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Merge branch 'master' into feature/Windows-GUI 

# Conflicts:
#	.gitignore
#	exe/FaceLandmarkVid/FaceLandmarkVid.cpp
#	exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp
#	exe/FeatureExtraction/FeatureExtraction.cpp
#	lib/local/FaceAnalyser/src/FaceAnalyser.cpp
#	lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h
#	lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp
#	matlab_runners/Action Unit Experiments/results/DISFA_valid_res.txt
#	matlab_runners/Action Unit Experiments/results/FERA2011_res_class.txt
#	matlab_runners/Action Unit Experiments/results/SEMAINE_valid_res.txt
#	matlab_runners/Demos/Read_HOG_file.m
#	matlab_runners/Demos/feature_extraction_demo_img_seq.m
#	matlab_runners/Feature Point Experiments/results/fps_yt.mat
#	matlab_runners/Feature Point Experiments/results/fps_yt.txt
#	matlab_runners/Feature Point Experiments/results/in-the-wild-res-no-outline.pdf
#	matlab_runners/Feature Point Experiments/results/landmark_detections.mat
#	matlab_runners/Gaze Experiments/mpii_1500_errs.mat
#	matlab_runners/Head Pose Experiments/results/Pose_OF.mat
#	matlab_runners/Head Pose Experiments/results/Pose_OF.txt
This commit is contained in:
Tadas Baltrusaitis 2017-10-18 03:01:47 -04:00
parent ddd0536b70 a47cbc5fd8
commit 0de2379463
170 changed files with 2011 additions and 15930 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

51
.gitignore vendored
View File

@ -1,11 +1,3 @@
exe/Recording/x64/
exe/FaceLandmarkVidMulti/x64/
lib/3rdParty/dlib/x64/
lib/local/FaceAnalyser/x64/
lib/local/LandmarkDetector/x64/
exe/FaceLandmarkImg/x64/
exe/FaceLandmarkVid/x64/
exe/FeatureExtraction/x64/
/x64/Release/
OpenFace.VC.opendb
OpenFace.sdf
@ -34,20 +26,44 @@ matlab_runners/Action Unit Experiments/out_DISFA/
matlab_runners/Action Unit Experiments/out_fera/
matlab_runners/Demos/output_features_seq/
matlab_runners/Demos/output_features_vid/
exe/FaceLandmarkImg/Release/
exe/FaceLandmarkImg/Debug/
exe/FaceLandmarkVid/Release/
exe/FaceLandmarkVid/Debug/
exe/FaceLandmarkVidMulti/Release/
exe/FaceLandmarkVidMulti/Debug/
exe/FeatureExtraction/Debug/
exe/FeatureExtraction/Release/
exe/Recording/Release/
exe/Recording/Debug/
exe/Recording/x64/
exe/FaceLandmarkVidMulti/x64/
exe/FaceLandmarkImg/x64/
exe/FaceLandmarkVid/x64/
exe/FeatureExtraction/x64/
gui/OpenFaceOffline/bin/
gui/OpenFaceOffline/obj/
gui/OpenFaceDemo/obj/
gui/HeadPose-live/obj/
lib/3rdParty/dlib/Release/
lib/3rdParty/dlib/Debug/
lib/local/CamCom/Release/
lib/local/CamCom/Debug/
lib/local/CamCom/x64/
lib/local/CppInerop/x64/
lib/local/CppInerop/Release/
lib/local/CppInerop/Debug/
lib/local/CppInerop/x64/Debug/
lib/local/FaceAnalyser/Release/
lib/local/FaceAnalyser/Debug/
lib/local/LandmarkDetector/Release/
lib/local/LandmarkDetector/Debug/
lib/3rdParty/dlib/x64/
lib/local/FaceAnalyser/x64/
lib/local/LandmarkDetector/x64/
lib/local/GazeAnalyser/x64/
lib/local/GazeAnalyser/Release/
lib/local/GazeAnalyser/Debug/
OpenFaceDemo/bin/
OpenFaceDemo/obj/
Debug/model/
@ -58,19 +74,6 @@ lib/local/LandmarkDetector/Debug/
matlab_runners/Head Pose Experiments/experiments/biwi_out/
matlab_runners/Head Pose Experiments/experiments/bu_out/
matlab_runners/Head Pose Experiments/experiments/ict_out/
exe/FaceLandmarkImg/Release/
exe/FaceLandmarkVid/Release/
exe/FaceLandmarkVidMulti/Release/
exe/FeatureExtraction/Release/
exe/Recording/Release/
gui/OpenFaceDemo/obj/
lib/3rdParty/dlib/Release/
lib/local/CamCom/Release/
lib/local/CppInerop/Release/
lib/local/CppInerop/x64/
lib/local/FaceAnalyser/Release/
lib/local/LandmarkDetector/Release/
gui/HeadPose-live/obj/
lib/local/GazeAnalyser/x64/
lib/local/GazeAnalyser/Release/
lib/local/GazeAnalyser/Debug/
OpenFace\.VC\.db
matlab_version/face_validation/vlfeat-0.9.20/
matlab_version/face_validation/trained/intermediate/

5
.travis.yml
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@ -7,7 +7,6 @@ branches:
- /^feature-.*$/
compiler:
- gcc
- clang
os:
- osx
@ -45,7 +44,7 @@ before_install:
if [ ${TRAVIS_OS_NAME} = linux ]; then
$CXX --version;
sudo sed -i -e 's/^Defaults\tsecure_path.*$//' /etc/sudoers;
sudo cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_V4L=ON -D WITH_OPENCL=OFF -D INSTALL_C_EXAMPLES=OFF -D BUILD_EXAMPLES=OFF -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF -D INSTALL_PYTHON_EXAMPLES=OFF ..;
sudo cmake -D CMAKE_BUILD_TYPE=RELEASE -D WITH_V4L=ON -D WITH_OPENCL=OFF -D INSTALL_C_EXAMPLES=OFF -D BUILD_EXAMPLES=OFF -D BUILD_TESTS=OFF -D BUILD_PERF_TESTS=OFF -D BUILD_EXAMPLES=OFF -D INSTALL_PYTHON_EXAMPLES=OFF -D WITH_TBB=ON ..;
fi
fi
@ -69,7 +68,7 @@ script:
- $CXX --version
- mkdir build
- cd build
- cmake -D CMAKE_BUILD_TYPE=RELEASE ..
- cmake -D CMAKE_BUILD_TYPE=RELEASE CMAKE_CXX_FLAGS="-std=c++11" -D CMAKE_EXE_LINKER_FLAGS="-std=c++11" ..
- make -j2
- ../build/bin/FaceLandmarkImg -fdir "../samples/" -ofdir "./demo_img/" -oidir "./demo_img/" -wild -q
- ../build/bin/FaceLandmarkImg -inroot ../samples -f sample1.jpg -outroot data -of sample1.txt -op sample1.3d -oi sample1.bmp -multi_view 1 -wild -q

17
Copyright.txt
View File

@ -1,12 +1,11 @@
///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, 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.
//
Copyright (C) 2017, University of Southern California, University of Cambridge, and Carnegie Mellon University, all rights reserved
ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
THIS SOFTWARE IS PROVIDED "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 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. Carnegie Mellon shall provide a list of Open Source Components for a particular version of the Software upon Licensee’s request. Licensee will comply with the applicable terms of such licenses and to the extent required by the licenses covering Open Source Components, the terms of such licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the licenses applicable to Open Source Components prohibit any of the restrictions in this License Agreement with respect to such Open Source Component, such restrictions will not apply to such Open Source Component. To the extent the terms of the licenses applicable to Open Source Components require Carnegie Mellon 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 Tadas Baltrusaitis. Licensee acknowledges receipt of notices for the Open Source Components for the initial delivery of the Software.
// License can be found in OpenFace-license.txt
//
// * Any publications arising from the use of this software, including but

12
README.md
View File

@ -68,13 +68,17 @@ Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
in *Facial Expression Recognition and Analysis Challenge*,
*IEEE International Conference on Automatic Face and Gesture Recognition*, 2015
# Final remarks
I did my best to make sure that the code runs out of the box but there are always issues and I would be grateful for your understanding that this is research code and not a commercial level product. However, if you encounter any problems/bugs/issues please contact me on github or by emailing me at Tadas.Baltrusaitis@cl.cam.ac.uk for any bug reports/questions/suggestions.
# Copyright
Copyright can be found in the Copyright.txt
You have to respect boost, TBB, dlib, and OpenCV licenses.
# Commercial license
For inquiries about the commercial licensing of the OpenFace toolkit please contact innovation@cmu.edu
# Final remarks
I did my best to make sure that the code runs out of the box but there are always issues and I would be grateful for your understanding that this is research code and not full fledged product. However, if you encounter any problems/bugs/issues please contact me on github or by emailing me at Tadas.Baltrusaitis@cl.cam.ac.uk for any bug reports/questions/suggestions.

18
exe/FaceLandmarkImg/FaceLandmarkImg.cpp
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;
@ -357,17 +357,7 @@ int main (int argc, char **argv)
cout << "Could not read the input image" << endl;
return 1;
}
// Loading depth file if exists (optional)
cv::Mat_<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);
@ -413,7 +403,7 @@ 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::GetPose(clnf_model, fx, fy, cx, cy);

44
exe/FaceLandmarkVid/FaceLandmarkVid.cpp
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;
@ -169,7 +162,7 @@ int main (int argc, char **argv)
// Indicates that rotation should be with respect to world or camera coordinates
string output_codec;
LandmarkDetector::get_video_input_output_params(files, depth_directories, out_dummy, output_video_files, output_codec, arguments);
LandmarkDetector::get_video_input_output_params(files, out_dummy, output_video_files, output_codec, arguments);
// The modules that are being used for tracking
LandmarkDetector::CLNF clnf_model(det_parameters.model_location);
@ -214,8 +207,6 @@ 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 )
@ -291,7 +282,6 @@ int main (int argc, char **argv)
{
// Reading the images
cv::Mat_<float> depth_image;
cv::Mat_<uchar> grayscale_image;
if(captured_image.channels() == 3)
@ -302,31 +292,9 @@ int main (int argc, char **argv)
{
grayscale_image = captured_image.clone();
}
// Get depth image
if(use_depth)
{
char* dst = new char[100];
std::stringstream sstream;
sstream << depth_directories[f_n] << "\\depth%05d.png";
sprintf(dst, sstream.str().c_str(), frame_count + 1);
// Reading in 16-bit png image representing depth
cv::Mat_<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
@ -342,7 +310,7 @@ int main (int argc, char **argv)
GazeAnalysis::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())

33
exe/FaceLandmarkVidMulti/FaceLandmarkVidMulti.cpp
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;
@ -127,7 +127,8 @@ int main (int argc, char **argv)
// Get the input output file parameters
string output_codec;
LandmarkDetector::get_video_input_output_params(files, depth_directories, dummy_out, tracked_videos_output, output_codec, arguments);
LandmarkDetector::get_video_input_output_params(files, dummy_out, tracked_videos_output, output_codec, arguments);
// Get camera parameters
LandmarkDetector::get_camera_params(device, fx, fy, cx, cy, arguments);
@ -176,8 +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 )
@ -253,28 +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;
@ -336,7 +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;
@ -350,7 +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]);
}
});

78
exe/FeatureExtraction/FeatureExtraction.cpp
View File

@ -132,8 +132,9 @@ void create_directory(string output_path)
}
}
void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, bool& verbose,
bool &output_2D_landmarks, bool &output_3D_landmarks, bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze, vector<string> &arguments);
void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
int &similarity_size, bool &grayscale, bool& visualize_track, bool& visualize_align, bool& visualize_hog, bool& dynamic, bool &output_2D_landmarks, bool &output_3D_landmarks,
bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze, vector<string> &arguments);
void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments);
@ -219,16 +220,19 @@ 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> 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
det_parameters.track_gaze = true;
// Get the input output file parameters
// Indicates that rotation should be with respect to camera or world coordinates
string output_codec; //not used but should
LandmarkDetector::get_video_input_output_params(input_files, depth_directories, output_files, tracked_videos_output, output_codec, arguments);
LandmarkDetector::get_video_input_output_params(input_files, output_files, tracked_videos_output, output_codec, arguments);
bool video_input = true;
bool verbose = true;
bool images_as_video = false;
vector<vector<string> > input_image_files;
@ -270,6 +274,14 @@ int main (int argc, char **argv)
bool video_output = false;
double sim_scale = -1;
int sim_size = 112;
bool grayscale = false;
bool video_output = false;
bool dynamic = true; // Indicates if a dynamic AU model should be used (dynamic is useful if the video is long enough to include neutral expressions)
int num_hog_rows;
int num_hog_cols;
// By default output all parameters, but these can be turned off to get smaller files or slightly faster processing times
// use -no2Dfp, -no3Dfp, -noMparams, -noPose, -noAUs, -noGaze to turn them off
bool output_2D_landmarks = true;
@ -279,10 +291,12 @@ int main (int argc, char **argv)
bool output_AUs = true;
bool output_gaze = true;
get_output_feature_params(output_similarity_align, output_hog_align_files, verbose,
bool visualize_track = false;
bool visualize_align = false;
bool visualize_hog = false;
get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, visualize_track, visualize_align, visualize_hog, dynamic,
output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
// If multiple video files are tracked, use this to indicate if we are done
bool done = false;
int f_n = -1;
@ -292,9 +306,6 @@ int main (int argc, char **argv)
// Load the modules that are being used for tracking and face analysis
// Load face landmark detector
LandmarkDetector::FaceModelParameters det_parameters(arguments);
// Always track gaze in feature extraction
det_parameters.track_gaze = true;
LandmarkDetector::CLNF face_model(det_parameters.model_location);
// Load facial feature extractor and AU analyser
@ -370,6 +381,8 @@ int main (int argc, char **argv)
return 1;
}
// If image sequence provided, assume the fps is 30
fps_vid_in = 30;
}
// If optical centers are not defined just use center of image
@ -430,8 +443,6 @@ int main (int argc, char **argv)
// Use for timestamping if using a webcam
int64 t_initial = cv::getTickCount();
bool visualise_hog = verbose;
// Timestamp in seconds of current processing
double time_stamp = 0;
@ -498,18 +509,19 @@ int main (int argc, char **argv)
// But only if needed in output
if(!output_similarity_align.empty() || hog_output_file.is_open() || output_AUs)
{
face_analyser.AddNextFrame(captured_image, face_model.detected_landmarks, face_model.detection_success, time_stamp, false, !det_parameters.quiet_mode);
face_analyser.AddNextFrame(captured_image, face_model.detected_landmarks, face_model.detection_success, time_stamp, false, !det_parameters.quiet_mode && (visualize_align || visualize_hog));
face_analyser.GetLatestAlignedFace(sim_warped_img);
if(!det_parameters.quiet_mode)
if(!det_parameters.quiet_mode && visualize_align)
{
cv::imshow("sim_warp", sim_warped_img);
}
if(hog_output_file.is_open())
if(hog_output_file.is_open() || (visualize_hog && !det_parameters.quiet_mode))
{
face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
if(visualise_hog && !det_parameters.quiet_mode)
if(visualize_hog && !det_parameters.quiet_mode)
{
cv::Mat_<double> hog_descriptor_vis;
FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_vis);
@ -549,7 +561,10 @@ int main (int argc, char **argv)
}
// Visualising the tracker
visualise_tracking(captured_image, face_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
if(visualize_track && !det_parameters.quiet_mode)
{
visualise_tracking(captured_image, face_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
}
// Output the landmarks, pose, gaze, parameters and AUs
outputAllFeatures(&output_file, output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze,
@ -910,7 +925,8 @@ void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, boo
}
void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, bool& verbose,
void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
int &similarity_size, bool &grayscale, bool& visualize_track, bool& visualize_align, bool& visualize_hog, bool& dynamic,
bool &output_2D_landmarks, bool &output_3D_landmarks, bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze,
vector<string> &arguments)
{
@ -926,6 +942,13 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
string output_root = "";
// By default the model is dynamic
dynamic = true;
visualize_align = false;
visualize_hog = false;
visualize_track = false;
string separator = string(1, boost::filesystem::path::preferred_separator);
// First check if there is a root argument (so that videos and outputs could be defined more easilly)
@ -963,7 +986,24 @@ void get_output_feature_params(vector<string> &output_similarity_aligned, vector
}
else if (arguments[i].compare("-verbose") == 0)
{
verbose = true;
visualize_track = true;
visualize_align = true;
visualize_hog = true;
}
else if (arguments[i].compare("-vis-align") == 0)
{
visualize_align = true;
valid[i] = false;
}
else if (arguments[i].compare("-vis-hog") == 0)
{
visualize_hog = true;
valid[i] = false;
}
else if (arguments[i].compare("-vis-track") == 0)
{
visualize_track = true;
valid[i] = false;
}
else if (arguments[i].compare("-no2Dfp") == 0)
{

5
lib/local/FaceAnalyser/src/FaceAnalyser.cpp
View File

@ -416,11 +416,6 @@ void FaceAnalyser::AddNextFrame(const cv::Mat& frame, const cv::Mat_<double>& de
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)
{

54
lib/local/LandmarkDetector/include/LandmarkDetectionValidator.h
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
// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
// the extent required by the licenses covering Open Source Components, the terms of such
// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
// licenses applicable to Open Source Components prohibit any of the restrictions in this
// License Agreement with respect to such Open Source Component, such restrictions will not
// apply to such Open Source Component. To the extent the terms of the licenses applicable to
// Open Source Components require Licensor to make an offer to provide source code or
// related information in connection with the Software, such offer is hereby made. Any request
// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
// Licensee acknowledges receipt of notices for the Open Source Components for the initial
// delivery of the Software.
// * Any publications arising from the use of this software, including but
// not limited to academic journal and conference publications, technical
// reports and manuals, must cite at least one of the following works:
@ -59,7 +83,7 @@ class DetectionValidator
public:
// What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net
// What type of validator we're using - 0 - linear svr, 1 - feed forward neural net, 2 - convolutional neural net, 3 - new version of convolutional neural net
int validator_type;
// The orientations of each of the landmark detection validator
@ -98,11 +122,15 @@ public:
vector<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);

8
lib/local/LandmarkDetector/include/LandmarkDetectorFunc.h
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

7
lib/local/LandmarkDetector/include/LandmarkDetectorModel.h
View File

@ -157,7 +157,7 @@ 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
@ -184,7 +184,7 @@ 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);
@ -193,9 +193,6 @@ 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);

4
lib/local/LandmarkDetector/include/LandmarkDetectorUtils.h
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, 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);
//===========================================================================

9
lib/local/LandmarkDetector/include/Patch_experts.h
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);

8
lib/local/LandmarkDetector/model/clm-z.txt
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

BIN
lib/local/LandmarkDetector/model/detection_validation/validator_cnn.txt Normal file
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lib/local/LandmarkDetector/model/detection_validation/validator_general_66.txt
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3
lib/local/LandmarkDetector/model/main_clm-z.txt
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@ -1,3 +0,0 @@
LandmarkDetector clm-z.txt
FaceDetConversion haarAlign.txt
DetectionValidator detection_validation/validator_general_66.txt

2
lib/local/LandmarkDetector/model/main_clm_general.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

2
lib/local/LandmarkDetector/model/main_clm_wild.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

2
lib/local/LandmarkDetector/model/main_clnf_general.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

2
lib/local/LandmarkDetector/model/main_clnf_wild.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

1276
lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.25.txt
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1276
lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.35.txt
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1276
lib/local/LandmarkDetector/model/patch_experts/depth_patches_0.5.txt
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1870
lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.25.txt
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1870
lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.35.txt
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1870
lib/local/LandmarkDetector/model/patch_experts/intensity_patches_0.5.txt
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409
lib/local/LandmarkDetector/model/pdms/Multi-PIE_aligned_PDM_66.txt
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@ -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
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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

518
lib/local/LandmarkDetector/model/tris_66.txt
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

675
lib/local/LandmarkDetector/src/LandmarkDetectionValidator.cpp
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
// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
// the extent required by the licenses covering Open Source Components, the terms of such
// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
// licenses applicable to Open Source Components prohibit any of the restrictions in this
// License Agreement with respect to such Open Source Component, such restrictions will not
// apply to such Open Source Component. To the extent the terms of the licenses applicable to
// Open Source Components require Licensor to make an offer to provide source code or
// related information in connection with the Software, such offer is hereby made. Any request
// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
// Licensee acknowledges receipt of notices for the Open Source Components for the initial
// delivery of the Software.
// * Any publications arising from the use of this software, including but
// not limited to academic journal and conference publications, technical
// reports and manuals, must cite at least one of the following works:
@ -40,6 +64,9 @@
#include <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();

49
lib/local/LandmarkDetector/src/LandmarkDetectorFunc.cpp
View File

@ -207,7 +207,7 @@ 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
@ -236,7 +236,8 @@ 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
@ -318,7 +319,7 @@ 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
@ -338,7 +339,7 @@ bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_i
}
else
{
clnf_model.failures_in_a_row = -1;
clnf_model.failures_in_a_row = -1;
UpdateTemplate(grayscale_image, clnf_model);
return true;
}
@ -361,7 +362,7 @@ 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)
{
@ -373,27 +374,17 @@ 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
@ -446,7 +437,7 @@ 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)
{
@ -456,11 +447,8 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_i
best_detected_landmarks = clnf_model.detected_landmarks.clone();
best_landmark_likelihoods = clnf_model.landmark_likelihoods.clone();
best_success = success;
}
for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
{
if (hypothesis == 0 || best_likelihood < clnf_model.hierarchical_models[part].model_likelihood)
for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
{
best_likelihood_h[part] = clnf_model.hierarchical_models[part].model_likelihood;
best_global_parameters_h[part] = clnf_model.hierarchical_models[part].params_global;
@ -469,7 +457,6 @@ bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_i
best_landmark_likelihoods_h[part] = clnf_model.hierarchical_models[part].landmark_likelihoods.clone();
}
}
}
// Store the best estimates in the clnf_model
@ -491,7 +478,7 @@ 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;
@ -520,18 +507,6 @@ 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);
}

132
lib/local/LandmarkDetector/src/LandmarkDetectorModel.cpp
View File

@ -76,7 +76,7 @@ CLNF::CLNF(const CLNF& other): pdm(other.pdm), params_local(other.params_local.c
this->detection_certainty = other.detection_certainty;
this->model_likelihood = other.model_likelihood;
this->failures_in_a_row = other.failures_in_a_row;
// Load the CascadeClassifier (as it does not have a proper copy constructor)
if(!face_detector_location.empty())
{
@ -251,7 +251,6 @@ 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
@ -316,10 +315,6 @@ 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);
@ -327,7 +322,7 @@ 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();
@ -590,11 +585,11 @@ 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);
@ -636,7 +631,7 @@ 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
@ -701,7 +696,7 @@ 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);
@ -710,21 +705,7 @@ bool CLNF::Fit(const cv::Mat_<uchar>& im, const cv::Mat_<float>& depthImg, const
cv::Mat_<double> current_shape(2 * pdm.NumberOfPoints() , 1, 0.0);
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
@ -746,15 +727,7 @@ 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)
{
@ -1139,95 +1112,6 @@ 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
{

15
lib/local/LandmarkDetector/src/LandmarkDetectorParameters.cpp
View File

@ -83,6 +83,15 @@ FaceModelParameters::FaceModelParameters(vector<string> &arguments)
i++;
}
if (arguments[i].compare("-fdloc") ==0)
{
string face_detector_loc = arguments[i + 1];
face_detector_location = face_detector_loc;
curr_face_detector = HAAR_DETECTOR;
valid[i] = false;
valid[i + 1] = false;
i++;
}
if (arguments[i].compare("-sigma") == 0)
{
stringstream data(arguments[i + 1]);
@ -144,7 +153,6 @@ FaceModelParameters::FaceModelParameters(vector<string> &arguments)
track_gaze = true;
valid[i] = false;
i++;
}
else if (arguments[i].compare("-q") == 0)
{
@ -253,12 +261,7 @@ void FaceModelParameters::init()
reinit_video_every = 4;
// Face detection
#if OS_UNIX
face_detector_location = "classifiers/haarcascade_frontalface_alt.xml";
#else
face_detector_location = "classifiers/haarcascade_frontalface_alt.xml";
#endif
quiet_mode = false;
// By default use HOG SVM

30
lib/local/LandmarkDetector/src/LandmarkDetectorUtils.cpp
View File

@ -14,20 +14,20 @@
// reports and manuals, must cite at least one of the following works:
//
// OpenFace: an open source facial behavior analysis toolkit
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
// in IEEE Winter Conference on Applications of Computer Vision, 2016
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
// Constrained Local Neural Fields for robust facial landmark detection in the wild.
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
//
///////////////////////////////////////////////////////////////////////////////
@ -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, string& output_codec, vector<string> &arguments)
{
bool* valid = new bool[arguments.size()];
@ -146,13 +146,6 @@ 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]);
@ -173,6 +166,7 @@ void get_video_input_output_params(vector<string> &input_video_files, vector<str
{
if(arguments[i + 1].length() == 4)
output_codec = arguments[i + 1];
i++;
}
}
@ -244,7 +238,7 @@ 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()];
@ -287,13 +281,6 @@ 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)
{
@ -1537,7 +1524,6 @@ bool DetectSingleFaceHOG(cv::Rect_<double>& o_region, const cv::Mat_<uchar>& int
{
dist = sqrt((preference.x - (face_detections[i].width/2 + face_detections[i].x)) * (preference.x - (face_detections[i].width/2 + face_detections[i].x)) +
(preference.y - (face_detections[i].height/2 + face_detections[i].y)) * (preference.y - (face_detections[i].height/2 + face_detections[i].y)));
better = dist < best_so_far;
}
else if (use_size)

119
lib/local/LandmarkDetector/src/Patch_experts.cpp
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)

2
matlab_runners/Action Unit Experiments/helpers/find_FERA2011.m
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@ -2,6 +2,8 @@ if(exist('D:\Datasets\fera/au_training', 'file'))
FERA2011_dir = 'D:\Datasets\fera/au_training/';
elseif(exist('/multicomp/datasets/fera2011/', 'file'))
FERA2011_dir = '/multicomp/datasets/fera2011/au_training/';
elseif(exist('E:\Datasets\fera/au_training', 'file'))
FERA2011_dir = 'E:\Datasets\fera/au_training/';
else
fprintf('FERA2011 location not found (or not defined)\n');
end

22
matlab_runners/Action Unit Experiments/results/DISFA_valid_res.txt
View File

@ -1,12 +1,12 @@
AU1 results - corr 0.825, rms 0.414, ccc - 0.802
AU2 results - corr 0.766, rms 0.444, ccc - 0.659
AU4 results - corr 0.863, rms 0.583, ccc - 0.837
AU5 results - corr 0.748, rms 0.179, ccc - 0.713
AU6 results - corr 0.706, rms 0.591, ccc - 0.665
AU1 results - corr 0.827, rms 0.412, ccc - 0.804
AU2 results - corr 0.762, rms 0.446, ccc - 0.656
AU4 results - corr 0.874, rms 0.565, ccc - 0.848
AU5 results - corr 0.744, rms 0.181, ccc - 0.714
AU6 results - corr 0.702, rms 0.604, ccc - 0.657
AU9 results - corr 0.741, rms 0.384, ccc - 0.688
AU12 results - corr 0.869, rms 0.494, ccc - 0.857
AU15 results - corr 0.747, rms 0.268, ccc - 0.714
AU17 results - corr 0.648, rms 0.512, ccc - 0.580
AU20 results - corr 0.638, rms 0.303, ccc - 0.595
AU25 results - corr 0.927, rms 0.499, ccc - 0.920
AU26 results - corr 0.804, rms 0.448, ccc - 0.763
AU12 results - corr 0.864, rms 0.510, ccc - 0.850
AU15 results - corr 0.745, rms 0.269, ccc - 0.712
AU17 results - corr 0.640, rms 0.521, ccc - 0.571
AU20 results - corr 0.618, rms 0.311, ccc - 0.580
AU25 results - corr 0.926, rms 0.500, ccc - 0.920
AU26 results - corr 0.803, rms 0.449, ccc - 0.763

22
matlab_runners/Action Unit Experiments/results/FERA2011_res_class.txt
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@ -1,11 +1,11 @@
AU1 class, Precision - 0.587, Recall - 0.715, F1 - 0.644
AU2 class, Precision - 0.470, Recall - 0.794, F1 - 0.590
AU4 class, Precision - 0.500, Recall - 0.753, F1 - 0.601
AU6 class, Precision - 0.835, Recall - 0.674, F1 - 0.746
AU7 class, Precision - 0.683, Recall - 0.791, F1 - 0.733
AU10 class, Precision - 0.523, Recall - 0.748, F1 - 0.616
AU12 class, Precision - 0.919, Recall - 0.654, F1 - 0.764
AU15 class, Precision - 0.359, Recall - 0.635, F1 - 0.458
AU17 class, Precision - 0.229, Recall - 0.286, F1 - 0.254
AU25 class, Precision - 0.205, Recall - 0.869, F1 - 0.332
AU26 class, Precision - 0.122, Recall - 0.972, F1 - 0.216
AU1 class, Precision - 0.590, Recall - 0.714, F1 - 0.646
AU2 class, Precision - 0.474, Recall - 0.750, F1 - 0.581
AU4 class, Precision - 0.509, Recall - 0.745, F1 - 0.605
AU6 class, Precision - 0.835, Recall - 0.667, F1 - 0.741
AU7 class, Precision - 0.686, Recall - 0.792, F1 - 0.735
AU10 class, Precision - 0.522, Recall - 0.738, F1 - 0.612
AU12 class, Precision - 0.919, Recall - 0.658, F1 - 0.767
AU15 class, Precision - 0.361, Recall - 0.638, F1 - 0.461
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

12
matlab_runners/Action Unit Experiments/results/SEMAINE_valid_res.txt
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@ -1,6 +1,6 @@
AU2 class, Precision - 0.371, Recall - 0.749, F1 - 0.497
AU12 class, Precision - 0.425, Recall - 0.782, F1 - 0.550
AU17 class, Precision - 0.126, Recall - 0.810, F1 - 0.218
AU25 class, Precision - 0.345, Recall - 0.577, F1 - 0.432
AU28 class, Precision - 0.479, Recall - 0.476, F1 - 0.478
AU45 class, Precision - 0.290, Recall - 0.621, F1 - 0.395
AU2 class, Precision - 0.360, Recall - 0.742, F1 - 0.485
AU12 class, Precision - 0.427, Recall - 0.781, F1 - 0.552
AU17 class, Precision - 0.111, Recall - 0.813, F1 - 0.195
AU25 class, Precision - 0.337, Recall - 0.523, F1 - 0.410
AU28 class, Precision - 0.430, Recall - 0.471, F1 - 0.450
AU45 class, Precision - 0.295, Recall - 0.615, F1 - 0.399

2
matlab_runners/Action Unit Experiments/run_AU_prediction_DISFA.m
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@ -8,6 +8,8 @@ end
if(exist('D:/Datasets/DISFA/Videos_LeftCamera/', 'file'))
DISFA_dir = 'D:/Datasets/DISFA/Videos_LeftCamera/';
elseif(exist('E:/Datasets/DISFA/Videos_LeftCamera/', 'file'))
DISFA_dir = 'E:/Datasets/DISFA/Videos_LeftCamera/';
else
DISFA_dir = '/multicomp/datasets/face_datasets/DISFA/Videos_LeftCamera/';
end

12
matlab_runners/Demos/Read_HOG_file.m
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@ -1,9 +1,10 @@
function [hog_data, valid_inds] = Read_HOG_file(hog_file)
valid_inds = [];
f = fopen(hog_file, 'r');
% Pre-allocated data
curr_data = [];
curr_ind = 0;
@ -50,10 +51,9 @@ function [hog_data, valid_inds] = Read_HOG_file(hog_file)
fclose(f);
hog_data = curr_data(1:curr_ind,:);
if(~isempty(hog_data))
valid_inds = hog_data(:,1);
hog_data = hog_data(:,2:end);
% Do some cleanup, remove un-allocated data
if(~isempty(curr_data))
valid_inds = curr_data(1:curr_ind,1);
hog_data = curr_data(1:curr_ind,2:end);
end
end

2
matlab_runners/Demos/feature_extraction_demo_img_seq.m
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@ -134,7 +134,7 @@ title('Pose (rotation and translation)');
xlabel('Time (s)');
%% Output HOG files
[hog_data, valid_inds] = Read_HOG_file([output, '/', name, '.hog']);
[hog_data, valid_inds] = Read_HOG_file(outputHOG_aligned);
%% Output aligned images
img_files = dir([outputDir_aligned, '/*.png']);

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matlab_runners/Feature Point Experiments/results/fps_yt.mat
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4
matlab_runners/Feature Point Experiments/results/fps_yt.txt
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@ -1,3 +1,3 @@
Model, mean, median
OpenFace (CLNF): 0.0563, 0.0515
CLM: 0.0683, 0.0602
OpenFace (CLNF): 0.0564, 0.0515
CLM: 0.0631, 0.0587

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matlab_runners/Feature Point Experiments/results/in-the-wild-res-no-outline.pdf
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matlab_runners/Feature Point Experiments/results/landmark_detections.mat
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2
matlab_runners/Feature Point Experiments/run_OpenFace_feature_point_tests_300W.m
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@ -7,6 +7,8 @@ if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
database_root = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];
elseif(exist('D:/Dropbox/Dropbox/AAM/test data/', 'file'))
database_root = 'D:/Dropbox/Dropbox/AAM/test data/';
elseif(exist('F:/Dropbox/AAM/test data/', 'file'))
database_root = 'F:/Dropbox/AAM/test data/';
else
database_root = '/multicomp/datasets/300-W/';
end

2
matlab_runners/Feature Point Experiments/run_yt_dataset.m
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@ -16,6 +16,8 @@ if(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
database_root = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];
elseif(exist('D:/Dropbox/Dropbox/AAM/test data/', 'file'))
database_root = 'D:/Dropbox/Dropbox/AAM/test data/';
elseif(exist('F:/Dropbox/AAM/test data/', 'file'))
database_root = 'F:/Dropbox/AAM/test data/';
else
database_root = '/multicomp/datasets/';
end

4
matlab_runners/Full_test_suite.m
View File

@ -36,7 +36,7 @@ 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);
@ -46,7 +46,7 @@ cd('../');
%% Gaze
cd('Gaze Experiments');
extract_mpii_gaze_test
assert(mean_error < 9.5)
assert(mean_error < 9.6)
assert(median_error < 9.0)
cd('../');

3
matlab_runners/Gaze Experiments/extract_mpii_gaze_test.m
View File

@ -7,6 +7,8 @@ if(exist([getenv('USERPROFILE') '/Dropbox/AAM/eye_clm/mpii_data/'], 'file'))
database_root = [getenv('USERPROFILE') '/Dropbox/AAM/eye_clm/mpii_data/'];
elseif(exist('D:\Dropbox/Dropbox/AAM/eye_clm/mpii_data/', 'file'))
database_root = 'D:\Dropbox/Dropbox/AAM/eye_clm/mpii_data/';
elseif(exist('F:\Dropbox/AAM/eye_clm/mpii_data/', 'file'))
database_root = 'F:\Dropbox/AAM/eye_clm/mpii_data/';
elseif(exist('/multicomp/datasets/mpii_gaze/mpii_data/', 'file'))
database_root = '/multicomp/datasets/mpii_gaze/mpii_data/';
else
@ -38,7 +40,6 @@ parfor p=1:numel(p_dirs)
out_p_loc = ['-opdir "', [output, p_dirs(p).name], '" '];
command_c = cat(2, command, input_loc, out_img_loc, out_p_loc);
command_c = cat(2, command_c, ' -wild');
if(isunix)
unix(command_c, '-echo');
else

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matlab_runners/Gaze Experiments/mpii_1500_errs.mat
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matlab_runners/Head Pose Experiments/results/Pose_OF.mat
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6
matlab_runners/Head Pose Experiments/results/Pose_OF.txt
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@ -1,4 +1,4 @@
Dataset and model, pitch, yaw, roll, mean, median
biwi error: 7.955, 5.583, 4.403, 5.980, 2.624
bu error: 2.734, 3.350, 2.459, 2.848, 1.974
ict error: 3.610, 3.959, 3.369, 3.646, 1.967
biwi error: 7.092, 5.170, 4.657, 5.640, 2.607
bu error: 2.769, 4.105, 2.569, 3.147, 2.118
ict error: 3.489, 3.632, 3.538, 3.553, 2.029

2
matlab_runners/Head Pose Experiments/run_biwi_experiment.m
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@ -53,7 +53,7 @@ for i=3 + offset:numTogether:numel(dbSeqDir)
command = cat(2, command, [' -ov "' outputVideo '"']);
end
end
command = cat(2, command, [' -fx 505 -fy 505 -cx 320 -cy 240 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze']);
command = cat(2, command, [' -fx 505 -fy 505 -cx 320 -cy 240 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze -vis-track']);
if(any(strcmp('model', varargin)))
command = cat(2, command, [' -mloc "', varargin{find(strcmp('model', varargin))+1}, '"']);

2
matlab_runners/Head Pose Experiments/run_bu_experiment.m
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@ -41,7 +41,7 @@ function [output_dir] = run_bu_experiment(bu_dir, verbose, varargin)
end
end
command = cat(2, command, ' -fx 500 -fy 500 -cx 160 -cy 120 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze ');
command = cat(2, command, ' -fx 500 -fy 500 -cx 160 -cy 120 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze -vis-track ');
if(any(strcmp('model', varargin)))
command = cat(2, command, [' -mloc "', varargin{find(strcmp('model', varargin))+1}, '"']);

65
matlab_runners/Head Pose Experiments/run_head_pose_tests_CLM.m
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@ -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'

2
matlab_runners/Head Pose Experiments/run_head_pose_tests_OpenFace.m
View File

@ -10,6 +10,8 @@ elseif(exist([getenv('USERPROFILE') '/Dropbox/AAM/test data/'], 'file'))
database_root = [getenv('USERPROFILE') '/Dropbox/AAM/test data/'];
elseif(exist([getenv('USERPROFILE') 'F:/Dropbox/Dropbox/AAM/test data/'], 'file'))
database_root = 'F:/Dropbox/Dropbox/AAM/test data/';
elseif(exist('F:/Dropbox/AAM/test data/', 'file'))
database_root = 'F:/Dropbox/AAM/test data/';
else
database_root = '/multicomp/datasets/head_pose_dbs/';
end

2
matlab_runners/Head Pose Experiments/run_ict_experiment.m
View File

@ -22,7 +22,7 @@ numTogether = 10;
for i=3:numTogether:numel(dbSeqDir)
command = [executable ' -fx 535 -fy 536 -cx 327 -cy 241 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze '];
command = [executable ' -fx 535 -fy 536 -cx 327 -cy 241 -no2Dfp -no3Dfp -noMparams -noAUs -noGaze -vis-track '];
command = cat(2, command, [' -inroot ' '"' rootDir '/"']);

9
matlab_version/demo/face_video_demo.m
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;];

221
matlab_version/face_validation/Collect_wild_imgs.m
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@ -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

337
matlab_version/face_validation/Create_data_66.m
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@ -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

334
matlab_version/face_validation/Create_data_68.m
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

38
matlab_version/face_validation/Create_data_68_large.m → matlab_version/face_validation/Create_data_test.m
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']));
@ -72,10 +74,21 @@ for r=1:size(centres_all,1)
mirror = true;
label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
61,65;62,64;68,66];
61,65;62,64;68,66];
% Make sure we take the subset of visibilities from all the
% datasets
load([dataset_loc, '/', data_files_c(mirror_inds(r)).name], 'visiIndex');
visiIndex_t = visiIndex;
load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
visiIndex = visiIndex_t & visiIndex;
else
load([dataset_loc, '/', data_files_c(r).name], 'visiIndex');
visiIndex_t = visiIndex;
load([dataset_loc, '/', data_files(r).name]);
visiIndex = visiIndex_t & visiIndex;
end
visiCurrent = logical(visiIndex);
@ -312,8 +325,7 @@ for r=1:size(centres_all,1)
examples = examples(1:curr_filled,:);
errors = errors(1:curr_filled);
% svm training
filename = sprintf('%s/face_checker_general_training_large_68_%d.mat', outputLocation, r);
filename = sprintf('%s/face_validator_test_%d.mat', outputLocation, r);
save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');

73
matlab_version/face_validation/Create_data_66_large.m → matlab_version/face_validation/Create_data_train.m
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)
@ -71,15 +75,31 @@ for r=1:size(centres_all,1)
mirror = true;
label_mirror_inds = [1,17;2,16;3,15;4,14;5,13;6,12;7,11;8,10;18,27;19,26;20,25;21,24;22,23;...
32,36;33,35;37,46;38,45;39,44;40,43;41,48;42,47;49,55;50,54;51,53;60,56;59,57;...
61,63;66,64];
61,65;62,64;68,66];
load([dataset_loc, '/', data_files_c(mirror_inds(r)).name]);
all_images_t = all_images;
landmark_locations_t = landmark_locations;
visiIndex_t = visiIndex;
load([dataset_loc, '/', data_files(mirror_inds(r)).name]);
% Combining Menpo + MPIE + 300W
all_images = cat(1, all_images, all_images_t);
landmark_locations = cat(1, landmark_locations, landmark_locations_t);
% Taking a subset of visibilities from all the datasets
visiIndex = visiIndex_t & visiIndex;
else
load([dataset_loc, '/', data_files_c(r).name]);
all_images_t = all_images;
landmark_locations_t = landmark_locations;
visiIndex_t = visiIndex;
load([dataset_loc, '/', data_files(r).name]);
all_images = cat(1, all_images, all_images_t);
landmark_locations = cat(1, landmark_locations, landmark_locations_t);
visiIndex = visiIndex_t & visiIndex;
end
% Convert to 66 point model
landmark_locations = landmark_locations(:,label_inds,:);
visiCurrent = logical(visiIndex);
if(mirror)
@ -161,7 +181,7 @@ for r=1:size(centres_all,1)
img = fliplr(img);
imgSize = size(img);
flippedLbls = labels;
flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1);
flippedLbls(:,1) = imgSize(1) - flippedLbls(:,1) + 1;
tmp1 = flippedLbls(label_mirror_inds(:,1),:);
tmp2 = flippedLbls(label_mirror_inds(:,2),:);
flippedLbls(label_mirror_inds(:,2),:) = tmp1;
@ -178,8 +198,12 @@ for r=1:size(centres_all,1)
continue;
end
% Centering the pixel so that 0,0 is center of the top left pixel
labels = labels - 1;
curr_filled = curr_filled + 1;
[features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
examples(curr_filled,:) = features;
errors(curr_filled,:) = 0;
@ -197,6 +221,7 @@ for r=1:size(centres_all,1)
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
@ -214,7 +239,8 @@ for r=1:size(centres_all,1)
labels_mod = labels_mod(:,1:2);
[features] = ExtractFaceFeatures(img, labels_mod, triangulation, triX, mask, alphas, betas, nPix, minX, minY);
% sample_img = zeros(size(mask));sample_img(mask) = features;imagesc(sample_img)
curr_filled = curr_filled + 1;
examples(curr_filled,:) = features;
@ -223,7 +249,7 @@ for r=1:size(centres_all,1)
% A somewhat offset example
trans_mod = trans_orig + randn(2,1) * 10;
trans_mod = trans_orig + randn(2,1) * 20;
p_global = [a_orig; eul_orig'; trans_mod];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
@ -238,7 +264,7 @@ for r=1:size(centres_all,1)
errors(curr_filled,:) = error;
% A rotated sample
eul_mod = eul_orig + randn(1,3)*0.2;
eul_mod = eul_orig + randn(1,3)*0.3;
p_global = [a_orig; eul_mod'; trans_orig];
labels_mod = GetShapeOrtho(M, V, params_orig, p_global);
@ -308,8 +334,7 @@ for r=1:size(centres_all,1)
examples = examples(1:curr_filled,:);
errors = errors(1:curr_filled);
% svm training
filename = sprintf('%s/face_checker_general_training_large_66_%d.mat', outputLocation, r);
filename = sprintf('%s/face_validator_train_%d.mat', outputLocation, r);
save(filename, 'examples', 'errors', 'alphas', 'betas', 'triangulation', 'minX', 'minY', 'nPix', 'shape', 'triX', 'mask', 'centres');
@ -321,8 +346,8 @@ function [features] = ExtractFaceFeatures(img, labels, triangulation, triX, mask
% Make sure labels are within range
[hRes, wRes] = size(img);
labels(labels(:,1) < 1,1) = 1;
labels(labels(:,2) < 1,2) = 1;
labels(labels(:,1) < 0,1) = 0;
labels(labels(:,2) < 0,2) = 0;
labels(labels(:,1) > wRes-1,1) = wRes-1;
labels(labels(:,2) > hRes-1,2) = hRes-1;

10
matlab_version/face_validation/DeepLearnToolbox/.travis.yml
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

29
matlab_version/face_validation/DeepLearnToolbox/CAE/caeapplygrads.m
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

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matlab_version/face_validation/DeepLearnToolbox/CAE/caebbp.m
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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

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matlab_version/face_validation/DeepLearnToolbox/CAE/caebp.m
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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

13
matlab_version/face_validation/DeepLearnToolbox/CAE/caedown.m
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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

32
matlab_version/face_validation/DeepLearnToolbox/CAE/caeexamples.m
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@ -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')

107
matlab_version/face_validation/DeepLearnToolbox/CAE/caenumgradcheck.m
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@ -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

26
matlab_version/face_validation/DeepLearnToolbox/CAE/caesdlm.m
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@ -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

38
matlab_version/face_validation/DeepLearnToolbox/CAE/caetrain.m
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@ -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

25
matlab_version/face_validation/DeepLearnToolbox/CAE/caeup.m
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@ -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

8
matlab_version/face_validation/DeepLearnToolbox/CAE/max3d.m
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@ -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

58
matlab_version/face_validation/DeepLearnToolbox/CAE/scaesetup.m
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@ -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

8
matlab_version/face_validation/DeepLearnToolbox/CAE/scaetrain.m
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@ -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

15
matlab_version/face_validation/DeepLearnToolbox/CNN/cnnapplygrads.m
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@ -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

56
matlab_version/face_validation/DeepLearnToolbox/CNN/cnnbp.m
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@ -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

47
matlab_version/face_validation/DeepLearnToolbox/CNN/cnnff.m
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@ -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

79
matlab_version/face_validation/DeepLearnToolbox/CNN/cnnnumgradcheck.m
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@ -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

36
matlab_version/face_validation/DeepLearnToolbox/CNN/cnnsetup.m
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@ -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

9
matlab_version/face_validation/DeepLearnToolbox/CNN/cnntest.m
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@ -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

29
matlab_version/face_validation/DeepLearnToolbox/CNN/cnntrain.m
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@ -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

20
matlab_version/face_validation/DeepLearnToolbox/CONTRIBUTING.md
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@ -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

19
matlab_version/face_validation/DeepLearnToolbox/DBN/dbnsetup.m
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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

10
matlab_version/face_validation/DeepLearnToolbox/DBN/dbntrain.m
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@ -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

15
matlab_version/face_validation/DeepLearnToolbox/DBN/dbnunfoldtonn.m
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@ -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

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

37
matlab_version/face_validation/DeepLearnToolbox/DBN/rbmtrain.m
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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

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

8
matlab_version/face_validation/DeepLearnToolbox/LICENSE
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@ -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.

22
matlab_version/face_validation/DeepLearnToolbox/NN/nnapplygrads.m
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@ -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

47
matlab_version/face_validation/DeepLearnToolbox/NN/nnbp.m
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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

22
matlab_version/face_validation/DeepLearnToolbox/NN/nnchecknumgrad.m
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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

28
matlab_version/face_validation/DeepLearnToolbox/NN/nneval.m
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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

60
matlab_version/face_validation/DeepLearnToolbox/NN/nnff.m
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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

8
matlab_version/face_validation/DeepLearnToolbox/NN/nnpredict.m
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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

29
matlab_version/face_validation/DeepLearnToolbox/NN/nnsetup.m
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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

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