Merged develop into master
This commit is contained in:
Tadas Baltrusaitis
commit
ed1422dbf7
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@ -44,3 +44,6 @@ exe/Recording/Debug/
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lib/3rdParty/dlib/Debug/
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lib/local/FaceAnalyser/Debug/
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lib/local/LandmarkDetector/Debug/
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matlab_runners/Head Pose Experiments/experiments/biwi_out/
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matlab_runners/Head Pose Experiments/experiments/bu_out/
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matlab_runners/Head Pose Experiments/experiments/ict_out/
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@ -75,4 +75,5 @@ script:
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- ../build/bin/FaceLandmarkImg -inroot ../videos -f Obama.jpg -outroot data -of obama.txt -op obama.3d -oi obama.bmp -multi_view 1 -wild -q
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- ../build/bin/FaceLandmarkVidMulti -inroot ../videos -f multi_face.avi -outroot output -ov multi_face.avi -q
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- ../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
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- ../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
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- ../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
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@ -25,4 +25,5 @@ test_script:
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- 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)
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- 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)
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- 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)
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- 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)
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- 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)
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@ -308,7 +308,7 @@ int main (int argc, char **argv)
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vector<string> output_similarity_align;
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vector<string> output_hog_align_files;
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double sim_scale = 0.7;
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double sim_scale = -1;
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int sim_size = 112;
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bool grayscale = false;
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bool video_output = false;
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@ -391,7 +391,10 @@ int main (int argc, char **argv)
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}
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// Creating a face analyser that will be used for AU extraction
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FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), 0.7, 112, 112, au_loc, tri_loc);
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// Make sure sim_scale is proportional to sim_size if not set
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if (sim_scale == -1) sim_scale = sim_size * (0.7 / 112.0);
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FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), sim_scale, sim_size, sim_size, au_loc, tri_loc);
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while(!done) // this is not a for loop as we might also be reading from a webcam
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{
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@ -593,7 +596,7 @@ int main (int argc, char **argv)
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}
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if(hog_output_file.is_open())
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{
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FaceAnalysis::Extract_FHOG_descriptor(hog_descriptor, sim_warped_img, num_hog_rows, num_hog_cols);
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face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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if(visualise_hog && !det_parameters.quiet_mode)
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{
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@ -615,13 +618,13 @@ int main (int argc, char **argv)
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pose_estimate = LandmarkDetector::GetCorrectedPoseCamera(face_model, fx, fy, cx, cy);
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}
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if(hog_output_file.is_open())
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if (hog_output_file.is_open())
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{
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output_HOG_frame(&hog_output_file, detection_success, hog_descriptor, num_hog_rows, num_hog_cols);
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}
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// Write the similarity normalised output
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if(!output_similarity_align.empty())
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if (!output_similarity_align.empty())
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{
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if (sim_warped_img.channels() == 3 && grayscale)
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@ -631,8 +634,8 @@ int main (int argc, char **argv)
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char name[100];
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// output the frame number
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std::sprintf(name, "frame_det_%06d.bmp", frame_count);
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// Filename is based on frame number
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std::sprintf(name, "frame_det_%06d.bmp", frame_count + 1);
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// Construct the output filename
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boost::filesystem::path slash("/");
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@ -714,10 +717,10 @@ int main (int argc, char **argv)
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output_file.close();
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if(output_files.size() > 0 && output_AUs)
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if (output_files.size() > 0 && output_AUs)
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{
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cout << "Postprocessing the Action Unit predictions" << endl;
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post_process_output_file(face_analyser, output_files[f_n], dynamic);
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face_analyser.PostprocessOutputFile(output_files[f_n], dynamic);
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}
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// Reset the models for the next video
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face_analyser.Reset();
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@ -741,121 +744,6 @@ int main (int argc, char **argv)
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return 0;
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}
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// Allows for post processing of the AU signal
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void post_process_output_file(FaceAnalysis::FaceAnalyser& face_analyser, string output_file, bool dynamic)
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{
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vector<double> certainties;
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vector<bool> successes;
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vector<double> timestamps;
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vector<std::pair<std::string, vector<double>>> predictions_reg;
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vector<std::pair<std::string, vector<double>>> predictions_class;
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// Construct the new values to overwrite the output file with
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face_analyser.ExtractAllPredictionsOfflineReg(predictions_reg, certainties, successes, timestamps, dynamic);
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face_analyser.ExtractAllPredictionsOfflineClass(predictions_class, certainties, successes, timestamps, dynamic);
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int num_class = predictions_class.size();
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int num_reg = predictions_reg.size();
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// Extract the indices of writing out first
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vector<string> au_reg_names = face_analyser.GetAURegNames();
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std::sort(au_reg_names.begin(), au_reg_names.end());
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vector<int> inds_reg;
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// write out ar the correct index
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for (string au_name : au_reg_names)
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{
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for (int i = 0; i < num_reg; ++i)
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{
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if (au_name.compare(predictions_reg[i].first) == 0)
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{
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inds_reg.push_back(i);
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break;
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}
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}
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}
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vector<string> au_class_names = face_analyser.GetAUClassNames();
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std::sort(au_class_names.begin(), au_class_names.end());
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vector<int> inds_class;
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// write out ar the correct index
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for (string au_name : au_class_names)
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{
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for (int i = 0; i < num_class; ++i)
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{
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if (au_name.compare(predictions_class[i].first) == 0)
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{
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inds_class.push_back(i);
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break;
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}
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}
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}
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// Read all of the output file in
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vector<string> output_file_contents;
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std::ifstream infile(output_file);
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string line;
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while (std::getline(infile, line))
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output_file_contents.push_back(line);
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infile.close();
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// Read the header and find all _r and _c parts in a file and use their indices
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std::vector<std::string> tokens;
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boost::split(tokens, output_file_contents[0], boost::is_any_of(","));
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int begin_ind = -1;
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for (size_t i = 0; i < tokens.size(); ++i)
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{
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if (tokens[i].find("AU") != string::npos && begin_ind == -1)
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{
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begin_ind = i;
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break;
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}
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}
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int end_ind = begin_ind + num_class + num_reg;
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// Now overwrite the whole file
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std::ofstream outfile(output_file, ios_base::out);
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// Write the header
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outfile << output_file_contents[0].c_str() << endl;
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// Write the contents
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for (int i = 1; i < (int)output_file_contents.size(); ++i)
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{
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std::vector<std::string> tokens;
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boost::split(tokens, output_file_contents[i], boost::is_any_of(","));
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outfile << tokens[0];
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for (int t = 1; t < (int)tokens.size(); ++t)
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{
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if (t >= begin_ind && t < end_ind)
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{
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if(t - begin_ind < num_reg)
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{
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outfile << ", " << predictions_reg[inds_reg[t - begin_ind]].second[i - 1];
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}
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else
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{
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outfile << ", " << predictions_class[inds_class[t - begin_ind - num_reg]].second[i - 1];
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}
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}
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else
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{
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outfile << ", " << tokens[t];
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}
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}
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outfile << endl;
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}
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}
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void prepareOutputFile(std::ofstream* output_file, bool output_2D_landmarks, bool output_3D_landmarks,
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bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze,
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int num_landmarks, int num_model_modes, vector<string> au_names_class, vector<string> au_names_reg)
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@ -1206,6 +1094,7 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
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}
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// Can process images via directories creating a separate output file per directory
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void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments)
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{
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@ -74,12 +74,12 @@
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namespace FaceAnalysis
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{
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class FaceAnalyser{
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class FaceAnalyser {
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public:
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public:
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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};
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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 };
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// Constructor from a model file (or a default one if not provided
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// TODO scale width and height should be read in as part of the model as opposed to being here?
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@ -112,12 +112,8 @@ public:
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cv::Mat_<int> GetTriangulation();
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cv::Mat_<uchar> GetLatestAlignedFaceGrayscale();
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void GetGeomDescriptor(cv::Mat_<double>& geom_desc);
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void ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations);
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// Grab the names of AUs being predicted
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std::vector<std::string> GetAUClassNames() const; // Presence
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std::vector<std::string> GetAURegNames() const; // Intensity
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@ -130,7 +126,10 @@ public:
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void ExtractAllPredictionsOfflineReg(vector<std::pair<std::string, vector<double>>>& au_predictions, vector<double>& confidences, vector<bool>& successes, vector<double>& timestamps, bool dynamic);
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void ExtractAllPredictionsOfflineClass(vector<std::pair<std::string, vector<double>>>& au_predictions, vector<double>& confidences, vector<bool>& successes, vector<double>& timestamps, bool dynamic);
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private:
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// Helper function for post-processing AU output files
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void PostprocessOutputFile(string output_file, bool dynamic);
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private:
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// Where the predictions are kept
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std::vector<std::pair<std::string, double>> AU_predictions_reg;
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@ -148,8 +147,8 @@ private:
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int frames_tracking;
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// Cache of intermediate images
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cv::Mat_<uchar> aligned_face_grayscale;
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cv::Mat aligned_face;
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cv::Mat aligned_face_for_au;
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cv::Mat aligned_face_for_output;
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cv::Mat hog_descriptor_visualisation;
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// Private members to be used for predictions
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@ -218,7 +217,7 @@ private:
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// 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
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// 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
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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);
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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);
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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);
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void PostprocessPredictions();
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@ -251,7 +250,7 @@ private:
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bool postprocessed = false;
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int frames_tracking_succ = 0;
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};
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};
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//===========================================================================
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}
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#endif
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@ -226,7 +226,7 @@ void FaceAnalyser::GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows,
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void FaceAnalyser::GetLatestAlignedFace(cv::Mat& image)
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{
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image = this->aligned_face.clone();
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image = this->aligned_face_for_output.clone();
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}
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void FaceAnalyser::GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols)
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@ -267,50 +267,15 @@ int GetViewId(const vector<cv::Vec3d> orientations_all, const cv::Vec3d& orienta
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}
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void FaceAnalyser::ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations)
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{
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orientations = this->head_orientations;
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for(size_t i = 0; i < orientations.size(); ++i)
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{
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cv::Mat_<double> median_face(this->face_image_median.rows, this->face_image_median.cols, 0.0);
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cv::Mat_<double> median_hog(this->hog_desc_median.rows, this->hog_desc_median.cols, 0.0);
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ExtractMedian(this->face_image_hist[i], this->face_image_hist_sum[i], median_face, 256, 0, 255);
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ExtractMedian(this->hog_desc_hist[i], this->hog_hist_sum[i], median_hog, this->num_bins_hog, 0, 1);
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// Add the HOG sample
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hog_medians.push_back(median_hog.clone());
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// For the face image need to convert it to suitable format
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cv::Mat_<uchar> aligned_face_cols_uchar;
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median_face.convertTo(aligned_face_cols_uchar, CV_8U);
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cv::Mat aligned_face_uchar;
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if(aligned_face.channels() == 1)
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{
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aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8U, aligned_face_cols_uchar.data);
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}
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else
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{
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aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8UC3, aligned_face_cols_uchar.data);
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}
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face_image_medians.push_back(aligned_face_uchar.clone());
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}
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}
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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)
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{
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// First align the face
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AlignFaceMask(aligned_face, frame, clnf, triangulation, true, align_scale, align_width, align_height);
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AlignFaceMask(aligned_face_for_au, frame, clnf, triangulation, true, 0.7, 112, 112);
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// Extract HOG descriptor from the frame and convert it to a useable format
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cv::Mat_<double> hog_descriptor;
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Extract_FHOG_descriptor(hog_descriptor, aligned_face, this->num_hog_rows, this->num_hog_cols);
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Extract_FHOG_descriptor(hog_descriptor, aligned_face_for_au, this->num_hog_rows, this->num_hog_cols);
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// Store the descriptor
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hog_desc_frame = hog_descriptor;
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||||
|
|
@ -326,10 +291,10 @@ std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string,
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|||
|
||||
cv::hconcat(locs.t(), geom_descriptor_frame.clone(), geom_descriptor_frame);
|
||||
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||||
// First convert the face image to double representation as a row vector
|
||||
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);
|
||||
// First convert the face image to double representation as a row vector, TODO rem
|
||||
//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;
|
||||
//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
|
||||
|
||||
// Visualising the median HOG
|
||||
if (visualise)
|
||||
|
|
@ -361,28 +326,33 @@ 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);
|
||||
|
||||
// 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)
|
||||
{
|
||||
cv::cvtColor(aligned_face, aligned_face_grayscale, CV_BGR2GRAY);
|
||||
aligned_face_for_output = aligned_face_for_au.clone();
|
||||
}
|
||||
else
|
||||
{
|
||||
aligned_face_grayscale = aligned_face.clone();
|
||||
AlignFaceMask(aligned_face_for_output, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
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;
|
||||
|
|
@ -425,7 +395,7 @@ 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);
|
||||
|
|
@ -434,7 +404,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
// 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);
|
||||
}
|
||||
|
|
@ -445,21 +415,18 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
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
|
||||
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);
|
||||
|
||||
// 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);
|
||||
// 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)
|
||||
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);
|
||||
}
|
||||
|
|
@ -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);
|
||||
}
|
||||
|
|
@ -507,7 +474,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
}
|
||||
|
||||
|
||||
if(online)
|
||||
if (online)
|
||||
{
|
||||
AU_predictions_reg = AU_predictions_reg_corrected;
|
||||
}
|
||||
|
|
@ -531,8 +498,6 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
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;
|
||||
|
|
@ -1299,3 +1258,121 @@ 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;
|
||||
}
|
||||
|
||||
|
||||
}
|
||||
|
|
|
|||
|
|
@ -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>(16,1) -= 30;
|
||||
destination_landmarks.at<double>(0,1) -= (30/0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(16,1) -= (30 / 0.7)*sim_scale;
|
||||
|
||||
destination_landmarks.at<double>(17,1) -= 30;
|
||||
destination_landmarks.at<double>(18,1) -= 30;
|
||||
destination_landmarks.at<double>(19,1) -= 30;
|
||||
destination_landmarks.at<double>(20,1) -= 30;
|
||||
destination_landmarks.at<double>(21,1) -= 30;
|
||||
destination_landmarks.at<double>(22,1) -= 30;
|
||||
destination_landmarks.at<double>(23,1) -= 30;
|
||||
destination_landmarks.at<double>(24,1) -= 30;
|
||||
destination_landmarks.at<double>(25,1) -= 30;
|
||||
destination_landmarks.at<double>(26,1) -= 30;
|
||||
destination_landmarks.at<double>(17,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(18,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(19,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(20,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(21,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(22,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(23,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(24,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(25,1) -= (30 / 0.7)*sim_scale;
|
||||
destination_landmarks.at<double>(26,1) -= (30 / 0.7)*sim_scale;
|
||||
|
||||
destination_landmarks = cv::Mat(destination_landmarks.t()).reshape(1, 1).t();
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
|
|
@ -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
|
||||
|
||||
|
|
|
|||
Binary file not shown.
Loading…
Reference in New Issue