Towards AU post-processing in the GUI.
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3b1770c106
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29d13878da
11 changed files with 142 additions and 132 deletions
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@ -234,9 +234,6 @@ void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, boo
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cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, cv::Vec2d gaze_angle, const cv::Vec6d& pose_estimate, double fx, double fy, double cx, double cy,
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const FaceAnalysis::FaceAnalyser& face_analyser);
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void post_process_output_file(FaceAnalysis::FaceAnalyser& face_analyser, string output_file, bool dynamic);
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int main (int argc, char **argv)
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{
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@ -499,8 +496,6 @@ int main (int argc, char **argv)
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{
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WARN_STREAM( "Could not open VideoWriter, OUTPUT FILE WILL NOT BE WRITTEN. Currently using codec " << output_codec << ", try using an other one (-oc option)");
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}
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}
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int frame_count = 0;
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@ -700,7 +695,7 @@ int main (int argc, char **argv)
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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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@ -724,124 +719,6 @@ 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 << std::setprecision(4);
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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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boost::trim(tokens[0]);
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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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boost::trim(tokens[t]);
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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_eye_lmks, int num_model_modes, vector<string> au_names_class, vector<string> au_names_reg)
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@ -420,7 +420,7 @@ namespace OpenFaceOffline
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// Setup the recorder first
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recorder = new Recorder(record_root, output_file_name, capture.width, capture.height, Record2DLandmarks, Record3DLandmarks, RecordModelParameters, RecordPose,
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RecordAUs, RecordGaze, RecordAligned, RecordHOG, clnf_model, face_analyser, fx, fy, cx, cy);
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RecordAUs, RecordGaze, RecordAligned, RecordHOG, clnf_model, face_analyser, fx, fy, cx, cy, DynamicAUModels);
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int frame_id = 0;
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@ -20,9 +20,13 @@ namespace OpenFaceOffline
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List<string> au_reg_names;
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List<string> au_class_names;
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String out_filename;
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bool dynamic_AU_model;
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public Recorder(string root, string filename, int width, int height, bool output_2D_landmarks, bool output_3D_landmarks, bool output_model_params,
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bool output_pose, bool output_AUs, bool output_gaze, bool record_aligned, bool record_HOG,
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CLNF clnf_model, FaceAnalyserManaged face_analyser, double fx, double fy, double cx, double cy)
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CLNF clnf_model, FaceAnalyserManaged face_analyser, double fx, double fy, double cx, double cy, bool dynamic_AU_model)
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{
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this.output_2D_landmarks = output_2D_landmarks; this.output_3D_landmarks = output_3D_landmarks;
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@ -32,12 +36,14 @@ namespace OpenFaceOffline
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this.fx = fx; this.fy = fy; this.cx = cx; this.cy = cy;
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this.dynamic_AU_model = dynamic_AU_model;
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if (!System.IO.Directory.Exists(root))
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{
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System.IO.Directory.CreateDirectory(root);
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}
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output_features_file = new StreamWriter(root + "/" + filename + ".txt");
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out_filename = root + "/" + filename + ".csv";
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output_features_file = new StreamWriter(out_filename);
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output_features_file.Write("frame, timestamp, confidence, success");
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if (output_gaze)
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@ -217,6 +223,7 @@ namespace OpenFaceOffline
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if (record_HOG)
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face_analyser.StopHOGRecording();
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face_analyser.PostProcessOutputFile(out_filename, dynamic_AU_model);
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}
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}
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@ -260,6 +260,11 @@ public:
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tracked_vid_writer->write(*tracked_face);
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}
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void PostProcessOutputFile(System::String^ file, bool dynamic)
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{
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face_analyser->PostprocessOutputFile(msclr::interop::marshal_as<std::string>(file), dynamic);
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}
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void AddNextFrame(OpenCVWrappers::RawImage^ frame, CppInterop::LandmarkDetector::CLNF^ clnf, double fx, double fy, double cx, double cy, bool online, bool vis_hog, bool vis_tracked) {
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face_analyser->AddNextFrame(frame->Mat, *clnf->getCLNF(), 0, online, vis_hog);
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@ -126,6 +126,9 @@ 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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// Helper function for post-processing AU output files
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void FaceAnalyser::PostprocessOutputFile(string output_file, bool dynamic);
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private:
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// Where the predictions are kept
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@ -437,7 +437,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
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std::vector<std::pair<std::string, double>> AU_predictions_reg_corrected;
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if(online)
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{
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AU_predictions_reg_corrected = CorrectOnlineAUs(AU_predictions_reg, orientation_to_use, true, false, clnf_model.detection_success);
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AU_predictions_reg_corrected = CorrectOnlineAUs(AU_predictions_reg, orientation_to_use, true, false, clnf_model.detection_success, true);
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}
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// Add the reg predictions to the historic data
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@ -1256,3 +1256,121 @@ void FaceAnalyser::ReadRegressor(std::string fname, const vector<string>& au_nam
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double FaceAnalyser::GetCurrentTimeSeconds() {
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return current_time_seconds;
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}
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// Allows for post processing of the AU signal
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void FaceAnalyser::PostprocessOutputFile(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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ExtractAllPredictionsOfflineReg(predictions_reg, certainties, successes, timestamps, dynamic);
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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 = 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 = 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 << std::setprecision(4);
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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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boost::trim(tokens[0]);
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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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boost::trim(tokens[t]);
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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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