Merged develop into master

.gitignore

@@ -44,3 +44,6 @@ exe/Recording/Debug/
 lib/3rdParty/dlib/Debug/
 lib/local/FaceAnalyser/Debug/
 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/

.travis.yml

@@ -75,4 +75,5 @@ script:
   - ../build/bin/FaceLandmarkImg -inroot ../videos -f Obama.jpg -outroot data -of obama.txt -op obama.3d -oi obama.bmp -multi_view 1 -wild -q
   - ../build/bin/FaceLandmarkVidMulti -inroot ../videos -f multi_face.avi -outroot output -ov multi_face.avi -q
   - ../build/bin/FeatureExtraction -f "../videos/1815_01_008_tony_blair.avi" -outroot output_features -ov blair.avi -of "1815_01_008_tony_blair.txt" -simalign aligned -ov feat_test.avi -hogalign hog_test.dat -q
+  - ../build/bin/FeatureExtraction -f "../videos/1815_01_008_tony_blair.avi" -outroot output_features -simsize 200 -simscale 0.5 -ov blair.avi -of "1815_01_008_tony_blair.txt" -simalign aligned -ov feat_test.avi -hogalign hog_test.dat -q
   - ../build/bin/FaceLandmarkVid -inroot ../videos -f 1815_01_008_tony_blair.avi -f 0188_03_021_al_pacino.avi -f 0217_03_006_alanis_morissette.avi -outroot output_data -ov 1.avi -ov 2.avi -ov 3.avi -q

appveyor.yml

@@ -25,4 +25,5 @@ test_script:
   - cmd: if exist "../videos" (FaceLandmarkImg.exe -inroot ../videos -f obama.jpg -outroot out_data -of obama.pts -op obama.3d -oi obama.bmp -q) else (FaceLandmarkImg.exe -inroot ../../videos -f obama.jpg -outroot out_data -of obama.pts -op obama.3d -oi obama.bmp -q)
   - cmd: if exist "../videos" (FaceLandmarkVidMulti.exe -inroot ../videos -f multi_face.avi -ov multi_face.avi -q) else (FaceLandmarkVidMulti.exe -inroot ../../videos -f multi_face.avi -ov multi_face.avi -q)
   - cmd: if exist "../videos" (FeatureExtraction.exe -f "../videos/1815_01_008_tony_blair.avi" -outroot output_features -of "1815_01_008_tony_blair.txt" -simalign aligned -ov feat_track.avi -hogalign hog_test.dat -q) else (FeatureExtraction.exe -f "../../videos/1815_01_008_tony_blair.avi" -outroot output_features -of "1815_01_008_tony_blair.txt" -simalign aligned -ov feat_track.avi -hogalign hog_test.dat -q)
+  - cmd: if exist "../videos" (FeatureExtraction.exe -f "../videos/1815_01_008_tony_blair.avi" -outroot output_features -of "1815_01_008_tony_blair.txt" -simalign aligned -simsize 200 -simscale 0.5 -ov feat_track.avi -hogalign hog_test.dat -q) else (FeatureExtraction.exe -f "../../videos/1815_01_008_tony_blair.avi" -outroot output_features -of "1815_01_008_tony_blair.txt" -simalign aligned -simsize 200 -simscale 0.5 -ov feat_track.avi -hogalign hog_test.dat -q)
   - cmd: if exist "../videos" (FaceLandmarkVid.exe -f "../videos/1815_01_008_tony_blair.avi" -ov track.avi -q) else (FaceLandmarkVid.exe -f "../../videos/1815_01_008_tony_blair.avi" -ov track.avi -q)

exe/FeatureExtraction/FeatureExtraction.cpp

@@ -308,9 +308,9 @@ int main (int argc, char **argv)
 	vector<string> output_similarity_align;
 	vector<string> output_hog_align_files;
 
-	double sim_scale = 0.7;
+	double sim_scale = -1;
 	int sim_size = 112;
-	bool grayscale = false;	
+	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;
@@ -321,13 +321,13 @@ int main (int argc, char **argv)
 	bool output_2D_landmarks = true;
 	bool output_3D_landmarks = true;
 	bool output_model_params = true;
-	bool output_pose = true; 
+	bool output_pose = true;
 	bool output_AUs = true;
 	bool output_gaze = true;
 
 	get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, verbose, dynamic,
 		output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
-	
+
 	// Used for image masking
 	string tri_loc;
 	boost::filesystem::path tri_loc_path = boost::filesystem::path("model/tris_68_full.txt");
@@ -391,7 +391,10 @@ int main (int argc, char **argv)
 	}
 
 	// Creating a  face analyser that will be used for AU extraction
-	FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), 0.7, 112, 112, au_loc, tri_loc);
+	// Make sure sim_scale is proportional to sim_size if not set
+	if (sim_scale == -1) sim_scale = sim_size * (0.7 / 112.0);
+
+	FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), sim_scale, sim_size, sim_size, au_loc, tri_loc);
 		
 	while(!done) // this is not a for loop as we might also be reading from a webcam
 	{
@@ -593,7 +596,7 @@ int main (int argc, char **argv)
 				}
 				if(hog_output_file.is_open())
 				{
-					FaceAnalysis::Extract_FHOG_descriptor(hog_descriptor, sim_warped_img, num_hog_rows, num_hog_cols);						
+					face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
 
 					if(visualise_hog && !det_parameters.quiet_mode)
 					{
@@ -615,13 +618,13 @@ int main (int argc, char **argv)
 				pose_estimate = LandmarkDetector::GetCorrectedPoseCamera(face_model, fx, fy, cx, cy);
 			}
 
-			if(hog_output_file.is_open())
+			if (hog_output_file.is_open())
 			{
 				output_HOG_frame(&hog_output_file, detection_success, hog_descriptor, num_hog_rows, num_hog_cols);
 			}
 
 			// Write the similarity normalised output
-			if(!output_similarity_align.empty())
+			if (!output_similarity_align.empty())
 			{
 
 				if (sim_warped_img.channels() == 3 && grayscale)
@@ -630,18 +633,18 @@ int main (int argc, char **argv)
 				}
 
 				char name[100];
-					
+
-				// output the frame number
+				// Filename is based on frame number
-				std::sprintf(name, "frame_det_%06d.bmp", frame_count);
+				std::sprintf(name, "frame_det_%06d.bmp", frame_count + 1);
 
 				// Construct the output filename
 				boost::filesystem::path slash("/");
-					
+
 				std::string preferredSlash = slash.make_preferred().string();
-				
+
 				string out_file = output_similarity_align[f_n] + preferredSlash + string(name);
 				bool write_success = imwrite(out_file, sim_warped_img);
-				
+
 				if (!write_success)
 				{
 					cout << "Could not output similarity aligned image image" << endl;
@@ -714,10 +717,10 @@ int main (int argc, char **argv)
 		
 		output_file.close();
 
-		if(output_files.size() > 0 && output_AUs)
+		if (output_files.size() > 0 && output_AUs)
 		{
 			cout << "Postprocessing the Action Unit predictions" << endl;
-			post_process_output_file(face_analyser, output_files[f_n], dynamic);
+			face_analyser.PostprocessOutputFile(output_files[f_n], dynamic);
 		}
 		// Reset the models for the next video
 		face_analyser.Reset();
@@ -741,121 +744,6 @@ int main (int argc, char **argv)
 	return 0;
 }
 
-// Allows for post processing of the AU signal
-void post_process_output_file(FaceAnalysis::FaceAnalyser& face_analyser, string output_file, bool dynamic)
-{
-
-	vector<double> certainties;
-	vector<bool> successes;
-	vector<double> timestamps;
-	vector<std::pair<std::string, vector<double>>> predictions_reg;
-	vector<std::pair<std::string, vector<double>>> predictions_class;
-
-	// Construct the new values to overwrite the output file with
-	face_analyser.ExtractAllPredictionsOfflineReg(predictions_reg, certainties, successes, timestamps, dynamic);
-	face_analyser.ExtractAllPredictionsOfflineClass(predictions_class, certainties, successes, timestamps, dynamic);
-
-	int num_class = predictions_class.size();
-	int num_reg = predictions_reg.size();
-
-	// Extract the indices of writing out first
-	vector<string> au_reg_names = face_analyser.GetAURegNames();
-	std::sort(au_reg_names.begin(), au_reg_names.end());
-	vector<int> inds_reg;
-
-	// write out ar the correct index
-	for (string au_name : au_reg_names)
-	{
-		for (int i = 0; i < num_reg; ++i)
-		{
-			if (au_name.compare(predictions_reg[i].first) == 0)
-			{
-				inds_reg.push_back(i);
-				break;
-			}
-		}
-	}
-
-	vector<string> au_class_names = face_analyser.GetAUClassNames();
-	std::sort(au_class_names.begin(), au_class_names.end());
-	vector<int> inds_class;
-
-	// write out ar the correct index
-	for (string au_name : au_class_names)
-	{
-		for (int i = 0; i < num_class; ++i)
-		{
-			if (au_name.compare(predictions_class[i].first) == 0)
-			{
-				inds_class.push_back(i);
-				break;
-			}
-		}
-	}
-	// Read all of the output file in
-	vector<string> output_file_contents;
-
-	std::ifstream infile(output_file);
-	string line;
-
-	while (std::getline(infile, line))
-		output_file_contents.push_back(line);
-
-	infile.close();
-
-	// Read the header and find all _r and _c parts in a file and use their indices
-	std::vector<std::string> tokens;
-	boost::split(tokens, output_file_contents[0], boost::is_any_of(","));
-
-	int begin_ind = -1;
-	
-	for (size_t i = 0; i < tokens.size(); ++i)
-	{
-		if (tokens[i].find("AU") != string::npos && begin_ind == -1)
-		{
-			begin_ind = i;
-			break;
-		}
-	}
-	int end_ind = begin_ind + num_class + num_reg;
-
-	// Now overwrite the whole file
-	std::ofstream outfile(output_file, ios_base::out);
-	// Write the header
-	outfile << output_file_contents[0].c_str() << endl;
-	
-	// Write the contents
-	for (int i = 1; i < (int)output_file_contents.size(); ++i)
-	{
-		std::vector<std::string> tokens;
-		boost::split(tokens, output_file_contents[i], boost::is_any_of(","));
-
-		outfile << tokens[0];
-
-		for (int t = 1; t < (int)tokens.size(); ++t)
-		{
-			if (t >= begin_ind && t < end_ind)
-			{
-				if(t - begin_ind < num_reg)
-				{
-					outfile << ", " << predictions_reg[inds_reg[t - begin_ind]].second[i - 1];
-				}
-				else
-				{
-					outfile << ", " << predictions_class[inds_class[t - begin_ind - num_reg]].second[i - 1];
-				}
-			}
-			else
-			{
-				outfile << ", " << tokens[t];
-			}
-		}
-		outfile << endl;
-	}
-		
-
-}
-
 void prepareOutputFile(std::ofstream* output_file, bool output_2D_landmarks, bool output_3D_landmarks,
 	bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze,
 	int num_landmarks, int num_model_modes, vector<string> au_names_class, vector<string> au_names_reg)
@@ -1206,6 +1094,7 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
 
 }
 
+
 // Can process images via directories creating a separate output file per directory
 void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments)
 {

lib/local/FaceAnalyser/include/FaceAnalyser.h

@@ -74,184 +74,183 @@
 namespace FaceAnalysis
 {
 
-class FaceAnalyser{
+	class FaceAnalyser {
 
-public:
+	public:
 
 
-	enum RegressorType{ SVR_appearance_static_linear = 0, SVR_appearance_dynamic_linear = 1, SVR_dynamic_geom_linear = 2, SVR_combined_linear = 3, SVM_linear_stat = 4, SVM_linear_dyn = 5, SVR_linear_static_seg = 6, SVR_linear_dynamic_seg =7};
+		enum RegressorType { SVR_appearance_static_linear = 0, SVR_appearance_dynamic_linear = 1, SVR_dynamic_geom_linear = 2, SVR_combined_linear = 3, SVM_linear_stat = 4, SVM_linear_dyn = 5, SVR_linear_static_seg = 6, SVR_linear_dynamic_seg = 7 };
 
-	// Constructor from a model file (or a default one if not provided
+		// Constructor from a model file (or a default one if not provided
-	// TODO scale width and height should be read in as part of the model as opposed to being here?
+		// TODO scale width and height should be read in as part of the model as opposed to being here?
-	FaceAnalyser(vector<cv::Vec3d> orientation_bins = vector<cv::Vec3d>(), double scale = 0.7, int width = 112, int height = 112, std::string au_location = "AU_predictors/AU_all_best.txt", std::string tri_location = "model/tris_68_full.txt");
+		FaceAnalyser(vector<cv::Vec3d> orientation_bins = vector<cv::Vec3d>(), double scale = 0.7, int width = 112, int height = 112, std::string au_location = "AU_predictors/AU_all_best.txt", std::string tri_location = "model/tris_68_full.txt");
 
-	void AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, double timestamp_seconds, bool online = false, bool visualise = true);
+		void AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, double timestamp_seconds, bool online = false, bool visualise = true);
 
-	// If the features are extracted manually (shouldn't really be used)
+		// If the features are extracted manually (shouldn't really be used)
-	void PredictAUs(const cv::Mat_<double>& hog_features, const cv::Mat_<double>& geom_features, const LandmarkDetector::CLNF& clnf_model, bool online);
+		void PredictAUs(const cv::Mat_<double>& hog_features, const cv::Mat_<double>& geom_features, const LandmarkDetector::CLNF& clnf_model, bool online);
 
-	cv::Mat GetLatestHOGDescriptorVisualisation();
+		cv::Mat GetLatestHOGDescriptorVisualisation();
 
-	double GetCurrentTimeSeconds();
+		double GetCurrentTimeSeconds();
-	
-	// Grab the current predictions about AUs from the face analyser
-	std::vector<std::pair<std::string, double>> GetCurrentAUsClass() const; // AU presence
-	std::vector<std::pair<std::string, double>> GetCurrentAUsReg() const;   // AU intensity
-	std::vector<std::pair<std::string, double>> GetCurrentAUsCombined() const; // Both presense and intensity
 
-	// A standalone call for predicting AUs from a static image, the first element in the pair represents occurence the second intensity
+		// Grab the current predictions about AUs from the face analyser
-	// This call is useful for detecting action units in images
+		std::vector<std::pair<std::string, double>> GetCurrentAUsClass() const; // AU presence
-	std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string, double>>> PredictStaticAUs(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, bool visualise = true);
+		std::vector<std::pair<std::string, double>> GetCurrentAUsReg() const;   // AU intensity
+		std::vector<std::pair<std::string, double>> GetCurrentAUsCombined() const; // Both presense and intensity
 
-	void Reset();
+																				   // A standalone call for predicting AUs from a static image, the first element in the pair represents occurence the second intensity
+																				   // This call is useful for detecting action units in images
+		std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string, double>>> PredictStaticAUs(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, bool visualise = true);
 
-	void GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols);
+		void Reset();
-	void GetLatestAlignedFace(cv::Mat& image);
-	
-	void GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols);
-	
-	cv::Mat_<int> GetTriangulation();
 
-	cv::Mat_<uchar> GetLatestAlignedFaceGrayscale();
+		void GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols);
-	
+		void GetLatestAlignedFace(cv::Mat& image);
-	void GetGeomDescriptor(cv::Mat_<double>& geom_desc);
 
-	void ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations);
+		void GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols);
 
-	// Grab the names of AUs being predicted
+		cv::Mat_<int> GetTriangulation();
-	std::vector<std::string> GetAUClassNames() const; // Presence
-	std::vector<std::string> GetAURegNames() const; // Intensity
 
-	// Identify if models are static or dynamic (useful for correction and shifting)
+		void GetGeomDescriptor(cv::Mat_<double>& geom_desc);
-	std::vector<bool> GetDynamicAUClass() const; // Presence
+
-	std::vector<std::pair<string, bool>> GetDynamicAUReg() const; // Intensity
+		// Grab the names of AUs being predicted
+		std::vector<std::string> GetAUClassNames() const; // Presence
+		std::vector<std::string> GetAURegNames() const; // Intensity
+
+														// Identify if models are static or dynamic (useful for correction and shifting)
+		std::vector<bool> GetDynamicAUClass() const; // Presence
+		std::vector<std::pair<string, bool>> GetDynamicAUReg() const; // Intensity
 
 
-	void ExtractAllPredictionsOfflineReg(vector<std::pair<std::string, vector<double>>>& au_predictions, vector<double>& confidences, vector<bool>& successes, vector<double>& timestamps, bool dynamic);
+		void ExtractAllPredictionsOfflineReg(vector<std::pair<std::string, vector<double>>>& au_predictions, vector<double>& confidences, vector<bool>& successes, vector<double>& timestamps, bool dynamic);
-	void ExtractAllPredictionsOfflineClass(vector<std::pair<std::string, vector<double>>>& au_predictions, vector<double>& confidences, vector<bool>& successes, vector<double>& timestamps, bool dynamic);
+		void ExtractAllPredictionsOfflineClass(vector<std::pair<std::string, vector<double>>>& au_predictions, vector<double>& confidences, vector<bool>& successes, vector<double>& timestamps, bool dynamic);
 
-private:
+		// Helper function for post-processing AU output files
+		void PostprocessOutputFile(string output_file, bool dynamic);
 
-	// Where the predictions are kept
+	private:
-	std::vector<std::pair<std::string, double>> AU_predictions_reg;
-	std::vector<std::pair<std::string, double>> AU_predictions_class;
 
-	std::vector<std::pair<std::string, double>> AU_predictions_combined;
+		// Where the predictions are kept
+		std::vector<std::pair<std::string, double>> AU_predictions_reg;
+		std::vector<std::pair<std::string, double>> AU_predictions_class;
 
-	// Keeping track of AU predictions over time (useful for post-processing)
+		std::vector<std::pair<std::string, double>> AU_predictions_combined;
-	vector<double> timestamps;
-	std::map<std::string, vector<double>> AU_predictions_reg_all_hist;
-	std::map<std::string, vector<double>> AU_predictions_class_all_hist;
-	std::vector<double> confidences;
-	std::vector<bool> valid_preds;
 
-	int frames_tracking;
+		// Keeping track of AU predictions over time (useful for post-processing)
+		vector<double> timestamps;
+		std::map<std::string, vector<double>> AU_predictions_reg_all_hist;
+		std::map<std::string, vector<double>> AU_predictions_class_all_hist;
+		std::vector<double> confidences;
+		std::vector<bool> valid_preds;
 
-	// Cache of intermediate images
+		int frames_tracking;
-	cv::Mat_<uchar> aligned_face_grayscale;
-	cv::Mat aligned_face;
-	cv::Mat hog_descriptor_visualisation;
 
-	// Private members to be used for predictions
+		// Cache of intermediate images
-	// The HOG descriptor of the last frame
+		cv::Mat aligned_face_for_au;
-	cv::Mat_<double> hog_desc_frame;
+		cv::Mat aligned_face_for_output;
-	int num_hog_rows;
+		cv::Mat hog_descriptor_visualisation;
-	int num_hog_cols;
 
-	// Keep a running median of the hog descriptors and a aligned images
+		// Private members to be used for predictions
-	cv::Mat_<double> hog_desc_median;
+		// The HOG descriptor of the last frame
-	cv::Mat_<double> face_image_median;
+		cv::Mat_<double> hog_desc_frame;
+		int num_hog_rows;
+		int num_hog_cols;
 
-	// Use histograms for quick (but approximate) median computation
+		// Keep a running median of the hog descriptors and a aligned images
-	// Use the same for
+		cv::Mat_<double> hog_desc_median;
-	vector<cv::Mat_<unsigned int> > hog_desc_hist;
+		cv::Mat_<double> face_image_median;
 
-	// This is not being used at the moment as it is a bit slow
+		// Use histograms for quick (but approximate) median computation
-	vector<cv::Mat_<unsigned int> > face_image_hist;
+		// Use the same for
-	vector<int> face_image_hist_sum;
+		vector<cv::Mat_<unsigned int> > hog_desc_hist;
 
-	vector<cv::Vec3d> head_orientations;
+		// This is not being used at the moment as it is a bit slow
+		vector<cv::Mat_<unsigned int> > face_image_hist;
+		vector<int> face_image_hist_sum;
 
-	int num_bins_hog;
+		vector<cv::Vec3d> head_orientations;
-	double min_val_hog;
-	double max_val_hog;
-	vector<int> hog_hist_sum;
-	int view_used;
 
-	// The geometry descriptor (rigid followed by non-rigid shape parameters from CLNF)
+		int num_bins_hog;
-	cv::Mat_<double> geom_descriptor_frame;
+		double min_val_hog;
-	cv::Mat_<double> geom_descriptor_median;
+		double max_val_hog;
-	
+		vector<int> hog_hist_sum;
-	int geom_hist_sum;
+		int view_used;
-	cv::Mat_<unsigned int> geom_desc_hist;
-	int num_bins_geom;
-	double min_val_geom;
-	double max_val_geom;
-	
-	// Using the bounding box of previous analysed frame to determine if a reset is needed
-	cv::Rect_<double> face_bounding_box;
-	
-	// The AU predictions internally
-	std::vector<std::pair<std::string, double>> PredictCurrentAUs(int view);
-	std::vector<std::pair<std::string, double>> PredictCurrentAUsClass(int view);
 
-	// special step for online (rather than offline AU prediction)
+		// The geometry descriptor (rigid followed by non-rigid shape parameters from CLNF)
-	std::vector<pair<string, double>> CorrectOnlineAUs(std::vector<std::pair<std::string, double>> predictions_orig, int view, bool dyn_shift = false, bool dyn_scale = false, bool update_track = true, bool clip_values = false);
+		cv::Mat_<double> geom_descriptor_frame;
+		cv::Mat_<double> geom_descriptor_median;
 
-	void ReadAU(std::string au_location);
+		int geom_hist_sum;
+		cv::Mat_<unsigned int> geom_desc_hist;
+		int num_bins_geom;
+		double min_val_geom;
+		double max_val_geom;
 
-	void ReadRegressor(std::string fname, const vector<string>& au_names);
+		// Using the bounding box of previous analysed frame to determine if a reset is needed
+		cv::Rect_<double> face_bounding_box;
 
-	// A utility function for keeping track of approximate running medians used for AU and emotion inference using a set of histograms (the histograms are evenly spaced from min_val to max_val)
+		// The AU predictions internally
-	// Descriptor has to be a row vector
+		std::vector<std::pair<std::string, double>> PredictCurrentAUs(int view);
-	// TODO this duplicates some other code
+		std::vector<std::pair<std::string, double>> PredictCurrentAUsClass(int view);
-	void UpdateRunningMedian(cv::Mat_<unsigned int>& histogram, int& hist_sum, cv::Mat_<double>& median, const cv::Mat_<double>& descriptor, bool update, int num_bins, double min_val, double max_val);
-	void ExtractMedian(cv::Mat_<unsigned int>& histogram, int hist_count, cv::Mat_<double>& median, int num_bins, double min_val, double max_val);
-	
-	// The linear SVR regressors
-	SVR_static_lin_regressors AU_SVR_static_appearance_lin_regressors;
-	SVR_dynamic_lin_regressors AU_SVR_dynamic_appearance_lin_regressors;
-		
-	// The linear SVM classifiers
-	SVM_static_lin AU_SVM_static_appearance_lin;
-	SVM_dynamic_lin AU_SVM_dynamic_appearance_lin;
 
-	// The AUs predicted by the model are not always 0 calibrated to a person. That is they don't always predict 0 for a neutral expression
+		// special step for online (rather than offline AU prediction)
-	// Keeping track of the predictions we can correct for this, by assuming that at least "ratio" of frames are neutral and subtract that value of prediction, only perform the correction after min_frames
+		std::vector<pair<string, double>> CorrectOnlineAUs(std::vector<std::pair<std::string, double>> predictions_orig, int view, bool dyn_shift = false, bool dyn_scale = false, bool update_track = true, bool clip_values = false);
-	void UpdatePredictionTrack(cv::Mat_<unsigned int>& prediction_corr_histogram, int& prediction_correction_count, vector<double>& correction, const vector<pair<string, double>>& predictions, double ratio=0.25, int num_bins = 200, double min_val = -3, double max_val = 5, int min_frames = 10);
-	void GetSampleHist(cv::Mat_<unsigned int>& prediction_corr_histogram, int prediction_correction_count, vector<double>& sample, double ratio, int num_bins = 200, double min_val = 0, double max_val = 5);
 
-	void PostprocessPredictions();
+		void ReadAU(std::string au_location);
 
-	vector<cv::Mat_<unsigned int>> au_prediction_correction_histogram;
+		void ReadRegressor(std::string fname, const vector<string>& au_names);
-	vector<int> au_prediction_correction_count;
 
-	// Some dynamic scaling (the logic is that before the extreme versions of expression or emotion are shown,
+		// A utility function for keeping track of approximate running medians used for AU and emotion inference using a set of histograms (the histograms are evenly spaced from min_val to max_val)
-	// it is hard to tell the boundaries, this allows us to scale the model to the most extreme seen)
+		// Descriptor has to be a row vector
-	// They have to be view specific
+		// TODO this duplicates some other code
-	vector<vector<double>> dyn_scaling;
+		void UpdateRunningMedian(cv::Mat_<unsigned int>& histogram, int& hist_sum, cv::Mat_<double>& median, const cv::Mat_<double>& descriptor, bool update, int num_bins, double min_val, double max_val);
-	
+		void ExtractMedian(cv::Mat_<unsigned int>& histogram, int hist_count, cv::Mat_<double>& median, int num_bins, double min_val, double max_val);
-	// Keeping track of predictions for summary stats
-	cv::Mat_<double> AU_prediction_track;
-	cv::Mat_<double> geom_desc_track;
 
-	double current_time_seconds;
+		// The linear SVR regressors
+		SVR_static_lin_regressors AU_SVR_static_appearance_lin_regressors;
+		SVR_dynamic_lin_regressors AU_SVR_dynamic_appearance_lin_regressors;
 
-	// Used for face alignment
+		// The linear SVM classifiers
-	cv::Mat_<int> triangulation;
+		SVM_static_lin AU_SVM_static_appearance_lin;
-	double align_scale;	
+		SVM_dynamic_lin AU_SVM_dynamic_appearance_lin;
-	int align_width;
-	int align_height;
 
-	// Useful placeholder for renormalizing the initial frames of shorter videos
+		// The AUs predicted by the model are not always 0 calibrated to a person. That is they don't always predict 0 for a neutral expression
-	int max_init_frames = 3000;
+		// Keeping track of the predictions we can correct for this, by assuming that at least "ratio" of frames are neutral and subtract that value of prediction, only perform the correction after min_frames
-	vector<cv::Mat_<double>> hog_desc_frames_init;
+		void UpdatePredictionTrack(cv::Mat_<unsigned int>& prediction_corr_histogram, int& prediction_correction_count, vector<double>& correction, const vector<pair<string, double>>& predictions, double ratio = 0.25, int num_bins = 200, double min_val = -3, double max_val = 5, int min_frames = 10);
-	vector<cv::Mat_<double>> geom_descriptor_frames_init;
+		void GetSampleHist(cv::Mat_<unsigned int>& prediction_corr_histogram, int prediction_correction_count, vector<double>& sample, double ratio, int num_bins = 200, double min_val = 0, double max_val = 5);
-	vector<int> views;
-	bool postprocessed = false;
-	int frames_tracking_succ = 0;
 
-};
+		void PostprocessPredictions();
-  //===========================================================================
+
+		vector<cv::Mat_<unsigned int>> au_prediction_correction_histogram;
+		vector<int> au_prediction_correction_count;
+
+		// Some dynamic scaling (the logic is that before the extreme versions of expression or emotion are shown,
+		// it is hard to tell the boundaries, this allows us to scale the model to the most extreme seen)
+		// They have to be view specific
+		vector<vector<double>> dyn_scaling;
+
+		// Keeping track of predictions for summary stats
+		cv::Mat_<double> AU_prediction_track;
+		cv::Mat_<double> geom_desc_track;
+
+		double current_time_seconds;
+
+		// Used for face alignment
+		cv::Mat_<int> triangulation;
+		double align_scale;
+		int align_width;
+		int align_height;
+
+		// Useful placeholder for renormalizing the initial frames of shorter videos
+		int max_init_frames = 3000;
+		vector<cv::Mat_<double>> hog_desc_frames_init;
+		vector<cv::Mat_<double>> geom_descriptor_frames_init;
+		vector<int> views;
+		bool postprocessed = false;
+		int frames_tracking_succ = 0;
+
+	};
+	//===========================================================================
 }
 #endif

lib/local/FaceAnalyser/src/FaceAnalyser.cpp

@@ -226,7 +226,7 @@ void FaceAnalyser::GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows,
 
 void FaceAnalyser::GetLatestAlignedFace(cv::Mat& image)
 {
-	image = this->aligned_face.clone();
+	image = this->aligned_face_for_output.clone();
 }
 
 void FaceAnalyser::GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols)
@@ -267,57 +267,22 @@ int GetViewId(const vector<cv::Vec3d> orientations_all, const cv::Vec3d& orienta
 	
 }
 
-void FaceAnalyser::ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations)
-{
-
-	orientations = this->head_orientations;
-
-	for(size_t i = 0; i < orientations.size(); ++i)
-	{
-		cv::Mat_<double> median_face(this->face_image_median.rows, this->face_image_median.cols, 0.0);
-		cv::Mat_<double> median_hog(this->hog_desc_median.rows, this->hog_desc_median.cols, 0.0);
-
-		ExtractMedian(this->face_image_hist[i], this->face_image_hist_sum[i], median_face, 256, 0, 255);		
-		ExtractMedian(this->hog_desc_hist[i], this->hog_hist_sum[i], median_hog, this->num_bins_hog, 0, 1);
-
-		// Add the HOG sample
-		hog_medians.push_back(median_hog.clone());
-
-		// For the face image need to convert it to suitable format
-		cv::Mat_<uchar> aligned_face_cols_uchar;
-		median_face.convertTo(aligned_face_cols_uchar, CV_8U);
-
-		cv::Mat aligned_face_uchar;
-		if(aligned_face.channels() == 1)
-		{
-			aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8U, aligned_face_cols_uchar.data);
-		}
-		else
-		{
-			aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8UC3, aligned_face_cols_uchar.data);
-		}
-
-		face_image_medians.push_back(aligned_face_uchar.clone());
-		
-	}
-}
-
 std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string, double>>> FaceAnalyser::PredictStaticAUs(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, bool visualise)
 {
-	
+
 	// First align the face
-	AlignFaceMask(aligned_face, frame, clnf, triangulation, true, align_scale, align_width, align_height);
+	AlignFaceMask(aligned_face_for_au, frame, clnf, triangulation, true, 0.7, 112, 112);
-	
+
 	// Extract HOG descriptor from the frame and convert it to a useable format
 	cv::Mat_<double> hog_descriptor;
-	Extract_FHOG_descriptor(hog_descriptor, aligned_face, this->num_hog_rows, this->num_hog_cols);
+	Extract_FHOG_descriptor(hog_descriptor, aligned_face_for_au, this->num_hog_rows, this->num_hog_cols);
 
 	// Store the descriptor
 	hog_desc_frame = hog_descriptor;
 
 	cv::Vec3d curr_orient(clnf.params_global[1], clnf.params_global[2], clnf.params_global[3]);
 	int orientation_to_use = GetViewId(this->head_orientations, curr_orient);
-	
+
 	// Geom descriptor and its median
 	geom_descriptor_frame = clnf.params_local.t();
 
@@ -325,11 +290,11 @@ std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string,
 	cv::Mat_<double> locs = clnf.pdm.princ_comp * geom_descriptor_frame.t();
 
 	cv::hconcat(locs.t(), geom_descriptor_frame.clone(), geom_descriptor_frame);
-	
+
-	// First convert the face image to double representation as a row vector
+	// 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_<uchar> aligned_face_cols(1, aligned_face_for_au.cols * aligned_face_for_au.rows * aligned_face_for_au.channels(), aligned_face_for_au.data, 1);
-	cv::Mat_<double> aligned_face_cols_double;
+	//cv::Mat_<double> aligned_face_cols_double;
-	aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
+	//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
 
 	// Visualising the median HOG
 	if (visualise)
@@ -361,29 +326,34 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 	frames_tracking++;
 
 	// First align the face if tracking was successfull
-	if(clnf_model.detection_success)
+	if (clnf_model.detection_success)
 	{
-		AlignFaceMask(aligned_face, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
-	}
-	else
-	{
-		aligned_face = cv::Mat(align_height, align_width, CV_8UC3);
-		aligned_face.setTo(0);
-	}
 
-	if(aligned_face.channels() == 3)
+		// The aligned face requirement for AUs
-	{
+		AlignFaceMask(aligned_face_for_au, frame, clnf_model, triangulation, true, 0.7, 112, 112);
-		cv::cvtColor(aligned_face, aligned_face_grayscale, CV_BGR2GRAY);
+
+		// If the output requirement matches use the already computed one, else compute it again
+		if (align_scale == 0.7 && align_width == 112 && align_height == 112)
+		{
+			aligned_face_for_output = aligned_face_for_au.clone();
+		}
+		else
+		{
+			AlignFaceMask(aligned_face_for_output, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
+		}
 	}
 	else
 	{
-		aligned_face_grayscale = aligned_face.clone();
+		aligned_face_for_output = cv::Mat(align_height, align_width, CV_8UC3);
+		aligned_face_for_au = cv::Mat(112, 112, CV_8UC3);
+		aligned_face_for_output.setTo(0);
+		aligned_face_for_au.setTo(0);
 	}
 
 	// Extract HOG descriptor from the frame and convert it to a useable format
 	cv::Mat_<double> hog_descriptor;
-	Extract_FHOG_descriptor(hog_descriptor, aligned_face, this->num_hog_rows, this->num_hog_cols);
+	Extract_FHOG_descriptor(hog_descriptor, aligned_face_for_au, this->num_hog_rows, this->num_hog_cols);
-	
+
 	// Store the descriptor
 	hog_desc_frame = hog_descriptor;
 
@@ -425,41 +395,38 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 		frames_tracking_succ++;
 
 	// A small speedup
-	if(frames_tracking % 2 == 1)
+	if (frames_tracking % 2 == 1)
 	{
 		UpdateRunningMedian(this->hog_desc_hist[orientation_to_use], this->hog_hist_sum[orientation_to_use], this->hog_desc_median, hog_descriptor, update_median, this->num_bins_hog, this->min_val_hog, this->max_val_hog);
 		this->hog_desc_median.setTo(0, this->hog_desc_median < 0);
-	}	
+	}
 
 	// Geom descriptor and its median
 	geom_descriptor_frame = clnf_model.params_local.t();
-	
+
-	if(!clnf_model.detection_success)
+	if (!clnf_model.detection_success)
 	{
 		geom_descriptor_frame.setTo(0);
 	}
 
 	// Stack with the actual feature point locations (without mean)
 	cv::Mat_<double> locs = clnf_model.pdm.princ_comp * geom_descriptor_frame.t();
-	
+
 	cv::hconcat(locs.t(), geom_descriptor_frame.clone(), geom_descriptor_frame);
-	
+
 	// A small speedup
-	if(frames_tracking % 2 == 1)
+	if (frames_tracking % 2 == 1)
 	{
 		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
+	// 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_<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;
+	//cv::Mat_<double> aligned_face_cols_double;
-	aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
+	//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
-	
-	// TODO get rid of this completely as it takes too long?
-	//UpdateRunningMedian(this->face_image_hist[orientation_to_use], this->face_image_hist_sum[orientation_to_use], this->face_image_median, aligned_face_cols_double, update_median, 256, 0, 255);
 
 	// Visualising the median HOG
-	if(visualise)
+	if (visualise)
 	{
 		FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_visualisation);
 	}
@@ -468,9 +435,9 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 	AU_predictions_reg = PredictCurrentAUs(orientation_to_use);
 
 	std::vector<std::pair<std::string, double>> AU_predictions_reg_corrected;
-	if(online)
+	if (online)
 	{
-		AU_predictions_reg_corrected = CorrectOnlineAUs(AU_predictions_reg, orientation_to_use, true, false, clnf_model.detection_success);
+		AU_predictions_reg_corrected = CorrectOnlineAUs(AU_predictions_reg, orientation_to_use, true, false, clnf_model.detection_success, true);
 	}
 
 	// Add the reg predictions to the historic data
@@ -479,7 +446,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 
 		// Find the appropriate AU (if not found add it)		
 		// Only add if the detection was successful
-		if(clnf_model.detection_success)
+		if (clnf_model.detection_success)
 		{
 			AU_predictions_reg_all_hist[AU_predictions_reg[au].first].push_back(AU_predictions_reg[au].second);
 		}
@@ -488,7 +455,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 			AU_predictions_reg_all_hist[AU_predictions_reg[au].first].push_back(0);
 		}
 	}
-	
+
 	AU_predictions_class = PredictCurrentAUsClass(orientation_to_use);
 
 	for (size_t au = 0; au < AU_predictions_class.size(); ++au)
@@ -496,7 +463,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 
 		// Find the appropriate AU (if not found add it)		
 		// Only add if the detection was successful
-		if(clnf_model.detection_success)
+		if (clnf_model.detection_success)
 		{
 			AU_predictions_class_all_hist[AU_predictions_class[au].first].push_back(AU_predictions_class[au].second);
 		}
@@ -505,9 +472,9 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 			AU_predictions_class_all_hist[AU_predictions_class[au].first].push_back(0);
 		}
 	}
-	
 
-	if(online)
+
+	if (online)
 	{
 		AU_predictions_reg = AU_predictions_reg_corrected;
 	}
@@ -524,15 +491,13 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
 	this->current_time_seconds = timestamp_seconds;
 
 	view_used = orientation_to_use;
-			
+
 	bool success = clnf_model.detection_success;
 
 	confidences.push_back(clnf_model.detection_certainty);
 	valid_preds.push_back(success);
 	timestamps.push_back(timestamp_seconds);
 
-
-
 }
 
 void FaceAnalyser::GetGeomDescriptor(cv::Mat_<double>& geom_desc)
@@ -1101,12 +1066,6 @@ vector<pair<string, double>> FaceAnalyser::PredictCurrentAUsClass(int view)
 	return predictions;
 }
 
-
-cv::Mat_<uchar> FaceAnalyser::GetLatestAlignedFaceGrayscale()
-{
-	return aligned_face_grayscale.clone();
-}
-
 cv::Mat FaceAnalyser::GetLatestHOGDescriptorVisualisation()
 {
 	return hog_descriptor_visualisation;
@@ -1298,4 +1257,122 @@ void FaceAnalyser::ReadRegressor(std::string fname, const vector<string>& au_nam
 
 double FaceAnalyser::GetCurrentTimeSeconds() {
 	return current_time_seconds;
-}
+}
+
+// Allows for post processing of the AU signal
+void FaceAnalyser::PostprocessOutputFile(string output_file, bool dynamic)
+{
+
+	vector<double> certainties;
+	vector<bool> successes;
+	vector<double> timestamps;
+	vector<std::pair<std::string, vector<double>>> predictions_reg;
+	vector<std::pair<std::string, vector<double>>> predictions_class;
+
+	// Construct the new values to overwrite the output file with
+	ExtractAllPredictionsOfflineReg(predictions_reg, certainties, successes, timestamps, dynamic);
+	ExtractAllPredictionsOfflineClass(predictions_class, certainties, successes, timestamps, dynamic);
+
+	int num_class = predictions_class.size();
+	int num_reg = predictions_reg.size();
+
+	// Extract the indices of writing out first
+	vector<string> au_reg_names = GetAURegNames();
+	std::sort(au_reg_names.begin(), au_reg_names.end());
+	vector<int> inds_reg;
+
+	// write out ar the correct index
+	for (string au_name : au_reg_names)
+	{
+		for (int i = 0; i < num_reg; ++i)
+		{
+			if (au_name.compare(predictions_reg[i].first) == 0)
+			{
+				inds_reg.push_back(i);
+				break;
+			}
+		}
+	}
+
+	vector<string> au_class_names = GetAUClassNames();
+	std::sort(au_class_names.begin(), au_class_names.end());
+	vector<int> inds_class;
+
+	// write out ar the correct index
+	for (string au_name : au_class_names)
+	{
+		for (int i = 0; i < num_class; ++i)
+		{
+			if (au_name.compare(predictions_class[i].first) == 0)
+			{
+				inds_class.push_back(i);
+				break;
+			}
+		}
+	}
+	// Read all of the output file in
+	vector<string> output_file_contents;
+
+	std::ifstream infile(output_file);
+	string line;
+
+	while (std::getline(infile, line))
+		output_file_contents.push_back(line);
+
+	infile.close();
+
+	// Read the header and find all _r and _c parts in a file and use their indices
+	std::vector<std::string> tokens;
+	boost::split(tokens, output_file_contents[0], boost::is_any_of(","));
+
+	int begin_ind = -1;
+
+	for (size_t i = 0; i < tokens.size(); ++i)
+	{
+		if (tokens[i].find("AU") != string::npos && begin_ind == -1)
+		{
+			begin_ind = i;
+			break;
+		}
+	}
+	int end_ind = begin_ind + num_class + num_reg;
+
+	// Now overwrite the whole file
+	std::ofstream outfile(output_file, ios_base::out);
+	// Write the header
+	outfile << std::setprecision(4);
+	outfile << output_file_contents[0].c_str() << endl;
+
+	// Write the contents
+	for (int i = 1; i < (int)output_file_contents.size(); ++i)
+	{
+		std::vector<std::string> tokens;
+		boost::split(tokens, output_file_contents[i], boost::is_any_of(","));
+
+		boost::trim(tokens[0]);
+		outfile << tokens[0];
+
+		for (int t = 1; t < (int)tokens.size(); ++t)
+		{
+			if (t >= begin_ind && t < end_ind)
+			{
+				if (t - begin_ind < num_reg)
+				{
+					outfile << ", " << predictions_reg[inds_reg[t - begin_ind]].second[i - 1];
+				}
+				else
+				{
+					outfile << ", " << predictions_class[inds_class[t - begin_ind - num_reg]].second[i - 1];
+				}
+			}
+			else
+			{
+				boost::trim(tokens[t]);
+				outfile << ", " << tokens[t];
+			}
+		}
+		outfile << endl;
+	}
+
+
+}

lib/local/FaceAnalyser/src/Face_utils.cpp

@@ -221,19 +221,19 @@ namespace FaceAnalysis
 		destination_landmarks.col(1) = destination_landmarks.col(1) + warp_matrix(1,2);
 		
 		// Move the eyebrows up to include more of upper face
-		destination_landmarks.at<double>(0,1) -= 30; 
+		destination_landmarks.at<double>(0,1) -= (30/0.7)*sim_scale; 
-		destination_landmarks.at<double>(16,1) -= 30;
+		destination_landmarks.at<double>(16,1) -= (30 / 0.7)*sim_scale;
 
-		destination_landmarks.at<double>(17,1) -= 30;
+		destination_landmarks.at<double>(17,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(18,1) -= 30;
+		destination_landmarks.at<double>(18,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(19,1) -= 30;
+		destination_landmarks.at<double>(19,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(20,1) -= 30;
+		destination_landmarks.at<double>(20,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(21,1) -= 30;
+		destination_landmarks.at<double>(21,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(22,1) -= 30;
+		destination_landmarks.at<double>(22,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(23,1) -= 30;
+		destination_landmarks.at<double>(23,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(24,1) -= 30;
+		destination_landmarks.at<double>(24,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(25,1) -= 30;
+		destination_landmarks.at<double>(25,1) -= (30 / 0.7)*sim_scale;
-		destination_landmarks.at<double>(26,1) -= 30;
+		destination_landmarks.at<double>(26,1) -= (30 / 0.7)*sim_scale;
 
 		destination_landmarks = cv::Mat(destination_landmarks.t()).reshape(1, 1).t();
 

lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp

@@ -366,6 +366,9 @@ void CLNF::Read(string main_location)
 	// The other module locations should be defined as relative paths from the main model
 	boost::filesystem::path root = boost::filesystem::path(main_location).parent_path();	
 
+	// Assume no eye model, unless read-in
+	eye_model = false;
+
 	// The main file contains the references to other files
 	while (!locations.eof())
 	{ 
@@ -387,6 +390,7 @@ void CLNF::Read(string main_location)
 			location = location.substr(0, location.size()-1);
 		}
 
+
 		// append to root
 		location = (root / location).string();
 		if (module.compare("LandmarkDetector") == 0) 
@@ -536,7 +540,6 @@ void CLNF::Read(string main_location)
 	tracking_initialised = false;
 	model_likelihood = -10; // very low
 	detection_certainty = 1; // very uncertain
-	eye_model = false;
 
 	// Initialising default values for the rest of the variables
 

matlab_runners/Demos/feature_extraction_demo_img_seq.m

@@ -35,7 +35,7 @@ for i=1:numel(in_dirs)
     
     command = cat(2, command, ['-asvid -fdir "' in_dirs{i} '" -of "' outputFile '" ']);
         
-    command = cat(2, command, [' -simalign "' outputDir_aligned '" -hogalign "' outputHOG_aligned '"']);    
+    command = cat(2, command, [' -simalign "' outputDir_aligned '" -simsize 200 -hogalign "' outputHOG_aligned '"']);    
                  
 end