555 lines
18 KiB
C++
555 lines
18 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: 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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#include <Face_utils.h>
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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#include <opencv2/imgproc.hpp>
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#include <opencv2/calib3d.hpp>
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// For FHOG visualisation
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#include <dlib/opencv.h>
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#include <dlib/image_processing/frontal_face_detector.h>
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using namespace std;
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namespace FaceAnalysis
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{
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// Pick only the more stable/rigid points under changes of expression
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void extract_rigid_points(cv::Mat_<float>& source_points, cv::Mat_<float>& destination_points)
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{
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if(source_points.rows == 68)
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{
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cv::Mat_<float> tmp_source = source_points.clone();
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source_points = cv::Mat_<float>();
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// Push back the rigid points (some face outline, eyes, and nose)
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source_points.push_back(tmp_source.row(1));
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source_points.push_back(tmp_source.row(2));
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source_points.push_back(tmp_source.row(3));
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source_points.push_back(tmp_source.row(4));
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source_points.push_back(tmp_source.row(12));
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source_points.push_back(tmp_source.row(13));
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source_points.push_back(tmp_source.row(14));
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source_points.push_back(tmp_source.row(15));
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source_points.push_back(tmp_source.row(27));
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source_points.push_back(tmp_source.row(28));
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source_points.push_back(tmp_source.row(29));
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source_points.push_back(tmp_source.row(31));
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source_points.push_back(tmp_source.row(32));
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source_points.push_back(tmp_source.row(33));
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source_points.push_back(tmp_source.row(34));
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source_points.push_back(tmp_source.row(35));
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source_points.push_back(tmp_source.row(36));
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source_points.push_back(tmp_source.row(39));
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source_points.push_back(tmp_source.row(40));
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source_points.push_back(tmp_source.row(41));
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source_points.push_back(tmp_source.row(42));
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source_points.push_back(tmp_source.row(45));
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source_points.push_back(tmp_source.row(46));
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source_points.push_back(tmp_source.row(47));
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cv::Mat_<float> tmp_dest = destination_points.clone();
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destination_points = cv::Mat_<float>();
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// Push back the rigid points
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destination_points.push_back(tmp_dest.row(1));
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destination_points.push_back(tmp_dest.row(2));
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destination_points.push_back(tmp_dest.row(3));
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destination_points.push_back(tmp_dest.row(4));
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destination_points.push_back(tmp_dest.row(12));
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destination_points.push_back(tmp_dest.row(13));
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destination_points.push_back(tmp_dest.row(14));
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destination_points.push_back(tmp_dest.row(15));
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destination_points.push_back(tmp_dest.row(27));
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destination_points.push_back(tmp_dest.row(28));
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destination_points.push_back(tmp_dest.row(29));
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destination_points.push_back(tmp_dest.row(31));
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destination_points.push_back(tmp_dest.row(32));
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destination_points.push_back(tmp_dest.row(33));
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destination_points.push_back(tmp_dest.row(34));
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destination_points.push_back(tmp_dest.row(35));
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destination_points.push_back(tmp_dest.row(36));
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destination_points.push_back(tmp_dest.row(39));
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destination_points.push_back(tmp_dest.row(40));
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destination_points.push_back(tmp_dest.row(41));
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destination_points.push_back(tmp_dest.row(42));
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destination_points.push_back(tmp_dest.row(45));
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destination_points.push_back(tmp_dest.row(46));
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destination_points.push_back(tmp_dest.row(47));
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}
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}
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// Aligning a face to a common reference frame
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void AlignFace(cv::Mat& aligned_face, const cv::Mat& frame, const cv::Mat_<float>& detected_landmarks, cv::Vec6f params_global, const PDM& pdm, bool rigid, float sim_scale, int out_width, int out_height)
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{
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// Will warp to scaled mean shape
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cv::Mat_<float> similarity_normalised_shape = pdm.mean_shape * sim_scale;
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// Discard the z component
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similarity_normalised_shape = similarity_normalised_shape(cv::Rect(0, 0, 1, 2*similarity_normalised_shape.rows/3)).clone();
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cv::Mat_<float> source_landmarks = detected_landmarks.reshape(1, 2).t();
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cv::Mat_<float> destination_landmarks = similarity_normalised_shape.reshape(1, 2).t();
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// Aligning only the more rigid points
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if(rigid)
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{
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extract_rigid_points(source_landmarks, destination_landmarks);
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}
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cv::Matx22f scale_rot_matrix = AlignShapesWithScale(source_landmarks, destination_landmarks);
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cv::Matx23f warp_matrix;
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warp_matrix(0,0) = scale_rot_matrix(0,0);
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warp_matrix(0,1) = scale_rot_matrix(0,1);
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warp_matrix(1,0) = scale_rot_matrix(1,0);
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warp_matrix(1,1) = scale_rot_matrix(1,1);
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float tx = params_global[4];
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float ty = params_global[5];
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cv::Vec2f T(tx, ty);
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T = scale_rot_matrix * T;
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// Make sure centering is correct
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warp_matrix(0,2) = -T(0) + out_width/2;
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warp_matrix(1,2) = -T(1) + out_height/2;
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cv::warpAffine(frame, aligned_face, warp_matrix, cv::Size(out_width, out_height), cv::INTER_LINEAR);
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}
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// Aligning a face to a common reference frame
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void AlignFaceMask(cv::Mat& aligned_face, const cv::Mat& frame, const cv::Mat_<float>& detected_landmarks, cv::Vec6f params_global, const PDM& pdm, const cv::Mat_<int>& triangulation, bool rigid, float sim_scale, int out_width, int out_height)
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{
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// Will warp to scaled mean shape
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cv::Mat_<float> similarity_normalised_shape = pdm.mean_shape * sim_scale;
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// Discard the z component
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similarity_normalised_shape = similarity_normalised_shape(cv::Rect(0, 0, 1, 2*similarity_normalised_shape.rows/3)).clone();
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cv::Mat_<float> source_landmarks = detected_landmarks.reshape(1, 2).t();
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cv::Mat_<float> destination_landmarks = similarity_normalised_shape.reshape(1, 2).t();
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// Aligning only the more rigid points
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if(rigid)
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{
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extract_rigid_points(source_landmarks, destination_landmarks);
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}
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cv::Matx22f scale_rot_matrix = AlignShapesWithScale(source_landmarks, destination_landmarks);
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cv::Matx23f warp_matrix;
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warp_matrix(0,0) = scale_rot_matrix(0,0);
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warp_matrix(0,1) = scale_rot_matrix(0,1);
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warp_matrix(1,0) = scale_rot_matrix(1,0);
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warp_matrix(1,1) = scale_rot_matrix(1,1);
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float tx = params_global[4];
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float ty = params_global[5];
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cv::Vec2f T(tx, ty);
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T = scale_rot_matrix * T;
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// Make sure centering is correct
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warp_matrix(0,2) = -T(0) + out_width/2;
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warp_matrix(1,2) = -T(1) + out_height/2;
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cv::warpAffine(frame, aligned_face, warp_matrix, cv::Size(out_width, out_height), cv::INTER_LINEAR);
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// Move the destination landmarks there as well
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cv::Matx22f warp_matrix_2d(warp_matrix(0,0), warp_matrix(0,1), warp_matrix(1,0), warp_matrix(1,1));
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destination_landmarks = cv::Mat(detected_landmarks.reshape(1, 2).t()) * cv::Mat(warp_matrix_2d).t();
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destination_landmarks.col(0) = destination_landmarks.col(0) + warp_matrix(0,2);
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destination_landmarks.col(1) = destination_landmarks.col(1) + warp_matrix(1,2);
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// Move the eyebrows up to include more of upper face
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destination_landmarks.at<float>(0,1) -= (30/0.7)*sim_scale;
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destination_landmarks.at<float>(16,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(17,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(18,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(19,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(20,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(21,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(22,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(23,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(24,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(25,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks.at<float>(26,1) -= (30 / 0.7)*sim_scale;
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destination_landmarks = cv::Mat(destination_landmarks.t()).reshape(1, 1).t();
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FaceAnalysis::PAW paw(destination_landmarks, triangulation, 0, 0, aligned_face.cols-1, aligned_face.rows-1);
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// Mask each of the channels (a bit of a roundabout way, but OpenCV 3.1 in debug mode doesn't seem to be able to handle a more direct way using split and merge)
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vector<cv::Mat> aligned_face_channels(aligned_face.channels());
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for (int c = 0; c < aligned_face.channels(); ++c)
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{
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cv::extractChannel(aligned_face, aligned_face_channels[c], c);
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}
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for(size_t i = 0; i < aligned_face_channels.size(); ++i)
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{
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cv::multiply(aligned_face_channels[i], paw.pixel_mask, aligned_face_channels[i], 1.0, CV_8U);
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}
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if(aligned_face.channels() == 3)
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{
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cv::Mat planes[] = { aligned_face_channels[0], aligned_face_channels[1], aligned_face_channels[2] };
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cv::merge(planes, 3, aligned_face);
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}
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else
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{
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aligned_face = aligned_face_channels[0];
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}
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}
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// Create a row vector Felzenszwalb HOG descriptor from a given image
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void Extract_FHOG_descriptor(cv::Mat_<double>& descriptor, const cv::Mat& image, int& num_rows, int& num_cols, int cell_size)
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{
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dlib::array2d<dlib::matrix<float,31,1> > hog;
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if(image.channels() == 1)
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{
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dlib::cv_image<uchar> dlib_warped_img(image);
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dlib::extract_fhog_features(dlib_warped_img, hog, cell_size);
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}
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else
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{
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dlib::cv_image<dlib::bgr_pixel> dlib_warped_img(image);
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dlib::extract_fhog_features(dlib_warped_img, hog, cell_size);
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}
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// Convert to a usable format
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num_cols = hog.nc();
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num_rows = hog.nr();
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descriptor = cv::Mat_<double>(1, num_cols * num_rows * 31);
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cv::MatIterator_<double> descriptor_it = descriptor.begin();
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for(int y = 0; y < num_cols; ++y)
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{
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for(int x = 0; x < num_rows; ++x)
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{
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for(unsigned int o = 0; o < 31; ++o)
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{
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*descriptor_it++ = (double)hog[y][x](o);
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}
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}
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}
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}
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// Extract summary statistics (mean, stdev, min, max) from each dimension of a descriptor, each row is a descriptor
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void ExtractSummaryStatistics(const cv::Mat_<double>& descriptors, cv::Mat_<double>& sum_stats, bool use_mean, bool use_stdev, bool use_max_min)
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{
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// Using four summary statistics at the moment
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// Means, stds, mins, maxs
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int num_stats = 0;
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if(use_mean)
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num_stats++;
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if(use_stdev)
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num_stats++;
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if(use_max_min)
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num_stats++;
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sum_stats = cv::Mat_<double>(1, descriptors.cols * num_stats, 0.0);
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for(int i = 0; i < descriptors.cols; ++i)
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{
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cv::Scalar mean, stdev;
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cv::meanStdDev(descriptors.col(i), mean, stdev);
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int add = 0;
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if(use_mean)
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{
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sum_stats.at<double>(0, i*num_stats + add) = mean[0];
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add++;
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}
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if(use_stdev)
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{
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sum_stats.at<double>(0, i*num_stats + add) = stdev[0];
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add++;
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}
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if(use_max_min)
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{
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double min, max;
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cv::minMaxIdx(descriptors.col(i), &min, &max);
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sum_stats.at<double>(0, i*num_stats + add) = max - min;
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add++;
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}
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}
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}
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void AddDescriptor(cv::Mat_<double>& descriptors, cv::Mat_<double> new_descriptor, int curr_frame, int num_frames_to_keep)
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{
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if(descriptors.empty())
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{
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descriptors = cv::Mat_<double>(num_frames_to_keep, new_descriptor.cols, 0.0);
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}
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int row_to_change = curr_frame % num_frames_to_keep;
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new_descriptor.copyTo(descriptors.row(row_to_change));
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}
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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::Matx22f AlignShapesKabsch2D(const cv::Mat_<float>& align_from, const cv::Mat_<float>& align_to)
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{
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cv::SVD svd(align_from.t() * align_to);
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// make sure no reflection is there
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// corr ensures that we do only rotaitons and not reflections
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float d = cv::determinant(svd.vt.t() * svd.u.t());
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cv::Matx22f corr = cv::Matx22f::eye();
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if (d > 0)
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{
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corr(1, 1) = 1;
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}
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else
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{
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corr(1, 1) = -1;
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}
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cv::Matx22f R;
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cv::Mat(svd.vt.t()*cv::Mat(corr)*svd.u.t()).copyTo(R);
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return R;
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}
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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::Matx22f AlignShapesWithScale(cv::Mat_<float>& src, cv::Mat_<float> dst)
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{
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int n = src.rows;
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// First we mean normalise both src and dst
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float mean_src_x = cv::mean(src.col(0))[0];
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float mean_src_y = cv::mean(src.col(1))[0];
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float mean_dst_x = cv::mean(dst.col(0))[0];
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float mean_dst_y = cv::mean(dst.col(1))[0];
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cv::Mat_<float> src_mean_normed = src.clone();
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src_mean_normed.col(0) = src_mean_normed.col(0) - mean_src_x;
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src_mean_normed.col(1) = src_mean_normed.col(1) - mean_src_y;
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cv::Mat_<float> dst_mean_normed = dst.clone();
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dst_mean_normed.col(0) = dst_mean_normed.col(0) - mean_dst_x;
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dst_mean_normed.col(1) = dst_mean_normed.col(1) - mean_dst_y;
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// Find the scaling factor of each
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cv::Mat src_sq;
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cv::pow(src_mean_normed, 2, src_sq);
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cv::Mat dst_sq;
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cv::pow(dst_mean_normed, 2, dst_sq);
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float s_src = sqrt(cv::sum(src_sq)[0] / n);
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float s_dst = sqrt(cv::sum(dst_sq)[0] / n);
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src_mean_normed = src_mean_normed / s_src;
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dst_mean_normed = dst_mean_normed / s_dst;
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float s = s_dst / s_src;
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// Get the rotation
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cv::Matx22f R = AlignShapesKabsch2D(src_mean_normed, dst_mean_normed);
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cv::Matx22f A;
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cv::Mat(s * R).copyTo(A);
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cv::Mat_<float> aligned = (cv::Mat(cv::Mat(A) * src.t())).t();
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cv::Mat_<float> offset = dst - aligned;
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float t_x = cv::mean(offset.col(0))[0];
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float t_y = cv::mean(offset.col(1))[0];
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return A;
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}
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//===========================================================================
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// Visualisation functions, TODO rem
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//===========================================================================
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void Project(cv::Mat_<float>& dest, const cv::Mat_<float>& mesh, float fx, float fy, float cx, float cy)
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{
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dest = cv::Mat_<float>(mesh.rows, 2, 0.0);
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int num_points = mesh.rows;
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float X, Y, Z;
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cv::Mat_<float>::const_iterator mData = mesh.begin();
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cv::Mat_<float>::iterator projected = dest.begin();
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for (int i = 0; i < num_points; i++)
|
|
{
|
|
// Get the points
|
|
X = *(mData++);
|
|
Y = *(mData++);
|
|
Z = *(mData++);
|
|
|
|
float x;
|
|
float y;
|
|
|
|
// if depth is 0 the projection is different
|
|
if (Z != 0)
|
|
{
|
|
x = ((X * fx / Z) + cx);
|
|
y = ((Y * fy / Z) + cy);
|
|
}
|
|
else
|
|
{
|
|
x = X;
|
|
y = Y;
|
|
}
|
|
|
|
// Project and store in dest matrix
|
|
(*projected++) = x;
|
|
(*projected++) = y;
|
|
}
|
|
|
|
}
|
|
|
|
|
|
//============================================================================
|
|
// Matrix reading functionality
|
|
//============================================================================
|
|
|
|
// Reading in a matrix from a stream
|
|
void ReadMat(std::ifstream& stream, cv::Mat &output_mat)
|
|
{
|
|
// Read in the number of rows, columns and the data type
|
|
int row, col, type;
|
|
|
|
stream >> row >> col >> type;
|
|
|
|
output_mat = cv::Mat(row, col, type);
|
|
|
|
switch (output_mat.type())
|
|
{
|
|
case CV_64FC1:
|
|
{
|
|
cv::MatIterator_<double> begin_it = output_mat.begin<double>();
|
|
cv::MatIterator_<double> end_it = output_mat.end<double>();
|
|
|
|
while (begin_it != end_it)
|
|
{
|
|
stream >> *begin_it++;
|
|
}
|
|
}
|
|
break;
|
|
case CV_32FC1:
|
|
{
|
|
cv::MatIterator_<float> begin_it = output_mat.begin<float>();
|
|
cv::MatIterator_<float> end_it = output_mat.end<float>();
|
|
|
|
while (begin_it != end_it)
|
|
{
|
|
stream >> *begin_it++;
|
|
}
|
|
}
|
|
break;
|
|
case CV_32SC1:
|
|
{
|
|
cv::MatIterator_<int> begin_it = output_mat.begin<int>();
|
|
cv::MatIterator_<int> end_it = output_mat.end<int>();
|
|
while (begin_it != end_it)
|
|
{
|
|
stream >> *begin_it++;
|
|
}
|
|
}
|
|
break;
|
|
case CV_8UC1:
|
|
{
|
|
cv::MatIterator_<uchar> begin_it = output_mat.begin<uchar>();
|
|
cv::MatIterator_<uchar> end_it = output_mat.end<uchar>();
|
|
while (begin_it != end_it)
|
|
{
|
|
stream >> *begin_it++;
|
|
}
|
|
}
|
|
break;
|
|
default:
|
|
printf("ERROR(%s,%d) : Unsupported Matrix type %d!\n", __FILE__, __LINE__, output_mat.type()); abort();
|
|
|
|
|
|
}
|
|
}
|
|
|
|
void ReadMatBin(std::ifstream& stream, cv::Mat &output_mat)
|
|
{
|
|
// Read in the number of rows, columns and the data type
|
|
int row, col, type;
|
|
|
|
stream.read((char*)&row, 4);
|
|
stream.read((char*)&col, 4);
|
|
stream.read((char*)&type, 4);
|
|
|
|
output_mat = cv::Mat(row, col, type);
|
|
int size = output_mat.rows * output_mat.cols * output_mat.elemSize();
|
|
stream.read((char *)output_mat.data, size);
|
|
|
|
}
|
|
|
|
// Skipping lines that start with # (together with empty lines)
|
|
void SkipComments(std::ifstream& stream)
|
|
{
|
|
while (stream.peek() == '#' || stream.peek() == '\n' || stream.peek() == ' ' || stream.peek() == '\r')
|
|
{
|
|
std::string skipped;
|
|
std::getline(stream, skipped);
|
|
}
|
|
}
|
|
|
|
|
|
} |