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More fine grained visualization during FeatureExtraction, and updating face validation readme.

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Tadas Baltrusaitis 2017-10-13 08:38:12 +01:00
parent a4d20fc9b4
commit 46bd0869ad
2 changed files with 37 additions and 16 deletions
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51
exe/FeatureExtraction/FeatureExtraction.cpp
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@ -132,7 +132,7 @@ void create_directory(string output_path)
} }
void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale, void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
int &similarity_size, bool &grayscale, bool& verbose, bool& dynamic, bool &output_2D_landmarks, bool &output_3D_landmarks, int &similarity_size, bool &grayscale, bool& visualize_track, bool& visualize_align, bool& visualize_hog, bool& dynamic, bool &output_2D_landmarks, bool &output_3D_landmarks,
bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze, vector<string> &arguments); bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze, vector<string> &arguments);
void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments); void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments);
@ -213,9 +213,6 @@ void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, boo
cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, const cv::Vec6d& pose_estimate, double fx, double fy, double cx, double cy, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, const cv::Vec6d& pose_estimate, double fx, double fy, double cx, double cy,
const FaceAnalysis::FaceAnalyser& face_analyser); 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) int main (int argc, char **argv)
{ {
@ -240,7 +237,6 @@ int main (int argc, char **argv)
LandmarkDetector::get_video_input_output_params(input_files, output_files, tracked_videos_output, use_world_coordinates, output_codec, arguments); LandmarkDetector::get_video_input_output_params(input_files, output_files, tracked_videos_output, use_world_coordinates, output_codec, arguments);
bool video_input = true; bool video_input = true;
bool verbose = true;
bool images_as_video = false; bool images_as_video = false;
vector<vector<string> > input_image_files; vector<vector<string> > input_image_files;
@ -301,7 +297,10 @@ int main (int argc, char **argv)
bool output_AUs = true; bool output_AUs = true;
bool output_gaze = true; bool output_gaze = true;
get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, verbose, dynamic, bool visualize_track = false;
bool visualize_align = false;
bool visualize_hog = false;
get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, visualize_track, visualize_align, visualize_hog, dynamic,
output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments); output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
// Used for image masking // Used for image masking
@ -504,8 +503,6 @@ int main (int argc, char **argv)
// Use for timestamping if using a webcam // Use for timestamping if using a webcam
int64 t_initial = cv::getTickCount(); int64 t_initial = cv::getTickCount();
bool visualise_hog = verbose;
// Timestamp in seconds of current processing // Timestamp in seconds of current processing
double time_stamp = 0; double time_stamp = 0;
@ -565,18 +562,18 @@ int main (int argc, char **argv)
// But only if needed in output // But only if needed in output
if(!output_similarity_align.empty() || hog_output_file.is_open() || output_AUs) if(!output_similarity_align.empty() || hog_output_file.is_open() || output_AUs)
{ {
face_analyser.AddNextFrame(captured_image, face_model, time_stamp, false, !det_parameters.quiet_mode); face_analyser.AddNextFrame(captured_image, face_model, time_stamp, false, !det_parameters.quiet_mode && (visualize_align || visualize_hog));
face_analyser.GetLatestAlignedFace(sim_warped_img); face_analyser.GetLatestAlignedFace(sim_warped_img);
if(!det_parameters.quiet_mode) if(!det_parameters.quiet_mode && visualize_align)
{ {
cv::imshow("sim_warp", sim_warped_img); cv::imshow("sim_warp", sim_warped_img);
} }
if(hog_output_file.is_open()) if(hog_output_file.is_open() || (visualize_hog && !det_parameters.quiet_mode))
{ {
face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols); face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
if(visualise_hog && !det_parameters.quiet_mode) if(visualize_hog && !det_parameters.quiet_mode)
{ {
cv::Mat_<double> hog_descriptor_vis; cv::Mat_<double> hog_descriptor_vis;
FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_vis); FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_vis);
@ -631,7 +628,10 @@ int main (int argc, char **argv)
} }
// Visualising the tracker // Visualising the tracker
visualise_tracking(captured_image, face_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy); if(visualize_track && !det_parameters.quiet_mode)
{
visualise_tracking(captured_image, face_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
}
// Output the landmarks, pose, gaze, parameters and AUs // Output the landmarks, pose, gaze, parameters and AUs
outputAllFeatures(&output_file, output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, outputAllFeatures(&output_file, output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze,
@ -949,7 +949,7 @@ void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, boo
void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale, void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
int &similarity_size, bool &grayscale, bool& verbose, bool& dynamic, int &similarity_size, bool &grayscale, bool& visualize_track, bool& visualize_align, bool& visualize_hog, bool& dynamic,
bool &output_2D_landmarks, bool &output_3D_landmarks, bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze, bool &output_2D_landmarks, bool &output_3D_landmarks, bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze,
vector<string> &arguments) vector<string> &arguments)
{ {
@ -968,6 +968,10 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
// By default the model is dynamic // By default the model is dynamic
dynamic = true; dynamic = true;
visualize_align = false;
visualize_hog = false;
visualize_track = false;
string separator = string(1, boost::filesystem::path::preferred_separator); string separator = string(1, boost::filesystem::path::preferred_separator);
// First check if there is a root argument (so that videos and outputs could be defined more easilly) // First check if there is a root argument (so that videos and outputs could be defined more easilly)
@ -1005,7 +1009,24 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
} }
else if (arguments[i].compare("-verbose") == 0) else if (arguments[i].compare("-verbose") == 0)
{ {
verbose = true; visualize_track = true;
visualize_align = true;
visualize_hog = true;
}
else if (arguments[i].compare("-vis-align") == 0)
{
visualize_align = true;
valid[i] = false;
}
else if (arguments[i].compare("-vis-hog") == 0)
{
visualize_hog = true;
valid[i] = false;
}
else if (arguments[i].compare("-vis-track") == 0)
{
visualize_track = true;
valid[i] = false;
} }
else if (arguments[i].compare("-au_static") == 0) else if (arguments[i].compare("-au_static") == 0)
{ {

2
matlab_version/face_validation/readme.txt
View file

@ -6,7 +6,7 @@ Create_data_train.m and Create_data_test.m
The data generation code requires you to have the patch expert training data (Menpo, Multi-PIE and 300W data, not included) for positive examples, and inriaperson dataset for negative samples (not included as well). The data generation code requires you to have the patch expert training data (Menpo, Multi-PIE and 300W data, not included) for positive examples, and inriaperson dataset for negative samples (not included as well).
To train Convolutional Neural Network based face landmark validation model use: To train Convolutional Neural Network based face landmark validation model use:
Train_face_checker_cnn.m train_CNN_model.m
This will produce trained/face_checker_cnn_*.mat and trained/face_checker_cnn_*.txt files that can be used in C++ and matlab versions of OpenFace for face validation. Old versions can also be found in trained folder (they are simpler CNN models trained on smaller datasets). This will produce trained/face_checker_cnn_*.mat and trained/face_checker_cnn_*.txt files that can be used in C++ and matlab versions of OpenFace for face validation. Old versions can also be found in trained folder (they are simpler CNN models trained on smaller datasets).