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110 lines
7.0 KiB
C++
110 lines
7.0 KiB
C++
///////////////////////////////////////////////////////////////////////////////
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// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
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// all rights reserved.
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//
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// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
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//
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// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
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// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
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//
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// License can be found in OpenFace-license.txt
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//
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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 2.0: Facial Behavior Analysis Toolkit
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// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
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// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
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//
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// Convolutional experts constrained local model for facial landmark detection.
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// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
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// in Computer Vision and Pattern Recognition Workshops, 2017.
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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-specific 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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///////////////////////////////////////////////////////////////////////////////
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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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#include "FaceDetectorMTCNN.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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// 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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// 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)
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void ExtractBoundingBox(const cv::Mat_<float>& landmarks, float &min_x, float &max_x, float &min_y, float &max_y);
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vector<cv::Point2f> CalculateVisibleLandmarks(const cv::Mat_<float>& shape2D, const cv::Mat_<int>& visibilities);
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vector<cv::Point2f> CalculateVisibleLandmarks(const CLNF& clnf_model);
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vector<cv::Point2f> CalculateVisibleEyeLandmarks(const CLNF& clnf_model);
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vector<cv::Point2f> CalculateAllLandmarks(const cv::Mat_<float>& shape2D);
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vector<cv::Point2f> CalculateAllLandmarks(const CLNF& clnf_model);
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vector<cv::Point2f> CalculateAllEyeLandmarks(const CLNF& clnf_model);
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vector<cv::Point3f> Calculate3DEyeLandmarks(const CLNF& clnf_model, float fx, float fy, float cx, float cy);
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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_<float> >& o_regions, const cv::Mat_<uchar>& intensity, float min_width = -1, cv::Rect_<float> roi = cv::Rect_<float>(0.0, 0.0, 1.0, 1.0));
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bool DetectFaces(vector<cv::Rect_<float> >& o_regions, const cv::Mat_<uchar>& intensity, cv::CascadeClassifier& classifier, float min_width = -1, cv::Rect_<float> roi = cv::Rect_<float>(0.0, 0.0, 1.0, 1.0));
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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_<float>& o_region, const cv::Mat_<uchar>& intensity, cv::CascadeClassifier& classifier, const cv::Point preference = cv::Point(-1, -1), float min_width = -1, cv::Rect_<float> roi = cv::Rect_<float>(0.0, 0.0, 1.0, 1.0));
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// Face detection using HOG-SVM classifier
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bool DetectFacesHOG(vector<cv::Rect_<float> >& o_regions, const cv::Mat_<uchar>& intensity, std::vector<float>& confidences, float min_width = -1, cv::Rect_<float> roi = cv::Rect_<float>(0.0, 0.0, 1.0, 1.0));
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bool DetectFacesHOG(vector<cv::Rect_<float> >& o_regions, const cv::Mat_<uchar>& intensity, dlib::frontal_face_detector& classifier, std::vector<float>& confidences, float min_width = -1, cv::Rect_<float> roi = cv::Rect_<float>(0.0, 0.0, 1.0, 1.0));
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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_<float>& o_region, const cv::Mat_<uchar>& intensity, dlib::frontal_face_detector& classifier, float& confidence, const cv::Point preference = cv::Point(-1, -1), float min_width = -1, cv::Rect_<float> roi = cv::Rect_<float>(0.0, 0.0, 1.0, 1.0));
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// Face detection using Multi-task Convolutional Neural Network
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bool DetectFacesMTCNN(vector<cv::Rect_<float> >& o_regions, const cv::Mat& image, LandmarkDetector::FaceDetectorMTCNN& detector, std::vector<float>& 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 DetectSingleFaceMTCNN(cv::Rect_<float>& o_region, const cv::Mat& image, LandmarkDetector::FaceDetectorMTCNN& detector, float& 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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