.size(); ++in)
+			{
+				cv::Mat_<float> add = input_maps[in].t();
+				add = add.reshape(0, 1);
+				cv::hconcat(input_concat, add, input_concat);
+			}
+
+			input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer];
+			input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t();
+
+			outputs.clear();
+			outputs.push_back(input_concat);
+
+			fully_connected_layer++;
+		}
+		if (layer_type == 3) // ReLU
+		{
+			outputs.clear();
+			for (size_t k = 0; k < input_maps.size(); ++k)
+			{
+				// Apply the ReLU
+				cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO);
+				outputs.push_back(input_maps[k]);
+
+			}
+		}
+		if (layer_type == 4)
+		{
+			outputs.clear();
+			for (size_t k = 0; k < input_maps.size(); ++k)
+			{
+				// Apply the sigmoid
+				cv::exp(-input_maps[k], input_maps[k]);
+				input_maps[k] = 1.0 / (1.0 + input_maps[k]);
+
+				outputs.push_back(input_maps[k]);
+
+			}
+		}
+		// Set the outputs of this layer to inputs of the next
+		input_maps = outputs;
+
+	}
+
+	// First turn to the 0-3 range
+	double max_val = 0;
+	cv::Point max_loc;
+	cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc);
+	int max_idx = max_loc.y;
+	double max = 3;
+	double min = 0;
+	double bins = (double)outputs[0].cols;
+	// Unquantizing the softmax layer to continuous value
+	double step_size = (max - min) / bins; // This should be saved somewhere
+	double unquantized = min + step_size / 2.0 + max_idx * step_size;
+
+	// Turn it to -1, 1 range
+	double dec = (unquantized - 1.5) / 1.5;
+
+	return dec;
+}
+
 void DetectionValidator::NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id)
 {
 	cv::Mat_<double> warped_t = warped_img.t();

lib/local/LandmarkDetector/src/LandmarkDetectorFunc.cpp

@@ -207,7 +207,7 @@ void CorrectGlobalParametersVideo(const cv::Mat_<uchar> &grayscale_image, CLNF&
 	
 }
 
-bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, CLNF& clnf_model, FaceModelParameters& params)
+bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
 {
 	// First need to decide if the landmarks should be "detected" or "tracked"
 	// Detected means running face detection and a larger search area, tracked means initialising from previous step
@@ -236,7 +236,8 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 			CorrectGlobalParametersVideo(grayscale_image, clnf_model, params);
 		}
 
-		bool track_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);
+		bool track_success = clnf_model.DetectLandmarks(grayscale_image, params);
+		
 		if(!track_success)
 		{
 			// Make a record that tracking failed
@@ -318,7 +319,7 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 			params.window_sizes_current = params.window_sizes_init;
 
 			// Do the actual landmark detection (and keep it only if successful)
-			bool landmark_detection_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);
+			bool landmark_detection_success = clnf_model.DetectLandmarks(grayscale_image, params);
 
 			// If landmark reinitialisation unsucessful continue from previous estimates
 			// if it's initial detection however, do not care if it was successful as the validator might be wrong, so continue trackig
@@ -361,7 +362,7 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 	
 }
 
-bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
+bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
 {
 	if(bounding_box.width > 0)
 	{
@@ -373,27 +374,17 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
 		clnf_model.tracking_initialised = true;
 	}
 
-	return DetectLandmarksInVideo(grayscale_image, depth_image, clnf_model, params);
+	return DetectLandmarksInVideo(grayscale_image, clnf_model, params);
 
 }
 
-bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
-{
-	return DetectLandmarksInVideo(grayscale_image, cv::Mat_<float>(), clnf_model, params);
-}
-
-bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
-{
-	return DetectLandmarksInVideo(grayscale_image, cv::Mat_<float>(), bounding_box, clnf_model, params);
-}
-
 //================================================================================================================
 // Landmark detection in image, need to provide an image and optionally CLNF model together with parameters (default values work well)
 // Optionally can provide a bounding box in which detection is performed (this is useful if multiple faces are to be detected in images)
 //================================================================================================================
 
 // This is the one where the actual work gets done, other DetectLandmarksInImage calls lead to this one
-bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
+bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
 {
 
 	// Can have multiple hypotheses
@@ -446,7 +437,7 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_i
 		// calculate the local and global parameters from the generated 2D shape (mapping from the 2D to 3D because camera params are unknown)
 		clnf_model.pdm.CalcParams(clnf_model.params_global, bounding_box, clnf_model.params_local, rotation_hypotheses[hypothesis]);
 	
-		bool success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);	
+		bool success = clnf_model.DetectLandmarks(grayscale_image, params);	
 
 		if(hypothesis == 0 || best_likelihood < clnf_model.model_likelihood)
 		{
@@ -456,11 +447,8 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_i
 			best_detected_landmarks = clnf_model.detected_landmarks.clone();
 			best_landmark_likelihoods = clnf_model.landmark_likelihoods.clone();
 			best_success = success;
-		}
 
 			for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
-		{
-			if (hypothesis == 0 || best_likelihood < clnf_model.hierarchical_models[part].model_likelihood)
 			{
 				best_likelihood_h[part] = clnf_model.hierarchical_models[part].model_likelihood;
 				best_global_parameters_h[part] = clnf_model.hierarchical_models[part].params_global;
@@ -469,7 +457,6 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_i
 				best_landmark_likelihoods_h[part] = clnf_model.hierarchical_models[part].landmark_likelihoods.clone();
 			}
 		}
-
 	}
 
 	// Store the best estimates in the clnf_model
@@ -491,7 +478,7 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_i
 	return best_success;
 }
 
-bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, CLNF& clnf_model, FaceModelParameters& params)
+bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
 {
 
 	cv::Rect_<double> bounding_box;
@@ -520,18 +507,6 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_i
 	}
 	else
 	{
-		return DetectLandmarksInImage(grayscale_image, depth_image, bounding_box, clnf_model, params);
+		return DetectLandmarksInImage(grayscale_image, bounding_box, clnf_model, params);
 	}
 }
-
-// Versions not using depth images
-bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
-{
-	return DetectLandmarksInImage(grayscale_image, cv::Mat_<float>(), bounding_box, clnf_model, params);
-}
-
-bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
-{
-	return DetectLandmarksInImage(grayscale_image, cv::Mat_<float>(), clnf_model, params);
-}
-

lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp

@@ -251,7 +251,6 @@ void CLNF::Read_CLNF(string clnf_location)
 	string line;
 	
 	vector<string> intensity_expert_locations;
-	vector<string> depth_expert_locations;
 	vector<string> ccnf_expert_locations;
 
 	// The other module locations should be defined as relative paths from the main model
@@ -316,10 +315,6 @@ void CLNF::Read_CLNF(string clnf_location)
 		{
 			intensity_expert_locations.push_back(location);
 		}
-		else if(module.compare("PatchesDepth") == 0)
-		{
-			depth_expert_locations.push_back(location);
-		}
 		else if(module.compare("PatchesCCNF") == 0)
 		{
 			ccnf_expert_locations.push_back(location);
@@ -327,7 +322,7 @@ void CLNF::Read_CLNF(string clnf_location)
 	}
   
 	// Initialise the patch experts
-	patch_experts.Read(intensity_expert_locations, depth_expert_locations, ccnf_expert_locations);
+	patch_experts.Read(intensity_expert_locations, ccnf_expert_locations);
 
 	// Read in a face detector
 	face_detector_HOG = dlib::get_frontal_face_detector();
@@ -590,11 +585,11 @@ void CLNF::Reset(double x, double y)
 }
 
 // The main internal landmark detection call (should not be used externally?)
-bool CLNF::DetectLandmarks(const cv::Mat_<uchar> &image, const cv::Mat_<float> &depth, FaceModelParameters& params)
+bool CLNF::DetectLandmarks(const cv::Mat_<uchar> &image, FaceModelParameters& params)
 {
 
 	// Fits from the current estimate of local and global parameters in the model
-	bool fit_success = Fit(image, depth, params.window_sizes_current, params);
+	bool fit_success = Fit(image, params.window_sizes_current, params);
 
 	// Store the landmarks converged on in detected_landmarks
 	pdm.CalcShape2D(detected_landmarks, params_local, params_global);	
@@ -636,7 +631,7 @@ bool CLNF::DetectLandmarks(const cv::Mat_<uchar> &image, const cv::Mat_<float> &
 					this->hierarchical_params[part_model].window_sizes_current = this->hierarchical_params[part_model].window_sizes_init;
 
 					// Do the actual landmark detection
-					hierarchical_models[part_model].DetectLandmarks(image, depth, hierarchical_params[part_model]);
+					hierarchical_models[part_model].DetectLandmarks(image, hierarchical_params[part_model]);
 
 				}
 				else
@@ -701,7 +696,7 @@ bool CLNF::DetectLandmarks(const cv::Mat_<uchar> &image, const cv::Mat_<float> &
 }
 
 //=============================================================================
-bool CLNF::Fit(const cv::Mat_<uchar>& im, const cv::Mat_<float>& depthImg, const std::vector<int>& window_sizes, const FaceModelParameters& parameters)
+bool CLNF::Fit(const cv::Mat_<uchar>& im, const std::vector<int>& window_sizes, const FaceModelParameters& parameters)
 {
 	// Making sure it is a single channel image
 	assert(im.channels() == 1);	
@@ -711,20 +706,6 @@ bool CLNF::Fit(const cv::Mat_<uchar>& im, const cv::Mat_<float>& depthImg, const
 
 	int n = pdm.NumberOfPoints(); 
 		
-	cv::Mat_<float> depth_img_no_background;
-	
-	// Background elimination from the depth image
-	if(!depthImg.empty())
-	{
-		bool success = RemoveBackground(depth_img_no_background, depthImg);
-
-		// The attempted background removal can fail leading to tracking failure
-		if(!success)
-		{
-			return false;
-		}
-	}
-
 	int num_scales = patch_experts.patch_scaling.size();
 
 	// Storing the patch expert response maps
@@ -746,15 +727,7 @@ bool CLNF::Fit(const cv::Mat_<uchar>& im, const cv::Mat_<float>& depthImg, const
 			continue;
 
 		// The patch expert response computation
-		if(scale != window_sizes.size() - 1)
+		patch_experts.Response(patch_expert_responses, sim_ref_to_img, sim_img_to_ref, im,  pdm, params_global, params_local, window_size, scale);
-		{
-			patch_experts.Response(patch_expert_responses, sim_ref_to_img, sim_img_to_ref, im, depth_img_no_background, pdm, params_global, params_local, window_size, scale);
-		}
-		else
-		{
-			// Do not use depth for the final iteration as it is not as accurate
-			patch_experts.Response(patch_expert_responses, sim_ref_to_img, sim_img_to_ref, im, cv::Mat(), pdm, params_global, params_local, window_size, scale);
-		}
 		
 		if(parameters.refine_parameters == true)
 		{
@@ -1139,95 +1112,6 @@ double CLNF::NU_RLMS(cv::Vec6d& final_global, cv::Mat_<double>& final_local, con
 
 }
 
-
-bool CLNF::RemoveBackground(cv::Mat_<float>& out_depth_image, const cv::Mat_<float>& depth_image)
-{
-	// use the current estimate of the face location to determine what is foreground and background
-	double tx = this->params_global[4];
-	double ty = this->params_global[5];
-
-	// if we are too close to the edge fail
-	if(tx - 9 <= 0 || ty - 9 <= 0 || tx + 9 >= depth_image.cols || ty + 9 >= depth_image.rows)
-	{
-		cout << "Face estimate is too close to the edge, tracking failed" << endl;
-		return false;
-	}
-
-	cv::Mat_<double> current_shape;
-
-	pdm.CalcShape2D(current_shape, params_local, params_global);
-
-	double min_x, max_x, min_y, max_y;
-
-	int n = this->pdm.NumberOfPoints();
-
-	cv::minMaxLoc(current_shape(cv::Range(0, n), cv::Range(0,1)), &min_x, &max_x);
-	cv::minMaxLoc(current_shape(cv::Range(n, n*2), cv::Range(0,1)), &min_y, &max_y);
-
-	// the area of interest: size of face with some scaling ( these scalings are fairly ad-hoc)
-	double width = 3 * (max_x - min_x); 
-	double height = 2.5 * (max_y - min_y); 
-
-	// getting the region of interest from the depth image,
-	// so we don't get other objects lying at same depth as head in the image but away from it
-	cv::Rect_<int> roi((int)(tx-width/2), (int)(ty - height/2), (int)width, (int)height);
-
-	// clamp it if it does not lie fully in the image
-	if(roi.x < 0) roi.x = 0;
-	if(roi.y < 0) roi.y = 0;
-	if(roi.width + roi.x >= depth_image.cols) roi.x = depth_image.cols - roi.width;
-	if(roi.height + roi.y >= depth_image.rows) roi.y = depth_image.rows - roi.height;
-		
-	if(width > depth_image.cols)
-	{
-		roi.x = 0; roi.width = depth_image.cols;
-	}
-	if(height > depth_image.rows)
-	{
-		roi.y = 0; roi.height = depth_image.rows;
-	}
-
-	if(roi.width == 0) roi.width = depth_image.cols;
-	if(roi.height == 0) roi.height = depth_image.rows;
-
-	if(roi.x >= depth_image.cols) roi.x = 0;
-	if(roi.y >= depth_image.rows) roi.y = 0;
-
-	// Initialise the mask
-	cv::Mat_<uchar> mask(depth_image.rows, depth_image.cols, (uchar)0);
-
-	cv::Mat_<uchar> valid_pixels = depth_image > 0;
-
-	// check if there is any depth near the estimate
-	if(cv::sum(valid_pixels(cv::Rect((int)tx - 8, (int)ty - 8, 16, 16))/255)[0] > 0)
-	{
-		double Z = cv::mean(depth_image(cv::Rect((int)tx - 8, (int)ty - 8, 16, 16)), valid_pixels(cv::Rect((int)tx - 8, (int)ty - 8, 16, 16)))[0]; // Z offset from the surface of the face
-				
-		// Only operate within region of interest of the depth image
-		cv::Mat dRoi = depth_image(roi);
-
-		cv::Mat mRoi = mask(roi);
-
-		// Filter all pixels further than 20cm away from the current pose depth estimate
-		cv::inRange(dRoi, Z - 200, Z + 200, mRoi);
-		
-		// Convert to be either 0 or 1
-		mask = mask / 255;
-		
-		cv::Mat_<float> maskF;
-		mask.convertTo(maskF, CV_32F);
-
-		//Filter the depth image
-		out_depth_image = depth_image.mul(maskF);
-	}
-	else
-	{
-		cout << "No depth signal found in foreground, tracking failed" << endl;
-		return false;
-	}
-	return true;
-}
-
 // Getting a 3D shape model from the current detected landmarks (in camera space)
 cv::Mat_<double> CLNF::GetShape(double fx, double fy, double cx, double cy) const
 {

lib/local/LandmarkDetector/src/LandmarkDetectorParameters.cpp

@@ -83,6 +83,15 @@ FaceModelParameters::FaceModelParameters(vector<string> &arguments)
 			i++;
 
 		}
+		if (arguments[i].compare("-fdloc") ==0)
+		{
+			string face_detector_loc = arguments[i + 1];
+			face_detector_location = face_detector_loc;
+			curr_face_detector = HAAR_DETECTOR;
+			valid[i] = false;
+			valid[i + 1] = false;
+			i++;
+		}
 		if (arguments[i].compare("-sigma") == 0)
 		{
 			stringstream data(arguments[i + 1]);
@@ -144,7 +153,6 @@ FaceModelParameters::FaceModelParameters(vector<string> &arguments)
 			track_gaze = true;
 
 			valid[i] = false;
-			i++;
 		}
 		else if (arguments[i].compare("-q") == 0)
 		{
@@ -253,12 +261,7 @@ void FaceModelParameters::init()
 	reinit_video_every = 4;
 
 	// Face detection
-#if OS_UNIX
 	face_detector_location = "classifiers/haarcascade_frontalface_alt.xml";
-#else
-	face_detector_location = "classifiers/haarcascade_frontalface_alt.xml";
-#endif
-
 	quiet_mode = false;
 
 	// By default use HOG SVM

lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp

@@ -14,20 +14,20 @@
 //       reports and manuals, must cite at least one of the following works:
 //
 //       OpenFace: an open source facial behavior analysis toolkit
-//       Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
+//       Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
 //       in IEEE Winter Conference on Applications of Computer Vision, 2016  
 //
 //       Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
-//       Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling 
+//       Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling 
 //       in IEEE International. Conference on Computer Vision (ICCV),  2015 
 //
 //       Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
-//       Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson 
+//       Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson 
 //       in Facial Expression Recognition and Analysis Challenge, 
 //       IEEE International Conference on Automatic Face and Gesture Recognition, 2015 
 //
 //       Constrained Local Neural Fields for robust facial landmark detection in the wild.
-//       Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. 
+//       Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency. 
 //       in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.    
 //
 ///////////////////////////////////////////////////////////////////////////////
@@ -95,8 +95,8 @@ void create_directories(string output_path)
 	}
 }
 
-// Extracting the following command line arguments -f, -fd, -op, -of, -ov (and possible ordered repetitions)
+// Extracting the following command line arguments -f, -op, -of, -ov (and possible ordered repetitions)
-void get_video_input_output_params(vector<string> &input_video_files, vector<string> &depth_dirs, vector<string> &output_files,
+void get_video_input_output_params(vector<string> &input_video_files, vector<string> &output_files,
 	vector<string> &output_video_files, string& output_codec, vector<string> &arguments)
 {
 	bool* valid = new bool[arguments.size()];
@@ -146,13 +146,6 @@ void get_video_input_output_params(vector<string> &input_video_files, vector<str
 			valid[i+1] = false;			
 			i++;
 		}		
-		else if (arguments[i].compare("-fd") == 0) 
-		{                    
-			depth_dirs.push_back(input_root + arguments[i + 1]);
-			valid[i] = false;
-			valid[i+1] = false;		
-			i++;
-		}
 		else if (arguments[i].compare("-of") == 0)
 		{
 			output_files.push_back(output_root + arguments[i + 1]);
@@ -173,6 +166,7 @@ void get_video_input_output_params(vector<string> &input_video_files, vector<str
 		{
 			if(arguments[i + 1].length() == 4)
 				output_codec = arguments[i + 1];
+			i++;
 		}
 	}
 
@@ -244,7 +238,7 @@ void get_camera_params(int &device, float &fx, float &fy, float &cx, float &cy,
 	}
 }
 
-void get_image_input_output_params(vector<string> &input_image_files, vector<string> &input_depth_files, vector<string> &output_feature_files, vector<string> &output_pose_files, vector<string> &output_image_files,
+void get_image_input_output_params(vector<string> &input_image_files, vector<string> &output_feature_files, vector<string> &output_pose_files, vector<string> &output_image_files,
 		vector<cv::Rect_<double>> &input_bounding_boxes, vector<string> &arguments)
 {
 	bool* valid = new bool[arguments.size()];
@@ -287,13 +281,6 @@ void get_image_input_output_params(vector<string> &input_image_files, vector<str
 			valid[i+1] = false;			
 			i++;
 		}		
-		else if (arguments[i].compare("-fd") == 0) 
-		{                    
-			input_depth_files.push_back(input_root + arguments[i + 1]);
-			valid[i] = false;
-			valid[i+1] = false;		
-			i++;
-		}
 		else if (arguments[i].compare("-fdir") == 0) 
 		{                    
 
@@ -1537,7 +1524,6 @@ bool DetectSingleFaceHOG(cv::Rect_<double>& o_region, const cv::Mat_<uchar>& int
 			{
 				dist = sqrt((preference.x - (face_detections[i].width/2 + face_detections[i].x)) * (preference.x - (face_detections[i].width/2 + face_detections[i].x)) + 
 							   (preference.y - (face_detections[i].height/2 + face_detections[i].y)) * (preference.y - (face_detections[i].height/2 + face_detections[i].y)));
-
 				better = dist < best_so_far;
 			}
 			else if (use_size)

lib/local/LandmarkDetector/src/Patch_experts.cpp

@@ -56,7 +56,7 @@
 using namespace LandmarkDetector;
 
 // A copy constructor
-Patch_experts::Patch_experts(const Patch_experts& other) : patch_scaling(other.patch_scaling), centers(other.centers), svr_expert_intensity(other.svr_expert_intensity), svr_expert_depth(other.svr_expert_depth), ccnf_expert_intensity(other.ccnf_expert_intensity)
+Patch_experts::Patch_experts(const Patch_experts& other) : patch_scaling(other.patch_scaling), centers(other.centers), svr_expert_intensity(other.svr_expert_intensity), ccnf_expert_intensity(other.ccnf_expert_intensity)
 {
 
 	// Make sure the matrices are allocated properly
@@ -86,11 +86,11 @@ Patch_experts::Patch_experts(const Patch_experts& other) : patch_scaling(other.p
 	}
 }
 
-// Returns the patch expert responses given a grayscale and an optional depth image.
+// Returns the patch expert responses given a grayscale image.
 // Additionally returns the transform from the image coordinates to the response coordinates (and vice versa).
 // The computation also requires the current landmark locations to compute response around, the PDM corresponding to the desired model, and the parameters describing its instance
 // Also need to provide the size of the area of interest and the desired scale of analysis
-void Patch_experts::Response(vector<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image, const cv::Mat_<float>& depth_image,
+void Patch_experts::Response(vector<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image, 
 							 const PDM& pdm, const cv::Vec6d& params_global, const cv::Mat_<double>& params_local, int window_size, int scale)
 {
 
@@ -126,15 +126,6 @@ void Patch_experts::Response(vector<cv::Mat_<float> >& patch_expert_responses, c
 	sim_ref_to_img(1,0) = (float)sim_ref_to_img_d(1,0);
 	sim_ref_to_img(1,1) = (float)sim_ref_to_img_d(1,1);
 
-	// Indicates the legal pixels in a depth image, if available (used for CLM-Z area of interest (window) interpolation)
-	cv::Mat_<uchar> mask;
-	if(!depth_image.empty())
-	{
-		mask = depth_image > 0;			
-		mask = mask / 255;
-	}		
-	
-
 	bool use_ccnf = !this->ccnf_expert_intensity.empty();
 
 	// If using CCNF patch experts might need to precalculate Sigmas
@@ -222,53 +213,6 @@ void Patch_experts::Response(vector<cv::Mat_<float> >& patch_expert_responses, c
 					svr_expert_intensity[scale][view_id][i].Response(area_of_interest, patch_expert_responses[i]);
 				}
 			
-				// if we have a corresponding depth patch and it is visible		
-				if(!svr_expert_depth.empty() && !depth_image.empty() && visibilities[scale][view_id].at<int>(i,0))
-				{
-
-					cv::Mat_<float> dProb = patch_expert_responses[i].clone();
-					cv::Mat_<float> depthWindow(area_of_interest_height, area_of_interest_width);
-			
-
-					CvMat dimg_o = depthWindow;
-					cv::Mat maskWindow(area_of_interest_height, area_of_interest_width, CV_32F);
-					CvMat mimg_o = maskWindow;
-
-					IplImage d_o = depth_image;
-					IplImage m_o = mask;
-
-					cvGetQuadrangleSubPix(&d_o,&dimg_o,&sim_o);
-				
-					cvGetQuadrangleSubPix(&m_o,&mimg_o,&sim_o);
-
-					depthWindow.setTo(0, maskWindow < 1);
-
-					svr_expert_depth[scale][view_id][i].ResponseDepth(depthWindow, dProb);
-							
-					// Sum to one
-					double sum = cv::sum(patch_expert_responses[i])[0];
-
-					// To avoid division by 0 issues
-					if(sum == 0)
-					{
-						sum = 1;
-					}
-
-					patch_expert_responses[i] /= sum;
-
-					// Sum to one
-					sum = cv::sum(dProb)[0];
-					// To avoid division by 0 issues
-					if(sum == 0)
-					{
-						sum = 1;
-					}
-
-					dProb /= sum;
-
-					patch_expert_responses[i] = patch_expert_responses[i] + dProb;
-
-				}
 			}
 		}
 	}
@@ -303,7 +247,7 @@ int Patch_experts::GetViewIdx(const cv::Vec6d& params_global, int scale) const
 
 
 //===========================================================================
-void Patch_experts::Read(vector<string> intensity_svr_expert_locations, vector<string> depth_svr_expert_locations, vector<string> intensity_ccnf_expert_locations)
+void Patch_experts::Read(vector<string> intensity_svr_expert_locations, vector<string> intensity_ccnf_expert_locations)
 {
 
 	// initialise the SVR intensity patch expert parameters
@@ -341,61 +285,6 @@ void Patch_experts::Read(vector<string> intensity_svr_expert_locations, vector<s
 		Read_CCNF_patch_experts(location,  centers[scale], visibilities[scale], ccnf_expert_intensity[scale], patch_scaling[scale]);
 	}
 
-
-	// initialise the SVR depth patch expert parameters
-	int num_depth_scales = depth_svr_expert_locations.size();
-	int num_intensity_scales = centers.size();
-	
-	if(num_depth_scales > 0 && num_intensity_scales != num_depth_scales)
-	{
-		cout << "Intensity and depth patch experts have a different number of scales, can't read depth" << endl;
-		return;
-	}
-
-	// Have these to confirm that depth patch experts have the same number of views and scales and have the same visibilities
-	vector<vector<cv::Vec3d> > centers_depth(num_depth_scales);
-	vector<vector<cv::Mat_<int> > > visibilities_depth(num_depth_scales);
-	vector<double> patch_scaling_depth(num_depth_scales);
-	
-	svr_expert_depth.resize(num_depth_scales);	
-
-	// Reading in SVR intensity patch experts for each scales it is defined in
-	for(int scale = 0; scale < num_depth_scales; ++scale)
-	{		
-		string location = depth_svr_expert_locations[scale];
-		cout << "Reading the depth SVR patch experts from: " << location << "....";
-		Read_SVR_patch_experts(location,  centers_depth[scale], visibilities_depth[scale], svr_expert_depth[scale], patch_scaling_depth[scale]);
-
-		// Check if the scales are identical
-		if(patch_scaling_depth[scale] != patch_scaling[scale])
-		{
-			cout << "Intensity and depth patch experts have a different scales, can't read depth" << endl;
-			svr_expert_depth.clear();
-			return;			
-		}
-
-		int num_views_intensity = centers[scale].size();
-		int num_views_depth = centers_depth[scale].size();
-
-		// Check if the number of views is identical
-		if(num_views_intensity != num_views_depth)
-		{
-			cout << "Intensity and depth patch experts have a different number of scales, can't read depth" << endl;
-			svr_expert_depth.clear();
-			return;			
-		}
-
-		for(int view = 0; view < num_views_depth; ++view)
-		{
-			if(cv::countNonZero(centers_depth[scale][view] != centers[scale][view]) || cv::countNonZero(visibilities[scale][view] != visibilities_depth[scale][view]))
-			{
-				cout << "Intensity and depth patch experts have different visibilities or centers" << endl;
-				svr_expert_depth.clear();
-				return;		
-			}
-		}
-	}
-
 }
 //======================= Reading the SVR patch experts =========================================//
 void Patch_experts::Read_SVR_patch_experts(string expert_location, std::vector<cv::Vec3d>& centers, std::vector<cv::Mat_<int> >& visibility, std::vector<std::vector<Multi_SVR_patch_expert> >& patches, double& scale)

matlab_runners/Action Unit Experiments/helpers/find_FERA2011.m

@@ -2,6 +2,8 @@ if(exist('D:\Datasets\fera/au_training', 'file'))
     FERA2011_dir = 'D:\Datasets\fera/au_training/';   
 elseif(exist('/multicomp/datasets/fera2011/', 'file'))
     FERA2011_dir = '/multicomp/datasets/fera2011/au_training/';   
+elseif(exist('E:\Datasets\fera/au_training', 'file'))
+    FERA2011_dir = 'E:\Datasets\fera/au_training/';  
 else
     fprintf('FERA2011 location not found (or not defined)\n'); 
 end

matlab_runners/Action Unit Experiments/results/DISFA_valid_res.txt

@@ -1,12 +1,12 @@
-AU1 results - corr 0.825, rms 0.414, ccc - 0.802
+AU1 results - corr 0.827, rms 0.412, ccc - 0.804
-AU2 results - corr 0.766, rms 0.444, ccc - 0.659
+AU2 results - corr 0.762, rms 0.446, ccc - 0.656
-AU4 results - corr 0.863, rms 0.583, ccc - 0.837
+AU4 results - corr 0.874, rms 0.565, ccc - 0.848
-AU5 results - corr 0.748, rms 0.179, ccc - 0.713
+AU5 results - corr 0.744, rms 0.181, ccc - 0.714
-AU6 results - corr 0.706, rms 0.591, ccc - 0.665
+AU6 results - corr 0.702, rms 0.604, ccc - 0.657
 AU9 results - corr 0.741, rms 0.384, ccc - 0.688
-AU12 results - corr 0.869, rms 0.494, ccc - 0.857
+AU12 results - corr 0.864, rms 0.510, ccc - 0.850
-AU15 results - corr 0.747, rms 0.268, ccc - 0.714
+AU15 results - corr 0.745, rms 0.269, ccc - 0.712
-AU17 results - corr 0.648, rms 0.512, ccc - 0.580
+AU17 results - corr 0.640, rms 0.521, ccc - 0.571
-AU20 results - corr 0.638, rms 0.303, ccc - 0.595
+AU20 results - corr 0.618, rms 0.311, ccc - 0.580
-AU25 results - corr 0.927, rms 0.499, ccc - 0.920
+AU25 results - corr 0.926, rms 0.500, ccc - 0.920
-AU26 results - corr 0.804, rms 0.448, ccc - 0.763
+AU26 results - corr 0.803, rms 0.449, ccc - 0.763

matlab_runners/Action Unit Experiments/results/FERA2011_res_class.txt

@@ -1,11 +1,11 @@
-AU1 class, Precision - 0.587, Recall - 0.715, F1 - 0.644
+AU1 class, Precision - 0.590, Recall - 0.714, F1 - 0.646
-AU2 class, Precision - 0.470, Recall - 0.794, F1 - 0.590
+AU2 class, Precision - 0.474, Recall - 0.750, F1 - 0.581
-AU4 class, Precision - 0.500, Recall - 0.753, F1 - 0.601
+AU4 class, Precision - 0.509, Recall - 0.745, F1 - 0.605
-AU6 class, Precision - 0.835, Recall - 0.674, F1 - 0.746
+AU6 class, Precision - 0.835, Recall - 0.667, F1 - 0.741
-AU7 class, Precision - 0.683, Recall - 0.791, F1 - 0.733
+AU7 class, Precision - 0.686, Recall - 0.792, F1 - 0.735
-AU10 class, Precision - 0.523, Recall - 0.748, F1 - 0.616
+AU10 class, Precision - 0.522, Recall - 0.738, F1 - 0.612
-AU12 class, Precision - 0.919, Recall - 0.654, F1 - 0.764
+AU12 class, Precision - 0.919, Recall - 0.658, F1 - 0.767
-AU15 class, Precision - 0.359, Recall - 0.635, F1 - 0.458
+AU15 class, Precision - 0.361, Recall - 0.638, F1 - 0.461
-AU17 class, Precision - 0.229, Recall - 0.286, F1 - 0.254
+AU17 class, Precision - 0.231, Recall - 0.280, F1 - 0.253
-AU25 class, Precision - 0.205, Recall - 0.869, F1 - 0.332
+AU25 class, Precision - 0.205, Recall - 0.871, F1 - 0.332
-AU26 class, Precision - 0.122, Recall - 0.972, F1 - 0.216
+AU26 class, Precision - 0.122, Recall - 0.974, F1 - 0.217

matlab_runners/Action Unit Experiments/results/SEMAINE_valid_res.txt

@@ -1,6 +1,6 @@
-AU2 class, Precision - 0.371, Recall - 0.749, F1 - 0.497
+AU2 class, Precision - 0.360, Recall - 0.742, F1 - 0.485
-AU12 class, Precision - 0.425, Recall - 0.782, F1 - 0.550
+AU12 class, Precision - 0.427, Recall - 0.781, F1 - 0.552
-AU17 class, Precision - 0.126, Recall - 0.810, F1 - 0.218
+AU17 class, Precision - 0.111, Recall - 0.813, F1 - 0.195
-AU25 class, Precision - 0.345, Recall - 0.577, F1 - 0.432
+AU25 class, Precision - 0.337, Recall - 0.523, F1 - 0.410
-AU28 class, Precision - 0.479, Recall - 0.476, F1 - 0.478
+AU28 class, Precision - 0.430, Recall - 0.471, F1 - 0.450
-AU45 class, Precision - 0.290, Recall - 0.621, F1 - 0.395
+AU45 class, Precision - 0.295, Recall - 0.615, F1 - 0.399

matlab_runners/Action Unit Experiments/run_AU_prediction_DISFA.m

@@ -8,6 +8,8 @@ end
 
 if(exist('D:/Datasets/DISFA/Videos_LeftCamera/', 'file'))   
     DISFA_dir = 'D:/Datasets/DISFA/Videos_LeftCamera/';  
+elseif(exist('E:/Datasets/DISFA/Videos_LeftCamera/', 'file'))   
+    DISFA_dir = 'E:/Datasets/DISFA/Videos_LeftCamera/';  
 else
     DISFA_dir = '/multicomp/datasets/face_datasets/DISFA/Videos_LeftCamera/';
 end

matlab_runners/Demos/Read_HOG_file.m

@@ -4,6 +4,7 @@ function [hog_data, valid_inds] = Read_HOG_file(hog_file)
             
     f = fopen(hog_file, 'r');
 
+    % Pre-allocated data
     curr_data = [];
     curr_ind = 0;
 
@@ -50,10 +51,9 @@ function [hog_data, valid_inds] = Read_HOG_file(hog_file)
 
     fclose(f);
 
-    hog_data = curr_data(1:curr_ind,:);
+    % Do some cleanup, remove un-allocated data    
-    
+    if(~isempty(curr_data))        
-    if(~isempty(hog_data))        
+        valid_inds = curr_data(1:curr_ind,1);
-        valid_inds = hog_data(:,1);
+        hog_data = curr_data(1:curr_ind,2:end);    
-        hog_data = hog_data(:,2:end);
     end
 end

matlab_runners/Demos/feature_extraction_demo_img_seq.m

@@ -134,7 +134,7 @@ title('Pose (rotation and translation)');
 xlabel('Time (s)');
 
 %% Output HOG files
-[hog_data, valid_inds] = Read_HOG_file([output, '/', name, '.hog']);
+[hog_data, valid_inds] = Read_HOG_file(outputHOG_aligned);
 
 %% Output aligned images
 img_files = dir([outputDir_aligned, '/*.png']);

matlab_runners/Feature Point Experiments/results/fps_yt.txt

@@ -1,3 +1,3 @@
 Model, mean,  median
-OpenFace (CLNF):  0.0563,   0.0515
+OpenFace (CLNF):  0.0564,   0.0515
-CLM:   0.0683,   0.0602
+CLM:   0.0631,   0.0587

matlab_runners/Feature Point Experiments/run_OpenFace_feature_point_tests_300W.m

@@ -7,6 +7,8 @@ if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
     database_root = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
 elseif(exist('D:/Dropbox/Dropbox/AAM/test data/', 'file'))
     database_root = 'D:/Dropbox/Dropbox/AAM/test data/';
+elseif(exist('F:/Dropbox/AAM/test data/', 'file'))
+    database_root = 'F:/Dropbox/AAM/test data/';
 else
     database_root = '/multicomp/datasets/300-W/';
 end

matlab_runners/Feature Point Experiments/run_yt_dataset.m

@@ -16,6 +16,8 @@ if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
     database_root = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
 elseif(exist('D:/Dropbox/Dropbox/AAM/test data/', 'file'))
     database_root = 'D:/Dropbox/Dropbox/AAM/test data/';
+elseif(exist('F:/Dropbox/AAM/test data/', 'file'))
+    database_root = 'F:/Dropbox/AAM/test data/';
 else
     database_root = '/multicomp/datasets/';
 end

matlab_runners/Full_test_suite.m

@@ -36,7 +36,7 @@ run_AU_prediction_DISFA
 assert(mean(au_res) > 0.7);
 
 run_AU_prediction_SEMAINE
-assert(mean(f1s) > 0.42);
+assert(mean(f1s) > 0.41);
 
 run_AU_prediction_FERA2011
 assert(mean(au_res) > 0.5);
@@ -46,7 +46,7 @@ cd('../');
 %% Gaze
 cd('Gaze Experiments');
 extract_mpii_gaze_test
-assert(mean_error < 9.5)
+assert(mean_error < 9.6)
 assert(median_error < 9.0)
 cd('../');
 

matlab_runners/Gaze Experiments/extract_mpii_gaze_test.m

@@ -7,6 +7,8 @@ if(exist([getenv('USERPROFILE') '/Dropbox/AAM/eye_clm/mpii_data/'], 'file'))
     database_root = [getenv('USERPROFILE') '/Dropbox/AAM/eye_clm/mpii_data/'];    
 elseif(exist('D:\Dropbox/Dropbox/AAM/eye_clm/mpii_data/', 'file'))
     database_root = 'D:\Dropbox/Dropbox/AAM/eye_clm/mpii_data/';    
+elseif(exist('F:\Dropbox/AAM/eye_clm/mpii_data/', 'file'))
+    database_root = 'F:\Dropbox/AAM/eye_clm/mpii_data/';    
 elseif(exist('/multicomp/datasets/mpii_gaze/mpii_data/', 'file'))
     database_root = '/multicomp/datasets/mpii_gaze/mpii_data/';    
 else
@@ -38,7 +40,6 @@ parfor p=1:numel(p_dirs)
     out_p_loc = ['-opdir "', [output, p_dirs(p).name], '" '];
     command_c = cat(2, command, input_loc, out_img_loc, out_p_loc);
 
-    command_c = cat(2, command_c, ' -wild');
     if(isunix)
         unix(command_c, '-echo');
     else

matlab_runners/Head Pose Experiments/results/Pose_OF.txt

@@ -1,4 +1,4 @@
 Dataset and model,        pitch,  yaw,  roll,  mean,  median
-biwi error:  7.955,   5.583, 4.403,  5.980,  2.624
+biwi error:  7.092,   5.170, 4.657,  5.640,  2.607
-bu error:    2.734,   3.350, 2.459,  2.848,  1.974
+bu error:    2.769,   4.105, 2.569,  3.147,  2.118
-ict error:   3.610,   3.959, 3.369,  3.646,  1.967
+ict error:   3.489,   3.632, 3.538,  3.553,  2.029

matlab_runners/Head Pose Experiments/run_biwi_experiment.m

@@ -53,7 +53,7 @@ for i=3 + offset:numTogether:numel(dbSeqDir)
             command = cat(2, command, [' -ov "' outputVideo '"']);    
         end
     end    
-    command = cat(2, command, [' -fx 505 -fy 505 -cx 320 -cy 240 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze']);
+    command = cat(2, command, [' -fx 505 -fy 505 -cx 320 -cy 240 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze -vis-track']);
         
     if(any(strcmp('model', varargin)))
         command = cat(2, command, [' -mloc "', varargin{find(strcmp('model', varargin))+1}, '"']);

matlab_runners/Head Pose Experiments/run_bu_experiment.m

@@ -41,7 +41,7 @@ function [output_dir] = run_bu_experiment(bu_dir, verbose, varargin)
             end
         end
         
-        command = cat(2, command,  ' -fx 500 -fy 500 -cx 160 -cy 120 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze ');        
+        command = cat(2, command,  ' -fx 500 -fy 500 -cx 160 -cy 120 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze -vis-track ');        
     
         if(any(strcmp('model', varargin)))
             command = cat(2, command, [' -mloc "', varargin{find(strcmp('model', varargin))+1}, '"']);

matlab_runners/Head Pose Experiments/run_head_pose_tests_CLM.m

@@ -1,65 +0,0 @@
-clear;
-
-%%
-% Run the BU test with CLM
-if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
-    database_root = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
-else
-    database_root = 'F:/Dropbox/Dropbox/AAM/test data/';
-end
-
-buDir = [database_root, '/bu/uniform-light/'];
-
-% The fast and accurate single light models
-%%
-v = 1;
-[fps_bu_general, resFolderBUCLM_general] = run_bu_experiment(buDir, false, v, 'model', 'model/main_clm_general.txt');
-[bu_error_clm_svr_general, ~, ~, all_errors_bu_svr_general] = calcBUerror(resFolderBUCLM_general, buDir);
-
-%%
-% Run the CLM-Z and general Biwi test
-biwi_dir = '/biwi pose/';
-biwi_results_root = '/biwi pose results/';
-
-% Intensity
-v = 1;
-[fps_biwi_clm, res_folder_clm_biwi] = run_biwi_experiment(database_root, biwi_dir, biwi_results_root, false, false, v, 'model', 'model/main_clm-z.txt');
-% Calculate the resulting errors
-[biwi_error_clm, ~, ~, ~, all_errors_biwi_clm] = calcBiwiError([database_root res_folder_clm_biwi], [database_root biwi_dir]);
-
-% Intensity with depth
-v = 2;
-[fps_biwi_clmz, res_folder_clmz_biwi] = run_biwi_experiment(database_root, biwi_dir, biwi_results_root, false, true, v, 'model', 'model/main_clm-z.txt');
-% Calculate the resulting errors
-[biwi_error_clmz, ~, ~, ~, all_errors_biwi_clm_z] = calcBiwiError([database_root res_folder_clmz_biwi], [database_root biwi_dir]);
-
-%% Run the CLM-Z and general ICT test
-ict_dir = ['ict/'];
-ict_results_root = ['ict results/'];
-
-v = 1;
-% Intensity
-[fps_ict_clm, res_folder_ict_clm] = run_ict_experiment(database_root, ict_dir, ict_results_root, false, false, v, 'model', 'model/main_clm-z.txt');
-[ict_error_clm, ~, ~, ~, all_errors_ict_clm] = calcIctError([database_root res_folder_ict_clm], [database_root ict_dir]);
-
-v = 2;
-% Intensity and depth
-[fps_ict_clmz, res_folder_ict_clmz] = run_ict_experiment(database_root, ict_dir, ict_results_root, false, true, v, 'model', 'model/main_clm-z.txt');
-[ict_error_clmz, ~, ~, ~, all_errors_ict_clm_z] = calcIctError([database_root res_folder_ict_clmz], [database_root ict_dir]);
-
-%% Save the results
-v = 1;
-filename = 'results/Pose_clm';
-save(filename);
-% 
-% Also save them in a reasonable .txt format for easy comparison
-f = fopen('results/Pose_clm.txt', 'w');
-fprintf(f, 'Dataset and model,        pitch,  yaw,  roll,  mean,  median\n');
-fprintf(f, 'biwi error clm:           %.3f,   %.3f, %.3f,  %.3f,  %.3f\n', biwi_error_clm, mean(all_errors_biwi_clm(:)), median(all_errors_biwi_clm(:)));
-fprintf(f, 'biwi error clm-z:         %.3f,   %.3f, %.3f,  %.3f,  %.3f\n', biwi_error_clmz, mean(all_errors_biwi_clm_z(:)), median(all_errors_biwi_clm_z(:)));
-fprintf(f, 'bu error clm general:     %.3f,   %.3f, %.3f,  %.3f,  %.3f\n', bu_error_clm_svr_general, mean(all_errors_bu_svr_general(:)), median(all_errors_bu_svr_general(:)));
-fprintf(f, 'ict error clm:            %.3f,   %.3f, %.3f,  %.3f,  %.3f\n', ict_error_clm, mean(all_errors_ict_clm(:)), median(all_errors_ict_clm(:)));
-fprintf(f, 'ict error clm-z:          %.3f,   %.3f, %.3f,  %.3f,  %.3f\n', ict_error_clmz, mean(all_errors_ict_clm_z(:)), median(all_errors_ict_clm_z(:)));
-
-fclose(f);
-clear 'f'

matlab_runners/Head Pose Experiments/run_head_pose_tests_OpenFace.m

@@ -10,6 +10,8 @@ elseif(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
     database_root = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];    
 elseif(exist([getenv('USERPROFILE') 'F:/Dropbox/Dropbox/AAM/test data/'], 'file'))
     database_root = 'F:/Dropbox/Dropbox/AAM/test data/';
+elseif(exist('F:/Dropbox/AAM/test data/', 'file'))
+    database_root = 'F:/Dropbox/AAM/test data/';    
 else
     database_root = '/multicomp/datasets/head_pose_dbs/';
 end

matlab_runners/Head Pose Experiments/run_ict_experiment.m

@@ -22,7 +22,7 @@ numTogether = 10;
 
 for i=3:numTogether:numel(dbSeqDir)
         
-    command = [executable  ' -fx 535 -fy 536 -cx 327 -cy 241 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze '];
+    command = [executable  ' -fx 535 -fy 536 -cx 327 -cy 241 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze -vis-track '];
 
     command = cat(2, command, [' -inroot ' '"' rootDir '/"']);
 

matlab_version/demo/face_video_demo.m

@@ -27,8 +27,11 @@ record = true;
 clmParams.multi_modal_types  = patches(1).multi_modal_types;
 
 % load the face validator and add its dependency
-load('../face_validation/trained/face_check_cnn_68.mat', 'face_check_cnns');
+load('../face_validation/trained/faceCheckers.mat', 'faceCheckers');
 addpath(genpath('../face_validation'));
+od = cd('../face_validation/');
+setup;
+cd(od);
 
 %%
 for v=1:numel(vids)
@@ -126,7 +129,7 @@ for v=1:numel(vids)
                         % detection
                         shape_new = GetShapeOrtho(pdm.M, pdm.V, params, g_param_n);
                     
-                        dec = face_check_cnn(image, shape_new, g_param, face_check_cnns);
+                        dec = face_check_cnn(image, shape_new, g_param, faceCheckers);
                         
                         if(dec < 0.5)
                             det = true;
@@ -153,7 +156,7 @@ for v=1:numel(vids)
             all_local_params(i,:) = l_param;
             all_global_params(i,:) = g_param;
             
-            dec = face_check_cnn(image, shape, g_param, face_check_cnns);
+            dec = face_check_cnn(image, shape, g_param, faceCheckers);
             
             if(dec < 0.5)
                 clmParams.window_size = [19,19; 17,17;];

matlab_version/face_validation/Collect_wild_imgs.m

@@ -1,221 +0,0 @@
-function [images, detections, labels] = Collect_wild_imgs(root_test_data)
-       
-    use_afw = true;
-    use_lfpw = true;
-    use_helen = true;
-    use_ibug = true;
-    
-    use_68 = true;           
-    
-    images = [];
-    labels = [];
-    detections = [];
-    
-    if(use_afw)
-        [img, det, lbl] = Collect_AFW(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end
-    
-    if(use_lfpw)
-        [img, det, lbl] = Collect_LFPW(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end    
-    
-    if(use_ibug)
-        [img, det, lbl] = Collect_ibug(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end
-    
-    if(use_helen)
-        [img, det, lbl] = Collect_helen(root_test_data, use_68);
-        images = cat(1, images, img');
-        detections = cat(1, detections, det);
-        labels = cat(1, labels, lbl);        
-    end    
-
-    % convert to format expected by the Fitting method
-    detections(:,3) = detections(:,1) + detections(:,3);
-    detections(:,4) = detections(:,2) + detections(:,4);
-    
-end
-
-
-function [images, detections, labels] = Collect_AFW(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/AFW/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_afw.mat']);
-
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-    
-    landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3);
-    landmarks = landmarks.data;
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.jpg'];
-    labels(imgs,:,:) = landmarks;
-                   
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-end
-
-function [images, detections, labels] = Collect_LFPW(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/lfpw/testset/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_lfpw_testset.mat']);
-
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-    
-    landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3);
-    landmarks = landmarks.data;
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.png'];
-    
-    labels(imgs,:,:) = landmarks;
-    
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-
-end
-
-function [images, detections, labels] = Collect_ibug(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/ibug/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_ibug.mat']);
-
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-    
-    landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3);
-    landmarks = landmarks.data;
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.jpg'];
-    
-    labels(imgs,:,:) = landmarks;
-    
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-end
-
-function [images, detections, labels] = Collect_helen(root_test_data, use_68)
-    
-dataset_loc = [root_test_data, '/helen/testset/'];
-
-landmarkLabels = dir([dataset_loc '\*.pts']);
-
-num_imgs = size(landmarkLabels,1);
-
-images = struct;
-
-if(use_68)
-    labels = zeros(num_imgs, 68, 2);    
-else
-    labels = zeros(num_imgs, 66, 2);
-end
-
-detections = zeros(num_imgs, 4);
-
-load([root_test_data, '/Bounding Boxes/bounding_boxes_helen_testset.mat']);
-
-for imgs = 1:num_imgs
-
-    [~,name,~] = fileparts(landmarkLabels(imgs).name);
-    
-    landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3);
-    landmarks = landmarks.data;
-    if(~use_68)
-        inds_frontal = [1:60,62:64,66:68];
-        landmarks = landmarks(inds_frontal,:);
-    end
-    
-    images(imgs).img = [dataset_loc,  name '.jpg'];
-    
-    labels(imgs,:,:) = landmarks;
-    
-    detections(imgs,:) = bounding_boxes{imgs}.bb_detector;
-    
-end
-
-detections(:,3) = detections(:,3) - detections(:,1);
-detections(:,4) = detections(:,4) - detections(:,2);
-
-end

matlab_version/face_validation/Create_data_66.m

@@ -1,337 +0,0 @@
-function Create_data_66()
-
-load '../models/pdm/pdm_66_multi_pie';
-load '../models/tri_66.mat';
-
-% This script uses the same format used for patch expert training, and
-% expects the data to be there
-dataset_loc = '../../../CCNF experiments/clnf/patch training/data_preparation/prepared_data/';
-addpath('../PDM_helpers/');
-
-scale = '0.5';
-prefix= 'combined_';
-
-% Find the available positive training data
-data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
-centres_all = [];
-for i=1:numel(data_files)
-   
-    % Load the orientation of the training data
-    load([dataset_loc, '/', data_files(i).name], 'centres');
-    centres_all = cat(1, centres_all, centres);
-    
-end
-
-label_inds = [1:60,62:64,66:68];
-
-% Construct mirror indices (which views need to be flipped to create other
-% profile training data)
-mirror_inds = zeros(size(centres_all,1), 1);
-for i=1:numel(data_files)
-    
-    % mirrored image has inverse yaw
-    mirrored_centre = centres_all(i,:);
-    mirrored_centre(2) = -mirrored_centre(2);
-
-    % if mirrored version has same orientation, do not need mirroring
-    if(~isequal(mirrored_centre, centres_all(i,:)))
-       
-        centres_all = cat(1, centres_all, mirrored_centre);
-        mirror_inds = cat(1, mirror_inds, i);
-        
-    end    
-    
-end
-        
-outputLocation = './prep_data/';
-    
-num_more_neg = 10;
-
-% Make sure same data generated all the time
-rng(0);
-
-neg_image_loc = './neg/';
-
-neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
-
-max_img_used = 1500;
-
-%% do it separately for centers due to memory limitations
-for r=1:size(centres_all,1)
-    
-    a_mod = 0.3;
-    
-    mirror = false;
-    
-    if(mirror_inds(r) ~= 0 )
-        mirror = true;
-        label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
-                  32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
-                  61,63;66,64];          
-        load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
-    else            
-        load([dataset_loc, '/', data_files(r).name]);
-    end
-    
-    % Convert to 66 point model
-    landmark_locations = landmark_locations(:,label_inds,:);
-    
-    visiCurrent = logical(visiIndex);
-    
-    % Flip the orientation and indices for mirror data
-    if(mirror)
-        centres = [centres(1), -centres(2), -centres(3)];
-        tmp1 = visiCurrent(label_mirror_inds(:,1));
-        tmp2 = visiCurrent(label_mirror_inds(:,2));            
-        visiCurrent(label_mirror_inds(:,2)) = tmp1;
-        visiCurrent(label_mirror_inds(:,1)) = tmp2; 
-    end    
-            
-    visibleVerts = 1:numel(visiCurrent);
-    visibleVerts = visibleVerts(visiCurrent)-1;
-
-    % Correct the triangulation to take into account the vertex
-    % visibilities
-    triangulation = [];
-
-    shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)';
-    shape = shape';
-
-    for i=1:size(T,1)
-        visib = 0;
-        for j=1:numel(visibleVerts)
-            if(T(i,1)==visibleVerts(j))
-                visib = visib+1;
-            end
-            if(T(i,2)==visibleVerts(j))
-                visib = visib+1;
-            end
-            if(T(i,3)==visibleVerts(j))
-                visib = visib+1;
-            end
-        end
-        
-        % Only if all three of the vertices are visible
-        if(visib == 3)            
-                                    
-            % Also want to remove triangles facing the wrong way (self occluded)
-            v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)];
-            v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)];
-            v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)];
-            normal = cross((v2-v1), v3 - v2);
-            normal = normal / norm(normal);
-            direction = normal * [0,0,1]';
-
-            % And only if the triangle is facing the camera
-            if(direction > 0)
-                triangulation = cat(1, triangulation, T(i,:)); 
-            end
-        end                
-    end
-    
-    % Initialise the warp       
-    [ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape);
-
-    imgs_to_use = randperm(size(landmark_locations, 1));
-    
-    if(size(landmark_locations, 1) > max_img_used)
-        imgs_to_use = imgs_to_use(1:max_img_used);
-    end
-    
-    % Extracting relevant filenames
-    examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix);    
-    errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1);
-    
-    unused_pos = 0;
-    
-    curr_filled = 0;
-           
-    for j=imgs_to_use
-        
-        labels = squeeze(landmark_locations(j,:,:));
-
-        img = squeeze(all_images(j,:,:));
-
-        if(mirror)
-            img = fliplr(img);
-            imgSize = size(img);
-            flippedLbls = labels;
-            flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1);
-            tmp1 = flippedLbls(label_mirror_inds(:,1),:);
-            tmp2 = flippedLbls(label_mirror_inds(:,2),:);            
-            flippedLbls(label_mirror_inds(:,2),:) = tmp1;
-            flippedLbls(label_mirror_inds(:,1),:) = tmp2;   
-            labels = flippedLbls;
-        end            
-        
-        % If for some reason some of the labels are not visible in the
-        % current sample skip this label
-        non_existent_labels = labels(:,1)==0 | labels(:,2)==0;
-        non_existent_inds = find(non_existent_labels)-1;
-        if(numel(intersect(triangulation(:), non_existent_inds)) > 0)
-           unused_pos = unused_pos + 1; 
-           continue;
-        end
-        
-        curr_filled = curr_filled + 1;        
-        [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-        examples(curr_filled,:) = features;
-        
-        % Extract the correct PDM parameters for the model (we will perturb
-        % them for some negative examples)
-        [ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
-        eul_orig = Rot2Euler(R_orig);
-        
-        % a slightly perturbed example, too tight
-        % from 0.3 to 0.9
-        a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1);                        
-        p_global = [a_mod; eul_orig'; trans_orig];
-        
-        labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-        
-        % Compute the badness of fit
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;
-
-        % a slightly perturbed example, too broad
-        % from 1.2 to 0.6
-        a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1);        
-        p_global = [a_mod; eul_orig'; trans_orig];
-        
-        labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-                
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-        
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;
-
-        % A somewhat offset example
-        
-        trans_mod = trans_orig + randn(2,1) * 10;
-        p_global = [a_orig; eul_orig'; trans_mod];
-        
-        labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-        
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-    
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;
-       
-        % A rotated sample
-        eul_mod = eul_orig + randn(1,3)*0.2;
-        p_global = [a_orig; eul_mod'; trans_orig];
-        
-        labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-        
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-    
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;
-        
-        % A sample with modified shape parameters
-        p_global = [a_orig; eul_orig'; trans_orig];
-        params_mod = params_orig + randn(size(params_orig)).*sqrt(E);
-        labels_mod = GetShapeOrtho(M, V, params_mod, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-        
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-    
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;     
-                
-        % pick a random image from negative inriaperson dataset, use original location if
-        % first, otherwhise resize it to fit
-        for n=6:num_more_neg
-            n_img = randi(numel(neg_images));
-            
-            neg_image = imread([neg_image_loc, neg_images(n_img).name]);
-            
-            if(size(neg_image,3) == 3)
-                neg_image = rgb2gray(neg_image);
-            end
-            
-            [h_neg, w_neg] = size(neg_image);
-            
-            % if the current labels fit just use them, if not, then resize
-            % to fit
-            max_x = max(labels(:,1));
-            max_y = max(labels(:,2));
-            
-            if(max_x > w_neg || max_y > h_neg)
-               neg_image = imresize(neg_image, [max_y, max_x]);
-            end
-            
-            [features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-            
-            curr_filled = curr_filled + 1;        
-            examples(curr_filled,:) = features;
-
-            % Set high error to 3
-            errors(curr_filled,:) = 3;
-        end
-        
-        
-        if(mod(curr_filled, 10) == 0)
-            fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use));
-        end
-        % add the pos example to the background
-        
-    end    
-                
-    examples = examples(1:curr_filled,:);
-    errors = errors(1:curr_filled);
-    
-    % svm training
-    filename = sprintf('%s/face_checker_general_training_66_%d.mat', outputLocation, r);
-    save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
-    
-    
-end
-
-end
-
-function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY)
-
-    % Make sure labels are within range
-    [hRes, wRes] = size(img);
-    labels(labels(:,1) < 1,1) = 1;
-    labels(labels(:,2) < 1,2) = 1;
-
-    labels(labels(:,1) > wRes-1,1) = wRes-1;
-    labels(labels(:,2) > hRes-1,2) = hRes-1;   
-
-    crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-    crop_img(isnan(crop_img)) = 0;
-    
-    % vectorised version
-    features = reshape(crop_img(logical(mask)), 1, nPix);
-
-    % normalisations
-    features = (features - mean(features));
-    norms = std(features);
-    if(norms==0)            
-        norms = 1;
-    end
-    features = features / norms;
-end

matlab_version/face_validation/Create_data_68.m

@@ -1,334 +0,0 @@
-function Create_data_68()
-
-load '../models/pdm/pdm_68_multi_pie';
-load '../models/tri_68.mat';
-
-% This script uses the same format used for patch expert training, and
-% expects the data to be there
-dataset_loc = '../../../CCNF experiments/clnf/patch_training/data_preparation/prepared_data/';
-addpath('../PDM_helpers/');
-
-scale = '0.5';
-prefix= 'combined_';
-
-% Find the available positive training data
-data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
-centres_all = [];
-for i=1:numel(data_files)
-   
-    % Load the orientation of the training data
-    load([dataset_loc, '/', data_files(i).name], 'centres');
-    centres_all = cat(1, centres_all, centres);
-    
-end
-
-% Construct mirror indices (which views need to be flipped to create other
-% profile training data)
-mirror_inds = zeros(size(centres_all,1), 1);
-for i=1:numel(data_files)
-    
-    % mirrored image has inverse yaw
-    mirrored_centre = centres_all(i,:);
-    mirrored_centre(2) = -mirrored_centre(2);
-
-    % if mirrored version has same orientation, do not need mirroring
-    if(~isequal(mirrored_centre, centres_all(i,:)))
-       
-        centres_all = cat(1, centres_all, mirrored_centre);
-        mirror_inds = cat(1, mirror_inds, i);
-        
-    end    
-    
-end
-        
-outputLocation = './prep_data/';
-    
-num_more_neg = 10;
-
-% Make sure same data generated all the time
-rng(0);
-
-neg_image_loc = './neg/';
-
-neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
-
-max_img_used = 1500;
-
-% do it separately for centers due to memory limitations
-for r=1:size(centres_all,1)
-    
-    a_mod = 0.3;
-    
-    mirror = false;
-    
-    if(mirror_inds(r) ~= 0 )
-        mirror = true;
-        label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
-                  32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
-                  61,65;62,64;68,66];    
-        load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
-    else            
-        load([dataset_loc, '/', data_files(r).name]);
-    end
-    
-    visiCurrent = logical(visiIndex);
-    
-    if(mirror)
-        centres = [centres(1), -centres(2), -centres(3)];
-        tmp1 = visiCurrent(label_mirror_inds(:,1));
-        tmp2 = visiCurrent(label_mirror_inds(:,2));            
-        visiCurrent(label_mirror_inds(:,2)) = tmp1;
-        visiCurrent(label_mirror_inds(:,1)) = tmp2; 
-    end    
-            
-    visibleVerts = 1:numel(visiCurrent);
-    visibleVerts = visibleVerts(visiCurrent)-1;
-
-    % Correct the triangulation to take into account the vertex
-    % visibilities
-    triangulation = [];
-
-    shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)';
-    shape = shape';
-
-    for i=1:size(T,1)
-        visib = 0;
-        for j=1:numel(visibleVerts)
-            if(T(i,1)==visibleVerts(j))
-                visib = visib+1;
-            end
-            if(T(i,2)==visibleVerts(j))
-                visib = visib+1;
-            end
-            if(T(i,3)==visibleVerts(j))
-                visib = visib+1;
-            end
-        end
-        
-        % Only if all three of the vertices are visible
-        if(visib == 3)            
-            
-            % Also want to remove triangles facing the wrong way (self occluded)
-            v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)];
-            v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)];
-            v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)];
-            normal = cross((v2-v1), v3 - v2);
-            normal = normal / norm(normal);
-            direction = normal * [0,0,1]';
-
-            % And only if the triangle is facing the camera
-            if(direction > 0)
-                triangulation = cat(1, triangulation, T(i,:)); 
-            end
-        end                
-    end
-        
-    % Initialise the warp
-    [ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape);
-
-    mask = logical(mask);
-    
-    imgs_to_use = randperm(size(landmark_locations, 1));
-    
-    if(size(landmark_locations, 1) > max_img_used)
-        imgs_to_use = imgs_to_use(1:max_img_used);
-    end
-    
-    % Extracting relevant filenames
-    examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix);    
-    errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1);
-    
-    unused_pos = 0;
-    
-    curr_filled = 0;
-           
-    for j=imgs_to_use
-        
-        labels = squeeze(landmark_locations(j,:,:));
-
-        img = squeeze(all_images(j,:,:));
-
-        if(mirror)
-            img = fliplr(img);
-            imgSize = size(img);
-            flippedLbls = labels;
-            flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1);
-            tmp1 = flippedLbls(label_mirror_inds(:,1),:);
-            tmp2 = flippedLbls(label_mirror_inds(:,2),:);            
-            flippedLbls(label_mirror_inds(:,2),:) = tmp1;
-            flippedLbls(label_mirror_inds(:,1),:) = tmp2;   
-            labels = flippedLbls;
-        end            
-        
-        % If for some reason some of the labels are not visible in the
-        % current sample skip this label
-        non_existent_labels = labels(:,1)==0 | labels(:,2)==0;
-        non_existent_inds = find(non_existent_labels)-1;
-        if(numel(intersect(triangulation(:), non_existent_inds)) > 0)
-           unused_pos = unused_pos + 1; 
-           continue;
-        end
-        
-        curr_filled = curr_filled + 1;        
-        [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-        examples(curr_filled,:) = features;
-        
-        % Extract the correct PDM parameters for the model (we will perturb
-        % them for some negative examples)
-        [ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels);
-        eul_orig = Rot2Euler(R_orig);
-        
-        % a slightly perturbed example, too tight
-        % from 0.3 to 0.9
-        a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1);                        
-        p_global = [a_mod; eul_orig'; trans_orig];
-        
-        labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-        
-        % Compute the badness of fit
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;
-
-        % a slightly perturbed example, too broad
-        % from 1.2 to 0.6
-        a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1);        
-        p_global = [a_mod; eul_orig'; trans_orig];
-        
-        labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-                
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-        
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;
-
-        % A somewhat offset example
-        
-        trans_mod = trans_orig + randn(2,1) * 10;
-        p_global = [a_orig; eul_orig'; trans_mod];
-        
-        labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-        
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-    
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;
-       
-        % A rotated sample
-        eul_mod = eul_orig + randn(1,3)*0.2;
-        p_global = [a_orig; eul_mod'; trans_orig];
-        
-        labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-        
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-    
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;
-        
-        % A sample with modified shape parameters
-        p_global = [a_orig; eul_orig'; trans_orig];
-        params_mod = params_orig + randn(size(params_orig)).*sqrt(E);
-        labels_mod = GetShapeOrtho(M, V, params_mod, p_global);
-        labels_mod = labels_mod(:,1:2);
-        
-        [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-        
-        curr_filled = curr_filled + 1;        
-        examples(curr_filled,:) = features;
-    
-        error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2)));
-        errors(curr_filled,:) = error;     
-                
-        % pick a random image from negative inriaperson dataset, use original location if
-        % first, otherwhise resize it to fit
-        for n=6:num_more_neg
-            n_img = randi(numel(neg_images));
-            
-            neg_image = imread([neg_image_loc, neg_images(n_img).name]);
-            
-            if(size(neg_image,3) == 3)
-                neg_image = rgb2gray(neg_image);
-            end
-            
-            [h_neg, w_neg] = size(neg_image);
-            
-            % if the current labels fit just use them, if not, then resize
-            % to fit
-            max_x = max(labels(:,1));
-            max_y = max(labels(:,2));
-            
-            if(max_x > w_neg || max_y > h_neg)
-               neg_image = imresize(neg_image, [max_y, max_x]);
-            end
-            
-            [features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-            
-            curr_filled = curr_filled + 1;        
-            examples(curr_filled,:) = features;
-
-            % Set high error to 3
-            errors(curr_filled,:) = 3;
-        end
-        
-        
-        if(mod(curr_filled, 10) == 0)
-            fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use));
-        end
-        % add the pos example to the background
-        
-    end    
-                
-    examples = examples(1:curr_filled,:);
-    errors = errors(1:curr_filled);
-    
-    % svm training
-    filename = sprintf('%s/face_checker_general_training_68_%d.mat', outputLocation, r);
-    save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
-    
-    
-end
-
-end
-
-function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY)
-
-    % Make sure labels are within range
-    [hRes, wRes] = size(img);
-    labels(labels(:,1) < 1,1) = 1;
-    labels(labels(:,2) < 1,2) = 1;
-
-    labels(labels(:,1) > wRes-1,1) = wRes-1;
-    labels(labels(:,2) > hRes-1,2) = hRes-1;   
-
-    crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
-    crop_img(isnan(crop_img)) = 0;
-    
-    % vectorised version
-    features = reshape(crop_img(logical(mask)), 1, nPix);
-
-    % normalisations
-    features = (features - mean(features));
-    norms = std(features);
-    if(norms==0)            
-        norms = 1;
-    end
-    features = features / norms;
-
-end

matlab_version/face_validation/Create_data_test.m

@@ -1,6 +1,6 @@
-function Create_data_68_large()
+function Create_data_test()
 
-load '../models/pdm/pdm_68_aligned_wild';
+load '../models/pdm/pdm_68_aligned_menpo';
 load '../models/tri_68.mat';
 
 % This script uses the same format used for patch expert training, and
@@ -8,14 +8,19 @@ load '../models/tri_68.mat';
 % https://github.com/TadasBaltrusaitis/CCNF)
 
 % Replace with your location of training data
-dataset_loc = 'C:/Users/Tadas/Documents/CCNF/patch_experts/data_preparation/prepared_data/';
+dataset_loc = 'C:\Users\tbaltrus\Documents\CCNF\patch_experts\data_preparation/prepared_data/';
 addpath('../PDM_helpers/');
+addpath('./paw_helpers/');
 
+% Collect Menpo, Multi-PIE and 300W data for training the validator
 scale = '0.5';
-prefix= 'combined_';
+prefix_menpo= 'menpo_valid_';
+prefix_mpie_300W = 'combined_';
 
 % Find the available positive training data
-data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
+data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_menpo, scale));
+data_files_c = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_mpie_300W, scale));
+
 centres_all = [];
 for i=1:numel(data_files)
    
@@ -25,9 +30,6 @@ for i=1:numel(data_files)
     
 end
 
-% Do not use extreme pose
-centres_all = centres_all(1:3,:);
-
 % Construct mirror indices (which views need to be flipped to create other
 % profile training data)
 mirror_inds = zeros(size(centres_all,1), 1);
@@ -48,14 +50,14 @@ for i=1:numel(data_files)
 end
         
 % Replace with your location of training data
-outputLocation = 'E:/datasets/detection_validation/prep_data/';
+outputLocation = 'D:\Datasets/detection_validation/prep_data/';
     
 num_more_neg = 10;
 
 % Make sure same data generated all the time
 rng(0);
 
-neg_image_loc = 'E:/datasets/detection_validation/neg/';
+neg_image_loc = 'D:\Datasets\INRIAPerson\INRIAPerson\Train\neg/';
 
 neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
 
@@ -73,9 +75,20 @@ for r=1:size(centres_all,1)
         label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
                   32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
                   61,65;62,64;68,66];   
+        
+        % Make sure we take the subset of visibilities from all the
+        % datasets
+        load([dataset_loc, '/', data_files_c(mirror_inds(r)).name], 'visiIndex');
+        visiIndex_t = visiIndex;
+        
         load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
+        visiIndex = visiIndex_t & visiIndex;
     else            
+        load([dataset_loc, '/', data_files_c(r).name], 'visiIndex');
+        visiIndex_t = visiIndex;
+        
         load([dataset_loc, '/', data_files(r).name]);
+        visiIndex = visiIndex_t & visiIndex;
     end
     
     visiCurrent = logical(visiIndex);
@@ -312,8 +325,7 @@ for r=1:size(centres_all,1)
     examples = examples(1:curr_filled,:);
     errors = errors(1:curr_filled);
     
-    % svm training
+    filename = sprintf('%s/face_validator_test_%d.mat', outputLocation, r);
-    filename = sprintf('%s/face_checker_general_training_large_68_%d.mat', outputLocation, r);
     save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
     
     

matlab_version/face_validation/Create_data_train.m

@@ -1,22 +1,26 @@
-function Create_data_66_large()
+function Create_data_train()
 
-load '../models/pdm/pdm_66_multi_pie';
+load '../models/pdm/pdm_68_aligned_menpo';
-load '../models/tri_66.mat';
+load '../models/tri_68.mat';
 
 % This script uses the same format used for patch expert training, and
 % expects the data to be there (this can be found in
 % https://github.com/TadasBaltrusaitis/CCNF)
 
 % Replace with your location of training data
-dataset_loc = 'C:/Users/Tadas/Documents/CCNF/patch_experts/data_preparation/prepared_data/';
+dataset_loc = 'C:\Users\tbaltrus\Documents\CCNF\patch_experts\data_preparation/prepared_data/';
-
 addpath('../PDM_helpers/');
+addpath('./paw_helpers/');
 
+% Collect Menpo, Multi-PIE and 300W data for training the validator
 scale = '0.5';
-prefix= 'combined_';
+prefix_menpo= 'menpo_train_';
+prefix_mpie_300W = 'combined_';
 
 % Find the available positive training data
-data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale));
+data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_menpo, scale));
+data_files_c = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_mpie_300W, scale));
+
 centres_all = [];
 for i=1:numel(data_files)
    
@@ -26,8 +30,6 @@ for i=1:numel(data_files)
     
 end
 
-label_inds = [1:60,62:64,66:68];
-
 % Construct mirror indices (which views need to be flipped to create other
 % profile training data)
 mirror_inds = zeros(size(centres_all,1), 1);
@@ -47,18 +49,20 @@ for i=1:numel(data_files)
     
 end
         
-outputLocation = 'F:/datasets/detection_validation/prep_data/';
+% Replace with your location of training data
+outputLocation = 'D:\Datasets/detection_validation/prep_data/';
     
 num_more_neg = 10;
 
 % Make sure same data generated all the time
 rng(0);
 
-neg_image_loc = 'F:/datasets/detection_validation/neg/';
+% Negative samples from teh INRIAPerson dataset
+neg_image_loc = 'D:\Datasets\INRIAPerson\INRIAPerson\Train\neg/';
 
 neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png']));
 
-max_img_used = 2500;
+max_img_used = 8000;
 
 % do it separately for centers due to memory limitations
 for r=1:size(centres_all,1)
@@ -71,14 +75,30 @@ for r=1:size(centres_all,1)
         mirror = true;
         label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
                   32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
-                  61,63;66,64];    
+                  61,65;62,64;68,66];    
-        load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
+        load([dataset_loc, '/', data_files_c(mirror_inds(r)).name]);
-    else            
+        all_images_t = all_images;
-        load([dataset_loc, '/', data_files(r).name]);
+        landmark_locations_t = landmark_locations;
-    end
+        visiIndex_t = visiIndex;
 
-    % Convert to 66 point model
+        load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
-    landmark_locations = landmark_locations(:,label_inds,:);
+
+        % Combining Menpo + MPIE + 300W
+        all_images = cat(1, all_images, all_images_t);
+        landmark_locations = cat(1, landmark_locations, landmark_locations_t);        
+        % Taking a subset of visibilities from all the datasets
+        visiIndex = visiIndex_t & visiIndex;
+    else            
+        load([dataset_loc, '/', data_files_c(r).name]);
+        all_images_t = all_images;
+        landmark_locations_t = landmark_locations;
+        visiIndex_t = visiIndex;
+        
+        load([dataset_loc, '/', data_files(r).name]);
+        all_images = cat(1, all_images, all_images_t);
+        landmark_locations = cat(1, landmark_locations, landmark_locations_t);
+        visiIndex = visiIndex_t & visiIndex;
+    end
     
     visiCurrent = logical(visiIndex);
     
@@ -161,7 +181,7 @@ for r=1:size(centres_all,1)
             img = fliplr(img);
             imgSize = size(img);
             flippedLbls = labels;
-            flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1);
+            flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1) + 1;
             tmp1 = flippedLbls(label_mirror_inds(:,1),:);
             tmp2 = flippedLbls(label_mirror_inds(:,2),:);            
             flippedLbls(label_mirror_inds(:,2),:) = tmp1;
@@ -178,8 +198,12 @@ for r=1:size(centres_all,1)
            continue;
         end
         
+        % Centering the pixel so that 0,0 is center of the top left pixel
+        labels = labels - 1;
+        
         curr_filled = curr_filled + 1;        
         [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
+%         sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
         examples(curr_filled,:) = features;
         errors(curr_filled,:) = 0;
         
@@ -197,6 +221,7 @@ for r=1:size(centres_all,1)
         labels_mod = labels_mod(:,1:2);
         
         [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
+%         sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
 
         curr_filled = curr_filled + 1;        
         examples(curr_filled,:) = features;
@@ -214,6 +239,7 @@ for r=1:size(centres_all,1)
         labels_mod = labels_mod(:,1:2);
         
         [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
+%         sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
         
         curr_filled = curr_filled + 1;        
         examples(curr_filled,:) = features;
@@ -223,7 +249,7 @@ for r=1:size(centres_all,1)
 
         % A somewhat offset example
         
-        trans_mod = trans_orig + randn(2,1) * 10;
+        trans_mod = trans_orig + randn(2,1) * 20;
         p_global = [a_orig; eul_orig'; trans_mod];
         
         labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
@@ -238,7 +264,7 @@ for r=1:size(centres_all,1)
         errors(curr_filled,:) = error;
        
         % A rotated sample
-        eul_mod = eul_orig + randn(1,3)*0.2;
+        eul_mod = eul_orig + randn(1,3)*0.3;
         p_global = [a_orig; eul_mod'; trans_orig];
         
         labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
@@ -308,8 +334,7 @@ for r=1:size(centres_all,1)
     examples = examples(1:curr_filled,:);
     errors = errors(1:curr_filled);
     
-    % svm training
+    filename = sprintf('%s/face_validator_train_%d.mat', outputLocation, r);
-    filename = sprintf('%s/face_checker_general_training_large_66_%d.mat', outputLocation, r);
     save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
     
     
@@ -321,8 +346,8 @@ function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask
 
     % Make sure labels are within range
     [hRes, wRes] = size(img);
-    labels(labels(:,1) < 1,1) = 1;
+    labels(labels(:,1) < 0,1) = 0;
-    labels(labels(:,2) < 1,2) = 1;
+    labels(labels(:,2) < 0,2) = 0;
 
     labels(labels(:,1) > wRes-1,1) = wRes-1;
     labels(labels(:,2) > hRes-1,2) = hRes-1;   

matlab_version/face_validation/DeepLearnToolbox/.travis.yml

@@ -1,10 +0,0 @@
-before_script:
-  - sudo apt-add-repository ppa:octave/stable --yes
-  - sudo apt-get update -y
-  - sudo apt-get install octave -y
-  - sudo apt-get install liboctave-dev -y
-script:
-  - sh -c "octave tests/runalltests.m"
-
-notifications:
-  email: false

matlab_version/face_validation/DeepLearnToolbox/CAE/caeapplygrads.m

@@ -1,29 +0,0 @@
-function cae = caeapplygrads(cae)
-    cae.sv = 0;
-    for j = 1 : numel(cae.a)
-        for i = 1 : numel(cae.i)
-%             cae.vik{i}{j} = cae.momentum * cae.vik{i}{j} + cae.alpha ./ (cae.sigma + cae.ddik{i}{j}) .* cae.dik{i}{j};
-%             cae.vok{i}{j} = cae.momentum * cae.vok{i}{j} + cae.alpha ./ (cae.sigma + cae.ddok{i}{j}) .* cae.dok{i}{j};
-            cae.vik{i}{j} = cae.alpha * cae.dik{i}{j};
-            cae.vok{i}{j} = cae.alpha * cae.dok{i}{j};
-            cae.sv = cae.sv + sum(cae.vik{i}{j}(:) .^ 2);
-            cae.sv = cae.sv + sum(cae.vok{i}{j}(:) .^ 2);
-
-            cae.ik{i}{j} = cae.ik{i}{j} - cae.vik{i}{j};
-            cae.ok{i}{j} = cae.ok{i}{j} - cae.vok{i}{j};
-        end
-%         cae.vb{j} = cae.momentum * cae.vb{j} + cae.alpha / (cae.sigma + cae.ddb{j}) * cae.db{j};
-        cae.vb{j} = cae.alpha * cae.db{j};
-        cae.sv = cae.sv + sum(cae.vb{j} .^ 2);
-
-        cae.b{j} = cae.b{j} - cae.vb{j};
-    end
-
-    for i = 1 : numel(cae.o)
-%         cae.vc{i} = cae.momentum * cae.vc{i} + cae.alpha / (cae.sigma + cae.ddc{i}) * cae.dc{i};
-        cae.vc{i} = cae.alpha * cae.dc{i};
-        cae.sv = cae.sv + sum(cae.vc{i} .^ 2);
-
-        cae.c{i} = cae.c{i} - cae.vc{i};
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/caebbp.m

@@ -1,29 +0,0 @@
-function cae = caebbp(cae)
-
-    %%  backprop deltas
-    for i = 1 : numel(cae.o)
-        %  output delta delta
-        cae.odd{i} = (cae.o{i} .* (1 - cae.o{i}) .* cae.edgemask) .^ 2;
-        %  delta delta c
-        cae.ddc{i} = sum(cae.odd{i}(:)) / size(cae.odd{i}, 1);
-    end
-
-    for j = 1 : numel(cae.a)   %  calc activation delta deltas
-        z = 0;
-        for i = 1 : numel(cae.o)
-             z = z + convn(cae.odd{i}, flipall(cae.ok{i}{j} .^ 2), 'full');
-        end
-        cae.add{j} = (cae.a{j} .* (1 - cae.a{j})) .^ 2 .* z;
-    end
-
-    %%  calc params delta deltas
-    ns = size(cae.odd{1}, 1);
-    for j = 1 : numel(cae.a)
-        cae.ddb{j} = sum(cae.add{j}(:)) / ns;
-        for i = 1 : numel(cae.o)
-            cae.ddok{i}{j} = convn(flipall(cae.a{j} .^ 2), cae.odd{i}, 'valid') / ns;
-            cae.ddik{i}{j} = convn(cae.add{j}, flipall(cae.i{i} .^ 2), 'valid') / ns;
-        end
-    end
-
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/caebp.m

@@ -1,34 +0,0 @@
-function cae = caebp(cae, y)
-
-    %%  backprop deltas
-    cae.L = 0;
-    for i = 1 : numel(cae.o)
-        %  error
-        cae.e{i} = (cae.o{i} - y{i}) .* cae.edgemask;
-        %  loss function
-        cae.L = cae.L + 1/2 * sum(cae.e{i}(:) .^2 ) / size(cae.e{i}, 1);
-        %  output delta
-        cae.od{i} = cae.e{i} .* (cae.o{i} .* (1 - cae.o{i}));
-
-        cae.dc{i} = sum(cae.od{i}(:)) / size(cae.e{i}, 1);
-    end
-
-    for j = 1 : numel(cae.a)   %  calc activation deltas
-        z = 0;
-        for i = 1 : numel(cae.o)
-             z = z + convn(cae.od{i}, flipall(cae.ok{i}{j}), 'full');
-        end
-        cae.ad{j} = cae.a{j} .* (1 - cae.a{j}) .* z;
-    end
-
-    %%  calc gradients
-    ns = size(cae.e{1}, 1);
-    for j = 1 : numel(cae.a)
-        cae.db{j} = sum(cae.ad{j}(:)) / ns;
-        for i = 1 : numel(cae.o)
-            cae.dok{i}{j} = convn(flipall(cae.a{j}), cae.od{i}, 'valid') / ns;
-            cae.dik{i}{j} = convn(cae.ad{j}, flipall(cae.i{i}), 'valid') / ns;
-        end
-    end
-
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/caedown.m

@@ -1,13 +0,0 @@
-function cae = caedown(cae)
-    pa  = cae.a;
-    pok = cae.ok;
-
-    for i = 1 : numel(cae.o)
-        z = 0;
-        for j = 1 : numel(cae.a)
-            z = z + convn(pa{j}, pok{i}{j}, 'valid');
-        end
-        cae.o{i} = sigm(z + cae.c{i});
-
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/caeexamples.m

@@ -1,32 +0,0 @@
-%%  mnist data
-clear all; close all; clc;
-load mnist_uint8;
-x = cell(100, 1);
-N = 600;
-for i = 1 : 100
-    x{i}{1} = reshape(train_x(((i - 1) * N + 1) : (i) * N, :), N, 28, 28) * 255;
-end
-%% ex 1
-scae = {
-    struct('outputmaps', 10, 'inputkernel', [1 5 5], 'outputkernel', [1 5 5], 'scale', [1 2 2], 'sigma', 0.1, 'momentum', 0.9, 'noise', 0)
-};
-
-opts.rounds     = 1000;
-opts.batchsize  =    1;
-opts.alpha      = 0.01;
-opts.ddinterval =   10;
-opts.ddhist     =  0.5;
-scae = scaesetup(scae, x, opts);
-scae = scaetrain(scae, x, opts);
-cae = scae{1};
-
-%Visualize the average reconstruction error
-plot(cae.rL);
-
-%Visualize the output kernels
-ff=[];
-for i=1:numel(cae.ok{1}); 
-    mm = cae.ok{1}{i}(1,:,:); 
-    ff(i,:) = mm(:); 
-end; 
-figure;visualize(ff')

matlab_version/face_validation/DeepLearnToolbox/CAE/caenumgradcheck.m

@@ -1,107 +0,0 @@
-function cae = caenumgradcheck(cae, x, y)
-    epsilon = 1e-4;
-    er = 1e-6;
-    disp('performing numerical gradient checking...')
-    for i = 1 : numel(cae.o)
-        p_cae = cae; p_cae.c{i} = p_cae.c{i} + epsilon;
-        m_cae = cae; m_cae.c{i} = m_cae.c{i} - epsilon;
-
-        [m_cae, p_cae] = caerun(m_cae, p_cae, x, y);
-        d = (p_cae.L - m_cae.L) / (2 * epsilon);
-
-        e = abs(d - cae.dc{i});
-        if e > er
-            disp('OUTPUT BIAS numerical gradient checking failed');
-            disp(e);
-            disp(d / cae.dc{i});
-            keyboard
-        end
-    end
-
-    for a = 1 : numel(cae.a)
-
-        p_cae = cae; p_cae.b{a} = p_cae.b{a} + epsilon;
-        m_cae = cae; m_cae.b{a} = m_cae.b{a} - epsilon;
-
-        [m_cae, p_cae] = caerun(m_cae, p_cae, x, y);
-        d = (p_cae.L - m_cae.L) / (2 * epsilon);
-%        cae.dok{i}{a}(u) = d;
-        e = abs(d - cae.db{a});
-        if e > er
-            disp('BIAS numerical gradient checking failed');
-            disp(e);
-            disp(d / cae.db{a});
-            keyboard
-        end
-
-        for i = 1 : numel(cae.o)
-            for u = 1 : numel(cae.ok{i}{a})
-                p_cae = cae; p_cae.ok{i}{a}(u) = p_cae.ok{i}{a}(u) + epsilon;
-                m_cae = cae; m_cae.ok{i}{a}(u) = m_cae.ok{i}{a}(u) - epsilon;
-
-                [m_cae, p_cae] = caerun(m_cae, p_cae, x, y);
-                d = (p_cae.L - m_cae.L) / (2 * epsilon);
-%                cae.dok{i}{a}(u) = d;
-                e = abs(d - cae.dok{i}{a}(u));
-                if e > er
-                    disp('OUTPUT KERNEL numerical gradient checking failed');
-                    disp(e);
-                    disp(d / cae.dok{i}{a}(u));
-%                    keyboard
-                end
-            end
-        end
-
-        for i = 1 : numel(cae.i)
-            for u = 1 : numel(cae.ik{i}{a})
-                p_cae = cae; 
-                m_cae = cae;
-                p_cae.ik{i}{a}(u) = p_cae.ik{i}{a}(u) + epsilon;
-                m_cae.ik{i}{a}(u) = m_cae.ik{i}{a}(u) - epsilon;
-                [m_cae, p_cae] = caerun(m_cae, p_cae, x, y);
-                d = (p_cae.L - m_cae.L) / (2 * epsilon);
-%                cae.dik{i}{a}(u) = d;
-                e = abs(d - cae.dik{i}{a}(u));
-                if e > er
-                    disp('INPUT KERNEL numerical gradient checking failed');
-                    disp(e);
-                    disp(d / cae.dik{i}{a}(u));
-                end
-            end
-        end
-    end
-
-    disp('done')
-
-end
-
-function [m_cae, p_cae] = caerun(m_cae, p_cae, x, y)
-    m_cae = caeup(m_cae, x); m_cae = caedown(m_cae); m_cae = caebp(m_cae, y);
-    p_cae = caeup(p_cae, x); p_cae = caedown(p_cae); p_cae = caebp(p_cae, y);
-end
-
-%function checknumgrad(cae,what,x,y)
-%    epsilon = 1e-4;
-%    er = 1e-9;
-%
-%    for i = 1 : numel(eval(what))
-%        if iscell(eval(['cae.' what]))
-%            checknumgrad(cae,[what '{' num2str(i) '}'], x, y)
-%        else
-%            p_cae = cae;
-%            m_cae = cae;
-%            eval(['p_cae.' what '(' num2str(i) ')']) = eval([what '(' num2str(i) ')']) + epsilon;
-%            eval(['m_cae.' what '(' num2str(i) ')']) = eval([what '(' num2str(i) ')']) - epsilon;
-%
-%            m_cae = caeff(m_cae, x); m_cae = caedown(m_cae); m_cae = caebp(m_cae, y);
-%            p_cae = caeff(p_cae, x); p_cae = caedown(p_cae); p_cae = caebp(p_cae, y);
-%
-%            d = (p_cae.L - m_cae.L) / (2 * epsilon);
-%            e = abs(d - eval(['cae.d' what '(' num2str(i) ')']));
-%            if e > er
-%                error('numerical gradient checking failed');
-%            end
-%         end
-%     end
-%
-% end

matlab_version/face_validation/DeepLearnToolbox/CAE/caesdlm.m

@@ -1,26 +0,0 @@
-function cae = caesdlm(cae, opts, m)
-    %stochastic diagonal levenberg-marquardt
-    
-    %first round
-    if isfield(cae,'ddok') == 0
-        cae = caebbp(cae);
-    end
-
-    %recalculate double grads every opts.ddinterval
-    if mod(m, opts.ddinterval) == 0  
-        cae_n = caebbp(cae);
-
-        for ii = 1 : numel(cae.o)
-            cae.ddc{ii} = opts.ddhist * cae.ddc{ii} + (1 - opts.ddhist) * cae_n.ddc{ii};
-        end
-
-        for jj = 1 : numel(cae.a)
-            cae.ddb{jj} = opts.ddhist * cae.ddb{jj} + (1 - opts.ddhist) * cae_n.ddb{jj};
-            for ii = 1 : numel(cae.o)
-                cae.ddok{ii}{jj} = opts.ddhist * cae.ddok{ii}{jj} + (1 - opts.ddhist) * cae_n.ddok{ii}{jj};
-                cae.ddik{ii}{jj} = opts.ddhist * cae.ddik{ii}{jj} + (1 - opts.ddhist) * cae_n.ddik{ii}{jj};
-            end
-        end
-
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/caetrain.m

@@ -1,38 +0,0 @@
-function cae = caetrain(cae, x, opts)
-    n = cae.inputkernel(1);
-    cae.rL = [];
-    for m = 1 : opts.rounds
-        tic;
-        disp([num2str(m) '/' num2str(opts.rounds) ' rounds']);
-        i1 = randi(numel(x));
-        l  = randi(size(x{i1}{1},1) - opts.batchsize - n + 1);
-        x1{1} = double(x{i1}{1}(l : l + opts.batchsize - 1, :, :)) / 255;
-
-        if n == 1   %Auto Encoder
-            x2{1} = x1{1};
-        else        %Predictive Encoder
-            x2{1} = double(x{i1}{1}(l + n : l + n + opts.batchsize - 1, :, :)) / 255;
-        end
-        %  Add noise to input, for denoising stacked autoenoder
-        x1{1} = x1{1} .* (rand(size(x1{1})) > cae.noise);
-
-        cae = caeup(cae, x1);
-        cae = caedown(cae);
-        cae = caebp(cae, x2);
-        cae = caesdlm(cae, opts, m);
-%         caenumgradcheck(cae,x1,x2);
-        cae = caeapplygrads(cae);
-
-        if m == 1
-            cae.rL(1) = cae.L;
-        end
-%         cae.rL(m + 1) = 0.99 * cae.rL(m) + 0.01 * cae.L;
-        cae.rL(m + 1) = cae.L;
-%         if cae.sv < 1e-10
-%             disp('Converged');
-%             break;
-%         end
-        toc;
-    end
-
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/caeup.m

@@ -1,25 +0,0 @@
-function cae = caeup(cae, x)
-    cae.i = x;
-
-    %init temp vars for parrallel processing
-    pa  = cell(size(cae.a));
-    pi  = cae.i;
-    pik = cae.ik;
-    pb  = cae.b;
-
-    for j = 1 : numel(cae.a)
-        z = 0;
-        for i = 1 : numel(pi)
-            z = z + convn(pi{i}, pik{i}{j}, 'full');
-        end
-        pa{j} = sigm(z + pb{j});
-
-        %  Max pool.
-        if ~isequal(cae.scale, [1 1 1])
-            pa{j} = max3d(pa{j}, cae.M);
-        end
-
-    end
-    cae.a = pa;
-
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/max3d.m

@@ -1,8 +0,0 @@
-function X = max3d(X, M)
-    ll = size(X);
-    B=X(M);
-    B=B+rand(size(B))*1e-12;
-    B=(B.*(B==repmat(max(B,[],2),[1 size(B,2) 1])));
-    X(M) = B;
-    reshape(X,ll);
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/scaesetup.m

@@ -1,58 +0,0 @@
-function scae = scaesetup(cae, x, opts)
-    x = x{1};
-    for l = 1 : numel(cae)
-        cae = cae{l};
-        ll= [opts.batchsize size(x{1}, 2) size(x{1}, 3)] + cae.inputkernel - 1;
-        X = zeros(ll);
-        cae.M = nbmap(X, cae.scale);
-        bounds = cae.outputmaps * prod(cae.inputkernel) + numel(x) * prod(cae.outputkernel);
-        for j = 1 : cae.outputmaps   %  activation maps
-            cae.a{j} = zeros(size(x{1}) + cae.inputkernel - 1);
-            for i = 1 : numel(x)    %  input map
-                cae.ik{i}{j}  = (rand(cae.inputkernel)  - 0.5) * 2 * sqrt(6 / bounds);
-                cae.ok{i}{j}  = (rand(cae.outputkernel) - 0.5) * 2 * sqrt(6 / bounds);
-                cae.vik{i}{j} = zeros(size(cae.ik{i}{j}));
-                cae.vok{i}{j} = zeros(size(cae.ok{i}{j}));
-            end
-            cae.b{j} = 0;
-            cae.vb{j} = zeros(size(cae.b{j}));
-        end
-
-        cae.alpha = opts.alpha;
-
-        cae.i = cell(numel(x), 1);
-        cae.o = cae.i;
-
-        for i = 1 : numel(cae.o)
-            cae.c{i}  = 0;
-            cae.vc{i} = zeros(size(cae.c{i}));
-        end
-
-        ss = cae.outputkernel;
-
-        cae.edgemask = zeros([opts.batchsize size(x{1}, 2) size(x{1}, 3)]);
-
-        cae.edgemask(ss(1) : end - ss(1) + 1, ...
-                     ss(2) : end - ss(2) + 1, ...
-                     ss(3) : end - ss(3) + 1) = 1;
-
-        scae{l} = cae;
-    end
-    
-    function B = nbmap(X,n)
-        assert(numel(n)==3,'n should have 3 elements (x,y,z) scaling.');
-        X = reshape(1:numel(X),size(X,1),size(X,2),size(X,3));
-        B = zeros(size(X,1)/n(1),prod(n),size(X,2)*size(X,3)/prod(n(2:3)));
-        u=1;
-        p=1;
-        for m=1:size(X,1)
-            B(u,(p-1)*prod(n(2:3))+1:p*prod(n(2:3)),:) = im2col(squeeze(X(m,:,:)),n(2:3),'distinct');
-            p=p+1;
-            if(mod(m,n(1))==0)
-                u=u+1;
-                p=1;
-            end
-        end
-
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/CAE/scaetrain.m

@@ -1,8 +0,0 @@
-function scae = scaetrain(scae, x, opts)
-    %TODO: Transform x through scae{1} into new x. Only works for a single PAE. 
-%     for i=1:numel(scae)
-%         scae{i} = paetrain(scae{i}, x, opts);        
-%     end
-    scae{1} = caetrain(scae{1}, x, opts);        
-  
-end

matlab_version/face_validation/DeepLearnToolbox/CNN/cnnapplygrads.m

@@ -1,15 +0,0 @@
-function net = cnnapplygrads(net, opts)
-    for l = 2 : numel(net.layers)
-        if strcmp(net.layers{l}.type, 'c')
-            for j = 1 : numel(net.layers{l}.a)
-                for ii = 1 : numel(net.layers{l - 1}.a)
-                    net.layers{l}.k{ii}{j} = net.layers{l}.k{ii}{j} - opts.alpha * net.layers{l}.dk{ii}{j};
-                end
-                net.layers{l}.b{j} = net.layers{l}.b{j} - opts.alpha * net.layers{l}.db{j};
-            end
-        end
-    end
-
-    net.ffW = net.ffW - opts.alpha * net.dffW;
-    net.ffb = net.ffb - opts.alpha * net.dffb;
-end

matlab_version/face_validation/DeepLearnToolbox/CNN/cnnbp.m

@@ -1,56 +0,0 @@
-function net = cnnbp(net, y)
-    n = numel(net.layers);
-
-    %   error
-    net.e = net.o - y;
-    %  loss function
-    net.L = 1/2* sum(net.e(:) .^ 2) / size(net.e, 2);
-
-    %%  backprop deltas
-    net.od = net.e .* (net.o .* (1 - net.o));   %  output delta
-    net.fvd = (net.ffW' * net.od);              %  feature vector delta
-    if strcmp(net.layers{n}.type, 'c')         %  only conv layers has sigm function
-        net.fvd = net.fvd .* (net.fv .* (1 - net.fv));
-    end
-
-    %  reshape feature vector deltas into output map style
-    sa = size(net.layers{n}.a{1});
-    fvnum = sa(1) * sa(2);
-    for j = 1 : numel(net.layers{n}.a)
-        net.layers{n}.d{j} = reshape(net.fvd(((j - 1) * fvnum + 1) : j * fvnum, :), sa(1), sa(2), sa(3));
-    end
-
-    for l = (n - 1) : -1 : 1
-        if strcmp(net.layers{l}.type, 'c')
-            for j = 1 : numel(net.layers{l}.a)
-                net.layers{l}.d{j} = net.layers{l}.a{j} .* (1 - net.layers{l}.a{j}) .* (expand(net.layers{l + 1}.d{j}, [net.layers{l + 1}.scale net.layers{l + 1}.scale 1]) / net.layers{l + 1}.scale ^ 2);
-            end
-        elseif strcmp(net.layers{l}.type, 's')
-            for i = 1 : numel(net.layers{l}.a)
-                z = zeros(size(net.layers{l}.a{1}));
-                for j = 1 : numel(net.layers{l + 1}.a)
-                     z = z + convn(net.layers{l + 1}.d{j}, rot180(net.layers{l + 1}.k{i}{j}), 'full');
-                end
-                net.layers{l}.d{i} = z;
-            end
-        end
-    end
-
-    %%  calc gradients
-    for l = 2 : n
-        if strcmp(net.layers{l}.type, 'c')
-            for j = 1 : numel(net.layers{l}.a)
-                for i = 1 : numel(net.layers{l - 1}.a)
-                    net.layers{l}.dk{i}{j} = convn(flipall(net.layers{l - 1}.a{i}), net.layers{l}.d{j}, 'valid') / size(net.layers{l}.d{j}, 3);
-                end
-                net.layers{l}.db{j} = sum(net.layers{l}.d{j}(:)) / size(net.layers{l}.d{j}, 3);
-            end
-        end
-    end
-    net.dffW = net.od * (net.fv)' / size(net.od, 2);
-    net.dffb = mean(net.od, 2);
-
-    function X = rot180(X)
-        X = flipdim(flipdim(X, 1), 2);
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/CNN/cnnff.m

@@ -1,47 +0,0 @@
-function net = cnnff(net, x)
-    n = numel(net.layers);
-    net.layers{1}.a{1} = x;
-    inputmaps = 1;
-
-    for l = 2 : n   %  for each layer
-        if strcmp(net.layers{l}.type, 'c')
-            %  !!below can probably be handled by insane matrix operations
-            for j = 1 : net.layers{l}.outputmaps   %  for each output map
-                %  create temp output map
-                if(size(x,3) > 1)
-                    z = zeros(size(net.layers{l - 1}.a{1}) - [net.layers{l}.kernelsize - 1 net.layers{l}.kernelsize - 1 0]);
-                else
-                    z = zeros(size(net.layers{l - 1}.a{1}) - [net.layers{l}.kernelsize - 1 net.layers{l}.kernelsize - 1]);
-                end
-                for i = 1 : inputmaps   %  for each input map
-                    %  convolve with corresponding kernel and add to temp output map
-                    z = z + convn(net.layers{l - 1}.a{i}, net.layers{l}.k{i}{j}, 'valid');
-                end
-                %  add bias, pass through nonlinearity
-                net.layers{l}.a{j} = sigm(z + net.layers{l}.b{j});
-            end
-            %  set number of input maps to this layers number of outputmaps
-            inputmaps = net.layers{l}.outputmaps;
-        elseif strcmp(net.layers{l}.type, 's')
-            %  downsample
-            for j = 1 : inputmaps
-                z = convn(net.layers{l - 1}.a{j}, ones(net.layers{l}.scale) / (net.layers{l}.scale ^ 2), 'valid');   %  !! replace with variable
-                net.layers{l}.a{j} = z(1 : net.layers{l}.scale : end, 1 : net.layers{l}.scale : end, :);
-            end
-        end
-    end
-
-    %  concatenate all end layer feature maps into vector
-    net.fv = [];
-    for j = 1 : numel(net.layers{n}.a)
-        sa = size(net.layers{n}.a{j});
-        if(numel(sa) == 3)            
-            net.fv = [net.fv; reshape(net.layers{n}.a{j}, sa(1) * sa(2), sa(3))];
-        else
-            net.fv = [net.fv; reshape(net.layers{n}.a{j}, sa(1) * sa(2), 1)];
-        end
-    end
-    %  feedforward into output perceptrons
-    net.o = sigm(net.ffW * net.fv + repmat(net.ffb, 1, size(net.fv, 2)));
-
-end

matlab_version/face_validation/DeepLearnToolbox/CNN/cnnnumgradcheck.m

@@ -1,79 +0,0 @@
-function cnnnumgradcheck(net, x, y)
-    epsilon = 1e-4;
-    er      = 1e-8;
-    n = numel(net.layers);
-    for j = 1 : numel(net.ffb)
-        net_m = net; net_p = net;
-        net_p.ffb(j) = net_m.ffb(j) + epsilon;
-        net_m.ffb(j) = net_m.ffb(j) - epsilon;
-        net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
-        net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
-        d = (net_p.L - net_m.L) / (2 * epsilon);
-        e = abs(d - net.dffb(j));
-        if e > er
-            error('numerical gradient checking failed');
-        end
-    end
-
-    for i = 1 : size(net.ffW, 1)
-        for u = 1 : size(net.ffW, 2)
-            net_m = net; net_p = net;
-            net_p.ffW(i, u) = net_m.ffW(i, u) + epsilon;
-            net_m.ffW(i, u) = net_m.ffW(i, u) - epsilon;
-            net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
-            net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
-            d = (net_p.L - net_m.L) / (2 * epsilon);
-            e = abs(d - net.dffW(i, u));
-            if e > er
-                error('numerical gradient checking failed');
-            end
-        end
-    end
-
-    for l = n : -1 : 2
-        if strcmp(net.layers{l}.type, 'c')
-            for j = 1 : numel(net.layers{l}.a)
-                net_m = net; net_p = net;
-                net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon;
-                net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon;
-                net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
-                net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
-                d = (net_p.L - net_m.L) / (2 * epsilon);
-                e = abs(d - net.layers{l}.db{j});
-                if e > er
-                    error('numerical gradient checking failed');
-                end
-                for i = 1 : numel(net.layers{l - 1}.a)
-                    for u = 1 : size(net.layers{l}.k{i}{j}, 1)
-                        for v = 1 : size(net.layers{l}.k{i}{j}, 2)
-                            net_m = net; net_p = net;
-                            net_p.layers{l}.k{i}{j}(u, v) = net_p.layers{l}.k{i}{j}(u, v) + epsilon;
-                            net_m.layers{l}.k{i}{j}(u, v) = net_m.layers{l}.k{i}{j}(u, v) - epsilon;
-                            net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
-                            net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
-                            d = (net_p.L - net_m.L) / (2 * epsilon);
-                            e = abs(d - net.layers{l}.dk{i}{j}(u, v));
-                            if e > er
-                                error('numerical gradient checking failed');
-                            end
-                        end
-                    end
-                end
-            end
-        elseif strcmp(net.layers{l}.type, 's')
-%            for j = 1 : numel(net.layers{l}.a)
-%                net_m = net; net_p = net;
-%                net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon;
-%                net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon;
-%                net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y);
-%                net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y);
-%                d = (net_p.L - net_m.L) / (2 * epsilon);
-%                e = abs(d - net.layers{l}.db{j});
-%                if e > er
-%                    error('numerical gradient checking failed');
-%                end
-%            end
-        end
-    end
-%    keyboard
-end

matlab_version/face_validation/DeepLearnToolbox/CNN/cnnsetup.m

@@ -1,36 +0,0 @@
-function net = cnnsetup(net, x, y)
-    assert(~isOctave() || compare_versions(OCTAVE_VERSION, '3.8.0', '>='), ['Octave 3.8.0 or greater is required for CNNs as there is a bug in convolution in previous versions. See http://savannah.gnu.org/bugs/?39314. Your version is ' myOctaveVersion]);
-    inputmaps = 1;
-    mapsize = size(squeeze(x(:, :, 1)));
-
-    for l = 1 : numel(net.layers)   %  layer
-        if strcmp(net.layers{l}.type, 's')
-            mapsize = mapsize / net.layers{l}.scale;
-            assert(all(floor(mapsize)==mapsize), ['Layer ' num2str(l) ' size must be integer. Actual: ' num2str(mapsize)]);
-            for j = 1 : inputmaps
-                net.layers{l}.b{j} = 0;
-            end
-        end
-        if strcmp(net.layers{l}.type, 'c')
-            mapsize = mapsize - net.layers{l}.kernelsize + 1;
-            fan_out = net.layers{l}.outputmaps * net.layers{l}.kernelsize ^ 2;
-            for j = 1 : net.layers{l}.outputmaps  %  output map
-                fan_in = inputmaps * net.layers{l}.kernelsize ^ 2;
-                for i = 1 : inputmaps  %  input map
-                    net.layers{l}.k{i}{j} = (rand(net.layers{l}.kernelsize) - 0.5) * 2 * sqrt(6 / (fan_in + fan_out));
-                end
-                net.layers{l}.b{j} = 0;
-            end
-            inputmaps = net.layers{l}.outputmaps;
-        end
-    end
-    % 'onum' is the number of labels, that's why it is calculated using size(y, 1). If you have 20 labels so the output of the network will be 20 neurons.
-    % 'fvnum' is the number of output neurons at the last layer, the layer just before the output layer.
-    % 'ffb' is the biases of the output neurons.
-    % 'ffW' is the weights between the last layer and the output neurons. Note that the last layer is fully connected to the output layer, that's why the size of the weights is (onum * fvnum)
-    fvnum = prod(mapsize) * inputmaps;
-    onum = size(y, 1);
-
-    net.ffb = zeros(onum, 1);
-    net.ffW = (rand(onum, fvnum) - 0.5) * 2 * sqrt(6 / (onum + fvnum));
-end

matlab_version/face_validation/DeepLearnToolbox/CNN/cnntest.m

@@ -1,9 +0,0 @@
-function [er, bad] = cnntest(net, x, y)
-    %  feedforward
-    net = cnnff(net, x);
-    [~, h] = max(net.o);
-    [~, a] = max(y);
-    bad = find(h ~= a);
-
-    er = numel(bad) / size(y, 2);
-end

matlab_version/face_validation/DeepLearnToolbox/CNN/cnntrain.m

@@ -1,29 +0,0 @@
-function net = cnntrain(net, x, y, opts)
-    m = size(x, 3);
-    numbatches = floor(m / opts.batchsize);
-    if rem(numbatches, 1) ~= 0
-        error('numbatches not integer');
-    end
-    net.rL = [];
-    for i = 1 : opts.numepochs
-        net = cnnff(net, x);
-        error_curr = sqrt(mean((net.o - y).^2));
-        disp(['epoch ' num2str(i) '/' num2str(opts.numepochs), ' RMSE-', num2str(error_curr)]);
-        tic;
-        kk = randperm(m);
-        for l = 1 : numbatches
-            batch_x = x(:, :, kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize));
-            batch_y = y(:,    kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize));
-
-            net = cnnff(net, batch_x);
-            net = cnnbp(net, batch_y);
-            net = cnnapplygrads(net, opts);
-            if isempty(net.rL)
-                net.rL(1) = net.L;
-            end
-            net.rL(end + 1) = 0.99 * net.rL(end) + 0.01 * net.L;
-        end
-        toc;
-    end
-    
-end

matlab_version/face_validation/DeepLearnToolbox/CONTRIBUTING.md

@@ -1,20 +0,0 @@
-Thank you so much for wanting to give back to the toolbox. Here's some info on how to contribute:
-
-#General:
- 
-Don't bunch up changes, e.g. if you have bug-fixes, new features and style changes, rather make 3 seperate pull requests.
-
-Ensure that you introduce tests/examples for any new functionality
-
-# Guide
-1. Fork repository
-2. Create a new branch, e.g. `checkout -b my-stuff`
-3. Commit and push your changes to that branch
-4. Make sure that the test works (!) (see known errors)
-5. Create a pull request
-6. I accept your pull request
-
-
-
-
-

matlab_version/face_validation/DeepLearnToolbox/DBN/dbnsetup.m

@@ -1,19 +0,0 @@
-function dbn = dbnsetup(dbn, x, opts)
-    n = size(x, 2);
-    dbn.sizes = [n, dbn.sizes];
-
-    for u = 1 : numel(dbn.sizes) - 1
-        dbn.rbm{u}.alpha    = opts.alpha;
-        dbn.rbm{u}.momentum = opts.momentum;
-
-        dbn.rbm{u}.W  = zeros(dbn.sizes(u + 1), dbn.sizes(u));
-        dbn.rbm{u}.vW = zeros(dbn.sizes(u + 1), dbn.sizes(u));
-
-        dbn.rbm{u}.b  = zeros(dbn.sizes(u), 1);
-        dbn.rbm{u}.vb = zeros(dbn.sizes(u), 1);
-
-        dbn.rbm{u}.c  = zeros(dbn.sizes(u + 1), 1);
-        dbn.rbm{u}.vc = zeros(dbn.sizes(u + 1), 1);
-    end
-
-end

matlab_version/face_validation/DeepLearnToolbox/DBN/dbntrain.m

@@ -1,10 +0,0 @@
-function dbn = dbntrain(dbn, x, opts)
-    n = numel(dbn.rbm);
-
-    dbn.rbm{1} = rbmtrain(dbn.rbm{1}, x, opts);
-    for i = 2 : n
-        x = rbmup(dbn.rbm{i - 1}, x);
-        dbn.rbm{i} = rbmtrain(dbn.rbm{i}, x, opts);
-    end
-
-end

matlab_version/face_validation/DeepLearnToolbox/DBN/dbnunfoldtonn.m

@@ -1,15 +0,0 @@
-function nn = dbnunfoldtonn(dbn, outputsize)
-%DBNUNFOLDTONN Unfolds a DBN to a NN
-%   dbnunfoldtonn(dbn, outputsize ) returns the unfolded dbn with a final
-%   layer of size outputsize added.
-    if(exist('outputsize','var'))
-        size = [dbn.sizes outputsize];
-    else
-        size = [dbn.sizes];
-    end
-    nn = nnsetup(size);
-    for i = 1 : numel(dbn.rbm)
-        nn.W{i} = [dbn.rbm{i}.c dbn.rbm{i}.W];
-    end
-end
-

matlab_version/face_validation/DeepLearnToolbox/DBN/rbmdown.m

@@ -1,3 +0,0 @@
-function x = rbmdown(rbm, x)
-    x = sigm(repmat(rbm.b', size(x, 1), 1) + x * rbm.W);
-end

matlab_version/face_validation/DeepLearnToolbox/DBN/rbmtrain.m

@@ -1,37 +0,0 @@
-function rbm = rbmtrain(rbm, x, opts)
-    assert(isfloat(x), 'x must be a float');
-    assert(all(x(:)>=0) && all(x(:)<=1), 'all data in x must be in [0:1]');
-    m = size(x, 1);
-    numbatches = m / opts.batchsize;
-    
-    assert(rem(numbatches, 1) == 0, 'numbatches not integer');
-
-    for i = 1 : opts.numepochs
-        kk = randperm(m);
-        err = 0;
-        for l = 1 : numbatches
-            batch = x(kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize), :);
-            
-            v1 = batch;
-            h1 = sigmrnd(repmat(rbm.c', opts.batchsize, 1) + v1 * rbm.W');
-            v2 = sigmrnd(repmat(rbm.b', opts.batchsize, 1) + h1 * rbm.W);
-            h2 = sigm(repmat(rbm.c', opts.batchsize, 1) + v2 * rbm.W');
-
-            c1 = h1' * v1;
-            c2 = h2' * v2;
-
-            rbm.vW = rbm.momentum * rbm.vW + rbm.alpha * (c1 - c2)     / opts.batchsize;
-            rbm.vb = rbm.momentum * rbm.vb + rbm.alpha * sum(v1 - v2)' / opts.batchsize;
-            rbm.vc = rbm.momentum * rbm.vc + rbm.alpha * sum(h1 - h2)' / opts.batchsize;
-
-            rbm.W = rbm.W + rbm.vW;
-            rbm.b = rbm.b + rbm.vb;
-            rbm.c = rbm.c + rbm.vc;
-
-            err = err + sum(sum((v1 - v2) .^ 2)) / opts.batchsize;
-        end
-        
-        disp(['epoch ' num2str(i) '/' num2str(opts.numepochs)  '. Average reconstruction error is: ' num2str(err / numbatches)]);
-        
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/DBN/rbmup.m

@@ -1,3 +0,0 @@
-function x = rbmup(rbm, x)
-    x = sigm(repmat(rbm.c', size(x, 1), 1) + x * rbm.W');
-end

matlab_version/face_validation/DeepLearnToolbox/LICENSE

@@ -1,8 +0,0 @@
-Copyright (c) 2012, Rasmus Berg Palm (rasmusbergpalm@gmail.com)
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
-
-Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
-Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

matlab_version/face_validation/DeepLearnToolbox/NN/nnapplygrads.m

@@ -1,22 +0,0 @@
-function nn = nnapplygrads(nn)
-%NNAPPLYGRADS updates weights and biases with calculated gradients
-% nn = nnapplygrads(nn) returns an neural network structure with updated
-% weights and biases
-    
-    for i = 1 : (nn.n - 1)
-        if(nn.weightPenaltyL2>0)
-            dW = nn.dW{i} + nn.weightPenaltyL2 * [zeros(size(nn.W{i},1),1) nn.W{i}(:,2:end)];
-        else
-            dW = nn.dW{i};
-        end
-        
-        dW = nn.learningRate * dW;
-        
-        if(nn.momentum>0)
-            nn.vW{i} = nn.momentum*nn.vW{i} + dW;
-            dW = nn.vW{i};
-        end
-            
-        nn.W{i} = nn.W{i} - dW;
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/NN/nnbp.m

@@ -1,47 +0,0 @@
-function nn = nnbp(nn)
-%NNBP performs backpropagation
-% nn = nnbp(nn) returns an neural network structure with updated weights 
-    
-    n = nn.n;
-    sparsityError = 0;
-    switch nn.output
-        case 'sigm'
-            d{n} = - nn.e .* (nn.a{n} .* (1 - nn.a{n}));
-        case {'softmax','linear'}
-            d{n} = - nn.e;
-    end
-    for i = (n - 1) : -1 : 2
-        % Derivative of the activation function
-        switch nn.activation_function 
-            case 'sigm'
-                d_act = nn.a{i} .* (1 - nn.a{i});
-            case 'tanh_opt'
-                d_act = 1.7159 * 2/3 * (1 - 1/(1.7159)^2 * nn.a{i}.^2);
-        end
-        
-        if(nn.nonSparsityPenalty>0)
-            pi = repmat(nn.p{i}, size(nn.a{i}, 1), 1);
-            sparsityError = [zeros(size(nn.a{i},1),1) nn.nonSparsityPenalty * (-nn.sparsityTarget ./ pi + (1 - nn.sparsityTarget) ./ (1 - pi))];
-        end
-        
-        % Backpropagate first derivatives
-        if i+1==n % in this case in d{n} there is not the bias term to be removed             
-            d{i} = (d{i + 1} * nn.W{i} + sparsityError) .* d_act; % Bishop (5.56)
-        else % in this case in d{i} the bias term has to be removed
-            d{i} = (d{i + 1}(:,2:end) * nn.W{i} + sparsityError) .* d_act;
-        end
-        
-        if(nn.dropoutFraction>0)
-            d{i} = d{i} .* [ones(size(d{i},1),1) nn.dropOutMask{i}];
-        end
-
-    end
-
-    for i = 1 : (n - 1)
-        if i+1==n
-            nn.dW{i} = (d{i + 1}' * nn.a{i}) / size(d{i + 1}, 1);
-        else
-            nn.dW{i} = (d{i + 1}(:,2:end)' * nn.a{i}) / size(d{i + 1}, 1);      
-        end
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/NN/nnchecknumgrad.m

@@ -1,22 +0,0 @@
-function nnchecknumgrad(nn, x, y)
-    epsilon = 1e-6;
-    er = 1e-7;
-    n = nn.n;
-    for l = 1 : (n - 1)
-        for i = 1 : size(nn.W{l}, 1)
-            for j = 1 : size(nn.W{l}, 2)
-                nn_m = nn; nn_p = nn;
-                nn_m.W{l}(i, j) = nn.W{l}(i, j) - epsilon;
-                nn_p.W{l}(i, j) = nn.W{l}(i, j) + epsilon;
-				rand('state',0)
-                nn_m = nnff(nn_m, x, y);
-                rand('state',0)
-                nn_p = nnff(nn_p, x, y);
-                dW = (nn_p.L - nn_m.L) / (2 * epsilon);
-                e = abs(dW - nn.dW{l}(i, j));
-                
-                assert(e < er, 'numerical gradient checking failed');
-            end
-        end
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/NN/nneval.m

@@ -1,28 +0,0 @@
-function [loss] = nneval(nn, loss, train_x, train_y, val_x, val_y)
-%NNEVAL evaluates performance of neural network
-% Returns a updated loss struct
-assert(nargin == 4 || nargin == 6, 'Wrong number of arguments');
-
-nn.testing = 1;
-% training performance
-nn                    = nnff(nn, train_x, train_y);
-loss.train.e(end + 1) = nn.L;
-
-% validation performance
-if nargin == 6
-    nn                    = nnff(nn, val_x, val_y);
-    loss.val.e(end + 1)   = nn.L;
-end
-nn.testing = 0;
-%calc misclassification rate if softmax
-if strcmp(nn.output,'softmax')
-    [er_train, dummy]               = nntest(nn, train_x, train_y);
-    loss.train.e_frac(end+1)    = er_train;
-    
-    if nargin == 6
-        [er_val, dummy]             = nntest(nn, val_x, val_y);
-        loss.val.e_frac(end+1)  = er_val;
-    end
-end
-
-end

matlab_version/face_validation/DeepLearnToolbox/NN/nnff.m

@@ -1,60 +0,0 @@
-function nn = nnff(nn, x, y)
-%NNFF performs a feedforward pass
-% nn = nnff(nn, x, y) returns an neural network structure with updated
-% layer activations, error and loss (nn.a, nn.e and nn.L)
-
-    n = nn.n;
-    m = size(x, 1);
-    
-    x = [ones(m,1) x];
-    nn.a{1} = x;
-
-    %feedforward pass
-    for i = 2 : n-1
-        switch nn.activation_function 
-            case 'sigm'
-                % Calculate the unit's outputs (including the bias term)
-                nn.a{i} = sigm(nn.a{i - 1} * nn.W{i - 1}');
-            case 'tanh_opt'
-                nn.a{i} = tanh_opt(nn.a{i - 1} * nn.W{i - 1}');
-        end
-        
-        %dropout
-        if(nn.dropoutFraction > 0)
-            if(nn.testing)
-                nn.a{i} = nn.a{i}.*(1 - nn.dropoutFraction);
-            else
-                nn.dropOutMask{i} = (rand(size(nn.a{i}))>nn.dropoutFraction);
-                nn.a{i} = nn.a{i}.*nn.dropOutMask{i};
-            end
-        end
-        
-        %calculate running exponential activations for use with sparsity
-        if(nn.nonSparsityPenalty>0)
-            nn.p{i} = 0.99 * nn.p{i} + 0.01 * mean(nn.a{i}, 1);
-        end
-        
-        %Add the bias term
-        nn.a{i} = [ones(m,1) nn.a{i}];
-    end
-    switch nn.output 
-        case 'sigm'
-            nn.a{n} = sigm(nn.a{n - 1} * nn.W{n - 1}');
-        case 'linear'
-            nn.a{n} = nn.a{n - 1} * nn.W{n - 1}';
-        case 'softmax'
-            nn.a{n} = nn.a{n - 1} * nn.W{n - 1}';
-            nn.a{n} = exp(bsxfun(@minus, nn.a{n}, max(nn.a{n},[],2)));
-            nn.a{n} = bsxfun(@rdivide, nn.a{n}, sum(nn.a{n}, 2)); 
-    end
-
-    %error and loss
-    nn.e = y - nn.a{n};
-    
-    switch nn.output
-        case {'sigm', 'linear'}
-            nn.L = 1/2 * sum(sum(nn.e .^ 2)) / m; 
-        case 'softmax'
-            nn.L = -sum(sum(y .* log(nn.a{n}))) / m;
-    end
-end

matlab_version/face_validation/DeepLearnToolbox/NN/nnpredict.m

@@ -1,8 +0,0 @@
-function labels = nnpredict(nn, x)
-    nn.testing = 1;
-    nn = nnff(nn, x, zeros(size(x,1), nn.size(end)));
-    nn.testing = 0;
-    
-    [dummy, i] = max(nn.a{end},[],2);
-    labels = i;
-end

matlab_version/face_validation/DeepLearnToolbox/NN/nnsetup.m

@@ -1,29 +0,0 @@
-function nn = nnsetup(architecture)
-%NNSETUP creates a Feedforward Backpropagate Neural Network
-% nn = nnsetup(architecture) returns an neural network structure with n=numel(architecture)
-% layers, architecture being a n x 1 vector of layer sizes e.g. [784 100 10]
-
-    nn.size   = architecture;
-    nn.n      = numel(nn.size);
-    
-    nn.activation_function              = 'tanh_opt';   %  Activation functions of hidden layers: 'sigm' (sigmoid) or 'tanh_opt' (optimal tanh).
-    nn.learningRate                     = 2;            %  learning rate Note: typically needs to be lower when using 'sigm' activation function and non-normalized inputs.
-    nn.momentum                         = 0.5;          %  Momentum
-    nn.scaling_learningRate             = 1;            %  Scaling factor for the learning rate (each epoch)
-    nn.weightPenaltyL2                  = 0;            %  L2 regularization
-    nn.nonSparsityPenalty               = 0;            %  Non sparsity penalty
-    nn.sparsityTarget                   = 0.05;         %  Sparsity target
-    nn.inputZeroMaskedFraction          = 0;            %  Used for Denoising AutoEncoders
-    nn.dropoutFraction                  = 0;            %  Dropout level (http://www.cs.toronto.edu/~hinton/absps/dropout.pdf)
-    nn.testing                          = 0;            %  Internal variable. nntest sets this to one.
-    nn.output                           = 'sigm';       %  output unit 'sigm' (=logistic), 'softmax' and 'linear'
-
-    for i = 2 : nn.n   
-        % weights and weight momentum
-        nn.W{i - 1} = (rand(nn.size(i), nn.size(i - 1)+1) - 0.5) * 2 * 4 * sqrt(6 / (nn.size(i) + nn.size(i - 1)));
-        nn.vW{i - 1} = zeros(size(nn.W{i - 1}));
-        
-        % average activations (for use with sparsity)
-        nn.p{i}     = zeros(1, nn.size(i));   
-    end
-end