r/sustaining_gazes
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

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

Browse source
This commit is contained in:
Tadas Baltrusaitis 2017-10-13 08:38:12 +01:00
parent a4d20fc9b4
commit 46bd0869ad
2 changed files with 37 additions and 16 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

49
exe/FeatureExtraction/FeatureExtraction.cpp
View file

@ -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,
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);
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,
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)
{
@ -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);
bool video_input = true;
bool verbose = true;
bool images_as_video = false;
vector<vector<string> > input_image_files;
@ -301,7 +297,10 @@ int main (int argc, char **argv)
bool output_AUs = 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);
// Used for image masking
@ -504,8 +503,6 @@ int main (int argc, char **argv)
// Use for timestamping if using a webcam
int64 t_initial = cv::getTickCount();
bool visualise_hog = verbose;
// Timestamp in seconds of current processing
double time_stamp = 0;
@ -565,18 +562,18 @@ int main (int argc, char **argv)
// But only if needed in output
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);
if(!det_parameters.quiet_mode)
if(!det_parameters.quiet_mode && visualize_align)
{
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);
if(visualise_hog && !det_parameters.quiet_mode)
if(visualize_hog && !det_parameters.quiet_mode)
{
cv::Mat_<double> 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
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
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,
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,
vector<string> &arguments)
{
@ -968,6 +968,10 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
// By default the model is dynamic
dynamic = true;
visualize_align = false;
visualize_hog = false;
visualize_track = false;
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)
@ -1005,7 +1009,24 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
}
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)
{

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).
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).