Video writing bug fix, work on demo scripts for feature extraction.

lib/local/Utilities/src/RecorderOpenFace.cpp

@@ -189,7 +189,7 @@ void RecorderOpenFace::SetObservationVisualization(const cv::Mat &vis_track)
 	if (params.outputTracked())
 	{
 		// Initialize the video writer if it has not been opened yet
-		if(video_writer.isOpened())
+		if(params.isSequence())
 		{
 			std::string output_codec = params.outputCodec();
 			try

matlab_runners/Demos/feature_extraction_demo_vid.m

@@ -1,50 +1,24 @@
+% A demo script that demonstrates how to process a single video file using
+% OpenFace and extract and visualize all of the features
+
 clear
 
+% The location executable will depend on the OS
 if(isunix)
     executable = '"../../build/bin/FeatureExtraction"';
 else
     executable = '"../../x64/Release/FeatureExtraction.exe"';
 end
 
-output = './output_features_vid/';
+% Input file
+in_file = '../../samples/default.wmv';
 
-if(~exist(output, 'file'))
+% Where to store the output
-    mkdir(output)
+output_dir = './processed_features/';
-end
 
-in_files = dir('../../samples/default.wmv');
+% This will take file after -f and output all the features to directory
-% some parameters
+% after -out_dir
-verbose = true;
+command = sprintf('%s -f "%s" -out_dir "%s" -verbose', executable, in_file, output_dir);
-
-command = executable;
-
-% Remove for a speedup
-command = cat(2, command, ' -verbose ');
-
-% add all videos to single argument list (so as not to load the model anew
-% for every video)
-for i=1:numel(in_files)
-    
-    inputFile = ['../../samples/', in_files(i).name];
-    [~, name, ~] = fileparts(inputFile);
-    
-    % where to output tracking results
-    outputFile = [output name '.txt'];
-            
-    if(~exist([output name], 'file'))
-        mkdir([output name]);
-    end
-    
-    outputDir_aligned = [output name];
-    
-    outputHOG_aligned = [output name '.hog'];
-    
-    output_shape_params = [output name '.params.txt'];
-    
-    command = cat(2, command, [' -f "' inputFile '" -of "' outputFile '"']);        
-    command = cat(2, command, [' -simalign "' outputDir_aligned '" -hogalign "' outputHOG_aligned '"' ]);    
-                 
-end
                  
 if(isunix)
     unix(command);
@@ -54,15 +28,30 @@ end
 
 %% Demonstrating reading the output files
 
-% First read in the column names
+% Most of the features will be in the csv file in the output directory with
-tab = readtable(outputFile);
+% the same name as the input file
+[~,name,~] = fileparts(in_file);
+output_csv = sprintf('%s/%s.csv', output_dir, name);
+
+% First read in the column names, to know which columns to read for
+% particular features
+tab = readtable(output_csv);
 column_names = tab.Properties.VariableNames;
 
-all_params  = dlmread(outputFile, ',', 1, 0);
+% Read all of the data
+all_params  = dlmread(output_csv, ',', 1, 0);
 
 % This indicates which frames were succesfully tracked
-valid_frames = logical(all_params(:,4));
+
-time = all_params(valid_frames, 2);
+% Find which column contains success of tracking data and timestamp data
+valid_ind = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'success'));
+time_stamp_ind = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'timestamp'));
+
+% Extract tracking success data and only read those frame
+valid_frames = logical(all_params(:,valid_ind));
+
+% Get the timestamp data
+time_stamps = all_params(valid_frames, time_stamp_ind);
 
 %% Finding which header line starts with p_ (basically model params)
 shape_inds = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'p_'));
@@ -71,7 +60,7 @@ shape_inds = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'p_'));
 shape_params  = all_params(valid_frames, shape_inds);
 
 figure
-plot(time, shape_params);
+plot(time_stamps, shape_params);
 title('Shape parameters');
 xlabel('Time (s)');
 
@@ -113,9 +102,20 @@ xs = all_params(valid_frames, landmark_inds_x);
 ys = all_params(valid_frames, landmark_inds_y);
 zs = all_params(valid_frames, landmark_inds_z);
 
+eye_landmark_inds_x = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'eye_lmk_X_'));
+eye_landmark_inds_y = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'eye_lmk_Y_'));
+eye_landmark_inds_z = cellfun(@(x) ~isempty(x) && x==1, strfind(column_names, 'eye_lmk_Z_'));
+
+eye_xs = all_params(valid_frames, eye_landmark_inds_x);
+eye_ys = all_params(valid_frames, eye_landmark_inds_y);
+eye_zs = all_params(valid_frames, eye_landmark_inds_z);
+
 figure
 for j = 1:size(xs,1)
     plot3(xs(j,:), ys(j,:), zs(j,:), '.');axis equal;
+    hold on;
+    plot3(eye_xs(j,:), eye_ys(j,:), eye_zs(j,:), '.r');
+    hold off;
     xlabel('X (mm)');
     ylabel('Y (mm)');    
     zlabel('Z (mm)');    

matlab_runners/Demos/run_demo_video_multi.m

@@ -8,12 +8,6 @@ else
     executable = '"../../x64/Release/FaceLandmarkVidMulti.exe"';
 end
     
-output = './demo_vid/';
-
-if(~exist(output, 'file'))
-    mkdir(output)
-end
-    
 in_files = dir('../../samples/multi_face.avi');
 
 model = 'model/main_clnf_general.txt'; % Trained on in the wild and multi-pie data (a CLNF model)

matlab_runners/Demos/run_demo_videos.m

@@ -22,8 +22,8 @@ model = 'model/main_clnf_general.txt'; % Trained on in the wild and multi-pie da
 % Create a command that will run the tracker on set of videos and display the output 
 command = sprintf('%s -mloc "%s" ', executable, model);
 
-% add all videos to single argument list (so as not to load the model anew
+% add all videos to single argument list by appending -f comments
-% for every video)
+% so as not to load the model anew for every video)
 for i=1:numel(in_files)
     inputFile = ['../../samples/', in_files(i).name];        
     command = cat(2, command, [' -f "' inputFile '" ']);