- Fixing the issue with gaze not tracking properly in video and landmark modes.
- Fixing the simscale/simalign bug
This commit is contained in:
Tadas Baltrusaitis
parent
a3e66319b5
commit
52c50b4ff3
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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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@ -308,9 +308,9 @@ 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 grayscale = false;
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bool video_output = false;
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bool dynamic = true; // Indicates if a dynamic AU model should be used (dynamic is useful if the video is long enough to include neutral expressions)
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int num_hog_rows;
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@ -321,13 +321,13 @@ int main (int argc, char **argv)
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bool output_2D_landmarks = true;
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bool output_3D_landmarks = true;
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bool output_model_params = true;
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bool output_pose = true;
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bool output_pose = true;
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bool output_AUs = true;
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bool output_gaze = true;
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get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, verbose, dynamic,
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output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
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// Used for image masking
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string tri_loc;
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boost::filesystem::path tri_loc_path = boost::filesystem::path("model/tris_68_full.txt");
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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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@ -630,18 +633,18 @@ int main (int argc, char **argv)
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}
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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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std::string preferredSlash = slash.make_preferred().string();
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string out_file = output_similarity_align[f_n] + preferredSlash + string(name);
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bool write_success = imwrite(out_file, sim_warped_img);
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if (!write_success)
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{
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cout << "Could not output similarity aligned image image" << endl;
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@ -1206,6 +1209,7 @@ 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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@ -74,184 +74,183 @@
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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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FaceAnalyser(vector<cv::Vec3d> orientation_bins = vector<cv::Vec3d>(), double scale = 0.7, int width = 112, int height = 112, std::string au_location = "AU_predictors/AU_all_best.txt", std::string tri_location = "model/tris_68_full.txt");
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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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FaceAnalyser(vector<cv::Vec3d> orientation_bins = vector<cv::Vec3d>(), double scale = 0.7, int width = 112, int height = 112, std::string au_location = "AU_predictors/AU_all_best.txt", std::string tri_location = "model/tris_68_full.txt");
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void AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, double timestamp_seconds, bool online = false, bool visualise = true);
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void AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, double timestamp_seconds, bool online = false, bool visualise = true);
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// If the features are extracted manually (shouldn't really be used)
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void PredictAUs(const cv::Mat_<double>& hog_features, const cv::Mat_<double>& geom_features, const LandmarkDetector::CLNF& clnf_model, bool online);
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// If the features are extracted manually (shouldn't really be used)
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void PredictAUs(const cv::Mat_<double>& hog_features, const cv::Mat_<double>& geom_features, const LandmarkDetector::CLNF& clnf_model, bool online);
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cv::Mat GetLatestHOGDescriptorVisualisation();
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cv::Mat GetLatestHOGDescriptorVisualisation();
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double GetCurrentTimeSeconds();
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// Grab the current predictions about AUs from the face analyser
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std::vector<std::pair<std::string, double>> GetCurrentAUsClass() const; // AU presence
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std::vector<std::pair<std::string, double>> GetCurrentAUsReg() const; // AU intensity
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std::vector<std::pair<std::string, double>> GetCurrentAUsCombined() const; // Both presense and intensity
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double GetCurrentTimeSeconds();
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// A standalone call for predicting AUs from a static image, the first element in the pair represents occurence the second intensity
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// This call is useful for detecting action units in images
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std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string, double>>> PredictStaticAUs(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, bool visualise = true);
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// Grab the current predictions about AUs from the face analyser
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std::vector<std::pair<std::string, double>> GetCurrentAUsClass() const; // AU presence
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std::vector<std::pair<std::string, double>> GetCurrentAUsReg() const; // AU intensity
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std::vector<std::pair<std::string, double>> GetCurrentAUsCombined() const; // Both presense and intensity
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void Reset();
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// A standalone call for predicting AUs from a static image, the first element in the pair represents occurence the second intensity
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// This call is useful for detecting action units in images
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std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string, double>>> PredictStaticAUs(const cv::Mat& frame, const LandmarkDetector::CLNF& clnf, bool visualise = true);
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void GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols);
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void GetLatestAlignedFace(cv::Mat& image);
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void GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols);
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cv::Mat_<int> GetTriangulation();
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void Reset();
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cv::Mat_<uchar> GetLatestAlignedFaceGrayscale();
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void GetGeomDescriptor(cv::Mat_<double>& geom_desc);
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void GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols);
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void GetLatestAlignedFace(cv::Mat& image);
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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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void GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols);
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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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cv::Mat_<int> GetTriangulation();
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// Identify if models are static or dynamic (useful for correction and shifting)
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std::vector<bool> GetDynamicAUClass() const; // Presence
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std::vector<std::pair<string, bool>> GetDynamicAUReg() const; // Intensity
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void GetGeomDescriptor(cv::Mat_<double>& geom_desc);
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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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// Identify if models are static or dynamic (useful for correction and shifting)
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std::vector<bool> GetDynamicAUClass() const; // Presence
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std::vector<std::pair<string, bool>> GetDynamicAUReg() const; // Intensity
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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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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 FaceAnalyser::PostprocessOutputFile(string output_file, bool dynamic);
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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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std::vector<std::pair<std::string, double>> AU_predictions_class;
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private:
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std::vector<std::pair<std::string, double>> AU_predictions_combined;
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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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std::vector<std::pair<std::string, double>> AU_predictions_class;
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// Keeping track of AU predictions over time (useful for post-processing)
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vector<double> timestamps;
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std::map<std::string, vector<double>> AU_predictions_reg_all_hist;
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std::map<std::string, vector<double>> AU_predictions_class_all_hist;
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std::vector<double> confidences;
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std::vector<bool> valid_preds;
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std::vector<std::pair<std::string, double>> AU_predictions_combined;
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int frames_tracking;
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// Keeping track of AU predictions over time (useful for post-processing)
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vector<double> timestamps;
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std::map<std::string, vector<double>> AU_predictions_reg_all_hist;
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std::map<std::string, vector<double>> AU_predictions_class_all_hist;
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std::vector<double> confidences;
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std::vector<bool> valid_preds;
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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 hog_descriptor_visualisation;
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int frames_tracking;
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// Private members to be used for predictions
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// The HOG descriptor of the last frame
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cv::Mat_<double> hog_desc_frame;
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int num_hog_rows;
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int num_hog_cols;
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// Cache of intermediate images
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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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// Keep a running median of the hog descriptors and a aligned images
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cv::Mat_<double> hog_desc_median;
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cv::Mat_<double> face_image_median;
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// Private members to be used for predictions
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// The HOG descriptor of the last frame
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cv::Mat_<double> hog_desc_frame;
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int num_hog_rows;
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int num_hog_cols;
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// Use histograms for quick (but approximate) median computation
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// Use the same for
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vector<cv::Mat_<unsigned int> > hog_desc_hist;
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// Keep a running median of the hog descriptors and a aligned images
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cv::Mat_<double> hog_desc_median;
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cv::Mat_<double> face_image_median;
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// This is not being used at the moment as it is a bit slow
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vector<cv::Mat_<unsigned int> > face_image_hist;
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vector<int> face_image_hist_sum;
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// Use histograms for quick (but approximate) median computation
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// Use the same for
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vector<cv::Mat_<unsigned int> > hog_desc_hist;
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vector<cv::Vec3d> head_orientations;
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// This is not being used at the moment as it is a bit slow
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vector<cv::Mat_<unsigned int> > face_image_hist;
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vector<int> face_image_hist_sum;
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int num_bins_hog;
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double min_val_hog;
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double max_val_hog;
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vector<int> hog_hist_sum;
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int view_used;
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vector<cv::Vec3d> head_orientations;
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// The geometry descriptor (rigid followed by non-rigid shape parameters from CLNF)
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cv::Mat_<double> geom_descriptor_frame;
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cv::Mat_<double> geom_descriptor_median;
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int geom_hist_sum;
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cv::Mat_<unsigned int> geom_desc_hist;
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int num_bins_geom;
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double min_val_geom;
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double max_val_geom;
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// Using the bounding box of previous analysed frame to determine if a reset is needed
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cv::Rect_<double> face_bounding_box;
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// The AU predictions internally
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std::vector<std::pair<std::string, double>> PredictCurrentAUs(int view);
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std::vector<std::pair<std::string, double>> PredictCurrentAUsClass(int view);
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int num_bins_hog;
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double min_val_hog;
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double max_val_hog;
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vector<int> hog_hist_sum;
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int view_used;
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// special step for online (rather than offline AU prediction)
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std::vector<pair<string, double>> CorrectOnlineAUs(std::vector<std::pair<std::string, double>> predictions_orig, int view, bool dyn_shift = false, bool dyn_scale = false, bool update_track = true, bool clip_values = false);
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// The geometry descriptor (rigid followed by non-rigid shape parameters from CLNF)
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cv::Mat_<double> geom_descriptor_frame;
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cv::Mat_<double> geom_descriptor_median;
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void ReadAU(std::string au_location);
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int geom_hist_sum;
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cv::Mat_<unsigned int> geom_desc_hist;
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int num_bins_geom;
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double min_val_geom;
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double max_val_geom;
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void ReadRegressor(std::string fname, const vector<string>& au_names);
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// Using the bounding box of previous analysed frame to determine if a reset is needed
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cv::Rect_<double> face_bounding_box;
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|
||||
// A utility function for keeping track of approximate running medians used for AU and emotion inference using a set of histograms (the histograms are evenly spaced from min_val to max_val)
|
||||
// Descriptor has to be a row vector
|
||||
// TODO this duplicates some other code
|
||||
void UpdateRunningMedian(cv::Mat_<unsigned int>& histogram, int& hist_sum, cv::Mat_<double>& median, const cv::Mat_<double>& descriptor, bool update, int num_bins, double min_val, double max_val);
|
||||
void ExtractMedian(cv::Mat_<unsigned int>& histogram, int hist_count, cv::Mat_<double>& median, int num_bins, double min_val, double max_val);
|
||||
|
||||
// The linear SVR regressors
|
||||
SVR_static_lin_regressors AU_SVR_static_appearance_lin_regressors;
|
||||
SVR_dynamic_lin_regressors AU_SVR_dynamic_appearance_lin_regressors;
|
||||
|
||||
// The linear SVM classifiers
|
||||
SVM_static_lin AU_SVM_static_appearance_lin;
|
||||
SVM_dynamic_lin AU_SVM_dynamic_appearance_lin;
|
||||
// The AU predictions internally
|
||||
std::vector<std::pair<std::string, double>> PredictCurrentAUs(int view);
|
||||
std::vector<std::pair<std::string, double>> PredictCurrentAUsClass(int view);
|
||||
|
||||
// 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
|
||||
// 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
|
||||
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);
|
||||
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);
|
||||
// special step for online (rather than offline AU prediction)
|
||||
std::vector<pair<string, double>> CorrectOnlineAUs(std::vector<std::pair<std::string, double>> predictions_orig, int view, bool dyn_shift = false, bool dyn_scale = false, bool update_track = true, bool clip_values = false);
|
||||
|
||||
void PostprocessPredictions();
|
||||
void ReadAU(std::string au_location);
|
||||
|
||||
vector<cv::Mat_<unsigned int>> au_prediction_correction_histogram;
|
||||
vector<int> au_prediction_correction_count;
|
||||
void ReadRegressor(std::string fname, const vector<string>& au_names);
|
||||
|
||||
// Some dynamic scaling (the logic is that before the extreme versions of expression or emotion are shown,
|
||||
// it is hard to tell the boundaries, this allows us to scale the model to the most extreme seen)
|
||||
// They have to be view specific
|
||||
vector<vector<double>> dyn_scaling;
|
||||
|
||||
// Keeping track of predictions for summary stats
|
||||
cv::Mat_<double> AU_prediction_track;
|
||||
cv::Mat_<double> geom_desc_track;
|
||||
// A utility function for keeping track of approximate running medians used for AU and emotion inference using a set of histograms (the histograms are evenly spaced from min_val to max_val)
|
||||
// Descriptor has to be a row vector
|
||||
// TODO this duplicates some other code
|
||||
void UpdateRunningMedian(cv::Mat_<unsigned int>& histogram, int& hist_sum, cv::Mat_<double>& median, const cv::Mat_<double>& descriptor, bool update, int num_bins, double min_val, double max_val);
|
||||
void ExtractMedian(cv::Mat_<unsigned int>& histogram, int hist_count, cv::Mat_<double>& median, int num_bins, double min_val, double max_val);
|
||||
|
||||
double current_time_seconds;
|
||||
// The linear SVR regressors
|
||||
SVR_static_lin_regressors AU_SVR_static_appearance_lin_regressors;
|
||||
SVR_dynamic_lin_regressors AU_SVR_dynamic_appearance_lin_regressors;
|
||||
|
||||
// Used for face alignment
|
||||
cv::Mat_<int> triangulation;
|
||||
double align_scale;
|
||||
int align_width;
|
||||
int align_height;
|
||||
// The linear SVM classifiers
|
||||
SVM_static_lin AU_SVM_static_appearance_lin;
|
||||
SVM_dynamic_lin AU_SVM_dynamic_appearance_lin;
|
||||
|
||||
// Useful placeholder for renormalizing the initial frames of shorter videos
|
||||
int max_init_frames = 3000;
|
||||
vector<cv::Mat_<double>> hog_desc_frames_init;
|
||||
vector<cv::Mat_<double>> geom_descriptor_frames_init;
|
||||
vector<int> views;
|
||||
bool postprocessed = false;
|
||||
int frames_tracking_succ = 0;
|
||||
// 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
|
||||
// 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
|
||||
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);
|
||||
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);
|
||||
|
||||
};
|
||||
//===========================================================================
|
||||
void PostprocessPredictions();
|
||||
|
||||
vector<cv::Mat_<unsigned int>> au_prediction_correction_histogram;
|
||||
vector<int> au_prediction_correction_count;
|
||||
|
||||
// Some dynamic scaling (the logic is that before the extreme versions of expression or emotion are shown,
|
||||
// it is hard to tell the boundaries, this allows us to scale the model to the most extreme seen)
|
||||
// They have to be view specific
|
||||
vector<vector<double>> dyn_scaling;
|
||||
|
||||
// Keeping track of predictions for summary stats
|
||||
cv::Mat_<double> AU_prediction_track;
|
||||
cv::Mat_<double> geom_desc_track;
|
||||
|
||||
double current_time_seconds;
|
||||
|
||||
// Used for face alignment
|
||||
cv::Mat_<int> triangulation;
|
||||
double align_scale;
|
||||
int align_width;
|
||||
int align_height;
|
||||
|
||||
// Useful placeholder for renormalizing the initial frames of shorter videos
|
||||
int max_init_frames = 3000;
|
||||
vector<cv::Mat_<double>> hog_desc_frames_init;
|
||||
vector<cv::Mat_<double>> geom_descriptor_frames_init;
|
||||
vector<int> views;
|
||||
bool postprocessed = false;
|
||||
int frames_tracking_succ = 0;
|
||||
|
||||
};
|
||||
//===========================================================================
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -226,7 +226,7 @@ void FaceAnalyser::GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows,
|
|||
|
||||
void FaceAnalyser::GetLatestAlignedFace(cv::Mat& image)
|
||||
{
|
||||
image = this->aligned_face.clone();
|
||||
image = this->aligned_face_for_output.clone();
|
||||
}
|
||||
|
||||
void FaceAnalyser::GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols)
|
||||
|
|
@ -267,57 +267,22 @@ int GetViewId(const vector<cv::Vec3d> orientations_all, const cv::Vec3d& orienta
|
|||
|
||||
}
|
||||
|
||||
void FaceAnalyser::ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations)
|
||||
{
|
||||
|
||||
orientations = this->head_orientations;
|
||||
|
||||
for(size_t i = 0; i < orientations.size(); ++i)
|
||||
{
|
||||
cv::Mat_<double> median_face(this->face_image_median.rows, this->face_image_median.cols, 0.0);
|
||||
cv::Mat_<double> median_hog(this->hog_desc_median.rows, this->hog_desc_median.cols, 0.0);
|
||||
|
||||
ExtractMedian(this->face_image_hist[i], this->face_image_hist_sum[i], median_face, 256, 0, 255);
|
||||
ExtractMedian(this->hog_desc_hist[i], this->hog_hist_sum[i], median_hog, this->num_bins_hog, 0, 1);
|
||||
|
||||
// Add the HOG sample
|
||||
hog_medians.push_back(median_hog.clone());
|
||||
|
||||
// For the face image need to convert it to suitable format
|
||||
cv::Mat_<uchar> aligned_face_cols_uchar;
|
||||
median_face.convertTo(aligned_face_cols_uchar, CV_8U);
|
||||
|
||||
cv::Mat aligned_face_uchar;
|
||||
if(aligned_face.channels() == 1)
|
||||
{
|
||||
aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8U, aligned_face_cols_uchar.data);
|
||||
}
|
||||
else
|
||||
{
|
||||
aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8UC3, aligned_face_cols_uchar.data);
|
||||
}
|
||||
|
||||
face_image_medians.push_back(aligned_face_uchar.clone());
|
||||
|
||||
}
|
||||
}
|
||||
|
||||
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)
|
||||
{
|
||||
|
||||
|
||||
// First align the face
|
||||
AlignFaceMask(aligned_face, frame, clnf, triangulation, true, align_scale, align_width, align_height);
|
||||
|
||||
AlignFaceMask(aligned_face_for_au, frame, clnf, triangulation, true, 0.7, 112, 112);
|
||||
|
||||
// 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;
|
||||
|
||||
cv::Vec3d curr_orient(clnf.params_global[1], clnf.params_global[2], clnf.params_global[3]);
|
||||
int orientation_to_use = GetViewId(this->head_orientations, curr_orient);
|
||||
|
||||
|
||||
// Geom descriptor and its median
|
||||
geom_descriptor_frame = clnf.params_local.t();
|
||||
|
||||
|
|
@ -325,11 +290,11 @@ std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string,
|
|||
cv::Mat_<double> locs = clnf.pdm.princ_comp * geom_descriptor_frame.t();
|
||||
|
||||
cv::hconcat(locs.t(), geom_descriptor_frame.clone(), geom_descriptor_frame);
|
||||
|
||||
// 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)
|
||||
|
|
@ -361,29 +326,34 @@ 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)
|
||||
{
|
||||
cv::cvtColor(aligned_face, aligned_face_grayscale, CV_BGR2GRAY);
|
||||
// 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)
|
||||
{
|
||||
aligned_face_for_output = aligned_face_for_au.clone();
|
||||
}
|
||||
else
|
||||
{
|
||||
AlignFaceMask(aligned_face_for_output, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
aligned_face_grayscale = aligned_face.clone();
|
||||
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;
|
||||
|
||||
|
|
@ -425,41 +395,38 @@ 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);
|
||||
}
|
||||
}
|
||||
|
||||
// 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);
|
||||
}
|
||||
|
||||
// Stack with the actual feature point locations (without mean)
|
||||
cv::Mat_<double> locs = clnf_model.pdm.princ_comp * geom_descriptor_frame.t();
|
||||
|
||||
|
||||
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);
|
||||
}
|
||||
|
|
@ -488,7 +455,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
AU_predictions_reg_all_hist[AU_predictions_reg[au].first].push_back(0);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
AU_predictions_class = PredictCurrentAUsClass(orientation_to_use);
|
||||
|
||||
for (size_t au = 0; au < AU_predictions_class.size(); ++au)
|
||||
|
|
@ -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);
|
||||
}
|
||||
|
|
@ -505,9 +472,9 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
AU_predictions_class_all_hist[AU_predictions_class[au].first].push_back(0);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
if(online)
|
||||
|
||||
if (online)
|
||||
{
|
||||
AU_predictions_reg = AU_predictions_reg_corrected;
|
||||
}
|
||||
|
|
@ -524,15 +491,13 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
this->current_time_seconds = timestamp_seconds;
|
||||
|
||||
view_used = orientation_to_use;
|
||||
|
||||
|
||||
bool success = clnf_model.detection_success;
|
||||
|
||||
confidences.push_back(clnf_model.detection_certainty);
|
||||
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;
|
||||
|
|
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|||
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|
@ -221,19 +221,19 @@ namespace FaceAnalysis
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destination_landmarks.col(1) = destination_landmarks.col(1) + warp_matrix(1,2);
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// Move the eyebrows up to include more of upper face
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destination_landmarks.at<double>(0,1) -= 30;
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destination_landmarks.at<double>(16,1) -= 30;
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destination_landmarks.at<double>(0,1) -= (30/0.7)*sim_scale;
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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;
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destination_landmarks.at<double>(19,1) -= 30;
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||||
destination_landmarks.at<double>(20,1) -= 30;
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||||
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
|
||||
|
||||
|
|
|
|||
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Reference in New Issue