diff --git a/.gitignore b/.gitignore index 0a0287b..fd4c176 100644 --- a/.gitignore +++ b/.gitignore @@ -49,3 +49,5 @@ matlab_runners/Head Pose Experiments/experiments/bu_out/ matlab_runners/Head Pose Experiments/experiments/ict_out/ OpenFace\.VC\.db +matlab_version/face_validation/vlfeat-0.9.20/ +matlab_version/face_validation/trained/intermediate/ diff --git a/exe/FaceLandmarkImg/FaceLandmarkImg.cpp b/exe/FaceLandmarkImg/FaceLandmarkImg.cpp index e7473ab..08e2776 100644 --- a/exe/FaceLandmarkImg/FaceLandmarkImg.cpp +++ b/exe/FaceLandmarkImg/FaceLandmarkImg.cpp @@ -302,12 +302,12 @@ int main (int argc, char **argv) boost::filesystem::path parent_path = boost::filesystem::path(arguments[0]).parent_path(); // Some initial parameters that can be overriden from command line - vector files, depth_files, output_images, output_landmark_locations, output_pose_locations; + vector files, output_images, output_landmark_locations, output_pose_locations; // Bounding boxes for a face in each image (optional) vector > bounding_boxes; - LandmarkDetector::get_image_input_output_params(files, depth_files, output_landmark_locations, output_pose_locations, output_images, bounding_boxes, arguments); + LandmarkDetector::get_image_input_output_params(files, output_landmark_locations, output_pose_locations, output_images, bounding_boxes, arguments); LandmarkDetector::FaceModelParameters det_parameters(arguments); // No need to validate detections, as we're not doing tracking det_parameters.validate_detections = false; @@ -397,17 +397,7 @@ int main (int argc, char **argv) cout << "Could not read the input image" << endl; return 1; } - - // Loading depth file if exists (optional) - cv::Mat_ depth_image; - - if(depth_files.size() > 0) - { - string dFile = depth_files.at(i); - cv::Mat dTemp = cv::imread(dFile, -1); - dTemp.convertTo(depth_image, CV_32F); - } - + // Making sure the image is in uchar grayscale cv::Mat_ grayscale_image; convert_to_grayscale(read_image, grayscale_image); @@ -453,7 +443,7 @@ int main (int argc, char **argv) for(size_t face=0; face < face_detections.size(); ++face) { // if there are multiple detections go through them - bool success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, depth_image, face_detections[face], clnf_model, det_parameters); + bool success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, face_detections[face], clnf_model, det_parameters); // Estimate head pose and eye gaze cv::Vec6d headPose = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy); diff --git a/exe/FaceLandmarkVid/FaceLandmarkVid.cpp b/exe/FaceLandmarkVid/FaceLandmarkVid.cpp index 577ad75..a17e6f5 100644 --- a/exe/FaceLandmarkVid/FaceLandmarkVid.cpp +++ b/exe/FaceLandmarkVid/FaceLandmarkVid.cpp @@ -87,7 +87,7 @@ double fps_tracker = -1.0; int64 t0 = 0; // Visualising the results -void visualise_tracking(cv::Mat& captured_image, cv::Mat_& depth_image, const LandmarkDetector::CLNF& face_model, const LandmarkDetector::FaceModelParameters& det_parameters, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, int frame_count, double fx, double fy, double cx, double cy) +void visualise_tracking(cv::Mat& captured_image, const LandmarkDetector::CLNF& face_model, const LandmarkDetector::FaceModelParameters& det_parameters, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, int frame_count, double fx, double fy, double cx, double cy) { // Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised @@ -142,13 +142,6 @@ void visualise_tracking(cv::Mat& captured_image, cv::Mat_& depth_image, c { cv::namedWindow("tracking_result", 1); cv::imshow("tracking_result", captured_image); - - if (!depth_image.empty()) - { - // Division needed for visualisation purposes - imshow("depth", depth_image / 2000.0); - } - } } @@ -158,7 +151,7 @@ int main (int argc, char **argv) vector arguments = get_arguments(argc, argv); // Some initial parameters that can be overriden from command line - vector files, depth_directories, output_video_files, out_dummy; + vector files, output_video_files, out_dummy; // By default try webcam 0 int device = 0; @@ -170,7 +163,7 @@ int main (int argc, char **argv) // Indicates that rotation should be with respect to world or camera coordinates bool u; string output_codec; - LandmarkDetector::get_video_input_output_params(files, depth_directories, out_dummy, output_video_files, u, output_codec, arguments); + LandmarkDetector::get_video_input_output_params(files, out_dummy, output_video_files, u, output_codec, arguments); // The modules that are being used for tracking LandmarkDetector::CLNF clnf_model(det_parameters.model_location); @@ -215,8 +208,6 @@ int main (int argc, char **argv) f_n = 0; } - bool use_depth = !depth_directories.empty(); - // Do some grabbing cv::VideoCapture video_capture; if( current_file.size() > 0 ) @@ -292,7 +283,6 @@ int main (int argc, char **argv) { // Reading the images - cv::Mat_ depth_image; cv::Mat_ grayscale_image; if(captured_image.channels() == 3) @@ -303,31 +293,9 @@ int main (int argc, char **argv) { grayscale_image = captured_image.clone(); } - - // Get depth image - if(use_depth) - { - char* dst = new char[100]; - std::stringstream sstream; - - sstream << depth_directories[f_n] << "\\depth%05d.png"; - sprintf(dst, sstream.str().c_str(), frame_count + 1); - // Reading in 16-bit png image representing depth - cv::Mat_ depth_image_16_bit = cv::imread(string(dst), -1); - - // Convert to a floating point depth image - if(!depth_image_16_bit.empty()) - { - depth_image_16_bit.convertTo(depth_image, CV_32F); - } - else - { - WARN_STREAM( "Can't find depth image" ); - } - } - + // The actual facial landmark detection / tracking - bool detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, depth_image, clnf_model, det_parameters); + bool detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, clnf_model, det_parameters); // Visualising the results // Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised @@ -343,7 +311,7 @@ int main (int argc, char **argv) FaceAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false); } - visualise_tracking(captured_image, depth_image, clnf_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy); + visualise_tracking(captured_image, clnf_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy); // output the tracked video if (!output_video_files.empty()) diff --git a/exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp b/exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp index 58f8aec..e3d2ab6 100644 --- a/exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp +++ b/exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp @@ -107,7 +107,7 @@ int main (int argc, char **argv) vector arguments = get_arguments(argc, argv); // Some initial parameters that can be overriden from command line - vector files, depth_directories, tracked_videos_output, dummy_out; + vector files, tracked_videos_output, dummy_out; // By default try webcam 0 int device = 0; @@ -128,7 +128,7 @@ int main (int argc, char **argv) // Get the input output file parameters bool u; string output_codec; - LandmarkDetector::get_video_input_output_params(files, depth_directories, dummy_out, tracked_videos_output, u, output_codec, arguments); + LandmarkDetector::get_video_input_output_params(files, dummy_out, tracked_videos_output, u, output_codec, arguments); // Get camera parameters LandmarkDetector::get_camera_params(device, fx, fy, cx, cy, arguments); @@ -177,8 +177,6 @@ int main (int argc, char **argv) current_file = files[f_n]; } - bool use_depth = !depth_directories.empty(); - // Do some grabbing cv::VideoCapture video_capture; if( current_file.size() > 0 ) @@ -254,28 +252,6 @@ int main (int argc, char **argv) grayscale_image = captured_image.clone(); } - // Get depth image - if(use_depth) - { - char* dst = new char[100]; - std::stringstream sstream; - - sstream << depth_directories[f_n] << "\\depth%05d.png"; - sprintf(dst, sstream.str().c_str(), frame_count + 1); - // Reading in 16-bit png image representing depth - cv::Mat_ depth_image_16_bit = cv::imread(string(dst), -1); - - // Convert to a floating point depth image - if(!depth_image_16_bit.empty()) - { - depth_image_16_bit.convertTo(depth_image, CV_32F); - } - else - { - WARN_STREAM( "Can't find depth image" ); - } - } - vector > face_detections; bool all_models_active = true; @@ -337,7 +313,7 @@ int main (int argc, char **argv) // This ensures that a wider window is used for the initial landmark localisation clnf_models[model].detection_success = false; - detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, depth_image, face_detections[detection_ind], clnf_models[model], det_parameters[model]); + detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_detections[detection_ind], clnf_models[model], det_parameters[model]); // This activates the model active_models[model] = true; @@ -351,7 +327,7 @@ int main (int argc, char **argv) else { // The actual facial landmark detection / tracking - detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, depth_image, clnf_models[model], det_parameters[model]); + detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, clnf_models[model], det_parameters[model]); } }); diff --git a/exe/FeatureExtraction/FeatureExtraction.cpp b/exe/FeatureExtraction/FeatureExtraction.cpp index 0e684cc..939942a 100644 --- a/exe/FeatureExtraction/FeatureExtraction.cpp +++ b/exe/FeatureExtraction/FeatureExtraction.cpp @@ -226,7 +226,7 @@ int main (int argc, char **argv) boost::filesystem::path parent_path = boost::filesystem::path(arguments[0]).parent_path(); // Some initial parameters that can be overriden from command line - vector input_files, depth_directories, output_files, tracked_videos_output; + vector input_files, output_files, tracked_videos_output; LandmarkDetector::FaceModelParameters det_parameters(arguments); // Always track gaze in feature extraction @@ -237,7 +237,7 @@ int main (int argc, char **argv) // Indicates that rotation should be with respect to camera or world coordinates bool use_world_coordinates; string output_codec; //not used but should - LandmarkDetector::get_video_input_output_params(input_files, depth_directories, output_files, tracked_videos_output, use_world_coordinates, output_codec, arguments); + LandmarkDetector::get_video_input_output_params(input_files, output_files, tracked_videos_output, use_world_coordinates, output_codec, arguments); bool video_input = true; bool verbose = true; diff --git a/lib/local/FaceAnalyser/src/FaceAnalyser.cpp b/lib/local/FaceAnalyser/src/FaceAnalyser.cpp index 064ae88..e91afc6 100644 --- a/lib/local/FaceAnalyser/src/FaceAnalyser.cpp +++ b/lib/local/FaceAnalyser/src/FaceAnalyser.cpp @@ -396,11 +396,6 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const LandmarkDetector::CL 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, TODO rem? - //cv::Mat_ aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1); - //cv::Mat_ aligned_face_cols_double; - //aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F); - // Visualising the median HOG if (visualise) { diff --git a/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h b/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h index 2a141a7..e6e590d 100644 --- a/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h +++ b/lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h @@ -1,14 +1,38 @@ /////////////////////////////////////////////////////////////////////////////// -// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge, +// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge, // all rights reserved. // -// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY -// -// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT. -// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE. -// -// License can be found in OpenFace-license.txt +// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS +// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO, +// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS +// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY. +// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) +// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, +// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN +// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +// POSSIBILITY OF SUCH DAMAGE. // +// Notwithstanding the license granted herein, Licensee acknowledges that certain components +// of the Software may be covered by so-called “open source” software licenses (“Open Source +// Components”), which means any software licenses approved as open source licenses by the +// Open Source Initiative or any substantially similar licenses, including without limitation any +// license that, as a condition of distribution of the software licensed under such license, +// requires that the distributor make the software available in source code format. Licensor shall +// provide a list of Open Source Components for a particular version of the Software upon +// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to +// the extent required by the licenses covering Open Source Components, the terms of such +// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the +// licenses applicable to Open Source Components prohibit any of the restrictions in this +// License Agreement with respect to such Open Source Component, such restrictions will not +// apply to such Open Source Component. To the extent the terms of the licenses applicable to +// Open Source Components require Licensor to make an offer to provide source code or +// related information in connection with the Software, such offer is hereby made. Any request +// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk +// Licensee acknowledges receipt of notices for the Open Source Components for the initial +// delivery of the Software. + // * Any publications arising from the use of this software, including but // not limited to academic journal and conference publications, technical // reports and manuals, must cite at least one of the following works: @@ -59,7 +83,7 @@ class DetectionValidator public: - // What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net + // What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net, 3 - new version of convolutional neural net int validator_type; // The orientations of each of the landmark detection validator @@ -98,11 +122,15 @@ public: vector > > > > > cnn_convolutional_layers_dft; vector > > cnn_convolutional_layers_bias; vector< vector > cnn_subsampling_layers; - vector< vector > > cnn_fully_connected_layers; + vector< vector > > cnn_fully_connected_layers_weights; vector< vector > cnn_fully_connected_layers_bias; - // 0 - convolutional, 1 - subsampling, 2 - fully connected + // OLD CNN: 0 - convolutional, 1 - subsampling, 2 - fully connected + // NEW CNN: 0 - convolutional, 1 - max pooling (2x2 stride 2), 2 - fully connected, 3 - relu, 4 - sigmoid vector > cnn_layer_types; + // Extra params for the new CNN + vector< vector > > cnn_fully_connected_layers_biases; + //========================================== // Normalisation for face validation @@ -134,9 +162,15 @@ private: // Feed-forward Neural Network double CheckNN(const cv::Mat_& warped_img, int view_id); + // Convolutional Neural Network + double CheckCNN_tbb(const cv::Mat_& warped_img, int view_id); + // Convolutional Neural Network double CheckCNN(const cv::Mat_& warped_img, int view_id); + // Convolutional Neural Network + double CheckCNN_old(const cv::Mat_& warped_img, int view_id); + // A normalisation helper void NormaliseWarpedToVector(const cv::Mat_& warped_img, cv::Mat_& feature_vec, int view_id); diff --git a/lib/local/LandmarkDetector/include/LandmarkDetectorFunc.h b/lib/local/LandmarkDetector/include/LandmarkDetectorFunc.h index 3377de8..e6bccb7 100644 --- a/lib/local/LandmarkDetector/include/LandmarkDetectorFunc.h +++ b/lib/local/LandmarkDetector/include/LandmarkDetectorFunc.h @@ -55,10 +55,7 @@ namespace LandmarkDetector // Optionally can provide a bounding box from which to start tracking //================================================================================================================ bool DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, CLNF& clnf_model, FaceModelParameters& params); - bool DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, const cv::Mat_ &depth_image, CLNF& clnf_model, FaceModelParameters& params); - bool DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params); - bool DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, const cv::Mat_ &depth_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params); //================================================================================================================ // Landmark detection in image, need to provide an image and optionally CLNF model together with parameters (default values work well) @@ -68,11 +65,6 @@ namespace LandmarkDetector // Providing a bounding box bool DetectLandmarksInImage(const cv::Mat_ &grayscale_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params); - //================================================ - // CLM-Z versions - bool DetectLandmarksInImage(const cv::Mat_ &grayscale_image, const cv::Mat_ depth_image, CLNF& clnf_model, FaceModelParameters& params); - bool DetectLandmarksInImage(const cv::Mat_ &grayscale_image, const cv::Mat_ depth_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params); - //================================================================ // Helper function for getting head pose from CLNF parameters diff --git a/lib/local/LandmarkDetector/include/LandmarkDetectorModel.h b/lib/local/LandmarkDetector/include/LandmarkDetectorModel.h index be63769..52ba242 100644 --- a/lib/local/LandmarkDetector/include/LandmarkDetectorModel.h +++ b/lib/local/LandmarkDetector/include/LandmarkDetectorModel.h @@ -153,7 +153,7 @@ public: CLNF & operator= (const CLNF&& other); // Does the actual work - landmark detection - bool DetectLandmarks(const cv::Mat_ &image, const cv::Mat_ &depth, FaceModelParameters& params); + bool DetectLandmarks(const cv::Mat_ &image, FaceModelParameters& params); // Gets the shape of the current detected landmarks in camera space (given camera calibration) // Can only be called after a call to DetectLandmarksInVideo or DetectLandmarksInImage @@ -180,7 +180,7 @@ private: map > kde_resp_precalc; // The model fitting: patch response computation and optimisation steps - bool Fit(const cv::Mat_& intensity_image, const cv::Mat_& depth_image, const std::vector& window_sizes, const FaceModelParameters& parameters); + bool Fit(const cv::Mat_& intensity_image, const std::vector& window_sizes, const FaceModelParameters& parameters); // Mean shift computation that uses precalculated kernel density estimators (the one actually used) void NonVectorisedMeanShift_precalc_kde(cv::Mat_& out_mean_shifts, const vector >& patch_expert_responses, const cv::Mat_ &dxs, const cv::Mat_ &dys, int resp_size, float a, int scale, int view_id, map >& mean_shifts); @@ -189,9 +189,6 @@ private: double NU_RLMS(cv::Vec6d& final_global, cv::Mat_& final_local, const vector >& patch_expert_responses, const cv::Vec6d& initial_global, const cv::Mat_& initial_local, const cv::Mat_& base_shape, const cv::Matx22d& sim_img_to_ref, const cv::Matx22f& sim_ref_to_img, int resp_size, int view_idx, bool rigid, int scale, cv::Mat_& landmark_lhoods, const FaceModelParameters& parameters); - // Removing background image from the depth - bool RemoveBackground(cv::Mat_& out_depth_image, const cv::Mat_& depth_image); - // Generating the weight matrix for the Weighted least squares void GetWeightMatrix(cv::Mat_& WeightMatrix, int scale, int view_id, const FaceModelParameters& parameters); diff --git a/lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h b/lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h index 19e856b..d4a360d 100644 --- a/lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h +++ b/lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h @@ -52,12 +52,12 @@ namespace LandmarkDetector //============================================================================================= // Helper functions for parsing the inputs //============================================================================================= - void get_video_input_output_params(vector &input_video_file, vector &depth_dir, vector &output_files, + void get_video_input_output_params(vector &input_video_file, vector &output_files, vector &output_video_files, bool& world_coordinates_pose, string &output_codec, vector &arguments); void get_camera_params(int &device, float &fx, float &fy, float &cx, float &cy, vector &arguments); - void get_image_input_output_params(vector &input_image_files, vector &input_depth_files, vector &output_feature_files, vector &output_pose_files, vector &output_image_files, + void get_image_input_output_params(vector &input_image_files, vector &output_feature_files, vector &output_pose_files, vector &output_image_files, vector> &input_bounding_boxes, vector &arguments); //=========================================================================== diff --git a/lib/local/LandmarkDetector/include/Patch_experts.h b/lib/local/LandmarkDetector/include/Patch_experts.h index 7624b98..ff1ef84 100644 --- a/lib/local/LandmarkDetector/include/Patch_experts.h +++ b/lib/local/LandmarkDetector/include/Patch_experts.h @@ -57,9 +57,6 @@ public: // The collection of SVR patch experts (for intensity/grayscale images), the experts are laid out scale->view->landmark vector > > svr_expert_intensity; - // The collection of SVR patch experts (for depth/range images), the experts are laid out scale->view->landmark - vector > > svr_expert_depth; - // The collection of LNF (CCNF) patch experts (for intensity images), the experts are laid out scale->view->landmark vector > > ccnf_expert_intensity; @@ -81,11 +78,11 @@ public: // A copy constructor Patch_experts(const Patch_experts& other); - // Returns the patch expert responses given a grayscale and an optional depth image. + // Returns the patch expert responses given a grayscale image. // Additionally returns the transform from the image coordinates to the response coordinates (and vice versa). // The computation also requires the current landmark locations to compute response around, the PDM corresponding to the desired model, and the parameters describing its instance // Also need to provide the size of the area of interest and the desired scale of analysis - void Response(vector >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_& grayscale_image, const cv::Mat_& depth_image, + void Response(vector >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_& grayscale_image, const PDM& pdm, const cv::Vec6d& params_global, const cv::Mat_& params_local, int window_size, int scale); // Getting the best view associated with the current orientation @@ -95,7 +92,7 @@ public: inline int nViews(size_t scale = 0) const { return (int)centers[scale].size(); }; // Reading in all of the patch experts - void Read(vector intensity_svr_expert_locations, vector depth_svr_expert_locations, vector intensity_ccnf_expert_locations); + void Read(vector intensity_svr_expert_locations, vector intensity_ccnf_expert_locations); diff --git a/lib/local/LandmarkDetector/model/clm-z.txt b/lib/local/LandmarkDetector/model/clm-z.txt deleted file mode 100644 index 6d185b6..0000000 --- a/lib/local/LandmarkDetector/model/clm-z.txt +++ /dev/null @@ -1,8 +0,0 @@ -PDM pdms/Multi-PIE_aligned_PDM_66.txt -Triangulations tris_66.txt -PatchesIntensity patch_experts/intensity_patches_0.25.txt -PatchesIntensity patch_experts/intensity_patches_0.35.txt -PatchesIntensity patch_experts/intensity_patches_0.5.txt -PatchesDepth patch_experts/depth_patches_0.25.txt -PatchesDepth patch_experts/depth_patches_0.35.txt -PatchesDepth patch_experts/depth_patches_0.5.txt \ No newline at end of file diff --git a/lib/local/LandmarkDetector/model/detection_validation/validator_cnn.txt b/lib/local/LandmarkDetector/model/detection_validation/validator_cnn.txt new file mode 100644 index 0000000..c1dff94 Binary files /dev/null and b/lib/local/LandmarkDetector/model/detection_validation/validator_cnn.txt differ diff --git a/lib/local/LandmarkDetector/model/detection_validation/validator_general_66.txt b/lib/local/LandmarkDetector/model/detection_validation/validator_general_66.txt deleted file mode 100644 index 9e36344..0000000 Binary files a/lib/local/LandmarkDetector/model/detection_validation/validator_general_66.txt and /dev/null differ diff --git a/lib/local/LandmarkDetector/model/main_clm-z.txt b/lib/local/LandmarkDetector/model/main_clm-z.txt deleted file mode 100644 index 33eab20..0000000 --- a/lib/local/LandmarkDetector/model/main_clm-z.txt +++ /dev/null @@ -1,3 +0,0 @@ -LandmarkDetector clm-z.txt -FaceDetConversion haarAlign.txt -DetectionValidator detection_validation/validator_general_66.txt \ No newline at end of file diff --git a/lib/local/LandmarkDetector/model/main_clm_general.txt b/lib/local/LandmarkDetector/model/main_clm_general.txt index da0eb3a..d13bd0a 100644 --- a/lib/local/LandmarkDetector/model/main_clm_general.txt +++ b/lib/local/LandmarkDetector/model/main_clm_general.txt @@ -1,3 +1,3 @@ LandmarkDetector clm_general.txt FaceDetConversion haarAlign.txt -DetectionValidator detection_validation/validator_general_68.txt \ No newline at end of file +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/lib/local/LandmarkDetector/model/main_clm_wild.txt b/lib/local/LandmarkDetector/model/main_clm_wild.txt index 00df7fe..886f6f7 100644 --- a/lib/local/LandmarkDetector/model/main_clm_wild.txt +++ b/lib/local/LandmarkDetector/model/main_clm_wild.txt @@ -1,3 +1,3 @@ LandmarkDetector clm_wild.txt FaceDetConversion haarAlign.txt -DetectionValidator detection_validation/validator_general_68.txt \ No newline at end of file +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/lib/local/LandmarkDetector/model/main_clnf_general.txt b/lib/local/LandmarkDetector/model/main_clnf_general.txt index ecb758d..0e5192b 100644 --- a/lib/local/LandmarkDetector/model/main_clnf_general.txt +++ b/lib/local/LandmarkDetector/model/main_clnf_general.txt @@ -3,4 +3,4 @@ LandmarkDetector_part model_inner/main_clnf_inner.txt inner 17 0 18 1 19 2 20 3 LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 FaceDetConversion haarAlign.txt -DetectionValidator detection_validation/validator_general_68.txt \ No newline at end of file +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/lib/local/LandmarkDetector/model/main_clnf_wild.txt b/lib/local/LandmarkDetector/model/main_clnf_wild.txt index 83e198e..f017262 100644 --- a/lib/local/LandmarkDetector/model/main_clnf_wild.txt +++ b/lib/local/LandmarkDetector/model/main_clnf_wild.txt @@ -2,4 +2,4 @@ LandmarkDetector clnf_wild.txt LandmarkDetector_part model_eye/main_clnf_synth_left.txt left_eye_28 36 8 37 10 38 12 39 14 40 16 41 18 LandmarkDetector_part model_eye/main_clnf_synth_right.txt right_eye_28 42 8 43 10 44 12 45 14 46 16 47 18 FaceDetConversion haarAlign.txt -DetectionValidator detection_validation/validator_general_68.txt \ No newline at end of file +DetectionValidator detection_validation/validator_cnn.txt \ No newline at end of file diff --git a/lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.25.txt b/lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.25.txt deleted file mode 100644 index ad268ff..0000000 --- a/lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.25.txt +++ /dev/null @@ -1,1276 +0,0 @@ -# scaling factor of training -0.250000 -# number of views -9 -# centers of the views -3 -1 -6 -0.000000000 -0.000000000 -0.000000000 -3 -1 -6 -0.000000000 --75.000000000 -0.000000000 -3 -1 -6 -0.000000000 --45.000000000 -0.000000000 -3 -1 -6 -0.000000000 --20.000000000 -0.000000000 -3 -1 -6 -0.000000000 -20.000000000 --0.000000000 -3 -1 -6 -0.000000000 -45.000000000 --0.000000000 -3 -1 -6 -0.000000000 -75.000000000 --0.000000000 -3 -1 -6 --30.000000000 -0.000000000 -0.000000000 -3 -1 -6 -30.000000000 -0.000000000 -0.000000000 -# visibility indices per view -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -1 -0 -0 -0 -1 -1 -1 -1 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -0 -0 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -0 -1 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -0 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -0 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -# Patches themselves (1 line patches of a vertex) -3 11 11 1 2 0 0.159082 39.711420 -7.008995 11 11 5 -0.006329 0.001232 0.000183 0.000229 -0.001605 -0.000946 0.000057 0.000712 -0.000257 0.002078 -0.001841 -0.000251 0.000336 -0.004557 -0.000868 -0.000865 -0.000241 0.001143 0.002128 0.000899 0.001650 -0.000422 0.001092 0.000454 -0.002494 -0.000104 0.001396 0.000511 0.001732 0.001847 0.001199 0.001192 0.005007 0.000736 0.000795 0.001001 0.004658 0.003272 0.001369 -0.000044 -0.000989 -0.001170 -0.001336 0.008332 0.000777 0.001405 0.001182 0.004664 0.003990 0.000214 0.000455 -0.004425 -0.002676 -0.003793 0.012554 -0.004243 0.001864 -0.000677 0.000550 0.000635 -0.001459 0.000642 -0.000057 -0.000798 -0.009787 0.034964 -0.005733 0.003370 0.005702 0.005404 0.003129 0.001340 0.001336 0.000211 -0.005615 -0.014742 0.021797 0.008684 0.010152 0.004408 0.001123 -0.001337 -0.000102 0.001138 0.000500 -0.005634 -0.012191 -0.009911 0.003320 -0.001486 -0.001776 -0.005354 -0.002559 0.002697 0.004310 -0.000069 -0.001577 0.000528 -0.031047 -0.005363 -0.003531 -0.005881 -0.003506 0.000392 0.001991 0.000912 -0.000330 0.001657 0.008334 -0.015300 -0.025713 -0.016458 -0.004803 0.000907 0.005025 0.007903 0.009484 0.011890 0.007919 0.007151 -0.019597 -3 11 11 1 2 0 0.168860 38.333402 -7.041743 11 11 5 -0.007703 -0.001704 0.001372 0.000352 -0.001452 0.002512 0.001379 0.002959 0.002802 -0.001638 -0.004939 -0.003706 -0.001240 0.002786 0.003403 -0.001694 0.002933 0.001195 0.001819 0.001792 -0.000434 -0.005367 0.005065 -0.002809 0.001399 0.002427 -0.002258 0.002169 0.001227 0.003173 0.005127 0.002927 -0.007570 0.011698 -0.001192 0.001194 -0.000245 -0.002623 0.000613 -0.001094 0.001094 0.005062 0.003777 -0.007095 0.011453 -0.000710 0.000676 -0.003244 -0.001019 0.002141 -0.000184 0.000958 0.003191 0.001314 0.001789 0.014044 -0.005045 -0.003906 -0.002182 0.000779 0.000711 0.000791 0.004386 0.004863 0.000215 0.017965 0.014010 -0.003652 -0.003786 -0.002065 -0.000896 -0.002354 -0.004868 -0.003515 -0.004242 -0.004884 0.040894 0.003278 0.003753 0.000192 -0.005483 -0.007648 -0.006693 -0.006445 -0.003051 -0.002772 -0.005175 0.003815 -0.014430 0.013239 0.005732 -0.004410 -0.005685 -0.006666 -0.010495 -0.012685 -0.007404 -0.002863 -0.008167 -0.021231 0.020630 0.010417 -0.000026 -0.001994 -0.001044 0.000235 0.001257 0.007024 0.008669 -0.021685 -0.039792 0.012898 0.013923 0.008897 0.004903 0.001667 0.000696 0.000437 0.002773 0.005584 -0.025294 -3 11 11 1 2 0 0.253421 33.104279 -6.592556 11 11 5 -0.003116 0.003791 0.001035 -0.000399 -0.000153 0.000112 -0.001497 -0.003656 -0.002360 -0.000539 0.002964 0.001819 0.003343 0.001084 0.001421 0.002358 0.003331 0.000936 -0.000860 -0.002648 -0.003781 0.004562 0.008165 -0.001097 -0.000657 0.001516 0.002238 0.002574 0.002264 0.001023 -0.005031 -0.005654 0.001139 0.012690 -0.002839 -0.002507 0.000901 0.001851 0.000948 0.001964 0.003786 -0.000912 -0.004472 -0.003699 0.013935 -0.006782 0.001417 0.004564 0.004359 -0.005035 0.000401 0.005394 0.007299 0.001859 -0.011173 0.006869 -0.008299 -0.004882 0.000975 0.008760 0.002497 0.012274 0.013900 0.014769 0.010103 -0.014292 -0.001773 -0.008377 -0.003683 -0.001435 -0.000575 -0.001644 0.001335 0.010643 0.017224 0.011894 -0.014784 -0.003944 -0.017003 -0.010888 -0.005958 -0.003617 -0.001028 -0.001761 -0.003222 -0.002299 0.004371 -0.011234 0.009598 -0.010678 -0.007661 -0.003814 -0.006566 -0.008530 -0.008427 -0.009231 -0.006570 -0.002450 -0.002741 0.022664 -0.004873 0.004837 0.007423 0.008790 0.009102 0.005119 0.001052 -0.002015 -0.001217 0.003063 0.006973 -0.008958 0.001033 -0.000756 -0.004737 -0.004222 -0.007278 -0.007091 -0.008757 -0.001193 0.005025 -3 11 11 1 2 0 0.190873 30.853722 -6.322284 11 11 5 0.009250 -0.000114 0.003125 -0.000380 -0.003875 -0.005065 -0.003829 0.000574 0.001949 0.001204 -0.001567 0.009947 0.000254 0.001537 0.000179 -0.001502 -0.001280 -0.001573 -0.002139 0.000212 0.002441 0.002274 0.009463 -0.001508 -0.000521 0.000951 0.001071 0.000069 -0.002177 -0.005096 -0.001842 0.003263 0.004392 0.011893 -0.003547 -0.001029 0.001399 0.005262 0.004294 -0.000156 -0.005542 -0.003319 0.001034 0.001746 -0.002115 0.009173 0.012448 0.007028 0.010331 0.009031 0.002967 -0.004948 -0.008371 -0.005524 -0.001258 -0.015499 0.003202 0.007123 0.014043 0.018336 0.009941 0.000135 -0.005653 -0.008440 -0.006405 0.002520 -0.028256 0.004334 0.002542 -0.001212 -0.003991 -0.003129 -0.004340 -0.005346 -0.003594 -0.000839 0.011894 -0.022233 -0.004842 -0.005603 -0.006727 -0.008006 -0.007806 -0.004414 0.001112 -0.000676 -0.003818 0.017777 -0.000010 0.001034 -0.001110 -0.005768 -0.007156 -0.003929 0.000039 0.002068 -0.000520 -0.004811 0.007982 0.016235 0.011359 0.008362 0.003833 0.001830 0.003460 0.005639 0.009064 0.008062 0.001966 -0.001696 0.002092 -0.012553 -0.008614 -0.005142 -0.002596 -0.000429 -0.003441 -0.001995 -0.000158 -0.005713 -0.019997 -3 11 11 1 2 0 0.181028 30.903630 -6.382260 11 11 5 0.009722 -0.001936 -0.001248 0.000414 -0.001542 0.001465 0.001194 0.000391 0.001538 -0.000206 -0.003566 0.014374 -0.000699 -0.001981 -0.002240 -0.000621 0.003789 0.001601 -0.000140 -0.000220 0.001322 -0.000685 0.020152 0.004035 -0.002168 -0.005619 -0.002165 0.000151 -0.003029 -0.003539 -0.002171 -0.000097 -0.000478 0.032521 0.004335 -0.004343 -0.005585 -0.003142 -0.004569 -0.006344 -0.004895 -0.003498 -0.000212 0.002826 0.006029 0.005928 -0.001637 -0.003085 -0.000914 -0.001631 -0.001807 -0.001213 -0.002024 0.000899 0.011315 -0.019163 -0.000128 -0.001982 -0.001005 0.000610 0.001795 0.004878 0.002775 -0.001174 -0.001332 0.014068 -0.031046 -0.000794 0.000056 -0.000621 -0.002554 -0.001629 0.002810 0.003922 0.000370 -0.005307 0.011653 -0.013917 0.001746 -0.000062 -0.000713 -0.001127 -0.003702 -0.002956 -0.001166 -0.002323 -0.006592 0.022121 -0.003120 0.006399 0.003278 -0.000018 -0.000078 -0.001638 -0.001532 -0.001330 -0.004501 -0.006462 0.032031 0.001422 0.008035 0.004921 0.001447 0.000543 -0.000100 -0.000231 0.002076 0.002938 -0.001043 0.010238 -0.006504 -0.002815 -0.002489 -0.000942 -0.000161 0.000552 0.001035 0.000804 -0.005311 -0.019416 -0.030289 -3 11 11 1 2 0 0.140038 30.415912 -6.554378 11 11 5 0.020447 0.001914 0.001992 0.001629 -0.001156 -0.001466 -0.002410 -0.006039 -0.003336 -0.002711 0.000669 0.018757 -0.008207 -0.009626 -0.008373 -0.007290 -0.001343 0.001812 -0.000120 0.002690 -0.000272 0.008129 0.016344 -0.007277 -0.006886 -0.003941 -0.000902 0.003052 0.002758 0.000706 0.000655 -0.004051 0.009821 -0.005298 0.000087 0.006016 0.002599 0.003056 0.002641 0.002730 0.002216 0.000983 -0.004342 0.007799 -0.019253 -0.002098 0.004326 0.005689 0.003550 0.002107 0.002812 0.003861 0.000470 -0.003574 0.001952 -0.017870 -0.003218 -0.002103 -0.002122 -0.002401 -0.000859 0.005102 0.007377 0.002173 -0.003441 -0.001673 -0.010763 -0.000099 -0.002012 -0.002943 -0.003110 -0.005262 -0.002238 0.008032 0.010755 0.001850 -0.003228 -0.007256 0.000194 -0.000325 -0.000740 -0.001195 -0.004404 -0.006876 -0.002069 0.009436 0.014525 0.006179 -0.000388 0.002964 0.002331 0.001019 0.000872 -0.002650 -0.006061 -0.002253 0.004046 0.008750 0.022160 0.004893 0.005609 0.002635 0.002477 0.005138 0.003399 -0.002354 -0.003334 -0.000058 0.001802 0.024752 0.000503 -0.001385 -0.004610 -0.001825 0.004290 0.004097 -0.005019 -0.018150 -0.033616 -0.035489 0.001746 -3 11 11 1 2 0 0.244927 29.003906 -6.941800 11 11 5 0.004938 0.003587 0.002633 0.000061 0.001788 -0.001548 -0.002474 0.001573 0.002369 0.004079 0.008104 -0.011594 0.005905 0.009344 0.002523 0.004358 0.002583 0.000231 -0.001116 -0.000046 -0.001990 0.003071 -0.024687 -0.003410 0.002389 0.005262 0.009721 0.003709 -0.003229 -0.003636 0.001489 0.002309 -0.001968 -0.021409 -0.004621 -0.000013 -0.002665 0.003884 0.013332 0.003475 0.000464 0.002888 0.001100 -0.004776 -0.008288 -0.002374 -0.004739 -0.005248 -0.002385 0.006669 0.015544 0.007635 0.000028 -0.003633 -0.006502 0.002512 0.001943 -0.003390 -0.007442 -0.004502 0.000553 0.008129 0.012848 0.006258 -0.001707 -0.005504 0.007847 0.002478 0.000592 -0.003702 -0.009562 -0.007038 0.003549 0.010534 0.009848 0.002068 -0.006110 0.007573 0.000407 0.001218 0.000259 -0.005190 -0.013090 -0.006965 0.004186 0.007281 0.007981 0.003095 0.003329 0.001228 0.001998 0.000491 -0.001100 -0.007145 -0.013186 -0.005083 0.003254 0.006740 0.010662 -0.001998 0.006446 0.008384 0.002213 0.001034 0.001453 -0.004959 -0.006419 -0.001220 0.000111 0.009546 -0.012833 0.002821 0.008492 0.001991 -0.003825 -0.008846 -0.018818 -0.028295 -0.019046 0.000869 0.020062 -3 11 11 1 2 0 0.295938 26.035543 -6.538823 11 11 5 -0.027948 0.006109 0.012910 0.017058 0.012609 0.008543 0.009700 0.002259 0.000654 0.003777 -0.003500 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-0.003035 -0.007405 0.024595 0.006729 0.000504 -0.010016 -0.007233 0.002790 0.004244 0.000987 0.006772 -0.003038 0.019213 -3 11 11 1 2 0 0.091161 32.259552 -6.236043 11 11 5 0.021633 0.013950 0.011755 0.002568 -0.008892 -0.003018 0.008939 0.001544 -0.000383 -0.000976 0.002716 -0.012139 -0.007758 -0.006478 -0.000038 -0.002132 -0.001290 0.003751 -0.002992 -0.003023 -0.000902 -0.003955 -0.023544 0.001090 -0.002993 0.003916 -0.001698 -0.002923 0.006495 0.000818 -0.003027 0.001594 0.000025 -0.032775 0.003644 0.001627 0.005911 -0.003604 -0.003832 0.007618 0.003459 -0.000147 0.004463 0.003317 -0.025662 0.005109 -0.002139 0.005691 -0.003228 -0.001481 0.009288 0.001317 -0.002210 0.002761 0.001819 -0.013837 -0.000134 -0.001484 0.005668 -0.002581 0.000860 0.008890 -0.002756 -0.003830 0.001557 -0.002180 -0.000977 -0.002443 0.006135 0.002894 -0.007572 0.000346 0.006874 -0.002563 -0.001632 0.002298 0.000968 0.014760 0.000945 0.005860 -0.003051 -0.007954 0.001599 0.004506 -0.004455 -0.002896 0.001010 0.001842 0.016447 0.005153 0.002113 -0.005186 -0.004697 0.000564 0.000752 -0.002030 -0.000810 -0.003487 -0.005179 0.008032 0.006151 0.002609 -0.001035 0.003440 0.004895 0.000773 0.000007 0.001878 -0.006621 -0.014227 -0.013188 -0.004911 -0.005334 -0.005484 -0.002146 -0.001154 -0.002140 -0.000308 0.007962 0.004960 0.031955 -3 11 11 1 2 0 0.081135 33.017316 -6.196015 11 11 5 0.024080 0.012014 0.002152 -0.002749 -0.001259 0.009780 0.004935 -0.005016 -0.004863 -0.000875 0.006311 -0.012094 -0.007487 -0.003877 0.002626 -0.001835 0.002579 0.001103 -0.005185 -0.004560 -0.000558 -0.004097 -0.026521 0.002856 -0.000366 0.001450 -0.003598 0.004656 0.005452 -0.004119 -0.003489 0.004537 -0.000933 -0.036224 0.007791 0.002530 0.000436 -0.003262 0.004956 0.006665 -0.003016 -0.001426 0.008571 0.004370 -0.025306 0.005110 0.002486 0.002380 -0.001478 0.005709 0.005653 -0.005801 -0.002381 0.005293 0.003555 -0.009957 -0.001883 0.000192 0.003746 0.001634 0.007064 0.002044 -0.009954 -0.001485 0.002963 -0.002210 0.004226 -0.003590 -0.000620 0.000692 0.002782 0.004901 -0.001768 -0.008655 0.001157 0.004107 -0.000971 0.016540 -0.001774 -0.001243 -0.002933 -0.000207 0.004022 -0.001402 -0.005216 -0.000571 0.001402 -0.001341 0.017597 0.001049 -0.000906 -0.004677 -0.002772 0.002198 0.000243 0.000307 -0.000370 -0.003275 -0.005978 0.009475 0.004063 0.001149 -0.001542 0.002427 0.005147 0.001250 -0.000951 -0.001811 -0.007231 -0.010146 -0.013266 -0.001671 -0.002783 -0.006194 -0.006531 -0.004559 -0.001869 0.006312 0.010311 0.003524 0.032128 -3 11 11 1 2 0 0.048563 36.643874 -6.149869 11 11 5 0.013743 -0.002126 -0.004779 -0.007372 -0.001287 0.003493 0.002460 0.005849 0.008483 -0.002872 0.024229 0.014765 -0.003761 -0.002486 -0.001340 0.000704 0.002729 -0.002576 -0.005004 -0.004048 -0.005828 -0.009135 0.010083 -0.005360 -0.001358 0.000174 0.001197 0.001872 -0.004355 -0.004922 -0.003085 -0.001294 -0.002049 -0.001832 -0.002058 0.002663 0.002415 0.001625 0.002269 -0.003303 -0.006009 -0.000648 0.003118 -0.002640 -0.021398 0.004977 0.002341 0.001068 0.001990 0.005839 -0.000820 -0.005964 0.001281 0.006731 -0.001805 -0.037928 0.013721 0.000469 -0.000361 0.001641 0.007491 0.001647 -0.002353 0.004310 0.006722 0.000251 -0.033905 0.012488 0.001405 -0.000697 0.002011 0.006373 0.001988 -0.002191 0.004098 0.006667 0.002552 -0.016431 0.001920 0.000963 0.001356 0.001898 0.003936 -0.001633 -0.005769 -0.000205 0.003931 0.001079 0.001497 -0.004662 -0.000518 0.001399 0.002107 0.002627 -0.003638 -0.006925 -0.003358 -0.000012 -0.003916 0.012187 -0.006008 -0.001907 -0.000986 0.002147 0.001303 -0.005938 -0.007319 -0.002636 -0.005328 -0.008954 0.022883 0.000172 -0.003133 -0.008222 -0.001668 0.004765 0.002137 0.004022 0.009425 -0.003919 0.020345 -3 11 11 1 2 0 0.069222 31.956727 -6.314833 11 11 5 -0.021832 0.001083 -0.001940 -0.007352 -0.007917 -0.005185 0.000122 0.007416 0.012283 0.004219 0.029991 0.003471 0.003689 0.000860 0.000203 0.002808 0.001848 -0.000141 0.001875 -0.000329 -0.008357 -0.002423 0.012912 0.001835 -0.001811 -0.004344 -0.000770 0.004248 0.001754 -0.001365 -0.003234 -0.004354 -0.001217 0.018279 0.001722 -0.001307 -0.005116 0.000354 0.003363 -0.001705 -0.002844 -0.000378 0.002060 -0.007053 0.012060 -0.000044 -0.000133 -0.002628 -0.000189 0.005432 -0.002152 -0.005032 0.003328 0.002373 -0.003329 0.000414 -0.002465 0.001236 0.002275 0.000731 0.005921 -0.001625 -0.010472 -0.000214 0.002786 -0.001696 -0.012888 -0.001386 0.001307 0.005002 0.001853 0.007553 0.002095 -0.009797 -0.001372 0.004957 0.002021 -0.032603 0.005622 0.002055 0.000576 -0.000997 0.006989 0.005605 -0.006076 -0.001782 0.006426 0.002783 -0.037043 0.005601 -0.000875 -0.000290 -0.003865 0.004610 0.005659 -0.003708 -0.002413 0.007537 0.000057 -0.020999 -0.003912 -0.006689 0.001021 -0.003710 0.002137 0.003194 -0.003367 -0.003057 0.003748 -0.000877 0.015726 0.010948 0.007567 0.000716 0.000405 0.012397 0.004725 -0.007040 -0.008546 -0.002223 0.002625 diff --git a/lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.35.txt b/lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.35.txt deleted file mode 100644 index 69c6e95..0000000 --- a/lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.35.txt +++ /dev/null @@ -1,1276 +0,0 @@ -# scaling factor of training -0.350000 -# number of views -9 -# centers of the views -3 -1 -6 -0.000000000 -0.000000000 -0.000000000 -3 -1 -6 -0.000000000 --75.000000000 -0.000000000 -3 -1 -6 -0.000000000 --45.000000000 -0.000000000 -3 -1 -6 -0.000000000 --20.000000000 -0.000000000 -3 -1 -6 -0.000000000 -20.000000000 --0.000000000 -3 -1 -6 -0.000000000 -45.000000000 --0.000000000 -3 -1 -6 -0.000000000 -75.000000000 --0.000000000 -3 -1 -6 --30.000000000 -0.000000000 -0.000000000 -3 -1 -6 -30.000000000 -0.000000000 -0.000000000 -# visibility indices per view -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -1 -0 -0 -0 -1 -1 -1 -1 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -0 -0 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -0 -1 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -0 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -0 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -# Patches themselves (1 line patches of a vertex) -3 11 11 1 2 0 0.062994 47.949356 -6.889757 11 11 5 -0.006442 -0.005002 -0.000255 0.003554 0.001399 -0.001084 -0.001265 0.000339 0.000727 0.001936 0.001853 -0.000709 -0.003136 0.004879 0.004514 0.001243 0.000834 -0.000691 -0.001311 0.001303 0.001395 0.004867 0.001334 -0.001701 0.004616 0.003390 -0.000699 0.000673 -0.001739 -0.001903 -0.001289 -0.002173 0.006699 0.001833 -0.002394 0.006247 0.001096 -0.001193 0.000666 -0.000667 -0.000597 -0.003204 -0.003725 0.008365 -0.004109 -0.001701 0.002561 -0.000307 -0.000222 -0.000441 0.001899 0.001696 -0.004902 -0.004273 0.012041 -0.005093 0.000913 0.001174 -0.000408 -0.003292 -0.001919 0.000237 0.000907 -0.002901 -0.008649 0.024596 -0.001460 0.004689 0.005196 0.003332 -0.000267 0.002043 0.005732 0.001337 -0.003120 -0.003888 0.014613 0.011446 0.007861 0.005066 0.001221 -0.001845 0.000365 0.002813 -0.001431 -0.002577 -0.001551 -0.016129 0.004524 0.002983 -0.003088 -0.003895 -0.003997 -0.001317 0.001275 -0.001311 -0.000587 0.005741 -0.030814 -0.008804 0.007940 -0.001513 -0.005326 -0.004812 -0.002098 -0.001036 -0.001130 0.002534 0.007471 -0.018464 -0.039219 -0.003717 -0.002295 0.002270 0.010349 0.012623 0.006101 0.005641 0.007678 0.006363 -0.013907 -3 11 11 1 2 0 0.061585 44.839388 -6.997380 11 11 5 -0.003282 -0.000850 -0.000201 0.001812 0.000369 0.000741 0.002738 -0.000686 0.001702 0.002351 -0.003853 0.004912 -0.002109 0.000282 0.002004 0.001512 0.000385 0.002222 0.000001 0.000109 0.000793 -0.001211 0.005262 -0.002660 -0.003127 -0.000635 0.001873 0.000339 -0.000206 0.000829 -0.000395 0.002139 -0.000132 0.007806 -0.000197 -0.004583 -0.000406 0.001679 -0.000692 0.001515 0.000218 -0.001417 -0.000572 0.003757 0.009300 -0.000886 -0.004811 0.000084 0.001540 0.000625 0.000568 -0.000308 -0.004374 -0.001654 0.009560 0.006477 -0.003142 -0.003790 0.002290 0.006434 0.004032 -0.001781 -0.000888 0.003221 -0.001956 0.020782 0.008076 -0.004851 -0.006389 0.002089 0.004798 -0.001771 -0.005234 -0.003328 -0.001851 -0.006154 0.036594 0.008173 -0.001493 -0.008197 -0.006043 -0.003138 -0.004378 -0.003973 -0.005504 -0.005344 -0.006694 0.004666 0.000159 0.003758 -0.003879 -0.007966 -0.005486 -0.003981 -0.000941 -0.003770 -0.005786 -0.005542 -0.009169 -0.009107 0.009789 0.005352 -0.002655 -0.003064 -0.003763 -0.002030 0.000018 0.001967 0.003957 -0.018015 -0.020454 0.012392 0.015881 0.007754 0.001401 -0.001701 0.000435 0.004186 0.007226 0.007221 -0.025702 -3 11 11 1 2 0 0.149811 36.401099 -6.732030 11 11 5 0.003180 0.000537 0.002180 0.001517 -0.000069 0.003271 0.005353 0.001580 -0.001012 -0.002164 -0.002962 0.003253 -0.000366 -0.001312 0.000191 -0.000176 0.003515 0.002239 -0.000246 -0.000699 -0.002382 -0.004223 0.007186 -0.003004 -0.001448 -0.001823 0.002187 0.003073 0.000795 0.001536 0.000318 -0.001920 -0.005664 0.010497 -0.003557 -0.000398 0.001912 0.002477 -0.000128 -0.003576 0.001537 0.000510 0.002719 -0.006082 0.005035 -0.007094 0.007444 0.009538 0.002070 -0.007483 -0.003393 0.003313 0.003381 0.010615 -0.005368 0.000121 -0.006255 -0.000483 0.006523 0.004555 -0.007545 0.007974 0.013459 0.010368 0.018041 -0.005016 -0.006319 -0.005782 -0.001851 0.001784 -0.000137 -0.004175 -0.000472 0.005822 0.012140 0.022202 -0.014095 -0.011976 -0.007149 -0.003854 0.000314 -0.002732 -0.005044 -0.000367 -0.001791 -0.002267 0.008192 -0.012381 -0.002977 -0.008042 -0.004788 0.001345 -0.002429 -0.006722 -0.004536 -0.005465 -0.003084 0.002427 -0.008338 0.007349 -0.008399 -0.006093 0.003731 0.004764 0.001443 -0.000167 -0.004082 -0.007064 -0.004732 -0.017221 0.028154 -0.004521 -0.005790 -0.000589 0.002122 0.000692 -0.002222 -0.003755 -0.003547 0.001843 0.012486 -3 11 11 1 2 0 0.124353 32.232349 -6.205911 11 11 5 0.010375 0.003914 0.001175 0.001121 0.001971 -0.000446 -0.002081 -0.005066 -0.003336 -0.000514 0.002099 0.005837 -0.001218 -0.004524 -0.001726 0.003094 -0.000661 -0.001400 -0.004764 -0.002660 0.001263 0.007567 0.004016 -0.002380 -0.004312 -0.000941 0.007101 0.004489 -0.001257 -0.002479 -0.003185 -0.000183 0.004935 0.004165 0.003287 -0.000091 0.002546 0.007962 0.006404 0.002381 -0.001168 -0.005449 -0.005169 -0.001380 -0.006010 0.023434 0.013838 0.007469 0.006652 0.005805 0.001185 -0.003102 -0.006941 -0.006638 -0.003376 -0.014113 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b/lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.5.txt deleted file mode 100644 index bf879df..0000000 --- a/lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.5.txt +++ /dev/null @@ -1,1276 +0,0 @@ -# scaling factor of training -0.500000 -# number of views -9 -# centers of the views -3 -1 -6 -0.000000000 -0.000000000 -0.000000000 -3 -1 -6 -0.000000000 --75.000000000 -0.000000000 -3 -1 -6 -0.000000000 --45.000000000 -0.000000000 -3 -1 -6 -0.000000000 --20.000000000 -0.000000000 -3 -1 -6 -0.000000000 -20.000000000 --0.000000000 -3 -1 -6 -0.000000000 -45.000000000 --0.000000000 -3 -1 -6 -0.000000000 -75.000000000 --0.000000000 -3 -1 -6 --30.000000000 -0.000000000 -0.000000000 -3 -1 -6 -30.000000000 -0.000000000 -0.000000000 -# visibility indices per view -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 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-1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -0 -1 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -0 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -0 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -# Patches themselves (1 line patches of a vertex) -3 11 11 1 2 0 0.014442 69.820355 -6.535775 11 11 5 -0.009456 0.006184 0.005283 0.000648 -0.000396 0.000198 0.000019 -0.000842 -0.000848 -0.000594 0.007264 -0.004801 0.007818 0.004775 -0.001253 -0.000001 0.001303 -0.002492 -0.003301 -0.000695 -0.002337 0.003257 -0.006534 0.005124 0.004716 -0.000539 0.001799 0.000263 -0.002282 -0.001910 0.000366 -0.002219 0.004811 -0.008812 -0.000028 0.002064 0.001093 0.000988 0.000084 0.000413 -0.000617 -0.001165 -0.002206 0.008175 -0.004275 0.000217 0.001323 -0.002049 -0.002827 0.000209 0.001374 -0.001130 -0.003702 -0.003306 0.010168 -0.001678 0.002510 0.001128 -0.002451 -0.002676 0.000103 0.000955 -0.001294 -0.002875 -0.004086 0.009451 0.001757 0.003255 0.000868 -0.001665 -0.001044 0.002624 0.002799 -0.000684 0.002939 0.004327 0.005394 0.009963 0.005463 0.001868 -0.001040 -0.000160 0.003114 0.002415 0.000765 0.003105 0.003939 -0.014704 0.009740 0.003299 -0.002726 -0.004430 -0.002751 -0.000201 -0.002253 -0.000725 0.003542 0.005233 -0.024159 -0.002244 0.002174 -0.002331 -0.004260 -0.002326 -0.001584 -0.002784 -0.001944 0.002910 0.004457 -0.020856 -0.023519 -0.004061 -0.001107 0.004637 0.008835 0.007683 0.003083 0.003242 0.005716 0.006433 -0.010426 -3 11 11 1 2 0 0.027164 56.436437 -6.649092 11 11 5 0.002281 -0.002814 -0.000786 0.002950 0.000932 0.000056 0.001200 0.000232 0.000193 0.001290 -0.000260 0.004411 -0.006046 -0.001414 0.002471 0.000817 -0.000511 -0.000123 -0.001071 -0.001113 0.000891 0.001333 0.004863 -0.005277 -0.000786 0.001722 0.002836 0.002491 0.001112 -0.002416 -0.001072 -0.000261 0.003045 0.003144 -0.003618 -0.001012 0.001257 0.002609 0.004401 0.000058 -0.004492 -0.001748 -0.000959 0.006155 0.001813 -0.005848 -0.000749 0.003298 0.000845 -0.000528 -0.002900 -0.004776 -0.002205 -0.000036 0.009716 0.004785 -0.006230 0.001496 0.007087 0.005008 0.001849 -0.000229 -0.005582 -0.004479 0.000091 0.018904 0.011411 -0.002422 0.001418 0.004719 0.005098 0.002035 -0.000791 -0.007158 -0.007905 -0.003145 0.028055 0.005198 -0.006461 -0.005683 -0.003691 0.001095 -0.000451 -0.002105 -0.005071 -0.005671 -0.007425 0.009622 0.000948 -0.008661 -0.007678 -0.004544 0.000526 0.000307 -0.000474 -0.001628 -0.003529 -0.009808 -0.003898 -0.001215 -0.004130 -0.003525 -0.003509 -0.003325 -0.002888 -0.001456 0.000587 0.001226 -0.006702 -0.015097 0.014166 0.004074 0.003133 0.002051 0.000596 -0.000193 0.002725 0.004072 0.007711 0.005302 -0.014138 -3 11 11 1 2 0 0.067184 41.667022 -6.493267 11 11 5 0.002340 -0.000507 0.000535 0.002074 0.001051 0.004150 0.004004 0.000804 0.002067 0.002965 -0.008591 0.001947 -0.002338 0.001065 0.000522 -0.001190 0.000478 0.000255 -0.002874 -0.000356 0.002246 -0.005849 0.004921 0.000357 0.001042 0.000934 -0.002408 -0.001975 -0.000076 0.000463 0.000723 0.002292 -0.002709 0.003345 0.003166 0.001832 -0.000863 -0.000507 -0.002552 -0.000848 0.001959 0.001773 0.001969 -0.000403 -0.001235 0.005829 0.003026 -0.003077 0.002366 -0.001138 -0.006530 -0.002253 0.006810 0.004090 0.001901 -0.007232 0.000118 0.006885 0.003900 0.006533 0.004849 -0.010383 -0.001762 0.023215 0.010360 0.000722 -0.012747 -0.000464 0.000930 0.001020 -0.001544 0.003375 -0.003731 -0.003799 0.018869 0.017664 -0.006654 -0.013290 -0.000331 0.000369 -0.000129 -0.006060 -0.000797 0.002228 -0.007082 -0.003840 0.009102 -0.008445 -0.008354 -0.003337 -0.001224 0.002239 -0.003848 -0.005315 -0.001053 -0.002881 -0.003033 0.003737 -0.011892 -0.000811 -0.007677 -0.006114 -0.000500 -0.001821 -0.003264 -0.004084 -0.004732 -0.006108 0.001042 -0.019632 0.022967 0.000288 -0.001525 0.001648 0.003649 0.004464 -0.000071 -0.000644 0.008772 0.012273 -0.012031 -3 11 11 1 2 0 0.089739 36.175575 -5.994599 11 11 5 0.001698 -0.000759 0.000679 0.003823 0.002135 0.002359 0.000090 -0.001476 -0.005385 -0.005339 0.005994 0.000450 -0.001486 -0.000635 0.002161 0.001342 0.002254 -0.000250 -0.000528 -0.002499 -0.000899 0.002775 0.002250 0.000839 -0.001530 0.001694 0.002225 0.004063 0.003163 0.002340 -0.004218 -0.001894 -0.002126 0.009565 0.003864 0.001320 0.005091 0.002703 0.004445 0.006164 0.003340 -0.003927 -0.003824 -0.006482 0.006186 0.017977 0.010697 0.006365 -0.000112 0.001260 0.001650 -0.000603 -0.003518 -0.005492 -0.006365 -0.011739 0.003957 0.009219 0.000987 -0.002880 0.001196 -0.001232 -0.002054 -0.000907 -0.004575 0.006450 -0.016725 0.003619 -0.002108 -0.002208 0.001337 -0.000930 -0.002793 -0.001532 -0.004103 -0.004275 0.016993 -0.017154 0.003491 -0.004980 -0.006576 -0.002810 -0.001909 0.001115 -0.002298 -0.006262 -0.006791 0.011873 -0.013600 0.004151 -0.003164 -0.006604 -0.006388 -0.004401 0.000553 0.001983 -0.002316 -0.004448 0.002681 -0.009935 -0.000462 -0.002094 -0.003379 -0.005173 -0.002995 -0.000643 0.002791 0.001526 -0.000383 -0.005637 0.007091 0.009681 0.004406 0.003364 0.001467 0.001933 0.003501 0.007646 0.006359 0.001109 -0.005632 -3 11 11 1 2 0 0.043318 44.920313 -5.360330 11 11 5 0.011681 -0.002162 -0.002545 -0.000811 -0.000634 -0.002562 -0.001238 -0.000746 -0.000404 -0.000556 -0.000972 0.011953 -0.004286 -0.004043 0.001838 -0.002422 -0.002954 0.000375 -0.000185 -0.002465 -0.002052 0.002195 0.011236 -0.005852 -0.004477 0.002411 -0.001013 -0.000548 0.001791 0.000055 -0.003343 -0.004234 0.005165 0.013691 -0.005698 -0.002910 0.004380 0.001487 0.001885 0.002528 0.001880 -0.002127 -0.003939 0.005958 -0.001386 0.000156 0.002653 0.003298 0.002555 0.001749 0.002169 0.000528 -0.001067 -0.002056 0.004491 -0.011861 -0.002218 -0.001778 -0.002040 0.003280 0.005553 0.000716 -0.001921 0.000900 0.000660 0.003351 -0.009419 0.000215 -0.001380 -0.002338 -0.001319 -0.000131 0.001651 0.001593 -0.000383 -0.001029 0.002394 -0.008684 0.001232 0.001016 -0.001507 -0.002765 -0.003363 -0.001370 0.000310 0.000742 0.000198 0.010092 -0.005561 0.002510 0.002654 0.000072 -0.001002 -0.002226 -0.003374 -0.003418 -0.002471 -0.001757 0.012277 -0.003001 0.001705 0.000956 -0.002119 -0.001035 -0.000258 -0.001782 -0.002852 -0.002094 -0.002887 0.002370 0.007157 0.004240 0.001500 -0.000807 0.001053 0.002719 0.003049 0.003618 0.001249 -0.004812 -0.014493 -3 11 11 1 2 0 0.034469 43.741300 -6.247053 11 11 5 0.004842 0.000658 -0.001588 -0.001809 -0.001304 -0.001984 -0.002514 0.000020 -0.000373 -0.002083 0.003877 0.005286 0.003208 0.001531 0.000701 -0.000626 0.000455 0.000101 -0.000544 0.000983 -0.000355 0.002455 0.000672 0.006281 0.002832 -0.000653 -0.001586 0.000983 0.000647 -0.000290 0.001171 0.001769 0.001141 -0.011486 0.004148 0.004447 0.001143 -0.000618 0.000793 -0.000620 0.000829 -0.000431 0.000093 -0.000812 -0.013740 0.000002 0.003504 0.004501 0.003340 -0.001450 -0.001694 0.001156 -0.002237 0.000286 -0.001503 -0.012274 -0.003300 -0.001946 -0.000325 0.004242 0.004292 0.000767 0.000473 -0.001166 0.000863 0.000588 -0.007043 -0.000228 -0.000799 -0.003512 -0.001176 0.001357 0.001088 0.001420 0.001135 0.001150 0.001775 -0.001986 0.002160 -0.000583 -0.001582 0.000763 -0.001102 -0.003418 -0.001373 0.001737 0.003003 0.013109 -0.002553 -0.000595 -0.000287 0.000439 0.001510 -0.002875 -0.005231 -0.003384 -0.003969 -0.000526 0.033389 -0.003642 -0.004199 -0.000584 0.000167 0.002600 0.001560 0.000295 0.001500 0.001478 0.001709 0.021145 0.020128 0.002901 -0.001360 -0.002636 0.002795 0.002769 -0.002213 -0.010518 -0.014478 -0.020836 -0.026163 -3 11 11 1 2 0 0.087134 31.586777 -6.822201 11 11 5 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-0.002909 0.001890 0.004469 0.012780 0.027034 -0.000747 -0.006459 -0.007181 -0.010676 -0.012985 -0.009701 -0.015635 -0.022817 -0.021072 -0.002810 -3 11 11 1 2 0 0.113390 32.092207 -6.990639 11 11 5 -0.013199 -0.000596 0.010517 0.018337 0.014092 0.008581 0.008638 0.012032 0.007973 -0.006989 0.000496 -0.010095 -0.004373 -0.002358 0.001407 0.002694 0.000194 -0.000690 -0.001590 -0.004920 -0.006077 -0.002017 -0.004570 -0.005261 -0.004907 -0.001095 0.002076 0.004678 0.002638 -0.001022 0.000684 0.007592 0.006053 0.002423 -0.005206 -0.007107 -0.004187 -0.000848 0.003602 0.002695 -0.001234 -0.000567 0.008085 0.009259 0.007312 -0.005184 -0.006356 -0.007348 -0.007122 -0.000211 0.002439 0.002610 -0.000254 -0.001387 -0.002133 0.006207 -0.005614 -0.002909 -0.003106 -0.007776 -0.005103 0.001959 0.002216 -0.001619 -0.002294 -0.005997 0.006839 -0.005225 -0.000792 -0.001430 -0.004732 -0.005445 0.001245 0.002731 -0.001836 -0.002088 -0.002370 0.008949 0.001056 0.001686 0.000182 0.000024 -0.002199 0.001081 0.002626 -0.001939 -0.002596 -0.002844 0.008459 0.004223 0.004204 0.002396 0.002298 -0.001845 -0.001531 0.000330 -0.001318 -0.003749 -0.003692 0.000435 0.005776 0.004099 0.002206 0.002482 0.000206 -0.000296 0.002832 0.004359 -0.002680 -0.004942 -0.015524 -0.001186 -0.001194 -0.008073 -0.011561 -0.017115 -0.018433 -0.006053 0.013892 0.018819 0.025091 -3 11 11 1 2 0 0.125605 37.147957 -6.555323 11 11 5 -0.014366 -0.014790 -0.015198 -0.014257 -0.003916 0.010926 0.016582 0.015536 0.009924 0.002571 0.004112 0.000355 0.003359 0.001518 -0.003706 -0.008728 -0.011378 -0.009460 -0.005932 -0.006853 -0.004636 -0.000539 0.000406 0.001923 0.001182 -0.000937 -0.006642 -0.008797 0.002124 0.005677 0.001106 -0.001464 0.002497 0.004035 -0.000231 -0.001216 -0.000106 -0.002281 0.000395 0.008503 0.006156 0.000839 -0.000838 0.003039 0.009574 0.000468 0.000421 0.002616 0.003185 0.003224 0.000070 -0.001355 0.000334 -0.000481 0.000759 0.010937 0.002763 0.002353 0.003505 0.005144 0.004226 -0.005760 -0.003093 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0.002140 -0.000352 -0.004003 -0.003267 -0.001343 0.001538 0.000738 -0.003549 0.000446 0.000635 0.001849 0.002421 0.001200 -0.002950 -0.003310 -0.002592 0.001062 0.001994 -0.006712 -0.000938 -0.001523 0.001636 0.003329 0.001208 -0.002434 -0.001415 -0.001601 0.001303 0.003926 -0.006347 -0.001302 -0.000150 0.002973 0.002728 0.000275 -0.001509 -0.000400 0.000384 -0.000884 0.007779 -0.004391 -0.003526 -0.002648 0.002484 0.004807 0.003287 0.000484 0.000997 -0.001379 0.006777 -3 11 11 1 2 0 0.030536 54.551986 -5.007240 11 11 5 0.004177 -0.001572 0.000158 0.002793 0.006944 0.004604 -0.001447 -0.002764 0.000722 -0.005611 0.007949 0.001241 -0.000060 0.000635 0.000338 0.000101 0.000282 -0.002887 -0.004615 -0.000693 -0.002890 -0.000188 -0.004456 -0.000008 -0.002365 -0.001934 -0.001143 0.002712 0.000143 -0.002969 -0.000889 -0.004522 0.003006 -0.005423 0.003036 -0.003179 -0.002371 -0.001030 0.004117 0.002777 -0.002901 -0.000900 -0.002882 0.003793 -0.004272 0.005360 -0.001304 0.001306 0.000167 0.003510 0.003660 -0.001420 -0.002270 -0.002164 0.003971 -0.002984 0.005112 -0.000954 0.001675 -0.001097 0.002753 0.004016 -0.001226 -0.003220 -0.001613 0.003315 -0.003427 0.005106 -0.002681 0.000623 -0.001272 0.003722 0.004220 -0.001884 -0.002468 -0.001844 0.003682 -0.004910 0.003945 -0.002249 -0.000206 -0.000413 0.004378 0.002770 -0.002705 -0.000779 -0.002814 0.004009 -0.004526 0.002554 -0.002875 -0.001937 -0.000893 0.003061 0.001224 -0.002579 -0.000328 -0.004637 0.003646 -0.001203 -0.000787 -0.001272 -0.002364 -0.000849 0.001693 -0.001592 -0.003363 0.000057 -0.002937 0.000496 0.003372 -0.001084 0.001539 0.003422 0.007612 0.004437 -0.002686 -0.004667 -0.000563 -0.007104 0.008182 -3 11 11 1 2 0 0.019618 55.270737 -5.164987 11 11 5 0.004879 -0.004900 -0.002817 -0.001889 0.002552 0.002690 0.001452 0.000490 0.000524 -0.000536 0.005958 0.004137 -0.004306 -0.000634 0.000241 0.002523 0.002479 0.000474 -0.000130 0.000583 0.000328 -0.001166 0.004290 -0.006258 -0.001082 -0.001164 0.002157 0.003499 0.000433 -0.002036 -0.000560 -0.001120 0.001109 0.002452 -0.005436 -0.000646 0.000278 0.002030 0.002717 0.000398 -0.002591 -0.001546 -0.002150 0.001020 0.000184 -0.002438 0.001949 0.001061 0.002512 0.001903 0.001093 -0.002237 -0.003130 -0.002376 0.001425 -0.000515 0.001538 0.001672 0.000406 0.002130 0.002765 0.000086 -0.005364 -0.004856 -0.000170 0.001207 -0.000264 0.001612 -0.000963 -0.001080 0.000872 0.002932 0.002500 -0.002278 -0.004598 -0.001283 0.000060 -0.002280 0.003685 -0.002924 -0.000835 -0.001200 0.001160 0.004658 0.000707 -0.005836 -0.004046 0.002085 -0.005374 0.007246 -0.001535 -0.000613 -0.002681 -0.000362 0.003864 0.002253 -0.004240 -0.003932 0.005417 -0.001965 0.007426 -0.002336 -0.000410 -0.003678 -0.002637 -0.000029 0.001851 0.000994 -0.001986 0.004256 -0.019243 0.011956 -0.005390 -0.001048 0.000683 0.006925 0.008811 0.003870 -0.000157 -0.010421 0.002230 diff --git a/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.25.txt b/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.25.txt deleted file mode 100644 index fe614c3..0000000 --- a/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.25.txt +++ /dev/null @@ -1,1870 +0,0 @@ -# scaling factor of training -0.250000 -# number of views -9 -# centers of the views -3 -1 -6 -0.000000000 -0.000000000 -0.000000000 -3 -1 -6 -0.000000000 --75.000000000 -0.000000000 -3 -1 -6 -0.000000000 --45.000000000 -0.000000000 -3 -1 -6 -0.000000000 --20.000000000 -0.000000000 -3 -1 -6 -0.000000000 -20.000000000 --0.000000000 -3 -1 -6 -0.000000000 -45.000000000 --0.000000000 -3 -1 -6 -0.000000000 -75.000000000 --0.000000000 -3 -1 -6 --30.000000000 -0.000000000 -0.000000000 -3 -1 -6 -30.000000000 -0.000000000 -0.000000000 -# visibility indices per view -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -1 -0 -0 -0 -1 -1 -1 -1 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -0 -0 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -0 -1 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -0 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -0 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -# Patches themselves (1 line patches of a vertex) -3 11 11 2 2 0 0.171450 12.290416 -6.196853 11 11 5 -0.038363 0.019611 0.044081 0.020396 -0.004113 -0.031242 0.029815 0.014202 -0.009326 0.002278 0.186750 0.096844 -0.033936 -0.030803 -0.012278 0.036206 0.021305 -0.025093 -0.027395 -0.039987 -0.041330 -0.106929 0.000946 -0.024464 -0.120361 0.025426 0.133888 0.123078 -0.019155 -0.035904 -0.015619 -0.034270 0.103924 -0.117145 -0.002398 -0.135283 0.036900 0.120485 0.127591 0.066259 -0.003947 0.054337 0.036558 0.133033 -0.229356 -0.024639 -0.185960 -0.154583 -0.153881 -0.025561 0.039995 -0.006318 0.019203 0.046618 -0.142265 -0.150655 0.019473 -0.075736 -0.142476 -0.241119 -0.189650 -0.069898 -0.063291 -0.094937 -0.043500 -0.266164 -0.150348 -0.005698 -0.018246 -0.006156 -0.022671 -0.013493 0.040517 0.035952 -0.021733 0.041361 -0.009050 -0.002393 0.100896 0.055096 0.064757 0.099272 0.096087 0.076845 0.075697 0.037319 0.033197 0.084907 0.014789 0.065398 0.066862 0.013511 0.044362 0.031876 0.029322 0.045796 0.060510 0.013550 0.099198 0.060603 -0.035234 0.021028 -0.005590 0.066627 0.084754 0.128728 0.116822 0.113176 0.021170 0.043665 0.199612 -0.102188 0.017306 -0.179522 -0.197828 -0.151595 -0.074523 0.010530 0.139747 0.105784 0.149764 -2 1 0.038211 5.307873 -4.660062 11 11 5 -0.031253 0.007923 0.047256 0.059905 -0.021950 -0.040814 -0.005768 0.004557 0.026123 0.058182 0.138114 -0.031674 -0.040419 0.013096 0.076447 0.062962 0.022076 -0.021170 -0.003985 -0.022048 0.006313 -0.000674 -0.055219 -0.050573 0.027691 0.104893 0.106914 -0.004619 -0.040491 -0.036922 0.013773 0.033081 0.082545 -0.113375 -0.006635 0.009385 0.123630 0.116654 0.062683 -0.042486 -0.036301 0.001932 -0.004623 0.009875 -0.202174 -0.066444 -0.133683 -0.083707 -0.015512 -0.005781 -0.043944 -0.053110 -0.058498 -0.049586 -0.114989 -0.188402 0.000444 0.002986 0.037318 0.066850 0.084502 0.069472 0.040835 -0.010053 0.018785 0.059942 -0.031659 0.092657 0.118390 0.143087 0.207415 0.211852 0.194400 0.136152 0.098218 0.115576 0.185387 0.046487 0.060349 0.035224 0.030818 0.080182 0.070989 0.045996 0.038823 0.030689 0.063479 0.023570 -0.013211 -0.020773 -0.036031 -0.036810 -0.019174 -0.035001 -0.042313 -0.005290 -0.001443 0.040283 -0.199600 0.004194 0.009775 -0.004545 -0.028079 -0.063912 -0.063861 -0.063202 -0.079384 -0.086037 -0.021597 -0.241950 -0.027104 -0.003594 -0.001989 -0.047525 -0.056663 -0.008724 0.005792 -0.013591 -0.085643 -0.220940 -0.490398 -3 11 11 2 2 0 0.145874 13.791229 -5.806274 11 11 5 -0.122014 0.046358 -0.012111 -0.000088 0.022875 0.078808 0.073145 0.019051 0.005222 0.106588 0.011822 0.066353 0.009475 -0.046216 -0.050744 -0.087230 -0.066142 -0.030304 -0.040863 -0.069137 0.023527 -0.063888 0.080282 0.051967 -0.032443 0.015274 0.031588 0.009906 0.016923 0.027499 -0.014420 -0.000905 -0.118103 -0.131504 0.152655 -0.019940 0.025832 0.057571 -0.000079 0.008118 0.028027 0.055006 0.071673 -0.044304 -0.484185 0.120271 -0.086833 -0.036446 -0.062545 -0.105437 -0.110081 -0.083876 -0.020527 0.115966 0.163178 -0.457454 0.035030 -0.080721 -0.058187 -0.096010 -0.100658 -0.102857 -0.121327 -0.101895 0.053858 0.109730 -0.019147 0.014251 0.010414 0.037347 0.038916 0.064799 0.053659 0.020844 -0.023954 0.006873 -0.089293 0.241769 0.006072 0.062212 0.067975 0.052577 0.048894 0.033950 0.014419 0.009175 0.018839 -0.022696 0.135421 0.023061 0.071271 0.046843 0.019996 0.032341 0.046038 0.035194 0.011170 -0.016102 0.022462 -0.102556 0.020187 0.074972 0.072976 0.053090 0.021427 0.017289 0.008710 -0.007220 -0.042071 0.012647 -0.160797 -0.081486 -0.041402 0.060101 0.107236 0.074130 0.057704 0.063425 0.056377 -0.009516 0.035081 -2 1 0.044327 6.650613 -4.888743 11 11 5 0.010111 -0.017525 -0.005217 0.040555 0.052964 0.087576 0.033474 0.018740 0.000488 0.089028 -0.046947 0.057245 0.007414 0.009255 -0.003968 -0.020100 0.021710 0.002638 -0.023844 -0.020644 0.073872 0.003468 0.100037 -0.015336 -0.014984 0.015291 -0.002566 -0.015651 0.040431 0.065417 0.048183 0.037249 0.087250 0.099304 -0.025955 -0.062724 0.014085 -0.009615 -0.061692 -0.038605 0.013727 0.083650 0.072992 0.175870 -0.019031 0.025138 -0.035704 -0.046819 -0.048833 -0.062336 -0.032131 -0.036876 0.047543 0.000739 0.139928 0.098420 0.101711 0.074182 0.039158 0.058683 0.092205 0.112919 0.085426 0.026544 -0.078052 -0.016136 0.266794 0.152142 0.132046 0.080288 0.086974 0.119597 0.127280 0.093557 0.032952 -0.076875 -0.004009 0.091690 0.066873 0.064730 0.053601 0.045730 0.052602 0.051798 0.017302 -0.029997 -0.061173 -0.062271 -0.116457 -0.049513 -0.052886 -0.018829 -0.035068 -0.056323 -0.079787 -0.087370 -0.092868 -0.069679 -0.203153 -0.052464 0.006588 -0.048147 -0.072227 -0.018942 -0.028088 -0.017095 -0.022782 -0.024821 0.003991 -0.251828 -0.028233 0.055606 0.025490 -0.025438 -0.155405 -0.241527 -0.225256 -0.163646 -0.139483 -0.064983 -0.454335 -3 11 11 2 2 0 0.144148 14.393891 -5.707466 11 11 5 -0.062930 0.094562 0.011402 -0.009236 0.085097 0.057058 -0.037498 -0.004756 0.016578 -0.010662 0.045396 -0.052101 0.004534 -0.002607 -0.024158 -0.008353 -0.050303 -0.077938 -0.034137 0.041830 -0.001528 0.009100 0.020924 -0.015757 0.001752 0.026648 0.007690 0.005309 -0.018324 -0.019063 -0.011749 -0.048155 -0.009124 -0.068440 -0.035426 -0.100742 -0.034351 0.045345 0.060379 0.081221 0.057105 0.045679 -0.004504 -0.003213 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0.080220 -0.169070 -0.084101 0.104266 0.084936 0.026498 0.052778 -0.086840 -0.371127 -0.103804 0.067838 -3 11 11 2 2 0 0.424433 12.644872 -5.674611 11 11 5 0.232314 -0.044030 0.009194 -0.020288 0.101610 0.190685 0.048668 -0.083619 -0.125200 -0.052647 -0.167202 0.109632 -0.025426 0.039659 -0.013153 -0.073904 -0.030474 0.020549 0.010265 -0.040457 0.032635 0.020889 0.108325 0.080824 0.003498 0.021083 -0.075180 -0.125405 0.019212 0.138443 0.014410 -0.056876 -0.054329 -0.013801 0.140708 -0.011331 0.014662 -0.069355 -0.221000 -0.069904 0.142207 0.122164 0.022094 -0.008815 -0.154215 0.064314 0.033875 0.029493 -0.100518 -0.259024 -0.088559 0.028716 0.089350 0.077046 0.039084 -0.191469 -0.048191 0.009299 0.077337 -0.040669 -0.195318 -0.098734 -0.003382 0.048672 0.047475 0.083596 -0.073342 -0.062057 -0.015416 0.077249 0.071761 -0.056496 -0.100020 0.000709 0.050938 -0.019963 0.079042 0.169188 0.019603 0.034053 0.031381 0.112051 0.022290 -0.109512 -0.070820 -0.015285 -0.039674 0.068495 0.285223 0.095272 0.054047 -0.003075 0.046531 0.009339 -0.108924 -0.135014 -0.060606 -0.029789 0.061106 -0.007026 0.006290 0.007193 -0.012806 0.010712 0.028029 0.044300 0.004681 0.030586 -0.009785 0.035427 -0.161376 -0.079435 0.029518 0.119694 0.049784 -0.148120 -0.191777 -0.088213 0.036220 0.025399 0.172907 -2 1 0.142085 9.660151 -5.191326 11 11 5 -0.387447 -0.171964 -0.175755 -0.248957 -0.245899 -0.097072 0.000937 0.030434 0.041489 0.029224 0.069763 -0.068425 -0.017771 0.038735 0.064687 0.099223 0.052588 -0.047996 -0.124250 -0.026271 0.030346 0.063080 0.087525 -0.098491 -0.066902 0.056612 0.102478 0.103021 0.088077 -0.071440 -0.241338 0.009168 0.078254 0.175243 -0.046762 -0.102015 0.113671 0.154274 0.059909 0.064939 0.101528 -0.209867 -0.121779 0.095074 0.034427 -0.079908 -0.139404 0.064104 0.142684 0.000176 -0.002185 0.068291 -0.026712 -0.185906 0.027667 -0.027878 -0.018885 -0.103837 0.029429 0.145267 0.049121 -0.027396 0.036303 0.052921 -0.103808 -0.049822 0.082808 0.059010 -0.045345 0.019442 0.117749 0.096503 0.014238 -0.001102 0.018375 -0.039775 -0.059746 0.087935 0.057179 -0.020522 0.015014 0.068648 0.076377 0.042534 -0.034054 -0.038456 -0.001462 -0.044059 0.061238 0.039646 -0.024026 0.015415 0.062846 0.063412 0.043791 -0.023188 -0.080527 0.012567 -0.057686 0.089062 0.055796 -0.014717 0.031845 0.062336 0.019004 0.030936 0.004299 -0.051481 -0.000442 -0.047904 0.033630 0.002005 -0.054790 0.047640 0.118823 0.072364 0.063109 0.035875 -0.038450 -0.067349 -0.100895 diff --git a/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.35.txt b/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.35.txt deleted file mode 100644 index f1fada9..0000000 --- a/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.35.txt +++ /dev/null @@ -1,1870 +0,0 @@ -# scaling factor of training -0.350000 -# number of views -9 -# centers of the views -3 -1 -6 -0.000000000 -0.000000000 -0.000000000 -3 -1 -6 -0.000000000 --75.000000000 -0.000000000 -3 -1 -6 -0.000000000 --45.000000000 -0.000000000 -3 -1 -6 -0.000000000 --20.000000000 -0.000000000 -3 -1 -6 -0.000000000 -20.000000000 --0.000000000 -3 -1 -6 -0.000000000 -45.000000000 --0.000000000 -3 -1 -6 -0.000000000 -75.000000000 --0.000000000 -3 -1 -6 --30.000000000 -0.000000000 -0.000000000 -3 -1 -6 -30.000000000 -0.000000000 -0.000000000 -# visibility indices per view -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -1 -0 -0 -0 -1 -1 -1 -1 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -0 -0 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -0 -1 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -0 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -0 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -# Patches themselves (1 line patches of a vertex) -3 11 11 2 2 0 0.123013 11.450707 -6.314733 11 11 5 -0.008676 -0.021879 -0.043242 -0.024492 0.107554 0.035050 -0.004436 0.024994 -0.057377 -0.086166 0.002089 -0.033716 -0.123116 0.064887 0.135163 0.133911 0.072960 -0.046690 -0.075478 -0.001971 0.029267 0.114169 -0.059840 -0.126361 0.028383 0.091891 0.030018 0.076426 0.042456 -0.060004 -0.001465 -0.013440 0.127272 -0.139031 -0.140513 -0.097140 -0.000793 0.025502 0.113928 0.125866 -0.016340 0.004554 0.064006 0.052557 -0.060738 -0.015685 -0.137371 -0.203965 -0.226977 -0.087503 0.072420 0.003079 -0.034110 0.137347 -0.184200 -0.072413 -0.003501 -0.096359 -0.158374 -0.226735 -0.195025 -0.043013 -0.034783 -0.104472 -0.022680 -0.343296 -0.065913 -0.020034 -0.085620 0.000916 -0.011017 -0.025096 -0.003697 0.030894 0.005347 0.032420 0.018629 0.046568 0.033757 -0.001015 0.072691 0.092444 0.070127 0.076516 0.080571 0.089543 0.016476 0.116553 0.083589 0.023344 -0.005157 0.038843 0.045401 0.000621 -0.012514 -0.004682 0.080672 0.023758 0.091371 0.114684 0.025783 0.021219 0.101266 0.162236 0.088765 0.088264 0.090797 0.080029 -0.011107 -0.020159 0.013238 -0.041912 -0.078994 -0.148554 -0.078577 -0.098289 -0.009484 0.057428 0.117091 0.076460 0.231127 -2 1 0.038445 5.424684 -4.750105 11 11 5 -0.079296 0.039266 0.032896 0.125149 0.048937 0.007100 -0.000101 -0.026509 -0.012275 -0.016117 0.087467 -0.197990 0.033849 0.038454 0.103050 0.106345 -0.040750 -0.075530 -0.001342 0.018609 0.047466 -0.006841 -0.236048 0.079448 0.095303 0.088424 0.130199 0.022358 -0.054577 -0.027634 -0.004474 0.014576 0.031986 -0.329363 -0.006200 0.033266 0.027403 0.068828 0.069731 -0.024292 -0.017335 -0.003235 0.009894 -0.059225 -0.296395 -0.175039 -0.133472 -0.073616 -0.057207 0.020876 -0.014020 -0.025169 -0.024465 -0.068778 -0.112991 -0.117397 0.039164 0.027555 0.079855 0.055273 0.097823 0.101887 0.055824 0.030378 0.002644 0.075075 0.026967 0.069100 0.084678 0.159497 0.168780 0.153664 0.169258 0.157671 0.133238 0.086987 0.188871 0.060822 0.054351 0.037284 0.074745 0.088349 0.054151 0.052814 0.047772 0.045010 0.026355 0.049972 0.011276 0.036426 -0.012731 -0.010944 -0.015058 -0.045107 -0.018232 0.002853 0.002781 -0.008453 -0.099646 -0.033548 -0.006394 -0.035086 -0.008644 -0.015770 -0.048164 -0.027507 -0.005731 -0.031447 -0.016337 -0.077466 -0.058397 -0.032565 -0.044344 -0.102723 -0.083389 -0.033197 -0.076397 -0.139584 -0.172782 -0.126016 -0.286687 -3 11 11 2 2 0 0.095673 13.746695 -5.797354 11 11 5 0.001568 -0.041931 -0.066377 -0.052360 -0.080621 -0.010665 0.045994 0.053633 -0.023759 0.024343 -0.044992 0.125298 -0.052115 -0.027124 0.040616 0.028062 0.003027 0.014677 0.009856 -0.014589 0.092739 -0.104337 0.014759 -0.038894 0.003746 0.083174 0.061494 0.003626 -0.026277 -0.011549 0.000741 0.041885 0.026984 -0.106354 0.071555 -0.035137 0.059536 0.057095 -0.010584 -0.045487 -0.034641 -0.003706 -0.105850 0.234102 -0.407537 0.143007 -0.065340 -0.041903 -0.103832 -0.111575 -0.094821 -0.130196 -0.059233 -0.139845 0.301004 -0.441022 0.125086 -0.096833 -0.078038 -0.135492 -0.052117 -0.007845 -0.057946 -0.033726 -0.143406 0.150329 -0.056800 0.083283 -0.025831 0.036780 0.013956 0.040822 0.040762 0.030814 0.044096 -0.060512 -0.099374 0.184556 -0.004308 0.061562 0.073033 0.058719 0.055766 0.042740 0.009994 0.017678 0.068346 -0.009962 0.142794 -0.032233 0.038656 0.007108 0.023079 0.040019 0.035991 0.021311 -0.002567 0.007360 -0.011884 0.066887 0.069786 0.012287 -0.014123 0.010389 0.006901 0.017664 0.010444 -0.008984 -0.007876 0.021429 -0.140978 0.053968 0.053755 0.084439 0.084495 0.044358 0.032818 0.052316 0.036123 -0.005002 -0.036730 -2 1 0.045976 6.320946 -4.936916 11 11 5 0.008917 0.013453 -0.017188 -0.006896 -0.010360 0.011929 0.034894 0.004549 0.027846 0.016919 0.148790 0.022140 -0.011029 0.025854 0.016358 -0.015450 -0.008757 0.041629 0.062673 0.074654 0.043462 0.037409 -0.003539 -0.025813 -0.019042 -0.002831 0.025596 -0.015190 0.003884 0.014291 0.070961 0.043441 0.106800 -0.025315 -0.013908 -0.005581 0.013287 0.015119 -0.025061 -0.052207 -0.052919 0.046732 0.070707 0.128396 0.005055 0.005573 -0.010447 -0.023464 -0.026464 -0.026002 -0.019450 0.018437 0.053322 0.066145 0.010620 0.186583 0.060073 0.071767 0.036460 0.045476 0.072063 0.075864 0.084446 0.060543 0.069697 -0.060449 0.275057 0.094420 0.116096 0.075793 0.107845 0.131879 0.125517 0.116544 0.046675 0.082663 -0.107616 0.123067 0.063280 0.036330 0.027689 0.039689 0.050034 0.050166 0.039123 -0.014175 0.043754 -0.147378 -0.078420 0.016644 -0.010987 0.011124 -0.007513 -0.027907 -0.045169 -0.043543 -0.099858 -0.089072 -0.220448 -0.100615 -0.054329 -0.066844 0.028756 0.042203 0.007441 -0.006563 0.011674 -0.026484 0.001043 -0.148433 -0.107480 -0.061052 -0.137929 -0.188643 -0.183772 -0.239677 -0.229183 -0.186575 -0.196851 -0.109470 -0.403968 -3 11 11 2 2 0 0.091098 13.994056 -5.465962 11 11 5 -0.004607 0.032784 0.005646 0.041568 -0.022450 -0.004775 -0.007334 -0.037697 -0.024516 0.027939 0.022104 -0.057333 0.004878 -0.011877 -0.032510 -0.016454 -0.007910 -0.023022 0.013442 -0.013032 -0.046144 -0.037220 -0.076443 -0.018136 0.005814 0.021204 0.007401 0.045200 0.019968 0.044111 0.006333 0.013969 0.004303 -0.178602 -0.009291 -0.051334 0.040095 0.041878 0.093130 0.107723 0.122623 0.024878 0.033291 -0.086340 -0.273477 0.024873 -0.162794 -0.043924 0.040159 0.092105 0.112011 0.168221 0.048373 0.035013 -0.259387 -0.102163 0.082334 -0.037315 -0.063170 -0.042451 0.025832 0.030571 0.060716 -0.039495 0.058578 -0.547477 0.116484 0.031182 0.041909 -0.020359 -0.020294 -0.006681 -0.053639 -0.021719 -0.028286 0.125320 -0.354320 0.134156 -0.034184 0.044563 0.019143 0.028651 -0.015823 -0.009663 -0.000599 0.017883 0.136354 0.104738 0.101320 -0.033236 0.030147 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-0.084746 0.100974 -0.143457 0.009521 0.017424 0.106563 0.039946 -0.081713 -0.083740 0.011041 0.059266 -0.070643 0.064050 0.061836 0.118483 0.043164 0.103213 0.102119 -0.061531 -0.121972 0.055629 0.131932 -0.016819 0.027072 0.097644 -0.016477 -0.072376 -0.035741 0.038266 -0.002904 -0.121656 -0.114840 -0.035554 -0.050284 0.044558 0.263888 0.003322 -0.060777 0.013680 0.178297 0.171686 0.071305 -0.081750 -0.054287 -0.061898 -0.043439 -0.072302 -0.026560 0.081634 0.084349 -0.102920 -0.179291 -0.099432 -0.086396 0.042318 0.009230 0.182596 -2 1 0.174935 9.754434 -5.392299 11 11 5 -0.250211 -0.108022 -0.130451 -0.120081 -0.072802 -0.007490 -0.050729 -0.108827 -0.068680 -0.038424 0.021120 -0.107435 0.016071 0.108020 0.188452 0.190145 0.170413 0.150906 0.089968 -0.111025 -0.272198 0.040460 -0.175471 -0.144893 0.041903 0.114178 0.073009 0.021160 0.045001 0.071455 -0.006633 -0.298047 -0.166269 -0.074821 -0.130414 0.072347 0.182176 0.134149 -0.023344 0.014132 0.065239 -0.015836 -0.020688 -0.230546 -0.057800 -0.155349 -0.007313 0.128168 0.096186 -0.073264 -0.085921 0.021089 -0.004755 0.040295 -0.089579 -0.015262 -0.094588 -0.061808 0.101696 0.113463 -0.014468 -0.086237 -0.013709 0.025233 0.048854 -0.049337 0.057576 -0.021649 -0.057313 0.050334 0.119087 0.051756 -0.016938 -0.037888 0.019972 0.040524 -0.031788 0.098962 0.024377 -0.009588 0.015378 0.096868 0.080563 0.044057 -0.013243 -0.036913 0.036255 -0.009730 0.071204 0.001452 -0.017977 0.009127 0.075035 0.094265 0.068485 0.007534 -0.102182 -0.000451 -0.004316 0.056951 -0.013754 -0.016389 0.007918 0.051122 0.055526 0.054828 0.028034 -0.065381 -0.005044 0.037086 0.088627 -0.000384 -0.018970 0.080149 0.103862 0.049500 0.082197 0.052705 -0.074676 -0.032731 -0.066571 diff --git a/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.5.txt b/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.5.txt deleted file mode 100644 index ae4f623..0000000 --- a/lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.5.txt +++ /dev/null @@ -1,1870 +0,0 @@ -# scaling factor of training -0.500000 -# number of views -9 -# centers of the views -3 -1 -6 -0.000000000 -0.000000000 -0.000000000 -3 -1 -6 -0.000000000 --75.000000000 -0.000000000 -3 -1 -6 -0.000000000 --45.000000000 -0.000000000 -3 -1 -6 -0.000000000 --20.000000000 -0.000000000 -3 -1 -6 -0.000000000 -20.000000000 --0.000000000 -3 -1 -6 -0.000000000 -45.000000000 --0.000000000 -3 -1 -6 -0.000000000 -75.000000000 --0.000000000 -3 -1 -6 --30.000000000 -0.000000000 -0.000000000 -3 -1 -6 -30.000000000 -0.000000000 -0.000000000 -# visibility indices per view -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -1 -0 -0 -0 -1 -1 -1 -1 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -0 -0 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -0 -0 -0 -0 -0 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -0 -1 -0 -0 -0 -0 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -1 -1 -1 -0 -0 -0 -1 -1 -1 -1 -0 -66 -1 -4 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -66 -1 -4 -1 -1 -0 -1 -1 -0 -0 -0 -0 -0 -0 -0 -1 -1 -0 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -# Patches themselves (1 line patches of a vertex) -3 11 11 2 2 0 0.081962 11.971007 -6.384019 11 11 5 -0.272360 0.135592 0.180807 0.140405 0.085659 0.095738 -0.012037 -0.097040 -0.076357 -0.036281 0.067131 -0.128099 0.091027 0.029544 -0.117841 -0.092914 0.057224 0.024433 -0.004955 0.056936 -0.011851 0.073363 -0.216054 0.052646 0.060951 -0.048169 -0.018009 0.139141 0.109807 0.050896 0.038844 -0.056148 0.032091 -0.061387 -0.075113 -0.055537 -0.061654 -0.089218 0.053387 0.120999 0.077559 -0.012287 0.017623 0.147926 -0.012838 -0.100992 -0.101856 -0.110768 -0.203742 -0.099812 0.000408 -0.015309 -0.126643 -0.025185 -0.190186 -0.031798 -0.092161 -0.074676 -0.098266 -0.174895 -0.124003 -0.025551 0.001690 -0.036507 0.069290 -0.333166 -0.024602 -0.102260 -0.007896 0.003294 -0.058947 -0.047109 0.003350 0.060706 0.056855 0.083028 -0.093193 0.044286 -0.058854 0.080328 0.067969 0.037964 0.047488 0.043474 0.043867 0.054023 0.015536 0.121730 0.117447 -0.120633 0.021837 0.036674 0.017093 -0.012047 0.025918 0.060116 0.047241 0.038006 0.113147 0.168457 -0.044958 0.046043 0.086590 0.142956 0.038610 0.013976 0.029312 0.011761 0.009149 -0.005567 0.026889 -0.135695 0.012375 0.017084 0.035251 -0.030506 -0.032091 0.041895 0.119111 0.107174 0.136894 -2 1 0.027728 5.162303 -4.776492 11 11 5 -0.117770 0.168072 0.025617 0.074958 0.088540 -0.056078 -0.088132 -0.048821 -0.000689 0.032050 0.018575 -0.150402 0.053757 -0.077144 0.058279 0.151560 0.032975 -0.040744 -0.042728 -0.059778 -0.002709 0.003353 -0.216355 0.097129 -0.038025 0.017467 0.132211 0.094214 0.021760 0.005930 -0.057633 0.032075 0.005831 -0.437455 0.019440 -0.113670 -0.131452 -0.005973 0.077597 -0.000971 -0.009529 0.016567 0.031941 -0.085545 -0.287784 -0.025449 -0.039215 -0.098125 -0.100227 0.005502 -0.043353 -0.062245 -0.049905 -0.055449 -0.107116 -0.023171 0.018791 0.050170 0.091174 0.048736 0.095868 0.100002 0.086195 0.055969 0.019106 0.012808 0.044631 0.017976 0.086156 0.141574 0.130721 0.119844 0.139620 0.166452 0.124541 0.097374 0.206540 0.070683 0.035545 0.090247 0.101051 0.099367 0.071044 0.064347 0.058006 0.043572 0.050784 0.107064 0.032470 0.003629 0.007660 0.028958 0.025739 0.010505 -0.012912 -0.017234 -0.051752 -0.014197 -0.078913 -0.000707 0.010077 -0.000634 -0.006725 -0.023790 -0.029408 -0.037219 -0.031587 -0.010196 0.025613 -0.067555 -0.128207 -0.047907 -0.075086 -0.081682 -0.126992 -0.123429 -0.102052 -0.059541 -0.066797 -0.038473 -0.149375 -3 11 11 2 2 0 0.075134 12.169766 -5.823451 11 11 5 -0.033084 -0.013957 -0.040454 0.024853 0.008230 -0.035707 -0.014831 -0.029808 -0.043738 0.014244 0.022475 -0.031056 0.047855 0.012409 0.027829 0.021254 0.043885 0.063024 -0.006242 0.007468 -0.003229 0.154688 -0.073256 0.063721 0.039430 0.093911 0.051969 0.033420 -0.004911 -0.063131 -0.045455 -0.108775 0.148088 -0.172127 0.150239 0.020040 0.067165 -0.033832 -0.086791 -0.071270 -0.094313 -0.100308 -0.233342 0.263831 -0.417310 0.140156 -0.119671 -0.156063 -0.156899 -0.089895 -0.015049 -0.010748 -0.084140 -0.184831 0.232784 -0.338585 0.100695 -0.128540 -0.100264 -0.077522 0.007100 0.020784 0.018441 0.022345 -0.036051 -0.064599 -0.022180 0.056944 -0.011868 0.034609 0.026538 0.033316 0.034560 -0.026117 0.020968 0.067333 -0.168510 0.105793 0.020575 0.052438 0.045283 0.045822 0.057435 0.050884 -0.011598 0.039492 0.081638 0.007850 0.155070 -0.014055 0.029289 0.020273 0.034009 0.016013 0.018904 0.010295 0.017611 0.007591 0.049287 0.115859 -0.066323 0.006497 0.022365 0.023149 -0.006707 0.027727 0.027126 0.013588 0.004853 0.031468 0.129163 0.005216 0.048015 0.035662 0.044429 0.024086 0.050498 0.030004 -0.004198 -0.023531 -0.026987 -2 1 0.038008 5.262231 -4.870518 11 11 5 -0.072008 -0.011278 0.004247 0.000403 -0.019754 -0.014218 -0.027737 0.000779 0.056214 0.108412 0.093846 -0.036235 0.019054 0.003246 0.026498 0.016303 -0.037942 -0.029198 0.008409 0.024275 0.019070 0.092526 -0.037729 -0.008217 0.009247 0.016102 -0.004545 -0.047424 -0.026522 -0.046220 -0.040764 0.001784 0.151986 -0.068618 0.000850 0.025780 -0.001731 -0.010089 -0.028493 0.012760 -0.018048 -0.002467 0.076716 0.156150 0.015338 -0.017484 -0.011947 -0.052319 -0.041020 -0.040487 0.003077 0.054993 0.065619 0.076254 0.118503 0.194716 0.067860 0.093920 0.052374 0.095876 0.112341 0.109840 0.069274 0.055038 0.034947 -0.041470 0.252826 0.069373 0.148444 0.102391 0.138667 0.135197 0.097149 0.078086 0.052711 0.052844 -0.027440 0.147741 0.027663 0.039929 0.038297 0.035975 0.034258 0.004485 0.013980 0.021982 0.039430 -0.032403 -0.073364 -0.043109 -0.059432 -0.036861 -0.038942 -0.029091 -0.034975 -0.030191 -0.006361 0.014784 -0.181696 -0.097570 0.068868 0.020585 0.024532 0.011976 0.016008 0.017593 -0.007173 -0.017347 -0.024812 -0.241007 -0.364395 -0.146351 -0.160620 -0.149418 -0.134696 -0.167010 -0.178405 -0.170958 -0.174972 -0.163556 -0.264281 -3 11 11 2 2 0 0.047571 11.904327 -5.149863 11 11 5 -0.045367 0.033588 0.006740 0.013192 -0.016271 -0.024981 -0.021753 0.028726 0.006398 -0.065271 0.028074 -0.115622 0.010059 -0.038057 0.015505 0.036518 0.005083 -0.041287 0.040838 0.052182 -0.007439 -0.013278 -0.171542 0.029478 -0.034060 0.059736 0.103329 0.100673 0.017036 0.038125 0.063543 0.061273 -0.078369 -0.198239 -0.007470 -0.071354 -0.000657 0.038072 0.092966 0.077617 0.080790 0.095059 0.133402 -0.221455 -0.107852 -0.046620 -0.123093 -0.043135 0.009105 0.081434 0.102960 0.092405 0.083366 0.159104 -0.411576 0.031031 -0.013128 -0.039428 -0.021165 -0.002309 0.005543 0.002316 -0.016206 -0.006456 0.118974 -0.510083 0.089055 -0.016932 0.012579 -0.017380 -0.029268 -0.021829 -0.027049 -0.052759 -0.045109 0.087295 -0.243343 0.101579 -0.018391 0.015516 0.010269 0.009674 -0.001629 0.021591 0.000871 -0.006199 0.052990 0.149694 0.082051 -0.038009 0.002072 0.022079 0.003347 -0.024478 -0.004145 0.008996 -0.000108 -0.025290 0.187894 0.059028 -0.009351 0.040286 0.032047 0.013527 -0.017363 -0.003457 0.007969 0.008396 -0.042628 0.108730 0.121036 -0.039330 -0.000842 -0.003025 -0.001791 -0.017185 -0.004984 -0.024314 -0.019234 -0.041546 0.107679 -2 1 0.025679 7.064274 -4.886751 11 11 5 -0.050714 0.017724 0.048171 0.011005 -0.014630 0.020287 0.015300 -0.016118 0.038944 -0.000464 0.058899 -0.017958 0.053791 0.015860 0.056099 -0.005130 0.020315 0.002051 -0.052578 0.063902 0.048560 0.071704 0.000576 0.050247 0.030936 0.045494 0.018589 -0.022182 -0.031026 -0.088438 0.047721 0.025891 0.081508 0.022505 0.024320 0.053827 0.068973 -0.009102 -0.042120 -0.057207 -0.092302 0.031401 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0.004899 -0.024894 0.022172 0.150877 0.110712 0.021495 0.037544 -0.033970 0.160388 0.086995 0.063436 -0.004389 -0.069356 -0.014090 0.123280 0.061236 0.013396 0.089784 0.031759 -0.148119 -0.066624 -0.050957 0.047833 -0.042299 -0.046810 0.081141 0.063266 0.036297 0.012999 -0.033122 -0.057809 0.230769 0.039884 -0.066274 -0.173203 -0.025060 -0.052700 -0.150896 -0.052491 -0.125972 0.146928 -2 1 0.059280 9.215689 -4.855578 11 11 5 -0.642308 -0.079643 0.137209 0.127628 0.016344 0.014715 0.005419 -0.030322 -0.035235 0.009607 -0.115510 -0.057465 0.041688 0.043295 0.011871 -0.023732 -0.006078 0.022686 0.000534 -0.015483 0.033070 0.010851 -0.060617 -0.083202 0.077002 0.068893 -0.000285 0.011785 0.059770 0.052699 0.007434 0.015077 -0.013792 0.062039 -0.042965 0.046765 0.055629 0.044624 0.045308 0.086725 0.060662 0.024625 0.011485 0.012718 0.042928 -0.084904 -0.004201 0.066431 0.065178 0.039880 0.044074 0.044834 0.022149 0.004846 -0.018129 0.076557 -0.113942 -0.116173 0.011713 0.044695 -0.015519 -0.021771 -0.027375 -0.032851 -0.038956 -0.035392 0.039714 -0.118334 -0.065871 0.043376 0.061772 0.003883 -0.006667 0.014908 0.005157 -0.039276 -0.042881 0.019854 -0.049132 0.038658 0.069078 0.057840 0.044528 0.056635 0.053182 0.033841 -0.016912 0.000856 -0.058152 -0.099805 0.050250 0.083505 0.042237 0.047701 0.047297 0.041201 -0.006775 -0.004433 -0.019989 0.049974 0.056583 0.065904 0.024189 -0.023095 -0.004663 0.042160 0.060274 0.025007 0.052129 0.017380 -0.498941 -0.126941 0.063454 0.095647 0.028655 0.005602 -0.002206 -0.022354 -0.057647 -0.021943 -0.122007 -3 11 11 2 2 0 0.212663 12.757742 -5.474941 11 11 5 0.211330 -0.000305 0.083514 0.084795 0.020061 -0.306689 -0.277575 0.014501 0.144525 0.110319 -0.095902 -0.018093 -0.245220 -0.046957 0.042885 0.117104 0.042440 0.016244 0.086574 0.068732 0.006019 -0.086240 0.108678 -0.035380 0.101123 0.073902 0.020648 -0.066509 -0.065287 0.035151 0.074903 0.065530 -0.010975 0.125829 0.063855 0.052735 0.022359 -0.002016 -0.010280 -0.036528 -0.054476 -0.013823 0.021140 -0.052394 -0.019160 0.124217 0.024605 -0.016059 -0.048291 -0.065146 0.004092 0.116151 0.121169 0.055092 0.016950 -0.229634 0.044207 -0.050792 -0.002566 -0.042353 -0.131681 0.051998 0.090122 -0.052339 -0.100476 0.027746 -0.175784 0.045609 0.004032 0.047091 0.010968 -0.070521 0.032254 0.000752 -0.081595 -0.053641 0.030353 -0.040271 0.029035 0.011788 0.010170 -0.016775 -0.056378 -0.027164 0.004364 -0.019087 -0.025553 0.009781 0.110506 0.077968 0.054359 -0.007300 -0.089601 -0.075526 0.000315 0.027056 -0.028624 -0.011417 0.040302 -0.054589 -0.002140 0.149563 0.089138 -0.029932 -0.080642 -0.075072 -0.107233 -0.121813 -0.061803 -0.010257 0.245913 0.034492 0.114680 -0.127047 -0.197710 -0.118970 -0.124114 0.005882 0.147877 0.076954 0.171260 -2 1 0.143137 12.451949 -5.532990 11 11 5 0.062649 -0.014553 -0.028940 0.021443 0.061411 0.028364 0.039584 0.071996 0.024097 0.032400 0.005018 0.021740 -0.038523 -0.054198 0.006756 0.059679 0.033856 0.024723 0.031449 0.000988 0.013391 0.035273 0.059298 -0.011483 -0.021028 0.037122 0.046108 0.015392 0.027350 0.054818 0.034266 0.026276 0.028363 0.033048 -0.038739 0.002233 0.047987 0.048507 0.015721 0.021441 0.026822 0.022616 0.010017 0.011358 0.043538 -0.031484 0.014198 0.033967 0.021244 0.020014 0.040137 0.038371 0.023627 0.015098 0.011451 -0.032084 -0.075576 -0.021310 -0.003230 0.007259 0.001291 0.022325 0.029805 0.008933 0.013910 0.000058 -0.119259 -0.118763 -0.025412 0.029347 0.049935 0.020090 0.017744 0.002554 -0.018780 -0.004191 -0.014132 -0.157097 -0.089074 0.041957 0.073458 0.055164 -0.007507 -0.035204 -0.031718 -0.023161 0.018289 -0.033286 -0.364938 -0.145837 0.031353 0.106841 0.074821 0.004963 -0.010461 0.007608 0.005148 0.002480 -0.118001 -0.366138 0.100813 0.147962 0.111776 0.046870 -0.015795 -0.026708 0.011849 0.020880 0.019522 -0.034709 -0.585766 -0.150452 0.080001 0.155480 0.100638 0.056768 0.064692 0.100580 0.054248 -0.014038 -0.221044 -3 11 11 2 2 0 0.199936 11.080626 -5.359743 11 11 5 0.238687 -0.082793 0.078296 0.010477 0.032511 -0.119169 -0.069363 -0.061558 -0.030768 -0.033263 0.115639 -0.068645 -0.198631 0.070443 0.137733 0.109947 0.088981 0.087470 -0.061017 -0.092923 -0.076682 -0.123369 0.107771 -0.039478 -0.027727 -0.054576 0.020717 -0.032726 -0.081422 -0.024698 0.045077 0.093351 0.003046 0.122297 0.042782 0.053952 0.090373 0.126076 -0.021332 -0.180205 -0.055812 0.088182 0.167456 0.022802 0.027514 0.055332 0.075522 0.100217 0.033683 -0.067685 -0.078083 0.046101 0.125776 0.039060 -0.013042 -0.138351 -0.020508 -0.013387 0.007983 -0.089593 -0.174841 -0.075182 0.032659 0.017337 -0.100128 0.012652 -0.112307 0.064302 0.060559 0.016727 -0.044049 -0.099170 0.048453 0.069728 -0.026542 -0.080943 0.029420 -0.024403 0.075097 0.043956 -0.062181 -0.161464 -0.103456 0.061547 0.027992 -0.053837 -0.075212 0.061892 0.045553 0.059509 -0.002429 -0.089393 -0.166910 -0.093884 0.024285 -0.018074 -0.028760 -0.026823 0.142787 -0.056693 -0.019143 0.103451 0.006757 -0.042576 -0.058247 -0.052228 -0.119014 -0.068881 0.043167 0.138484 0.285796 -0.040126 0.078628 -0.125970 -0.165156 -0.110516 0.007979 0.128911 0.214147 0.142864 -0.074551 -2 1 0.138955 10.501051 -5.270357 11 11 5 0.032854 0.004913 0.033817 0.079027 0.075435 0.049761 0.078500 0.072060 -0.058132 -0.062884 -0.015657 -0.009661 -0.013834 -0.016313 0.012003 0.045355 0.037871 0.037431 0.034799 -0.040649 -0.032611 0.020528 0.025515 -0.010894 -0.011541 0.028992 0.050198 0.056722 0.068483 0.034628 -0.036295 -0.021591 0.019686 0.014564 0.003573 0.013352 0.028655 0.055986 0.049260 0.021042 0.007487 -0.026047 0.007218 0.021875 0.006277 -0.006338 -0.001023 0.040864 0.067858 0.054050 0.017850 0.006674 0.003606 0.019196 0.032482 -0.066700 -0.039939 -0.001320 0.028109 0.040675 -0.002818 -0.010103 0.013533 -0.028096 -0.001137 0.000647 -0.068750 -0.064077 0.014228 0.073313 0.039023 -0.031082 -0.045662 -0.045261 -0.081117 -0.016626 -0.029484 -0.069635 0.020037 0.094617 0.112302 0.034941 -0.090622 -0.094529 -0.034904 -0.019903 0.039241 -0.003834 -0.224900 -0.004073 0.075257 0.121923 0.032274 -0.066420 -0.024675 0.025388 0.004077 -0.013545 -0.116605 -0.164488 0.107893 0.116450 0.100921 0.007698 -0.074838 -0.052867 0.003463 -0.000406 0.076431 -0.092610 -0.249152 -0.050531 0.092216 0.155896 0.106452 0.060015 0.093832 0.112928 0.034883 -0.161807 -0.703130 -3 11 11 2 2 0 0.242152 18.445863 -5.695371 11 11 5 0.074455 0.157234 -0.060348 -0.203286 -0.264565 -0.085325 0.044361 -0.107706 -0.029520 -0.076674 0.290264 -0.175341 -0.029455 -0.013361 0.078297 0.086805 0.031291 0.062453 0.064716 0.004159 -0.077906 -0.044076 0.050160 0.036229 0.033099 -0.034850 -0.082689 -0.092009 0.012988 0.092891 0.068223 0.007985 0.044673 0.171035 0.084328 0.106017 0.080804 0.047638 -0.058167 -0.003176 0.043788 0.070489 0.022546 -0.039971 0.106745 0.002853 0.041459 0.006044 0.022374 -0.061513 -0.042306 -0.022296 0.009488 0.046314 -0.033046 -0.144899 -0.164905 -0.019813 0.012737 0.025900 -0.103520 -0.100606 -0.075017 -0.002265 0.053163 -0.097578 -0.046465 -0.094269 -0.007721 0.012886 0.010241 -0.098297 -0.068509 -0.026213 -0.017912 0.022921 -0.036234 0.114772 0.044746 0.095582 0.088921 0.074623 -0.034659 -0.033166 0.018408 0.055331 0.038198 -0.046453 0.156513 0.114245 0.065554 -0.009493 -0.059154 -0.087040 -0.003303 0.060850 0.096598 0.044710 0.012391 -0.162251 -0.025051 0.016616 0.088971 0.048189 0.014738 0.033075 0.088663 0.087866 -0.024044 -0.081073 0.021789 0.152178 -0.073848 -0.187007 -0.260549 -0.097437 0.046900 -0.041075 -0.094172 -0.145997 0.287098 -2 1 0.068495 5.241411 -4.727409 11 11 5 -0.320572 0.071966 0.246513 0.173946 -0.071389 -0.145166 -0.151129 -0.156555 -0.043001 0.013258 -0.185448 -0.064925 -0.000864 0.039589 0.041607 -0.010050 0.054115 0.101967 0.000634 -0.018832 0.061173 0.002174 -0.033008 -0.019593 0.130383 0.165693 0.037663 -0.023538 0.024648 -0.021818 -0.059408 0.015434 -0.066906 0.052708 0.003968 0.035265 0.126051 0.098802 0.039724 0.056592 0.054296 -0.085271 0.023286 -0.005884 0.035498 0.018107 0.059180 0.141840 0.134226 0.024654 -0.000245 -0.019694 -0.100072 -0.021388 -0.031683 -0.040692 -0.150179 -0.152620 -0.007878 0.057245 -0.008516 -0.044941 -0.058656 -0.076334 -0.003961 -0.032484 -0.015188 -0.042769 0.002932 0.103383 0.116645 0.002548 -0.023082 -0.043305 -0.088919 -0.056604 -0.036003 0.022583 -0.022752 0.038861 0.129067 0.120952 0.020430 0.044172 0.024111 -0.057781 0.002967 0.012490 -0.031684 -0.049544 0.092583 0.186825 0.088181 -0.006514 0.025640 0.003668 -0.102655 0.027097 -0.066729 -0.007186 0.021084 0.050756 0.076555 -0.010867 0.014748 0.114377 0.077436 -0.032429 0.083295 -0.019413 -0.307397 -0.000875 0.166203 0.149742 -0.037086 -0.076376 -0.102553 -0.191265 -0.095259 0.012621 -0.217218 -3 11 11 2 2 0 0.174379 12.099864 -5.480745 11 11 5 0.333199 -0.101121 0.041795 -0.109520 -0.121819 -0.041160 0.045462 0.146120 0.145851 0.065284 -0.107030 -0.066194 -0.018901 0.117925 -0.003053 -0.076564 -0.068673 -0.082716 -0.089342 0.029584 0.074574 0.122134 0.042555 0.069453 0.018331 -0.039263 -0.123205 -0.109931 -0.013923 -0.032762 0.034609 -0.002990 0.152182 -0.015661 0.029923 0.002710 -0.067593 -0.152695 -0.074898 0.054582 -0.064624 -0.035104 -0.065573 0.119012 -0.061086 0.126203 0.075399 -0.050269 -0.141571 -0.098267 0.097295 0.025549 -0.058939 -0.059022 0.013739 -0.175856 0.039179 0.013427 -0.023011 -0.032227 -0.084127 0.006253 0.067909 -0.044270 -0.014713 0.014970 -0.084029 0.041173 -0.013951 0.005623 -0.059072 -0.162963 -0.057264 0.065139 0.028631 -0.007629 0.002445 0.054393 0.092512 0.111568 0.139613 0.119734 -0.038284 -0.125303 0.005267 0.096332 0.074068 -0.005465 0.151601 0.005576 0.069054 0.037864 0.116441 -0.056675 -0.217885 -0.048046 0.097669 0.106274 0.063904 0.059527 -0.197223 -0.045472 -0.085631 -0.023869 0.073548 0.071958 -0.016959 -0.011276 -0.045084 -0.053894 0.152089 -0.133792 0.113087 0.180887 0.131485 -0.076989 -0.024998 -0.187812 -0.061813 -0.028810 0.005185 -2 1 0.152259 10.252286 -5.353906 11 11 5 -0.191524 -0.040628 0.053724 0.125470 0.099850 0.095633 0.123366 0.108074 -0.014074 -0.132183 -0.633790 -0.136455 0.069963 0.121689 0.128642 0.027064 -0.041644 -0.012081 0.038102 0.039158 0.051844 -0.189999 -0.203098 0.003096 0.074346 0.109534 0.021603 -0.057453 -0.004757 0.050971 -0.005287 -0.037323 -0.150704 -0.196041 0.045254 0.101850 0.120804 0.003266 -0.117593 -0.114027 -0.012555 -0.009544 0.013911 -0.024622 -0.153386 -0.027996 0.091431 0.123296 0.045742 -0.067625 -0.076908 -0.078227 -0.055361 0.008090 -0.006888 -0.072363 -0.055130 -0.001544 0.045550 0.030403 -0.032949 -0.014217 -0.035021 -0.045077 0.015601 0.007586 -0.027784 -0.012488 -0.000257 0.048604 0.068159 0.037406 0.011073 0.005511 -0.025271 0.007163 0.019118 0.021451 -0.013423 0.001468 0.040288 0.076619 0.043336 0.016029 -0.006778 -0.009993 0.019678 0.048371 0.055892 -0.014448 0.013302 0.048276 0.073090 0.068386 0.058379 0.023557 -0.051851 -0.009960 0.001985 0.057758 -0.021417 -0.036788 0.008105 0.040445 0.047429 0.052273 0.041257 -0.032956 0.014232 0.041878 0.012002 0.031061 0.000530 0.049257 0.098190 0.073908 0.086986 0.070825 -0.077818 -0.076315 -0.062569 diff --git a/lib/local/LandmarkDetector/model/pdms/Multi-PIE_aligned_PDM_66.txt b/lib/local/LandmarkDetector/model/pdms/Multi-PIE_aligned_PDM_66.txt deleted file mode 100644 index 68aa218..0000000 --- a/lib/local/LandmarkDetector/model/pdms/Multi-PIE_aligned_PDM_66.txt +++ /dev/null @@ -1,409 +0,0 @@ -# The mean values of the components (in mm) -198 -1 -6 --73.447014 --72.593444 --70.508936 --66.868861 --60.414326 --49.631717 --35.391108 --18.754141 -0.114597 -19.016913 -35.598673 -49.771346 -60.477080 -66.851714 -70.423155 -72.449235 -73.242955 --58.203530 --49.478500 --38.505934 --27.177561 --16.300987 -16.156900 -27.016187 -38.332565 -49.304224 -58.038529 --0.040354 --0.033363 --0.017873 -0.004084 --12.557676 --6.454282 -0.021435 -6.508846 -12.601163 --43.222559 --36.097646 --27.574181 --20.557521 --28.056401 --35.953854 -20.451629 -27.455875 -35.977844 -43.113320 -35.851506 -27.955232 --26.517287 --19.122816 --10.001914 -0.033411 -10.096699 -19.222369 -26.631040 -19.826245 -10.719912 -0.073977 --10.546541 --19.679901 --10.743288 -0.054542 -10.855802 -11.052930 -0.066245 --10.914660 --24.746512 --5.563341 -13.586104 -31.935985 -48.494172 -62.540756 -73.747225 -81.753306 -83.703222 -81.701197 -73.643185 -62.391933 -48.310331 -31.731624 -13.372130 --5.780379 --24.966474 --55.382734 --62.743537 --65.767452 --65.366721 --62.577889 --62.609412 --65.431918 --65.867839 --62.878026 --55.545001 --42.794388 --31.898600 --21.092928 --10.293158 -2.090045 -3.917313 -4.546474 -3.913121 -2.066722 --38.786133 --43.150534 --43.235540 --38.961437 --36.861866 --36.568711 --39.010632 --43.306353 --43.245963 --38.901782 --36.662572 --36.932904 -30.400416 -23.498452 -18.833915 -19.206597 -18.816801 -23.452358 -30.330913 -37.487315 -41.984978 -42.996350 -42.005283 -37.535894 -26.744930 -26.063568 -26.723925 -32.163959 -33.052706 -32.187531 -78.681255 -76.697610 -75.100729 -67.144829 -48.865709 -28.603564 -11.177305 --4.295402 --17.795472 --4.239701 -11.236558 -28.650983 -48.897325 -67.163067 -75.133633 -76.746544 -78.714887 -1.731363 --4.557739 --9.659810 --14.615709 --19.199130 --19.060969 --14.484504 --9.533992 --4.439184 -1.839139 --17.498844 --27.916448 --38.687865 --48.878729 --23.622361 --26.603554 --29.386742 --26.488498 --23.509038 --2.510802 --3.222846 --3.712807 --2.897381 --3.340207 --3.258814 --2.796041 --3.614055 --3.114678 --2.391598 --3.139280 --3.231401 --10.336622 --18.125938 --24.157414 --30.363981 --24.048139 --18.017482 --10.235142 --15.032664 --19.983990 --25.047636 --20.087424 --15.139002 --19.356132 --27.503141 --19.258370 --16.964648 --23.973005 --17.050167 -# The principal components (eigenvectors) of identity or combined identity and expression model -198 -24 -6 --0.131273 0.061642 -0.012510 0.102280 -0.050892 0.032543 -0.075885 0.038389 -0.127712 0.121736 -0.051410 0.006800 -0.109190 0.068214 -0.191040 0.005581 -0.001793 0.030290 -0.014701 0.054678 -0.099910 0.129486 -0.039664 0.030211 --0.130535 0.076644 0.005955 0.128992 -0.087766 0.003856 -0.062712 0.011035 -0.104311 0.121196 -0.051551 -0.031437 -0.072782 0.078546 -0.149843 0.002235 -0.009600 0.014969 -0.023432 0.076747 -0.057106 0.057651 -0.045418 0.037971 --0.131360 0.097772 0.019892 0.147686 -0.117376 -0.003328 -0.044024 -0.011450 -0.072751 0.113932 -0.046556 -0.055102 0.000908 0.067126 -0.101982 -0.000479 -0.013325 -0.013626 -0.032762 0.091546 -0.014217 -0.020913 -0.046980 0.035342 --0.137970 0.110929 0.030689 0.139783 -0.115369 0.026630 -0.019212 -0.029754 -0.010726 0.072704 -0.021451 -0.056577 0.082231 0.025464 -0.003511 0.005508 -0.024960 -0.025544 -0.036912 0.062564 0.039080 -0.055324 -0.046860 0.017641 --0.135135 0.110637 0.050661 0.102334 -0.098664 0.080015 0.005429 -0.036857 0.072405 0.012258 0.033956 -0.045079 0.148257 -0.040902 0.133200 0.012816 -0.043055 -0.021025 -0.013320 -0.013401 0.114790 -0.030526 -0.022542 -0.008660 --0.100221 0.089414 0.064057 0.061401 -0.066814 0.071042 0.011659 -0.031420 0.110299 -0.020965 0.069839 -0.033725 0.138080 -0.122631 0.195694 -0.001764 -0.028688 -0.044580 0.021239 -0.074578 0.157209 -0.004682 0.016493 -0.018792 --0.051976 0.055863 0.055121 0.032228 -0.022272 0.031244 0.013022 -0.025100 0.085963 -0.019700 0.073384 -0.035730 0.068265 -0.200993 0.161503 -0.009745 0.021018 -0.059450 0.046151 -0.071870 0.151306 0.019038 0.028337 0.009588 --0.012118 0.022021 0.029027 0.018045 0.000934 0.009319 0.003756 -0.016914 0.037117 -0.004160 0.068224 -0.022136 0.020698 -0.271194 0.080781 -0.005545 0.106564 -0.049508 0.044223 -0.049700 0.116584 0.030734 0.037658 0.019211 --0.001496 -0.003386 -0.001635 0.001533 -0.003370 0.023643 -0.004987 0.000648 -0.000476 0.004318 0.062219 0.001774 0.003329 -0.347090 -0.004710 -0.005730 0.179671 0.001995 0.003775 -0.039326 0.114391 0.011512 0.046072 -0.002301 -0.010156 -0.026698 -0.034295 -0.017574 -0.007795 0.007692 -0.010272 0.019016 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-0.026825 0.025950 0.037674 -0.074353 -0.008133 0.019461 -0.076201 -0.015515 -0.131982 -0.097616 -0.021277 -0.149817 0.116660 -0.002810 0.044505 0.014100 0.072832 -0.115723 -0.042691 0.054624 -0.000761 0.060145 0.100303 0.001612 -0.013223 0.012744 0.032584 -0.067128 -0.068838 -0.010357 -0.061317 -0.038451 -0.131569 -0.075260 -0.006352 -0.131216 0.089215 0.006368 0.064227 -0.008742 0.103968 -0.124092 -0.045800 0.030087 0.073130 0.073717 0.148378 -0.001695 -0.009349 -0.015794 0.022791 -0.030641 -0.093172 -0.078192 -0.021389 -0.044189 -0.132692 -0.059063 0.012959 -0.103988 0.056043 0.038127 0.077019 -0.035808 0.126882 -0.125517 -0.043474 -0.008736 0.110533 0.064482 0.189624 -0.005446 -0.001524 -0.030614 0.013591 0.011999 -0.112879 -0.138665 0.020561 -0.038379 --0.103256 0.020891 -0.110162 -0.212634 0.058183 0.138030 -0.200849 0.100068 -0.022495 0.002709 0.153038 0.054887 -0.024530 -0.007414 0.049195 0.197049 -0.022827 -0.045753 0.186816 -0.052449 -0.132988 0.030237 0.015215 0.024104 --0.080994 0.010985 -0.094549 -0.264861 0.003874 0.102996 -0.116142 0.005617 -0.017761 0.061246 0.173548 0.032150 -0.019111 -0.056125 0.005898 0.140163 -0.037308 -0.052262 0.076745 -0.021728 -0.140151 -0.032839 -0.049225 0.028646 --0.060263 0.002721 -0.066287 -0.241913 -0.043231 0.048059 0.014406 -0.119970 -0.045232 0.070887 0.164744 0.001511 -0.015282 -0.070804 -0.009665 0.049746 -0.010035 0.008282 0.038497 0.009992 -0.108441 -0.053610 -0.130623 0.013733 --0.043438 -0.004202 -0.035388 -0.188880 -0.081593 -0.012313 0.148663 -0.247184 -0.088321 0.038230 0.140939 -0.042380 -0.011204 -0.066379 -0.012701 -0.020756 0.039141 0.069640 0.060648 0.028443 -0.068939 -0.065957 -0.207395 0.000253 --0.027456 -0.012648 -0.010305 -0.128762 -0.118023 -0.076298 0.272941 -0.355960 -0.129333 -0.008980 0.109842 -0.090833 -0.003982 -0.049622 -0.009264 -0.079023 0.098614 0.109283 0.109170 0.037788 -0.032760 -0.076614 -0.277675 -0.009655 -0.028658 0.015537 0.014071 0.124422 0.114523 -0.115827 -0.262002 0.348923 0.124202 0.009677 0.119551 0.093386 0.008350 -0.050918 0.011467 0.069446 0.111352 -0.106208 -0.111719 -0.010214 -0.041439 -0.068982 -0.278486 0.025867 -0.044696 0.007255 0.038872 0.186692 0.083366 -0.041438 -0.141963 0.242179 0.082905 -0.037066 0.149989 0.046493 0.015225 -0.069457 0.013907 0.018541 0.044200 -0.065992 -0.064310 0.019432 -0.066650 -0.044316 -0.214883 0.006808 -0.061510 0.000372 0.069425 0.241930 0.049925 0.030814 -0.011846 0.116905 0.039652 -0.069373 0.171756 0.003798 0.018542 -0.075797 0.009708 -0.045986 -0.013506 -0.004452 -0.042694 0.057660 -0.089105 -0.017260 -0.145119 -0.016908 -0.082170 -0.007997 0.097171 0.267100 0.007247 0.099038 0.114834 -0.006757 0.012478 -0.059880 0.177191 -0.026227 0.021483 -0.062985 -0.007118 -0.133388 -0.050566 0.056066 -0.080337 0.100740 -0.095460 0.005097 -0.066940 -0.041625 -0.104329 -0.018258 0.112126 0.216590 -0.044296 0.147034 0.196819 -0.099590 0.018126 -0.002082 0.153447 -0.049659 0.025491 -0.015134 -0.051658 -0.192842 -0.041726 0.049416 -0.188101 0.118016 -0.067590 -0.017735 0.019713 -0.041620 -0.000663 0.000944 0.002028 -0.002485 -0.004325 -0.178450 0.006940 -0.002647 -0.000452 -0.000260 -0.036127 -0.002157 -0.001769 0.085386 0.003664 -0.001368 0.012609 -0.000348 0.002466 -0.036630 0.109712 -0.022803 -0.093546 -0.000547 -0.000288 0.000448 0.002317 -0.000912 -0.003640 -0.137716 0.004390 -0.002725 -0.000322 0.001001 -0.035680 -0.002530 -0.004788 0.091072 0.001471 0.002255 -0.028343 -0.000838 0.003056 -0.051336 0.154680 -0.028982 -0.119923 -0.000914 --0.000001 0.000083 0.002277 0.000559 -0.002598 -0.092125 0.002081 -0.002291 -0.000077 0.001848 -0.034709 -0.002701 -0.006719 0.095246 -0.000088 0.004200 -0.062383 -0.001457 0.003110 -0.065545 0.197098 -0.035205 -0.145076 -0.001671 --0.000249 -0.000228 0.001999 0.001898 -0.001304 -0.044349 -0.000049 -0.001452 0.000185 0.002371 -0.035322 -0.002749 -0.007660 0.093895 -0.000988 0.005450 -0.093930 -0.002147 0.002397 -0.077738 0.233063 -0.038852 -0.158652 -0.002510 --0.018175 0.032540 -0.046381 0.072624 0.076758 -0.059237 0.017791 -0.046361 -0.028705 0.026262 -0.065072 -0.040634 -0.156135 0.085741 0.051510 0.014526 -0.086807 -0.021631 -0.076788 -0.116557 0.108914 -0.513944 0.078194 -0.015943 --0.009972 0.017183 -0.024354 0.040287 0.043472 -0.082474 0.009076 -0.020686 -0.018070 0.017280 -0.071368 -0.020552 -0.088495 0.092577 0.031774 0.012613 -0.099299 -0.024190 -0.038465 -0.086399 0.124173 -0.285954 -0.014114 -0.021193 --0.000140 -0.000550 0.001209 -0.000441 -0.004329 -0.107015 0.002219 -0.001344 0.000205 0.001041 -0.076844 -0.002757 -0.005753 0.102871 0.001296 0.005455 -0.107458 -0.002422 0.001475 -0.047062 0.142812 -0.027540 -0.116449 -0.004795 -0.010137 -0.017300 0.025196 -0.041630 -0.049844 -0.081928 -0.004971 0.018240 0.019669 -0.017799 -0.072194 0.017115 0.082066 0.095586 -0.027996 -0.005137 -0.101295 0.021371 0.041818 -0.002708 0.151466 0.245495 -0.155914 0.015694 -0.018362 -0.032464 0.047071 -0.073353 -0.081048 -0.055564 -0.015023 0.044733 0.030168 -0.027108 -0.065188 0.037657 0.150432 0.091558 -0.048341 -0.007899 -0.090804 0.019455 0.080044 0.030478 0.158906 0.486632 -0.188186 0.013055 --0.062179 0.016062 -0.055023 -0.062336 0.062633 0.015002 -0.139789 0.011415 -0.012883 -0.257736 0.051063 -0.125679 -0.062414 0.075253 -0.035691 -0.258638 0.116601 -0.007012 -0.024872 0.054285 0.050491 0.018896 0.155783 0.098688 --0.055414 0.013285 -0.037771 -0.058857 0.017543 -0.008107 -0.059850 0.006637 -0.050576 -0.215639 0.052160 -0.035553 -0.049198 0.068918 -0.025862 -0.132344 0.120040 -0.033448 -0.103534 0.030552 0.049017 0.047455 0.160755 -0.008346 --0.042484 0.032992 -0.022698 -0.029477 -0.040529 -0.018012 0.042014 0.015150 -0.126555 -0.155830 0.044106 0.140958 -0.059714 0.068312 0.063184 -0.034734 0.122973 -0.048511 -0.121531 -0.003780 0.034849 0.078048 0.159977 -0.057993 --0.035440 0.019458 -0.017848 -0.004969 -0.075476 -0.024694 0.116267 -0.001407 -0.162960 -0.132404 0.034346 0.190808 -0.021034 0.078356 0.100739 0.093001 0.133670 -0.080129 -0.174964 -0.044027 0.017142 0.122386 0.140941 -0.159570 --0.044064 0.017262 -0.035061 -0.017594 -0.023286 -0.002500 0.026448 0.003865 -0.112249 -0.174655 0.038768 0.080754 -0.033141 0.082713 0.066483 -0.030953 0.128027 -0.050641 -0.114281 -0.017467 0.025057 0.086708 0.136329 -0.067118 --0.054128 0.008177 -0.043584 -0.039099 0.020889 0.009912 -0.060465 -0.004176 -0.052947 -0.228032 0.043482 -0.053136 -0.041201 0.082949 0.006132 -0.134940 0.124448 -0.035251 -0.078194 0.013263 0.034128 0.059040 0.138889 0.002496 -0.036429 -0.017173 0.020074 0.003056 0.076476 -0.040706 -0.110829 -0.001936 0.160311 0.130160 0.044100 -0.189477 0.022333 0.075611 -0.101573 -0.108789 0.118490 0.084245 0.178783 0.027137 0.027026 -0.042416 0.177492 0.162700 -0.043372 -0.030810 0.025028 0.027904 0.042779 -0.027092 -0.037252 -0.018723 0.123958 0.153502 0.050374 -0.140335 0.061145 0.068850 -0.064475 0.020012 0.121849 0.052851 0.125271 -0.017183 0.022247 0.006394 0.175965 0.061268 -0.056262 -0.011187 0.039919 0.057812 -0.014179 -0.007524 0.063630 -0.009916 0.048136 0.213352 0.047852 0.036046 0.050505 0.070893 0.024268 0.117516 0.131104 0.037653 0.107485 -0.054386 0.015812 0.034711 0.164552 0.011119 -0.062997 -0.014037 0.056777 0.062384 -0.057710 0.023599 0.141727 -0.013554 0.010630 0.255766 0.040524 0.126602 0.063146 0.079571 0.033942 0.243078 0.142713 0.010601 0.029329 -0.076942 0.008834 0.059028 0.148989 -0.097300 -0.055001 -0.006044 0.045393 0.038929 -0.016762 0.010702 0.062907 0.002209 0.050447 0.226291 0.037698 0.054580 0.041441 0.084706 -0.007500 0.118756 0.136432 0.038761 0.082581 -0.032660 0.015190 0.014654 0.150716 -0.000936 -0.044997 -0.015025 0.036985 0.016777 0.026234 -0.009436 -0.022901 -0.006263 0.109640 0.172791 0.041228 -0.079125 0.033710 0.082359 -0.067516 0.014768 0.127674 0.054423 0.118491 -0.001109 0.022631 -0.011923 0.159215 0.069256 --0.023578 0.120370 -0.206325 0.064439 0.117295 0.061900 0.048174 -0.094861 0.156753 -0.048033 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0.001946 --0.014891 -0.008411 -0.036029 -0.015021 0.034034 -0.016902 -0.000349 -0.017533 0.008122 -0.003290 -0.006353 -0.011299 -0.025911 0.001618 0.002149 -0.022488 -0.009485 0.009421 0.019984 -0.027222 0.008181 -0.016199 0.011061 -0.004347 --0.007543 -0.015474 -0.040189 0.015384 0.038625 -0.027574 -0.000491 -0.031990 0.023963 0.036850 0.011293 -0.042285 -0.082698 -0.016002 0.021122 -0.035817 0.009754 0.014281 0.004730 -0.087198 0.033671 -0.000797 0.015535 0.007350 -0.007930 0.023774 0.036743 -0.014038 -0.036477 -0.006774 0.011374 0.022541 -0.022535 0.042305 0.026335 0.036750 0.021916 0.007611 -0.005653 -0.015182 -0.047638 -0.014341 0.054505 -0.043196 0.023290 -0.056673 -0.015723 -0.007742 --0.004915 0.015933 0.017345 -0.015680 -0.017967 -0.004915 0.011614 0.016946 0.029827 0.028125 0.011868 -0.004711 -0.005276 -0.001960 -0.009475 0.009741 -0.046956 0.011883 0.007010 -0.018339 -0.002865 -0.004312 -0.013932 -0.006942 --0.003521 0.004654 -0.015590 -0.013889 0.004395 -0.008732 0.007700 -0.004791 0.067569 -0.003581 0.005624 -0.039265 -0.012761 -0.005771 -0.015968 0.006255 -0.033275 -0.009690 -0.041270 0.014617 -0.023760 0.048481 -0.000051 -0.022552 -0.000458 0.005431 -0.018104 -0.000980 0.011696 -0.042760 -0.002120 -0.006394 0.021871 -0.044384 0.017191 0.023310 -0.058988 -0.023639 0.049743 -0.022378 0.011228 -0.017895 -0.021546 0.054782 -0.005029 0.079345 0.046779 0.008618 --0.000473 -0.003483 -0.000317 -0.000032 -0.013100 -0.027729 -0.010188 -0.006816 0.024254 -0.025317 0.025138 -0.003575 -0.027818 -0.016550 0.033888 -0.020679 -0.003022 -0.026972 0.004090 0.018494 0.016316 0.039096 0.026870 -0.021700 --0.000384 0.006795 0.002821 0.001952 -0.016537 -0.017312 -0.006126 -0.003212 0.008095 0.008878 0.020727 0.007447 -0.003471 -0.004029 0.015335 -0.019076 -0.026372 -0.019477 0.020929 -0.007807 0.020166 -0.006328 0.008143 -0.024274 -0.002071 0.006466 -0.023492 -0.007974 0.015683 0.036241 -0.004189 -0.003038 0.024520 -0.048551 -0.020624 0.025659 -0.052563 0.020100 0.044440 -0.026317 -0.003525 -0.010101 -0.021391 0.035466 0.034123 0.038210 -0.080648 0.006556 --0.002402 0.005398 -0.019679 -0.020452 0.005835 0.006932 0.007837 -0.002300 0.069086 -0.006995 -0.007547 -0.036643 -0.005613 0.005258 -0.021338 -0.000437 0.040201 0.000064 -0.039798 -0.010442 0.026457 0.034652 -0.023664 -0.024801 --0.003825 0.016801 0.014153 -0.022104 -0.016761 0.006166 0.011717 0.019730 0.031672 0.024962 -0.015533 -0.002099 0.002298 0.002520 -0.014551 0.001733 0.051460 0.020880 0.007994 -0.024340 -0.011603 -0.003149 0.015724 -0.010346 -0.009293 0.025153 0.034610 -0.021073 -0.035140 0.010154 0.010695 0.025885 -0.019886 0.039855 -0.033031 0.039020 0.029686 -0.005553 -0.011205 -0.023293 0.045845 -0.005817 0.054993 -0.027600 -0.047966 -0.047026 0.044749 -0.012053 -0.001196 0.008104 -0.000550 -0.005319 -0.014490 0.017382 -0.007183 0.000135 0.010803 0.005711 -0.024559 0.010185 0.003984 0.004249 0.009649 -0.025892 0.026929 -0.011175 0.020838 -0.000927 -0.021532 -0.016130 -0.001564 -0.027490 -0.001089 -0.002355 -0.004514 -0.007150 -0.010291 0.025128 -0.011639 -0.003498 0.027026 -0.029088 -0.027714 -0.001506 -0.021034 0.014769 0.028630 -0.025597 0.007130 -0.019412 0.003749 0.018448 -0.003519 0.014143 -0.041945 -0.024701 --0.014752 -0.007321 0.013877 -0.002884 -0.024108 0.002303 -0.026512 0.020183 -0.015461 -0.030184 -0.006267 0.026068 0.044433 -0.016670 0.030474 0.011557 0.027340 0.038203 -0.005839 0.006177 0.027136 0.000316 0.019255 0.000552 --0.016868 -0.029752 0.004473 -0.020764 -0.021435 0.027753 -0.000612 0.014380 -0.000613 0.002858 0.009706 -0.012946 0.027082 -0.001179 0.028245 0.039466 0.007036 0.048356 -0.016487 -0.020655 0.013899 0.035330 -0.037198 0.035797 --0.020584 -0.025552 0.009208 -0.010018 -0.001434 0.030711 0.008708 -0.025079 0.018193 -0.004982 0.019040 -0.024397 0.011371 0.005362 0.014803 0.024863 0.004075 -0.004452 0.006803 -0.034515 0.013896 0.032023 -0.042288 0.024877 --0.007172 -0.030730 -0.013156 0.002041 0.013713 0.018878 -0.002702 -0.036444 -0.004836 -0.014642 0.027953 -0.026214 0.006306 -0.001831 0.021305 -0.037591 0.021327 -0.044823 0.050411 -0.036042 0.008177 0.014946 -0.015597 -0.013038 --0.017181 -0.023576 0.003032 -0.025203 -0.001919 -0.036232 0.009083 -0.022286 0.022601 -0.010671 -0.019965 -0.020816 0.016805 0.003686 0.009380 0.019998 -0.010796 0.002809 0.008988 -0.025250 -0.024145 0.039778 0.034154 0.028887 --0.014072 -0.028410 -0.000822 -0.033387 -0.022436 -0.033373 -0.000370 0.016901 0.003275 -0.002184 -0.011401 -0.010105 0.031211 0.010900 0.023483 0.035466 -0.010793 0.053986 -0.014690 -0.011441 -0.019274 0.041437 0.027803 0.041183 --0.011745 -0.006901 0.008132 -0.012160 -0.024078 -0.016750 -0.027255 0.023374 -0.012432 -0.034736 0.001738 0.028410 0.047103 0.022609 0.024876 0.009700 -0.029335 0.042447 -0.004444 0.021981 -0.024490 -0.016464 -0.009335 0.005723 -0.013230 -0.012106 -0.008624 0.006624 -0.005346 -0.037879 -0.023831 0.001700 -0.000246 -0.040315 0.016171 -0.000787 0.040941 -0.027783 -0.034923 0.000944 -0.023770 -0.015075 0.022171 0.070943 0.004816 -0.018620 0.009349 0.002391 -0.017298 0.003296 0.001998 -0.019318 -0.015374 -0.040235 -0.032471 -0.025113 0.013205 -0.017560 -0.001988 -0.032933 0.018524 0.000793 -0.025342 0.018806 -0.022045 -0.076625 0.010375 0.067569 0.001278 -0.039650 0.037479 0.024286 -0.026498 0.011392 -0.000362 -0.042663 -0.029473 0.013919 -0.020835 -0.008795 -0.003774 0.015161 0.010246 -0.040784 0.002108 -0.020452 -0.003005 0.013382 0.020127 -0.090119 -0.018840 0.070445 0.011233 -0.062439 -0.022872 0.049291 -0.012895 0.001538 0.008666 -0.004337 -0.015103 0.016876 -0.032324 -0.028284 0.009022 -0.012605 0.000615 -0.037415 0.014420 0.004265 -0.019532 0.022244 0.021863 -0.083473 0.007511 0.058851 0.031450 -0.040010 -0.012168 0.018051 -0.009878 -0.013189 -0.002893 0.018337 -0.004429 0.018560 -0.023726 -0.000676 -0.003245 -0.036619 -0.018168 -0.004958 0.037474 0.033212 -0.029730 0.002752 0.018367 -0.021047 0.019956 0.061906 0.033994 -0.009713 0.002178 -0.002604 --0.036455 -0.002450 -0.017922 -0.006097 0.022282 0.049421 -0.011847 -0.011101 -0.004510 -0.004388 0.004868 0.031117 0.011638 -0.001303 -0.008047 0.039302 0.009212 0.001789 0.021092 -0.019512 -0.016745 0.011997 0.019345 0.006406 -0.000423 -0.007319 -0.057292 0.016176 0.031459 0.007973 -0.009437 -0.016403 0.000322 -0.021840 -0.027543 0.040725 0.012995 0.020647 -0.022551 -0.047601 0.009484 -0.033190 0.025752 -0.014163 -0.038431 -0.002155 0.032958 -0.015735 --0.032717 -0.001468 -0.026128 -0.020284 0.020055 -0.061717 -0.009130 -0.007757 -0.001532 -0.011309 -0.008803 0.034833 0.018287 0.008545 -0.015543 0.035259 -0.013646 0.010114 0.025166 -0.029561 0.005239 -0.010502 -0.011794 0.015129 --0.015427 -0.008969 -0.007325 0.001209 0.003884 -0.065420 -0.014325 0.005239 0.003813 -0.021528 0.005891 0.012697 -0.008585 -0.000428 -0.006703 0.035593 -0.008463 0.035073 0.011345 0.016756 -0.004326 -0.043418 0.005114 0.014734 --0.016076 0.002727 -0.004804 0.030704 0.014818 0.010395 -0.028232 0.002524 0.026332 0.003282 -0.053346 0.020559 -0.065119 0.005758 -0.005391 0.059666 -0.008954 0.058529 0.015960 0.017843 -0.028979 -0.059673 -0.002070 0.043925 --0.018698 -0.009367 0.000474 0.013333 0.006238 0.041309 -0.016795 0.002286 0.001409 -0.014775 -0.007769 0.009326 -0.013965 0.003250 0.000451 0.038211 0.013024 0.027985 0.007332 0.016022 0.004588 -0.027147 0.015319 0.006226 -# The variances of the components (eigenvalues) of identity or combined identity and expression model -1 -24 -6 -634.723159 232.843080 101.461353 70.892811 62.146634 61.253370 53.263370 41.886102 37.065997 30.948553 27.957356 24.039353 20.687504 19.610950 14.875372 14.281810 13.358140 11.883143 10.946700 9.934766 9.739733 7.997896 7.885247 6.790098 diff --git a/lib/local/LandmarkDetector/model/tris_66.txt b/lib/local/LandmarkDetector/model/tris_66.txt deleted file mode 100644 index 48462ff..0000000 --- a/lib/local/LandmarkDetector/model/tris_66.txt +++ /dev/null @@ -1,518 +0,0 @@ -# Number of triangulations -7 -# Triangulation 1 -# triangulation -91 -3 -4 -23 20 21 -23 21 22 -36 0 1 -15 16 45 -17 0 36 -16 26 45 -18 17 37 -26 25 44 -37 17 36 -45 26 44 -19 18 38 -25 24 43 -38 18 37 -44 25 43 -20 19 38 -24 23 43 -21 20 39 -23 22 42 -39 20 38 -43 23 42 -22 21 27 -27 21 39 -22 27 42 -27 28 42 -28 27 39 -42 28 47 -28 39 40 -36 1 41 -46 15 45 -41 1 2 -14 15 46 -29 28 40 -28 29 47 -41 2 40 -47 14 46 -2 29 40 -29 14 47 -29 2 3 -13 14 29 -30 29 31 -35 29 30 -29 3 31 -35 13 29 -33 30 32 -34 30 33 -32 30 31 -35 30 34 -31 3 4 -12 13 35 -4 5 48 -11 12 54 -5 6 48 -10 11 54 -48 6 59 -55 10 54 -6 7 59 -9 10 55 -59 7 58 -56 9 55 -58 8 57 -57 8 56 -7 8 58 -8 9 56 -31 4 48 -54 12 35 -49 31 48 -54 35 53 -50 31 49 -53 35 52 -32 31 50 -35 34 52 -33 32 50 -34 33 52 -51 33 50 -52 33 51 -49 48 60 -50 49 60 -61 50 60 -51 50 61 -52 51 61 -61 62 52 -53 52 62 -54 53 62 -55 54 63 -56 55 63 -64 56 63 -57 56 64 -64 65 57 -58 57 65 -59 58 65 -65 48 59 -# Triangulation 2 -# triangulation -90 -3 -4 -23 20 21 -23 21 22 -36 0 1 -15 16 45 -17 0 36 -16 26 45 -18 17 37 -26 25 44 -37 17 36 -45 26 44 -19 18 38 -25 24 43 -38 18 37 -44 25 43 -20 19 38 -24 23 43 -21 20 39 -23 22 42 -39 20 38 -43 23 42 -22 21 27 -27 21 39 -22 27 42 -27 28 42 -28 27 39 -42 28 47 -28 39 40 -36 1 41 -46 15 45 -41 1 2 -14 15 46 -29 28 40 -28 29 47 -41 2 40 -47 14 46 -2 29 40 -29 14 47 -29 2 3 -13 14 29 -30 29 31 -29 3 31 -35 13 29 -33 30 32 -34 30 33 -32 30 31 -35 30 34 -31 3 4 -12 13 35 -4 5 48 -11 12 54 -5 6 48 -10 11 54 -48 6 59 -55 10 54 -6 7 59 -9 10 55 -59 7 58 -56 9 55 -58 8 57 -57 8 56 -7 8 58 -8 9 56 -31 4 48 -54 12 35 -49 31 48 -54 35 53 -50 31 49 -53 35 52 -32 31 50 -35 34 52 -33 32 50 -34 33 52 -51 33 50 -52 33 51 -49 48 60 -50 49 60 -61 50 60 -51 50 61 -52 51 61 -61 62 52 -53 52 62 -54 53 62 -55 54 63 -56 55 63 -64 56 63 -57 56 64 -64 65 57 -58 57 65 -59 58 65 -65 48 59 -# Triangulation 3 -# triangulation -77 -3 -4 -23 20 21 -23 21 22 -36 0 1 -17 0 36 -18 17 37 -26 25 44 -37 17 36 -45 26 44 -19 18 38 -25 24 43 -38 18 37 -44 25 43 -20 19 38 -24 23 43 -21 20 39 -23 22 42 -39 20 38 -43 23 42 -22 21 27 -27 21 39 -22 27 42 -27 28 42 -28 27 39 -42 28 47 -28 39 40 -36 1 41 -46 15 45 -41 1 2 -29 28 40 -28 29 47 -41 2 40 -47 14 46 -2 29 40 -29 2 3 -30 29 31 -29 3 31 -33 30 32 -34 30 33 -32 30 31 -35 30 34 -31 3 4 -4 5 48 -5 6 48 -48 6 59 -6 7 59 -59 7 58 -56 9 55 -58 8 57 -57 8 56 -7 8 58 -8 9 56 -31 4 48 -49 31 48 -50 31 49 -53 35 52 -32 31 50 -35 34 52 -33 32 50 -34 33 52 -51 33 50 -52 33 51 -49 48 60 -50 49 60 -61 50 60 -51 50 61 -52 51 61 -61 62 52 -53 52 62 -54 53 62 -55 54 63 -56 55 63 -64 56 63 -57 56 64 -64 65 57 -58 57 65 -59 58 65 -65 48 59 -# Triangulation 4 -# triangulation -28 -3 -4 -36 0 1 -17 0 36 -18 17 37 -37 17 36 -19 18 38 -38 18 37 -20 19 38 -36 1 41 -41 1 2 -29 28 40 -41 2 40 -2 29 40 -29 2 3 -30 29 31 -29 3 31 -31 3 4 -4 5 48 -5 6 48 -48 6 59 -6 7 59 -59 7 58 -58 8 57 -7 8 58 -31 4 48 -49 31 48 -50 31 49 -51 33 50 -51 50 61 -# Triangulation 5 -# triangulation -90 -3 -4 -23 20 21 -23 21 22 -36 0 1 -15 16 45 -17 0 36 -16 26 45 -18 17 37 -26 25 44 -37 17 36 -45 26 44 -19 18 38 -25 24 43 -38 18 37 -44 25 43 -20 19 38 -24 23 43 -21 20 39 -23 22 42 -39 20 38 -43 23 42 -22 21 27 -27 21 39 -22 27 42 -27 28 42 -28 27 39 -42 28 47 -28 39 40 -36 1 41 -46 15 45 -41 1 2 -14 15 46 -29 28 40 -28 29 47 -41 2 40 -47 14 46 -2 29 40 -29 14 47 -29 2 3 -13 14 29 -35 29 30 -29 3 31 -35 13 29 -33 30 32 -34 30 33 -32 30 31 -35 30 34 -31 3 4 -12 13 35 -4 5 48 -11 12 54 -5 6 48 -10 11 54 -48 6 59 -55 10 54 -6 7 59 -9 10 55 -59 7 58 -56 9 55 -58 8 57 -57 8 56 -7 8 58 -8 9 56 -31 4 48 -54 12 35 -49 31 48 -54 35 53 -50 31 49 -53 35 52 -32 31 50 -35 34 52 -33 32 50 -34 33 52 -51 33 50 -52 33 51 -49 48 60 -50 49 60 -61 50 60 -51 50 61 -52 51 61 -61 62 52 -53 52 62 -54 53 62 -55 54 63 -56 55 63 -64 56 63 -57 56 64 -64 65 57 -58 57 65 -59 58 65 -65 48 59 -# Triangulation 6 -# triangulation -77 -3 -4 -23 20 21 -23 21 22 -15 16 45 -16 26 45 -18 17 37 -26 25 44 -37 17 36 -45 26 44 -19 18 38 -25 24 43 -38 18 37 -44 25 43 -20 19 38 -24 23 43 -21 20 39 -23 22 42 -39 20 38 -43 23 42 -22 21 27 -27 21 39 -22 27 42 -27 28 42 -28 27 39 -42 28 47 -28 39 40 -36 1 41 -46 15 45 -14 15 46 -29 28 40 -28 29 47 -41 2 40 -47 14 46 -29 14 47 -13 14 29 -35 29 30 -35 13 29 -33 30 32 -34 30 33 -32 30 31 -35 30 34 -12 13 35 -11 12 54 -10 11 54 -55 10 54 -9 10 55 -59 7 58 -56 9 55 -58 8 57 -57 8 56 -7 8 58 -8 9 56 -54 12 35 -54 35 53 -50 31 49 -53 35 52 -32 31 50 -35 34 52 -33 32 50 -34 33 52 -51 33 50 -52 33 51 -49 48 60 -50 49 60 -61 50 60 -51 50 61 -52 51 61 -61 62 52 -53 52 62 -54 53 62 -55 54 63 -56 55 63 -64 56 63 -57 56 64 -64 65 57 -58 57 65 -59 58 65 -65 48 59 -# Triangulation 7 -# triangulation -28 -3 -4 -15 16 45 -16 26 45 -26 25 44 -45 26 44 -25 24 43 -44 25 43 -24 23 43 -46 15 45 -14 15 46 -28 29 47 -47 14 46 -29 14 47 -13 14 29 -35 29 30 -35 13 29 -12 13 35 -11 12 54 -10 11 54 -55 10 54 -9 10 55 -56 9 55 -57 8 56 -8 9 56 -54 12 35 -54 35 53 -53 35 52 -52 33 51 -52 51 61 diff --git a/lib/local/LandmarkDetector/src/LandmarkDetectionValidator.cpp b/lib/local/LandmarkDetector/src/LandmarkDetectionValidator.cpp index edb31c6..2a3b90e 100644 --- a/lib/local/LandmarkDetector/src/LandmarkDetectionValidator.cpp +++ b/lib/local/LandmarkDetector/src/LandmarkDetectionValidator.cpp @@ -1,14 +1,38 @@ /////////////////////////////////////////////////////////////////////////////// -// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge, +// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge, // all rights reserved. // -// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY -// -// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT. -// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE. -// -// License can be found in OpenFace-license.txt +// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS +// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO, +// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR +// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS +// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY. +// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF +// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) +// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, +// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN +// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE +// POSSIBILITY OF SUCH DAMAGE. // +// Notwithstanding the license granted herein, Licensee acknowledges that certain components +// of the Software may be covered by so-called “open source” software licenses (“Open Source +// Components”), which means any software licenses approved as open source licenses by the +// Open Source Initiative or any substantially similar licenses, including without limitation any +// license that, as a condition of distribution of the software licensed under such license, +// requires that the distributor make the software available in source code format. Licensor shall +// provide a list of Open Source Components for a particular version of the Software upon +// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to +// the extent required by the licenses covering Open Source Components, the terms of such +// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the +// licenses applicable to Open Source Components prohibit any of the restrictions in this +// License Agreement with respect to such Open Source Component, such restrictions will not +// apply to such Open Source Component. To the extent the terms of the licenses applicable to +// Open Source Components require Licensor to make an offer to provide source code or +// related information in connection with the Software, such offer is hereby made. Any request +// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk +// Licensee acknowledges receipt of notices for the Open Source Components for the initial +// delivery of the Software. + // * Any publications arising from the use of this software, including but // not limited to academic journal and conference publications, technical // reports and manuals, must cite at least one of the following works: @@ -40,6 +64,9 @@ #include #include +// TBB includes +#include + // System includes #include @@ -108,15 +135,27 @@ cnn_convolutional_layers_bias(other.cnn_convolutional_layers_bias), cnn_convolut } } - this->cnn_fully_connected_layers.resize(other.cnn_fully_connected_layers.size()); - for (size_t v = 0; v < other.cnn_fully_connected_layers.size(); ++v) + this->cnn_fully_connected_layers_weights.resize(other.cnn_fully_connected_layers_weights.size()); + for (size_t v = 0; v < other.cnn_fully_connected_layers_weights.size(); ++v) { - this->cnn_fully_connected_layers[v].resize(other.cnn_fully_connected_layers[v].size()); + this->cnn_fully_connected_layers_weights[v].resize(other.cnn_fully_connected_layers_weights[v].size()); - for (size_t l = 0; l < other.cnn_fully_connected_layers[v].size(); ++l) + for (size_t l = 0; l < other.cnn_fully_connected_layers_weights[v].size(); ++l) { // Make sure the matrix is copied. - this->cnn_fully_connected_layers[v][l] = other.cnn_fully_connected_layers[v][l].clone(); + this->cnn_fully_connected_layers_weights[v][l] = other.cnn_fully_connected_layers_weights[v][l].clone(); + } + } + + this->cnn_fully_connected_layers_biases.resize(other.cnn_fully_connected_layers_biases.size()); + for (size_t v = 0; v < other.cnn_fully_connected_layers_biases.size(); ++v) + { + this->cnn_fully_connected_layers_biases[v].resize(other.cnn_fully_connected_layers_biases[v].size()); + + for (size_t l = 0; l < other.cnn_fully_connected_layers_biases[v].size(); ++l) + { + // Make sure the matrix is copied. + this->cnn_fully_connected_layers_biases[v][l] = other.cnn_fully_connected_layers_biases[v][l].clone(); } } @@ -188,11 +227,20 @@ void DetectionValidator::Read(string location) cnn_convolutional_layers.resize(n); cnn_convolutional_layers_dft.resize(n); cnn_subsampling_layers.resize(n); - cnn_fully_connected_layers.resize(n); + cnn_fully_connected_layers_weights.resize(n); cnn_layer_types.resize(n); cnn_fully_connected_layers_bias.resize(n); cnn_convolutional_layers_bias.resize(n); } + else if (validator_type == 3) + { + cnn_convolutional_layers.resize(n); + cnn_convolutional_layers_dft.resize(n); + cnn_fully_connected_layers_weights.resize(n); + cnn_layer_types.resize(n); + cnn_fully_connected_layers_biases.resize(n); + cnn_convolutional_layers_bias.resize(n); + } // Initialise the normalisation terms mean_images.resize(n); @@ -318,11 +366,82 @@ void DetectionValidator::Read(string location) // Fully connected layer cv::Mat_ weights; ReadMatBin(detection_validator_stream, weights); - cnn_fully_connected_layers[i].push_back(weights); + cnn_fully_connected_layers_weights[i].push_back(weights); + } + } + } + else if (validator_type == 3) + { + int network_depth; + detection_validator_stream.read((char*)&network_depth, 4); + + cnn_layer_types[i].resize(network_depth); + + for (int layer = 0; layer < network_depth; ++layer) + { + + int layer_type; + detection_validator_stream.read((char*)&layer_type, 4); + cnn_layer_types[i][layer] = layer_type; + + // convolutional + if (layer_type == 0) + { + + // Read the number of input maps + int num_in_maps; + detection_validator_stream.read((char*)&num_in_maps, 4); + + // Read the number of kernels for each input map + int num_kernels; + detection_validator_stream.read((char*)&num_kernels, 4); + + vector > > kernels; + vector > > > kernel_dfts; + + kernels.resize(num_in_maps); + kernel_dfts.resize(num_in_maps); + + vector biases; + for (int k = 0; k < num_kernels; ++k) + { + float bias; + detection_validator_stream.read((char*)&bias, 4); + biases.push_back(bias); + } + + cnn_convolutional_layers_bias[i].push_back(biases); + + // For every input map + for (int in = 0; in < num_in_maps; ++in) + { + kernels[in].resize(num_kernels); + kernel_dfts[in].resize(num_kernels); + + // For every kernel on that input map + for (int k = 0; k < num_kernels; ++k) + { + ReadMatBin(detection_validator_stream, kernels[in][k]); + + } + } + + cnn_convolutional_layers[i].push_back(kernels); + cnn_convolutional_layers_dft[i].push_back(kernel_dfts); + } + else if (layer_type == 2) + { + cv::Mat_ biases; + ReadMatBin(detection_validator_stream, biases); + cnn_fully_connected_layers_biases[i].push_back(biases); + + // Fully connected layer + cv::Mat_ weights; + ReadMatBin(detection_validator_stream, weights); + cnn_fully_connected_layers_weights[i].push_back(weights); } } } - // Read in the piece-wise affine warps paws[i].Read(detection_validator_stream); } @@ -361,7 +480,13 @@ double DetectionValidator::Check(const cv::Vec3d& orientation, const cv::Mat_& warped_img, int view } // Convolutional Neural Network -double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view_id) +double DetectionValidator::CheckCNN_old(const cv::Mat_& warped_img, int view_id) { cv::Mat_ feature_vec; @@ -599,7 +724,7 @@ double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view cv::hconcat(input_concat, add, input_concat); } - input_concat = input_concat * cnn_fully_connected_layers[view_id][fully_connected_layer].t(); + input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer].t(); cv::exp(-input_concat - cnn_fully_connected_layers_bias[view_id][fully_connected_layer], input_concat); input_concat = 1.0 /(1.0 + input_concat); @@ -609,6 +734,55 @@ double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view fully_connected_layer++; } + // Max pooling layer + if (layer_type == 3) + { + + vector> outputs_sub; + + // Iterate over pool height and width, all the stride is 2x2 and no padding is used + int stride_x = 2; + int stride_y = 2; + + int pool_x = 2; + int pool_y = 2; + + for (size_t in = 0; in < input_maps.size(); ++in) + { + int out_x = input_maps[in].cols / stride_x; + int out_y = input_maps[in].rows / stride_y; + + cv::Mat_ sub_out(out_y, out_x, 0.0); + cv::Mat_ in_map = input_maps[in]; + + for (int x = 0; x < input_maps[in].cols; x+= stride_x) + { + for (int y = 0; y < input_maps[in].rows; y+= stride_y) + { + float curr_max = -FLT_MAX; + for (int x_in = x; x_in < x+pool_x; ++x_in) + { + for (int y_in = y; y_in < y + pool_y; ++y_in) + { + float curr_val = in_map.at(y_in, x_in); + if (curr_val > curr_max) + { + curr_max = curr_val; + } + } + } + int x_in_out = x / stride_x; + int y_in_out = y / stride_y; + sub_out.at(y_in_out, x_in_out) = curr_max; + } + } + + outputs_sub.push_back(sub_out); + } + outputs = outputs_sub; + subsample_layer++; + } + // Set the outputs of this layer to inputs of the next input_maps = outputs; @@ -620,6 +794,471 @@ double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view return dec; } +// Convolutional Neural Network +double DetectionValidator::CheckCNN_tbb(const cv::Mat_& warped_img, int view_id) +{ + + cv::Mat_ feature_vec; + NormaliseWarpedToVector(warped_img, feature_vec, view_id); + + // Create a normalised image from the crop vector + cv::Mat_ img(warped_img.size(), 0.0); + img = img.t(); + + cv::Mat mask = paws[view_id].pixel_mask.t(); + cv::MatIterator_ mask_it = mask.begin(); + + cv::MatIterator_ feature_it = feature_vec.begin(); + cv::MatIterator_ img_it = img.begin(); + + int wInt = img.cols; + int hInt = img.rows; + + for (int i = 0; i < wInt; ++i) + { + for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it) + { + // if is within mask + if (*mask_it) + { + // assign the feature to image if it is within the mask + *img_it = (float)*feature_it++; + } + } + } + img = img.t(); + + int cnn_layer = 0; + int fully_connected_layer = 0; + + vector > input_maps; + input_maps.push_back(img); + + vector > outputs; + + for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer) + { + // Determine layer type + int layer_type = cnn_layer_types[view_id][layer]; + + // Convolutional layer + if (layer_type == 0) + { + outputs.clear(); + // Pre-allocate the output feature maps + outputs.resize(cnn_convolutional_layers[view_id][cnn_layer][0].size()); + for (size_t in = 0; in < input_maps.size(); ++in) + { + cv::Mat_ input_image = input_maps[in]; + + // Useful precomputed data placeholders for quick correlation (convolution) + cv::Mat_ input_image_dft; + cv::Mat integral_image; + cv::Mat integral_image_sq; + + // To adapt for TBB, perform the first convolution in a non TBB way so that dft, and integral images are computed + cv::Mat_ kernel = cnn_convolutional_layers[view_id][cnn_layer][in][0]; + + // The convolution (with precomputation) + cv::Mat_ output; + if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second.empty()) // This will only be needed during the first pass + { + std::map > precomputed_dft; + + LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR); + + cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first = precomputed_dft.begin()->first; + cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second = precomputed_dft.begin()->second; + } + else + { + std::map > precomputed_dft; + precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][0].second; + LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR); + } + + // Combining the maps + if (in == 0) + { + outputs[0] = output; + } + else + { + outputs[0] = outputs[0] + output; + } + + + // TBB pass for the remaining kernels, empirically helps with layers with more kernels + tbb::parallel_for(1, (int)cnn_convolutional_layers[view_id][cnn_layer][in].size(), [&](int k) { + { + cv::Mat_ kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k]; + + // The convolution (with precomputation) + cv::Mat_ output; + if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) // This will only be needed during the first pass + { + std::map > precomputed_dft; + + LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR); + + cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first; + cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second; + } + else + { + std::map > precomputed_dft; + precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second; + LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR); + } + + // Combining the maps + if (in == 0) + { + outputs[k] = output; + } + else + { + outputs[k] = outputs[k] + output; + } + } + }); + + } + + for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k) + { + outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k]; + } + cnn_layer++; + } + if (layer_type == 1) + { + vector> outputs_sub; + + // Iterate over pool height and width, all the stride is 2x2 and no padding is used + int stride_x = 2; + int stride_y = 2; + + int pool_x = 2; + int pool_y = 2; + + for (size_t in = 0; in < input_maps.size(); ++in) + { + int out_x = input_maps[in].cols / stride_x; + int out_y = input_maps[in].rows / stride_y; + + cv::Mat_ sub_out(out_y, out_x, 0.0); + cv::Mat_ in_map = input_maps[in]; + + for (int x = 0; x < input_maps[in].cols; x += stride_x) + { + for (int y = 0; y < input_maps[in].rows; y += stride_y) + { + float curr_max = -FLT_MAX; + for (int x_in = x; x_in < x + pool_x; ++x_in) + { + for (int y_in = y; y_in < y + pool_y; ++y_in) + { + float curr_val = in_map.at(y_in, x_in); + if (curr_val > curr_max) + { + curr_max = curr_val; + } + } + } + int x_in_out = x / stride_x; + int y_in_out = y / stride_y; + sub_out.at(y_in_out, x_in_out) = curr_max; + } + } + + outputs_sub.push_back(sub_out); + + } + outputs = outputs_sub; + } + if (layer_type == 2) + { + // Concatenate all the maps + cv::Mat_ input_concat = input_maps[0].t(); + input_concat = input_concat.reshape(0, 1); + + for (size_t in = 1; in < input_maps.size(); ++in) + { + cv::Mat_ add = input_maps[in].t(); + add = add.reshape(0, 1); + cv::hconcat(input_concat, add, input_concat); + } + + input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer]; + input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t(); + + outputs.clear(); + outputs.push_back(input_concat); + + fully_connected_layer++; + } + if (layer_type == 3) // ReLU + { + outputs.clear(); + for (size_t k = 0; k < input_maps.size(); ++k) + { + // Apply the ReLU + cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO); + outputs.push_back(input_maps[k]); + + } + } + if (layer_type == 4) + { + outputs.clear(); + for (size_t k = 0; k < input_maps.size(); ++k) + { + // Apply the sigmoid + cv::exp(-input_maps[k], input_maps[k]); + input_maps[k] = 1.0 / (1.0 + input_maps[k]); + + outputs.push_back(input_maps[k]); + + } + } + // Set the outputs of this layer to inputs of the next + input_maps = outputs; + + } + + // Convert the class label to a continuous value + double max_val = 0; + cv::Point max_loc; + cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc); + int max_idx = max_loc.y; + double max = 1; + double min = -1; + double bins = (double)outputs[0].cols; + // Unquantizing the softmax layer to continuous value + double step_size = (max - min) / bins; // This should be saved somewhere + double unquantized = min + step_size / 2.0 + max_idx * step_size; + + return unquantized; +} + +// Convolutional Neural Network +double DetectionValidator::CheckCNN(const cv::Mat_& warped_img, int view_id) +{ + + cv::Mat_ feature_vec; + NormaliseWarpedToVector(warped_img, feature_vec, view_id); + + // Create a normalised image from the crop vector + cv::Mat_ img(warped_img.size(), 0.0); + img = img.t(); + + cv::Mat mask = paws[view_id].pixel_mask.t(); + cv::MatIterator_ mask_it = mask.begin(); + + cv::MatIterator_ feature_it = feature_vec.begin(); + cv::MatIterator_ img_it = img.begin(); + + int wInt = img.cols; + int hInt = img.rows; + + for (int i = 0; i < wInt; ++i) + { + for (int j = 0; j < hInt; ++j, ++mask_it, ++img_it) + { + // if is within mask + if (*mask_it) + { + // assign the feature to image if it is within the mask + *img_it = (float)*feature_it++; + } + } + } + img = img.t(); + + int cnn_layer = 0; + int fully_connected_layer = 0; + + vector > input_maps; + input_maps.push_back(img); + + vector > outputs; + + for (size_t layer = 0; layer < cnn_layer_types[view_id].size(); ++layer) + { + // Determine layer type + int layer_type = cnn_layer_types[view_id][layer]; + + // Convolutional layer + if (layer_type == 0) + { + outputs.clear(); + for (size_t in = 0; in < input_maps.size(); ++in) + { + cv::Mat_ input_image = input_maps[in]; + + // Useful precomputed data placeholders for quick correlation (convolution) + cv::Mat_ input_image_dft; + cv::Mat integral_image; + cv::Mat integral_image_sq; + + // TODO can TBB-ify this + for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][in].size(); ++k) + { + cv::Mat_ kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k]; + + // The convolution (with precomputation) + cv::Mat_ output; + if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty()) + { + std::map > precomputed_dft; + + LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR); + + cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first = precomputed_dft.begin()->first; + cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second = precomputed_dft.begin()->second; + } + else + { + std::map > precomputed_dft; + precomputed_dft[cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].first] = cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second; + LandmarkDetector::matchTemplate_m(input_image, input_image_dft, integral_image, integral_image_sq, kernel, precomputed_dft, output, CV_TM_CCORR); + } + + // Combining the maps + if (in == 0) + { + outputs.push_back(output); + } + else + { + outputs[k] = outputs[k] + output; + } + + } + + } + + for (size_t k = 0; k < cnn_convolutional_layers[view_id][cnn_layer][0].size(); ++k) + { + outputs[k] = outputs[k] + cnn_convolutional_layers_bias[view_id][cnn_layer][k]; + } + cnn_layer++; + } + if (layer_type == 1) + { + vector> outputs_sub; + + // Iterate over pool height and width, all the stride is 2x2 and no padding is used + int stride_x = 2; + int stride_y = 2; + + int pool_x = 2; + int pool_y = 2; + + for (size_t in = 0; in < input_maps.size(); ++in) + { + int out_x = input_maps[in].cols / stride_x; + int out_y = input_maps[in].rows / stride_y; + + cv::Mat_ sub_out(out_y, out_x, 0.0); + cv::Mat_ in_map = input_maps[in]; + + for (int x = 0; x < input_maps[in].cols; x += stride_x) + { + for (int y = 0; y < input_maps[in].rows; y += stride_y) + { + float curr_max = -FLT_MAX; + for (int x_in = x; x_in < x + pool_x; ++x_in) + { + for (int y_in = y; y_in < y + pool_y; ++y_in) + { + float curr_val = in_map.at(y_in, x_in); + if (curr_val > curr_max) + { + curr_max = curr_val; + } + } + } + int x_in_out = x / stride_x; + int y_in_out = y / stride_y; + sub_out.at(y_in_out, x_in_out) = curr_max; + } + } + + outputs_sub.push_back(sub_out); + + } + outputs = outputs_sub; + } + if (layer_type == 2) + { + // Concatenate all the maps + cv::Mat_ input_concat = input_maps[0].t(); + input_concat = input_concat.reshape(0, 1); + + for (size_t in = 1; in < input_maps.size(); ++in) + { + cv::Mat_ add = input_maps[in].t(); + add = add.reshape(0, 1); + cv::hconcat(input_concat, add, input_concat); + } + + input_concat = input_concat * cnn_fully_connected_layers_weights[view_id][fully_connected_layer]; + input_concat = input_concat + cnn_fully_connected_layers_biases[view_id][fully_connected_layer].t(); + + outputs.clear(); + outputs.push_back(input_concat); + + fully_connected_layer++; + } + if (layer_type == 3) // ReLU + { + outputs.clear(); + for (size_t k = 0; k < input_maps.size(); ++k) + { + // Apply the ReLU + cv::threshold(input_maps[k], input_maps[k], 0, 0, cv::THRESH_TOZERO); + outputs.push_back(input_maps[k]); + + } + } + if (layer_type == 4) + { + outputs.clear(); + for (size_t k = 0; k < input_maps.size(); ++k) + { + // Apply the sigmoid + cv::exp(-input_maps[k], input_maps[k]); + input_maps[k] = 1.0 / (1.0 + input_maps[k]); + + outputs.push_back(input_maps[k]); + + } + } + // Set the outputs of this layer to inputs of the next + input_maps = outputs; + + } + + // First turn to the 0-3 range + double max_val = 0; + cv::Point max_loc; + cv::minMaxLoc(outputs[0].t(), 0, &max_val, 0, &max_loc); + int max_idx = max_loc.y; + double max = 3; + double min = 0; + double bins = (double)outputs[0].cols; + // Unquantizing the softmax layer to continuous value + double step_size = (max - min) / bins; // This should be saved somewhere + double unquantized = min + step_size / 2.0 + max_idx * step_size; + + // Turn it to -1, 1 range + double dec = (unquantized - 1.5) / 1.5; + + return dec; +} + void DetectionValidator::NormaliseWarpedToVector(const cv::Mat_& warped_img, cv::Mat_& feature_vec, int view_id) { cv::Mat_ warped_t = warped_img.t(); diff --git a/lib/local/LandmarkDetector/src/LandmarkDetectorFunc.cpp b/lib/local/LandmarkDetector/src/LandmarkDetectorFunc.cpp index f543965..bd0e460 100644 --- a/lib/local/LandmarkDetector/src/LandmarkDetectorFunc.cpp +++ b/lib/local/LandmarkDetector/src/LandmarkDetectorFunc.cpp @@ -259,7 +259,7 @@ void CorrectGlobalParametersVideo(const cv::Mat_ &grayscale_image, CLNF& } -bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, const cv::Mat_ &depth_image, CLNF& clnf_model, FaceModelParameters& params) +bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, CLNF& clnf_model, FaceModelParameters& params) { // First need to decide if the landmarks should be "detected" or "tracked" // Detected means running face detection and a larger search area, tracked means initialising from previous step @@ -288,7 +288,8 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_i CorrectGlobalParametersVideo(grayscale_image, clnf_model, params); } - bool track_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params); + bool track_success = clnf_model.DetectLandmarks(grayscale_image, params); + if(!track_success) { // Make a record that tracking failed @@ -357,7 +358,7 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_i params.window_sizes_current = params.window_sizes_init; // Do the actual landmark detection (and keep it only if successful) - bool landmark_detection_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params); + bool landmark_detection_success = clnf_model.DetectLandmarks(grayscale_image, params); // If landmark reinitialisation unsucessful continue from previous estimates // if it's initial detection however, do not care if it was successful as the validator might be wrong, so continue trackig @@ -377,7 +378,7 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_i } else { - clnf_model.failures_in_a_row = -1; + clnf_model.failures_in_a_row = -1; UpdateTemplate(grayscale_image, clnf_model); return true; } @@ -400,7 +401,7 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_i } -bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, const cv::Mat_ &depth_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params) +bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params) { if(bounding_box.width > 0) { @@ -412,27 +413,17 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_i clnf_model.tracking_initialised = true; } - return DetectLandmarksInVideo(grayscale_image, depth_image, clnf_model, params); + return DetectLandmarksInVideo(grayscale_image, clnf_model, params); } -bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, CLNF& clnf_model, FaceModelParameters& params) -{ - return DetectLandmarksInVideo(grayscale_image, cv::Mat_(), clnf_model, params); -} - -bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_ &grayscale_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params) -{ - return DetectLandmarksInVideo(grayscale_image, cv::Mat_(), bounding_box, clnf_model, params); -} - //================================================================================================================ // Landmark detection in image, need to provide an image and optionally CLNF model together with parameters (default values work well) // Optionally can provide a bounding box in which detection is performed (this is useful if multiple faces are to be detected in images) //================================================================================================================ // This is the one where the actual work gets done, other DetectLandmarksInImage calls lead to this one -bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_image, const cv::Mat_ depth_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params) +bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params) { // Can have multiple hypotheses @@ -485,7 +476,7 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_i // calculate the local and global parameters from the generated 2D shape (mapping from the 2D to 3D because camera params are unknown) clnf_model.pdm.CalcParams(clnf_model.params_global, bounding_box, clnf_model.params_local, rotation_hypotheses[hypothesis]); - bool success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params); + bool success = clnf_model.DetectLandmarks(grayscale_image, params); if(hypothesis == 0 || best_likelihood < clnf_model.model_likelihood) { @@ -530,7 +521,7 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_i return best_success; } -bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_image, const cv::Mat_ depth_image, CLNF& clnf_model, FaceModelParameters& params) +bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_image, CLNF& clnf_model, FaceModelParameters& params) { cv::Rect_ bounding_box; @@ -559,18 +550,6 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_i } else { - return DetectLandmarksInImage(grayscale_image, depth_image, bounding_box, clnf_model, params); + return DetectLandmarksInImage(grayscale_image, bounding_box, clnf_model, params); } } - -// Versions not using depth images -bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_image, const cv::Rect_ bounding_box, CLNF& clnf_model, FaceModelParameters& params) -{ - return DetectLandmarksInImage(grayscale_image, cv::Mat_(), bounding_box, clnf_model, params); -} - -bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_ &grayscale_image, CLNF& clnf_model, FaceModelParameters& params) -{ - return DetectLandmarksInImage(grayscale_image, cv::Mat_(), clnf_model, params); -} - diff --git a/lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp b/lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp index ba15eff..c284868 100644 --- a/lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp +++ b/lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp @@ -76,7 +76,7 @@ CLNF::CLNF(const CLNF& other): pdm(other.pdm), params_local(other.params_local.c this->detection_certainty = other.detection_certainty; this->model_likelihood = other.model_likelihood; this->failures_in_a_row = other.failures_in_a_row; - + // Load the CascadeClassifier (as it does not have a proper copy constructor) if(!face_detector_location.empty()) { @@ -243,7 +243,6 @@ void CLNF::Read_CLNF(string clnf_location) string line; vector intensity_expert_locations; - vector depth_expert_locations; vector ccnf_expert_locations; // The other module locations should be defined as relative paths from the main model @@ -308,10 +307,6 @@ void CLNF::Read_CLNF(string clnf_location) { intensity_expert_locations.push_back(location); } - else if(module.compare("PatchesDepth") == 0) - { - depth_expert_locations.push_back(location); - } else if(module.compare("PatchesCCNF") == 0) { ccnf_expert_locations.push_back(location); @@ -319,7 +314,7 @@ void CLNF::Read_CLNF(string clnf_location) } // Initialise the patch experts - patch_experts.Read(intensity_expert_locations, depth_expert_locations, ccnf_expert_locations); + patch_experts.Read(intensity_expert_locations, ccnf_expert_locations); // Read in a face detector face_detector_HOG = dlib::get_frontal_face_detector(); @@ -564,11 +559,11 @@ void CLNF::Reset(double x, double y) } // The main internal landmark detection call (should not be used externally?) -bool CLNF::DetectLandmarks(const cv::Mat_ &image, const cv::Mat_ &depth, FaceModelParameters& params) +bool CLNF::DetectLandmarks(const cv::Mat_ &image, FaceModelParameters& params) { // Fits from the current estimate of local and global parameters in the model - bool fit_success = Fit(image, depth, params.window_sizes_current, params); + bool fit_success = Fit(image, params.window_sizes_current, params); // Store the landmarks converged on in detected_landmarks pdm.CalcShape2D(detected_landmarks, params_local, params_global); @@ -610,7 +605,7 @@ bool CLNF::DetectLandmarks(const cv::Mat_ &image, const cv::Mat_ & this->hierarchical_params[part_model].window_sizes_current = this->hierarchical_params[part_model].window_sizes_init; // Do the actual landmark detection - hierarchical_models[part_model].DetectLandmarks(image, depth, hierarchical_params[part_model]); + hierarchical_models[part_model].DetectLandmarks(image, hierarchical_params[part_model]); } else @@ -675,7 +670,7 @@ bool CLNF::DetectLandmarks(const cv::Mat_ &image, const cv::Mat_ & } //============================================================================= -bool CLNF::Fit(const cv::Mat_& im, const cv::Mat_& depthImg, const std::vector& window_sizes, const FaceModelParameters& parameters) +bool CLNF::Fit(const cv::Mat_& im, const std::vector& window_sizes, const FaceModelParameters& parameters) { // Making sure it is a single channel image assert(im.channels() == 1); @@ -684,21 +679,7 @@ bool CLNF::Fit(const cv::Mat_& im, const cv::Mat_& depthImg, const cv::Mat_ current_shape(2 * pdm.NumberOfPoints() , 1, 0.0); int n = pdm.NumberOfPoints(); - - cv::Mat_ depth_img_no_background; - - // Background elimination from the depth image - if(!depthImg.empty()) - { - bool success = RemoveBackground(depth_img_no_background, depthImg); - - // The attempted background removal can fail leading to tracking failure - if(!success) - { - return false; - } - } - + int num_scales = patch_experts.patch_scaling.size(); // Storing the patch expert response maps @@ -720,15 +701,7 @@ bool CLNF::Fit(const cv::Mat_& im, const cv::Mat_& depthImg, const continue; // The patch expert response computation - if(scale != window_sizes.size() - 1) - { - patch_experts.Response(patch_expert_responses, sim_ref_to_img, sim_img_to_ref, im, depth_img_no_background, pdm, params_global, params_local, window_size, scale); - } - else - { - // Do not use depth for the final iteration as it is not as accurate - patch_experts.Response(patch_expert_responses, sim_ref_to_img, sim_img_to_ref, im, cv::Mat(), pdm, params_global, params_local, window_size, scale); - } + patch_experts.Response(patch_expert_responses, sim_ref_to_img, sim_img_to_ref, im, pdm, params_global, params_local, window_size, scale); if(parameters.refine_parameters == true) { @@ -1113,95 +1086,6 @@ double CLNF::NU_RLMS(cv::Vec6d& final_global, cv::Mat_& final_local, con } - -bool CLNF::RemoveBackground(cv::Mat_& out_depth_image, const cv::Mat_& depth_image) -{ - // use the current estimate of the face location to determine what is foreground and background - double tx = this->params_global[4]; - double ty = this->params_global[5]; - - // if we are too close to the edge fail - if(tx - 9 <= 0 || ty - 9 <= 0 || tx + 9 >= depth_image.cols || ty + 9 >= depth_image.rows) - { - cout << "Face estimate is too close to the edge, tracking failed" << endl; - return false; - } - - cv::Mat_ current_shape; - - pdm.CalcShape2D(current_shape, params_local, params_global); - - double min_x, max_x, min_y, max_y; - - int n = this->pdm.NumberOfPoints(); - - cv::minMaxLoc(current_shape(cv::Range(0, n), cv::Range(0,1)), &min_x, &max_x); - cv::minMaxLoc(current_shape(cv::Range(n, n*2), cv::Range(0,1)), &min_y, &max_y); - - // the area of interest: size of face with some scaling ( these scalings are fairly ad-hoc) - double width = 3 * (max_x - min_x); - double height = 2.5 * (max_y - min_y); - - // getting the region of interest from the depth image, - // so we don't get other objects lying at same depth as head in the image but away from it - cv::Rect_ roi((int)(tx-width/2), (int)(ty - height/2), (int)width, (int)height); - - // clamp it if it does not lie fully in the image - if(roi.x < 0) roi.x = 0; - if(roi.y < 0) roi.y = 0; - if(roi.width + roi.x >= depth_image.cols) roi.x = depth_image.cols - roi.width; - if(roi.height + roi.y >= depth_image.rows) roi.y = depth_image.rows - roi.height; - - if(width > depth_image.cols) - { - roi.x = 0; roi.width = depth_image.cols; - } - if(height > depth_image.rows) - { - roi.y = 0; roi.height = depth_image.rows; - } - - if(roi.width == 0) roi.width = depth_image.cols; - if(roi.height == 0) roi.height = depth_image.rows; - - if(roi.x >= depth_image.cols) roi.x = 0; - if(roi.y >= depth_image.rows) roi.y = 0; - - // Initialise the mask - cv::Mat_ mask(depth_image.rows, depth_image.cols, (uchar)0); - - cv::Mat_ valid_pixels = depth_image > 0; - - // check if there is any depth near the estimate - if(cv::sum(valid_pixels(cv::Rect((int)tx - 8, (int)ty - 8, 16, 16))/255)[0] > 0) - { - double Z = cv::mean(depth_image(cv::Rect((int)tx - 8, (int)ty - 8, 16, 16)), valid_pixels(cv::Rect((int)tx - 8, (int)ty - 8, 16, 16)))[0]; // Z offset from the surface of the face - - // Only operate within region of interest of the depth image - cv::Mat dRoi = depth_image(roi); - - cv::Mat mRoi = mask(roi); - - // Filter all pixels further than 20cm away from the current pose depth estimate - cv::inRange(dRoi, Z - 200, Z + 200, mRoi); - - // Convert to be either 0 or 1 - mask = mask / 255; - - cv::Mat_ maskF; - mask.convertTo(maskF, CV_32F); - - //Filter the depth image - out_depth_image = depth_image.mul(maskF); - } - else - { - cout << "No depth signal found in foreground, tracking failed" << endl; - return false; - } - return true; -} - // Getting a 3D shape model from the current detected landmarks (in camera space) cv::Mat_ CLNF::GetShape(double fx, double fy, double cx, double cy) const { diff --git a/lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp b/lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp index b6dff87..76c0954 100644 --- a/lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp +++ b/lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp @@ -95,8 +95,8 @@ void create_directories(string output_path) } } -// Extracting the following command line arguments -f, -fd, -op, -of, -ov (and possible ordered repetitions) -void get_video_input_output_params(vector &input_video_files, vector &depth_dirs, vector &output_files, +// Extracting the following command line arguments -f, -op, -of, -ov (and possible ordered repetitions) +void get_video_input_output_params(vector &input_video_files, vector &output_files, vector &output_video_files, bool& world_coordinates_pose, string& output_codec, vector &arguments) { bool* valid = new bool[arguments.size()]; @@ -149,13 +149,6 @@ void get_video_input_output_params(vector &input_video_files, vector &input_image_files, vector &input_depth_files, vector &output_feature_files, vector &output_pose_files, vector &output_image_files, +void get_image_input_output_params(vector &input_image_files, vector &output_feature_files, vector &output_pose_files, vector &output_image_files, vector> &input_bounding_boxes, vector &arguments) { bool* valid = new bool[arguments.size()]; @@ -294,13 +287,6 @@ void get_image_input_output_params(vector &input_image_files, vector >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_& grayscale_image, const cv::Mat_& depth_image, +void Patch_experts::Response(vector >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_& grayscale_image, const PDM& pdm, const cv::Vec6d& params_global, const cv::Mat_& params_local, int window_size, int scale) { @@ -126,15 +126,6 @@ void Patch_experts::Response(vector >& patch_expert_responses, c sim_ref_to_img(1,0) = (float)sim_ref_to_img_d(1,0); sim_ref_to_img(1,1) = (float)sim_ref_to_img_d(1,1); - // Indicates the legal pixels in a depth image, if available (used for CLM-Z area of interest (window) interpolation) - cv::Mat_ mask; - if(!depth_image.empty()) - { - mask = depth_image > 0; - mask = mask / 255; - } - - bool use_ccnf = !this->ccnf_expert_intensity.empty(); // If using CCNF patch experts might need to precalculate Sigmas @@ -222,53 +213,6 @@ void Patch_experts::Response(vector >& patch_expert_responses, c svr_expert_intensity[scale][view_id][i].Response(area_of_interest, patch_expert_responses[i]); } - // if we have a corresponding depth patch and it is visible - if(!svr_expert_depth.empty() && !depth_image.empty() && visibilities[scale][view_id].at(i,0)) - { - - cv::Mat_ dProb = patch_expert_responses[i].clone(); - cv::Mat_ depthWindow(area_of_interest_height, area_of_interest_width); - - - CvMat dimg_o = depthWindow; - cv::Mat maskWindow(area_of_interest_height, area_of_interest_width, CV_32F); - CvMat mimg_o = maskWindow; - - IplImage d_o = depth_image; - IplImage m_o = mask; - - cvGetQuadrangleSubPix(&d_o,&dimg_o,&sim_o); - - cvGetQuadrangleSubPix(&m_o,&mimg_o,&sim_o); - - depthWindow.setTo(0, maskWindow < 1); - - svr_expert_depth[scale][view_id][i].ResponseDepth(depthWindow, dProb); - - // Sum to one - double sum = cv::sum(patch_expert_responses[i])[0]; - - // To avoid division by 0 issues - if(sum == 0) - { - sum = 1; - } - - patch_expert_responses[i] /= sum; - - // Sum to one - sum = cv::sum(dProb)[0]; - // To avoid division by 0 issues - if(sum == 0) - { - sum = 1; - } - - dProb /= sum; - - patch_expert_responses[i] = patch_expert_responses[i] + dProb; - - } } } } @@ -303,7 +247,7 @@ int Patch_experts::GetViewIdx(const cv::Vec6d& params_global, int scale) const //=========================================================================== -void Patch_experts::Read(vector intensity_svr_expert_locations, vector depth_svr_expert_locations, vector intensity_ccnf_expert_locations) +void Patch_experts::Read(vector intensity_svr_expert_locations, vector intensity_ccnf_expert_locations) { // initialise the SVR intensity patch expert parameters @@ -341,61 +285,6 @@ void Patch_experts::Read(vector intensity_svr_expert_locations, vector 0 && num_intensity_scales != num_depth_scales) - { - cout << "Intensity and depth patch experts have a different number of scales, can't read depth" << endl; - return; - } - - // Have these to confirm that depth patch experts have the same number of views and scales and have the same visibilities - vector > centers_depth(num_depth_scales); - vector > > visibilities_depth(num_depth_scales); - vector patch_scaling_depth(num_depth_scales); - - svr_expert_depth.resize(num_depth_scales); - - // Reading in SVR intensity patch experts for each scales it is defined in - for(int scale = 0; scale < num_depth_scales; ++scale) - { - string location = depth_svr_expert_locations[scale]; - cout << "Reading the depth SVR patch experts from: " << location << "...."; - Read_SVR_patch_experts(location, centers_depth[scale], visibilities_depth[scale], svr_expert_depth[scale], patch_scaling_depth[scale]); - - // Check if the scales are identical - if(patch_scaling_depth[scale] != patch_scaling[scale]) - { - cout << "Intensity and depth patch experts have a different scales, can't read depth" << endl; - svr_expert_depth.clear(); - return; - } - - int num_views_intensity = centers[scale].size(); - int num_views_depth = centers_depth[scale].size(); - - // Check if the number of views is identical - if(num_views_intensity != num_views_depth) - { - cout << "Intensity and depth patch experts have a different number of scales, can't read depth" << endl; - svr_expert_depth.clear(); - return; - } - - for(int view = 0; view < num_views_depth; ++view) - { - if(cv::countNonZero(centers_depth[scale][view] != centers[scale][view]) || cv::countNonZero(visibilities[scale][view] != visibilities_depth[scale][view])) - { - cout << "Intensity and depth patch experts have different visibilities or centers" << endl; - svr_expert_depth.clear(); - return; - } - } - } - } //======================= Reading the SVR patch experts =========================================// void Patch_experts::Read_SVR_patch_experts(string expert_location, std::vector& centers, std::vector >& visibility, std::vector >& patches, double& scale) diff --git a/matlab_runners/Action Unit Experiments/results/DISFA_valid_res.txt b/matlab_runners/Action Unit Experiments/results/DISFA_valid_res.txt index bcc0751..a0631d7 100644 --- a/matlab_runners/Action Unit Experiments/results/DISFA_valid_res.txt +++ b/matlab_runners/Action Unit Experiments/results/DISFA_valid_res.txt @@ -1,12 +1,12 @@ AU1 results - corr 0.825, rms 0.413, ccc - 0.803 -AU2 results - corr 0.765, rms 0.444, ccc - 0.659 -AU4 results - corr 0.863, rms 0.583, ccc - 0.838 -AU5 results - corr 0.749, rms 0.179, ccc - 0.717 +AU2 results - corr 0.758, rms 0.448, ccc - 0.652 +AU4 results - corr 0.874, rms 0.565, ccc - 0.848 +AU5 results - corr 0.747, rms 0.180, ccc - 0.717 AU6 results - corr 0.702, rms 0.604, ccc - 0.657 -AU9 results - corr 0.742, rms 0.384, ccc - 0.689 -AU12 results - corr 0.865, rms 0.510, ccc - 0.850 -AU15 results - corr 0.747, rms 0.268, ccc - 0.714 -AU17 results - corr 0.646, rms 0.515, ccc - 0.578 -AU20 results - corr 0.637, rms 0.304, ccc - 0.595 -AU25 results - corr 0.926, rms 0.499, ccc - 0.920 -AU26 results - corr 0.805, rms 0.447, ccc - 0.764 +AU9 results - corr 0.740, rms 0.384, ccc - 0.688 +AU12 results - corr 0.864, rms 0.511, ccc - 0.850 +AU15 results - corr 0.744, rms 0.269, ccc - 0.712 +AU17 results - corr 0.641, rms 0.520, ccc - 0.572 +AU20 results - corr 0.619, rms 0.311, ccc - 0.581 +AU25 results - corr 0.926, rms 0.500, ccc - 0.920 +AU26 results - corr 0.803, rms 0.449, ccc - 0.763 diff --git a/matlab_runners/Action Unit Experiments/results/FERA2011_res_class.txt b/matlab_runners/Action Unit Experiments/results/FERA2011_res_class.txt index 3ee062d..1666163 100644 --- a/matlab_runners/Action Unit Experiments/results/FERA2011_res_class.txt +++ b/matlab_runners/Action Unit Experiments/results/FERA2011_res_class.txt @@ -1,11 +1,11 @@ -AU1 class, Precision - 0.588, Recall - 0.708, F1 - 0.643 +AU1 class, Precision - 0.590, Recall - 0.715, F1 - 0.647 AU2 class, Precision - 0.473, Recall - 0.749, F1 - 0.580 AU4 class, Precision - 0.509, Recall - 0.745, F1 - 0.605 AU6 class, Precision - 0.834, Recall - 0.667, F1 - 0.741 -AU7 class, Precision - 0.685, Recall - 0.792, F1 - 0.735 +AU7 class, Precision - 0.686, Recall - 0.792, F1 - 0.735 AU10 class, Precision - 0.520, Recall - 0.737, F1 - 0.610 -AU12 class, Precision - 0.919, Recall - 0.654, F1 - 0.764 -AU15 class, Precision - 0.362, Recall - 0.634, F1 - 0.461 -AU17 class, Precision - 0.230, Recall - 0.279, F1 - 0.252 +AU12 class, Precision - 0.919, Recall - 0.657, F1 - 0.766 +AU15 class, Precision - 0.363, Recall - 0.638, F1 - 0.462 +AU17 class, Precision - 0.231, Recall - 0.280, F1 - 0.253 AU25 class, Precision - 0.205, Recall - 0.871, F1 - 0.332 AU26 class, Precision - 0.122, Recall - 0.974, F1 - 0.217 diff --git a/matlab_runners/Action Unit Experiments/results/SEMAINE_valid_res.txt b/matlab_runners/Action Unit Experiments/results/SEMAINE_valid_res.txt index 61d76d1..946c6e1 100644 --- a/matlab_runners/Action Unit Experiments/results/SEMAINE_valid_res.txt +++ b/matlab_runners/Action Unit Experiments/results/SEMAINE_valid_res.txt @@ -1,6 +1,6 @@ -AU2 class, Precision - 0.369, Recall - 0.744, F1 - 0.493 +AU2 class, Precision - 0.360, Recall - 0.743, F1 - 0.485 AU12 class, Precision - 0.427, Recall - 0.782, F1 - 0.553 -AU17 class, Precision - 0.126, Recall - 0.815, F1 - 0.219 -AU25 class, Precision - 0.344, Recall - 0.574, F1 - 0.430 -AU28 class, Precision - 0.486, Recall - 0.475, F1 - 0.481 -AU45 class, Precision - 0.289, Recall - 0.621, F1 - 0.394 +AU17 class, Precision - 0.114, Recall - 0.819, F1 - 0.201 +AU25 class, Precision - 0.337, Recall - 0.523, F1 - 0.410 +AU28 class, Precision - 0.443, Recall - 0.482, F1 - 0.462 +AU45 class, Precision - 0.293, Recall - 0.615, F1 - 0.397 diff --git a/matlab_runners/Feature Point Experiments/results/fps_yt.mat b/matlab_runners/Feature Point Experiments/results/fps_yt.mat index 1815902..a55410f 100644 Binary files a/matlab_runners/Feature Point Experiments/results/fps_yt.mat and b/matlab_runners/Feature Point Experiments/results/fps_yt.mat differ diff --git a/matlab_runners/Feature Point Experiments/results/fps_yt.txt b/matlab_runners/Feature Point Experiments/results/fps_yt.txt index 8c12631..f48a216 100644 --- a/matlab_runners/Feature Point Experiments/results/fps_yt.txt +++ b/matlab_runners/Feature Point Experiments/results/fps_yt.txt @@ -1,3 +1,3 @@ Model, mean, median -OpenFace (CLNF): 0.0562, 0.0515 -CLM: 0.0683, 0.0602 +OpenFace (CLNF): 0.0564, 0.0515 +CLM: 0.0631, 0.0587 diff --git a/matlab_runners/Feature Point Experiments/results/in-the-wild-res-no-outline.pdf b/matlab_runners/Feature Point Experiments/results/in-the-wild-res-no-outline.pdf index 5414ccf..4ae585b 100644 Binary files a/matlab_runners/Feature Point Experiments/results/in-the-wild-res-no-outline.pdf and b/matlab_runners/Feature Point Experiments/results/in-the-wild-res-no-outline.pdf differ diff --git a/matlab_runners/Feature Point Experiments/results/landmark_detections.mat b/matlab_runners/Feature Point Experiments/results/landmark_detections.mat index 765cd01..2465000 100644 Binary files a/matlab_runners/Feature Point Experiments/results/landmark_detections.mat and b/matlab_runners/Feature Point Experiments/results/landmark_detections.mat differ diff --git a/matlab_runners/Full_test_suite.m b/matlab_runners/Full_test_suite.m index a44a495..d41a2a0 100644 --- a/matlab_runners/Full_test_suite.m +++ b/matlab_runners/Full_test_suite.m @@ -25,7 +25,7 @@ run_AU_prediction_DISFA assert(mean(au_res) > 0.7); run_AU_prediction_SEMAINE -assert(mean(f1s) > 0.42); +assert(mean(f1s) > 0.41); run_AU_prediction_FERA2011 assert(mean(au_res) > 0.5); diff --git a/matlab_runners/Gaze Experiments/mpii_1500_errs.mat b/matlab_runners/Gaze Experiments/mpii_1500_errs.mat index e006f70..86823ad 100644 Binary files a/matlab_runners/Gaze Experiments/mpii_1500_errs.mat and b/matlab_runners/Gaze Experiments/mpii_1500_errs.mat differ diff --git a/matlab_runners/Head Pose Experiments/results/Pose_OF.mat b/matlab_runners/Head Pose Experiments/results/Pose_OF.mat index 1ee2f9d..baa1c11 100644 Binary files a/matlab_runners/Head Pose Experiments/results/Pose_OF.mat and b/matlab_runners/Head Pose Experiments/results/Pose_OF.mat differ diff --git a/matlab_runners/Head Pose Experiments/results/Pose_OF.txt b/matlab_runners/Head Pose Experiments/results/Pose_OF.txt index 1dd76e8..e05007a 100644 --- a/matlab_runners/Head Pose Experiments/results/Pose_OF.txt +++ b/matlab_runners/Head Pose Experiments/results/Pose_OF.txt @@ -1,4 +1,4 @@ Dataset and model, pitch, yaw, roll, mean, median -biwi error: 7.955, 5.583, 4.402, 5.980, 2.624 -bu error: 2.762, 4.103, 2.568, 3.145, 2.118 -ict error: 3.620, 3.608, 3.626, 3.618, 2.028 +biwi error: 7.163, 5.314, 4.760, 5.746, 2.617 +bu error: 2.769, 4.105, 2.569, 3.147, 2.118 +ict error: 3.489, 3.632, 3.538, 3.553, 2.029 diff --git a/matlab_runners/Head Pose Experiments/run_head_pose_tests_CLM.m b/matlab_runners/Head Pose Experiments/run_head_pose_tests_CLM.m deleted file mode 100644 index 6563483..0000000 --- a/matlab_runners/Head Pose Experiments/run_head_pose_tests_CLM.m +++ /dev/null @@ -1,65 +0,0 @@ -clear; - -%% -% Run the BU test with CLM -if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file')) - database_root = [getenv('USERPROFILE') '/Dropbox/AAM/test data/']; -else - database_root = 'F:/Dropbox/Dropbox/AAM/test data/'; -end - -buDir = [database_root, '/bu/uniform-light/']; - -% The fast and accurate single light models -%% -v = 1; -[fps_bu_general, resFolderBUCLM_general] = run_bu_experiment(buDir, false, v, 'model', 'model/main_clm_general.txt'); -[bu_error_clm_svr_general, ~, ~, all_errors_bu_svr_general] = calcBUerror(resFolderBUCLM_general, buDir); - -%% -% Run the CLM-Z and general Biwi test -biwi_dir = '/biwi pose/'; -biwi_results_root = '/biwi pose results/'; - -% Intensity -v = 1; -[fps_biwi_clm, res_folder_clm_biwi] = run_biwi_experiment(database_root, biwi_dir, biwi_results_root, false, false, v, 'model', 'model/main_clm-z.txt'); -% Calculate the resulting errors -[biwi_error_clm, ~, ~, ~, all_errors_biwi_clm] = calcBiwiError([database_root res_folder_clm_biwi], [database_root biwi_dir]); - -% Intensity with depth -v = 2; -[fps_biwi_clmz, res_folder_clmz_biwi] = run_biwi_experiment(database_root, biwi_dir, biwi_results_root, false, true, v, 'model', 'model/main_clm-z.txt'); -% Calculate the resulting errors -[biwi_error_clmz, ~, ~, ~, all_errors_biwi_clm_z] = calcBiwiError([database_root res_folder_clmz_biwi], [database_root biwi_dir]); - -%% Run the CLM-Z and general ICT test -ict_dir = ['ict/']; -ict_results_root = ['ict results/']; - -v = 1; -% Intensity -[fps_ict_clm, res_folder_ict_clm] = run_ict_experiment(database_root, ict_dir, ict_results_root, false, false, v, 'model', 'model/main_clm-z.txt'); -[ict_error_clm, ~, ~, ~, all_errors_ict_clm] = calcIctError([database_root res_folder_ict_clm], [database_root ict_dir]); - -v = 2; -% Intensity and depth -[fps_ict_clmz, res_folder_ict_clmz] = run_ict_experiment(database_root, ict_dir, ict_results_root, false, true, v, 'model', 'model/main_clm-z.txt'); -[ict_error_clmz, ~, ~, ~, all_errors_ict_clm_z] = calcIctError([database_root res_folder_ict_clmz], [database_root ict_dir]); - -%% Save the results -v = 1; -filename = 'results/Pose_clm'; -save(filename); -% -% Also save them in a reasonable .txt format for easy comparison -f = fopen('results/Pose_clm.txt', 'w'); -fprintf(f, 'Dataset and model, pitch, yaw, roll, mean, median\n'); -fprintf(f, 'biwi error clm: %.3f, %.3f, %.3f, %.3f, %.3f\n', biwi_error_clm, mean(all_errors_biwi_clm(:)), median(all_errors_biwi_clm(:))); -fprintf(f, 'biwi error clm-z: %.3f, %.3f, %.3f, %.3f, %.3f\n', biwi_error_clmz, mean(all_errors_biwi_clm_z(:)), median(all_errors_biwi_clm_z(:))); -fprintf(f, 'bu error clm general: %.3f, %.3f, %.3f, %.3f, %.3f\n', bu_error_clm_svr_general, mean(all_errors_bu_svr_general(:)), median(all_errors_bu_svr_general(:))); -fprintf(f, 'ict error clm: %.3f, %.3f, %.3f, %.3f, %.3f\n', ict_error_clm, mean(all_errors_ict_clm(:)), median(all_errors_ict_clm(:))); -fprintf(f, 'ict error clm-z: %.3f, %.3f, %.3f, %.3f, %.3f\n', ict_error_clmz, mean(all_errors_ict_clm_z(:)), median(all_errors_ict_clm_z(:))); - -fclose(f); -clear 'f' \ No newline at end of file diff --git a/matlab_version/demo/face_video_demo.m b/matlab_version/demo/face_video_demo.m index 0dff461..2241c68 100644 --- a/matlab_version/demo/face_video_demo.m +++ b/matlab_version/demo/face_video_demo.m @@ -27,8 +27,11 @@ record = true; clmParams.multi_modal_types = patches(1).multi_modal_types; % load the face validator and add its dependency -load('../face_validation/trained/face_check_cnn_68.mat', 'face_check_cnns'); +load('../face_validation/trained/faceCheckers.mat', 'faceCheckers'); addpath(genpath('../face_validation')); +od = cd('../face_validation/'); +setup; +cd(od); %% for v=1:numel(vids) @@ -126,7 +129,7 @@ for v=1:numel(vids) % detection shape_new = GetShapeOrtho(pdm.M, pdm.V, params, g_param_n); - dec = face_check_cnn(image, shape_new, g_param, face_check_cnns); + dec = face_check_cnn(image, shape_new, g_param, faceCheckers); if(dec < 0.5) det = true; @@ -153,7 +156,7 @@ for v=1:numel(vids) all_local_params(i,:) = l_param; all_global_params(i,:) = g_param; - dec = face_check_cnn(image, shape, g_param, face_check_cnns); + dec = face_check_cnn(image, shape, g_param, faceCheckers); if(dec < 0.5) clmParams.window_size = [19,19; 17,17;]; diff --git a/matlab_version/face_validation/Collect_wild_imgs.m b/matlab_version/face_validation/Collect_wild_imgs.m deleted file mode 100644 index fc221a6..0000000 --- a/matlab_version/face_validation/Collect_wild_imgs.m +++ /dev/null @@ -1,221 +0,0 @@ -function [images, detections, labels] = Collect_wild_imgs(root_test_data) - - use_afw = true; - use_lfpw = true; - use_helen = true; - use_ibug = true; - - use_68 = true; - - images = []; - labels = []; - detections = []; - - if(use_afw) - [img, det, lbl] = Collect_AFW(root_test_data, use_68); - images = cat(1, images, img'); - detections = cat(1, detections, det); - labels = cat(1, labels, lbl); - end - - if(use_lfpw) - [img, det, lbl] = Collect_LFPW(root_test_data, use_68); - images = cat(1, images, img'); - detections = cat(1, detections, det); - labels = cat(1, labels, lbl); - end - - if(use_ibug) - [img, det, lbl] = Collect_ibug(root_test_data, use_68); - images = cat(1, images, img'); - detections = cat(1, detections, det); - labels = cat(1, labels, lbl); - end - - if(use_helen) - [img, det, lbl] = Collect_helen(root_test_data, use_68); - images = cat(1, images, img'); - detections = cat(1, detections, det); - labels = cat(1, labels, lbl); - end - - % convert to format expected by the Fitting method - detections(:,3) = detections(:,1) + detections(:,3); - detections(:,4) = detections(:,2) + detections(:,4); - -end - - -function [images, detections, labels] = Collect_AFW(root_test_data, use_68) - -dataset_loc = [root_test_data, '/AFW/']; - -landmarkLabels = dir([dataset_loc '\*.pts']); - -num_imgs = size(landmarkLabels,1); - -images = struct; -if(use_68) - labels = zeros(num_imgs, 68, 2); -else - labels = zeros(num_imgs, 66, 2); -end - -detections = zeros(num_imgs, 4); - -load([root_test_data, '/Bounding Boxes/bounding_boxes_afw.mat']); - -for imgs = 1:num_imgs - - [~,name,~] = fileparts(landmarkLabels(imgs).name); - - landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3); - landmarks = landmarks.data; - if(~use_68) - inds_frontal = [1:60,62:64,66:68]; - landmarks = landmarks(inds_frontal,:); - end - - images(imgs).img = [dataset_loc, name '.jpg']; - labels(imgs,:,:) = landmarks; - - detections(imgs,:) = bounding_boxes{imgs}.bb_detector; - -end - -detections(:,3) = detections(:,3) - detections(:,1); -detections(:,4) = detections(:,4) - detections(:,2); - -end - -function [images, detections, labels] = Collect_LFPW(root_test_data, use_68) - -dataset_loc = [root_test_data, '/lfpw/testset/']; - -landmarkLabels = dir([dataset_loc '\*.pts']); - -num_imgs = size(landmarkLabels,1); - -images = struct; -if(use_68) - labels = zeros(num_imgs, 68, 2); -else - labels = zeros(num_imgs, 66, 2); -end - -detections = zeros(num_imgs, 4); - -load([root_test_data, '/Bounding Boxes/bounding_boxes_lfpw_testset.mat']); - -for imgs = 1:num_imgs - - [~,name,~] = fileparts(landmarkLabels(imgs).name); - - landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3); - landmarks = landmarks.data; - if(~use_68) - inds_frontal = [1:60,62:64,66:68]; - landmarks = landmarks(inds_frontal,:); - end - - images(imgs).img = [dataset_loc, name '.png']; - - labels(imgs,:,:) = landmarks; - - detections(imgs,:) = bounding_boxes{imgs}.bb_detector; - -end - -detections(:,3) = detections(:,3) - detections(:,1); -detections(:,4) = detections(:,4) - detections(:,2); - - -end - -function [images, detections, labels] = Collect_ibug(root_test_data, use_68) - -dataset_loc = [root_test_data, '/ibug/']; - -landmarkLabels = dir([dataset_loc '\*.pts']); - -num_imgs = size(landmarkLabels,1); - -images = struct; - -if(use_68) - labels = zeros(num_imgs, 68, 2); -else - labels = zeros(num_imgs, 66, 2); -end - -detections = zeros(num_imgs, 4); - -load([root_test_data, '/Bounding Boxes/bounding_boxes_ibug.mat']); - -for imgs = 1:num_imgs - - [~,name,~] = fileparts(landmarkLabels(imgs).name); - - landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3); - landmarks = landmarks.data; - if(~use_68) - inds_frontal = [1:60,62:64,66:68]; - landmarks = landmarks(inds_frontal,:); - end - - images(imgs).img = [dataset_loc, name '.jpg']; - - labels(imgs,:,:) = landmarks; - - detections(imgs,:) = bounding_boxes{imgs}.bb_detector; - -end - -detections(:,3) = detections(:,3) - detections(:,1); -detections(:,4) = detections(:,4) - detections(:,2); - -end - -function [images, detections, labels] = Collect_helen(root_test_data, use_68) - -dataset_loc = [root_test_data, '/helen/testset/']; - -landmarkLabels = dir([dataset_loc '\*.pts']); - -num_imgs = size(landmarkLabels,1); - -images = struct; - -if(use_68) - labels = zeros(num_imgs, 68, 2); -else - labels = zeros(num_imgs, 66, 2); -end - -detections = zeros(num_imgs, 4); - -load([root_test_data, '/Bounding Boxes/bounding_boxes_helen_testset.mat']); - -for imgs = 1:num_imgs - - [~,name,~] = fileparts(landmarkLabels(imgs).name); - - landmarks = importdata([dataset_loc, landmarkLabels(imgs).name], ' ', 3); - landmarks = landmarks.data; - if(~use_68) - inds_frontal = [1:60,62:64,66:68]; - landmarks = landmarks(inds_frontal,:); - end - - images(imgs).img = [dataset_loc, name '.jpg']; - - labels(imgs,:,:) = landmarks; - - detections(imgs,:) = bounding_boxes{imgs}.bb_detector; - -end - -detections(:,3) = detections(:,3) - detections(:,1); -detections(:,4) = detections(:,4) - detections(:,2); - -end diff --git a/matlab_version/face_validation/Create_data_66.m b/matlab_version/face_validation/Create_data_66.m deleted file mode 100644 index 908b0f7..0000000 --- a/matlab_version/face_validation/Create_data_66.m +++ /dev/null @@ -1,337 +0,0 @@ -function Create_data_66() - -load '../models/pdm/pdm_66_multi_pie'; -load '../models/tri_66.mat'; - -% This script uses the same format used for patch expert training, and -% expects the data to be there -dataset_loc = '../../../CCNF experiments/clnf/patch training/data_preparation/prepared_data/'; -addpath('../PDM_helpers/'); - -scale = '0.5'; -prefix= 'combined_'; - -% Find the available positive training data -data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale)); -centres_all = []; -for i=1:numel(data_files) - - % Load the orientation of the training data - load([dataset_loc, '/', data_files(i).name], 'centres'); - centres_all = cat(1, centres_all, centres); - -end - -label_inds = [1:60,62:64,66:68]; - -% Construct mirror indices (which views need to be flipped to create other -% profile training data) -mirror_inds = zeros(size(centres_all,1), 1); -for i=1:numel(data_files) - - % mirrored image has inverse yaw - mirrored_centre = centres_all(i,:); - mirrored_centre(2) = -mirrored_centre(2); - - % if mirrored version has same orientation, do not need mirroring - if(~isequal(mirrored_centre, centres_all(i,:))) - - centres_all = cat(1, centres_all, mirrored_centre); - mirror_inds = cat(1, mirror_inds, i); - - end - -end - -outputLocation = './prep_data/'; - -num_more_neg = 10; - -% Make sure same data generated all the time -rng(0); - -neg_image_loc = './neg/'; - -neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png'])); - -max_img_used = 1500; - -%% do it separately for centers due to memory limitations -for r=1:size(centres_all,1) - - a_mod = 0.3; - - mirror = false; - - if(mirror_inds(r) ~= 0 ) - mirror = true; - label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;... - 32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;... - 61,63;66,64]; - load([dataset_loc, '/', data_files(mirror_inds(r)).name]); - else - load([dataset_loc, '/', data_files(r).name]); - end - - % Convert to 66 point model - landmark_locations = landmark_locations(:,label_inds,:); - - visiCurrent = logical(visiIndex); - - % Flip the orientation and indices for mirror data - if(mirror) - centres = [centres(1), -centres(2), -centres(3)]; - tmp1 = visiCurrent(label_mirror_inds(:,1)); - tmp2 = visiCurrent(label_mirror_inds(:,2)); - visiCurrent(label_mirror_inds(:,2)) = tmp1; - visiCurrent(label_mirror_inds(:,1)) = tmp2; - end - - visibleVerts = 1:numel(visiCurrent); - visibleVerts = visibleVerts(visiCurrent)-1; - - % Correct the triangulation to take into account the vertex - % visibilities - triangulation = []; - - shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)'; - shape = shape'; - - for i=1:size(T,1) - visib = 0; - for j=1:numel(visibleVerts) - if(T(i,1)==visibleVerts(j)) - visib = visib+1; - end - if(T(i,2)==visibleVerts(j)) - visib = visib+1; - end - if(T(i,3)==visibleVerts(j)) - visib = visib+1; - end - end - - % Only if all three of the vertices are visible - if(visib == 3) - - % Also want to remove triangles facing the wrong way (self occluded) - v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)]; - v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)]; - v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)]; - normal = cross((v2-v1), v3 - v2); - normal = normal / norm(normal); - direction = normal * [0,0,1]'; - - % And only if the triangle is facing the camera - if(direction > 0) - triangulation = cat(1, triangulation, T(i,:)); - end - end - end - - % Initialise the warp - [ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape); - - imgs_to_use = randperm(size(landmark_locations, 1)); - - if(size(landmark_locations, 1) > max_img_used) - imgs_to_use = imgs_to_use(1:max_img_used); - end - - % Extracting relevant filenames - examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix); - errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1); - - unused_pos = 0; - - curr_filled = 0; - - for j=imgs_to_use - - labels = squeeze(landmark_locations(j,:,:)); - - img = squeeze(all_images(j,:,:)); - - if(mirror) - img = fliplr(img); - imgSize = size(img); - flippedLbls = labels; - flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1); - tmp1 = flippedLbls(label_mirror_inds(:,1),:); - tmp2 = flippedLbls(label_mirror_inds(:,2),:); - flippedLbls(label_mirror_inds(:,2),:) = tmp1; - flippedLbls(label_mirror_inds(:,1),:) = tmp2; - labels = flippedLbls; - end - - % If for some reason some of the labels are not visible in the - % current sample skip this label - non_existent_labels = labels(:,1)==0 | labels(:,2)==0; - non_existent_inds = find(non_existent_labels)-1; - if(numel(intersect(triangulation(:), non_existent_inds)) > 0) - unused_pos = unused_pos + 1; - continue; - end - - curr_filled = curr_filled + 1; - [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - examples(curr_filled,:) = features; - - % Extract the correct PDM parameters for the model (we will perturb - % them for some negative examples) - [ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels); - eul_orig = Rot2Euler(R_orig); - - % a slightly perturbed example, too tight - % from 0.3 to 0.9 - a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1); - p_global = [a_mod; eul_orig'; trans_orig]; - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - % Compute the badness of fit - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % a slightly perturbed example, too broad - % from 1.2 to 0.6 - a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1); - p_global = [a_mod; eul_orig'; trans_orig]; - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % A somewhat offset example - - trans_mod = trans_orig + randn(2,1) * 10; - p_global = [a_orig; eul_orig'; trans_mod]; - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % A rotated sample - eul_mod = eul_orig + randn(1,3)*0.2; - p_global = [a_orig; eul_mod'; trans_orig]; - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % A sample with modified shape parameters - p_global = [a_orig; eul_orig'; trans_orig]; - params_mod = params_orig + randn(size(params_orig)).*sqrt(E); - labels_mod = GetShapeOrtho(M, V, params_mod, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % pick a random image from negative inriaperson dataset, use original location if - % first, otherwhise resize it to fit - for n=6:num_more_neg - n_img = randi(numel(neg_images)); - - neg_image = imread([neg_image_loc, neg_images(n_img).name]); - - if(size(neg_image,3) == 3) - neg_image = rgb2gray(neg_image); - end - - [h_neg, w_neg] = size(neg_image); - - % if the current labels fit just use them, if not, then resize - % to fit - max_x = max(labels(:,1)); - max_y = max(labels(:,2)); - - if(max_x > w_neg || max_y > h_neg) - neg_image = imresize(neg_image, [max_y, max_x]); - end - - [features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - % Set high error to 3 - errors(curr_filled,:) = 3; - end - - - if(mod(curr_filled, 10) == 0) - fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use)); - end - % add the pos example to the background - - end - - examples = examples(1:curr_filled,:); - errors = errors(1:curr_filled); - - % svm training - filename = sprintf('%s/face_checker_general_training_66_%d.mat', outputLocation, r); - save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres'); - - -end - -end - -function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY) - - % Make sure labels are within range - [hRes, wRes] = size(img); - labels(labels(:,1) < 1,1) = 1; - labels(labels(:,2) < 1,2) = 1; - - labels(labels(:,1) > wRes-1,1) = wRes-1; - labels(labels(:,2) > hRes-1,2) = hRes-1; - - crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - crop_img(isnan(crop_img)) = 0; - - % vectorised version - features = reshape(crop_img(logical(mask)), 1, nPix); - - % normalisations - features = (features - mean(features)); - norms = std(features); - if(norms==0) - norms = 1; - end - features = features / norms; -end \ No newline at end of file diff --git a/matlab_version/face_validation/Create_data_68.m b/matlab_version/face_validation/Create_data_68.m deleted file mode 100644 index 56623be..0000000 --- a/matlab_version/face_validation/Create_data_68.m +++ /dev/null @@ -1,334 +0,0 @@ -function Create_data_68() - -load '../models/pdm/pdm_68_multi_pie'; -load '../models/tri_68.mat'; - -% This script uses the same format used for patch expert training, and -% expects the data to be there -dataset_loc = '../../../CCNF experiments/clnf/patch_training/data_preparation/prepared_data/'; -addpath('../PDM_helpers/'); - -scale = '0.5'; -prefix= 'combined_'; - -% Find the available positive training data -data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale)); -centres_all = []; -for i=1:numel(data_files) - - % Load the orientation of the training data - load([dataset_loc, '/', data_files(i).name], 'centres'); - centres_all = cat(1, centres_all, centres); - -end - -% Construct mirror indices (which views need to be flipped to create other -% profile training data) -mirror_inds = zeros(size(centres_all,1), 1); -for i=1:numel(data_files) - - % mirrored image has inverse yaw - mirrored_centre = centres_all(i,:); - mirrored_centre(2) = -mirrored_centre(2); - - % if mirrored version has same orientation, do not need mirroring - if(~isequal(mirrored_centre, centres_all(i,:))) - - centres_all = cat(1, centres_all, mirrored_centre); - mirror_inds = cat(1, mirror_inds, i); - - end - -end - -outputLocation = './prep_data/'; - -num_more_neg = 10; - -% Make sure same data generated all the time -rng(0); - -neg_image_loc = './neg/'; - -neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png'])); - -max_img_used = 1500; - -% do it separately for centers due to memory limitations -for r=1:size(centres_all,1) - - a_mod = 0.3; - - mirror = false; - - if(mirror_inds(r) ~= 0 ) - mirror = true; - label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;... - 32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;... - 61,65;62,64;68,66]; - load([dataset_loc, '/', data_files(mirror_inds(r)).name]); - else - load([dataset_loc, '/', data_files(r).name]); - end - - visiCurrent = logical(visiIndex); - - if(mirror) - centres = [centres(1), -centres(2), -centres(3)]; - tmp1 = visiCurrent(label_mirror_inds(:,1)); - tmp2 = visiCurrent(label_mirror_inds(:,2)); - visiCurrent(label_mirror_inds(:,2)) = tmp1; - visiCurrent(label_mirror_inds(:,1)) = tmp2; - end - - visibleVerts = 1:numel(visiCurrent); - visibleVerts = visibleVerts(visiCurrent)-1; - - % Correct the triangulation to take into account the vertex - % visibilities - triangulation = []; - - shape = a_mod * Euler2Rot(centres * pi/180) * reshape(M, numel(M)/3, 3)'; - shape = shape'; - - for i=1:size(T,1) - visib = 0; - for j=1:numel(visibleVerts) - if(T(i,1)==visibleVerts(j)) - visib = visib+1; - end - if(T(i,2)==visibleVerts(j)) - visib = visib+1; - end - if(T(i,3)==visibleVerts(j)) - visib = visib+1; - end - end - - % Only if all three of the vertices are visible - if(visib == 3) - - % Also want to remove triangles facing the wrong way (self occluded) - v1 = [shape(T(i,1)+1,1), shape(T(i,1)+1,2), shape(T(i,1)+1,3)]; - v2 = [shape(T(i,2)+1,1), shape(T(i,2)+1,2), shape(T(i,2)+1,3)]; - v3 = [shape(T(i,3)+1,1), shape(T(i,3)+1,2), shape(T(i,3)+1,3)]; - normal = cross((v2-v1), v3 - v2); - normal = normal / norm(normal); - direction = normal * [0,0,1]'; - - % And only if the triangle is facing the camera - if(direction > 0) - triangulation = cat(1, triangulation, T(i,:)); - end - end - end - - % Initialise the warp - [ alphas, betas, triX, mask, minX, minY, nPix ] = InitialisePieceWiseAffine(triangulation, shape); - - mask = logical(mask); - - imgs_to_use = randperm(size(landmark_locations, 1)); - - if(size(landmark_locations, 1) > max_img_used) - imgs_to_use = imgs_to_use(1:max_img_used); - end - - % Extracting relevant filenames - examples = zeros(numel(imgs_to_use) * (num_more_neg+1), nPix); - errors = zeros(numel(imgs_to_use) * (num_more_neg+1), 1); - - unused_pos = 0; - - curr_filled = 0; - - for j=imgs_to_use - - labels = squeeze(landmark_locations(j,:,:)); - - img = squeeze(all_images(j,:,:)); - - if(mirror) - img = fliplr(img); - imgSize = size(img); - flippedLbls = labels; - flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1); - tmp1 = flippedLbls(label_mirror_inds(:,1),:); - tmp2 = flippedLbls(label_mirror_inds(:,2),:); - flippedLbls(label_mirror_inds(:,2),:) = tmp1; - flippedLbls(label_mirror_inds(:,1),:) = tmp2; - labels = flippedLbls; - end - - % If for some reason some of the labels are not visible in the - % current sample skip this label - non_existent_labels = labels(:,1)==0 | labels(:,2)==0; - non_existent_inds = find(non_existent_labels)-1; - if(numel(intersect(triangulation(:), non_existent_inds)) > 0) - unused_pos = unused_pos + 1; - continue; - end - - curr_filled = curr_filled + 1; - [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - examples(curr_filled,:) = features; - - % Extract the correct PDM parameters for the model (we will perturb - % them for some negative examples) - [ a_orig, R_orig, trans_orig, ~, params_orig] = fit_PDM_ortho_proj_to_2D(M, E, V, labels); - eul_orig = Rot2Euler(R_orig); - - % a slightly perturbed example, too tight - % from 0.3 to 0.9 - a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1); - p_global = [a_mod; eul_orig'; trans_orig]; - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - % Compute the badness of fit - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % a slightly perturbed example, too broad - % from 1.2 to 0.6 - a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1); - p_global = [a_mod; eul_orig'; trans_orig]; - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % A somewhat offset example - - trans_mod = trans_orig + randn(2,1) * 10; - p_global = [a_orig; eul_orig'; trans_mod]; - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % A rotated sample - eul_mod = eul_orig + randn(1,3)*0.2; - p_global = [a_orig; eul_mod'; trans_orig]; - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % A sample with modified shape parameters - p_global = [a_orig; eul_orig'; trans_orig]; - params_mod = params_orig + randn(size(params_orig)).*sqrt(E); - labels_mod = GetShapeOrtho(M, V, params_mod, p_global); - labels_mod = labels_mod(:,1:2); - - [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors(curr_filled,:) = error; - - % pick a random image from negative inriaperson dataset, use original location if - % first, otherwhise resize it to fit - for n=6:num_more_neg - n_img = randi(numel(neg_images)); - - neg_image = imread([neg_image_loc, neg_images(n_img).name]); - - if(size(neg_image,3) == 3) - neg_image = rgb2gray(neg_image); - end - - [h_neg, w_neg] = size(neg_image); - - % if the current labels fit just use them, if not, then resize - % to fit - max_x = max(labels(:,1)); - max_y = max(labels(:,2)); - - if(max_x > w_neg || max_y > h_neg) - neg_image = imresize(neg_image, [max_y, max_x]); - end - - [features] = ExtractFaceFeatures(neg_image, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - - curr_filled = curr_filled + 1; - examples(curr_filled,:) = features; - - % Set high error to 3 - errors(curr_filled,:) = 3; - end - - - if(mod(curr_filled, 10) == 0) - fprintf('%d/%d done\n', curr_filled/(num_more_neg+1), numel(imgs_to_use)); - end - % add the pos example to the background - - end - - examples = examples(1:curr_filled,:); - errors = errors(1:curr_filled); - - % svm training - filename = sprintf('%s/face_checker_general_training_68_%d.mat', outputLocation, r); - save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres'); - - -end - -end - -function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY) - - % Make sure labels are within range - [hRes, wRes] = size(img); - labels(labels(:,1) < 1,1) = 1; - labels(labels(:,2) < 1,2) = 1; - - labels(labels(:,1) > wRes-1,1) = wRes-1; - labels(labels(:,2) > hRes-1,2) = hRes-1; - - crop_img = Crop(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - crop_img(isnan(crop_img)) = 0; - - % vectorised version - features = reshape(crop_img(logical(mask)), 1, nPix); - - % normalisations - features = (features - mean(features)); - norms = std(features); - if(norms==0) - norms = 1; - end - features = features / norms; - -end \ No newline at end of file diff --git a/matlab_version/face_validation/Create_data_68_large.m b/matlab_version/face_validation/Create_data_test.m similarity index 91% rename from matlab_version/face_validation/Create_data_68_large.m rename to matlab_version/face_validation/Create_data_test.m index dba0053..7fa4798 100644 --- a/matlab_version/face_validation/Create_data_68_large.m +++ b/matlab_version/face_validation/Create_data_test.m @@ -1,6 +1,6 @@ -function Create_data_68_large() +function Create_data_test() -load '../models/pdm/pdm_68_aligned_wild'; +load '../models/pdm/pdm_68_aligned_menpo'; load '../models/tri_68.mat'; % This script uses the same format used for patch expert training, and @@ -8,14 +8,19 @@ load '../models/tri_68.mat'; % https://github.com/TadasBaltrusaitis/CCNF) % Replace with your location of training data -dataset_loc = 'C:/Users/Tadas/Documents/CCNF/patch_experts/data_preparation/prepared_data/'; +dataset_loc = 'C:\Users\tbaltrus\Documents\CCNF\patch_experts\data_preparation/prepared_data/'; addpath('../PDM_helpers/'); +addpath('./paw_helpers/'); +% Collect Menpo, Multi-PIE and 300W data for training the validator scale = '0.5'; -prefix= 'combined_'; +prefix_menpo= 'menpo_valid_'; +prefix_mpie_300W = 'combined_'; % Find the available positive training data -data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale)); +data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_menpo, scale)); +data_files_c = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_mpie_300W, scale)); + centres_all = []; for i=1:numel(data_files) @@ -25,9 +30,6 @@ for i=1:numel(data_files) end -% Do not use extreme pose -centres_all = centres_all(1:3,:); - % Construct mirror indices (which views need to be flipped to create other % profile training data) mirror_inds = zeros(size(centres_all,1), 1); @@ -48,14 +50,14 @@ for i=1:numel(data_files) end % Replace with your location of training data -outputLocation = 'E:/datasets/detection_validation/prep_data/'; +outputLocation = 'D:\Datasets/detection_validation/prep_data/'; num_more_neg = 10; % Make sure same data generated all the time rng(0); -neg_image_loc = 'E:/datasets/detection_validation/neg/'; +neg_image_loc = 'D:\Datasets\INRIAPerson\INRIAPerson\Train\neg/'; neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png'])); @@ -72,10 +74,21 @@ for r=1:size(centres_all,1) mirror = true; label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;... 32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;... - 61,65;62,64;68,66]; + 61,65;62,64;68,66]; + + % Make sure we take the subset of visibilities from all the + % datasets + load([dataset_loc, '/', data_files_c(mirror_inds(r)).name], 'visiIndex'); + visiIndex_t = visiIndex; + load([dataset_loc, '/', data_files(mirror_inds(r)).name]); + visiIndex = visiIndex_t & visiIndex; else + load([dataset_loc, '/', data_files_c(r).name], 'visiIndex'); + visiIndex_t = visiIndex; + load([dataset_loc, '/', data_files(r).name]); + visiIndex = visiIndex_t & visiIndex; end visiCurrent = logical(visiIndex); @@ -312,8 +325,7 @@ for r=1:size(centres_all,1) examples = examples(1:curr_filled,:); errors = errors(1:curr_filled); - % svm training - filename = sprintf('%s/face_checker_general_training_large_68_%d.mat', outputLocation, r); + filename = sprintf('%s/face_validator_test_%d.mat', outputLocation, r); save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres'); diff --git a/matlab_version/face_validation/Create_data_66_large.m b/matlab_version/face_validation/Create_data_train.m similarity index 82% rename from matlab_version/face_validation/Create_data_66_large.m rename to matlab_version/face_validation/Create_data_train.m index 86069b4..d240353 100644 --- a/matlab_version/face_validation/Create_data_66_large.m +++ b/matlab_version/face_validation/Create_data_train.m @@ -1,22 +1,26 @@ -function Create_data_66_large() +function Create_data_train() -load '../models/pdm/pdm_66_multi_pie'; -load '../models/tri_66.mat'; +load '../models/pdm/pdm_68_aligned_menpo'; +load '../models/tri_68.mat'; % This script uses the same format used for patch expert training, and % expects the data to be there (this can be found in % https://github.com/TadasBaltrusaitis/CCNF) % Replace with your location of training data -dataset_loc = 'C:/Users/Tadas/Documents/CCNF/patch_experts/data_preparation/prepared_data/'; - +dataset_loc = 'C:\Users\tbaltrus\Documents\CCNF\patch_experts\data_preparation/prepared_data/'; addpath('../PDM_helpers/'); +addpath('./paw_helpers/'); +% Collect Menpo, Multi-PIE and 300W data for training the validator scale = '0.5'; -prefix= 'combined_'; +prefix_menpo= 'menpo_train_'; +prefix_mpie_300W = 'combined_'; % Find the available positive training data -data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix, scale)); +data_files = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_menpo, scale)); +data_files_c = dir(sprintf('%s/%s%s*.mat', dataset_loc, prefix_mpie_300W, scale)); + centres_all = []; for i=1:numel(data_files) @@ -26,8 +30,6 @@ for i=1:numel(data_files) end -label_inds = [1:60,62:64,66:68]; - % Construct mirror indices (which views need to be flipped to create other % profile training data) mirror_inds = zeros(size(centres_all,1), 1); @@ -47,18 +49,20 @@ for i=1:numel(data_files) end -outputLocation = 'F:/datasets/detection_validation/prep_data/'; +% Replace with your location of training data +outputLocation = 'D:\Datasets/detection_validation/prep_data/'; num_more_neg = 10; % Make sure same data generated all the time rng(0); -neg_image_loc = 'F:/datasets/detection_validation/neg/'; +% Negative samples from teh INRIAPerson dataset +neg_image_loc = 'D:\Datasets\INRIAPerson\INRIAPerson\Train\neg/'; neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png'])); -max_img_used = 2500; +max_img_used = 8000; % do it separately for centers due to memory limitations for r=1:size(centres_all,1) @@ -71,15 +75,31 @@ for r=1:size(centres_all,1) mirror = true; label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;... 32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;... - 61,63;66,64]; + 61,65;62,64;68,66]; + load([dataset_loc, '/', data_files_c(mirror_inds(r)).name]); + all_images_t = all_images; + landmark_locations_t = landmark_locations; + visiIndex_t = visiIndex; + load([dataset_loc, '/', data_files(mirror_inds(r)).name]); + + % Combining Menpo + MPIE + 300W + all_images = cat(1, all_images, all_images_t); + landmark_locations = cat(1, landmark_locations, landmark_locations_t); + % Taking a subset of visibilities from all the datasets + visiIndex = visiIndex_t & visiIndex; else + load([dataset_loc, '/', data_files_c(r).name]); + all_images_t = all_images; + landmark_locations_t = landmark_locations; + visiIndex_t = visiIndex; + load([dataset_loc, '/', data_files(r).name]); + all_images = cat(1, all_images, all_images_t); + landmark_locations = cat(1, landmark_locations, landmark_locations_t); + visiIndex = visiIndex_t & visiIndex; end - % Convert to 66 point model - landmark_locations = landmark_locations(:,label_inds,:); - visiCurrent = logical(visiIndex); if(mirror) @@ -161,7 +181,7 @@ for r=1:size(centres_all,1) img = fliplr(img); imgSize = size(img); flippedLbls = labels; - flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1); + flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1) + 1; tmp1 = flippedLbls(label_mirror_inds(:,1),:); tmp2 = flippedLbls(label_mirror_inds(:,2),:); flippedLbls(label_mirror_inds(:,2),:) = tmp1; @@ -178,8 +198,12 @@ for r=1:size(centres_all,1) continue; end + % Centering the pixel so that 0,0 is center of the top left pixel + labels = labels - 1; + curr_filled = curr_filled + 1; [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY); +% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img) examples(curr_filled,:) = features; errors(curr_filled,:) = 0; @@ -197,6 +221,7 @@ for r=1:size(centres_all,1) labels_mod = labels_mod(:,1:2); [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); +% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img) curr_filled = curr_filled + 1; examples(curr_filled,:) = features; @@ -214,7 +239,8 @@ for r=1:size(centres_all,1) labels_mod = labels_mod(:,1:2); [features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY); - +% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img) + curr_filled = curr_filled + 1; examples(curr_filled,:) = features; @@ -223,7 +249,7 @@ for r=1:size(centres_all,1) % A somewhat offset example - trans_mod = trans_orig + randn(2,1) * 10; + trans_mod = trans_orig + randn(2,1) * 20; p_global = [a_orig; eul_orig'; trans_mod]; labels_mod = GetShapeOrtho(M, V, params_orig, p_global); @@ -238,7 +264,7 @@ for r=1:size(centres_all,1) errors(curr_filled,:) = error; % A rotated sample - eul_mod = eul_orig + randn(1,3)*0.2; + eul_mod = eul_orig + randn(1,3)*0.3; p_global = [a_orig; eul_mod'; trans_orig]; labels_mod = GetShapeOrtho(M, V, params_orig, p_global); @@ -308,8 +334,7 @@ for r=1:size(centres_all,1) examples = examples(1:curr_filled,:); errors = errors(1:curr_filled); - % svm training - filename = sprintf('%s/face_checker_general_training_large_66_%d.mat', outputLocation, r); + filename = sprintf('%s/face_validator_train_%d.mat', outputLocation, r); save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres'); @@ -321,8 +346,8 @@ function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask % Make sure labels are within range [hRes, wRes] = size(img); - labels(labels(:,1) < 1,1) = 1; - labels(labels(:,2) < 1,2) = 1; + labels(labels(:,1) < 0,1) = 0; + labels(labels(:,2) < 0,2) = 0; labels(labels(:,1) > wRes-1,1) = wRes-1; labels(labels(:,2) > hRes-1,2) = hRes-1; diff --git a/matlab_version/face_validation/DeepLearnToolbox/.travis.yml b/matlab_version/face_validation/DeepLearnToolbox/.travis.yml deleted file mode 100644 index 45ed2a2..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/.travis.yml +++ /dev/null @@ -1,10 +0,0 @@ -before_script: - - sudo apt-add-repository ppa:octave/stable --yes - - sudo apt-get update -y - - sudo apt-get install octave -y - - sudo apt-get install liboctave-dev -y -script: - - sh -c "octave tests/runalltests.m" - -notifications: - email: false diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caeapplygrads.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caeapplygrads.m deleted file mode 100644 index 57b788f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caeapplygrads.m +++ /dev/null @@ -1,29 +0,0 @@ -function cae = caeapplygrads(cae) - cae.sv = 0; - for j = 1 : numel(cae.a) - for i = 1 : numel(cae.i) -% cae.vik{i}{j} = cae.momentum * cae.vik{i}{j} + cae.alpha ./ (cae.sigma + cae.ddik{i}{j}) .* cae.dik{i}{j}; -% cae.vok{i}{j} = cae.momentum * cae.vok{i}{j} + cae.alpha ./ (cae.sigma + cae.ddok{i}{j}) .* cae.dok{i}{j}; - cae.vik{i}{j} = cae.alpha * cae.dik{i}{j}; - cae.vok{i}{j} = cae.alpha * cae.dok{i}{j}; - cae.sv = cae.sv + sum(cae.vik{i}{j}(:) .^ 2); - cae.sv = cae.sv + sum(cae.vok{i}{j}(:) .^ 2); - - cae.ik{i}{j} = cae.ik{i}{j} - cae.vik{i}{j}; - cae.ok{i}{j} = cae.ok{i}{j} - cae.vok{i}{j}; - end -% cae.vb{j} = cae.momentum * cae.vb{j} + cae.alpha / (cae.sigma + cae.ddb{j}) * cae.db{j}; - cae.vb{j} = cae.alpha * cae.db{j}; - cae.sv = cae.sv + sum(cae.vb{j} .^ 2); - - cae.b{j} = cae.b{j} - cae.vb{j}; - end - - for i = 1 : numel(cae.o) -% cae.vc{i} = cae.momentum * cae.vc{i} + cae.alpha / (cae.sigma + cae.ddc{i}) * cae.dc{i}; - cae.vc{i} = cae.alpha * cae.dc{i}; - cae.sv = cae.sv + sum(cae.vc{i} .^ 2); - - cae.c{i} = cae.c{i} - cae.vc{i}; - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caebbp.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caebbp.m deleted file mode 100644 index 6eca671..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caebbp.m +++ /dev/null @@ -1,29 +0,0 @@ -function cae = caebbp(cae) - - %% backprop deltas - for i = 1 : numel(cae.o) - % output delta delta - cae.odd{i} = (cae.o{i} .* (1 - cae.o{i}) .* cae.edgemask) .^ 2; - % delta delta c - cae.ddc{i} = sum(cae.odd{i}(:)) / size(cae.odd{i}, 1); - end - - for j = 1 : numel(cae.a) % calc activation delta deltas - z = 0; - for i = 1 : numel(cae.o) - z = z + convn(cae.odd{i}, flipall(cae.ok{i}{j} .^ 2), 'full'); - end - cae.add{j} = (cae.a{j} .* (1 - cae.a{j})) .^ 2 .* z; - end - - %% calc params delta deltas - ns = size(cae.odd{1}, 1); - for j = 1 : numel(cae.a) - cae.ddb{j} = sum(cae.add{j}(:)) / ns; - for i = 1 : numel(cae.o) - cae.ddok{i}{j} = convn(flipall(cae.a{j} .^ 2), cae.odd{i}, 'valid') / ns; - cae.ddik{i}{j} = convn(cae.add{j}, flipall(cae.i{i} .^ 2), 'valid') / ns; - end - end - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caebp.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caebp.m deleted file mode 100644 index bb8e084..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caebp.m +++ /dev/null @@ -1,34 +0,0 @@ -function cae = caebp(cae, y) - - %% backprop deltas - cae.L = 0; - for i = 1 : numel(cae.o) - % error - cae.e{i} = (cae.o{i} - y{i}) .* cae.edgemask; - % loss function - cae.L = cae.L + 1/2 * sum(cae.e{i}(:) .^2 ) / size(cae.e{i}, 1); - % output delta - cae.od{i} = cae.e{i} .* (cae.o{i} .* (1 - cae.o{i})); - - cae.dc{i} = sum(cae.od{i}(:)) / size(cae.e{i}, 1); - end - - for j = 1 : numel(cae.a) % calc activation deltas - z = 0; - for i = 1 : numel(cae.o) - z = z + convn(cae.od{i}, flipall(cae.ok{i}{j}), 'full'); - end - cae.ad{j} = cae.a{j} .* (1 - cae.a{j}) .* z; - end - - %% calc gradients - ns = size(cae.e{1}, 1); - for j = 1 : numel(cae.a) - cae.db{j} = sum(cae.ad{j}(:)) / ns; - for i = 1 : numel(cae.o) - cae.dok{i}{j} = convn(flipall(cae.a{j}), cae.od{i}, 'valid') / ns; - cae.dik{i}{j} = convn(cae.ad{j}, flipall(cae.i{i}), 'valid') / ns; - end - end - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caedown.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caedown.m deleted file mode 100644 index 8e6736d..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caedown.m +++ /dev/null @@ -1,13 +0,0 @@ -function cae = caedown(cae) - pa = cae.a; - pok = cae.ok; - - for i = 1 : numel(cae.o) - z = 0; - for j = 1 : numel(cae.a) - z = z + convn(pa{j}, pok{i}{j}, 'valid'); - end - cae.o{i} = sigm(z + cae.c{i}); - - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caeexamples.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caeexamples.m deleted file mode 100644 index 5be328e..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caeexamples.m +++ /dev/null @@ -1,32 +0,0 @@ -%% mnist data -clear all; close all; clc; -load mnist_uint8; -x = cell(100, 1); -N = 600; -for i = 1 : 100 - x{i}{1} = reshape(train_x(((i - 1) * N + 1) : (i) * N, :), N, 28, 28) * 255; -end -%% ex 1 -scae = { - struct('outputmaps', 10, 'inputkernel', [1 5 5], 'outputkernel', [1 5 5], 'scale', [1 2 2], 'sigma', 0.1, 'momentum', 0.9, 'noise', 0) -}; - -opts.rounds = 1000; -opts.batchsize = 1; -opts.alpha = 0.01; -opts.ddinterval = 10; -opts.ddhist = 0.5; -scae = scaesetup(scae, x, opts); -scae = scaetrain(scae, x, opts); -cae = scae{1}; - -%Visualize the average reconstruction error -plot(cae.rL); - -%Visualize the output kernels -ff=[]; -for i=1:numel(cae.ok{1}); - mm = cae.ok{1}{i}(1,:,:); - ff(i,:) = mm(:); -end; -figure;visualize(ff') diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caenumgradcheck.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caenumgradcheck.m deleted file mode 100644 index 125d926..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caenumgradcheck.m +++ /dev/null @@ -1,107 +0,0 @@ -function cae = caenumgradcheck(cae, x, y) - epsilon = 1e-4; - er = 1e-6; - disp('performing numerical gradient checking...') - for i = 1 : numel(cae.o) - p_cae = cae; p_cae.c{i} = p_cae.c{i} + epsilon; - m_cae = cae; m_cae.c{i} = m_cae.c{i} - epsilon; - - [m_cae, p_cae] = caerun(m_cae, p_cae, x, y); - d = (p_cae.L - m_cae.L) / (2 * epsilon); - - e = abs(d - cae.dc{i}); - if e > er - disp('OUTPUT BIAS numerical gradient checking failed'); - disp(e); - disp(d / cae.dc{i}); - keyboard - end - end - - for a = 1 : numel(cae.a) - - p_cae = cae; p_cae.b{a} = p_cae.b{a} + epsilon; - m_cae = cae; m_cae.b{a} = m_cae.b{a} - epsilon; - - [m_cae, p_cae] = caerun(m_cae, p_cae, x, y); - d = (p_cae.L - m_cae.L) / (2 * epsilon); -% cae.dok{i}{a}(u) = d; - e = abs(d - cae.db{a}); - if e > er - disp('BIAS numerical gradient checking failed'); - disp(e); - disp(d / cae.db{a}); - keyboard - end - - for i = 1 : numel(cae.o) - for u = 1 : numel(cae.ok{i}{a}) - p_cae = cae; p_cae.ok{i}{a}(u) = p_cae.ok{i}{a}(u) + epsilon; - m_cae = cae; m_cae.ok{i}{a}(u) = m_cae.ok{i}{a}(u) - epsilon; - - [m_cae, p_cae] = caerun(m_cae, p_cae, x, y); - d = (p_cae.L - m_cae.L) / (2 * epsilon); -% cae.dok{i}{a}(u) = d; - e = abs(d - cae.dok{i}{a}(u)); - if e > er - disp('OUTPUT KERNEL numerical gradient checking failed'); - disp(e); - disp(d / cae.dok{i}{a}(u)); -% keyboard - end - end - end - - for i = 1 : numel(cae.i) - for u = 1 : numel(cae.ik{i}{a}) - p_cae = cae; - m_cae = cae; - p_cae.ik{i}{a}(u) = p_cae.ik{i}{a}(u) + epsilon; - m_cae.ik{i}{a}(u) = m_cae.ik{i}{a}(u) - epsilon; - [m_cae, p_cae] = caerun(m_cae, p_cae, x, y); - d = (p_cae.L - m_cae.L) / (2 * epsilon); -% cae.dik{i}{a}(u) = d; - e = abs(d - cae.dik{i}{a}(u)); - if e > er - disp('INPUT KERNEL numerical gradient checking failed'); - disp(e); - disp(d / cae.dik{i}{a}(u)); - end - end - end - end - - disp('done') - -end - -function [m_cae, p_cae] = caerun(m_cae, p_cae, x, y) - m_cae = caeup(m_cae, x); m_cae = caedown(m_cae); m_cae = caebp(m_cae, y); - p_cae = caeup(p_cae, x); p_cae = caedown(p_cae); p_cae = caebp(p_cae, y); -end - -%function checknumgrad(cae,what,x,y) -% epsilon = 1e-4; -% er = 1e-9; -% -% for i = 1 : numel(eval(what)) -% if iscell(eval(['cae.' what])) -% checknumgrad(cae,[what '{' num2str(i) '}'], x, y) -% else -% p_cae = cae; -% m_cae = cae; -% eval(['p_cae.' what '(' num2str(i) ')']) = eval([what '(' num2str(i) ')']) + epsilon; -% eval(['m_cae.' what '(' num2str(i) ')']) = eval([what '(' num2str(i) ')']) - epsilon; -% -% m_cae = caeff(m_cae, x); m_cae = caedown(m_cae); m_cae = caebp(m_cae, y); -% p_cae = caeff(p_cae, x); p_cae = caedown(p_cae); p_cae = caebp(p_cae, y); -% -% d = (p_cae.L - m_cae.L) / (2 * epsilon); -% e = abs(d - eval(['cae.d' what '(' num2str(i) ')'])); -% if e > er -% error('numerical gradient checking failed'); -% end -% end -% end -% -% end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caesdlm.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caesdlm.m deleted file mode 100644 index 2c7c22d..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caesdlm.m +++ /dev/null @@ -1,26 +0,0 @@ -function cae = caesdlm(cae, opts, m) - %stochastic diagonal levenberg-marquardt - - %first round - if isfield(cae,'ddok') == 0 - cae = caebbp(cae); - end - - %recalculate double grads every opts.ddinterval - if mod(m, opts.ddinterval) == 0 - cae_n = caebbp(cae); - - for ii = 1 : numel(cae.o) - cae.ddc{ii} = opts.ddhist * cae.ddc{ii} + (1 - opts.ddhist) * cae_n.ddc{ii}; - end - - for jj = 1 : numel(cae.a) - cae.ddb{jj} = opts.ddhist * cae.ddb{jj} + (1 - opts.ddhist) * cae_n.ddb{jj}; - for ii = 1 : numel(cae.o) - cae.ddok{ii}{jj} = opts.ddhist * cae.ddok{ii}{jj} + (1 - opts.ddhist) * cae_n.ddok{ii}{jj}; - cae.ddik{ii}{jj} = opts.ddhist * cae.ddik{ii}{jj} + (1 - opts.ddhist) * cae_n.ddik{ii}{jj}; - end - end - - end -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caetrain.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caetrain.m deleted file mode 100644 index 0751728..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caetrain.m +++ /dev/null @@ -1,38 +0,0 @@ -function cae = caetrain(cae, x, opts) - n = cae.inputkernel(1); - cae.rL = []; - for m = 1 : opts.rounds - tic; - disp([num2str(m) '/' num2str(opts.rounds) ' rounds']); - i1 = randi(numel(x)); - l = randi(size(x{i1}{1},1) - opts.batchsize - n + 1); - x1{1} = double(x{i1}{1}(l : l + opts.batchsize - 1, :, :)) / 255; - - if n == 1 %Auto Encoder - x2{1} = x1{1}; - else %Predictive Encoder - x2{1} = double(x{i1}{1}(l + n : l + n + opts.batchsize - 1, :, :)) / 255; - end - % Add noise to input, for denoising stacked autoenoder - x1{1} = x1{1} .* (rand(size(x1{1})) > cae.noise); - - cae = caeup(cae, x1); - cae = caedown(cae); - cae = caebp(cae, x2); - cae = caesdlm(cae, opts, m); -% caenumgradcheck(cae,x1,x2); - cae = caeapplygrads(cae); - - if m == 1 - cae.rL(1) = cae.L; - end -% cae.rL(m + 1) = 0.99 * cae.rL(m) + 0.01 * cae.L; - cae.rL(m + 1) = cae.L; -% if cae.sv < 1e-10 -% disp('Converged'); -% break; -% end - toc; - end - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/caeup.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/caeup.m deleted file mode 100644 index 52c076e..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/caeup.m +++ /dev/null @@ -1,25 +0,0 @@ -function cae = caeup(cae, x) - cae.i = x; - - %init temp vars for parrallel processing - pa = cell(size(cae.a)); - pi = cae.i; - pik = cae.ik; - pb = cae.b; - - for j = 1 : numel(cae.a) - z = 0; - for i = 1 : numel(pi) - z = z + convn(pi{i}, pik{i}{j}, 'full'); - end - pa{j} = sigm(z + pb{j}); - - % Max pool. - if ~isequal(cae.scale, [1 1 1]) - pa{j} = max3d(pa{j}, cae.M); - end - - end - cae.a = pa; - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/max3d.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/max3d.m deleted file mode 100644 index 2e7651d..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/max3d.m +++ /dev/null @@ -1,8 +0,0 @@ -function X = max3d(X, M) - ll = size(X); - B=X(M); - B=B+rand(size(B))*1e-12; - B=(B.*(B==repmat(max(B,[],2),[1 size(B,2) 1]))); - X(M) = B; - reshape(X,ll); -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/scaesetup.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/scaesetup.m deleted file mode 100644 index 5ede971..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/scaesetup.m +++ /dev/null @@ -1,58 +0,0 @@ -function scae = scaesetup(cae, x, opts) - x = x{1}; - for l = 1 : numel(cae) - cae = cae{l}; - ll= [opts.batchsize size(x{1}, 2) size(x{1}, 3)] + cae.inputkernel - 1; - X = zeros(ll); - cae.M = nbmap(X, cae.scale); - bounds = cae.outputmaps * prod(cae.inputkernel) + numel(x) * prod(cae.outputkernel); - for j = 1 : cae.outputmaps % activation maps - cae.a{j} = zeros(size(x{1}) + cae.inputkernel - 1); - for i = 1 : numel(x) % input map - cae.ik{i}{j} = (rand(cae.inputkernel) - 0.5) * 2 * sqrt(6 / bounds); - cae.ok{i}{j} = (rand(cae.outputkernel) - 0.5) * 2 * sqrt(6 / bounds); - cae.vik{i}{j} = zeros(size(cae.ik{i}{j})); - cae.vok{i}{j} = zeros(size(cae.ok{i}{j})); - end - cae.b{j} = 0; - cae.vb{j} = zeros(size(cae.b{j})); - end - - cae.alpha = opts.alpha; - - cae.i = cell(numel(x), 1); - cae.o = cae.i; - - for i = 1 : numel(cae.o) - cae.c{i} = 0; - cae.vc{i} = zeros(size(cae.c{i})); - end - - ss = cae.outputkernel; - - cae.edgemask = zeros([opts.batchsize size(x{1}, 2) size(x{1}, 3)]); - - cae.edgemask(ss(1) : end - ss(1) + 1, ... - ss(2) : end - ss(2) + 1, ... - ss(3) : end - ss(3) + 1) = 1; - - scae{l} = cae; - end - - function B = nbmap(X,n) - assert(numel(n)==3,'n should have 3 elements (x,y,z) scaling.'); - X = reshape(1:numel(X),size(X,1),size(X,2),size(X,3)); - B = zeros(size(X,1)/n(1),prod(n),size(X,2)*size(X,3)/prod(n(2:3))); - u=1; - p=1; - for m=1:size(X,1) - B(u,(p-1)*prod(n(2:3))+1:p*prod(n(2:3)),:) = im2col(squeeze(X(m,:,:)),n(2:3),'distinct'); - p=p+1; - if(mod(m,n(1))==0) - u=u+1; - p=1; - end - end - - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CAE/scaetrain.m b/matlab_version/face_validation/DeepLearnToolbox/CAE/scaetrain.m deleted file mode 100644 index e249656..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CAE/scaetrain.m +++ /dev/null @@ -1,8 +0,0 @@ -function scae = scaetrain(scae, x, opts) - %TODO: Transform x through scae{1} into new x. Only works for a single PAE. -% for i=1:numel(scae) -% scae{i} = paetrain(scae{i}, x, opts); -% end - scae{1} = caetrain(scae{1}, x, opts); - -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnapplygrads.m b/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnapplygrads.m deleted file mode 100644 index 8f42146..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnapplygrads.m +++ /dev/null @@ -1,15 +0,0 @@ -function net = cnnapplygrads(net, opts) - for l = 2 : numel(net.layers) - if strcmp(net.layers{l}.type, 'c') - for j = 1 : numel(net.layers{l}.a) - for ii = 1 : numel(net.layers{l - 1}.a) - net.layers{l}.k{ii}{j} = net.layers{l}.k{ii}{j} - opts.alpha * net.layers{l}.dk{ii}{j}; - end - net.layers{l}.b{j} = net.layers{l}.b{j} - opts.alpha * net.layers{l}.db{j}; - end - end - end - - net.ffW = net.ffW - opts.alpha * net.dffW; - net.ffb = net.ffb - opts.alpha * net.dffb; -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnbp.m b/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnbp.m deleted file mode 100644 index 5a3f68e..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnbp.m +++ /dev/null @@ -1,56 +0,0 @@ -function net = cnnbp(net, y) - n = numel(net.layers); - - % error - net.e = net.o - y; - % loss function - net.L = 1/2* sum(net.e(:) .^ 2) / size(net.e, 2); - - %% backprop deltas - net.od = net.e .* (net.o .* (1 - net.o)); % output delta - net.fvd = (net.ffW' * net.od); % feature vector delta - if strcmp(net.layers{n}.type, 'c') % only conv layers has sigm function - net.fvd = net.fvd .* (net.fv .* (1 - net.fv)); - end - - % reshape feature vector deltas into output map style - sa = size(net.layers{n}.a{1}); - fvnum = sa(1) * sa(2); - for j = 1 : numel(net.layers{n}.a) - net.layers{n}.d{j} = reshape(net.fvd(((j - 1) * fvnum + 1) : j * fvnum, :), sa(1), sa(2), sa(3)); - end - - for l = (n - 1) : -1 : 1 - if strcmp(net.layers{l}.type, 'c') - for j = 1 : numel(net.layers{l}.a) - net.layers{l}.d{j} = net.layers{l}.a{j} .* (1 - net.layers{l}.a{j}) .* (expand(net.layers{l + 1}.d{j}, [net.layers{l + 1}.scale net.layers{l + 1}.scale 1]) / net.layers{l + 1}.scale ^ 2); - end - elseif strcmp(net.layers{l}.type, 's') - for i = 1 : numel(net.layers{l}.a) - z = zeros(size(net.layers{l}.a{1})); - for j = 1 : numel(net.layers{l + 1}.a) - z = z + convn(net.layers{l + 1}.d{j}, rot180(net.layers{l + 1}.k{i}{j}), 'full'); - end - net.layers{l}.d{i} = z; - end - end - end - - %% calc gradients - for l = 2 : n - if strcmp(net.layers{l}.type, 'c') - for j = 1 : numel(net.layers{l}.a) - for i = 1 : numel(net.layers{l - 1}.a) - net.layers{l}.dk{i}{j} = convn(flipall(net.layers{l - 1}.a{i}), net.layers{l}.d{j}, 'valid') / size(net.layers{l}.d{j}, 3); - end - net.layers{l}.db{j} = sum(net.layers{l}.d{j}(:)) / size(net.layers{l}.d{j}, 3); - end - end - end - net.dffW = net.od * (net.fv)' / size(net.od, 2); - net.dffb = mean(net.od, 2); - - function X = rot180(X) - X = flipdim(flipdim(X, 1), 2); - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnff.m b/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnff.m deleted file mode 100644 index 0d80256..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnff.m +++ /dev/null @@ -1,47 +0,0 @@ -function net = cnnff(net, x) - n = numel(net.layers); - net.layers{1}.a{1} = x; - inputmaps = 1; - - for l = 2 : n % for each layer - if strcmp(net.layers{l}.type, 'c') - % !!below can probably be handled by insane matrix operations - for j = 1 : net.layers{l}.outputmaps % for each output map - % create temp output map - if(size(x,3) > 1) - z = zeros(size(net.layers{l - 1}.a{1}) - [net.layers{l}.kernelsize - 1 net.layers{l}.kernelsize - 1 0]); - else - z = zeros(size(net.layers{l - 1}.a{1}) - [net.layers{l}.kernelsize - 1 net.layers{l}.kernelsize - 1]); - end - for i = 1 : inputmaps % for each input map - % convolve with corresponding kernel and add to temp output map - z = z + convn(net.layers{l - 1}.a{i}, net.layers{l}.k{i}{j}, 'valid'); - end - % add bias, pass through nonlinearity - net.layers{l}.a{j} = sigm(z + net.layers{l}.b{j}); - end - % set number of input maps to this layers number of outputmaps - inputmaps = net.layers{l}.outputmaps; - elseif strcmp(net.layers{l}.type, 's') - % downsample - for j = 1 : inputmaps - z = convn(net.layers{l - 1}.a{j}, ones(net.layers{l}.scale) / (net.layers{l}.scale ^ 2), 'valid'); % !! replace with variable - net.layers{l}.a{j} = z(1 : net.layers{l}.scale : end, 1 : net.layers{l}.scale : end, :); - end - end - end - - % concatenate all end layer feature maps into vector - net.fv = []; - for j = 1 : numel(net.layers{n}.a) - sa = size(net.layers{n}.a{j}); - if(numel(sa) == 3) - net.fv = [net.fv; reshape(net.layers{n}.a{j}, sa(1) * sa(2), sa(3))]; - else - net.fv = [net.fv; reshape(net.layers{n}.a{j}, sa(1) * sa(2), 1)]; - end - end - % feedforward into output perceptrons - net.o = sigm(net.ffW * net.fv + repmat(net.ffb, 1, size(net.fv, 2))); - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnnumgradcheck.m b/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnnumgradcheck.m deleted file mode 100644 index 665262a..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnnumgradcheck.m +++ /dev/null @@ -1,79 +0,0 @@ -function cnnnumgradcheck(net, x, y) - epsilon = 1e-4; - er = 1e-8; - n = numel(net.layers); - for j = 1 : numel(net.ffb) - net_m = net; net_p = net; - net_p.ffb(j) = net_m.ffb(j) + epsilon; - net_m.ffb(j) = net_m.ffb(j) - epsilon; - net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); - net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); - d = (net_p.L - net_m.L) / (2 * epsilon); - e = abs(d - net.dffb(j)); - if e > er - error('numerical gradient checking failed'); - end - end - - for i = 1 : size(net.ffW, 1) - for u = 1 : size(net.ffW, 2) - net_m = net; net_p = net; - net_p.ffW(i, u) = net_m.ffW(i, u) + epsilon; - net_m.ffW(i, u) = net_m.ffW(i, u) - epsilon; - net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); - net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); - d = (net_p.L - net_m.L) / (2 * epsilon); - e = abs(d - net.dffW(i, u)); - if e > er - error('numerical gradient checking failed'); - end - end - end - - for l = n : -1 : 2 - if strcmp(net.layers{l}.type, 'c') - for j = 1 : numel(net.layers{l}.a) - net_m = net; net_p = net; - net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon; - net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon; - net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); - net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); - d = (net_p.L - net_m.L) / (2 * epsilon); - e = abs(d - net.layers{l}.db{j}); - if e > er - error('numerical gradient checking failed'); - end - for i = 1 : numel(net.layers{l - 1}.a) - for u = 1 : size(net.layers{l}.k{i}{j}, 1) - for v = 1 : size(net.layers{l}.k{i}{j}, 2) - net_m = net; net_p = net; - net_p.layers{l}.k{i}{j}(u, v) = net_p.layers{l}.k{i}{j}(u, v) + epsilon; - net_m.layers{l}.k{i}{j}(u, v) = net_m.layers{l}.k{i}{j}(u, v) - epsilon; - net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); - net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); - d = (net_p.L - net_m.L) / (2 * epsilon); - e = abs(d - net.layers{l}.dk{i}{j}(u, v)); - if e > er - error('numerical gradient checking failed'); - end - end - end - end - end - elseif strcmp(net.layers{l}.type, 's') -% for j = 1 : numel(net.layers{l}.a) -% net_m = net; net_p = net; -% net_p.layers{l}.b{j} = net_m.layers{l}.b{j} + epsilon; -% net_m.layers{l}.b{j} = net_m.layers{l}.b{j} - epsilon; -% net_m = cnnff(net_m, x); net_m = cnnbp(net_m, y); -% net_p = cnnff(net_p, x); net_p = cnnbp(net_p, y); -% d = (net_p.L - net_m.L) / (2 * epsilon); -% e = abs(d - net.layers{l}.db{j}); -% if e > er -% error('numerical gradient checking failed'); -% end -% end - end - end -% keyboard -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnsetup.m b/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnsetup.m deleted file mode 100644 index 81f8466..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnnsetup.m +++ /dev/null @@ -1,36 +0,0 @@ -function net = cnnsetup(net, x, y) - assert(~isOctave() || compare_versions(OCTAVE_VERSION, '3.8.0', '>='), ['Octave 3.8.0 or greater is required for CNNs as there is a bug in convolution in previous versions. See http://savannah.gnu.org/bugs/?39314. Your version is ' myOctaveVersion]); - inputmaps = 1; - mapsize = size(squeeze(x(:, :, 1))); - - for l = 1 : numel(net.layers) % layer - if strcmp(net.layers{l}.type, 's') - mapsize = mapsize / net.layers{l}.scale; - assert(all(floor(mapsize)==mapsize), ['Layer ' num2str(l) ' size must be integer. Actual: ' num2str(mapsize)]); - for j = 1 : inputmaps - net.layers{l}.b{j} = 0; - end - end - if strcmp(net.layers{l}.type, 'c') - mapsize = mapsize - net.layers{l}.kernelsize + 1; - fan_out = net.layers{l}.outputmaps * net.layers{l}.kernelsize ^ 2; - for j = 1 : net.layers{l}.outputmaps % output map - fan_in = inputmaps * net.layers{l}.kernelsize ^ 2; - for i = 1 : inputmaps % input map - net.layers{l}.k{i}{j} = (rand(net.layers{l}.kernelsize) - 0.5) * 2 * sqrt(6 / (fan_in + fan_out)); - end - net.layers{l}.b{j} = 0; - end - inputmaps = net.layers{l}.outputmaps; - end - end - % 'onum' is the number of labels, that's why it is calculated using size(y, 1). If you have 20 labels so the output of the network will be 20 neurons. - % 'fvnum' is the number of output neurons at the last layer, the layer just before the output layer. - % 'ffb' is the biases of the output neurons. - % 'ffW' is the weights between the last layer and the output neurons. Note that the last layer is fully connected to the output layer, that's why the size of the weights is (onum * fvnum) - fvnum = prod(mapsize) * inputmaps; - onum = size(y, 1); - - net.ffb = zeros(onum, 1); - net.ffW = (rand(onum, fvnum) - 0.5) * 2 * sqrt(6 / (onum + fvnum)); -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnntest.m b/matlab_version/face_validation/DeepLearnToolbox/CNN/cnntest.m deleted file mode 100644 index 64ba271..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnntest.m +++ /dev/null @@ -1,9 +0,0 @@ -function [er, bad] = cnntest(net, x, y) - % feedforward - net = cnnff(net, x); - [~, h] = max(net.o); - [~, a] = max(y); - bad = find(h ~= a); - - er = numel(bad) / size(y, 2); -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnntrain.m b/matlab_version/face_validation/DeepLearnToolbox/CNN/cnntrain.m deleted file mode 100644 index 23ed976..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CNN/cnntrain.m +++ /dev/null @@ -1,29 +0,0 @@ -function net = cnntrain(net, x, y, opts) - m = size(x, 3); - numbatches = floor(m / opts.batchsize); - if rem(numbatches, 1) ~= 0 - error('numbatches not integer'); - end - net.rL = []; - for i = 1 : opts.numepochs - net = cnnff(net, x); - error_curr = sqrt(mean((net.o - y).^2)); - disp(['epoch ' num2str(i) '/' num2str(opts.numepochs), ' RMSE-', num2str(error_curr)]); - tic; - kk = randperm(m); - for l = 1 : numbatches - batch_x = x(:, :, kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize)); - batch_y = y(:, kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize)); - - net = cnnff(net, batch_x); - net = cnnbp(net, batch_y); - net = cnnapplygrads(net, opts); - if isempty(net.rL) - net.rL(1) = net.L; - end - net.rL(end + 1) = 0.99 * net.rL(end) + 0.01 * net.L; - end - toc; - end - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/CONTRIBUTING.md b/matlab_version/face_validation/DeepLearnToolbox/CONTRIBUTING.md deleted file mode 100644 index 5d92ac3..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/CONTRIBUTING.md +++ /dev/null @@ -1,20 +0,0 @@ -Thank you so much for wanting to give back to the toolbox. Here's some info on how to contribute: - -#General: - -Don't bunch up changes, e.g. if you have bug-fixes, new features and style changes, rather make 3 seperate pull requests. - -Ensure that you introduce tests/examples for any new functionality - -# Guide -1. Fork repository -2. Create a new branch, e.g. `checkout -b my-stuff` -3. Commit and push your changes to that branch -4. Make sure that the test works (!) (see known errors) -5. Create a pull request -6. I accept your pull request - - - - - diff --git a/matlab_version/face_validation/DeepLearnToolbox/DBN/dbnsetup.m b/matlab_version/face_validation/DeepLearnToolbox/DBN/dbnsetup.m deleted file mode 100644 index 0588fd7..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/DBN/dbnsetup.m +++ /dev/null @@ -1,19 +0,0 @@ -function dbn = dbnsetup(dbn, x, opts) - n = size(x, 2); - dbn.sizes = [n, dbn.sizes]; - - for u = 1 : numel(dbn.sizes) - 1 - dbn.rbm{u}.alpha = opts.alpha; - dbn.rbm{u}.momentum = opts.momentum; - - dbn.rbm{u}.W = zeros(dbn.sizes(u + 1), dbn.sizes(u)); - dbn.rbm{u}.vW = zeros(dbn.sizes(u + 1), dbn.sizes(u)); - - dbn.rbm{u}.b = zeros(dbn.sizes(u), 1); - dbn.rbm{u}.vb = zeros(dbn.sizes(u), 1); - - dbn.rbm{u}.c = zeros(dbn.sizes(u + 1), 1); - dbn.rbm{u}.vc = zeros(dbn.sizes(u + 1), 1); - end - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/DBN/dbntrain.m b/matlab_version/face_validation/DeepLearnToolbox/DBN/dbntrain.m deleted file mode 100644 index 5d710b3..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/DBN/dbntrain.m +++ /dev/null @@ -1,10 +0,0 @@ -function dbn = dbntrain(dbn, x, opts) - n = numel(dbn.rbm); - - dbn.rbm{1} = rbmtrain(dbn.rbm{1}, x, opts); - for i = 2 : n - x = rbmup(dbn.rbm{i - 1}, x); - dbn.rbm{i} = rbmtrain(dbn.rbm{i}, x, opts); - end - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/DBN/dbnunfoldtonn.m b/matlab_version/face_validation/DeepLearnToolbox/DBN/dbnunfoldtonn.m deleted file mode 100644 index 0b43edb..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/DBN/dbnunfoldtonn.m +++ /dev/null @@ -1,15 +0,0 @@ -function nn = dbnunfoldtonn(dbn, outputsize) -%DBNUNFOLDTONN Unfolds a DBN to a NN -% dbnunfoldtonn(dbn, outputsize ) returns the unfolded dbn with a final -% layer of size outputsize added. - if(exist('outputsize','var')) - size = [dbn.sizes outputsize]; - else - size = [dbn.sizes]; - end - nn = nnsetup(size); - for i = 1 : numel(dbn.rbm) - nn.W{i} = [dbn.rbm{i}.c dbn.rbm{i}.W]; - end -end - diff --git a/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmdown.m b/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmdown.m deleted file mode 100644 index f044cb4..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmdown.m +++ /dev/null @@ -1,3 +0,0 @@ -function x = rbmdown(rbm, x) - x = sigm(repmat(rbm.b', size(x, 1), 1) + x * rbm.W); -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmtrain.m b/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmtrain.m deleted file mode 100644 index 39aca0b..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmtrain.m +++ /dev/null @@ -1,37 +0,0 @@ -function rbm = rbmtrain(rbm, x, opts) - assert(isfloat(x), 'x must be a float'); - assert(all(x(:)>=0) && all(x(:)<=1), 'all data in x must be in [0:1]'); - m = size(x, 1); - numbatches = m / opts.batchsize; - - assert(rem(numbatches, 1) == 0, 'numbatches not integer'); - - for i = 1 : opts.numepochs - kk = randperm(m); - err = 0; - for l = 1 : numbatches - batch = x(kk((l - 1) * opts.batchsize + 1 : l * opts.batchsize), :); - - v1 = batch; - h1 = sigmrnd(repmat(rbm.c', opts.batchsize, 1) + v1 * rbm.W'); - v2 = sigmrnd(repmat(rbm.b', opts.batchsize, 1) + h1 * rbm.W); - h2 = sigm(repmat(rbm.c', opts.batchsize, 1) + v2 * rbm.W'); - - c1 = h1' * v1; - c2 = h2' * v2; - - rbm.vW = rbm.momentum * rbm.vW + rbm.alpha * (c1 - c2) / opts.batchsize; - rbm.vb = rbm.momentum * rbm.vb + rbm.alpha * sum(v1 - v2)' / opts.batchsize; - rbm.vc = rbm.momentum * rbm.vc + rbm.alpha * sum(h1 - h2)' / opts.batchsize; - - rbm.W = rbm.W + rbm.vW; - rbm.b = rbm.b + rbm.vb; - rbm.c = rbm.c + rbm.vc; - - err = err + sum(sum((v1 - v2) .^ 2)) / opts.batchsize; - end - - disp(['epoch ' num2str(i) '/' num2str(opts.numepochs) '. Average reconstruction error is: ' num2str(err / numbatches)]); - - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmup.m b/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmup.m deleted file mode 100644 index 9a75a06..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/DBN/rbmup.m +++ /dev/null @@ -1,3 +0,0 @@ -function x = rbmup(rbm, x) - x = sigm(repmat(rbm.c', size(x, 1), 1) + x * rbm.W'); -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/LICENSE b/matlab_version/face_validation/DeepLearnToolbox/LICENSE deleted file mode 100644 index fdf3b5f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/LICENSE +++ /dev/null @@ -1,8 +0,0 @@ -Copyright (c) 2012, Rasmus Berg Palm (rasmusbergpalm@gmail.com) -All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: - -Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. -Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nnapplygrads.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nnapplygrads.m deleted file mode 100644 index 781163b..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nnapplygrads.m +++ /dev/null @@ -1,22 +0,0 @@ -function nn = nnapplygrads(nn) -%NNAPPLYGRADS updates weights and biases with calculated gradients -% nn = nnapplygrads(nn) returns an neural network structure with updated -% weights and biases - - for i = 1 : (nn.n - 1) - if(nn.weightPenaltyL2>0) - dW = nn.dW{i} + nn.weightPenaltyL2 * [zeros(size(nn.W{i},1),1) nn.W{i}(:,2:end)]; - else - dW = nn.dW{i}; - end - - dW = nn.learningRate * dW; - - if(nn.momentum>0) - nn.vW{i} = nn.momentum*nn.vW{i} + dW; - dW = nn.vW{i}; - end - - nn.W{i} = nn.W{i} - dW; - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nnbp.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nnbp.m deleted file mode 100644 index 1217719..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nnbp.m +++ /dev/null @@ -1,47 +0,0 @@ -function nn = nnbp(nn) -%NNBP performs backpropagation -% nn = nnbp(nn) returns an neural network structure with updated weights - - n = nn.n; - sparsityError = 0; - switch nn.output - case 'sigm' - d{n} = - nn.e .* (nn.a{n} .* (1 - nn.a{n})); - case {'softmax','linear'} - d{n} = - nn.e; - end - for i = (n - 1) : -1 : 2 - % Derivative of the activation function - switch nn.activation_function - case 'sigm' - d_act = nn.a{i} .* (1 - nn.a{i}); - case 'tanh_opt' - d_act = 1.7159 * 2/3 * (1 - 1/(1.7159)^2 * nn.a{i}.^2); - end - - if(nn.nonSparsityPenalty>0) - pi = repmat(nn.p{i}, size(nn.a{i}, 1), 1); - sparsityError = [zeros(size(nn.a{i},1),1) nn.nonSparsityPenalty * (-nn.sparsityTarget ./ pi + (1 - nn.sparsityTarget) ./ (1 - pi))]; - end - - % Backpropagate first derivatives - if i+1==n % in this case in d{n} there is not the bias term to be removed - d{i} = (d{i + 1} * nn.W{i} + sparsityError) .* d_act; % Bishop (5.56) - else % in this case in d{i} the bias term has to be removed - d{i} = (d{i + 1}(:,2:end) * nn.W{i} + sparsityError) .* d_act; - end - - if(nn.dropoutFraction>0) - d{i} = d{i} .* [ones(size(d{i},1),1) nn.dropOutMask{i}]; - end - - end - - for i = 1 : (n - 1) - if i+1==n - nn.dW{i} = (d{i + 1}' * nn.a{i}) / size(d{i + 1}, 1); - else - nn.dW{i} = (d{i + 1}(:,2:end)' * nn.a{i}) / size(d{i + 1}, 1); - end - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nnchecknumgrad.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nnchecknumgrad.m deleted file mode 100644 index f6f983f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nnchecknumgrad.m +++ /dev/null @@ -1,22 +0,0 @@ -function nnchecknumgrad(nn, x, y) - epsilon = 1e-6; - er = 1e-7; - n = nn.n; - for l = 1 : (n - 1) - for i = 1 : size(nn.W{l}, 1) - for j = 1 : size(nn.W{l}, 2) - nn_m = nn; nn_p = nn; - nn_m.W{l}(i, j) = nn.W{l}(i, j) - epsilon; - nn_p.W{l}(i, j) = nn.W{l}(i, j) + epsilon; - rand('state',0) - nn_m = nnff(nn_m, x, y); - rand('state',0) - nn_p = nnff(nn_p, x, y); - dW = (nn_p.L - nn_m.L) / (2 * epsilon); - e = abs(dW - nn.dW{l}(i, j)); - - assert(e < er, 'numerical gradient checking failed'); - end - end - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nneval.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nneval.m deleted file mode 100644 index 5f2d18f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nneval.m +++ /dev/null @@ -1,28 +0,0 @@ -function [loss] = nneval(nn, loss, train_x, train_y, val_x, val_y) -%NNEVAL evaluates performance of neural network -% Returns a updated loss struct -assert(nargin == 4 || nargin == 6, 'Wrong number of arguments'); - -nn.testing = 1; -% training performance -nn = nnff(nn, train_x, train_y); -loss.train.e(end + 1) = nn.L; - -% validation performance -if nargin == 6 - nn = nnff(nn, val_x, val_y); - loss.val.e(end + 1) = nn.L; -end -nn.testing = 0; -%calc misclassification rate if softmax -if strcmp(nn.output,'softmax') - [er_train, dummy] = nntest(nn, train_x, train_y); - loss.train.e_frac(end+1) = er_train; - - if nargin == 6 - [er_val, dummy] = nntest(nn, val_x, val_y); - loss.val.e_frac(end+1) = er_val; - end -end - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nnff.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nnff.m deleted file mode 100644 index 32a2c69..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nnff.m +++ /dev/null @@ -1,60 +0,0 @@ -function nn = nnff(nn, x, y) -%NNFF performs a feedforward pass -% nn = nnff(nn, x, y) returns an neural network structure with updated -% layer activations, error and loss (nn.a, nn.e and nn.L) - - n = nn.n; - m = size(x, 1); - - x = [ones(m,1) x]; - nn.a{1} = x; - - %feedforward pass - for i = 2 : n-1 - switch nn.activation_function - case 'sigm' - % Calculate the unit's outputs (including the bias term) - nn.a{i} = sigm(nn.a{i - 1} * nn.W{i - 1}'); - case 'tanh_opt' - nn.a{i} = tanh_opt(nn.a{i - 1} * nn.W{i - 1}'); - end - - %dropout - if(nn.dropoutFraction > 0) - if(nn.testing) - nn.a{i} = nn.a{i}.*(1 - nn.dropoutFraction); - else - nn.dropOutMask{i} = (rand(size(nn.a{i}))>nn.dropoutFraction); - nn.a{i} = nn.a{i}.*nn.dropOutMask{i}; - end - end - - %calculate running exponential activations for use with sparsity - if(nn.nonSparsityPenalty>0) - nn.p{i} = 0.99 * nn.p{i} + 0.01 * mean(nn.a{i}, 1); - end - - %Add the bias term - nn.a{i} = [ones(m,1) nn.a{i}]; - end - switch nn.output - case 'sigm' - nn.a{n} = sigm(nn.a{n - 1} * nn.W{n - 1}'); - case 'linear' - nn.a{n} = nn.a{n - 1} * nn.W{n - 1}'; - case 'softmax' - nn.a{n} = nn.a{n - 1} * nn.W{n - 1}'; - nn.a{n} = exp(bsxfun(@minus, nn.a{n}, max(nn.a{n},[],2))); - nn.a{n} = bsxfun(@rdivide, nn.a{n}, sum(nn.a{n}, 2)); - end - - %error and loss - nn.e = y - nn.a{n}; - - switch nn.output - case {'sigm', 'linear'} - nn.L = 1/2 * sum(sum(nn.e .^ 2)) / m; - case 'softmax' - nn.L = -sum(sum(y .* log(nn.a{n}))) / m; - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nnpredict.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nnpredict.m deleted file mode 100644 index 9106d4c..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nnpredict.m +++ /dev/null @@ -1,8 +0,0 @@ -function labels = nnpredict(nn, x) - nn.testing = 1; - nn = nnff(nn, x, zeros(size(x,1), nn.size(end))); - nn.testing = 0; - - [dummy, i] = max(nn.a{end},[],2); - labels = i; -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nnsetup.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nnsetup.m deleted file mode 100644 index b8ec742..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nnsetup.m +++ /dev/null @@ -1,29 +0,0 @@ -function nn = nnsetup(architecture) -%NNSETUP creates a Feedforward Backpropagate Neural Network -% nn = nnsetup(architecture) returns an neural network structure with n=numel(architecture) -% layers, architecture being a n x 1 vector of layer sizes e.g. [784 100 10] - - nn.size = architecture; - nn.n = numel(nn.size); - - nn.activation_function = 'tanh_opt'; % Activation functions of hidden layers: 'sigm' (sigmoid) or 'tanh_opt' (optimal tanh). - nn.learningRate = 2; % learning rate Note: typically needs to be lower when using 'sigm' activation function and non-normalized inputs. - nn.momentum = 0.5; % Momentum - nn.scaling_learningRate = 1; % Scaling factor for the learning rate (each epoch) - nn.weightPenaltyL2 = 0; % L2 regularization - nn.nonSparsityPenalty = 0; % Non sparsity penalty - nn.sparsityTarget = 0.05; % Sparsity target - nn.inputZeroMaskedFraction = 0; % Used for Denoising AutoEncoders - nn.dropoutFraction = 0; % Dropout level (http://www.cs.toronto.edu/~hinton/absps/dropout.pdf) - nn.testing = 0; % Internal variable. nntest sets this to one. - nn.output = 'sigm'; % output unit 'sigm' (=logistic), 'softmax' and 'linear' - - for i = 2 : nn.n - % weights and weight momentum - nn.W{i - 1} = (rand(nn.size(i), nn.size(i - 1)+1) - 0.5) * 2 * 4 * sqrt(6 / (nn.size(i) + nn.size(i - 1))); - nn.vW{i - 1} = zeros(size(nn.W{i - 1})); - - % average activations (for use with sparsity) - nn.p{i} = zeros(1, nn.size(i)); - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nntest.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nntest.m deleted file mode 100644 index c17cbbe..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nntest.m +++ /dev/null @@ -1,6 +0,0 @@ -function [er, bad] = nntest(nn, x, y) - labels = nnpredict(nn, x); - [dummy, expected] = max(y,[],2); - bad = find(labels ~= expected); - er = numel(bad) / size(x, 1); -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nntrain.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nntrain.m deleted file mode 100644 index 3c6c828..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nntrain.m +++ /dev/null @@ -1,77 +0,0 @@ -function [nn, L] = nntrain(nn, train_x, train_y, opts, val_x, val_y) -%NNTRAIN trains a neural net -% [nn, L] = nnff(nn, x, y, opts) trains the neural network nn with input x and -% output y for opts.numepochs epochs, with minibatches of size -% opts.batchsize. Returns a neural network nn with updated activations, -% errors, weights and biases, (nn.a, nn.e, nn.W, nn.b) and L, the sum -% squared error for each training minibatch. - -assert(isfloat(train_x), 'train_x must be a float'); -assert(nargin == 4 || nargin == 6,'number ofinput arguments must be 4 or 6') - -loss.train.e = []; -loss.train.e_frac = []; -loss.val.e = []; -loss.val.e_frac = []; -opts.validation = 0; -if nargin == 6 - opts.validation = 1; -end - -fhandle = []; -if isfield(opts,'plot') && opts.plot == 1 - fhandle = figure(); -end - -m = size(train_x, 1); - -batchsize = opts.batchsize; -numepochs = opts.numepochs; - -numbatches = floor(m / batchsize); - -assert(rem(numbatches, 1) == 0, 'numbatches must be a integer'); - -L = zeros(numepochs*numbatches,1); -n = 1; -for i = 1 : numepochs - tic; - - kk = randperm(m); - for l = 1 : numbatches - batch_x = train_x(kk((l - 1) * batchsize + 1 : l * batchsize), :); - - %Add noise to input (for use in denoising autoencoder) - if(nn.inputZeroMaskedFraction ~= 0) - batch_x = batch_x.*(rand(size(batch_x))>nn.inputZeroMaskedFraction); - end - - batch_y = train_y(kk((l - 1) * batchsize + 1 : l * batchsize), :); - - nn = nnff(nn, batch_x, batch_y); - nn = nnbp(nn); - nn = nnapplygrads(nn); - - L(n) = nn.L; - - n = n + 1; - end - - t = toc; - - if opts.validation == 1 - loss = nneval(nn, loss, train_x, train_y, val_x, val_y); - str_perf = sprintf('; Full-batch train mse = %f, val mse = %f', loss.train.e(end), loss.val.e(end)); - else - loss = nneval(nn, loss, train_x, train_y); - str_perf = sprintf('; Full-batch train err = %f', loss.train.e(end)); - end - if ishandle(fhandle) - nnupdatefigures(nn, fhandle, loss, opts, i); - end - -% disp(['epoch ' num2str(i) '/' num2str(opts.numepochs) '. Took ' num2str(t) ' seconds' '. Mini-batch mean squared error on training set is ' num2str(mean(L((n-numbatches):(n-1)))) str_perf]); - nn.learningRate = nn.learningRate * nn.scaling_learningRate; -end -end - diff --git a/matlab_version/face_validation/DeepLearnToolbox/NN/nnupdatefigures.m b/matlab_version/face_validation/DeepLearnToolbox/NN/nnupdatefigures.m deleted file mode 100644 index fdcc775..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/NN/nnupdatefigures.m +++ /dev/null @@ -1,63 +0,0 @@ -function nnupdatefigures(nn,fhandle,L,opts,i) -%NNUPDATEFIGURES updates figures during training -if i > 1 %dont plot first point, its only a point - x_ax = 1:i; - % create legend - if opts.validation == 1 - M = {'Training','Validation'}; - else - M = {'Training'}; - end - - %create data for plots - if strcmp(nn.output,'softmax') - plot_x = x_ax'; - plot_ye = L.train.e'; - plot_yfrac = L.train.e_frac'; - - else - plot_x = x_ax'; - plot_ye = L.train.e'; - end - - %add error on validation data if present - if opts.validation == 1 - plot_x = [plot_x, x_ax']; - plot_ye = [plot_ye,L.val.e']; - end - - - %add classification error on validation data if present - if opts.validation == 1 && strcmp(nn.output,'softmax') - plot_yfrac = [plot_yfrac, L.val.e_frac']; - end - -% plotting - figure(fhandle); - if strcmp(nn.output,'softmax') %also plot classification error - - p1 = subplot(1,2,1); - plot(plot_x,plot_ye); - xlabel('Number of epochs'); ylabel('Error');title('Error'); - title('Error') - legend(p1, M,'Location','NorthEast'); - set(p1, 'Xlim',[0,opts.numepochs + 1]) - - p2 = subplot(1,2,2); - plot(plot_x,plot_yfrac); - xlabel('Number of epochs'); ylabel('Misclassification rate'); - title('Misclassification rate') - legend(p2, M,'Location','NorthEast'); - set(p2, 'Xlim',[0,opts.numepochs + 1]) - - else - - p = plot(plot_x,plot_ye); - xlabel('Number of epochs'); ylabel('Error');title('Error'); - legend(p, M,'Location','NorthEast'); - set(gca, 'Xlim',[0,opts.numepochs + 1]) - - end - drawnow; -end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/README.md b/matlab_version/face_validation/DeepLearnToolbox/README.md deleted file mode 100644 index be277e4..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/README.md +++ /dev/null @@ -1,298 +0,0 @@ - -DeepLearnToolbox -================ - -A Matlab toolbox for Deep Learning. - -Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data. -It is inspired by the human brain's apparent deep (layered, hierarchical) architecture. -A good overview of the theory of Deep Learning theory is -[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf) - -For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng. - -* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007) -* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010) -* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011) - -If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284) - -``` -@MASTERSTHESIS\{IMM2012-06284, - author = "R. B. Palm", - title = "Prediction as a candidate for learning deep hierarchical models of data", - year = "2012", -} -``` - -Contact: rasmusbergpalm at gmail dot com - -Directories included in the toolbox ------------------------------------ - -`NN/` - A library for Feedforward Backpropagation Neural Networks - -`CNN/` - A library for Convolutional Neural Networks - -`DBN/` - A library for Deep Belief Networks - -`SAE/` - A library for Stacked Auto-Encoders - -`CAE/` - A library for Convolutional Auto-Encoders - -`util/` - Utility functions used by the libraries - -`data/` - Data used by the examples - -`tests/` - unit tests to verify toolbox is working - -For references on each library check REFS.md - -Setup ------ - -1. Download. -2. addpath(genpath('DeepLearnToolbox')); - -Known errors ------------------------------- - -`test_cnn_gradients_are_numerically_correct` fails on Octave because of a bug in Octave's convn implementation. See http://savannah.gnu.org/bugs/?39314 - -`test_example_CNN` fails in Octave for the same reason. -Example: Deep Belief Network ---------------------- -```matlab - -function test_example_DBN -load mnist_uint8; - -train_x = double(train_x) / 255; -test_x = double(test_x) / 255; -train_y = double(train_y); -test_y = double(test_y); - -%% ex1 train a 100 hidden unit RBM and visualize its weights -rand('state',0) -dbn.sizes = [100]; -opts.numepochs = 1; -opts.batchsize = 100; -opts.momentum = 0; -opts.alpha = 1; -dbn = dbnsetup(dbn, train_x, opts); -dbn = dbntrain(dbn, train_x, opts); -figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights - -%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NN -rand('state',0) -%train dbn -dbn.sizes = [100 100]; -opts.numepochs = 1; -opts.batchsize = 100; -opts.momentum = 0; -opts.alpha = 1; -dbn = dbnsetup(dbn, train_x, opts); -dbn = dbntrain(dbn, train_x, opts); - -%unfold dbn to nn -nn = dbnunfoldtonn(dbn, 10); -nn.activation_function = 'sigm'; - -%train nn -opts.numepochs = 1; -opts.batchsize = 100; -nn = nntrain(nn, train_x, train_y, opts); -[er, bad] = nntest(nn, test_x, test_y); - -assert(er < 0.10, 'Too big error'); - -``` - - -Example: Stacked Auto-Encoders ---------------------- -```matlab - -function test_example_SAE -load mnist_uint8; - -train_x = double(train_x)/255; -test_x = double(test_x)/255; -train_y = double(train_y); -test_y = double(test_y); - -%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN -% Setup and train a stacked denoising autoencoder (SDAE) -rand('state',0) -sae = saesetup([784 100]); -sae.ae{1}.activation_function = 'sigm'; -sae.ae{1}.learningRate = 1; -sae.ae{1}.inputZeroMaskedFraction = 0.5; -opts.numepochs = 1; -opts.batchsize = 100; -sae = saetrain(sae, train_x, opts); -visualize(sae.ae{1}.W{1}(:,2:end)') - -% Use the SDAE to initialize a FFNN -nn = nnsetup([784 100 10]); -nn.activation_function = 'sigm'; -nn.learningRate = 1; -nn.W{1} = sae.ae{1}.W{1}; - -% Train the FFNN -opts.numepochs = 1; -opts.batchsize = 100; -nn = nntrain(nn, train_x, train_y, opts); -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.16, 'Too big error'); - -``` - - -Example: Convolutional Neural Nets ---------------------- -```matlab - -function test_example_CNN -load mnist_uint8; - -train_x = double(reshape(train_x',28,28,60000))/255; -test_x = double(reshape(test_x',28,28,10000))/255; -train_y = double(train_y'); -test_y = double(test_y'); - -%% ex1 Train a 6c-2s-12c-2s Convolutional neural network -%will run 1 epoch in about 200 second and get around 11% error. -%With 100 epochs you'll get around 1.2% error -rand('state',0) -cnn.layers = { - struct('type', 'i') %input layer - struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer - struct('type', 's', 'scale', 2) %sub sampling layer - struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer - struct('type', 's', 'scale', 2) %subsampling layer -}; -cnn = cnnsetup(cnn, train_x, train_y); - -opts.alpha = 1; -opts.batchsize = 50; -opts.numepochs = 1; - -cnn = cnntrain(cnn, train_x, train_y, opts); - -[er, bad] = cnntest(cnn, test_x, test_y); - -%plot mean squared error -figure; plot(cnn.rL); - -assert(er<0.12, 'Too big error'); - -``` - - -Example: Neural Networks ---------------------- -```matlab - -function test_example_NN -load mnist_uint8; - -train_x = double(train_x) / 255; -test_x = double(test_x) / 255; -train_y = double(train_y); -test_y = double(test_y); - -% normalize -[train_x, mu, sigma] = zscore(train_x); -test_x = normalize(test_x, mu, sigma); - -%% ex1 vanilla neural net -rand('state',0) -nn = nnsetup([784 100 10]); -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples -[nn, L] = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); - -assert(er < 0.08, 'Too big error'); - -%% ex2 neural net with L2 weight decay -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.weightPenaltyL2 = 1e-4; % L2 weight decay -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - - -%% ex3 neural net with dropout -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.dropoutFraction = 0.5; % Dropout fraction -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex4 neural net with sigmoid activation function -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.activation_function = 'sigm'; % Sigmoid activation function -nn.learningRate = 1; % Sigm require a lower learning rate -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex5 plotting functionality -rand('state',0) -nn = nnsetup([784 20 10]); -opts.numepochs = 5; % Number of full sweeps through data -nn.output = 'softmax'; % use softmax output -opts.batchsize = 1000; % Take a mean gradient step over this many samples -opts.plot = 1; % enable plotting - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex6 neural net with sigmoid activation and plotting of validation and training error -% split training data into training and validation data -vx = train_x(1:10000,:); -tx = train_x(10001:end,:); -vy = train_y(1:10000,:); -ty = train_y(10001:end,:); - -rand('state',0) -nn = nnsetup([784 20 10]); -nn.output = 'softmax'; % use softmax output -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 1000; % Take a mean gradient step over this many samples -opts.plot = 1; % enable plotting -nn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally) - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -``` - - - - -[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge") - diff --git a/matlab_version/face_validation/DeepLearnToolbox/README_header.md b/matlab_version/face_validation/DeepLearnToolbox/README_header.md deleted file mode 100644 index 42ef722..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/README_header.md +++ /dev/null @@ -1,63 +0,0 @@ -[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge") - -DeepLearnToolbox -================ - -A Matlab toolbox for Deep Learning. - -Deep Learning is a new subfield of machine learning that focuses on learning deep hierarchical models of data. -It is inspired by the human brain's apparent deep (layered, hierarchical) architecture. -A good overview of the theory of Deep Learning theory is -[Learning Deep Architectures for AI](http://www.iro.umontreal.ca/~bengioy/papers/ftml_book.pdf) - -For a more informal introduction, see the following videos by Geoffrey Hinton and Andrew Ng. - -* [The Next Generation of Neural Networks](http://www.youtube.com/watch?v=AyzOUbkUf3M) (Hinton, 2007) -* [Recent Developments in Deep Learning](http://www.youtube.com/watch?v=VdIURAu1-aU) (Hinton, 2010) -* [Unsupervised Feature Learning and Deep Learning](http://www.youtube.com/watch?v=ZmNOAtZIgIk) (Ng, 2011) - -If you use this toolbox in your research please cite [Prediction as a candidate for learning deep hierarchical models of data](http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6284) - -``` -@MASTERSTHESIS\{IMM2012-06284, - author = "R. B. Palm", - title = "Prediction as a candidate for learning deep hierarchical models of data", - year = "2012", -} -``` - -Contact: rasmusbergpalm at gmail dot com - -Directories included in the toolbox ------------------------------------ - -`NN/` - A library for Feedforward Backpropagation Neural Networks - -`CNN/` - A library for Convolutional Neural Networks - -`DBN/` - A library for Deep Belief Networks - -`SAE/` - A library for Stacked Auto-Encoders - -`CAE/` - A library for Convolutional Auto-Encoders - -`util/` - Utility functions used by the libraries - -`data/` - Data used by the examples - -`tests/` - unit tests to verify toolbox is working - -For references on each library check REFS.md - -Setup ------ - -1. Download. -2. addpath(genpath('DeepLearnToolbox')); - -Known errors ------------------------------- - -`test_cnn_gradients_are_numerically_correct` fails on Octave because of a bug in Octave's convn implementation. See http://savannah.gnu.org/bugs/?39314 - -`test_example_CNN` fails in Octave for the same reason. diff --git a/matlab_version/face_validation/DeepLearnToolbox/REFS.md b/matlab_version/face_validation/DeepLearnToolbox/REFS.md deleted file mode 100644 index 8b03750..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/REFS.md +++ /dev/null @@ -1,16 +0,0 @@ -Deep Belief Nets ----------------- - -* ["A Fast Learning Algorithm for Deep Belief Nets"](http://www.cs.toronto.edu/~hinton/absps/ncfast.pdf) Geoffrey Hinton 2006 - Introduces contrastive divergence and DBNs -* ["A Practical Guide to Training Restricted Boltzmann Machines"](http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf) Geoffrey Hinton 2010 - How to implement DBNs - -Convolutional Neural Nets -------------------------- - -* ["Handwritten Digit Recognition with a Back-Propagation Network"](http://yann.lecun.com/exdb/publis/pdf/lecun-90c.pdf) Yann LeCun 1990 - Introduces CNNs -* ["Notes on Convolutional Neural Networks"](http://cogprints.org/5869/1/cnn_tutorial.pdf) Jake Bouvrie 2006 - How to implement CNNs - -Auto Encoders -------------- - -* ["Extracting and Composing Robust Features with Denoising Autoencoders"](http://www.iro.umontreal.ca/~vincentp/Publications/vincent_icml_2008.pdf) Pascal Vincent 2008 - Introduces the Denoising Autoencoder diff --git a/matlab_version/face_validation/DeepLearnToolbox/SAE/saesetup.m b/matlab_version/face_validation/DeepLearnToolbox/SAE/saesetup.m deleted file mode 100644 index aee4f3f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/SAE/saesetup.m +++ /dev/null @@ -1,5 +0,0 @@ -function sae = saesetup(size) - for u = 2 : numel(size) - sae.ae{u-1} = nnsetup([size(u-1) size(u) size(u-1)]); - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/SAE/saetrain.m b/matlab_version/face_validation/DeepLearnToolbox/SAE/saetrain.m deleted file mode 100644 index ccd464b..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/SAE/saetrain.m +++ /dev/null @@ -1,10 +0,0 @@ -function sae = saetrain(sae, x, opts) - for i = 1 : numel(sae.ae); - disp(['Training AE ' num2str(i) '/' num2str(numel(sae.ae))]); - sae.ae{i} = nntrain(sae.ae{i}, x, x, opts); - t = nnff(sae.ae{i}, x, x); - x = t.a{2}; - %remove bias term - x = x(:,2:end); - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/create_readme.sh b/matlab_version/face_validation/DeepLearnToolbox/create_readme.sh deleted file mode 100644 index 47bc89a..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/create_readme.sh +++ /dev/null @@ -1,18 +0,0 @@ -echo "" > README.md -cat README_header.md >> README.md - -echo -e "Example: Deep Belief Network\n---------------------\n\`\`\`matlab\n" >> README.md -cat ./tests/test_example_DBN.m >> README.md -echo -e "\n\`\`\`\n\n" >> README.md - -echo -e "Example: Stacked Auto-Encoders\n---------------------\n\`\`\`matlab\n" >> README.md -cat ./tests/test_example_SAE.m >> README.md -echo -e "\n\`\`\`\n\n" >> README.md - -echo -e "Example: Convolutional Neural Nets\n---------------------\n\`\`\`matlab\n" >> README.md -cat ./tests/test_example_CNN.m >> README.md -echo -e "\n\`\`\`\n\n" >> README.md - -echo -e "Example: Neural Networks\n---------------------\n\`\`\`matlab\n" >> README.md -cat ./tests/test_example_NN.m >> README.md -echo -e "\n\`\`\`\n\n" >> README.md \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/runalltests.m b/matlab_version/face_validation/DeepLearnToolbox/tests/runalltests.m deleted file mode 100644 index a2ee47f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/runalltests.m +++ /dev/null @@ -1,8 +0,0 @@ -clear all; close all; clc; - -addpath(genpath('.')); -dirlist = dir('tests/test_*'); -for i = 1:length(dirlist) - name = dirlist(i).name(1:end-2); - feval(name) -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/test_cnn_gradients_are_numerically_correct.m b/matlab_version/face_validation/DeepLearnToolbox/tests/test_cnn_gradients_are_numerically_correct.m deleted file mode 100644 index 2391ae1..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/test_cnn_gradients_are_numerically_correct.m +++ /dev/null @@ -1,15 +0,0 @@ -function test_cnn_gradients_are_numerically_correct -batch_x = rand(28,28,5); -batch_y = rand(10,5); -cnn.layers = { - struct('type', 'i') %input layer - struct('type', 'c', 'outputmaps', 2, 'kernelsize', 5) %convolution layer - struct('type', 's', 'scale', 2) %sub sampling layer - struct('type', 'c', 'outputmaps', 2, 'kernelsize', 5) %convolution layer - struct('type', 's', 'scale', 2) %subsampling layer -}; -cnn = cnnsetup(cnn, batch_x, batch_y); - -cnn = cnnff(cnn, batch_x); -cnn = cnnbp(cnn, batch_y); -cnnnumgradcheck(cnn, batch_x, batch_y); \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_CNN.m b/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_CNN.m deleted file mode 100644 index 8f52824..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_CNN.m +++ /dev/null @@ -1,35 +0,0 @@ -%function test_example_CNN -load mnist_uint8; - -train_x = double(reshape(train_x',28,28,60000))/255; -test_x = double(reshape(test_x',28,28,10000))/255; -train_y = double(train_y'); -test_y = double(test_y'); - -%% ex1 Train a 6c-2s-12c-2s Convolutional neural network -%will run 1 epoch in about 200 second and get around 11% error. -%With 100 epochs you'll get around 1.2% error - -rand('state',0) - -cnn.layers = { - struct('type', 'i') %input layer - struct('type', 'c', 'outputmaps', 6, 'kernelsize', 5) %convolution layer - struct('type', 's', 'scale', 2) %sub sampling layer - struct('type', 'c', 'outputmaps', 12, 'kernelsize', 5) %convolution layer - struct('type', 's', 'scale', 2) %subsampling layer -}; - - -opts.alpha = 1; -opts.batchsize = 50; -opts.numepochs = 5; - -cnn = cnnsetup(cnn, train_x, train_y); -cnn = cnntrain(cnn, train_x, train_y, opts); - -[er, bad] = cnntest(cnn, test_x, test_y); - -%plot mean squared error -figure; plot(cnn.rL); -assert(er<0.12, 'Too big error'); diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_DBN.m b/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_DBN.m deleted file mode 100644 index f5a5e6f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_DBN.m +++ /dev/null @@ -1,41 +0,0 @@ -function test_example_DBN -load mnist_uint8; - -train_x = double(train_x) / 255; -test_x = double(test_x) / 255; -train_y = double(train_y); -test_y = double(test_y); - -%% ex1 train a 100 hidden unit RBM and visualize its weights -rand('state',0) -dbn.sizes = [100]; -opts.numepochs = 1; -opts.batchsize = 100; -opts.momentum = 0; -opts.alpha = 1; -dbn = dbnsetup(dbn, train_x, opts); -dbn = dbntrain(dbn, train_x, opts); -figure; visualize(dbn.rbm{1}.W'); % Visualize the RBM weights - -%% ex2 train a 100-100 hidden unit DBN and use its weights to initialize a NN -rand('state',0) -%train dbn -dbn.sizes = [100 100]; -opts.numepochs = 1; -opts.batchsize = 100; -opts.momentum = 0; -opts.alpha = 1; -dbn = dbnsetup(dbn, train_x, opts); -dbn = dbntrain(dbn, train_x, opts); - -%unfold dbn to nn -nn = dbnunfoldtonn(dbn, 10); -nn.activation_function = 'sigm'; - -%train nn -opts.numepochs = 1; -opts.batchsize = 100; -nn = nntrain(nn, train_x, train_y, opts); -[er, bad] = nntest(nn, test_x, test_y); - -assert(er < 0.10, 'Too big error'); diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN.m b/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN.m deleted file mode 100644 index 6c5302b..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN.m +++ /dev/null @@ -1,94 +0,0 @@ -%function test_example_NN -load mnist_uint8; - -train_x = double(train_x) / 255; -test_x = double(test_x) / 255; -train_y = double(train_y); -test_y = double(test_y); - -% normalize -[train_x, mu, sigma] = zscore(train_x); -test_x = normalize(test_x, mu, sigma); - -%% ex1 vanilla neural net -rand('state',0) -nn = nnsetup([784 100 10]); -opts.numepochs = 10; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples -[nn, L] = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); - -assert(er < 0.08, 'Too big error'); - -%% ex2 neural net with L2 weight decay -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.weightPenaltyL2 = 1e-4; % L2 weight decay -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - - -%% ex3 neural net with dropout -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.dropoutFraction = 0.5; % Dropout fraction -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex4 neural net with sigmoid activation function -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.activation_function = 'sigm'; % Sigmoid activation function -nn.learningRate = 1; % Sigm require a lower learning rate -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex5 plotting functionality -rand('state',0) -nn = nnsetup([784 20 10]); -opts.numepochs = 5; % Number of full sweeps through data -nn.output = 'softmax'; % use softmax output -opts.batchsize = 1000; % Take a mean gradient step over this many samples -opts.plot = 1; % enable plotting - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex6 neural net with sigmoid activation and plotting of validation and training error -% split training data into training and validation data -vx = train_x(1:10000,:); -tx = train_x(10001:end,:); -vy = train_y(1:10000,:); -ty = train_y(10001:end,:); - -rand('state',0) -nn = nnsetup([784 20 10]); -nn.output = 'softmax'; % use softmax output -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 1000; % Take a mean gradient step over this many samples -opts.plot = 1; % enable plotting -nn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally) - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN_single_out.asv b/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN_single_out.asv deleted file mode 100644 index 01cfd60..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN_single_out.asv +++ /dev/null @@ -1,102 +0,0 @@ -%function test_example_NN -load mnist_uint8; - -train_x = double(train_x) / 255; -test_x = double(test_x) / 255; -train_y = double(train_y); -test_y = double(test_y); - -train_y = train_y(:,1); -test_y = test_y(:,1); - -% normalize -[train_x, mu, sigma] = zscore(train_x); -test_x = normalize(test_x, mu, sigma); - -%% ex1 vanilla neural net -rand('state',0) -nn = nnsetup([784 100 1]); -nn.output = 'sigm'; -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples -[nn, L] = nntrain(nn, train_x, train_y, opts); - -% [er, bad] = nntest(nn, test_x, test_y); -nn = nnff(nn, test_x, zeros(size(test_x,1), nn.size(end))); -% pred_y = nnpredict(nn, test_x); - nn.a{end}; -fprintf('Prediction error %f\n', sqrt(mean((pred_y - test_y).^2))); - -% assert(er < 0.08, 'Too big error'); - -%% ex2 neural net with L2 weight decay -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.weightPenaltyL2 = 1e-4; % L2 weight decay -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - - -%% ex3 neural net with dropout -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.dropoutFraction = 0.5; % Dropout fraction -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex4 neural net with sigmoid activation function -rand('state',0) -nn = nnsetup([784 100 10]); - -nn.activation_function = 'sigm'; % Sigmoid activation function -nn.learningRate = 1; % Sigm require a lower learning rate -opts.numepochs = 1; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex5 plotting functionality -rand('state',0) -nn = nnsetup([784 20 10]); -opts.numepochs = 5; % Number of full sweeps through data -nn.output = 'softmax'; % use softmax output -opts.batchsize = 1000; % Take a mean gradient step over this many samples -opts.plot = 1; % enable plotting - -nn = nntrain(nn, train_x, train_y, opts); - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); - -%% ex6 neural net with sigmoid activation and plotting of validation and training error -% split training data into training and validation data -vx = train_x(1:10000,:); -tx = train_x(10001:end,:); -vy = train_y(1:10000,:); -ty = train_y(10001:end,:); - -rand('state',0) -nn = nnsetup([784 20 10]); -nn.output = 'softmax'; % use softmax output -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 1000; % Take a mean gradient step over this many samples -opts.plot = 1; % enable plotting -nn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally) - -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.1, 'Too big error'); diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN_single_out.m b/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN_single_out.m deleted file mode 100644 index fb0758b..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_NN_single_out.m +++ /dev/null @@ -1,123 +0,0 @@ -%function test_example_NN -load mnist_uint8; - -train_x = double(train_x) / 255; -test_x = double(test_x) / 255; -train_y = double(train_y); -test_y = double(test_y); - -train_y = train_y(:,1); -test_y = test_y(:,1); - -% normalize -[train_x, mu, sigma] = zscore(train_x); -test_x = normalize(test_x, mu, sigma); - -%% ex1 vanilla neural net -rand('state',0) -nn = nnsetup([784 100 1]); -nn.output = 'sigm'; -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples -[nn, L] = nntrain(nn, train_x, train_y, opts); - -% [er, bad] = nntest(nn, test_x, test_y); -nn = nnff(nn, test_x, zeros(size(test_x,1), nn.size(end))); -% pred_y = nnpredict(nn, test_x); -pred_y = nn.a{end}; - -fprintf('Prediction error 1 %f\n', sqrt(mean((pred_y - test_y).^2))); - -% assert(er < 0.08, 'Too big error'); - -%% ex2 neural net with L2 weight decay -rand('state',0) -nn = nnsetup([784 100 1]); - -nn.weightPenaltyL2 = 1e-4; % L2 weight decay -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -nn = nnff(nn, test_x, zeros(size(test_x,1), nn.size(end))); -% pred_y = nnpredict(nn, test_x); -pred_y = nn.a{end}; - -fprintf('Prediction error 2 %f\n', sqrt(mean((pred_y - test_y).^2))); - - - -%% ex3 neural net with dropout -rand('state',0) -nn = nnsetup([784 100 1]); - -nn.dropoutFraction = 0.5; % Dropout fraction -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -nn = nnff(nn, test_x, zeros(size(test_x,1), nn.size(end))); -% pred_y = nnpredict(nn, test_x); -pred_y = nn.a{end}; - -fprintf('Prediction error 3 %f\n', sqrt(mean((pred_y - test_y).^2))); - - -%% ex4 neural net with sigmoid activation function -rand('state',0) -nn = nnsetup([784 100 1]); - -nn.activation_function = 'sigm'; % Sigmoid activation function -nn.learningRate = 1; % Sigm require a lower learning rate -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 100; % Take a mean gradient step over this many samples - -nn = nntrain(nn, train_x, train_y, opts); - -nn = nnff(nn, test_x, zeros(size(test_x,1), nn.size(end))); -% pred_y = nnpredict(nn, test_x); -pred_y = nn.a{end}; - -fprintf('Prediction error 4 %f\n', sqrt(mean((pred_y - test_y).^2))); - - -%% ex5 plotting functionality -rand('state',0) -nn = nnsetup([784 20 1]); -opts.numepochs = 5; % Number of full sweeps through data -nn.output = 'sigm'; % use softmax output -opts.batchsize = 1000; % Take a mean gradient step over this many samples -opts.plot = 1; % enable plotting - -nn = nntrain(nn, train_x, train_y, opts); - -nn = nnff(nn, test_x, zeros(size(test_x,1), nn.size(end))); -% pred_y = nnpredict(nn, test_x); -pred_y = nn.a{end}; - -fprintf('Prediction error 5 %f\n', sqrt(mean((pred_y - test_y).^2))); - - -%% ex6 neural net with sigmoid activation and plotting of validation and training error -% split training data into training and validation data -vx = train_x(1:10000,:); -tx = train_x(10001:end,:); -vy = train_y(1:10000,:); -ty = train_y(10001:end,:); - -rand('state',0) -nn = nnsetup([784 20 1]); -nn.output = 'sigm'; % use softmax output -opts.numepochs = 5; % Number of full sweeps through data -opts.batchsize = 1000; % Take a mean gradient step over this many samples -opts.plot = 1; % enable plotting -nn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally) - -nn = nnff(nn, test_x, zeros(size(test_x,1), nn.size(end))); -% pred_y = nnpredict(nn, test_x); -pred_y = nn.a{end}; - -fprintf('Prediction error 6 %f\n', sqrt(mean((pred_y - test_y).^2))); - diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_SAE.m b/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_SAE.m deleted file mode 100644 index eed8a5c..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/test_example_SAE.m +++ /dev/null @@ -1,32 +0,0 @@ -function test_example_SAE -load mnist_uint8; - -train_x = double(train_x)/255; -test_x = double(test_x)/255; -train_y = double(train_y); -test_y = double(test_y); - -%% ex1 train a 100 hidden unit SDAE and use it to initialize a FFNN -% Setup and train a stacked denoising autoencoder (SDAE) -rand('state',0) -sae = saesetup([784 100]); -sae.ae{1}.activation_function = 'sigm'; -sae.ae{1}.learningRate = 1; -sae.ae{1}.inputZeroMaskedFraction = 0.5; -opts.numepochs = 1; -opts.batchsize = 100; -sae = saetrain(sae, train_x, opts); -visualize(sae.ae{1}.W{1}(:,2:end)') - -% Use the SDAE to initialize a FFNN -nn = nnsetup([784 100 10]); -nn.activation_function = 'sigm'; -nn.learningRate = 1; -nn.W{1} = sae.ae{1}.W{1}; - -% Train the FFNN -opts.numepochs = 1; -opts.batchsize = 100; -nn = nntrain(nn, train_x, train_y, opts); -[er, bad] = nntest(nn, test_x, test_y); -assert(er < 0.16, 'Too big error'); diff --git a/matlab_version/face_validation/DeepLearnToolbox/tests/test_nn_gradients_are_numerically_correct.m b/matlab_version/face_validation/DeepLearnToolbox/tests/test_nn_gradients_are_numerically_correct.m deleted file mode 100644 index 2b6df89..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/tests/test_nn_gradients_are_numerically_correct.m +++ /dev/null @@ -1,26 +0,0 @@ -function test_nn_gradients_are_numerically_correct -batch_x = rand(20, 5); -batch_y = rand(20, 2); - -for output = {'sigm', 'linear', 'softmax'} - y=batch_y; - if(strcmp(output,'softmax')) - % softmax output requires a binary output vector - y=(y==repmat(max(y,[],2),1,size(y,2))); - end - - for activation_function = {'sigm', 'tanh_opt'} - for dropoutFraction = {0 rand()} - nn = nnsetup([5 3 4 2]); - - nn.activation_function = activation_function{1}; - nn.output = output{1}; - nn.dropoutFraction = dropoutFraction{1}; - - rand('state',0) - nn = nnff(nn, batch_x, y); - nn = nnbp(nn); - nnchecknumgrad(nn, batch_x, y); - end - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/allcomb.m b/matlab_version/face_validation/DeepLearnToolbox/util/allcomb.m deleted file mode 100644 index c37c4b3..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/allcomb.m +++ /dev/null @@ -1,87 +0,0 @@ -function A = allcomb(varargin) -% ALLCOMB - All combinations -% B = ALLCOMB(A1,A2,A3,...,AN) returns all combinations of the elements -% in A1, A2, ..., and AN. B is P-by-N matrix is which P is the product -% of the number of elements of the N inputs. -% Empty inputs yields an empty matrix B of size 0-by-N. Note that -% previous versions (1.x) simply ignored empty inputs. -% -% Example: -% allcomb([1 3 5],[-3 8],[0 1]) ; -% 1 -3 0 -% 1 -3 1 -% 1 8 0 -% ... -% 5 -3 1 -% 5 8 0 -% 5 8 1 -% -% ALLCOMB(A1,..AN,'matlab') causes the first column to change fastest. -% This is more consistent with matlab indexing. Example: -% allcomb(1:2,3:4,5:6,'matlab') %-> -% 1 3 5 -% 2 3 5 -% 1 4 5 -% ... -% 2 4 6 -% -% This functionality is also known as the cartesian product. -% -% See also NCHOOSEK, PERMS, -% and COMBN (Matlab Central FEX) - -% for Matlab R13+ -% version 2.1 (feb 2011) -% (c) Jos van der Geest -% email: jos@jasen.nl - -% History -% 1.1 (feb 2006), removed minor bug when entering empty cell arrays; -% added option to let the first input run fastest (suggestion by JD) -% 1.2 (jan 2010), using ii as an index on the left-hand for the multiple -% output by NDGRID. Thanks to Jan Simon, for showing this little trick -% 2.0 (dec 2010). Bruno Luong convinced me that an empty input should -% return an empty output. -% 2.1 (feb 2011). A cell as input argument caused the check on the last -% argument (specifying the order) to crash. - -error(nargchk(1,Inf,nargin)) ; - -% check for empty inputs -q = ~cellfun('isempty',varargin) ; -if any(~q), - warning('ALLCOMB:EmptyInput','Empty inputs result in an empty output.') ; - A = zeros(0,nargin) ; -else - - ni = sum(q) ; - - argn = varargin{end} ; - ischar(argn) - if ischar(argn) && (strcmpi(argn,'matlab') || strcmpi(argn,'john')), - % based on a suggestion by JD on the FEX - ni = ni-1 ; - ii = 1:ni ; - q(end) = 0 ; - else - % enter arguments backwards, so last one (AN) is changing fastest - ii = ni:-1:1 ; - end - - if ni==0, - A = [] ; - else - args = varargin(q) ; - if ~all(cellfun('isclass',args,'double')), - error('All arguments should be arrays of doubles') ; - end - if ni==1, - A = args{1}(:) ; - else - % flip using ii if last column is changing fastest - [A{ii}] = ndgrid(args{ii}) ; - % concatenate - A = reshape(cat(ni+1,A{:}),[],ni) ; - end - end -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/expand.m b/matlab_version/face_validation/DeepLearnToolbox/util/expand.m deleted file mode 100644 index ebb3162..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/expand.m +++ /dev/null @@ -1,54 +0,0 @@ -function B = expand(A, S) -%EXPAND Replicate and tile each element of an array, similar to repmat. -% EXPAND(A,SZ), for array A and vector SZ replicates each element of A by -% SZ. The results are tiled into an array in the same order as the -% elements of A, so that the result is size: size(A).*SZ. Therefore the -% number of elements of SZ must equal the number of dimensions of A, or in -% MATLAB syntax: length(size(A))==length(SZ) must be true. -% The result will have the same number of dimensions as does A. -% There is no restriction on the number of dimensions for input A. -% -% Examples: -% -% A = [1 2; 3 4]; % 2x2 -% SZ = [6 5]; -% B = expand(A,[6 5]) % Creates a 12x10 array. -% -% The following demonstrates equivalence of EXPAND and expansion acheived -% through indexing the individual elements of the array: -% -% A = 1; B = 2; C = 3; D = 4; % Elements of the array to be expanded. -% Mat = [A B;C D]; % The array to expand. -% SZ = [2 3]; % The expansion vector. -% ONES = ones(SZ); % The index array. -% ExpMat1 = [A(ONES),B(ONES);C(ONES),D(ONES)]; % Element expansion. -% ExpMat2 = expand(Mat,SZ); % Calling EXPAND. -% isequal(ExpMat1,ExpMat2) % Yes -% -% -% See also, repmat, meshgrid, ones, zeros, kron -% -% Author: Matt Fig -% Date: 6/20/2009 -% Contact: popkenai@yahoo.com - -if nargin < 2 - error('Size vector must be provided. See help.'); -end - -SA = size(A); % Get the size (and number of dimensions) of input. - -if length(SA) ~= length(S) - error('Length of size vector must equal ndims(A). See help.') -elseif any(S ~= floor(S)) - error('The size vector must contain integers only. See help.') -end - -T = cell(length(SA), 1); -for ii = length(SA) : -1 : 1 - H = zeros(SA(ii) * S(ii), 1); % One index vector into A for each dim. - H(1 : S(ii) : SA(ii) * S(ii)) = 1; % Put ones in correct places. - T{ii} = cumsum(H); % Cumsumming creates the correct order. -end - -B = A(T{:}); % Feed the indices into A. \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/flicker.m b/matlab_version/face_validation/DeepLearnToolbox/util/flicker.m deleted file mode 100644 index a503af9..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/flicker.m +++ /dev/null @@ -1,11 +0,0 @@ -function flicker(X,fps) - figure; - colormap gray; - axis image; - while 1 - for i=1:size(X,1); - imagesc(squeeze(X(i,:,:))); drawnow; - pause(1/fps); - end - end -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/flipall.m b/matlab_version/face_validation/DeepLearnToolbox/util/flipall.m deleted file mode 100644 index ec81725..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/flipall.m +++ /dev/null @@ -1,5 +0,0 @@ -function X=flipall(X) - for i=1:ndims(X) - X = flipdim(X,i); - end -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/fliplrf.m b/matlab_version/face_validation/DeepLearnToolbox/util/fliplrf.m deleted file mode 100644 index 9237f2b..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/fliplrf.m +++ /dev/null @@ -1,20 +0,0 @@ -function y = fliplrf(x) -%FLIPLR Flip matrix in left/right direction. -% FLIPLR(X) returns X with row preserved and columns flipped -% in the left/right direction. -% -% X = 1 2 3 becomes 3 2 1 -% 4 5 6 6 5 4 -% -% Class support for input X: -% float: double, single -% -% See also FLIPUD, ROT90, FLIPDIM. - -% Copyright 1984-2010 The MathWorks, Inc. -% $Revision: 5.9.4.4 $ $Date: 2010/02/25 08:08:47 $ - -% if ~ismatrix(x) -% error('MATLAB:fliplr:SizeX', 'X must be a 2-D matrix.'); -% end -y = x(:,end:-1:1); diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/flipudf.m b/matlab_version/face_validation/DeepLearnToolbox/util/flipudf.m deleted file mode 100644 index 430d59f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/flipudf.m +++ /dev/null @@ -1,21 +0,0 @@ -function y = flipudf(x) -%FLIPUD Flip matrix in up/down direction. -% FLIPUD(X) returns X with columns preserved and rows flipped -% in the up/down direction. For example, -% -% X = 1 4 becomes 3 6 -% 2 5 2 5 -% 3 6 1 4 -% -% Class support for input X: -% float: double, single -% -% See also FLIPLR, ROT90, FLIPDIM. - -% Copyright 1984-2010 The MathWorks, Inc. -% $Revision: 5.9.4.4 $ $Date: 2010/02/25 08:08:49 $ - -% if ~ismatrix(x) -% error('MATLAB:flipud:SizeX', 'X must be a 2-D matrix.'); -% end -y = x(end:-1:1,:); diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/im2patches.m b/matlab_version/face_validation/DeepLearnToolbox/util/im2patches.m deleted file mode 100644 index a32739f..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/im2patches.m +++ /dev/null @@ -1,13 +0,0 @@ -function patches = im2patches(im,m,n) - assert(rem(size(im,1),m)==0) - assert(rem(size(im,2),n)==0) - - patches = []; - for i=1:m:size(im,1) - for u=1:n:size(im,2) - patch = im(i:i+n-1,u:u+m-1); - patches = [patches patch(:)]; - end - end - patches = patches'; -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/isOctave.m b/matlab_version/face_validation/DeepLearnToolbox/util/isOctave.m deleted file mode 100644 index 08ce876..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/isOctave.m +++ /dev/null @@ -1,4 +0,0 @@ -%detects if we're running Octave -function result = isOctave() - result = exist('OCTAVE_VERSION') ~= 0; -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/makeLMfilters.m b/matlab_version/face_validation/DeepLearnToolbox/util/makeLMfilters.m deleted file mode 100644 index 211c03a..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/makeLMfilters.m +++ /dev/null @@ -1,62 +0,0 @@ -function F=makeLMfilters -% Returns the LML filter bank of size 49x49x48 in F. To convolve an -% image I with the filter bank you can either use the matlab function -% conv2, i.e. responses(:,:,i)=conv2(I,F(:,:,i),'valid'), or use the -% Fourier transform. - - SUP=49; % Support of the largest filter (must be odd) - SCALEX=sqrt(2).^[1:3]; % Sigma_{x} for the oriented filters - NORIENT=6; % Number of orientations - - NROTINV=12; - NBAR=length(SCALEX)*NORIENT; - NEDGE=length(SCALEX)*NORIENT; - NF=NBAR+NEDGE+NROTINV; - F=zeros(SUP,SUP,NF); - hsup=(SUP-1)/2; - [x,y]=meshgrid([-hsup:hsup],[hsup:-1:-hsup]); - orgpts=[x(:) y(:)]'; - - count=1; - for scale=1:length(SCALEX), - for orient=0:NORIENT-1, - angle=pi*orient/NORIENT; % Not 2pi as filters have symmetry - c=cos(angle);s=sin(angle); - rotpts=[c -s;s c]*orgpts; - F(:,:,count)=makefilter(SCALEX(scale),0,1,rotpts,SUP); - F(:,:,count+NEDGE)=makefilter(SCALEX(scale),0,2,rotpts,SUP); - count=count+1; - end; - end; - - count=NBAR+NEDGE+1; - SCALES=sqrt(2).^[1:4]; - for i=1:length(SCALES), - F(:,:,count)=normalise(fspecial('gaussian',SUP,SCALES(i))); - F(:,:,count+1)=normalise(fspecial('log',SUP,SCALES(i))); - F(:,:,count+2)=normalise(fspecial('log',SUP,3*SCALES(i))); - count=count+3; - end; -return - -function f=makefilter(scale,phasex,phasey,pts,sup) - gx=gauss1d(3*scale,0,pts(1,:),phasex); - gy=gauss1d(scale,0,pts(2,:),phasey); - f=normalise(reshape(gx.*gy,sup,sup)); -return - -function g=gauss1d(sigma,mean,x,ord) -% Function to compute gaussian derivatives of order 0 <= ord < 3 -% evaluated at x. - - x=x-mean;num=x.*x; - variance=sigma^2; - denom=2*variance; - g=exp(-num/denom)/(pi*denom)^0.5; - switch ord, - case 1, g=-g.*(x/variance); - case 2, g=g.*((num-variance)/(variance^2)); - end; -return - -function f=normalise(f), f=f-mean(f(:)); f=f/sum(abs(f(:))); return \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/myOctaveVersion.m b/matlab_version/face_validation/DeepLearnToolbox/util/myOctaveVersion.m deleted file mode 100644 index f361cec..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/myOctaveVersion.m +++ /dev/null @@ -1,7 +0,0 @@ -% return OCTAVE_VERSION or 'undefined' as a string -function result = myOctaveVersion() - if isOctave() - result = OCTAVE_VERSION; - else - result = 'undefined'; -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/normalize.m b/matlab_version/face_validation/DeepLearnToolbox/util/normalize.m deleted file mode 100644 index ed32c36..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/normalize.m +++ /dev/null @@ -1,4 +0,0 @@ -function x = normalize(x, mu, sigma) - x=bsxfun(@minus,x,mu); - x=bsxfun(@rdivide,x,sigma); -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/patches2im.m b/matlab_version/face_validation/DeepLearnToolbox/util/patches2im.m deleted file mode 100644 index d1be454..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/patches2im.m +++ /dev/null @@ -1,11 +0,0 @@ -function im = patches2im(patches,n,m) - k=1; - im = zeros(n,m); - for i=1:10:800 - for u=1:10:1140 - patch = patches(:,k); - im(u:u+9,i:i+9) = reshape(patch,10,10); - k = k+1; - end - end -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/randcorr.m b/matlab_version/face_validation/DeepLearnToolbox/util/randcorr.m deleted file mode 100644 index c10ddfc..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/randcorr.m +++ /dev/null @@ -1,8 +0,0 @@ -function x=randcorr(n,R) -% RANDCORR Generates corremlated random variables -% Generates n vector valued variates with uniform marginals and correlation -% matrix R. -% Returns an nxk matrix, where k is the order of R. - k=size(R,1); - R=2*sin((pi/6)*R); - x=normcdf(randn(n,k)*chol(R)); \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/randp.m b/matlab_version/face_validation/DeepLearnToolbox/util/randp.m deleted file mode 100644 index 97101c2..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/randp.m +++ /dev/null @@ -1,76 +0,0 @@ -function X = randp(P,varargin) -% RANDP - pick random values with relative probability -% -% R = RANDP(PROB,..) returns integers in the range from 1 to -% NUMEL(PROB) with a relative probability, so that the value X is -% present approximately (PROB(X)./sum(PROB)) times in the matrix R. -% -% All values of PROB should be equal to or larger than 0. -% -% RANDP(PROB,N) is an N-by-N matrix, RANDP(PROB,M,N) and -% RANDP(PROB,[M,N]) are M-by-N matrices. RANDP(PROB, M1,M2,M3,...) or -% RANDP(PROB,[M1,M2,M3,...]) generate random arrays. -% RANDP(PROB,SIZE(A)) is the same size as A. -% -% Example: -% R = randp([1 3 2],1,10000) -% % return a row vector with 10000 values with about 16650% 2 -% histc(R,1:3) ./ numel(R) -% -% R = randp([1 1 0 0 1],10,1) -% % 10 samples evenly drawn from [1 2 5] -% -% -% Also see RAND, RANDPERM -% RANDPERMBREAK, RANDINTERVAL, RANDSWAP (MatLab File Exchange) - -% Created for Matlab R13+ -% version 2.0 (feb 2009) -% (c) Jos van der Geest -% email: jos@jasen.nl -% -% File history: -% 1.0 (nov 2005) - created -% 1.1 (nov 2005) - modified slightly to check input arguments to RAND first -% 1.2 (aug 2006) - fixed bug when called with scalar argument P -% 2.0 (feb 2009) - use HISTC for creating the integers (faster and simplier than -% previous algorithm) - -error(nargchk(2,Inf,nargin)) ; - -try - X = rand(varargin{:}) ; -catch - E = lasterror ; - E.message = strrep(E.message,'rand','randp') ; - rethrow(E) ; -end - -P = P(:) ; - -if any(P<0), - error('All probabilities should be 0 or larger.') ; -end - -if isempty(P) || sum(P)==0 - warning([mfilename ':ZeroProbabilities'],'All zero probabilities') ; - X(:) = 0 ; -else - [junk,X] = histc(X,[0 ; cumsum(P(:))] ./ sum(P)) ; -end - -% Method used before version 2 -% X = rand(varargin{:}) ; -% sz = size(X) ; -% P = reshape(P,1,[]) ; % row vector -% P = cumsum(P) ./ sum(P) ; -% X = repmat(X(:),1,numel(P)) < repmat(P,numel(X),1) ; -% X = numel(P) - sum(X,2) + 1 ; -% X = reshape(X,sz) ; - - - - - - - diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/rnd.m b/matlab_version/face_validation/DeepLearnToolbox/util/rnd.m deleted file mode 100644 index cb2e0bc..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/rnd.m +++ /dev/null @@ -1,3 +0,0 @@ -function y = rnd(x) - y = double(x>rand()); -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/sigm.m b/matlab_version/face_validation/DeepLearnToolbox/util/sigm.m deleted file mode 100644 index a5f8e15..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/sigm.m +++ /dev/null @@ -1,3 +0,0 @@ -function X = sigm(P) - X = 1./(1+exp(-P)); -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/sigmrnd.m b/matlab_version/face_validation/DeepLearnToolbox/util/sigmrnd.m deleted file mode 100644 index 7726a24..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/sigmrnd.m +++ /dev/null @@ -1,4 +0,0 @@ -function X = sigmrnd(P) -% X = double(1./(1+exp(-P)))+1*randn(size(P)); - X = double(1./(1+exp(-P)) > rand(size(P))); -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/softmax.m b/matlab_version/face_validation/DeepLearnToolbox/util/softmax.m deleted file mode 100644 index 1c48b83..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/softmax.m +++ /dev/null @@ -1,12 +0,0 @@ -function mu = softmax(eta) - % Softmax function - % mu(i,c) = exp(eta(i,c))/sum_c' exp(eta(i,c')) - - % This file is from matlabtools.googlecode.com - c = 3; - - tmp = exp(c*eta); - denom = sum(tmp, 2); - mu = bsxfun(@rdivide, tmp, denom); - -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/tanh_opt.m b/matlab_version/face_validation/DeepLearnToolbox/util/tanh_opt.m deleted file mode 100644 index e616531..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/tanh_opt.m +++ /dev/null @@ -1,3 +0,0 @@ -function f=tanh_opt(A) - f=1.7159*tanh(2/3.*A); -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/visualize.m b/matlab_version/face_validation/DeepLearnToolbox/util/visualize.m deleted file mode 100644 index 8739767..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/visualize.m +++ /dev/null @@ -1,51 +0,0 @@ -function r=visualize(X, mm, s1, s2) -%FROM RBMLIB http://code.google.com/p/matrbm/ -%Visualize weights X. If the function is called as a void method, -%it does the plotting. But if the function is assigned to a variable -%outside of this code, the formed image is returned instead. -if ~exist('mm','var') - mm = [min(X(:)) max(X(:))]; -end -if ~exist('s1','var') - s1 = 0; -end -if ~exist('s2','var') - s2 = 0; -end - -[D,N]= size(X); -s=sqrt(D); -if s==floor(s) || (s1 ~=0 && s2 ~=0) - if (s1 ==0 || s2 ==0) - s1 = s; s2 = s; - end - %its a square, so data is probably an image - num=ceil(sqrt(N)); - a=mm(2)*ones(num*s2+num-1,num*s1+num-1); - x=0; - y=0; - for i=1:N - im = reshape(X(:,i),s1,s2)'; - a(x*s2+1+x : x*s2+s2+x, y*s1+1+y : y*s1+s1+y)=im; - x=x+1; - if(x>=num) - x=0; - y=y+1; - end - end - d=true; -else - %there is not much we can do - a=X; -end - -%return the image, or plot the image -if nargout==1 - r=a; -else - - imagesc(a, [mm(1) mm(2)]); - axis equal - colormap gray - -end diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/whiten.m b/matlab_version/face_validation/DeepLearnToolbox/util/whiten.m deleted file mode 100644 index cf690db..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/whiten.m +++ /dev/null @@ -1,7 +0,0 @@ -function X = whiten(X, fudgefactor) - C = cov(X); - M = mean(X); - [V,D] = eig(C); - P = V * diag(sqrt(1./(diag(D) + fudgefactor))) * V'; - X = bsxfun(@minus, X, M) * P; -end \ No newline at end of file diff --git a/matlab_version/face_validation/DeepLearnToolbox/util/zscore.m b/matlab_version/face_validation/DeepLearnToolbox/util/zscore.m deleted file mode 100644 index b6f696e..0000000 --- a/matlab_version/face_validation/DeepLearnToolbox/util/zscore.m +++ /dev/null @@ -1,6 +0,0 @@ -function [x, mu, sigma] = zscore(x) - mu=mean(x); - sigma=max(std(x),eps); - x=bsxfun(@minus,x,mu); - x=bsxfun(@rdivide,x,sigma); -end diff --git a/matlab_version/face_validation/Test_face_checker.m b/matlab_version/face_validation/Test_face_checker.m deleted file mode 100644 index 3f71c90..0000000 --- a/matlab_version/face_validation/Test_face_checker.m +++ /dev/null @@ -1,161 +0,0 @@ -function [ predictions, gts, rmse, corr_coeff ] = Test_face_checker( face_check_fun ) - - addpath('../PDM_helpers'); - - % Read in the face images - [image_locs, ~, landmark_locations] = Collect_wild_imgs('C:/Users/Tadas/Dropbox/AAM/test data/'); - - load '../models/pdm/pdm_68_aligned_wild.mat'; - load '../models/tri_68.mat'; - - % Make sure same data generated all the time - rng(0); - - neg_image_loc = 'E:/datasets/detection_validation/neg/'; - - neg_images = cat(1,dir([neg_image_loc, '/*.jpg']),dir([neg_image_loc, '/*.png'])); - - curr_filled = 0; - - num_imgs = numel(image_locs); - - more_neg = 1; - - errors_all = zeros(num_imgs + num_imgs*more_neg, 1); - predictions_all = zeros(num_imgs + num_imgs*more_neg, 1); - - img_used = cell(size(errors_all)); - labels_used = cell(size(errors_all)); - - for j=1:num_imgs - - labels = squeeze(landmark_locations(j,:,:)); - - % Extract the correct PDM parameters for the model and evaluate how - % well the checker detects correct detections - curr_filled = curr_filled + 1; - img = imread(image_locs(j).img); - img_used(curr_filled) = {image_locs(j).img}; - - [ a_orig, R_orig, trans_orig, ~, params_orig, err, shape] = fit_PDM_ortho_proj_to_2D(M, E, V, labels); - eul_orig = Rot2Euler(R_orig); - - p_global = [a_orig; eul_orig'; trans_orig]; - - labels_used(curr_filled) = {shape}; - - % Test the positive example - predictions_all(curr_filled) = face_check_fun(img, shape, p_global); - error = norm(shape(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors_all(curr_filled) = error; - - curr_filled = curr_filled + 1; - - img_used(curr_filled) = {image_locs(j).img}; - - if(mod(j, 6) == 0) - % a slightly perturbed example, too tight - % from 0.3 to 0.9 - a_mod = a_orig * (0.6 + (randi(7) - 4)*0.1); - p_global = [a_mod; eul_orig'; trans_orig]; - elseif(mod(j,6) == 1) - % a slightly perturbed example, too broad - % from 1.2 to 0.6 - a_mod = a_orig * (1.4 + (randi(5) - 3)*0.1); - p_global = [a_mod; eul_orig'; trans_orig]; - elseif(mod(j,6) == 2) - % A somewhat offset example - trans_mod = trans_orig + randn(2,1) * 10; - p_global = [a_orig; eul_orig'; trans_mod]; - elseif(mod(j,6) == 3) - % A rotated sample - eul_mod = eul_orig + randn(1,3)*0.2; - p_global = [a_orig; eul_mod'; trans_orig]; - elseif(mod(j,6) == 4) - % A sample with modified shape parameters - p_global = [a_orig; eul_orig'; trans_orig]; - params_orig = params_orig + randn(size(params_orig)).*sqrt(E); - elseif(mod(j,6) == 5) - % pick a random image from negative inriaperson dataset, use original location if - % first, otherwhise resize it to fit - n_img = randi(numel(neg_images)); - - neg_image = imread([neg_image_loc, neg_images(n_img).name]); - img_used(curr_filled) = {[neg_image_loc, neg_images(n_img).name]}; - - if(size(neg_image,3) == 3) - neg_image = rgb2gray(neg_image); - end - - [h_neg, w_neg] = size(neg_image); - - % if the current labels fit just use them, if not, then resize - % to fit - max_x = max(labels(:,1)); - max_y = max(labels(:,2)); - - if(max_x > w_neg || max_y > h_neg) - neg_image = imresize(neg_image, [max_y, max_x]); - end - img = neg_image; - - end - - labels_mod = GetShapeOrtho(M, V, params_orig, p_global); - labels_mod = labels_mod(:,1:2); - - labels_used(curr_filled) = {labels_mod}; - - predictions_all(curr_filled) = face_check_fun(img, labels_mod, p_global); - % Compute the badness of fit - error = norm(labels_mod(:) - labels(:)) / (max(labels(:,2))-min(labels(:,2))); - errors_all(curr_filled,:) = error; - - if(mod(j,6) == 5) - % Set high error to 3, if inriaperson used - errors_all(curr_filled,:) = 3; - end - - if(mod(curr_filled, 10) == 0) - fprintf('%d/%d done\n', j, num_imgs); - end - end - - predictions = predictions_all(1:curr_filled,:); - gts = errors_all(1:curr_filled); - - gts(gts > 3) = 3; - - rmse = sqrt(mean((predictions - gts).^2)); - corr_coeff = corr(predictions, gts); - - [~, inds] = sort((predictions - gts).^2, 'descend'); - - %% Visualise the worst errors - num_to_vis = 20; - samples_per_row = 4; - num_cols = ceil(num_to_vis / samples_per_row); - figure; - for i=1:num_to_vis - subplot(num_cols, samples_per_row, i); - - curr_img = imread(img_used{inds(i)}); - curr_labels = labels_used{inds(i)}; - - min_x = min(curr_labels(:,1)); - min_y = min(curr_labels(:,2)); - - max_x = max(curr_labels(:,1)); - max_y = max(curr_labels(:,2)); - - curr_img = imcrop(curr_img, [min_x, min_y, max_x - min_x, max_y - min_y]); - - imshow(curr_img); - hold on; - plot(curr_labels(:,1) - min_x, curr_labels(:,2) - min_y, '.'); - text(20, 20, 0, sprintf('Pred:%f, Actual:%f', predictions(inds(i)), gts(inds(i)))); - - end - -end - diff --git a/matlab_version/face_validation/Train_face_checker_66.m b/matlab_version/face_validation/Train_face_checker_66.m deleted file mode 100644 index 3ae32c7..0000000 --- a/matlab_version/face_validation/Train_face_checker_66.m +++ /dev/null @@ -1,141 +0,0 @@ -clear -location = './prep_data/'; - -faceCheckersLoc = dir([location 'face_checker_general_training_66_*']); - -training_ratio = 0.7; - -% Current best rmse - 0.142, corr 0.766 (without PCA) -% Current best rmse - 0.12, corr 0.828 (with PCA) -% Best RMSE with PCA and more training - 0.84 - -rng(0); -for i=1:numel(faceCheckersLoc) - - load([location faceCheckersLoc(i).name]); - - num_examples = size(examples, 1); - - training_cutoff = round(num_examples * training_ratio); - - % picking training data for SVM (positive and negative samples) - examples_train = examples(1:training_cutoff,:); - - % Extract the mean and standard deviation and normalise by it - mean_ex = mean(examples_train); - std_ex = std(examples_train); - examples_train = bsxfun(@times, bsxfun(@minus, examples_train, mean_ex), 1./std_ex); - errors_train = errors(1:training_cutoff); - - [prin_comps, score, latent] = pca(examples_train); - - % Keep enough data to explain 95 percent of variability - variance_explained = cumsum(latent) / sum(latent); - num_components = find(variance_explained > 0.90, 1, 'first'); - prin_comps = prin_comps(:,1:num_components); - - examples_train_ld = prin_comps' * examples_train'; - examples_train_ld = examples_train_ld'; - - min_err = min( errors(1:training_cutoff)); - max_err = max( errors(1:training_cutoff)); - - labels_train = 2*((errors(1:training_cutoff) - min_err)/(max_err-min_err)-0.5); - - % liblinear SVR training - addpath('C:\liblinear\matlab'); - - cs = [-4:-1]; - ps = [-10:-2]; - - cmd = ['-s 11 -B 1 -q ']; - - res = zeros(numel(cs), numel(ps)); - for c=1:numel(cs) - for p=1:numel(ps) - - validation_p = sprintf('%s -v 5 -p %f -c %f', cmd, 10^cs(c), 2^ps(p)); - rms_valid = train(labels_train, sparse(double(examples_train_ld)), validation_p); - res(c,p) = rms_valid; - end - end - - [val,~] = min(min(res)); - [a, b] = ind2sub(size(res), find(res == val)); - - best_c = cs(a); - best_p = ps(b); - - best_params = sprintf('%s -p %f -c %f', cmd, 10^best_c, 2^best_p); - - regressor_lsvr = train(labels_train, sparse(double(examples_train_ld)), best_params); - - w = regressor_lsvr.w(1:end-1)'; - b = regressor_lsvr.w(end); - - examples_test = examples(training_cutoff+1:end,:); - examples_test = bsxfun(@times, bsxfun(@minus, examples_test, mean_ex), 1./std_ex); - - examples_test_ld = prin_comps' * examples_test'; - examples_test_ld = examples_test_ld'; - - labels_test = 2*((errors(training_cutoff+1:end) - min_err)/(max_err-min_err)-0.5); - - dec = examples_test_ld * w + b; - - rmse = sqrt(mean((dec - labels_test).^2)); - corr_dec = corr(dec, labels_test); - - % Need to decide on a threshold for positive and negative - - thresh_corr = -0.85; - - class_test = zeros(size(labels_test)); - class_predict = zeros(size(labels_test)); - - class_test(labels_test < thresh_corr) = 1; - class_test(labels_test >= thresh_corr) = -1; - - class_predict(dec < thresh_corr) = 1; - class_predict(dec >= thresh_corr) = -1; - - TP = sum(class_predict==1 & class_test == 1); - FP = sum(class_predict==1 & class_test == -1); - FN = sum(class_predict==-1 & class_test == 1); - - Precission = TP / (TP+FP); - Recall = TP / (TP+FN); - - F1 = 2 * (Precission * Recall) / (Precission + Recall); - - faceChecker.corr = corr_dec; - faceChecker.principal_components = prin_comps; - faceChecker.mean_ex = mean_ex; - faceChecker.std_ex = std_ex; - faceChecker.triangulation = triangulation; - faceChecker.centres = centres; - faceChecker.triX = triX; - faceChecker.nPix = nPix; - faceChecker.minX = minX; - faceChecker.minY = minY; - faceChecker.source = shape(:,1:2); - faceChecker.mask = mask; - faceChecker.alphas = alphas; - faceChecker.betas = betas; - faceChecker.w = w; - faceChecker.b = b; - - if(i==1) - faceCheckers = faceChecker; - else - faceCheckers = [faceCheckers faceChecker]; - end -end - -locationOut = './trained/face_check_general_66.txt'; -locationOutM = './trained/face_check_general_66.mat'; - -WriteOutFaceCheckers(locationOut, locationOutM, faceCheckers); - -% as a side effect write out a triangulation file as well -WriteOutTriangulation('./trained/tris_66.txt', './trained/tris_66.mat', faceCheckers); diff --git a/matlab_version/face_validation/Train_face_checker_66_cnn.m b/matlab_version/face_validation/Train_face_checker_66_cnn.m deleted file mode 100644 index 55bfde6..0000000 --- a/matlab_version/face_validation/Train_face_checker_66_cnn.m +++ /dev/null @@ -1,183 +0,0 @@ -clear - -% Replace with your location of training data -root_loc = 'F:/datasets/detection_validation/'; - -location = [root_loc, '/prep_data/']; - -faceCheckersLoc = dir([location 'face_checker_general_training_large_66_*']); - -training_ratio = 0.9; - -rng(0); -for i=1:numel(faceCheckersLoc) - - load([location faceCheckersLoc(i).name]); - - % set a max value to the error - errors(errors > 3) = 3; - - num_examples = size(examples, 1); - - training_cutoff = round(num_examples * training_ratio); - - % Randomise the samples before training - new_inds = randperm(size(examples,1)); - examples = examples(new_inds, :); - errors = errors(new_inds); - - % Extract the mean and standard deviation and normalise by it, the - % examples have been normalised individually, but this is a global - % normalisation that follows it - - % picking training data for normalisation - examples_train = examples(1:training_cutoff,:); - - mean_ex = mean(examples_train); - std_ex = std(examples_train); - - examples = bsxfun(@times, bsxfun(@minus, examples, mean_ex), 1./std_ex); - -% examples = examples / 255; - - kernel_1_size = 7; - kernel_2_size = 5; - - % Convert the examples to wanted format (number rows and cols to allow from proper subsampling) - - % keep adding rows - while(mod(size(mask,1)-kernel_1_size + 1, 2) == 1 || mod((size(mask,1)-kernel_1_size + 1)/2 - kernel_2_size + 1, 2) == 1) - mask = cat(1, mask, false(1, size(mask,2))); - triX = cat(1, triX, -ones(1, size(mask,2))); - end - - % keep adding cols - while(mod(size(mask,2)-kernel_1_size + 1, 2) == 1 || mod((size(mask,2)-kernel_1_size + 1)/2 - kernel_2_size + 1, 2) == 1) - mask = cat(2, mask, false(size(mask,1),1)); - triX = cat(2, triX, -ones(size(triX,1),1)); - end - - examples_r = zeros(size(mask, 1), size(mask, 2), num_examples); - - img_curr = zeros(size(mask)); - for e=1:num_examples - - img_curr(mask) = examples(e,:); - examples_r(:, :, e) = img_curr; - - end - - % picking training data for SVM (positive and negative samples) - examples_train = examples_r(:, :, 1:training_cutoff); - - examples_valid = examples_r(:, :, training_cutoff+1:end); - - % Normalise to 0-1 labels - labels_train = errors(1:training_cutoff) / 3; - labels_valid = errors(training_cutoff+1:end,:)/3; - - addpath(genpath('DeepLearnToolbox')); - - % This needs to be validated - num_kern = [2, 3]; - alphas_learn = [0.5, 0.75, 1]; - - res = zeros(numel(num_kern), numel(alphas_learn)); - corrs = zeros(numel(num_kern), numel(alphas_learn)); - - % Set up model options - opts.batchsize = 50; - opts.numepochs = 100; - - cnns = cell(numel(num_kern), numel(alphas_learn)); - - for n=1:numel(num_kern) - - for a=1:numel(alphas_learn) - - %% - opts.alpha = alphas_learn(a); - - rand(0); - - cnn.layers = { - struct('type', 'i') %input layer - struct('type', 'c', 'outputmaps', num_kern(n), 'kernelsize', kernel_1_size) %convolution layer - struct('type', 's', 'scale', 2) %sub sampling layer - struct('type', 'c', 'outputmaps', num_kern(n) * 2, 'kernelsize', kernel_2_size) %convolution layer - struct('type', 's', 'scale', 2) %subsampling layer - }; - - cnn = cnnsetup(cnn, examples_train, labels_train'); - - % For some reason the model does not always converge properly - cnn = cnntrain(cnn, examples_train, labels_train', opts); - - cnn = cnnff(cnn, examples_valid); - - pred_y = cnn.o'; - - res(n, a) = sqrt(mean((labels_valid - pred_y).^2)); - corrs(n, a) = corr(labels_valid, pred_y); - - fprintf('curr corr res %d layers: %f alpha %f\n', num_kern(n), alphas_learn(a), corrs(n, a)); - - % A hack to save space - cnn = cnnff(cnn, examples_valid(:,:,1)); - - cnn.o = []; - cnn.rL = []; - cnn.fV = []; - cnn.fvd = []; - cnn.dffW = []; - - for l=1:numel(cnn.layers) - cnn.layers{l}.a = []; - if(isfield(cnn.layers{l}, 'd')) - cnn.layers{l}.d = []; - end - end - - cnns(n, a) = {cnn}; - end - - end - - [val,~] = min(res(:)); - [a, b] = ind2sub(size(res), find(res == val)); - fprintf('---------------------------------\n'); - fprintf('Best model num kernels %d, alpha %f\n', num_kern(a), alphas_learn(b)); - fprintf('---------------------------------\n'); - - cnn = cnns{a, b}; - - face_check_cnns(i).cnn = cnn; - face_check_cnns(i).destination = shape(:,1:2); - face_check_cnns(i).triangulation = triangulation; - face_check_cnns(i).triX = triX; - face_check_cnns(i).mask = mask; - face_check_cnns(i).centres = centres; - face_check_cnns(i).alphas = alphas; - face_check_cnns(i).betas = betas; - face_check_cnns(i).minX = minX; - face_check_cnns(i).minY = minY; - face_check_cnns(i).nPix = nPix; - face_check_cnns(i).mean_ex = mean_ex; - face_check_cnns(i).std_ex = std_ex; -end - -%the actual testing before it is released -% Create the function -face_check_fun = @(img, shape, global_params) face_check_cnn(img, shape, global_params, face_check_cnns); - -[ predictions, gts, rmse, corr_coeff ] = Test_face_checker(face_check_fun); - -locationOut = ['./trained/face_check_cnn_66.txt']; -locationOutM = ['./trained/face_check_cnn_66.mat']; - -save(locationOutM, 'face_check_cnns', 'rmse', 'gts', 'predictions', 'corr_coeff'); - -WriteOutFaceCheckersCNNbinary(locationOut, face_check_cnns); - -% as a side effect write out a triangulation file as well -WriteOutTriangulation('./trained/tris_66.txt', './trained/tris_66.mat', face_check_cnns); diff --git a/matlab_version/face_validation/Train_face_checker_68.m b/matlab_version/face_validation/Train_face_checker_68.m deleted file mode 100644 index c262d59..0000000 --- a/matlab_version/face_validation/Train_face_checker_68.m +++ /dev/null @@ -1,137 +0,0 @@ -clear -location = './prep_data/'; - -faceCheckersLoc = dir([location 'face_checker_general_training_68_*']); - -training_ratio = 0.7; - -rng(0); -for i=1:numel(faceCheckersLoc) - - load([location faceCheckersLoc(i).name]); - - num_examples = size(examples, 1); - - training_cutoff = round(num_examples * training_ratio); - - % picking training data for SVM (positive and negative samples) - examples_train = examples(1:training_cutoff,:); - - % Extract the mean and standard deviation and normalise by it - mean_ex = mean(examples_train); - std_ex = std(examples_train); - examples_train = bsxfun(@times, bsxfun(@minus, examples_train, mean_ex), 1./std_ex); - errors_train = errors(1:training_cutoff); - - [prin_comps, score, latent] = pca(examples_train); - - % Keep enough data to explain 95 percent of variability - variance_explained = cumsum(latent) / sum(latent); - num_components = find(variance_explained > 0.90, 1, 'first'); - prin_comps = prin_comps(:,1:num_components); - - examples_train_ld = prin_comps' * examples_train'; - examples_train_ld = examples_train_ld'; - - min_err = min( errors(1:training_cutoff)); - max_err = max( errors(1:training_cutoff)); - - labels_train = 2*((errors(1:training_cutoff) - min_err)/(max_err-min_err)-0.5); - - % liblinear SVR training - addpath('C:\liblinear\matlab'); - - cs = [-4:-1]; - ps = [-10:-2]; - - cmd = ['-s 11 -B 1 -q ']; - - res = zeros(numel(cs), numel(ps)); - for c=1:numel(cs) - for p=1:numel(ps) - - validation_p = sprintf('%s -v 5 -p %f -c %f', cmd, 10^cs(c), 2^ps(p)); - rms_valid = train(labels_train, sparse(double(examples_train_ld)), validation_p); - res(c,p) = rms_valid; - end - end - - [val,~] = min(min(res)); - [a, b] = ind2sub(size(res), find(res == val)); - - best_c = cs(a); - best_p = ps(b); - - best_params = sprintf('%s -p %f -c %f', cmd, 10^best_c, 2^best_p); - - regressor_lsvr = train(labels_train, sparse(double(examples_train_ld)), best_params); - - w = regressor_lsvr.w(1:end-1)'; - b = regressor_lsvr.w(end); - - examples_test = examples(training_cutoff+1:end,:); - examples_test = bsxfun(@times, bsxfun(@minus, examples_test, mean_ex), 1./std_ex); - - examples_test_ld = prin_comps' * examples_test'; - examples_test_ld = examples_test_ld'; - - labels_test = 2*((errors(training_cutoff+1:end) - min_err)/(max_err-min_err)-0.5); - - dec = examples_test_ld * w + b; - - rmse = sqrt(mean((dec - labels_test).^2)); - corr_dec = corr(dec, labels_test); - - % Need to decide on a threshold for positive and negative - - thresh_corr = -0.85; - - class_test = zeros(size(labels_test)); - class_predict = zeros(size(labels_test)); - - class_test(labels_test < thresh_corr) = 1; - class_test(labels_test >= thresh_corr) = -1; - - class_predict(dec < thresh_corr) = 1; - class_predict(dec >= thresh_corr) = -1; - - TP = sum(class_predict==1 & class_test == 1); - FP = sum(class_predict==1 & class_test == -1); - FN = sum(class_predict==-1 & class_test == 1); - - Precission = TP / (TP+FP); - Recall = TP / (TP+FN); - - F1 = 2 * (Precission * Recall) / (Precission + Recall); - - faceChecker.corr = corr_dec; - faceChecker.principal_components = prin_comps; - faceChecker.mean_ex = mean_ex; - faceChecker.std_ex = std_ex; - faceChecker.triangulation = triangulation; - faceChecker.centres = centres; - faceChecker.triX = triX; - faceChecker.nPix = nPix; - faceChecker.minX = minX; - faceChecker.minY = minY; - faceChecker.source = shape(:,1:2); - faceChecker.mask = mask; - faceChecker.alphas = alphas; - faceChecker.betas = betas; - faceChecker.w = w; - faceChecker.b = b; - - if(i==1) - faceCheckers = faceChecker; - else - faceCheckers = [faceCheckers faceChecker]; - end -end - -locationOut = './trained/face_check_general_68.txt'; -locationOutM = './trained/face_check_general_68.mat'; - -WriteOutFaceCheckers(locationOut, locationOutM, faceCheckers); - -% as a side effect write out a triangulation file as well -WriteOutTriangulation('./trained/tris_68.txt', './trained/tris_68.mat', faceCheckers); diff --git a/matlab_version/face_validation/Train_face_checker_68_cnn.m b/matlab_version/face_validation/Train_face_checker_68_cnn.m deleted file mode 100644 index c2e6b45..0000000 --- a/matlab_version/face_validation/Train_face_checker_68_cnn.m +++ /dev/null @@ -1,183 +0,0 @@ -clear - -root_loc = 'E:/datasets/detection_validation/'; - -location = [root_loc, '/prep_data/']; - -faceCheckersLoc = dir([location 'face_checker_general_training_large_68_*']); - -training_ratio = 0.9; - -rng(0); -for i=1:numel(faceCheckersLoc) - - load([location faceCheckersLoc(i).name]); - - % set a max value to the error - errors(errors > 3) = 3; - - num_examples = size(examples, 1); - - training_cutoff = round(num_examples * training_ratio); - - % Randomise the samples before training - new_inds = randperm(size(examples,1)); - examples = examples(new_inds, :); - errors = errors(new_inds); - - % Extract the mean and standard deviation and normalise by it, the - % examples have been normalised individually, but this is a global - % normalisation that follows it - - % picking training data for normalisation - examples_train = examples(1:training_cutoff,:); - - mean_ex = mean(examples_train); - std_ex = std(examples_train); - - examples = bsxfun(@times, bsxfun(@minus, examples, mean_ex), 1./std_ex); - - kernel_1_size = 7; - kernel_2_size = 5; - - % Convert the examples to wanted format (number rows and cols to allow from proper subsampling) - - % keep adding rows - while(mod(size(mask,1)-kernel_1_size + 1, 2) == 1 || mod((size(mask,1)-kernel_1_size + 1)/2 - kernel_2_size + 1, 2) == 1) - mask = cat(1, mask, false(1, size(mask,2))); - triX = cat(1, triX, -ones(1, size(mask,2))); - end - - % keep adding cols - while(mod(size(mask,2)-kernel_1_size + 1, 2) == 1 || mod((size(mask,2)-kernel_1_size + 1)/2 - kernel_2_size + 1, 2) == 1) - mask = cat(2, mask, false(size(mask,1),1)); - triX = cat(2, triX, -ones(size(triX,1),1)); - end - - examples_r = zeros(size(mask, 1), size(mask, 2), num_examples); - - img_curr = zeros(size(mask)); - for e=1:num_examples - - img_curr(mask) = examples(e,:); - examples_r(:, :, e) = img_curr; - - end - - % picking training data for SVM (positive and negative samples) - examples_train = examples_r(:, :, 1:training_cutoff); - - examples_valid = examples_r(:, :, training_cutoff+1:end); - - % Normalise to 0-1 labels - labels_train = errors(1:training_cutoff) / 3; - labels_valid = errors(training_cutoff+1:end,:)/3; - - addpath(genpath('DeepLearnToolbox')); - - % This needs to be validated - - num_kern = [3]; - alphas_learn = [0.5, 0.75, 1]; - - res = zeros(numel(num_kern), numel(alphas_learn)); - corrs = zeros(numel(num_kern), numel(alphas_learn)); - - % Set up model options - opts.batchsize = 50; - - % Can go up if needed - opts.numepochs = 150; - - cnns = cell(numel(num_kern), numel(alphas_learn)); - - for n=1:numel(num_kern) - - for a=1:numel(alphas_learn) - - %% - opts.alpha = alphas_learn(a); - - rand(0); - - cnn.layers = { - struct('type', 'i') %input layer - struct('type', 'c', 'outputmaps', num_kern(n), 'kernelsize', kernel_1_size) %convolution layer - struct('type', 's', 'scale', 2) %sub sampling layer - struct('type', 'c', 'outputmaps', num_kern(n) * 2, 'kernelsize', kernel_2_size) %convolution layer - struct('type', 's', 'scale', 2) %subsampling layer - }; - - cnn = cnnsetup(cnn, examples_train, labels_train'); - - % For some reason the model does not always converge properly - cnn = cnntrain(cnn, examples_train, labels_train', opts); - - cnn = cnnff(cnn, examples_valid); - - pred_y = cnn.o'; - - res(n, a) = sqrt(mean((labels_valid - pred_y).^2)); - corrs(n, a) = corr(labels_valid, pred_y); - - fprintf('curr corr res %d layers: %f alpha %f\n', num_kern(n), alphas_learn(a), corrs(n, a)); - - % A hack to save space - cnn = cnnff(cnn, examples_valid(:,:,1)); - - cnn.o = []; - cnn.rL = []; - cnn.fV = []; - cnn.fvd = []; - cnn.dffW = []; - - for l=1:numel(cnn.layers) - cnn.layers{l}.a = []; - if(isfield(cnn.layers{l}, 'd')) - cnn.layers{l}.d = []; - end - end - - cnns(n, a) = {cnn}; - end - - end - - [val,~] = min(res(:)); - [a, b] = ind2sub(size(res), find(res == val)); - fprintf('---------------------------------\n'); - fprintf('Best model num kernels %d, alpha %f\n', num_kern(a), alphas_learn(b)); - fprintf('---------------------------------\n'); - - cnn = cnns{a, b}; - - face_check_cnns(i).cnn = cnn; - face_check_cnns(i).destination = shape(:,1:2); - face_check_cnns(i).triangulation = triangulation; - face_check_cnns(i).triX = triX; - face_check_cnns(i).mask = mask; - face_check_cnns(i).centres = centres; - face_check_cnns(i).alphas = alphas; - face_check_cnns(i).betas = betas; - face_check_cnns(i).minX = minX; - face_check_cnns(i).minY = minY; - face_check_cnns(i).nPix = nPix; - face_check_cnns(i).mean_ex = mean_ex; - face_check_cnns(i).std_ex = std_ex; -end - -%the actual testing before it is released -% Create the function -face_check_fun = @(img, shape, global_params) face_check_cnn(img, shape, global_params, face_check_cnns); - -[ predictions, gts, rmse, corr_coeff ] = Test_face_checker(face_check_fun); - -locationOut = ['./trained/face_check_cnn_68.txt']; -locationOutM = ['./trained/face_check_cnn_68.mat']; - -save(locationOutM, 'face_check_cnns', 'rmse', 'gts', 'predictions', 'corr_coeff'); - -WriteOutFaceCheckersCNNbinary(locationOut, face_check_cnns); - -% as a side effect write out a triangulation file as well -WriteOutTriangulation('./trained/tris_68.txt', './trained/tris_68.mat', face_check_cnns); diff --git a/matlab_version/face_validation/Train_face_checker_68_nn.m b/matlab_version/face_validation/Train_face_checker_68_nn.m deleted file mode 100644 index 4ba36f8..0000000 --- a/matlab_version/face_validation/Train_face_checker_68_nn.m +++ /dev/null @@ -1,137 +0,0 @@ -clear - -root_loc = 'F:/datasets/detection_validation/'; - -location = [root_loc, '/prep_data/']; - -faceCheckersLoc = dir([location 'face_checker_general_training_large_68_*']); - -training_ratio = 0.7; - -rng(0); -for i=1:numel(faceCheckersLoc) - - load([location faceCheckersLoc(i).name]); - - % set a max value to the error - errors(errors > 3) = 3; - - num_examples = size(examples, 1); - - training_cutoff = round(num_examples * training_ratio); - - % picking training data for SVM (positive and negative samples) - examples_train = examples(1:training_cutoff,:); - - % Extract the mean and standard deviation and normalise by it - mean_ex = mean(examples_train); - std_ex = std(examples_train); - examples_train = bsxfun(@times, bsxfun(@minus, examples_train, mean_ex), 1./std_ex); - - examples_valid = examples(training_cutoff+1:end,:); - examples_valid = bsxfun(@times, bsxfun(@minus, examples_valid, mean_ex), 1./std_ex); - - errors_train = errors(1:training_cutoff); - - % Normalise to 0-1 labels - labels_train = errors(1:training_cutoff) / 3; - labels_valid = errors(training_cutoff+1:end,:)/3; - - % liblinear SVR training - addpath(genpath('DeepLearnToolbox')); - - layer_depth = [1, 2]; - - neural_layers = [5, 10, 20, 50]; - - res = zeros(numel(neural_layers), numel(layer_depth)); - corrs = zeros(numel(neural_layers), numel(layer_depth)); - - for n=1:numel(neural_layers) - for l=1:numel(layer_depth) - - %% Vanilla neural net - rand(0); - - % input layer - layers = size(examples_train,2); - - % add hidden layers between input and output - for nl=1:layer_depth(l) - layers = cat(1, layers, neural_layers(n)); - end - - % output layer - layers = cat(1, layers, 1); - - nn = nnsetup(layers); - nn.output = 'sigm'; - opts.numepochs = 100; % Number of full sweeps through data - opts.batchsize = 200; % Take a mean gradient step over this many samples - opts.plot = 0; - [nn, L] = nntrain(nn, examples_train, labels_train, opts, examples_valid, labels_valid); - - % [er, bad] = nntest(nn, test_x, test_y); - nn = nnff(nn, examples_valid, zeros(size(examples_valid,1), nn.size(end))); - % pred_y = nnpredict(nn, test_x); - pred_y = nn.a{end}; - - rms_valid = sqrt(mean((labels_valid - pred_y).^2)); - - res(n, l) = rms_valid; - corrs(n, l) = corr(labels_valid, pred_y); - end - end - - [val,~] = min(res(:)); - [a, b] = ind2sub(size(res), find(res == val)); - - layers = size(examples_train,2); - - % add hidden layers between input and output - for nl=1:layer_depth(b) - layers = cat(1, layers, neural_layers(a)); - end - - % output layer - layers = cat(1, layers, 1); - - nn = nnsetup(layers); - - examples = bsxfun(@times, bsxfun(@minus, examples, mean_ex), 1./std_ex); - - [nn, L] = nntrain(nn, examples, errors/3, opts); - - face_check_nns(i).nn = nn; - face_check_nns(i).destination = shape(:,1:2); - face_check_nns(i).triangulation = triangulation; - face_check_nns(i).triX = triX; - face_check_nns(i).mask = mask; - face_check_nns(i).centres = centres; - face_check_nns(i).alphas = alphas; - face_check_nns(i).betas = betas; - face_check_nns(i).minX = minX; - face_check_nns(i).minY = minY; - face_check_nns(i).nPix = nPix; - face_check_nns(i).mean_ex = mean_ex; - face_check_nns(i).std_ex = std_ex; -end - -%the actual testing before it is released -% Create the function -face_check_fun = @(img, shape, global_params) face_check_nn(img, shape, global_params, face_check_nns); - -[ predictions, gts, rmse, corr_coeff ] = Test_face_checker(face_check_fun); - -locationOut = [root_loc, './trained/face_check_nn_68.txt']; -locationOutM = [root_loc, './trained/face_check_nn_68.mat']; - -save(locationOutM, 'face_check_nns', 'rmse', 'gts', 'predictions', 'corr_coeff'); - -WriteOutFaceCheckersNNbinary(locationOut, face_check_nns); -% WriteOutFaceCheckers(locationOut, locationOutM, faceCheckers); - -% as a side effect write out a triangulation file as well -% WriteOutTriangulation('./trained/tris_68.txt', './trained/tris_68.mat', faceCheckers); - -% Results, with global normalisation \ No newline at end of file diff --git a/matlab_version/face_validation/Train_face_checker_68_svr.m b/matlab_version/face_validation/Train_face_checker_68_svr.m deleted file mode 100644 index 8fcbb95..0000000 --- a/matlab_version/face_validation/Train_face_checker_68_svr.m +++ /dev/null @@ -1,152 +0,0 @@ -clear - -root_loc = 'E:/datasets/detection_validation/'; - -location = [root_loc, '/prep_data/']; - -faceCheckersLoc = dir([location 'face_checker_general_training_large_68_*']); - -training_ratio = 0.9; - -rng(0); -for i=1:numel(faceCheckersLoc) - - load([location faceCheckersLoc(i).name]); - - % set a max value to the error - errors(errors > 3) = 3; - - num_examples = size(examples, 1); - - training_cutoff = round(num_examples * training_ratio); - - % picking training data for SVM (positive and negative samples) - examples_train = examples(1:training_cutoff,:); - - % Extract the mean and standard deviation and normalise by it - mean_ex = mean(examples_train); - std_ex = std(examples_train); - examples_train = bsxfun(@times, bsxfun(@minus, examples_train, mean_ex), 1./std_ex); - errors_train = errors(1:training_cutoff); - - [prin_comps, score, latent] = pca(examples_train); - - % Keep enough data to explain 95 percent of variability - variance_explained = cumsum(latent) / sum(latent); - num_components = find(variance_explained > 0.90, 1, 'first'); - prin_comps = prin_comps(:,1:num_components); - - examples_train_ld = prin_comps' * examples_train'; - examples_train_ld = examples_train_ld'; - - min_err = min( errors(1:training_cutoff)); - max_err = max( errors(1:training_cutoff)); - - labels_train = 2*((errors(1:training_cutoff) - min_err)/(max_err-min_err)-0.5); - - % liblinear SVR training - addpath('C:\liblinear\matlab'); - - cs = [-4:-1]; - ps = [-10:-2]; - - cmd = ['-s 11 -B 1 -q ']; - - res = zeros(numel(cs), numel(ps)); - for c=1:numel(cs) - for p=1:numel(ps) - - validation_p = sprintf('%s -v 5 -p %f -c %f', cmd, 10^cs(c), 2^ps(p)); - rms_valid = train(labels_train, sparse(double(examples_train_ld)), validation_p); - res(c,p) = rms_valid; - end - end - - [val,~] = min(min(res)); - [a, b] = ind2sub(size(res), find(res == val)); - - best_c = cs(a); - best_p = ps(b); - - best_params = sprintf('%s -p %f -c %f', cmd, 10^best_c, 2^best_p); - - regressor_lsvr = train(labels_train, sparse(double(examples_train_ld)), best_params); - - w = regressor_lsvr.w(1:end-1)'; - b = regressor_lsvr.w(end); - - examples_test = examples(training_cutoff+1:end,:); - examples_test = bsxfun(@times, bsxfun(@minus, examples_test, mean_ex), 1./std_ex); - - examples_test_ld = prin_comps' * examples_test'; - examples_test_ld = examples_test_ld'; - - labels_test = 2*((errors(training_cutoff+1:end) - min_err)/(max_err-min_err)-0.5); - - dec = examples_test_ld * w + b; - - rmse = sqrt(mean((dec - labels_test).^2)); - corr_dec = corr(dec, labels_test); - - % Need to decide on a threshold for positive and negative - - thresh_corr = -0.85; - - class_test = zeros(size(labels_test)); - class_predict = zeros(size(labels_test)); - - class_test(labels_test < thresh_corr) = 1; - class_test(labels_test >= thresh_corr) = -1; - - class_predict(dec < thresh_corr) = 1; - class_predict(dec >= thresh_corr) = -1; - - TP = sum(class_predict==1 & class_test == 1); - FP = sum(class_predict==1 & class_test == -1); - FN = sum(class_predict==-1 & class_test == 1); - - Precission = TP / (TP+FP); - Recall = TP / (TP+FN); - - F1 = 2 * (Precission * Recall) / (Precission + Recall); - - faceChecker.corr = corr_dec; - faceChecker.principal_components = prin_comps; - faceChecker.mean_ex = mean_ex; - faceChecker.std_ex = std_ex; - faceChecker.triangulation = triangulation; - faceChecker.centres = centres; - faceChecker.triX = triX; - faceChecker.nPix = nPix; - faceChecker.minX = minX; - faceChecker.minY = minY; - faceChecker.source = shape(:,1:2); - faceChecker.mask = mask; - faceChecker.alphas = alphas; - faceChecker.betas = betas; - faceChecker.w = w; - faceChecker.b = b; - - if(i==1) - faceCheckers = faceChecker; - else - faceCheckers = [faceCheckers faceChecker]; - end -end - -% Testing before writing them out - -% Create the function -face_check_fun = @(img, shape, global_params) face_check_preloaded(img, shape, global_params, faceCheckers); - -[ predictions, gts, rmse, corr_coeff ] = Test_face_checker(face_check_fun); - -locationOut = [root_loc, './trained/face_check_general_68.txt']; -locationOutM = [root_loc, './trained/face_check_general_68.mat']; - -save(locationOutM, 'faceCheckers', 'rmse', 'gts', 'predictions', 'corr_coeff'); - -WriteOutFaceCheckers(locationOut, locationOutM, faceCheckers); - -% as a side effect write out a triangulation file as well -WriteOutTriangulation('./trained/tris_68.txt', './trained/tris_68.mat', faceCheckers); diff --git a/matlab_version/face_validation/WriteOutFaceCheckers.m b/matlab_version/face_validation/WriteOutFaceCheckers.m deleted file mode 100644 index 53c7147..0000000 --- a/matlab_version/face_validation/WriteOutFaceCheckers.m +++ /dev/null @@ -1,75 +0,0 @@ -function WriteOutFaceCheckers(locationTxt, locationMlab, faceCheckers) - - addpath('..\PDM_helpers\'); - - faceCheckerFile = fopen(locationTxt, 'w'); - - views = numel(faceCheckers); - - fprintf(faceCheckerFile, '# Number of face checkers\r\n%d\r\n', views); - - fprintf(faceCheckerFile, '# Face checker centres\r\n'); - - for i=1:views - % this indicates that we're writing a 3x1 double matrix - writeMatrix(faceCheckerFile, faceCheckers(i).centres', 6); - end - - for i = 1:views - - b = faceCheckers(i).b; - w = faceCheckers(i).w; - - fprintf(faceCheckerFile, '# Principal Components %d\r\n', i); - writeMatrix(faceCheckerFile, faceCheckers(i).principal_components, 6); - - fprintf(faceCheckerFile, '# Mean of images %d\r\n', i); - writeMatrix(faceCheckerFile, faceCheckers(i).mean_ex, 6); - - fprintf(faceCheckerFile, '# Standard deviation of images %d\r\n', i); - writeMatrix(faceCheckerFile, faceCheckers(i).std_ex, 6); - - fprintf(faceCheckerFile, '# Classifier %d\r\n', i); - - fprintf(faceCheckerFile, '# Linear SVR\r\n'); - fprintf(faceCheckerFile, '%f\r\n', b); - writeMatrix(faceCheckerFile, w, 6); - - fprintf(faceCheckerFile, '# Piecewise affine warp \r\n'); - - nPix = faceCheckers(i).nPix; - minX = faceCheckers(i).minX; - minY = faceCheckers(i).minY; - - source = reshape(faceCheckers(i).source, numel(faceCheckers(i).source), 1); - triangulation = faceCheckers(i).triangulation; - triX = faceCheckers(i).triX; - mask = faceCheckers(i).mask; - alphas = faceCheckers(i).alphas; - betas = faceCheckers(i).betas; - - fprintf(faceCheckerFile, '%d\r\n%f\r\n%f\r\n', nPix, minX, minY); - - fprintf(faceCheckerFile, '# source shape \r\n'); - writeMatrix(faceCheckerFile, source, 6); - - fprintf(faceCheckerFile, '# triangulation \r\n'); - writeMatrix(faceCheckerFile, triangulation, 4); - - fprintf(faceCheckerFile, '# triangle map \r\n'); - writeMatrix(faceCheckerFile, triX, 4); - - fprintf(faceCheckerFile, '# mask \r\n'); - writeMatrix(faceCheckerFile, mask, 4); - - fprintf(faceCheckerFile, '# alphas \r\n'); - writeMatrix(faceCheckerFile, alphas, 6); - - fprintf(faceCheckerFile, '# betas \r\n'); - writeMatrix(faceCheckerFile, betas, 6); - - end - - fclose(faceCheckerFile); - -end \ No newline at end of file diff --git a/matlab_version/face_validation/WriteOutFaceCheckersCNNbinary.m b/matlab_version/face_validation/WriteOutFaceCheckersCNNbinary.m index 98e30b2..24b28ec 100644 --- a/matlab_version/face_validation/WriteOutFaceCheckersCNNbinary.m +++ b/matlab_version/face_validation/WriteOutFaceCheckersCNNbinary.m @@ -1,15 +1,16 @@ function WriteOutFaceCheckersCNNbinary(locationTxt, faceCheckers) - addpath('..\PDM_helpers\'); + addpath('../PDM_helpers\'); % use little-endian faceCheckerFile = fopen(locationTxt, 'w', 'l'); views = numel(faceCheckers); - % Type 0 - linear SVR, 1 - feed forward neural net, 2 - CNN - fwrite(faceCheckerFile, 2, 'uint'); % 4 bytes - + % Type 0 - linear SVR, 1 - feed forward neural net, 2 - CNN, 3 - new + % CNN + fwrite(faceCheckerFile, 3, 'uint'); % 4 bytes + % Number of face checkers fwrite(faceCheckerFile, views, 'uint'); % 4 bytes @@ -30,59 +31,71 @@ function WriteOutFaceCheckersCNNbinary(locationTxt, faceCheckers) cnn = faceCheckers(i).cnn; - num_depth_layers = size(cnn.layers,1); + num_depth_layers = size(cnn.layers,2); % Get the number of layers fwrite(faceCheckerFile, num_depth_layers, 'uint'); % 4 bytes - for layers=2:num_depth_layers + % For disambiguation between FC and conv layers + res = vl_simplenn(cnn, single(faceCheckers(i).mask), [], []); + + for layers=1:num_depth_layers - % write layer type: 0 - convolutional, 1 - subsampling, 2- - % fully connected - if(cnn.layers{layers}.type == 'c') + % write layer type: 0 - convolutional, 1 - max pooling (2x2 stride 2), 2 - + % fully connected, 3 - relu, 4 - sigmoid + if(cnn.layers{layers}.type == 'conv') - % write the type (convolutional) - fwrite(faceCheckerFile, 0, 'uint'); % 4 bytes + % First check if it is an FC layer (they are represented + % like that in matconvnet) + if(numel(res(layers).x) == numel(cnn.layers{layers}.weights{1}(:,:,:,1))) + % This is the fully connected layer + fwrite(faceCheckerFile, 2, 'uint'); % 4 bytes + + % the bias term + writeMatrixBin(faceCheckerFile, cnn.layers{layers}.weights{2}(:), 5); + % the weights + + % Convert the filters to a matrix + weights_c = cnn.layers{layers}.weights{1}; + size_w = size(weights_c); + weights = zeros(size_w(1)*size_w(2)*size_w(3), size_w(4)); + weights(:) = weights_c; + writeMatrixBin(faceCheckerFile, weights, 5); + else - num_in_map = size(cnn.layers{layers}.k,2); + % write the type (convolutional) + fwrite(faceCheckerFile, 0, 'uint'); % 4 bytes - % write the number of input maps - fwrite(faceCheckerFile, num_in_map, 'uint'); % 4 bytes + num_in_map = size(cnn.layers{layers}.weights{1},3); - num_out_kerns = cnn.layers{layers}.outputmaps; + % write the number of input maps + fwrite(faceCheckerFile, num_in_map, 'uint'); % 4 bytes - % write the number of kernels for each output map - fwrite(faceCheckerFile, num_out_kerns, 'uint'); % 4 bytes - - % Write output map bias terms - for k2=1:num_out_kerns - fwrite(faceCheckerFile, cnn.layers{layers}.b{k2}, 'float32'); % 4 bytes - end - - for k=1:num_in_map - for k2=1:num_out_kerns - % Write out the bias term - W = cnn.layers{layers}.k{k}{k2}; - writeMatrixBin(faceCheckerFile, W, 5); + num_out_kerns = size(cnn.layers{layers}.weights{1},4); + + % write the number of kernels for each output map + fwrite(faceCheckerFile, num_out_kerns, 'uint'); % 4 bytes + + % Write output map bias terms + for k2=1:num_out_kerns + fwrite(faceCheckerFile, cnn.layers{layers}.weights{2}(k2), 'float32'); % 4 bytes end + + for k=1:num_in_map + for k2=1:num_out_kerns + % Write out the bias term + W = squeeze(cnn.layers{layers}.weights{1}(:,:,k,k2)); + writeMatrixBin(faceCheckerFile, W, 5); + end + end end - - else - fwrite(faceCheckerFile, 1, 'uint'); % 4 bytes - % size of scaling - fwrite(faceCheckerFile, cnn.layers{layers}.scale, 'uint'); % 4 bytes - end - + elseif(cnn.layers{layers}.type == 'pool') + fwrite(faceCheckerFile, 1, 'uint'); % 4 bytes, indicate max pooling layer, no params, assume (2x2 stride 2) + elseif(cnn.layers{layers}.type == 'relu') + fwrite(faceCheckerFile, 3, 'uint'); % 4 bytes, indicate relu layer, no params + end end - % This is the fully connected layer - fwrite(faceCheckerFile, 2, 'uint'); % 4 bytes - - % the bias term - fwrite(faceCheckerFile, cnn.ffb, 'float32'); - % the weights - writeMatrixBin(faceCheckerFile, cnn.ffW, 5); - %sigm(net.ffW * net.fv + repmat(net.ffb, 1, size(net.fv, 2))); % Piecewise affine warp diff --git a/matlab_version/face_validation/WriteOutFaceCheckersNNbinary.m b/matlab_version/face_validation/WriteOutFaceCheckersNNbinary.m deleted file mode 100644 index 99193db..0000000 --- a/matlab_version/face_validation/WriteOutFaceCheckersNNbinary.m +++ /dev/null @@ -1,103 +0,0 @@ -function WriteOutFaceCheckersNNbinary(locationTxt, faceCheckers) - - addpath('..\PDM_helpers\'); - - % use little-endian - faceCheckerFile = fopen(locationTxt, 'w', 'l'); - - views = numel(faceCheckers); - - % Type 0 - linear SVR, 1 - feed forward neural net - fwrite(faceCheckerFile, 1, 'uint'); % 4 bytes - - % Number of face checkers - fwrite(faceCheckerFile, views, 'uint'); % 4 bytes - - % Matrices representing view orientations - for i=1:views - % this indicates that we're writing a 3x1 double matrix - writeMatrixBin(faceCheckerFile, faceCheckers(i).centres', 6); - end - - for i = 1:views - - % The normalisation models - % Mean of images - writeMatrixBin(faceCheckerFile, faceCheckers(i).mean_ex, 6); - - % Standard deviation of images - writeMatrixBin(faceCheckerFile, faceCheckers(i).std_ex, 6); - - nn = faceCheckers(i).nn; - - num_depth_layers = numel(nn.size) - 1; - % Get the number of layers - fwrite(faceCheckerFile, num_depth_layers, 'uint'); % 4 bytes - - % The activation function used 0 - sigmoid, 1 - tanh, 2 - ReLu - act_fun = 0; - if(strcmp(nn.activation_function, 'tanh_opt')) - act_fun = 1; - elseif(strcmp(nn.activation_function, 'ReLu')) - act_fun = 2; - end - - fwrite(faceCheckerFile, act_fun, 'uint'); % 4 bytes - - % The activation function used 0 - sigmoid, 1 - tanh, 2 - ReLu - out_fun = 0; - if(strcmp(nn.output, 'tanh_opt')) - out_fun = 1; - elseif(strcmp(nn.output, 'ReLu')) - out_fun = 2; - end - - fwrite(faceCheckerFile, out_fun, 'uint'); % 4 bytes - - for layers=1:num_depth_layers - - W = nn.W{layers}; - writeMatrixBin(faceCheckerFile, W, 6); - end - - - % Piecewise affine warp - - nPix = faceCheckers(i).nPix; - minX = faceCheckers(i).minX; - minY = faceCheckers(i).minY; - - destination = reshape(faceCheckers(i).destination, numel(faceCheckers(i).destination), 1); - triangulation = faceCheckers(i).triangulation; - triX = faceCheckers(i).triX; - mask = faceCheckers(i).mask; - alphas = faceCheckers(i).alphas; - betas = faceCheckers(i).betas; - - fwrite(faceCheckerFile, nPix, 'uint'); % 4 bytes - fwrite(faceCheckerFile, minX, 'float64'); % 8 bytes - fwrite(faceCheckerFile, minY, 'float64'); % 8 bytes - - % Destination shape - writeMatrixBin(faceCheckerFile, destination, 6); - - % Triangulation - writeMatrixBin(faceCheckerFile, triangulation, 4); - - % Triangle map - writeMatrixBin(faceCheckerFile, triX, 4); - - % Mask - writeMatrixBin(faceCheckerFile, mask, 4); - - % Alphas - writeMatrixBin(faceCheckerFile, alphas, 6); - - % Betas - writeMatrixBin(faceCheckerFile, betas, 6); - - end - - fclose(faceCheckerFile); - -end \ No newline at end of file diff --git a/matlab_version/face_validation/WriteOutTriangulation.m b/matlab_version/face_validation/WriteOutTriangulation.m deleted file mode 100644 index e572f27..0000000 --- a/matlab_version/face_validation/WriteOutTriangulation.m +++ /dev/null @@ -1,30 +0,0 @@ -function WriteOutTriangulation(locationTxt, locationMlab, faceCheckers) - - addpath('..\PDM_helpers\'); - - triangulationFile = fopen(locationTxt, 'w'); - - views = numel(faceCheckers); - - fprintf(triangulationFile, '# Number of triangulations\r\n%d\r\n', views); - - for i = 1:views - - fprintf(triangulationFile, '# Triangulation %d\r\n', i); - - triangulation = faceCheckers(i).triangulation; - - % swap around for clockwise convention in openGL - tmp = triangulation(:,2); - triangulation(:,2) = triangulation(:,1); - triangulation(:,1) = tmp; - - fprintf(triangulationFile, '# triangulation \r\n'); - writeMatrix(triangulationFile, triangulation, 4); - - end - - fclose(triangulationFile); - - save(locationMlab); -end \ No newline at end of file diff --git a/matlab_version/face_validation/cnn_train_reg.m b/matlab_version/face_validation/cnn_train_reg.m new file mode 100644 index 0000000..25416ad --- /dev/null +++ b/matlab_version/face_validation/cnn_train_reg.m @@ -0,0 +1,632 @@ +function [net, stats] = cnn_train_reg(net, imdb, getBatch, varargin) +%cnn_train_reg An example implementation of SGD for training CNNs +% CNN_TRAIN() is an example learner implementing stochastic +% gradient descent with momentum to train a CNN. It can be used +% with different datasets and tasks by providing a suitable +% getBatch function. +% +% The function automatically restarts after each training epoch by +% checkpointing. +% +% The function supports training on CPU or on one or more GPUs +% (specify the list of GPU IDs in the `gpus` option). + +% Copyright (C) 2014-16 Andrea Vedaldi. +% All rights reserved. +% +% This file is part of the VLFeat library and is made available under +% the terms of the BSD license (see the COPYING file). + +% This is a modified version for regression using the CNN_TRAIN from +% MatConvNet + +addpath(fullfile(vl_rootnn, 'examples')); + +opts.expDir = fullfile('data','exp') ; +opts.continue = true ; +opts.batchSize = 256 ; +opts.numSubBatches = 1 ; +opts.train = [] ; +opts.val = [] ; +opts.gpus = [] ; +opts.epochSize = inf; +opts.prefetch = false ; +opts.numEpochs = 300 ; +opts.learningRate = 0.001 ; +opts.weightDecay = 0.0005 ; + +opts.solver = [] ; % Empty array means use the default SGD solver +[opts, varargin] = vl_argparse(opts, varargin) ; +if ~isempty(opts.solver) + assert(isa(opts.solver, 'function_handle') && nargout(opts.solver) == 2,... + 'Invalid solver; expected a function handle with two outputs.') ; + % Call without input arguments, to get default options + opts.solverOpts = opts.solver() ; +end + +opts.momentum = 0.9 ; +opts.saveSolverState = true ; +opts.nesterovUpdate = false ; +opts.randomSeed = 0 ; +opts.memoryMapFile = fullfile(tempdir, 'matconvnet.bin') ; +opts.profile = false ; +opts.parameterServer.method = 'mmap' ; +opts.parameterServer.prefix = 'mcn' ; + +opts.conserveMemory = true ; +opts.backPropDepth = +inf ; +opts.sync = false ; +opts.cudnn = true ; +opts.errorFunction = 'regression' ; +opts.errorLabels = {} ; +opts.plotDiagnostics = false ; +opts.plotStatistics = true; +opts.postEpochFn = [] ; % postEpochFn(net,params,state) called after each epoch; can return a new learning rate, 0 to stop, [] for no change +opts = vl_argparse(opts, varargin) ; + +if ~exist(opts.expDir, 'dir'), mkdir(opts.expDir) ; end +if isempty(opts.train), opts.train = find(imdb.images.set==1) ; end +if isempty(opts.val), opts.val = find(imdb.images.set==2) ; end +if isscalar(opts.train) && isnumeric(opts.train) && isnan(opts.train) + opts.train = [] ; +end +if isscalar(opts.val) && isnumeric(opts.val) && isnan(opts.val) + opts.val = [] ; +end + +hasError = true ; +opts.errorLabels = {'correlation', 'rmse'}; + +% ------------------------------------------------------------------------- +% Initialization +% ------------------------------------------------------------------------- + +net = vl_simplenn_tidy(net); % fill in some eventually missing values +net.layers{end-1}.precious = 1; % do not remove predictions, used for error +vl_simplenn_display(net, 'batchSize', opts.batchSize) ; + +evaluateMode = isempty(opts.train) ; +if ~evaluateMode + for i=1:numel(net.layers) + J = numel(net.layers{i}.weights) ; + if ~isfield(net.layers{i}, 'learningRate') + net.layers{i}.learningRate = ones(1, J) ; + end + if ~isfield(net.layers{i}, 'weightDecay') + net.layers{i}.weightDecay = ones(1, J) ; + end + end +end + +state.getBatch = getBatch ; +stats = [] ; + +% ------------------------------------------------------------------------- +% Train and validate +% ------------------------------------------------------------------------- + +modelPath = @(ep) fullfile(opts.expDir, sprintf('net-epoch-%d.mat', ep)); +modelFigPath = fullfile(opts.expDir, 'net-train.pdf') ; + +start = opts.continue * findLastCheckpoint(opts.expDir) ; +if start >= 1 + fprintf('%s: resuming by loading epoch %d\n', mfilename, start) ; + [net, state, stats] = loadState(modelPath(start)) ; +else + state = [] ; +end + +for epoch=start+1:opts.numEpochs + + % Set the random seed based on the epoch and opts.randomSeed. + % This is important for reproducibility, including when training + % is restarted from a checkpoint. + + rng(epoch + opts.randomSeed) ; + prepareGPUs(opts, epoch == start+1) ; + + % Train for one epoch. + params = opts ; + params.epoch = epoch ; + params.learningRate = opts.learningRate(min(epoch, numel(opts.learningRate))) ; + params.train = opts.train(randperm(numel(opts.train))) ; % shuffle + params.train = params.train(1:min(opts.epochSize, numel(opts.train))); + params.imdb = imdb ; + params.getBatch = getBatch ; + + if numel(params.gpus) <= 1 + [net, state] = processEpoch(net, state, params, 'train') ; + [net, state] = processEpoch(net, state, params, 'val') ; + if ~evaluateMode + saveState(modelPath(epoch), net, state) ; + end + lastStats = state.stats ; + else + spmd + [net, state] = processEpoch(net, state, params, 'train') ; + [net, state] = processEpoch(net, state, params, 'val') ; + if labindex == 1 && ~evaluateMode + saveState(modelPath(epoch), net, state) ; + end + lastStats = state.stats ; + end + lastStats = accumulateStats(lastStats) ; + end + + stats.train(epoch) = lastStats.train ; + stats.val(epoch) = lastStats.val ; + clear lastStats ; + if ~evaluateMode + saveStats(modelPath(epoch), stats) ; + end + + if params.plotStatistics + switchFigure(1) ; clf ; + plots = setdiff(... + cat(2,... + fieldnames(stats.train)', ... + fieldnames(stats.val)'), {'num', 'time'}) ; + for p = plots + p = char(p) ; + values = zeros(0, epoch) ; + leg = {} ; + for f = {'train', 'val'} + f = char(f) ; + if isfield(stats.(f), p) + tmp = [stats.(f).(p)] ; + values(end+1,:) = tmp(1,:)' ; + leg{end+1} = f ; + end + end + subplot(1,numel(plots),find(strcmp(p,plots))) ; + plot(1:epoch, values','o-') ; + xlabel('epoch') ; + title(p) ; + legend(leg{:}) ; + grid on ; + end + drawnow ; + print(1, modelFigPath, '-dpdf') ; + end + + if ~isempty(opts.postEpochFn) + if nargout(opts.postEpochFn) == 0 + opts.postEpochFn(net, params, state) ; + else + lr = opts.postEpochFn(net, params, state) ; + if ~isempty(lr), opts.learningRate = lr; end + if opts.learningRate == 0, break; end + end + end +end + +% Return the best performing model +[~,best_epoch] = min(cat(1,stats.val.rmse)); +fprintf('%s: Best model in epoch %d\n', mfilename, best_epoch) ; +[net, state, stats] = loadState(modelPath(best_epoch)) ; + +% With multiple GPUs, return one copy +if isa(net, 'Composite'), net = net{1} ; end + +% ------------------------------------------------------------------------- +function [net, state] = processEpoch(net, state, params, mode) +% ------------------------------------------------------------------------- +% Note that net is not strictly needed as an output argument as net +% is a handle class. However, this fixes some aliasing issue in the +% spmd caller. + +% initialize with momentum 0 +if isempty(state) || isempty(state.solverState) + for i = 1:numel(net.layers) + state.solverState{i} = cell(1, numel(net.layers{i}.weights)) ; + state.solverState{i}(:) = {0} ; + end +end + +% move CNN to GPU as needed +numGpus = numel(params.gpus) ; +if numGpus >= 1 + net = vl_simplenn_move(net, 'gpu') ; + for i = 1:numel(state.solverState) + for j = 1:numel(state.solverState{i}) + s = state.solverState{i}{j} ; + if isnumeric(s) + state.solverState{i}{j} = gpuArray(s) ; + elseif isstruct(s) + state.solverState{i}{j} = structfun(@gpuArray, s, 'UniformOutput', false) ; + end + end + end +end +if numGpus > 1 + parserv = ParameterServer(params.parameterServer) ; + vl_simplenn_start_parserv(net, parserv) ; +else + parserv = [] ; +end + +% profile +if params.profile + if numGpus <= 1 + profile clear ; + profile on ; + else + mpiprofile reset ; + mpiprofile on ; + end +end + +subset = params.(mode) ; +num = 0 ; +stats.num = 0 ; % return something even if subset = [] +stats.time = 0 ; +adjustTime = 0 ; +res = [] ; + +preds_all = []; +labels_all = []; + +err = 0; + +start = tic ; +for t=1:params.batchSize:numel(subset) + fprintf('%s: epoch %02d: %3d/%3d:', mode, params.epoch, ... + fix((t-1)/params.batchSize)+1, ceil(numel(subset)/params.batchSize)) ; + batchSize = min(params.batchSize, numel(subset) - t + 1) ; + + for s=1:params.numSubBatches + % get this image batch and prefetch the next + batchStart = t + (labindex-1) + (s-1) * numlabs ; + batchEnd = min(t+params.batchSize-1, numel(subset)) ; + batch = subset(batchStart : params.numSubBatches * numlabs : batchEnd) ; + num = num + numel(batch) ; + if numel(batch) == 0, continue ; end + + [im, labels] = params.getBatch(params.imdb, batch) ; + + if params.prefetch + if s == params.numSubBatches + batchStart = t + (labindex-1) + params.batchSize ; + batchEnd = min(t+2*params.batchSize-1, numel(subset)) ; + else + batchStart = batchStart + numlabs ; + end + nextBatch = subset(batchStart : params.numSubBatches * numlabs : batchEnd) ; + params.getBatch(params.imdb, nextBatch) ; + end + + if numGpus >= 1 + im = gpuArray(im) ; + end + + if strcmp(mode, 'train') + dzdy = 1 ; + evalMode = 'normal' ; + else + dzdy = [] ; + evalMode = 'test' ; + end + net.layers{end}.class = labels ; + res = vl_simplenn(net, im, dzdy, res, ... + 'accumulate', s ~= 1, ... + 'mode', evalMode, ... + 'conserveMemory', params.conserveMemory, ... + 'backPropDepth', params.backPropDepth, ... + 'sync', params.sync, ... + 'cudnn', params.cudnn, ... + 'parameterServer', parserv, ... + 'holdOn', s < params.numSubBatches) ; + + predictions = gather(res(end-1).x) ; + [~,predictions] = sort(predictions, 3, 'descend') ; + predictions = squeeze(predictions); + num_bins = size(predictions,1); + predictions = predictions(1,:); + + % Convert the class labels into the continuous values + labels = unQuantizeContinuous(squeeze(labels), 0, 3, num_bins)'; + + predictions = unQuantizeContinuous(squeeze(predictions), 0, 3, num_bins)'; + preds_all = cat(1, preds_all, predictions); + labels_all = cat(1, labels_all, labels); + + err = [err(1)+sum(double(gather(res(end).x)));... + corr(labels_all, preds_all);... + sqrt(mean((labels_all-preds_all).^2))]; + end + + % accumulate gradient + if strcmp(mode, 'train') + if ~isempty(parserv), parserv.sync() ; end + [net, res, state] = accumulateGradients(net, res, state, params, batchSize, parserv) ; + end + + % get statistics + time = toc(start) + adjustTime ; + batchTime = time - stats.time ; + + stats = extractStats(net, params, [err(1)/num; err(2); err(3)]) ; + stats.num = num ; + stats.time = time ; + currentSpeed = batchSize / batchTime ; + averageSpeed = (t + batchSize - 1) / time ; + if t == 3*params.batchSize + 1 + % compensate for the first three iterations, which are outliers + adjustTime = 4*batchTime - time ; + stats.time = time + adjustTime ; + end + + fprintf(' %.1f (%.1f) Hz', averageSpeed, currentSpeed) ; + for f = setdiff(fieldnames(stats)', {'num', 'time'}) + f = char(f) ; + fprintf(' %s: %.3f', f, stats.(f)) ; + end + fprintf('\n') ; + + % collect diagnostic statistics + if strcmp(mode, 'train') && params.plotDiagnostics + switchFigure(2) ; clf ; + diagn = [res.stats] ; + diagnvar = horzcat(diagn.variation) ; + diagnpow = horzcat(diagn.power) ; + subplot(2,2,1) ; barh(diagnvar) ; + set(gca,'TickLabelInterpreter', 'none', ... + 'YTick', 1:numel(diagnvar), ... + 'YTickLabel',horzcat(diagn.label), ... + 'YDir', 'reverse', ... + 'XScale', 'log', ... + 'XLim', [1e-5 1], ... + 'XTick', 10.^(-5:1)) ; + grid on ; title('Variation'); + subplot(2,2,2) ; barh(sqrt(diagnpow)) ; + set(gca,'TickLabelInterpreter', 'none', ... + 'YTick', 1:numel(diagnpow), ... + 'YTickLabel',{diagn.powerLabel}, ... + 'YDir', 'reverse', ... + 'XScale', 'log', ... + 'XLim', [1e-5 1e5], ... + 'XTick', 10.^(-5:5)) ; + grid on ; title('Power'); + subplot(2,2,3); plot(squeeze(res(end-1).x)) ; + drawnow ; + end +end + +% Save back to state. +state.stats.(mode) = stats ; +if params.profile + if numGpus <= 1 + state.prof.(mode) = profile('info') ; + profile off ; + else + state.prof.(mode) = mpiprofile('info'); + mpiprofile off ; + end +end +if ~params.saveSolverState + state.solverState = [] ; +else + for i = 1:numel(state.solverState) + for j = 1:numel(state.solverState{i}) + s = state.solverState{i}{j} ; + if isnumeric(s) + state.solverState{i}{j} = gather(s) ; + elseif isstruct(s) + state.solverState{i}{j} = structfun(@gather, s, 'UniformOutput', false) ; + end + end + end +end + +net = vl_simplenn_move(net, 'cpu') ; + +% ------------------------------------------------------------------------- +function [net, res, state] = accumulateGradients(net, res, state, params, batchSize, parserv) +% ------------------------------------------------------------------------- +numGpus = numel(params.gpus) ; +otherGpus = setdiff(1:numGpus, labindex) ; + +for l=numel(net.layers):-1:1 + for j=numel(res(l).dzdw):-1:1 + + if ~isempty(parserv) + tag = sprintf('l%d_%d',l,j) ; + parDer = parserv.pull(tag) ; + else + parDer = res(l).dzdw{j} ; + end + + if j == 3 && strcmp(net.layers{l}.type, 'bnorm') + % special case for learning bnorm moments + thisLR = net.layers{l}.learningRate(j) ; + net.layers{l}.weights{j} = vl_taccum(... + 1 - thisLR, ... + net.layers{l}.weights{j}, ... + thisLR / batchSize, ... + parDer) ; + else + % Standard gradient training. + thisDecay = params.weightDecay * net.layers{l}.weightDecay(j) ; + thisLR = params.learningRate * net.layers{l}.learningRate(j) ; + + if thisLR>0 || thisDecay>0 + % Normalize gradient and incorporate weight decay. + parDer = vl_taccum(1/batchSize, parDer, ... + thisDecay, net.layers{l}.weights{j}) ; + + if isempty(params.solver) + % Default solver is the optimised SGD. + % Update momentum. + state.solverState{l}{j} = vl_taccum(... + params.momentum, state.solverState{l}{j}, ... + -1, parDer) ; + + % Nesterov update (aka one step ahead). + if params.nesterovUpdate + delta = params.momentum * state.solverState{l}{j} - parDer ; + else + delta = state.solverState{l}{j} ; + end + + % Update parameters. + net.layers{l}.weights{j} = vl_taccum(... + 1, net.layers{l}.weights{j}, ... + thisLR, delta) ; + + else + % call solver function to update weights + [net.layers{l}.weights{j}, state.solverState{l}{j}] = ... + params.solver(net.layers{l}.weights{j}, state.solverState{l}{j}, ... + parDer, params.solverOpts, thisLR) ; + end + end + end + + % if requested, collect some useful stats for debugging + if params.plotDiagnostics + variation = [] ; + label = '' ; + switch net.layers{l}.type + case {'conv','convt'} + if isnumeric(state.solverState{l}{j}) + variation = thisLR * mean(abs(state.solverState{l}{j}(:))) ; + end + power = mean(res(l+1).x(:).^2) ; + if j == 1 % fiters + base = mean(net.layers{l}.weights{j}(:).^2) ; + label = 'filters' ; + else % biases + base = sqrt(power) ;%mean(abs(res(l+1).x(:))) ; + label = 'biases' ; + end + variation = variation / base ; + label = sprintf('%s_%s', net.layers{l}.name, label) ; + end + res(l).stats.variation(j) = variation ; + res(l).stats.power = power ; + res(l).stats.powerLabel = net.layers{l}.name ; + res(l).stats.label{j} = label ; + end + end +end + +% ------------------------------------------------------------------------- +function stats = accumulateStats(stats_) +% ------------------------------------------------------------------------- + +for s = {'train', 'val'} + s = char(s) ; + total = 0 ; + + % initialize stats stucture with same fields and same order as + % stats_{1} + stats__ = stats_{1} ; + names = fieldnames(stats__.(s))' ; + values = zeros(1, numel(names)) ; + fields = cat(1, names, num2cell(values)) ; + stats.(s) = struct(fields{:}) ; + + for g = 1:numel(stats_) + stats__ = stats_{g} ; + num__ = stats__.(s).num ; + total = total + num__ ; + + for f = setdiff(fieldnames(stats__.(s))', 'num') + f = char(f) ; + stats.(s).(f) = stats.(s).(f) + stats__.(s).(f) * num__ ; + + if g == numel(stats_) + stats.(s).(f) = stats.(s).(f) / total ; + end + end + end + stats.(s).num = total ; +end + +% ------------------------------------------------------------------------- +function stats = extractStats(net, params, errors) +% ------------------------------------------------------------------------- +stats.objective = errors(1) ; +for i = 1:numel(params.errorLabels) + stats.(params.errorLabels{i}) = errors(i+1) ; +end + +% ------------------------------------------------------------------------- +function saveState(fileName, net, state) +% ------------------------------------------------------------------------- +save(fileName, 'net', 'state') ; + +% ------------------------------------------------------------------------- +function saveStats(fileName, stats) +% ------------------------------------------------------------------------- +if exist(fileName) + save(fileName, 'stats', '-append') ; +else + save(fileName, 'stats') ; +end + +% ------------------------------------------------------------------------- +function [net, state, stats] = loadState(fileName) +% ------------------------------------------------------------------------- +load(fileName, 'net', 'state', 'stats') ; +net = vl_simplenn_tidy(net) ; +if isempty(whos('stats')) + error('Epoch ''%s'' was only partially saved. Delete this file and try again.', ... + fileName) ; +end + +% ------------------------------------------------------------------------- +function epoch = findLastCheckpoint(modelDir) +% ------------------------------------------------------------------------- +list = dir(fullfile(modelDir, 'net-epoch-*.mat')) ; +tokens = regexp({list.name}, 'net-epoch-([\d]+).mat', 'tokens') ; +epoch = cellfun(@(x) sscanf(x{1}{1}, '%d'), tokens) ; +epoch = max([epoch 0]) ; + +% ------------------------------------------------------------------------- +function switchFigure(n) +% ------------------------------------------------------------------------- +if get(0,'CurrentFigure') ~= n + try + set(0,'CurrentFigure',n) ; + catch + figure(n) ; + end +end + +% ------------------------------------------------------------------------- +function clearMex() +% ------------------------------------------------------------------------- +%clear vl_tmove vl_imreadjpeg ; +disp('Clearing mex files') ; +clear mex ; +clear vl_tmove vl_imreadjpeg ; + +% ------------------------------------------------------------------------- +function prepareGPUs(params, cold) +% ------------------------------------------------------------------------- +numGpus = numel(params.gpus) ; +if numGpus > 1 + % check parallel pool integrity as it could have timed out + pool = gcp('nocreate') ; + if ~isempty(pool) && pool.NumWorkers ~= numGpus + delete(pool) ; + end + pool = gcp('nocreate') ; + if isempty(pool) + parpool('local', numGpus) ; + cold = true ; + end +end +if numGpus >= 1 && cold + fprintf('%s: resetting GPU\n', mfilename) ; + clearMex() ; + if numGpus == 1 + disp(gpuDevice(params.gpus)) ; + else + spmd + clearMex() ; + disp(gpuDevice(params.gpus(labindex))) ; + end + end +end diff --git a/matlab_version/face_validation/face_check_cnn.m b/matlab_version/face_validation/face_check_cnn.m index c9d634d..b3f9b69 100644 --- a/matlab_version/face_validation/face_check_cnn.m +++ b/matlab_version/face_validation/face_check_cnn.m @@ -42,18 +42,17 @@ img_crop = img_crop ./ cnns(view_id).std_ex; mask = cnns(view_id).mask; -% Normalisation to 0-1 -% img(mask) = img_crop / 255; img = zeros(size(mask)); img(mask) = img_crop; -% img = cat(3, img, img); - % [er, bad] = nntest(nn, test_x, test_y); - cnn = cnns(view_id).cnn; +cnn = cnns(view_id).cnn; + %% -cnn = cnnff(cnn, img); +res = vl_simplenn(cnn, single(img), [], []); +res = gather(res(end).x); +num_bins = numel(res); +[~,res] = sort(res, 3, 'descend') ; +res = squeeze(res); +res = res(1,:); -decision = cnn.o(1); - -% normalise decision from ~ 0, 1 to [0,3] -decision = decision * 3; \ No newline at end of file +decision = unQuantizeContinuous(res, 0, 3, num_bins)'; diff --git a/matlab_version/face_validation/face_check_nn.m b/matlab_version/face_validation/face_check_nn.m deleted file mode 100644 index 0a44e6a..0000000 --- a/matlab_version/face_validation/face_check_nn.m +++ /dev/null @@ -1,44 +0,0 @@ -function [ decision ] = face_check_nn( img, shape, global_params, nns ) -%FACE_CHECK_NN Summary of this function goes here -% Detailed explanation goes here - -if(size(img,3) == 3) - img = rgb2gray(img); -end -% first need to determine the view -centres = cat(1, nns.centres); - -dists = centres*pi/180 - repmat(global_params(2:4)',size(centres,1),1); -[~,view_id] = min(sum(dists.^2,2)); - -img_crop = Crop(img, shape, nns(view_id).triangulation,... - nns(view_id).triX, nns(view_id).mask,... - nns(view_id).alphas, nns(view_id).betas,... - nns(view_id).nPix, nns(view_id).minX, ... - nns(view_id).minY); - -%% -img_crop = reshape(img_crop(logical(nns(view_id).mask)), 1, nns(view_id).nPix); -img_crop(isnan(img_crop)) = 0; - -%% -% normalisation (local) -img_crop = (img_crop - mean(img_crop)); -norms = std(img_crop); -if(norms==0) - norms = 1; -end -img_crop = img_crop / norms; - -% normalisation (global) -img_crop = img_crop - nns(view_id).mean_ex; -img_crop = img_crop ./ nns(view_id).std_ex; -nn = nns(view_id).nn; - % [er, bad] = nntest(nn, test_x, test_y); -nn = nnff(nn, img_crop, zeros(size(img_crop,1), nn.size(end))); - -decision = nn.a{end}; - -%% -% normalise decision from ~ 0, 1 to [0,3] -decision = decision * 3; \ No newline at end of file diff --git a/matlab_version/face_validation/face_check_preloaded.m b/matlab_version/face_validation/face_check_preloaded.m deleted file mode 100644 index f99145c..0000000 --- a/matlab_version/face_validation/face_check_preloaded.m +++ /dev/null @@ -1,42 +0,0 @@ -function [ decision ] = face_check_preloaded( img, shape, global_params, faceCheckers ) -%FACE_CHECK_WILD Summary of this function goes here -% Detailed explanation goes here - -if(size(img,3) == 3) - img = rgb2gray(img); -end -% first need to determine the view -centres = cat(1, faceCheckers.centres); - -dists = centres*pi/180 - repmat(global_params(2:4)',size(centres,1),1); -[~,view_id] = min(sum(dists.^2,2)); - -img_crop = Crop(img, shape, faceCheckers(view_id).triangulation,... - faceCheckers(view_id).triX, faceCheckers(view_id).mask,... - faceCheckers(view_id).alphas, faceCheckers(view_id).betas,... - faceCheckers(view_id).nPix, faceCheckers(view_id).minX, ... - faceCheckers(view_id).minY); - -img_crop = reshape(img_crop(logical(faceCheckers(view_id).mask)), 1, faceCheckers(view_id).nPix); -img_crop(isnan(img_crop)) = 0; - -% normalisation (local) -img_crop = (img_crop - mean(img_crop)); -norms = std(img_crop); -if(norms==0) - norms = 1; -end -img_crop = img_crop / norms; - -% Projection onto principal components - -% normalisation (global) -img_crop = img_crop - faceCheckers(view_id).mean_ex; -img_crop = img_crop ./ faceCheckers(view_id).std_ex; - -img_crop = img_crop * faceCheckers(view_id).principal_components; - -decision = faceCheckers(view_id).b + faceCheckers(view_id).w' * img_crop'; - -% normalise decision from ~ -1, 1 to [0,3] -decision = (decision + 1) * 1.5; \ No newline at end of file diff --git a/matlab_version/face_validation/face_check_wild.m b/matlab_version/face_validation/face_check_wild.m deleted file mode 100644 index 8ed0ffe..0000000 --- a/matlab_version/face_validation/face_check_wild.m +++ /dev/null @@ -1,39 +0,0 @@ -function [ decision ] = face_check_wild( img, shape, global_params ) -%FACE_CHECK_WILD Summary of this function goes here -% Detailed explanation goes here - -load '../models/pdm/pdm_68_multi_pie.mat'; -load '../models/tri_68.mat'; -load '../face_validation_svr/trained/face_check_general_68.mat'; - -% first need to determine the view - -centres = cat(1, faceCheckers.centres); - -dists = centres*pi/180 - repmat(global_params(2:4)',size(centres,1),1); -[~,view_id] = min(sum(dists.^2,2)); -% shape = faceCheckers(view_id).source; - -img_crop = Crop(img, shape, faceCheckers(view_id).triangulation,... - faceCheckers(view_id).triX, faceCheckers(view_id).mask,... - faceCheckers(view_id).alphas, faceCheckers(view_id).betas,... - faceCheckers(view_id).nPix, faceCheckers(view_id).minX, ... - faceCheckers(view_id).minY); - -img_crop = reshape(img_crop(logical(faceCheckers(view_id).mask)), 1, faceCheckers(view_id).nPix); -img_crop(isnan(img_crop)) = 0; -% normalisation -img_crop = (img_crop - mean(img_crop)); -norms = std(img_crop); -if(norms==0) - norms = 1; -end -img_crop = img_crop / norms; - -% Projection onto principal components -img_crop = img_crop - faceCheckers(view_id).mean_ex; -img_crop = img_crop ./ faceCheckers(view_id).std_ex; - -img_crop = img_crop * faceCheckers(view_id).principal_components; - -decision = faceCheckers(view_id).b + faceCheckers(view_id).w' * img_crop'; \ No newline at end of file diff --git a/matlab_version/face_validation/initializeFaceCNN.m b/matlab_version/face_validation/initializeFaceCNN.m new file mode 100644 index 0000000..6513fed --- /dev/null +++ b/matlab_version/face_validation/initializeFaceCNN.m @@ -0,0 +1,37 @@ +function net = initializeFaceCNN_simple_5(num_bins) + +f=1/100 ; +net.layers = {} ; +net.layers{end+1} = struct('type', 'conv', ... + 'weights', {{f*randn(9,9,1,10, 'single'), zeros(1, 10, 'single')}}, ... + 'stride', 1, ... + 'pad', 0) ; +net.layers{end+1} = struct('type', 'relu') ; +net.layers{end+1} = struct('type', 'pool', ... + 'method', 'max', ... + 'pool', [2 2], ... + 'stride', 2, ... + 'pad', 0) ; +net.layers{end+1} = struct('type', 'conv', ... + 'weights', {{f*randn(7,7,10,10, 'single'), zeros(1,10,'single')}}, ... + 'stride', 1, ... + 'pad', 0) ; +net.layers{end+1} = struct('type', 'relu') ; +net.layers{end+1} = struct('type', 'pool', ... + 'method', 'max', ... + 'pool', [2 2], ... + 'stride', 2, ... + 'pad', 0) ; +% This is basically an FC layer +net.layers{end+1} = struct('type', 'conv', ... + 'weights', {{f*randn(10,10,10,50, 'single'), zeros(1,50,'single')}}, ... + 'stride', 1, ... + 'pad', 0) ; +net.layers{end+1} = struct('type', 'relu') ; +net.layers{end+1} = struct('type', 'conv', ... + 'weights', {{f*randn(1,1,50,num_bins, 'single'), zeros(1,num_bins,'single')}}, ... + 'stride', 1, ... + 'pad', 0) ; +net.layers{end+1} = struct('type', 'softmaxloss') ; + +net = vl_simplenn_tidy(net) ; diff --git a/matlab_version/face_validation/CalculateCoefficients.m b/matlab_version/face_validation/paw_helpers/CalculateCoefficients.m similarity index 100% rename from matlab_version/face_validation/CalculateCoefficients.m rename to matlab_version/face_validation/paw_helpers/CalculateCoefficients.m diff --git a/matlab_version/face_validation/Crop.m b/matlab_version/face_validation/paw_helpers/Crop.m similarity index 100% rename from matlab_version/face_validation/Crop.m rename to matlab_version/face_validation/paw_helpers/Crop.m diff --git a/matlab_version/face_validation/InitialisePieceWiseAffine.m b/matlab_version/face_validation/paw_helpers/InitialisePieceWiseAffine.m similarity index 100% rename from matlab_version/face_validation/InitialisePieceWiseAffine.m rename to matlab_version/face_validation/paw_helpers/InitialisePieceWiseAffine.m diff --git a/matlab_version/face_validation/Remap.m b/matlab_version/face_validation/paw_helpers/Remap.m similarity index 100% rename from matlab_version/face_validation/Remap.m rename to matlab_version/face_validation/paw_helpers/Remap.m diff --git a/matlab_version/face_validation/WarpRegion.m b/matlab_version/face_validation/paw_helpers/WarpRegion.m similarity index 100% rename from matlab_version/face_validation/WarpRegion.m rename to matlab_version/face_validation/paw_helpers/WarpRegion.m diff --git a/matlab_version/face_validation/quantizeContinuous.m b/matlab_version/face_validation/quantizeContinuous.m new file mode 100644 index 0000000..1e1310e --- /dev/null +++ b/matlab_version/face_validation/quantizeContinuous.m @@ -0,0 +1,12 @@ + +function [quantized] = quantizeContinuous(values, min_v, max_v, num_bins) + + step_size = (max_v - min_v) / num_bins; + bin_centres = min_v + step_size/2 : step_size : max_v; + + bin_centres = repmat(bin_centres, numel(values),1); + values = repmat(values, 1, num_bins); + + [~,quantized] = min(abs(values - bin_centres)'); + quantized = quantized'; +end diff --git a/matlab_version/face_validation/readme.txt b/matlab_version/face_validation/readme.txt index 65ef7d7..99a0458 100644 --- a/matlab_version/face_validation/readme.txt +++ b/matlab_version/face_validation/readme.txt @@ -1,13 +1,21 @@ +Code for facial landmark detection validation (knowing if detection succeeded), to be use for face tracking in videos so as not to do face detection every frame. + To create the training data run: -Create_data_68_large.m or Create_data_66_large.m for 68 and 66 point PDM versions accordingly. +Create_data_train.m and Create_data_test.m -The data generation code requires you to have the patch expert training data (Multi-PIE and in-the-wild data, not included) for positive examples, and inriaperson dataset for negative samples (not included as well). +The data generation code requires you to have the patch expert training data (Menpo, Multi-PIE and 300W data, not included) for positive examples, and inriaperson dataset for negative samples (not included as well). -To train Convolutional Neural Network based face landmark validation model (used by the model now) use: -Train_face_checker_66_cnn.m and Train_face_checker_68_cnn.m +To train Convolutional Neural Network based face landmark validation model use: +Train_face_checker_cnn.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 CLM framework for face checking. +This will produce trained/face_checker_cnn_*.mat and trained/face_checker_cnn_*.txt files that can be used in C++ and matlab versions of OpenFace for face validation. Old versions can also be found in trained folder (they are simpler CNN models trained on smaller datasets). -This will also produces tris*.txt files that can be used in the C++ version of the CLM_framework. +This will also produces tris*.txt files that can be used in the C++ version of the OpenFace,just place it in the lib\local\LandmarkDetector\model\detection_validation folder and edit the appropriate "main_*.txt" files. -The code uses piece-wise affine warping to a neutral shape with an CNN regressor for error estimation (see http://www.cl.cam.ac.uk/~tb346/ThesisFinal.pdf Section 4.6.2 for a very similar model but with SVR regressor) \ No newline at end of file +The code uses piece-wise affine warping to a neutral shape with an CNN regressor for error estimation (see http://www.cl.cam.ac.uk/~tb346/ThesisFinal.pdf Section 4.6.2 for a very similar model but with SVR regressor) + +Dependencies: +- vlfeat-0.9.20 and extract it in the current directory (http://www.vlfeat.org/download.html) +- MatConvNet from http://www.vlfeat.org/matconvnet/ (tested with version 1.0-beta24), and install following the instructions + +Change the setup.m to match the locations of vlfeat and MatConvNet \ No newline at end of file diff --git a/matlab_version/face_validation/setup.m b/matlab_version/face_validation/setup.m new file mode 100644 index 0000000..37a90b0 --- /dev/null +++ b/matlab_version/face_validation/setup.m @@ -0,0 +1,25 @@ +function setup(varargin) + +run vlfeat-0.9.20/toolbox/vl_setup ; +run D:\soft\matconvnet-master\matconvnet-master\matlab/vl_setupnn ; +addpath D:\soft\matconvnet-master\matconvnet-master\examples ; + +opts.useGpu = false ; +opts.verbose = false ; +opts = vl_argparse(opts, varargin) ; + +try + vl_nnconv(single(1),single(1),[]) ; +catch + warning('VL_NNCONV() does not seem to be compiled. Trying to compile it now.') ; + vl_compilenn('enableGpu', opts.useGpu, 'verbose', opts.verbose) ; +end + +if opts.useGpu + try + vl_nnconv(gpuArray(single(1)),gpuArray(single(1)),[]) ; + catch + vl_compilenn('enableGpu', opts.useGpu, 'verbose', opts.verbose) ; + warning('GPU support does not seem to be compiled in MatConvNet. Trying to compile it now') ; + end +end diff --git a/matlab_version/face_validation/train_CNN_model.m b/matlab_version/face_validation/train_CNN_model.m new file mode 100644 index 0000000..8edfee2 --- /dev/null +++ b/matlab_version/face_validation/train_CNN_model.m @@ -0,0 +1,210 @@ +function train_CNN_model(varargin) + +setup('useGPU', true); + +tic + +% Load the data +root_loc = 'D:\Datasets\detection_validation'; +location = [root_loc, '/prep_data/']; + +faceCheckersLoc_train = dir([location 'face_validator_train_*']); +faceCheckersLoc_test = dir([location 'face_validator_test_*']); + +corrs_all = []; +rmses_all = []; + +faceCheckers = struct; + +% As we will be training a classifier that will act as a regressor, +% binarize it +num_bins = 30; + +for i=1:numel(faceCheckersLoc_train) + + load([location faceCheckersLoc_train(i).name]); + + % set a max value to the error + errors(errors > 3) = 3; + errors_train = quantizeContinuous(errors, 0, 3, num_bins); + examples_train = single(examples); + clear examples + + mean_ex = mean(examples_train); + std_ex = std(examples_train); + std_ex = std_ex / 256; + examples_train = bsxfun(@times, bsxfun(@minus, examples_train, mean_ex), 1./std_ex); + num_examples_train = size(examples_train,1); + + % Add rows and columns untill we have 60 x 60 image + + % keep adding rows + while(size(mask,1) < 60) + mask = cat(1, mask, false(1, size(mask,2))); + triX = cat(1, triX, -ones(1, size(mask,2))); + end + + % keep adding cols + while(size(mask,2) < 60) + mask = cat(2, mask, false(size(mask,1),1)); + triX = cat(2, triX, -ones(size(triX,1),1)); + end + + examples_r = single(zeros(size(mask, 1), size(mask, 2), num_examples_train)); + + img_curr = zeros(size(mask)); + for e=1:num_examples_train + + img_curr(mask) = examples_train(e,:); + examples_r(:, :, e) = img_curr; + + end + + imdb.images.data = examples_r; + clear examples_r + imdb.images.label = errors_train'; + imdb.images.id = 1:numel(errors_train); + + % Split data for training and validation (20%) + imdb.images.set = ones(1, numel(errors_train)); + imdb.images.set(round(4*end/5):end) = 2; + + % Visualize some of the data + figure(10) ; clf ; colormap gray ; + subplot(1,2,1) ; + pos_samples = squeeze(imdb.images.data(:,:,imdb.images.label==1)); + vl_imarraysc(pos_samples(:,:,1:20)) ; + axis image off ; + title('Positive training data') ; + + subplot(1,2,2) ; + neg_samples = squeeze(imdb.images.data(:,:,imdb.images.label>5)); + vl_imarraysc(neg_samples(:,:,1:20)) ; + axis image off ; + title('Negative training data') ; + + net = initializeFaceCNN(num_bins) ; + + trainOpts.batchSize = 100 ; + trainOpts.numEpochs = 20; + trainOpts.continue = true ; + trainOpts.gpus = [1] ; + trainOpts.learningRate = 0.001 ; + trainOpts.expDir = ['trained/intermediate/face_validator_' num2str(i)]; + trainOpts.errorFunction = 'regression'; + trainOpts = vl_argparse(trainOpts, varargin); + + % Call training function in MatConvNet + [net,info] = cnn_train_reg(net, imdb, @getBatch, trainOpts) ; + + % Move the CNN back to the CPU if it was trained on the GPU + if numel(trainOpts.gpus) > 0 + net = vl_simplenn_move(net, 'cpu') ; + end + + % Save the result for later use + net.layers(end) = [] ; + net.imageMean = mean_ex ; + net.imageStd = std_ex; + +% save('data/face-experiment/facecnn.mat', '-struct', 'net') ; + + + % Evaluate on the test data, also evaluate using correlation and RMSE + load([location faceCheckersLoc_test(i).name]); + + % set a max value to the error + errors(errors > 3) = 3; + errors_test = errors; + errors_q = quantizeContinuous(errors, 0, 3, num_bins); + errors_test_q = errors_q; + examples_test = single(examples); + examples_test = bsxfun(@times, bsxfun(@minus, examples_test, net.imageMean), 1./net.imageStd); + num_examples_test = size(examples_test,1); + + % keep adding rows + while(size(mask,1) < 60) + mask = cat(1, mask, false(1, size(mask,2))); + triX = cat(1, triX, -ones(1, size(mask,2))); + end + + % keep adding cols + while(size(mask,2) < 60) + mask = cat(2, mask, false(size(mask,1),1)); + triX = cat(2, triX, -ones(size(triX,1),1)); + end + + examples_r = single(zeros(size(mask, 1), size(mask, 2), num_examples_test)); + + img_curr = zeros(size(mask)); + for e=1:num_examples_test + + img_curr(mask) = examples_test(e,:); + examples_r(:, :, e) = img_curr; + + end + examples_test = zeros(size(examples_r,1), size(examples_r,2), 1, size(examples_r,3)); + examples_test(:) = single(examples_r(:)); + + ids = 1:99:num_examples_test; + + errors_all_test = zeros(num_examples_test,1); + for k=1:numel(ids)-1 + examples_test_sm = single(examples_test(:,:,:,ids(k):ids(k+1)-1)); + res = vl_simplenn(net, examples_test_sm, [], []); + res = gather(res(end).x); + [~,res] = sort(res, 3, 'descend') ; + res = squeeze(res); + res = res(1,:); + + errors_test_rec = unQuantizeContinuous(res, 0, 3, num_bins)'; + errors_all_test(ids(k):ids(k+1)-1) = errors_test_rec; + end + errors_all_test = errors_all_test(1:ids(end)); + corrs_test = corr(errors_all_test, errors_test(1:numel(errors_all_test))); + rmse_test = sqrt(mean((errors_all_test-errors_test(1:numel(errors_all_test))).^2)); + + corrs_all = cat(1, corrs_all, corrs_test); + rmses_all = cat(1, rmses_all, rmse_test); + + net.corrs = corrs_test; + net.rmse = rmse_test; + save([trainOpts.expDir, '/facecnn.mat'], '-struct', 'net'); + + % The CNN + faceCheckers(i).cnn = net; + + % The orientation + faceCheckers(i).centres = centres; + + % The info for preprocessing + faceCheckers(i).mask = mask; + faceCheckers(i).nPix = nPix; + faceCheckers(i).minX = minX; + faceCheckers(i).minY = minY; + + faceCheckers(i).destination = shape(:,1:2); + tri = load([location faceCheckersLoc_train(i).name], 'triangulation'); + faceCheckers(i).triangulation = tri.triangulation; + faceCheckers(i).triX = triX; + faceCheckers(i).mask = mask; + faceCheckers(i).alphas = alphas; + faceCheckers(i).betas = betas; + + faceCheckers(i).mean_ex = mean_ex; + faceCheckers(i).std_ex = std_ex; + + WriteOutFaceCheckersCNNbinary("trained/validator_cnn.txt", faceCheckers); +end + +save('trained/faceCheckers.mat', 'faceCheckers', 'corrs_all', 'rmses_all'); + +end +% -------------------------------------------------------------------- +function [im, labels] = getBatch(imdb, batch) +% -------------------------------------------------------------------- + im = imdb.images.data(:,:,batch) ; + im = reshape(im, 60, 60, 1, []) ; + labels = imdb.images.label(1,batch) ; +end + diff --git a/matlab_version/face_validation/trained/faceCheckers.mat b/matlab_version/face_validation/trained/faceCheckers.mat new file mode 100644 index 0000000..282cb74 Binary files /dev/null and b/matlab_version/face_validation/trained/faceCheckers.mat differ diff --git a/matlab_version/face_validation/trained/validator_cnn.txt b/matlab_version/face_validation/trained/validator_cnn.txt new file mode 100644 index 0000000..c1dff94 Binary files /dev/null and b/matlab_version/face_validation/trained/validator_cnn.txt differ diff --git a/matlab_version/face_validation/unQuantizeContinuous.m b/matlab_version/face_validation/unQuantizeContinuous.m new file mode 100644 index 0000000..44db381 --- /dev/null +++ b/matlab_version/face_validation/unQuantizeContinuous.m @@ -0,0 +1,6 @@ + +function [unquantized] = unQuantizeContinuous(values, min_v, max_v, num_bins) + step_size = (max_v - min_v) / num_bins; + unquantized = min_v + step_size/2 + (values-1) * step_size; + +end \ No newline at end of file diff --git a/matlab_version/models/pdm/pdm_68_aligned_menpo.mat b/matlab_version/models/pdm/pdm_68_aligned_menpo.mat new file mode 100644 index 0000000..3d40bba Binary files /dev/null and b/matlab_version/models/pdm/pdm_68_aligned_menpo.mat differ