/////////////////////////////////////////////////////////////////////////////// // 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. // // License can be found in OpenFace-license.txt // // * 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: // // OpenFace: an open source facial behavior analysis toolkit // 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 // 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 // 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. // in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013. // /////////////////////////////////////////////////////////////////////////////// // Header for all external CLNF/CLM-Z/CLM methods of interest to the user #ifndef __LANDMARK_DETECTOR_UTILS_h_ #define __LANDMARK_DETECTOR_UTILS_h_ // OpenCV includes #include #include "LandmarkDetectorModel.h" using namespace std; namespace LandmarkDetector { //=========================================================================== // Defining a set of useful utility functions to be used within CLNF //============================================================================================= // Helper functions for parsing the inputs //============================================================================================= void get_video_input_output_params(vector &input_video_file, vector &depth_dir, vector &output_files, vector &output_video_files, bool& world_coordinates_pose, string &output_codec, vector &arguments); void get_camera_params(int &device, float &fx, float &fy, float &cx, float &cy, vector &arguments); void get_image_input_output_params(vector &input_image_files, vector &input_depth_files, vector &output_feature_files, vector &output_pose_files, vector &output_image_files, vector> &input_bounding_boxes, vector &arguments); //=========================================================================== // Fast patch expert response computation (linear model across a ROI) using normalised cross-correlation //=========================================================================== // This is a modified version of openCV code that allows for precomputed dfts of templates and for precomputed dfts of an image // _img is the input img, _img_dft it's dft (optional), _integral_img the images integral image (optional), squared integral image (optional), // 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 void matchTemplate_m( const cv::Mat_& input_img, cv::Mat_& img_dft, cv::Mat& _integral_img, cv::Mat& _integral_img_sq, const cv::Mat_& templ, map >& templ_dfts, cv::Mat_& result, int method ); //=========================================================================== // Point set and landmark manipulation functions //=========================================================================== // Using Kabsch's algorithm for aligning shapes //This assumes that align_from and align_to are already mean normalised cv::Matx22d AlignShapesKabsch2D(const cv::Mat_& align_from, const cv::Mat_& align_to ); //============================================================================= // Basically Kabsch's algorithm but also allows the collection of points to be different in scale from each other cv::Matx22d AlignShapesWithScale(cv::Mat_& src, cv::Mat_ dst); //=========================================================================== // Visualisation functions //=========================================================================== void Project(cv::Mat_& dest, const cv::Mat_& mesh, double fx, double fy, double cx, double cy); void DrawBox(cv::Mat image, cv::Vec6d pose, cv::Scalar color, int thickness, float fx, float fy, float cx, float cy); // Drawing face bounding box vector> CalculateBox(cv::Vec6d pose, float fx, float fy, float cx, float cy); void DrawBox(vector> lines, cv::Mat image, cv::Scalar color, int thickness); vector CalculateLandmarks(const cv::Mat_& shape2D, cv::Mat_& visibilities); vector CalculateLandmarks(CLNF& clnf_model); void DrawLandmarks(cv::Mat img, vector landmarks); void Draw(cv::Mat img, const cv::Mat_& shape2D, const cv::Mat_& visibilities); void Draw(cv::Mat img, const cv::Mat_& shape2D); void Draw(cv::Mat img, const CLNF& clnf_model); //=========================================================================== // Angle representation conversion helpers //=========================================================================== cv::Matx33d Euler2RotationMatrix(const cv::Vec3d& eulerAngles); // Using the XYZ convention R = Rx * Ry * Rz, left-handed positive sign cv::Vec3d RotationMatrix2Euler(const cv::Matx33d& rotation_matrix); cv::Vec3d Euler2AxisAngle(const cv::Vec3d& euler); cv::Vec3d AxisAngle2Euler(const cv::Vec3d& axis_angle); cv::Matx33d AxisAngle2RotationMatrix(const cv::Vec3d& axis_angle); cv::Vec3d RotationMatrix2AxisAngle(const cv::Matx33d& rotation_matrix); //============================================================================ // Face detection helpers //============================================================================ // Face detection using Haar cascade classifier bool DetectFaces(vector >& o_regions, const cv::Mat_& intensity); bool DetectFaces(vector >& o_regions, const cv::Mat_& intensity, cv::CascadeClassifier& classifier); // 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 bool DetectSingleFace(cv::Rect_& o_region, const cv::Mat_& intensity, cv::CascadeClassifier& classifier, const cv::Point preference = cv::Point(-1,-1)); // Face detection using HOG-SVM classifier bool DetectFacesHOG(vector >& o_regions, const cv::Mat_& intensity, std::vector& confidences); bool DetectFacesHOG(vector >& o_regions, const cv::Mat_& intensity, dlib::frontal_face_detector& classifier, std::vector& confidences); // 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 bool DetectSingleFaceHOG(cv::Rect_& o_region, const cv::Mat_& intensity, dlib::frontal_face_detector& classifier, double& confidence, const cv::Point preference = cv::Point(-1,-1)); //============================================================================ // Matrix reading functionality //============================================================================ // Reading a matrix written in a binary format void ReadMatBin(std::ifstream& stream, cv::Mat &output_mat); // Reading in a matrix from a stream void ReadMat(std::ifstream& stream, cv::Mat& output_matrix); // Skipping comments (lines starting with # symbol) void SkipComments(std::ifstream& stream); } #endif