358cd6f9e2
A warning is now issued in the case the VideoWriter could not be instantiated, advising to change the codec This change applies to FaceLandmarkVid* and FeatureExtraction executables The option is used as "-oc CODEC_FOURCC" and defaults to DIVX. If the user inputs a FOURCC with a size different than 4, than the default codec is used The option is handled by the function get_video_input_output_params in the LandMarkDetector namespace
171 lines
10 KiB
C++
171 lines
10 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// THIS SOFTWARE IS PROVIDED “AS IS” FOR ACADEMIC USE ONLY AND ANY EXPRESS
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// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
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// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
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// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
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// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
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// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
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// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Notwithstanding the license granted herein, Licensee acknowledges that certain components
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// of the Software may be covered by so-called “open source” software licenses (“Open Source
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// Components”), which means any software licenses approved as open source licenses by the
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// Open Source Initiative or any substantially similar licenses, including without limitation any
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// license that, as a condition of distribution of the software licensed under such license,
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// requires that the distributor make the software available in source code format. Licensor shall
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// provide a list of Open Source Components for a particular version of the Software upon
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// Licensee’s request. Licensee will comply with the applicable terms of such licenses and to
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// the extent required by the licenses covering Open Source Components, the terms of such
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// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
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// licenses applicable to Open Source Components prohibit any of the restrictions in this
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// License Agreement with respect to such Open Source Component, such restrictions will not
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// apply to such Open Source Component. To the extent the terms of the licenses applicable to
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// Open Source Components require Licensor to make an offer to provide source code or
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// related information in connection with the Software, such offer is hereby made. Any request
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// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
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// Licensee acknowledges receipt of notices for the Open Source Components for the initial
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// delivery of the Software.
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// * Any publications arising from the use of this software, including but
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// not limited to academic journal and conference publications, technical
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// reports and manuals, must cite at least one of the following works:
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//
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// OpenFace: an open source facial behavior analysis toolkit
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
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// in IEEE Winter Conference on Applications of Computer Vision, 2016
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//
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// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
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// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
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// in IEEE International. Conference on Computer Vision (ICCV), 2015
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//
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// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
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// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
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// in Facial Expression Recognition and Analysis Challenge,
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// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
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//
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// Constrained Local Neural Fields for robust facial landmark detection in the wild.
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// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
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// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
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//
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///////////////////////////////////////////////////////////////////////////////
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// Header for all external CLNF/CLM-Z/CLM methods of interest to the user
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#ifndef __LANDMARK_DETECTOR_UTILS_h_
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#define __LANDMARK_DETECTOR_UTILS_h_
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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#include "LandmarkDetectorModel.h"
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using namespace std;
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namespace LandmarkDetector
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{
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//===========================================================================
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// Defining a set of useful utility functions to be used within CLNF
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//=============================================================================================
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// Helper functions for parsing the inputs
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//=============================================================================================
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void get_video_input_output_params(vector<string> &input_video_file, vector<string> &depth_dir, vector<string> &output_files,
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vector<string> &output_video_files, bool& world_coordinates_pose, string &output_codec, vector<string> &arguments);
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void get_camera_params(int &device, float &fx, float &fy, float &cx, float &cy, vector<string> &arguments);
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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,
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vector<cv::Rect_<double>> &input_bounding_boxes, vector<string> &arguments);
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//===========================================================================
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// Fast patch expert response computation (linear model across a ROI) using normalised cross-correlation
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//===========================================================================
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// This is a modified version of openCV code that allows for precomputed dfts of templates and for precomputed dfts of an image
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// _img is the input img, _img_dft it's dft (optional), _integral_img the images integral image (optional), squared integral image (optional),
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// templ is the template we are convolving with, templ_dfts it's dfts at varying windows sizes (optional), _result - the output, method the type of convolution
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void matchTemplate_m( const cv::Mat_<float>& input_img, cv::Mat_<double>& img_dft, cv::Mat& _integral_img, cv::Mat& _integral_img_sq, const cv::Mat_<float>& templ, map<int, cv::Mat_<double> >& templ_dfts, cv::Mat_<float>& result, int method );
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//===========================================================================
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// Point set and landmark manipulation functions
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//===========================================================================
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// Using Kabsch's algorithm for aligning shapes
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//This assumes that align_from and align_to are already mean normalised
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cv::Matx22d AlignShapesKabsch2D(const cv::Mat_<double>& align_from, const cv::Mat_<double>& align_to );
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//=============================================================================
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// Basically Kabsch's algorithm but also allows the collection of points to be different in scale from each other
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cv::Matx22d AlignShapesWithScale(cv::Mat_<double>& src, cv::Mat_<double> dst);
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//===========================================================================
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// Visualisation functions
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//===========================================================================
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void Project(cv::Mat_<double>& dest, const cv::Mat_<double>& mesh, double fx, double fy, double cx, double cy);
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void DrawBox(cv::Mat image, cv::Vec6d pose, cv::Scalar color, int thickness, float fx, float fy, float cx, float cy);
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// Drawing face bounding box
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vector<std::pair<cv::Point2d, cv::Point2d>> CalculateBox(cv::Vec6d pose, float fx, float fy, float cx, float cy);
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void DrawBox(vector<pair<cv::Point, cv::Point>> lines, cv::Mat image, cv::Scalar color, int thickness);
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vector<cv::Point2d> CalculateLandmarks(const cv::Mat_<double>& shape2D, cv::Mat_<int>& visibilities);
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vector<cv::Point2d> CalculateLandmarks(CLNF& clnf_model);
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void DrawLandmarks(cv::Mat img, vector<cv::Point> landmarks);
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void Draw(cv::Mat img, const cv::Mat_<double>& shape2D, const cv::Mat_<int>& visibilities);
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void Draw(cv::Mat img, const cv::Mat_<double>& shape2D);
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void Draw(cv::Mat img, const CLNF& clnf_model);
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//===========================================================================
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// Angle representation conversion helpers
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//===========================================================================
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cv::Matx33d Euler2RotationMatrix(const cv::Vec3d& eulerAngles);
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// Using the XYZ convention R = Rx * Ry * Rz, left-handed positive sign
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cv::Vec3d RotationMatrix2Euler(const cv::Matx33d& rotation_matrix);
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cv::Vec3d Euler2AxisAngle(const cv::Vec3d& euler);
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cv::Vec3d AxisAngle2Euler(const cv::Vec3d& axis_angle);
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cv::Matx33d AxisAngle2RotationMatrix(const cv::Vec3d& axis_angle);
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cv::Vec3d RotationMatrix2AxisAngle(const cv::Matx33d& rotation_matrix);
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//============================================================================
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// Face detection helpers
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//============================================================================
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// Face detection using Haar cascade classifier
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bool DetectFaces(vector<cv::Rect_<double> >& o_regions, const cv::Mat_<uchar>& intensity);
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bool DetectFaces(vector<cv::Rect_<double> >& o_regions, const cv::Mat_<uchar>& intensity, cv::CascadeClassifier& classifier);
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// The preference point allows for disambiguation if multiple faces are present (pick the closest one), if it is not set the biggest face is chosen
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bool DetectSingleFace(cv::Rect_<double>& o_region, const cv::Mat_<uchar>& intensity, cv::CascadeClassifier& classifier, const cv::Point preference = cv::Point(-1,-1));
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// Face detection using HOG-SVM classifier
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bool DetectFacesHOG(vector<cv::Rect_<double> >& o_regions, const cv::Mat_<uchar>& intensity, std::vector<double>& confidences);
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bool DetectFacesHOG(vector<cv::Rect_<double> >& o_regions, const cv::Mat_<uchar>& intensity, dlib::frontal_face_detector& classifier, std::vector<double>& confidences);
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// The preference point allows for disambiguation if multiple faces are present (pick the closest one), if it is not set the biggest face is chosen
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bool DetectSingleFaceHOG(cv::Rect_<double>& o_region, const cv::Mat_<uchar>& intensity, dlib::frontal_face_detector& classifier, double& confidence, const cv::Point preference = cv::Point(-1,-1));
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//============================================================================
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// Matrix reading functionality
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//============================================================================
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// Reading a matrix written in a binary format
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void ReadMatBin(std::ifstream& stream, cv::Mat &output_mat);
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// Reading in a matrix from a stream
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void ReadMat(std::ifstream& stream, cv::Mat& output_matrix);
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// Skipping comments (lines starting with # symbol)
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void SkipComments(std::ifstream& stream);
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}
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#endif
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