/////////////////////////////////////////////////////////////////////////////// // 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 2.0: Facial Behavior Analysis Toolkit // Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency // in IEEE International Conference on Automatic Face and Gesture Recognition, 2018 // // Convolutional experts constrained local model for facial landmark detection. // A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency, // in Computer Vision and Pattern Recognition Workshops, 2017. // // 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-specific 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 // /////////////////////////////////////////////////////////////////////////////// #ifndef LANDMARK_DETECTOR_UTILS_H #define LANDMARK_DETECTOR_UTILS_H // OpenCV includes #include #include "LandmarkDetectorModel.h" #include "FaceDetectorMTCNN.h" using namespace std; namespace LandmarkDetector { //=========================================================================== // Defining a set of useful utility functions to be used within CLNF //=========================================================================== // 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); // Useful utility for grabing a bounding box around a set of 2D landmarks (as a 1D 2n x 1 vector of xs followed by doubles or as an n x 2 vector) void ExtractBoundingBox(const cv::Mat_& landmarks, float &min_x, float &max_x, float &min_y, float &max_y); vector CalculateVisibleLandmarks(const cv::Mat_& shape2D, const cv::Mat_& visibilities); vector CalculateVisibleLandmarks(const CLNF& clnf_model); vector CalculateVisibleEyeLandmarks(const CLNF& clnf_model); vector CalculateAllLandmarks(const cv::Mat_& shape2D); vector CalculateAllLandmarks(const CLNF& clnf_model); vector CalculateAllEyeLandmarks(const CLNF& clnf_model); vector Calculate3DEyeLandmarks(const CLNF& clnf_model, float fx, float fy, float cx, float cy); //============================================================================ // Face detection helpers //============================================================================ // Face detection using Haar cascade classifier bool DetectFaces(vector >& o_regions, const cv::Mat_& intensity, float min_width = -1, cv::Rect_ roi = cv::Rect_(0.0, 0.0, 1.0, 1.0)); bool DetectFaces(vector >& o_regions, const cv::Mat_& intensity, cv::CascadeClassifier& classifier, float min_width = -1, cv::Rect_ roi = cv::Rect_(0.0, 0.0, 1.0, 1.0)); // 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), float min_width = -1, cv::Rect_ roi = cv::Rect_(0.0, 0.0, 1.0, 1.0)); // Face detection using HOG-SVM classifier bool DetectFacesHOG(vector >& o_regions, const cv::Mat_& intensity, std::vector& confidences, float min_width = -1, cv::Rect_ roi = cv::Rect_(0.0, 0.0, 1.0, 1.0)); bool DetectFacesHOG(vector >& o_regions, const cv::Mat_& intensity, dlib::frontal_face_detector& classifier, std::vector& confidences, float min_width = -1, cv::Rect_ roi = cv::Rect_(0.0, 0.0, 1.0, 1.0)); // 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, float& confidence, const cv::Point preference = cv::Point(-1, -1), float min_width = -1, cv::Rect_ roi = cv::Rect_(0.0, 0.0, 1.0, 1.0)); // Face detection using Multi-task Convolutional Neural Network bool DetectFacesMTCNN(vector >& o_regions, const cv::Mat& image, LandmarkDetector::FaceDetectorMTCNN& detector, 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 DetectSingleFaceMTCNN(cv::Rect_& o_region, const cv::Mat& image, LandmarkDetector::FaceDetectorMTCNN& detector, float& 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 // LANDMARK_DETECTOR_UTILS_H