- some cleanup with depth

exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp

@@ -238,7 +238,6 @@ int main (int argc, char **argv)
 		{		
 
 			// Reading the images
-			cv::Mat_<float> depth_image;
 			cv::Mat_<uchar> grayscale_image;
 
 			cv::Mat disp_image = captured_image.clone();
@@ -398,12 +397,6 @@ int main (int argc, char **argv)
 			{
 				cv::namedWindow("tracking_result",1);
 				cv::imshow("tracking_result", disp_image);
-
-				if(!depth_image.empty())
-				{
-					// Division needed for visualisation purposes
-					imshow("depth", depth_image/2000.0);
-				}
 			}
 
 			// output the tracked video

exe/FeatureExtraction/FeatureExtraction.cpp

@@ -232,7 +232,7 @@ int main (int argc, char **argv)
 	// Get the input output file parameters
 	
 	// Indicates that rotation should be with respect to camera or world coordinates
-	bool use_camera_coordinates;
+	bool use_camera_coordinates = false;
 	string output_codec; //not used but should
 	LandmarkDetector::get_video_input_output_params(input_files, output_files, tracked_videos_output, use_camera_coordinates, output_codec, arguments);
 

lib/local/LandmarkDetector/include/CCNF_patch_expert.h

@@ -51,7 +51,7 @@ class CCNF_neuron{
 
 public:
 
-	// Type of patch (0=raw,1=grad,3=depth, other types besides raw are not actually used now)
+	// Type of patch (0=raw,1=grad other types besides raw are not actually used now)
 	int     neuron_type; 
 
 	// scaling of weights (needed as the energy of neuron might not be 1) 
@@ -112,7 +112,7 @@ public:
 
 	void Read(std::ifstream &stream, std::vector<int> window_sizes, std::vector<std::vector<cv::Mat_<float> > > sigma_components);
 
-	// actual work (can pass in an image and a potential depth image, if the CCNF is trained with depth)
+	// actual work (can pass in an image)
 	void Response(cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
 
 	// Helper function to compute relevant sigmas

lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h

@@ -53,7 +53,7 @@ namespace LandmarkDetector
 	// Helper functions for parsing the inputs
 	//=============================================================================================
 	void get_video_input_output_params(vector<string> &input_video_file, vector<string> &output_files,
-		vector<string> &output_video_files, bool& world_coordinates_pose, string &output_codec, vector<string> &arguments);
+		vector<string> &output_video_files, bool& camera_coordinates_pose, string &output_codec, vector<string> &arguments);
 
 	void get_camera_params(int &device, float &fx, float &fy, float &cx, float &cy, vector<string> &arguments);
 

lib/local/LandmarkDetector/include/SVR_patch_expert.h

@@ -78,9 +78,8 @@ class SVR_patch_expert{
 		// Reading in the patch expert
 		void Read(std::ifstream &stream);
 
-		// The actual response computation from intensity or depth (for CLM-Z)
+		// The actual response computation from intensity
 		void Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
-		void ResponseDepth(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
 
 };
 //===========================================================================
@@ -105,9 +104,8 @@ class Multi_SVR_patch_expert{
 
 		void Read(std::ifstream &stream);
 
-		// actual response computation from intensity of depth (for CLM-Z)
+		// actual response computation from intensity
 		void Response(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
-		void ResponseDepth(const cv::Mat_<float> &area_of_interest, cv::Mat_<float> &response);
 
 };
 }

lib/local/LandmarkDetector/src/SVR_patch_expert.cpp

@@ -195,72 +195,6 @@ void SVR_patch_expert::Response(const cv::Mat_<float>& area_of_interest, cv::Mat
 
 }
 
-void SVR_patch_expert::ResponseDepth(const cv::Mat_<float>& area_of_interest, cv::Mat_<float> &response)
-{
-
-	// How big the response map will be
-	int response_height = area_of_interest.rows - weights.rows + 1;
-	int response_width = area_of_interest.cols - weights.cols + 1;
-	
-	// the patch area on which we will calculate reponses
-	cv::Mat_<float> normalised_area_of_interest;
-  
-	if(response.rows != response_height || response.cols != response_width)
-	{
-		response.create(response_height, response_width);
-	}
-
-	if(type == 0)
-	{
-		// Perform normalisation across whole patch
-		cv::Scalar mean;
-		cv::Scalar std;
-		
-		// ignore missing values
-		cv::Mat_<uchar> mask = area_of_interest > 0;
-		cv::meanStdDev(area_of_interest, mean, std, mask);
-
-		// if all values the same don't divide by 0
-		if(std[0] == 0)
-		{
-			std[0] = 1;
-		}
-
-		normalised_area_of_interest = (area_of_interest - mean[0]) / std[0];
-
-		// Set the invalid pixels to 0
-		normalised_area_of_interest.setTo(0, mask == 0);
-	}
-	else
-	{
-		printf("ERROR(%s,%d): Unsupported patch type %d!\n", __FILE__,__LINE__,type);
-		abort();
-	}
-  
-	cv::Mat_<float> svr_response;
-		
-	// The empty matrix as we don't pass precomputed dft's of image
-	cv::Mat_<double> empty_matrix_0(0,0,0.0);
-	cv::Mat_<float> empty_matrix_1(0,0,0.0);
-	cv::Mat_<float> empty_matrix_2(0,0,0.0);
-
-	// Efficient calc of patch expert response across the area of interest
-
-	matchTemplate_m(normalised_area_of_interest, empty_matrix_0, empty_matrix_1, empty_matrix_2, weights, weights_dfts, svr_response, CV_TM_CCOEFF); 
-	
-	response.create(svr_response.size());
-	cv::MatIterator_<float> p = response.begin();
-
-	cv::MatIterator_<float> q1 = svr_response.begin(); // respone for each pixel
-	cv::MatIterator_<float> q2 = svr_response.end();
-
-	while(q1 != q2)
-	{
-		// the SVR response passed through a logistic regressor
-		*p++ = 1.0/(1.0 + exp( -(*q1++ * scaling + bias )));
-	}	
-}
-
 // Copy constructor				
 Multi_SVR_patch_expert::Multi_SVR_patch_expert(const Multi_SVR_patch_expert& other) : svr_patch_experts(other.svr_patch_experts)
 {
@@ -321,17 +255,3 @@ void Multi_SVR_patch_expert::Response(const cv::Mat_<float> &area_of_interest, c
 
 }
 
-void Multi_SVR_patch_expert::ResponseDepth(const cv::Mat_<float>& area_of_interest, cv::Mat_<float>& response)
-{
-	int response_height = area_of_interest.rows - height + 1;
-	int response_width = area_of_interest.cols - width + 1;
-
-	if(response.rows != response_height || response.cols != response_width)
-	{
-		response.create(response_height, response_width);
-	}
-	
-	// With depth patch experts only do raw data modality
-	svr_patch_experts[0].ResponseDepth(area_of_interest, response);
-}
-//===========================================================================

matlab_runners/Gaze Experiments/mpii_1500_errs.txt

@@ -1,2 +1,2 @@
 Mean error, median error
-9.469, 8.773
+9.405, 8.580

matlab_runners/Head Pose Experiments/results/Pose_OF.txt

@@ -1,4 +1,4 @@
 Dataset and model,        pitch,  yaw,  roll,  mean,  median
 biwi error:  7.092,   5.170, 4.657,  5.640,  2.607
-bu error:    2.769,   4.105, 2.569,  3.147,  2.118
+bu error:    2.762,   3.328, 2.249,  2.780,  1.963
-ict error:   3.489,   3.632, 3.538,  3.553,  2.029
+ict error:   3.529,   3.873, 2.688,  3.363,  1.931

matlab_runners/Head Pose Experiments/run_biwi_experiment.m

@@ -1,4 +1,4 @@
-function [output_dir] = run_biwi_experiment(rootDir, biwiDir, verbose, depth, varargin)
+function [output_dir] = run_biwi_experiment(rootDir, biwiDir, verbose, varargin)
 % Biwi dataset experiment
 
 if(isunix)
@@ -11,10 +11,6 @@ output_dir = 'experiments/biwi_out';
 
 dbSeqDir = dir([rootDir biwiDir]);
    
-if(depth)
-    output_dir = cat(2, output_dir, '_depth');
-end
-
 output_dir = cat(2, output_dir, '/');
 
 offset = 0;
@@ -43,17 +39,12 @@ for i=3 + offset:numTogether:numel(dbSeqDir)
 
         command = cat(2, command, [' -f "' inputFile '" -of "' outputFile  '"']);
 
-        if(depth)
-            dDir = [biwiDir dbSeqDir(i+n).name '/depthAligned/'];
-            command = cat(2, command, [' -fd "' dDir '"']);    
-        end
-
         if(verbose)
             outputVideo = [output_dir dbSeqDir(i).name '.avi'];
             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 -vis-track']);
+    command = cat(2, command, [' -fx 505 -fy 505 -cx 320 -cy 240 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze -vis-track -camera_coord ']);
         
     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_OpenFace.m

@@ -27,7 +27,7 @@ buDir = [database_root, '/bu/uniform-light/'];
 % Run the Biwi test
 biwi_dir = '/biwi pose/';
 
-[res_folder_biwi_OF] = run_biwi_experiment(database_root, biwi_dir, false, false, 'model', 'model/main_clnf_general.txt');
+[res_folder_biwi_OF] = run_biwi_experiment(database_root, biwi_dir, false, 'model', 'model/main_clnf_general.txt');
 % Calculate the resulting errors
 [biwi_error_OF, pred_hp_biwi, gt_hp_biwi, ~, all_errors_biwi_OF, rels_biwi] = calcBiwiError(res_folder_biwi_OF, [database_root biwi_dir]);
 
@@ -35,7 +35,7 @@ biwi_dir = '/biwi pose/';
 ict_dir = ['/ict/'];
 
 % Intensity
-[res_folder_ict_OF] = run_ict_experiment(database_root, ict_dir, false, false, 'model', 'model/main_clnf_general.txt');
+[res_folder_ict_OF] = run_ict_experiment(database_root, ict_dir, false, 'model', 'model/main_clnf_general.txt');
 % Calculate the resulting errors
 [ict_error_OF, pred_hp_ict, gt_hp_ict, ~, all_errors_ict_OF, rel_ict] = calcIctError(res_folder_ict_OF, [database_root ict_dir]);
 

matlab_runners/Head Pose Experiments/run_ict_experiment.m

@@ -1,4 +1,4 @@
-function [output_dir] = run_ict_experiment(rootDir, ictDir, verbose, depth, varargin)
+function [output_dir] = run_ict_experiment(rootDir, ictDir, verbose, varargin)
 %EVALUATEICTDATABASE Summary of this function goes here
 %   Detailed explanation goes here
 
@@ -12,10 +12,6 @@ output_dir = 'experiments/ict_out';
 
 dbSeqDir = dir([rootDir ictDir]);
    
-if(depth)
-    output_dir = cat(2, output_dir, '_depth');
-end
-
 output_dir = cat(2, output_dir, '/');
 
 numTogether = 10;
@@ -37,12 +33,7 @@ for i=3:numTogether:numel(dbSeqDir)
         outputFile = [output_dir dbSeqDir(i+n).name '.txt'];
         
         command = cat(2, command,  [' -f "' inputFile '" -of "' outputFile  '" ']);
-        
+                
-        if(depth)
-            dDir = [ictDir dbSeqDir(i+n).name '/depthAligned/'];
-            command = cat(2, command, [' -fd "' dDir '"']);
-        end
-        
         if(verbose)
             outputVideo = [output_dir dbSeqDir(i+n).name '.avi'];
             command = cat(2, command, [' -ov "' outputVideo '"']);