Merge branch 'feature/new_validator' into develop

This commit is contained in:
Tadas Baltrusaitis 2017-08-02 13:40:56 -04:00
commit cdd258330f
155 changed files with 1864 additions and 15837 deletions

2
.gitignore vendored
View file

@ -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/

View file

@ -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<string> files, depth_files, output_images, output_landmark_locations, output_pose_locations;
vector<string> files, output_images, output_landmark_locations, output_pose_locations;
// Bounding boxes for a face in each image (optional)
vector<cv::Rect_<double> > 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;
@ -398,16 +398,6 @@ int main (int argc, char **argv)
return 1;
}
// Loading depth file if exists (optional)
cv::Mat_<float> 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_<uchar> 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);

View file

@ -87,7 +87,7 @@ double fps_tracker = -1.0;
int64 t0 = 0;
// Visualising the results
void visualise_tracking(cv::Mat& captured_image, cv::Mat_<float>& 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_<float>& 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<string> arguments = get_arguments(argc, argv);
// Some initial parameters that can be overriden from command line
vector<string> files, depth_directories, output_video_files, out_dummy;
vector<string> 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_<float> depth_image;
cv::Mat_<uchar> grayscale_image;
if(captured_image.channels() == 3)
@ -304,30 +294,8 @@ 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_<short> 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())

View file

@ -107,7 +107,7 @@ int main (int argc, char **argv)
vector<string> arguments = get_arguments(argc, argv);
// Some initial parameters that can be overriden from command line
vector<string> files, depth_directories, tracked_videos_output, dummy_out;
vector<string> 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_<short> 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<cv::Rect_<double> > 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]);
}
});

View file

@ -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<string> input_files, depth_directories, output_files, tracked_videos_output;
vector<string> 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;

View file

@ -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_<uchar> aligned_face_cols(1, aligned_face.cols * aligned_face.rows * aligned_face.channels(), aligned_face.data, 1);
//cv::Mat_<double> aligned_face_cols_double;
//aligned_face_cols.convertTo(aligned_face_cols_double, CV_64F);
// Visualising the median HOG
if (visualise)
{

View file

@ -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
// Licensees 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<vector<vector<vector<pair<int, cv::Mat_<double> > > > > > cnn_convolutional_layers_dft;
vector<vector<vector<float > > > cnn_convolutional_layers_bias;
vector< vector<int> > cnn_subsampling_layers;
vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers;
vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_weights;
vector< vector<float > > 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<vector<int> > cnn_layer_types;
// Extra params for the new CNN
vector< vector<cv::Mat_<float> > > cnn_fully_connected_layers_biases;
//==========================================
// Normalisation for face validation
@ -134,9 +162,15 @@ private:
// Feed-forward Neural Network
double CheckNN(const cv::Mat_<double>& warped_img, int view_id);
// Convolutional Neural Network
double CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id);
// Convolutional Neural Network
double CheckCNN(const cv::Mat_<double>& warped_img, int view_id);
// Convolutional Neural Network
double CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id);
// A normalisation helper
void NormaliseWarpedToVector(const cv::Mat_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id);

View file

@ -55,10 +55,7 @@ namespace LandmarkDetector
// Optionally can provide a bounding box from which to start tracking
//================================================================================================================
bool DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params);
bool DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, CLNF& clnf_model, FaceModelParameters& params);
bool DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params);
bool DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, const cv::Rect_<double> 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_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params);
//================================================
// CLM-Z versions
bool DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, CLNF& clnf_model, FaceModelParameters& params);
bool DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params);
//================================================================
// Helper function for getting head pose from CLNF parameters

View file

@ -153,7 +153,7 @@ public:
CLNF & operator= (const CLNF&& other);
// Does the actual work - landmark detection
bool DetectLandmarks(const cv::Mat_<uchar> &image, const cv::Mat_<float> &depth, FaceModelParameters& params);
bool DetectLandmarks(const cv::Mat_<uchar> &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<int, cv::Mat_<float> > kde_resp_precalc;
// The model fitting: patch response computation and optimisation steps
bool Fit(const cv::Mat_<uchar>& intensity_image, const cv::Mat_<float>& depth_image, const std::vector<int>& window_sizes, const FaceModelParameters& parameters);
bool Fit(const cv::Mat_<uchar>& intensity_image, const std::vector<int>& window_sizes, const FaceModelParameters& parameters);
// Mean shift computation that uses precalculated kernel density estimators (the one actually used)
void NonVectorisedMeanShift_precalc_kde(cv::Mat_<float>& out_mean_shifts, const vector<cv::Mat_<float> >& patch_expert_responses, const cv::Mat_<float> &dxs, const cv::Mat_<float> &dys, int resp_size, float a, int scale, int view_id, map<int, cv::Mat_<float> >& mean_shifts);
@ -189,9 +189,6 @@ private:
double NU_RLMS(cv::Vec6d& final_global, cv::Mat_<double>& final_local, const vector<cv::Mat_<float> >& patch_expert_responses, const cv::Vec6d& initial_global, const cv::Mat_<double>& initial_local,
const cv::Mat_<double>& 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_<double>& landmark_lhoods, const FaceModelParameters& parameters);
// Removing background image from the depth
bool RemoveBackground(cv::Mat_<float>& out_depth_image, const cv::Mat_<float>& depth_image);
// Generating the weight matrix for the Weighted least squares
void GetWeightMatrix(cv::Mat_<float>& WeightMatrix, int scale, int view_id, const FaceModelParameters& parameters);

View file

@ -52,12 +52,12 @@ namespace LandmarkDetector
//=============================================================================================
// Helper functions for parsing the inputs
//=============================================================================================
void get_video_input_output_params(vector<string> &input_video_file, vector<string> &depth_dir, vector<string> &output_files,
void get_video_input_output_params(vector<string> &input_video_file, vector<string> &output_files,
vector<string> &output_video_files, bool& world_coordinates_pose, string &output_codec, vector<string> &arguments);
void get_camera_params(int &device, float &fx, float &fy, float &cx, float &cy, vector<string> &arguments);
void get_image_input_output_params(vector<string> &input_image_files, vector<string> &input_depth_files, vector<string> &output_feature_files, vector<string> &output_pose_files, vector<string> &output_image_files,
void get_image_input_output_params(vector<string> &input_image_files, vector<string> &output_feature_files, vector<string> &output_pose_files, vector<string> &output_image_files,
vector<cv::Rect_<double>> &input_bounding_boxes, vector<string> &arguments);
//===========================================================================

View file

@ -57,9 +57,6 @@ public:
// The collection of SVR patch experts (for intensity/grayscale images), the experts are laid out scale->view->landmark
vector<vector<vector<Multi_SVR_patch_expert> > > svr_expert_intensity;
// The collection of SVR patch experts (for depth/range images), the experts are laid out scale->view->landmark
vector<vector<vector<Multi_SVR_patch_expert> > > svr_expert_depth;
// The collection of LNF (CCNF) patch experts (for intensity images), the experts are laid out scale->view->landmark
vector<vector<vector<CCNF_patch_expert> > > 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<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image, const cv::Mat_<float>& depth_image,
void Response(vector<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image,
const PDM& pdm, const cv::Vec6d& params_global, const cv::Mat_<double>& 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<string> intensity_svr_expert_locations, vector<string> depth_svr_expert_locations, vector<string> intensity_ccnf_expert_locations);
void Read(vector<string> intensity_svr_expert_locations, vector<string> intensity_ccnf_expert_locations);

View file

@ -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

View file

@ -1,3 +0,0 @@
LandmarkDetector clm-z.txt
FaceDetConversion haarAlign.txt
DetectionValidator detection_validation/validator_general_66.txt

View file

@ -1,3 +1,3 @@
LandmarkDetector clm_general.txt
FaceDetConversion haarAlign.txt
DetectionValidator detection_validation/validator_general_68.txt
DetectionValidator detection_validation/validator_cnn.txt

View file

@ -1,3 +1,3 @@
LandmarkDetector clm_wild.txt
FaceDetConversion haarAlign.txt
DetectionValidator detection_validation/validator_general_68.txt
DetectionValidator detection_validation/validator_cnn.txt

View file

@ -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
DetectionValidator detection_validation/validator_cnn.txt

View file

@ -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
DetectionValidator detection_validation/validator_cnn.txt

View file

@ -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 -0.035864 0.008549 0.064502 0.028715 -0.008884 -0.273566 -0.081852 -0.001133 0.107864 0.054134 -0.039650 -0.030460 0.123967 -0.011016 0.045932 -0.017967
0.050475 -0.060045 -0.059978 -0.031345 0.017962 0.028792 -0.020342 0.027766 -0.085041 0.023528 0.067875 0.042232 -0.060356 -0.206706 -0.162667 0.012045 0.023279 0.064628 -0.042433 -0.031479 0.167069 -0.007401 0.030965 -0.000492
0.099327 -0.092823 -0.068503 -0.059721 0.066651 0.067454 -0.020423 0.035253 -0.109560 0.023854 0.064577 0.039736 -0.133331 -0.132567 -0.197720 0.008731 -0.027289 0.049747 -0.018088 -0.031922 0.172937 0.005670 0.008896 0.030979
0.134832 -0.112930 -0.054432 -0.101018 0.100680 0.077060 -0.013236 0.041442 -0.071489 -0.011067 0.031190 0.049373 -0.145540 -0.052449 -0.135671 -0.005866 -0.044700 0.024214 0.015403 -0.056540 0.100198 0.008994 -0.039469 0.017290
0.138185 -0.111981 -0.033264 -0.140688 0.115232 0.026089 0.016312 0.033420 0.011362 -0.073244 -0.020374 0.057861 -0.080955 0.015932 0.001458 -0.002179 -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 -0.078564 0.161264 0.064195 -0.000899 -0.055363 0.160622 -0.073064 0.105794 -0.067154 0.094394 -0.067326 0.049608 0.060316 -0.120181
0.029259 0.097048 -0.122702 0.034425 0.039206 0.022570 0.003128 -0.017498 0.071592 -0.016638 -0.085460 0.066636 0.003404 0.023490 -0.025193 0.099620 -0.076253 0.199443 -0.086905 0.022589 -0.068385 -0.007717 0.037784 -0.108892
0.030709 0.048406 -0.048080 0.012620 -0.004611 0.002575 -0.013731 0.007570 0.030575 -0.003973 -0.088770 0.022909 -0.008814 0.038796 -0.013788 0.058480 -0.077010 0.147878 -0.060535 -0.009937 -0.059100 -0.003053 0.017061 -0.070454
-0.000302 -0.000943 0.000547 0.001449 -0.001239 -0.006491 -0.001734 0.000341 0.000847 0.001198 -0.093282 -0.003311 -0.005371 0.041839 -0.000402 0.005437 -0.074125 -0.002514 -0.001704 0.015020 -0.043990 0.000645 -0.005113 -0.001960
-0.030797 -0.048997 0.046937 -0.011391 0.003417 0.001209 0.011529 -0.006000 -0.027354 0.002930 -0.087701 -0.026732 0.004662 0.036964 0.016357 -0.051045 -0.081748 -0.151184 0.056099 0.046124 -0.042445 0.011467 0.014231 0.071140
-0.029604 -0.097770 0.120860 -0.032640 -0.038750 0.025383 -0.006595 0.019926 -0.067987 0.015490 -0.082251 -0.069769 -0.006133 0.021065 0.027135 -0.091877 -0.086183 -0.203228 0.082136 0.026697 -0.069388 0.024703 0.031778 0.112183
0.022926 -0.121370 0.203355 -0.061901 -0.114065 0.069420 -0.053980 0.098785 -0.152366 0.046567 -0.073896 -0.162999 -0.063898 -0.003711 0.056517 -0.153237 -0.090068 -0.110124 0.062269 -0.030591 -0.112204 -0.014302 0.080858 0.125147
-0.033733 -0.096371 0.176956 -0.058336 -0.085727 0.026810 -0.034571 0.089236 -0.078399 0.021782 -0.098726 -0.080928 -0.040063 -0.023172 0.014364 -0.122521 -0.074310 0.004259 0.019871 -0.077142 -0.147174 -0.004727 0.048434 0.050469
-0.051756 -0.056781 0.102870 -0.037546 -0.040576 0.002479 -0.010659 0.050596 -0.022705 -0.002075 -0.106003 -0.031065 -0.014993 -0.034761 -0.000659 -0.052928 -0.050979 0.046678 -0.015466 -0.021197 -0.154220 -0.002858 0.040511 0.026339
-0.001389 -0.001769 -0.000221 0.000668 -0.002293 -0.014354 -0.001470 0.001005 0.001826 0.001041 -0.106758 -0.002393 -0.001734 -0.050997 -0.000734 0.002124 -0.037690 -0.003636 -0.002295 0.041989 -0.130523 0.013248 0.034704 -0.002931
0.049819 0.054690 -0.105351 0.038014 0.038108 0.001525 0.008211 -0.047455 0.027967 0.001024 -0.106413 0.028066 0.016805 -0.033544 0.003369 0.055321 -0.044824 -0.053224 0.009920 0.107234 -0.109307 0.028491 0.031904 -0.029247
0.032388 0.094696 -0.179555 0.059657 0.085278 0.021859 0.030839 -0.085781 0.083274 -0.022804 -0.100987 0.078004 0.040824 -0.020810 -0.012061 0.127611 -0.059704 -0.010308 -0.024953 0.149707 -0.068486 0.031088 0.038623 -0.049272
0.055288 0.089987 -0.116420 0.055196 0.050866 -0.013567 -0.014283 -0.077197 0.126567 -0.055797 -0.100908 0.043115 0.020959 0.003353 -0.055083 -0.113788 -0.069784 0.127756 0.009788 -0.087710 -0.139159 0.025540 0.021607 0.253009
-0.000454 -0.000787 -0.000116 0.000621 -0.001841 -0.018428 -0.001368 0.000983 0.001188 0.000771 -0.104809 -0.002318 -0.002586 0.016991 -0.001054 0.003022 -0.060359 -0.003725 -0.003339 0.027248 -0.086137 0.001090 -0.002708 -0.003297
-0.055465 -0.090870 0.114742 -0.054605 -0.053530 -0.009652 0.012691 0.079117 -0.122882 0.054638 -0.102095 -0.047054 -0.022220 0.004312 0.057137 0.118816 -0.053099 -0.132196 -0.014616 0.152468 -0.052576 -0.010694 0.031747 -0.251650
-0.064337 -0.100340 0.139482 -0.063623 -0.067127 -0.027284 -0.001801 0.097292 -0.131144 0.051530 -0.114480 -0.051189 -0.025074 -0.019008 0.065639 0.123629 -0.049740 -0.055988 -0.035597 0.133740 -0.070551 -0.023888 0.035165 -0.253572
-0.001193 -0.001429 -0.000416 0.000442 -0.002304 -0.039911 -0.000185 0.001231 0.001785 0.000743 -0.115314 -0.002297 -0.001727 -0.012627 -0.000406 0.001330 -0.052773 -0.004296 -0.002723 0.032706 -0.100037 0.004905 0.010825 -0.002656
0.062870 0.098835 -0.141206 0.062837 0.063106 -0.032160 0.001745 -0.095313 0.135795 -0.053571 -0.113289 0.047120 0.026385 -0.020598 -0.061923 -0.119342 -0.066010 0.050733 0.030681 -0.061349 -0.141997 0.041136 0.016678 0.257293
0.013568 0.075283 0.073044 0.014178 -0.036832 0.113334 0.054730 -0.003736 0.092943 -0.048912 0.292026 -0.041422 0.028860 0.225432 0.108228 0.035625 -0.179377 -0.108097 0.055587 -0.019926 0.017807 0.031717 0.052884 0.058473
0.015213 0.048885 0.041282 -0.001124 -0.047273 0.002254 0.047494 0.012727 0.054730 -0.033674 0.241823 -0.016723 0.002475 0.164058 0.022547 0.036518 -0.119643 -0.048013 0.004933 -0.012554 -0.002398 -0.002715 0.046355 0.030392
0.017522 0.021690 0.010983 -0.017448 -0.056982 -0.109291 0.039563 0.031922 0.015108 -0.022673 0.188102 0.006317 -0.030263 0.109650 -0.077499 0.043786 -0.054529 0.018794 -0.044887 -0.005600 -0.029115 -0.027271 0.032934 0.002573
0.018987 -0.009590 -0.020454 -0.023044 -0.061384 -0.216509 0.013503 0.028882 -0.021498 -0.014168 0.132528 0.020703 -0.051878 0.060000 -0.122343 0.034133 0.013369 0.053221 -0.064304 0.006574 -0.063883 -0.035597 0.020759 -0.007359
0.014665 -0.047062 -0.056981 -0.003353 -0.046223 -0.273362 -0.044350 -0.011088 -0.059522 0.008654 0.083653 0.013501 -0.067898 0.018249 -0.097659 0.015458 0.074427 0.017393 -0.022201 0.032842 -0.105221 -0.016934 -0.002578 0.015161
-0.011450 -0.073527 -0.097697 0.035873 -0.049430 -0.245443 -0.102199 -0.064566 -0.087344 0.026273 0.044374 0.012634 -0.051313 -0.011410 -0.063484 0.000102 0.121608 -0.009751 0.007075 0.058464 -0.131329 -0.001915 -0.022626 0.029807
-0.060146 -0.080736 -0.127955 0.073043 -0.088358 -0.163765 -0.159562 -0.103110 -0.077882 0.023844 0.025374 0.032114 0.030673 -0.031697 -0.002302 -0.019082 0.139533 -0.006214 0.006395 0.031929 -0.121254 -0.013269 -0.023355 -0.014124
-0.121346 -0.076937 -0.136566 0.088866 -0.119102 -0.064832 -0.203154 -0.126655 -0.034705 0.003459 0.019500 0.039546 0.101804 -0.042472 0.103227 -0.047564 0.086433 0.019189 0.007404 -0.026891 -0.063047 -0.032690 -0.012970 -0.055948
-0.151220 -0.082702 -0.136965 0.091990 -0.112993 -0.000251 -0.234686 -0.167876 0.003752 -0.008060 -0.000066 0.044230 0.142201 -0.003374 0.230204 -0.053466 -0.000503 0.003982 0.054641 -0.082587 -0.028236 -0.020971 -0.004196 -0.045681
-0.120937 -0.075784 -0.136278 0.090171 -0.113089 0.058797 -0.206006 -0.125043 -0.033229 0.001417 -0.020487 0.037919 0.099750 0.039237 0.104876 -0.038389 -0.091944 0.018618 0.007584 -0.056502 0.035109 -0.026210 0.016043 -0.048495
-0.059629 -0.078558 -0.127244 0.077300 -0.073869 0.158607 -0.167426 -0.099163 -0.075768 0.020907 -0.028100 0.029161 0.029808 0.026459 0.000054 -0.004045 -0.140156 -0.005142 0.007623 -0.042074 0.117786 -0.005105 0.019598 -0.000500
-0.010940 -0.070512 -0.096799 0.042746 -0.027857 0.241765 -0.114078 -0.058395 -0.084784 0.022512 -0.046950 0.007989 -0.049498 0.004365 -0.061566 0.013184 -0.119265 -0.008286 0.010001 -0.026872 0.140208 0.004587 0.017846 0.042540
0.015145 -0.043448 -0.055769 0.004697 -0.021638 0.273023 -0.057270 -0.003856 -0.056858 0.004345 -0.086740 0.006239 -0.063529 -0.024252 -0.096149 0.023312 -0.069655 0.017608 -0.018139 -0.033257 0.101718 -0.016840 0.009047 0.021908
0.019533 -0.005664 -0.018585 -0.016212 -0.040292 0.220687 0.004373 0.035063 -0.019405 -0.018736 -0.136680 0.010393 -0.045248 -0.063788 -0.120611 0.035274 -0.004956 0.051791 -0.059390 -0.032128 0.051498 -0.042614 -0.003163 -0.006605
0.018270 0.025910 0.014076 -0.014226 -0.043367 0.115436 0.037225 0.035730 0.016547 -0.027137 -0.190705 -0.007109 -0.021203 -0.111254 -0.074472 0.037677 0.064831 0.016199 -0.039336 -0.023342 0.014105 -0.038187 -0.017379 -0.000412
0.016068 0.053215 0.045334 -0.001355 -0.041533 0.004764 0.052138 0.014289 0.055640 -0.038392 -0.241255 -0.032967 0.013979 -0.163460 0.026626 0.023886 0.128906 -0.051444 0.011454 -0.015064 -0.014736 -0.020704 -0.039340 0.024586
0.014398 0.079548 0.077832 0.010796 -0.038970 -0.105561 0.066264 -0.004530 0.093262 -0.053842 -0.288432 -0.060374 0.043064 -0.222726 0.113556 0.017171 0.188326 -0.112355 0.063384 -0.010729 -0.038365 0.008496 -0.060300 0.050271
-0.000666 -0.166461 -0.047638 0.226722 -0.062495 0.047061 0.180480 0.054964 -0.040678 -0.223687 -0.106798 -0.057279 -0.061243 0.212546 -0.044251 0.050233 0.280303 0.128676 0.284177 -0.033774 0.088486 -0.023176 -0.092996 -0.037713
-0.000972 -0.171244 -0.078496 0.083595 -0.066265 0.098378 0.068289 0.121816 0.047409 -0.107857 -0.045203 -0.023775 -0.038860 0.147556 -0.009404 0.089405 0.189064 -0.022525 0.157650 -0.029346 0.013491 -0.036902 -0.069676 0.009942
0.009278 -0.178350 -0.093628 -0.018883 -0.085033 0.127557 0.028476 0.129605 0.116840 0.026287 0.008063 0.026010 -0.025831 0.087597 0.010314 0.029459 0.105757 -0.079040 -0.008182 0.007583 -0.025090 -0.036933 -0.073199 0.029351
0.018752 -0.183143 -0.086363 -0.060625 -0.090889 0.110928 0.045066 0.077759 0.155975 0.112058 0.026685 0.054277 -0.009075 0.048565 0.025079 -0.075927 0.063430 -0.049367 -0.135403 0.062582 -0.017305 -0.026424 -0.102326 0.035178
0.026223 -0.182968 -0.060390 -0.077983 -0.094273 0.074346 0.089135 -0.028227 0.168586 0.167246 0.037509 0.062050 -0.006134 0.006573 0.041144 -0.171564 0.035741 0.019097 -0.234694 0.102822 -0.006954 -0.024876 -0.147405 0.024067
0.026767 -0.181701 -0.059223 -0.081503 -0.097861 -0.059682 0.095176 -0.031342 0.168331 0.165196 -0.043727 0.059636 -0.007020 -0.002405 0.042686 -0.169025 -0.049614 0.019903 -0.233971 0.072414 0.072296 0.054873 0.146194 0.018985
0.019428 -0.181307 -0.084402 -0.065649 -0.096576 -0.101837 0.054069 0.073217 0.154613 0.110317 -0.028598 0.051888 -0.008555 -0.043402 0.028212 -0.069800 -0.069257 -0.048068 -0.136040 0.035945 0.056853 0.030960 0.109225 0.035017
0.010064 -0.175942 -0.090992 -0.024661 -0.091222 -0.122171 0.039151 0.124128 0.114402 0.024926 -0.006400 0.024007 -0.024184 -0.082393 0.014850 0.040333 -0.102544 -0.077394 -0.010188 -0.010614 0.027989 0.007890 0.089474 0.030854
-0.000088 -0.168268 -0.075489 0.078533 -0.069718 -0.095585 0.077758 0.116529 0.043594 -0.108181 0.049478 -0.023579 -0.035863 -0.143648 -0.004009 0.110302 -0.181753 -0.020018 0.152575 -0.012880 -0.031069 0.005921 0.083920 0.007966
0.000303 -0.163017 -0.044570 0.223475 -0.061573 -0.041807 0.187180 0.050632 -0.045851 -0.222654 0.109567 -0.054439 -0.057083 -0.211724 -0.038411 0.082331 -0.278800 0.131770 0.276012 0.031916 -0.100194 0.029624 0.091170 -0.050609
0.051760 0.009814 0.124180 -0.028182 0.167985 -0.010920 -0.187859 -0.066584 0.132638 0.087357 -0.000635 0.115302 -0.129897 -0.002753 0.021796 -0.275762 -0.016521 0.093369 0.291072 0.266124 0.091270 -0.002682 0.004203 -0.408835
0.069592 0.022340 0.176048 0.000511 0.066476 -0.004060 -0.158509 -0.148885 0.137068 0.020040 0.001487 0.099014 -0.201573 -0.004521 0.004133 -0.090121 -0.003724 0.008600 0.144731 0.116573 0.038887 0.030887 -0.006416 -0.208614
0.086514 0.034269 0.228120 0.032208 -0.032565 0.001715 -0.132563 -0.232248 0.144765 -0.051312 0.001229 0.083422 -0.270941 -0.005987 -0.011569 0.094073 0.008026 -0.073257 -0.000307 -0.032488 -0.010769 0.064854 -0.015112 -0.008922
0.103713 0.046693 0.281192 0.065708 -0.133400 0.006840 -0.106934 -0.313780 0.153934 -0.126247 -0.001131 0.068642 -0.340988 -0.005884 -0.035197 0.278498 0.019359 -0.149850 -0.146846 -0.180591 -0.057995 0.094039 -0.020694 0.188561
0.107290 0.107291 0.075904 -0.066396 -0.091329 0.031634 -0.041196 0.045182 -0.048306 -0.067631 -0.008673 0.095721 0.147995 0.001302 -0.124149 0.017545 -0.009636 -0.037424 -0.003437 0.050152 0.003791 -0.212308 0.062173 -0.029743
0.113725 0.093832 0.098857 -0.095333 -0.140128 0.020449 -0.043266 0.022029 -0.015646 -0.093435 -0.003323 0.089404 0.213953 0.002776 -0.140828 0.001928 -0.007389 0.030961 0.032791 0.087900 0.023262 -0.073810 0.023020 0.046717
0.116067 0.085493 0.108752 -0.106261 -0.157436 0.006270 -0.055560 0.010898 0.005091 -0.104413 0.000135 0.086637 0.240119 0.004924 -0.133904 0.001894 -0.005766 0.050149 0.061238 0.103625 0.036166 -0.018672 0.005746 0.092861
0.114129 0.094588 0.099022 -0.096799 -0.140692 -0.009616 -0.041993 0.021925 -0.015505 -0.094195 -0.000581 0.089715 0.213590 0.006497 -0.141378 0.001992 -0.004080 0.031770 0.032399 0.083739 0.033574 -0.077325 0.018579 0.047336
0.107706 0.108389 0.076420 -0.068018 -0.092314 -0.025722 -0.038533 0.044227 -0.048742 -0.068489 0.004124 0.095815 0.147436 0.005377 -0.124322 0.017316 0.001500 -0.036277 -0.003589 0.041306 0.028987 -0.218020 0.052030 -0.027864
0.021075 -0.061699 0.044785 0.054934 0.160887 0.105590 0.006119 -0.059284 -0.096162 0.017034 -0.013413 -0.047746 0.093876 0.070868 0.051776 0.060390 0.073348 -0.004413 -0.017285 -0.041193 -0.057196 0.016766 -0.089974 0.013964
0.000566 -0.108307 0.039120 0.000595 0.212129 0.081625 -0.055977 -0.092907 -0.001599 -0.025075 -0.000796 -0.250824 0.159174 0.060991 -0.085016 0.100121 0.076776 -0.017621 -0.126803 -0.019434 -0.042586 0.013023 -0.084440 -0.073557
0.004728 -0.094037 0.050088 -0.015559 0.202189 0.081127 -0.048503 -0.082521 -0.025175 -0.013916 0.009790 -0.203476 0.144441 0.047373 -0.091747 0.078963 0.065318 -0.019187 -0.131514 -0.000356 -0.028973 0.027383 -0.045994 -0.062522
0.036544 -0.021301 0.079004 0.004288 0.123788 0.048902 0.021459 -0.024952 -0.144596 0.049707 0.002301 0.096121 0.029274 0.031194 0.018614 -0.001164 0.055624 0.008799 -0.048234 0.016083 -0.026506 0.020130 -0.019641 0.029235
0.040662 0.005334 0.058748 0.038985 0.155976 0.067252 0.016266 0.016341 -0.178937 0.078551 -0.003891 0.196079 0.021670 0.041840 0.130013 -0.079759 0.044697 0.059931 0.029592 -0.003383 -0.042202 -0.002612 -0.041503 0.142530
0.036321 -0.002710 0.048009 0.053270 0.158552 0.090876 0.026363 0.009405 -0.176808 0.074596 -0.006341 0.170349 0.041244 0.052134 0.142940 -0.053780 0.055031 0.045925 0.033286 -0.018257 -0.045071 0.001114 -0.054385 0.140177
0.037107 -0.019811 0.080257 0.001698 0.121329 -0.054543 0.026442 -0.027060 -0.145809 0.048061 -0.013255 0.095686 0.029206 -0.026493 0.020739 0.005343 -0.054777 0.009840 -0.050111 -0.002654 0.031999 0.027429 0.009560 0.031306
0.005338 -0.092159 0.052016 -0.019313 0.197770 -0.095517 -0.040939 -0.085941 -0.026978 -0.015799 -0.003747 -0.204368 0.144726 -0.038398 -0.088843 0.085859 -0.058638 -0.017713 -0.133151 -0.018160 0.028466 0.046749 0.030509 -0.059219
0.001269 -0.106010 0.041480 -0.003485 0.208432 -0.097705 -0.047985 -0.096850 -0.003676 -0.027406 0.009933 -0.251880 0.160196 -0.051456 -0.082076 0.108035 -0.068171 -0.014969 -0.128459 -0.040669 0.027766 0.052025 0.072297 -0.068702
0.021917 -0.058963 0.047688 0.050001 0.156292 -0.115845 0.015715 -0.064136 -0.098739 0.014539 0.011422 -0.049071 0.095376 -0.063594 0.055202 0.067948 -0.067973 -0.001179 -0.018618 -0.064793 0.021240 0.057766 0.075304 0.020151
0.037081 -0.000389 0.050415 0.048954 0.154353 -0.100674 0.034701 0.005422 -0.178809 0.072243 -0.006892 0.168929 0.042180 -0.047468 0.145839 -0.047358 -0.061246 0.048186 0.031794 -0.038594 0.021257 0.026704 0.048958 0.145316
0.041321 0.007238 0.060557 0.035574 0.152741 -0.077242 0.022817 0.013342 -0.180567 0.076531 -0.010939 0.195224 0.022261 -0.038193 0.132420 -0.074116 -0.053043 0.061780 0.028192 -0.025446 0.027559 0.017187 0.038533 0.146615
-0.008608 0.139928 -0.165726 0.019265 0.107354 0.053069 0.134201 -0.025768 -0.019242 0.051248 -0.003843 -0.018606 -0.033981 -0.026008 -0.037507 -0.003917 0.020300 -0.348075 0.083854 0.194618 -0.033770 0.031775 -0.020322 0.048880
0.065974 0.159841 -0.116613 -0.015936 0.017826 0.025887 0.053362 0.047682 -0.043986 0.043981 -0.004988 -0.122833 -0.081246 -0.019647 0.090258 0.006277 0.022327 -0.043979 -0.002374 0.118418 -0.031262 0.005206 -0.022271 0.030471
0.121060 0.146795 -0.049039 -0.047213 -0.085538 0.019092 -0.026017 0.091907 -0.048204 0.031425 0.001660 -0.173929 -0.096729 -0.007540 0.158115 0.036645 0.015751 0.184181 -0.058520 0.023155 -0.022803 0.035205 -0.019099 -0.010358
0.144633 0.148416 -0.045185 -0.037883 -0.100856 0.003832 -0.050526 0.097384 -0.035440 0.024961 0.006683 -0.179670 -0.096413 0.000115 0.207844 0.025215 0.004627 0.193394 -0.048757 -0.029672 -0.007457 0.073006 -0.019618 -0.037156
0.121676 0.147819 -0.048662 -0.048811 -0.086131 -0.014394 -0.024542 0.091577 -0.048031 0.030487 0.004271 -0.173617 -0.097380 0.008014 0.157887 0.038735 -0.008933 0.185889 -0.058490 0.005281 0.033574 0.037776 0.001551 -0.008432
0.066713 0.161493 -0.115911 -0.017655 0.017386 -0.025939 0.056130 0.046460 -0.044150 0.042595 0.010142 -0.122458 -0.082363 0.016867 0.090474 0.009783 -0.017509 -0.041090 -0.001486 0.078056 0.099052 0.011002 0.018775 0.035575
-0.007919 0.141969 -0.164759 0.016847 0.105618 -0.056715 0.139476 -0.028351 -0.019507 0.049370 0.007443 -0.018533 -0.035117 0.019770 -0.036919 0.000160 -0.017686 -0.345003 0.085695 0.138424 0.149596 0.031630 0.002508 0.058001
-0.126672 0.095819 -0.025587 -0.020411 0.069696 -0.040492 0.113408 0.031462 0.034512 0.033831 0.003643 0.030528 -0.016233 0.011701 -0.096173 -0.076485 -0.029675 -0.146836 0.048508 -0.081728 0.061181 0.021189 -0.014489 -0.041443
-0.250166 0.063268 0.083495 -0.054436 0.057312 -0.020981 0.104096 0.069033 0.091832 0.000402 0.000251 0.071967 0.008207 0.011854 -0.127690 -0.088722 -0.024205 0.035454 -0.007640 -0.213727 -0.029662 -0.007746 -0.007841 -0.085785
-0.296019 0.054209 0.115310 -0.058454 0.064304 0.001047 0.095382 0.064413 0.123368 -0.010259 0.003756 0.077430 0.018650 -0.004453 -0.087455 -0.086370 -0.006771 0.088859 -0.019705 -0.235003 -0.076648 -0.015417 -0.003684 -0.080678
-0.250897 0.062205 0.083305 -0.052857 0.058055 0.020288 0.102304 0.069589 0.091555 0.001374 0.003939 0.071836 0.009119 -0.017922 -0.127756 -0.091187 0.008043 0.034653 -0.008149 -0.191606 -0.101632 -0.002118 0.002213 -0.089753
-0.127456 0.094161 -0.026167 -0.018422 0.070970 0.038881 0.109672 0.033189 0.034640 0.035192 0.000349 0.030582 -0.015196 -0.018359 -0.096808 -0.080649 0.018355 -0.148838 0.047059 -0.033800 -0.095213 0.028324 -0.005043 -0.049366
0.104320 0.170913 -0.080998 -0.021930 -0.032618 0.025131 0.010807 0.067423 -0.036997 0.049471 0.000150 -0.100902 -0.019091 -0.013763 -0.036516 -0.100919 0.007221 -0.037267 0.000956 -0.160019 -0.086714 0.062270 -0.011209 -0.137345
0.143379 0.165763 -0.060587 -0.016963 -0.064358 0.001114 -0.036866 0.078487 -0.030741 0.055788 0.002031 -0.111664 -0.000937 0.002077 0.032607 -0.119558 -0.009742 -0.005622 0.036313 -0.263615 -0.087856 0.081412 -0.018026 -0.161589
0.105016 0.172060 -0.080874 -0.023381 -0.033528 -0.024000 0.012838 0.066580 -0.036656 0.048317 0.005201 -0.100551 -0.019986 0.012979 -0.036877 -0.099109 -0.022128 -0.035904 0.001548 -0.181239 -0.020639 0.057382 -0.019235 -0.134122
-0.205688 0.049919 0.069362 -0.072378 0.066333 -0.021241 0.053205 0.075885 0.026935 -0.034539 0.005560 -0.038648 -0.097809 0.004207 0.045232 0.071028 -0.010315 0.132832 -0.022870 0.175336 0.099108 -0.028911 0.005928 0.109927
-0.284218 0.019124 0.108561 -0.081450 0.085386 -0.000675 0.040237 0.073037 0.081899 -0.060470 -0.000721 -0.041618 -0.087622 -0.002093 0.131858 0.123116 0.007698 0.217754 -0.028622 0.209344 0.066249 -0.010693 -0.000377 0.151209
-0.206346 0.048788 0.069346 -0.071231 0.066851 0.018814 0.051430 0.076716 0.026560 -0.033380 -0.000869 -0.038955 -0.097134 -0.010612 0.045537 0.068786 0.024196 0.131653 -0.023820 0.199350 0.022820 -0.026651 0.003549 0.105548
0.027947 -0.014519 0.027372 0.027090 -0.014515 -0.118449 0.014779 -0.000186 -0.033562 0.024932 -0.042037 -0.006816 -0.010401 -0.004006 -0.030938 -0.006596 -0.003303 0.025464 0.010620 0.011829 -0.009179 -0.028271 -0.011793 -0.040691
0.025583 -0.022643 0.008028 0.037660 -0.003753 -0.102792 0.026260 -0.003599 -0.046445 0.055353 -0.023960 0.025986 -0.025782 -0.031085 0.011987 -0.036466 -0.000804 -0.031755 -0.013448 -0.019920 0.026063 -0.033933 -0.018291 -0.017150
0.030716 -0.019142 0.015104 0.048347 0.008061 -0.057675 0.034305 0.001908 -0.032288 0.037098 -0.025976 0.008895 -0.034955 -0.015408 -0.033946 -0.012214 0.004252 -0.031221 0.011547 -0.052185 0.013885 0.023495 -0.041625 -0.018600
0.017217 -0.005005 -0.000825 0.038309 -0.033691 -0.049929 0.035044 0.006099 -0.026296 -0.013340 -0.050572 0.018098 0.017580 0.008349 -0.037259 -0.027207 0.030530 0.047757 -0.042520 -0.027716 0.002119 0.011201 -0.013641 -0.023775
-0.002977 -0.004519 -0.005401 0.007102 -0.020136 0.003735 0.010466 0.031329 -0.051627 0.040202 -0.038370 -0.038383 0.005010 0.005392 -0.023086 0.029715 0.008523 0.055107 -0.010657 0.004865 -0.007244 0.004499 -0.049346 0.002795
-0.010096 0.001042 0.000059 0.011824 -0.026881 0.025272 0.041191 0.020275 -0.012375 0.000779 -0.033145 0.002458 -0.007586 0.007785 -0.013394 0.017417 0.014159 -0.005881 -0.014046 -0.035161 0.017520 -0.016349 -0.017341 -0.009992
0.019776 -0.000284 -0.012557 0.024116 -0.012968 0.027447 0.037641 -0.003555 0.001294 -0.019848 -0.025716 -0.009227 0.017166 0.003187 -0.003003 -0.013572 0.026600 -0.056937 -0.027703 -0.029589 0.036149 -0.017405 -0.005140 -0.014149
0.003043 0.002636 -0.007479 0.021282 0.022862 0.026962 0.012532 0.017070 -0.011934 -0.027974 -0.031975 -0.014367 0.009348 0.001082 -0.006111 0.023087 0.042828 -0.015792 -0.000859 -0.010996 0.010873 0.003257 0.004459 0.021874
0.003741 0.016913 -0.001204 -0.017634 -0.016073 0.036588 0.022771 0.035887 0.015065 -0.020207 -0.010793 -0.000589 0.017515 -0.028759 -0.005286 0.026388 0.045892 -0.023168 0.012083 0.037597 0.001342 0.008441 -0.027373 0.056358
0.006235 0.002336 -0.014149 0.012474 0.020734 -0.047581 0.012805 0.019052 -0.009281 -0.032040 0.036141 -0.011251 0.009517 0.003028 -0.009339 0.025537 -0.049242 -0.015047 0.003361 0.000855 -0.014520 -0.007131 0.002813 0.033132
0.021965 -0.001009 -0.015741 0.019932 -0.015721 -0.035531 0.037303 -0.002551 0.003316 -0.020857 0.030647 -0.007770 0.014825 -0.001334 -0.003715 -0.011282 -0.035968 -0.059442 -0.024581 0.000602 -0.041615 -0.016669 0.018792 -0.007243
-0.008996 -0.000549 -0.001255 0.012659 -0.029948 -0.024644 0.041247 0.020575 -0.011513 0.000978 0.039860 0.002944 -0.010132 -0.008154 -0.011641 0.019826 -0.019156 -0.010840 -0.011057 -0.014449 -0.029046 -0.005862 0.026793 -0.003840
-0.003031 -0.007050 -0.005025 0.013159 -0.022487 -0.002090 0.009577 0.031278 -0.051872 0.041429 0.045054 -0.039255 0.000680 -0.006840 -0.019272 0.033989 -0.010335 0.047850 -0.007969 0.005201 0.010707 0.029877 0.041337 0.009335
0.015909 -0.008608 0.000093 0.050988 -0.032880 0.056455 0.031226 0.007102 -0.028123 -0.011172 0.054224 0.016291 0.012312 -0.014037 -0.034003 -0.018029 -0.036754 0.038949 -0.041639 -0.016244 -0.014604 0.017632 -0.000615 -0.021925
0.028911 -0.020564 0.020663 0.057528 0.009832 0.063863 0.029412 0.000800 -0.032018 0.041995 0.029433 0.006736 -0.041794 0.006742 -0.027211 -0.007554 -0.006106 -0.039369 0.009745 -0.029838 -0.038610 0.047533 0.015143 -0.020532
0.023922 -0.023664 0.013385 0.048180 0.001429 0.109536 0.018845 -0.002766 -0.046065 0.060109 0.023434 0.024933 -0.030917 0.021761 0.016546 -0.033368 -0.000156 -0.037766 -0.016409 0.003256 -0.032063 -0.016957 0.022986 -0.020118
0.025479 -0.016511 0.031827 0.041393 -0.008403 0.126612 0.007288 0.001233 -0.035336 0.029687 0.040922 -0.007776 -0.013868 -0.005459 -0.027831 -0.001951 0.005347 0.020879 0.007693 0.007357 0.014333 -0.017694 0.012221 -0.043917
0.003189 0.026538 0.038597 0.018669 0.012396 -0.003064 -0.024561 -0.002817 0.002622 -0.000148 0.017020 0.022609 -0.004868 0.005963 0.023348 -0.028816 -0.032968 0.005698 -0.055577 0.020448 0.022584 0.024768 0.047859 0.001740
0.000463 0.019463 0.024278 -0.029989 0.006319 -0.005217 -0.013751 0.006275 0.010675 0.049628 0.022928 0.001551 0.009671 -0.024186 0.018628 0.002273 -0.059675 0.026343 -0.063193 0.033560 0.003140 0.020410 0.001326 0.016946
-0.000029 0.020658 0.017072 -0.027930 0.007062 -0.017970 -0.006258 -0.005185 0.004557 0.025434 0.022254 0.009542 0.011170 -0.004279 0.003404 0.005904 -0.053521 0.031271 -0.024813 0.038249 0.003914 0.022292 -0.001279 0.009964
0.003294 0.025279 0.021921 -0.021950 0.016281 -0.028948 -0.013909 -0.011395 -0.011052 -0.005952 0.021898 0.008144 0.008008 0.011346 -0.002342 0.008934 -0.040320 0.014749 0.007476 0.035145 0.004734 0.013044 0.014277 0.001855
0.008232 0.024965 0.029824 -0.004269 0.019641 -0.043536 -0.030921 -0.012568 -0.008932 -0.041767 0.012154 0.000783 -0.004622 0.026748 0.019253 0.007897 -0.032740 -0.002789 0.020610 0.019074 0.013518 -0.004310 0.037242 -0.007354
0.010354 0.027336 0.023251 -0.015652 0.025251 0.036485 -0.033761 -0.007033 -0.005841 -0.047403 -0.018907 0.006933 0.007124 -0.023824 0.008963 -0.000927 0.036586 0.012466 0.022380 0.013472 -0.003293 -0.035777 -0.027321 -0.012812
0.004813 0.027050 0.017023 -0.030956 0.020204 0.026792 -0.015645 -0.007011 -0.008335 -0.010363 -0.029818 0.012299 0.017923 -0.008348 -0.009660 -0.000164 0.045111 0.026769 0.008711 0.022239 0.011627 -0.005512 -0.015953 -0.003064
0.000766 0.021777 0.013939 -0.033866 0.009609 0.020486 -0.007124 -0.001779 0.006741 0.022061 -0.029365 0.012405 0.019471 0.006576 -0.002112 -0.003785 0.059780 0.041340 -0.023649 0.024858 0.014139 0.012995 -0.007512 0.005546
0.000800 0.019861 0.022529 -0.033132 0.007255 0.008814 -0.013329 0.008770 0.012220 0.047301 -0.028907 0.002944 0.016370 0.025151 0.014628 -0.007662 0.068549 0.034586 -0.062338 0.021464 0.011355 0.013293 -0.010281 0.012728
0.003226 0.025832 0.036768 0.019095 0.012590 0.005204 -0.023585 -0.000822 0.003035 -0.000961 -0.021105 0.022764 0.001173 -0.008868 0.020613 -0.035042 0.039000 0.010165 -0.056131 0.022830 -0.009516 -0.002921 -0.057879 -0.004983
0.001974 0.012342 0.008902 -0.001035 -0.014319 -0.014808 -0.005193 -0.002275 0.011929 -0.018206 0.019006 0.009941 0.038655 0.005308 0.018479 0.030950 -0.036894 -0.017445 0.032165 -0.091752 0.005902 -0.021467 0.001966 -0.009808
-0.004216 0.004994 -0.016522 -0.029024 0.004502 0.007034 0.013316 0.000795 -0.012080 0.017514 0.036897 -0.007448 0.020571 -0.003682 0.025414 0.004514 -0.022096 -0.025326 0.029382 -0.047762 -0.017414 0.005327 -0.013496 0.012521
-0.018832 -0.002126 -0.034571 -0.026174 0.029176 0.018516 0.009517 0.001888 -0.008390 0.017435 0.027928 -0.015628 0.017215 -0.007801 0.016744 -0.024043 -0.014056 0.003402 0.019569 -0.013736 -0.015438 0.013625 -0.001066 0.006400
-0.028577 -0.003106 -0.035186 0.006865 0.039718 0.022501 -0.012257 -0.000103 0.031018 -0.027697 -0.003165 -0.006225 0.003329 0.006415 -0.021346 -0.045658 -0.008373 0.037353 0.024522 0.008467 0.007100 0.009292 0.037864 -0.028296
-0.010439 -0.016610 -0.032891 0.026755 0.038727 0.019621 -0.001639 -0.035212 0.021531 0.043250 -0.011573 -0.047443 -0.090454 0.017415 0.029955 -0.029753 -0.016593 0.003223 0.000312 -0.050431 -0.067987 0.007906 -0.010813 0.001715
-0.018644 -0.010357 -0.028056 -0.000468 0.032730 0.008727 -0.000374 -0.021592 0.004388 0.003520 0.005170 -0.016747 -0.034045 0.003118 0.010412 -0.016663 0.001397 -0.000975 0.017040 -0.015979 -0.012284 -0.003719 0.001706 -0.008266
-0.024274 -0.003386 -0.017513 -0.036282 0.010578 0.005796 0.000745 -0.000204 -0.000937 -0.029211 0.016619 0.019301 0.020013 -0.001295 -0.025894 0.021730 0.004179 0.018245 0.011996 0.012299 0.036561 -0.004257 0.003014 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

View file

@ -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

View file

@ -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
// Licensees 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 <opencv2/core/core.hpp>
#include <opencv2/imgproc.hpp>
// TBB includes
#include <tbb/tbb.h>
// System includes
#include <fstream>
@ -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_<float> 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<vector<cv::Mat_<float> > > kernels;
vector<vector<pair<int, cv::Mat_<double> > > > kernel_dfts;
kernels.resize(num_in_maps);
kernel_dfts.resize(num_in_maps);
vector<float> 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_<float> biases;
ReadMatBin(detection_validator_stream, biases);
cnn_fully_connected_layers_biases[i].push_back(biases);
// Fully connected layer
cv::Mat_<float> 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_<uc
}
else if(validator_type == 2)
{
dec = CheckCNN(warped, id);
dec = CheckCNN_old(warped, id);
}
else if (validator_type == 3)
{
// On some machines the non-TBB version may be faster
//dec = CheckCNN(warped, id);
dec = CheckCNN_tbb(warped, id);
}
return dec;
}
@ -433,7 +558,7 @@ double DetectionValidator::CheckSVR(const cv::Mat_<double>& warped_img, int view
}
// Convolutional Neural Network
double DetectionValidator::CheckCNN(const cv::Mat_<double>& warped_img, int view_id)
double DetectionValidator::CheckCNN_old(const cv::Mat_<double>& warped_img, int view_id)
{
cv::Mat_<double> feature_vec;
@ -599,7 +724,7 @@ double DetectionValidator::CheckCNN(const cv::Mat_<double>& 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_<double>& warped_img, int view
fully_connected_layer++;
}
// Max pooling layer
if (layer_type == 3)
{
vector<cv::Mat_<float>> 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_<float> sub_out(out_y, out_x, 0.0);
cv::Mat_<float> 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<float>(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<float>(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_<double>& warped_img, int view
return dec;
}
// Convolutional Neural Network
double DetectionValidator::CheckCNN_tbb(const cv::Mat_<double>& warped_img, int view_id)
{
cv::Mat_<double> feature_vec;
NormaliseWarpedToVector(warped_img, feature_vec, view_id);
// Create a normalised image from the crop vector
cv::Mat_<float> img(warped_img.size(), 0.0);
img = img.t();
cv::Mat mask = paws[view_id].pixel_mask.t();
cv::MatIterator_<uchar> mask_it = mask.begin<uchar>();
cv::MatIterator_<double> feature_it = feature_vec.begin();
cv::MatIterator_<float> 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<cv::Mat_<float> > input_maps;
input_maps.push_back(img);
vector<cv::Mat_<float> > 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_<float> input_image = input_maps[in];
// Useful precomputed data placeholders for quick correlation (convolution)
cv::Mat_<double> 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_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][0];
// The convolution (with precomputation)
cv::Mat_<float> 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<int, cv::Mat_<double> > 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<int, cv::Mat_<double> > 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_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
// The convolution (with precomputation)
cv::Mat_<float> 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<int, cv::Mat_<double> > 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<int, cv::Mat_<double> > 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<cv::Mat_<float>> 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_<float> sub_out(out_y, out_x, 0.0);
cv::Mat_<float> 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<float>(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<float>(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_<float> 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_<float> 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_<double>& warped_img, int view_id)
{
cv::Mat_<double> feature_vec;
NormaliseWarpedToVector(warped_img, feature_vec, view_id);
// Create a normalised image from the crop vector
cv::Mat_<float> img(warped_img.size(), 0.0);
img = img.t();
cv::Mat mask = paws[view_id].pixel_mask.t();
cv::MatIterator_<uchar> mask_it = mask.begin<uchar>();
cv::MatIterator_<double> feature_it = feature_vec.begin();
cv::MatIterator_<float> 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<cv::Mat_<float> > input_maps;
input_maps.push_back(img);
vector<cv::Mat_<float> > 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_<float> input_image = input_maps[in];
// Useful precomputed data placeholders for quick correlation (convolution)
cv::Mat_<double> 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_<float> kernel = cnn_convolutional_layers[view_id][cnn_layer][in][k];
// The convolution (with precomputation)
cv::Mat_<float> output;
if (cnn_convolutional_layers_dft[view_id][cnn_layer][in][k].second.empty())
{
std::map<int, cv::Mat_<double> > 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<int, cv::Mat_<double> > 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<cv::Mat_<float>> 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_<float> sub_out(out_y, out_x, 0.0);
cv::Mat_<float> 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<float>(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<float>(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_<float> 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_<float> 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_<double>& warped_img, cv::Mat_<double>& feature_vec, int view_id)
{
cv::Mat_<double> warped_t = warped_img.t();

View file

@ -259,7 +259,7 @@ void CorrectGlobalParametersVideo(const cv::Mat_<uchar> &grayscale_image, CLNF&
}
bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, CLNF& clnf_model, FaceModelParameters& params)
bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &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_<uchar> &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_<uchar> &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
@ -400,7 +401,7 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
}
bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
{
if(bounding_box.width > 0)
{
@ -412,27 +413,17 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &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_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
{
return DetectLandmarksInVideo(grayscale_image, cv::Mat_<float>(), clnf_model, params);
}
bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
{
return DetectLandmarksInVideo(grayscale_image, cv::Mat_<float>(), 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_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
{
// Can have multiple hypotheses
@ -485,7 +476,7 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &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_<uchar> &grayscale_i
return best_success;
}
bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, CLNF& clnf_model, FaceModelParameters& params)
bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
{
cv::Rect_<double> bounding_box;
@ -559,18 +550,6 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &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_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
{
return DetectLandmarksInImage(grayscale_image, cv::Mat_<float>(), bounding_box, clnf_model, params);
}
bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
{
return DetectLandmarksInImage(grayscale_image, cv::Mat_<float>(), clnf_model, params);
}

View file

@ -243,7 +243,6 @@ void CLNF::Read_CLNF(string clnf_location)
string line;
vector<string> intensity_expert_locations;
vector<string> depth_expert_locations;
vector<string> 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_<uchar> &image, const cv::Mat_<float> &depth, FaceModelParameters& params)
bool CLNF::DetectLandmarks(const cv::Mat_<uchar> &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_<uchar> &image, const cv::Mat_<float> &
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_<uchar> &image, const cv::Mat_<float> &
}
//=============================================================================
bool CLNF::Fit(const cv::Mat_<uchar>& im, const cv::Mat_<float>& depthImg, const std::vector<int>& window_sizes, const FaceModelParameters& parameters)
bool CLNF::Fit(const cv::Mat_<uchar>& im, const std::vector<int>& window_sizes, const FaceModelParameters& parameters)
{
// Making sure it is a single channel image
assert(im.channels() == 1);
@ -685,20 +680,6 @@ bool CLNF::Fit(const cv::Mat_<uchar>& im, const cv::Mat_<float>& depthImg, const
int n = pdm.NumberOfPoints();
cv::Mat_<float> 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_<uchar>& im, const cv::Mat_<float>& 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_<double>& final_local, con
}
bool CLNF::RemoveBackground(cv::Mat_<float>& out_depth_image, const cv::Mat_<float>& 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_<double> 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_<int> 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_<uchar> mask(depth_image.rows, depth_image.cols, (uchar)0);
cv::Mat_<uchar> 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_<float> 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_<double> CLNF::GetShape(double fx, double fy, double cx, double cy) const
{

View file

@ -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<string> &input_video_files, vector<string> &depth_dirs, vector<string> &output_files,
// Extracting the following command line arguments -f, -op, -of, -ov (and possible ordered repetitions)
void get_video_input_output_params(vector<string> &input_video_files, vector<string> &output_files,
vector<string> &output_video_files, bool& world_coordinates_pose, string& output_codec, vector<string> &arguments)
{
bool* valid = new bool[arguments.size()];
@ -149,13 +149,6 @@ void get_video_input_output_params(vector<string> &input_video_files, vector<str
valid[i+1] = false;
i++;
}
else if (arguments[i].compare("-fd") == 0)
{
depth_dirs.push_back(input_root + arguments[i + 1]);
valid[i] = false;
valid[i+1] = false;
i++;
}
else if (arguments[i].compare("-of") == 0)
{
output_files.push_back(output_root + arguments[i + 1]);
@ -251,7 +244,7 @@ void get_camera_params(int &device, float &fx, float &fy, float &cx, float &cy,
}
}
void get_image_input_output_params(vector<string> &input_image_files, vector<string> &input_depth_files, vector<string> &output_feature_files, vector<string> &output_pose_files, vector<string> &output_image_files,
void get_image_input_output_params(vector<string> &input_image_files, vector<string> &output_feature_files, vector<string> &output_pose_files, vector<string> &output_image_files,
vector<cv::Rect_<double>> &input_bounding_boxes, vector<string> &arguments)
{
bool* valid = new bool[arguments.size()];
@ -294,13 +287,6 @@ void get_image_input_output_params(vector<string> &input_image_files, vector<str
valid[i+1] = false;
i++;
}
else if (arguments[i].compare("-fd") == 0)
{
input_depth_files.push_back(input_root + arguments[i + 1]);
valid[i] = false;
valid[i+1] = false;
i++;
}
else if (arguments[i].compare("-fdir") == 0)
{

View file

@ -56,7 +56,7 @@
using namespace LandmarkDetector;
// A copy constructor
Patch_experts::Patch_experts(const Patch_experts& other) : patch_scaling(other.patch_scaling), centers(other.centers), svr_expert_intensity(other.svr_expert_intensity), svr_expert_depth(other.svr_expert_depth), ccnf_expert_intensity(other.ccnf_expert_intensity)
Patch_experts::Patch_experts(const Patch_experts& other) : patch_scaling(other.patch_scaling), centers(other.centers), svr_expert_intensity(other.svr_expert_intensity), ccnf_expert_intensity(other.ccnf_expert_intensity)
{
// Make sure the matrices are allocated properly
@ -86,11 +86,11 @@ Patch_experts::Patch_experts(const Patch_experts& other) : patch_scaling(other.p
}
}
// 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 Patch_experts::Response(vector<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image, const cv::Mat_<float>& depth_image,
void Patch_experts::Response(vector<cv::Mat_<float> >& patch_expert_responses, cv::Matx22f& sim_ref_to_img, cv::Matx22d& sim_img_to_ref, const cv::Mat_<uchar>& grayscale_image,
const PDM& pdm, const cv::Vec6d& params_global, const cv::Mat_<double>& params_local, int window_size, int scale)
{
@ -126,15 +126,6 @@ void Patch_experts::Response(vector<cv::Mat_<float> >& 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_<uchar> 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<cv::Mat_<float> >& 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<int>(i,0))
{
cv::Mat_<float> dProb = patch_expert_responses[i].clone();
cv::Mat_<float> 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<string> intensity_svr_expert_locations, vector<string> depth_svr_expert_locations, vector<string> intensity_ccnf_expert_locations)
void Patch_experts::Read(vector<string> intensity_svr_expert_locations, vector<string> intensity_ccnf_expert_locations)
{
// initialise the SVR intensity patch expert parameters
@ -341,61 +285,6 @@ void Patch_experts::Read(vector<string> intensity_svr_expert_locations, vector<s
Read_CCNF_patch_experts(location, centers[scale], visibilities[scale], ccnf_expert_intensity[scale], patch_scaling[scale]);
}
// initialise the SVR depth patch expert parameters
int num_depth_scales = depth_svr_expert_locations.size();
int num_intensity_scales = centers.size();
if(num_depth_scales > 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<vector<cv::Vec3d> > centers_depth(num_depth_scales);
vector<vector<cv::Mat_<int> > > visibilities_depth(num_depth_scales);
vector<double> 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<cv::Vec3d>& centers, std::vector<cv::Mat_<int> >& visibility, std::vector<std::vector<Multi_SVR_patch_expert> >& patches, double& scale)

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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);

View file

@ -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

View file

@ -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'

View file

@ -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;];

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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']));
@ -73,9 +75,20 @@ for r=1:size(centres_all,1)
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];
% 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');

View file

@ -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)
@ -71,14 +75,30 @@ 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];
load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
else
load([dataset_loc, '/', data_files(r).name]);
end
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;
% Convert to 66 point model
landmark_locations = landmark_locations(:,label_inds,:);
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
visiCurrent = logical(visiIndex);
@ -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;
@ -214,6 +239,7 @@ 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;
@ -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;

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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')

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -1,3 +0,0 @@
function x = rbmdown(rbm, x)
x = sigm(repmat(rbm.b', size(x, 1), 1) + x * rbm.W);
end

View file

@ -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

View file

@ -1,3 +0,0 @@
function x = rbmup(rbm, x)
x = sigm(repmat(rbm.c', size(x, 1), 1) + x * rbm.W');
end

View file

@ -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.

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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")

View file

@ -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.

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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

View file

@ -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);

View file

@ -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');

View file

@ -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');

View file

@ -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');

View file

@ -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');

Some files were not shown because too many files have changed in this diff Show more