- 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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@ -308,7 +308,7 @@ 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 video_output = false;
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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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@ -631,8 +634,8 @@ int main (int argc, char **argv)
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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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@ -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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@ -74,12 +74,12 @@
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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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@ -112,12 +112,8 @@ public:
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cv::Mat_<int> GetTriangulation();
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cv::Mat_<uchar> GetLatestAlignedFaceGrayscale();
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void GetGeomDescriptor(cv::Mat_<double>& geom_desc);
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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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// 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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@ -130,7 +126,10 @@ 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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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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private:
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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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@ -148,8 +147,8 @@ private:
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int frames_tracking;
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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 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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// Private members to be used for predictions
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@ -218,7 +217,7 @@ private:
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// 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
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// 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
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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);
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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);
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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);
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void PostprocessPredictions();
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@ -251,7 +250,7 @@ private:
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bool postprocessed = false;
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int frames_tracking_succ = 0;
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};
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};
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//===========================================================================
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}
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#endif
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@ -226,7 +226,7 @@ void FaceAnalyser::GetLatestHOG(cv::Mat_<double>& hog_descriptor, int& num_rows,
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void FaceAnalyser::GetLatestAlignedFace(cv::Mat& image)
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{
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image = this->aligned_face.clone();
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image = this->aligned_face_for_output.clone();
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}
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void FaceAnalyser::GetLatestNeutralHOG(cv::Mat_<double>& hog_descriptor, int& num_rows, int& num_cols)
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@ -267,50 +267,15 @@ int GetViewId(const vector<cv::Vec3d> orientations_all, const cv::Vec3d& orienta
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}
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void FaceAnalyser::ExtractCurrentMedians(vector<cv::Mat>& hog_medians, vector<cv::Mat>& face_image_medians, vector<cv::Vec3d>& orientations)
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{
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orientations = this->head_orientations;
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for(size_t i = 0; i < orientations.size(); ++i)
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{
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cv::Mat_<double> median_face(this->face_image_median.rows, this->face_image_median.cols, 0.0);
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cv::Mat_<double> median_hog(this->hog_desc_median.rows, this->hog_desc_median.cols, 0.0);
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ExtractMedian(this->face_image_hist[i], this->face_image_hist_sum[i], median_face, 256, 0, 255);
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ExtractMedian(this->hog_desc_hist[i], this->hog_hist_sum[i], median_hog, this->num_bins_hog, 0, 1);
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// Add the HOG sample
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hog_medians.push_back(median_hog.clone());
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// For the face image need to convert it to suitable format
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cv::Mat_<uchar> aligned_face_cols_uchar;
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median_face.convertTo(aligned_face_cols_uchar, CV_8U);
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cv::Mat aligned_face_uchar;
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if(aligned_face.channels() == 1)
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{
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aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8U, aligned_face_cols_uchar.data);
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}
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else
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{
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aligned_face_uchar = cv::Mat(aligned_face.rows, aligned_face.cols, CV_8UC3, aligned_face_cols_uchar.data);
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}
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face_image_medians.push_back(aligned_face_uchar.clone());
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}
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}
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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)
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{
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// First align the face
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AlignFaceMask(aligned_face, frame, clnf, triangulation, true, align_scale, align_width, align_height);
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AlignFaceMask(aligned_face_for_au, frame, clnf, triangulation, true, 0.7, 112, 112);
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// Extract HOG descriptor from the frame and convert it to a useable format
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cv::Mat_<double> hog_descriptor;
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Extract_FHOG_descriptor(hog_descriptor, aligned_face, this->num_hog_rows, this->num_hog_cols);
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Extract_FHOG_descriptor(hog_descriptor, aligned_face_for_au, this->num_hog_rows, this->num_hog_cols);
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// Store the descriptor
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hog_desc_frame = hog_descriptor;
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@ -326,10 +291,10 @@ std::pair<std::vector<std::pair<string, double>>, std::vector<std::pair<string,
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cv::hconcat(locs.t(), geom_descriptor_frame.clone(), geom_descriptor_frame);
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// First convert the face image to double representation as a row vector
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cv::Mat_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
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cv::Mat_<double> aligned_face_cols_double;
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aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
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// First convert the face image to double representation as a row vector, TODO rem
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//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);
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//cv::Mat_<double> aligned_face_cols_double;
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//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
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// Visualising the median HOG
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if (visualise)
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@ -361,28 +326,33 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
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frames_tracking++;
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// First align the face if tracking was successfull
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if(clnf_model.detection_success)
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if (clnf_model.detection_success)
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{
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AlignFaceMask(aligned_face, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
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}
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else
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{
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aligned_face = cv::Mat(align_height, align_width, CV_8UC3);
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aligned_face.setTo(0);
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}
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if(aligned_face.channels() == 3)
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// The aligned face requirement for AUs
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AlignFaceMask(aligned_face_for_au, frame, clnf_model, triangulation, true, 0.7, 112, 112);
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// If the output requirement matches use the already computed one, else compute it again
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if (align_scale == 0.7 && align_width == 112 && align_height == 112)
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{
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cv::cvtColor(aligned_face, aligned_face_grayscale, CV_BGR2GRAY);
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aligned_face_for_output = aligned_face_for_au.clone();
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}
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else
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{
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aligned_face_grayscale = aligned_face.clone();
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AlignFaceMask(aligned_face_for_output, frame, clnf_model, triangulation, true, align_scale, align_width, align_height);
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}
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}
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else
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{
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aligned_face_for_output = cv::Mat(align_height, align_width, CV_8UC3);
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aligned_face_for_au = cv::Mat(112, 112, CV_8UC3);
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aligned_face_for_output.setTo(0);
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aligned_face_for_au.setTo(0);
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}
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// Extract HOG descriptor from the frame and convert it to a useable format
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cv::Mat_<double> hog_descriptor;
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Extract_FHOG_descriptor(hog_descriptor, aligned_face, this->num_hog_rows, this->num_hog_cols);
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Extract_FHOG_descriptor(hog_descriptor, aligned_face_for_au, this->num_hog_rows, this->num_hog_cols);
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// Store the descriptor
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hog_desc_frame = hog_descriptor;
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@ -425,7 +395,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
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frames_tracking_succ++;
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// A small speedup
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if(frames_tracking % 2 == 1)
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if (frames_tracking % 2 == 1)
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{
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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);
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this->hog_desc_median.setTo(0, this->hog_desc_median < 0);
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@ -434,7 +404,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
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// Geom descriptor and its median
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geom_descriptor_frame = clnf_model.params_local.t();
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if(!clnf_model.detection_success)
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if (!clnf_model.detection_success)
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{
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geom_descriptor_frame.setTo(0);
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}
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@ -445,21 +415,18 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
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cv::hconcat(locs.t(), geom_descriptor_frame.clone(), geom_descriptor_frame);
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// A small speedup
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if(frames_tracking % 2 == 1)
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if (frames_tracking % 2 == 1)
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{
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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);
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}
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// First convert the face image to double representation as a row vector
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cv::Mat_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
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cv::Mat_<double> aligned_face_cols_double;
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aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
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// TODO get rid of this completely as it takes too long?
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//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);
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// First convert the face image to double representation as a row vector, TODO rem?
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//cv::Mat_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
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//cv::Mat_<double> aligned_face_cols_double;
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//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
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// Visualising the median HOG
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if(visualise)
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if (visualise)
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{
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FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_visualisation);
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}
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|
@ -468,9 +435,9 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
AU_predictions_reg = PredictCurrentAUs(orientation_to_use);
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||||
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||||
std::vector<std::pair<std::string, double>> AU_predictions_reg_corrected;
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||||
if(online)
|
||||
if (online)
|
||||
{
|
||||
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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||||
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|
@ -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);
|
||||
}
|
||||
|
|
@ -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);
|
||||
}
|
||||
|
|
@ -507,7 +474,7 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
}
|
||||
|
||||
|
||||
if(online)
|
||||
if (online)
|
||||
{
|
||||
AU_predictions_reg = AU_predictions_reg_corrected;
|
||||
}
|
||||
|
|
@ -531,8 +498,6 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL
|
|||
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;
|
||||
|
|
|
|||
|
|
@ -221,19 +221,19 @@ namespace FaceAnalysis
|
|||
destination_landmarks.col(1) = destination_landmarks.col(1) + warp_matrix(1,2);
|
||||
|
||||
// Move the eyebrows up to include more of upper face
|
||||
destination_landmarks.at<double>(0,1) -= 30;
|
||||
destination_landmarks.at<double>(16,1) -= 30;
|
||||
destination_landmarks.at<double>(0,1) -= (30/0.7)*sim_scale;
|
||||
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;
|
||||
destination_landmarks.at<double>(19,1) -= 30;
|
||||
destination_landmarks.at<double>(20,1) -= 30;
|
||||
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