Towards AU post-processing in the GUI.

This commit is contained in:
Tadas Baltrusaitis 2016-12-09 10:06:04 -05:00
parent 3b1770c106
commit 29d13878da
11 changed files with 142 additions and 132 deletions

View File

@ -234,9 +234,6 @@ void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, boo
cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, cv::Vec2d gaze_angle, const cv::Vec6d& pose_estimate, double fx, double fy, double cx, double cy,
const FaceAnalysis::FaceAnalyser& face_analyser);
void post_process_output_file(FaceAnalysis::FaceAnalyser& face_analyser, string output_file, bool dynamic);
int main (int argc, char **argv)
{
@ -498,9 +495,7 @@ int main (int argc, char **argv)
catch(cv::Exception e)
{
WARN_STREAM( "Could not open VideoWriter, OUTPUT FILE WILL NOT BE WRITTEN. Currently using codec " << output_codec << ", try using an other one (-oc option)");
}
}
}
int frame_count = 0;
@ -700,7 +695,7 @@ int main (int argc, char **argv)
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();
@ -724,124 +719,6 @@ 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 << 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;
}
}
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_eye_lmks, int num_model_modes, vector<string> au_names_class, vector<string> au_names_reg)

View File

@ -420,7 +420,7 @@ namespace OpenFaceOffline
// Setup the recorder first
recorder = new Recorder(record_root, output_file_name, capture.width, capture.height, Record2DLandmarks, Record3DLandmarks, RecordModelParameters, RecordPose,
RecordAUs, RecordGaze, RecordAligned, RecordHOG, clnf_model, face_analyser, fx, fy, cx, cy);
RecordAUs, RecordGaze, RecordAligned, RecordHOG, clnf_model, face_analyser, fx, fy, cx, cy, DynamicAUModels);
int frame_id = 0;

View File

@ -20,9 +20,13 @@ namespace OpenFaceOffline
List<string> au_reg_names;
List<string> au_class_names;
String out_filename;
bool dynamic_AU_model;
public Recorder(string root, string filename, int width, int height, bool output_2D_landmarks, bool output_3D_landmarks, bool output_model_params,
bool output_pose, bool output_AUs, bool output_gaze, bool record_aligned, bool record_HOG,
CLNF clnf_model, FaceAnalyserManaged face_analyser, double fx, double fy, double cx, double cy)
CLNF clnf_model, FaceAnalyserManaged face_analyser, double fx, double fy, double cx, double cy, bool dynamic_AU_model)
{
this.output_2D_landmarks = output_2D_landmarks; this.output_3D_landmarks = output_3D_landmarks;
@ -32,12 +36,14 @@ namespace OpenFaceOffline
this.fx = fx; this.fy = fy; this.cx = cx; this.cy = cy;
this.dynamic_AU_model = dynamic_AU_model;
if (!System.IO.Directory.Exists(root))
{
System.IO.Directory.CreateDirectory(root);
}
output_features_file = new StreamWriter(root + "/" + filename + ".txt");
out_filename = root + "/" + filename + ".csv";
output_features_file = new StreamWriter(out_filename);
output_features_file.Write("frame, timestamp, confidence, success");
if (output_gaze)
@ -216,7 +222,8 @@ namespace OpenFaceOffline
if (record_HOG)
face_analyser.StopHOGRecording();
face_analyser.PostProcessOutputFile(out_filename, dynamic_AU_model);
}
}

View File

@ -260,6 +260,11 @@ public:
tracked_vid_writer->write(*tracked_face);
}
void PostProcessOutputFile(System::String^ file, bool dynamic)
{
face_analyser->PostprocessOutputFile(msclr::interop::marshal_as<std::string>(file), dynamic);
}
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) {
face_analyser->AddNextFrame(frame->Mat, *clnf->getCLNF(), 0, online, vis_hog);

View File

@ -126,6 +126,9 @@ public:
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);
// Helper function for post-processing AU output files
void FaceAnalyser::PostprocessOutputFile(string output_file, bool dynamic);
private:
// Where the predictions are kept

View File

@ -437,7 +437,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
std::vector<std::pair<std::string, double>> AU_predictions_reg_corrected;
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
@ -1255,4 +1255,122 @@ 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;
}
}