601 lines
25 KiB
C++
601 lines
25 KiB
C++
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///////////////////////////////////////////////////////////////////////////////
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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 <20>AS IS<49> 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 <20>open source<63> software licenses (<28>Open Source
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// Components<74>), 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<65>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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 "stdafx.h"
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#include <LandmarkDetectorFunc.h>
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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#include <opencv2/calib3d.hpp>
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#include <opencv2/imgproc.hpp>
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// System includes
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#include <vector>
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using namespace LandmarkDetector;
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// Getting a head pose estimate from the currently detected landmarks (rotation with respect to point camera)
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// The format returned is [Tx, Ty, Tz, Eul_x, Eul_y, Eul_z]
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cv::Vec6d LandmarkDetector::GetPoseCamera(const CLNF& clnf_model, double fx, double fy, double cx, double cy)
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{
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if(!clnf_model.detected_landmarks.empty() && clnf_model.params_global[0] != 0)
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{
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double Z = fx / clnf_model.params_global[0];
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double X = ((clnf_model.params_global[4] - cx) * (1.0/fx)) * Z;
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double Y = ((clnf_model.params_global[5] - cy) * (1.0/fy)) * Z;
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return cv::Vec6d(X, Y, Z, clnf_model.params_global[1], clnf_model.params_global[2], clnf_model.params_global[3]);
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}
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else
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{
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return cv::Vec6d(0,0,0,0,0,0);
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}
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}
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// Getting a head pose estimate from the currently detected landmarks (rotation in world coordinates)
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// The format returned is [Tx, Ty, Tz, Eul_x, Eul_y, Eul_z]
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cv::Vec6d LandmarkDetector::GetPoseWorld(const CLNF& clnf_model, double fx, double fy, double cx, double cy)
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{
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if(!clnf_model.detected_landmarks.empty() && clnf_model.params_global[0] != 0)
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{
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double Z = fx / clnf_model.params_global[0];
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double X = ((clnf_model.params_global[4] - cx) * (1.0/fx)) * Z;
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double Y = ((clnf_model.params_global[5] - cy) * (1.0/fy)) * Z;
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// Here we correct for the camera orientation, for this need to determine the angle the camera makes with the head pose
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double z_x = cv::sqrt(X * X + Z * Z);
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double eul_x = atan2(Y, z_x);
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double z_y = cv::sqrt(Y * Y + Z * Z);
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double eul_y = -atan2(X, z_y);
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cv::Matx33d camera_rotation = LandmarkDetector::Euler2RotationMatrix(cv::Vec3d(eul_x, eul_y, 0));
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cv::Matx33d head_rotation = LandmarkDetector::AxisAngle2RotationMatrix(cv::Vec3d(clnf_model.params_global[1], clnf_model.params_global[2], clnf_model.params_global[3]));
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cv::Matx33d corrected_rotation = camera_rotation.t() * head_rotation;
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cv::Vec3d euler_corrected = LandmarkDetector::RotationMatrix2Euler(corrected_rotation);
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return cv::Vec6d(X, Y, Z, euler_corrected[0], euler_corrected[1], euler_corrected[2]);
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}
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else
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{
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return cv::Vec6d(0,0,0,0,0,0);
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}
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}
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// Getting a head pose estimate from the currently detected landmarks, with appropriate correction due to orthographic camera issue
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// This is because rotation estimate under orthographic assumption is only correct close to the centre of the image
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// This method returns a corrected pose estimate with respect to world coordinates (Experimental)
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// The format returned is [Tx, Ty, Tz, Eul_x, Eul_y, Eul_z]
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cv::Vec6d LandmarkDetector::GetCorrectedPoseWorld(const CLNF& clnf_model, double fx, double fy, double cx, double cy)
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{
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if(!clnf_model.detected_landmarks.empty() && clnf_model.params_global[0] != 0)
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{
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// This is used as an initial estimate for the iterative PnP algorithm
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double Z = fx / clnf_model.params_global[0];
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double X = ((clnf_model.params_global[4] - cx) * (1.0/fx)) * Z;
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double Y = ((clnf_model.params_global[5] - cy) * (1.0/fy)) * Z;
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// Correction for orientation
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// 2D points
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cv::Mat_<double> landmarks_2D = clnf_model.detected_landmarks;
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landmarks_2D = landmarks_2D.reshape(1, 2).t();
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// 3D points
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cv::Mat_<double> landmarks_3D;
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clnf_model.pdm.CalcShape3D(landmarks_3D, clnf_model.params_local);
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landmarks_3D = landmarks_3D.reshape(1, 3).t();
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// Solving the PNP model
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// The camera matrix
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cv::Matx33d camera_matrix(fx, 0, cx, 0, fy, cy, 0, 0, 1);
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cv::Vec3d vec_trans(X, Y, Z);
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cv::Vec3d vec_rot(clnf_model.params_global[1], clnf_model.params_global[2], clnf_model.params_global[3]);
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cv::solvePnP(landmarks_3D, landmarks_2D, camera_matrix, cv::Mat(), vec_rot, vec_trans, true);
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cv::Vec3d euler = LandmarkDetector::AxisAngle2Euler(vec_rot);
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return cv::Vec6d(vec_trans[0], vec_trans[1], vec_trans[2], vec_rot[0], vec_rot[1], vec_rot[2]);
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}
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else
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{
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return cv::Vec6d(0,0,0,0,0,0);
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}
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}
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// Getting a head pose estimate from the currently detected landmarks, with appropriate correction due to perspective projection
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// This method returns a corrected pose estimate with respect to a point camera (NOTE not the world coordinates) (Experimental)
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// The format returned is [Tx, Ty, Tz, Eul_x, Eul_y, Eul_z]
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cv::Vec6d LandmarkDetector::GetCorrectedPoseCamera(const CLNF& clnf_model, double fx, double fy, double cx, double cy)
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{
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if(!clnf_model.detected_landmarks.empty() && clnf_model.params_global[0] != 0)
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{
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double Z = fx / clnf_model.params_global[0];
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double X = ((clnf_model.params_global[4] - cx) * (1.0/fx)) * Z;
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double Y = ((clnf_model.params_global[5] - cy) * (1.0/fy)) * Z;
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// Correction for orientation
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// 3D points
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cv::Mat_<double> landmarks_3D;
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clnf_model.pdm.CalcShape3D(landmarks_3D, clnf_model.params_local);
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landmarks_3D = landmarks_3D.reshape(1, 3).t();
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// 2D points
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cv::Mat_<double> landmarks_2D = clnf_model.detected_landmarks;
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landmarks_2D = landmarks_2D.reshape(1, 2).t();
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// Solving the PNP model
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// The camera matrix
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cv::Matx33d camera_matrix(fx, 0, cx, 0, fy, cy, 0, 0, 1);
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cv::Vec3d vec_trans(X, Y, Z);
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cv::Vec3d vec_rot(clnf_model.params_global[1], clnf_model.params_global[2], clnf_model.params_global[3]);
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cv::solvePnP(landmarks_3D, landmarks_2D, camera_matrix, cv::Mat(), vec_rot, vec_trans, true);
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// Here we correct for the camera orientation, for this need to determine the angle the camera makes with the head pose
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double z_x = cv::sqrt(vec_trans[0] * vec_trans[0] + vec_trans[2] * vec_trans[2]);
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double eul_x = atan2(vec_trans[1], z_x);
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double z_y = cv::sqrt(vec_trans[1] * vec_trans[1] + vec_trans[2] * vec_trans[2]);
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double eul_y = -atan2(vec_trans[0], z_y);
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cv::Matx33d camera_rotation = LandmarkDetector::Euler2RotationMatrix(cv::Vec3d(eul_x, eul_y, 0));
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cv::Matx33d head_rotation = LandmarkDetector::AxisAngle2RotationMatrix(vec_rot);
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cv::Matx33d corrected_rotation = camera_rotation * head_rotation;
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cv::Vec3d euler_corrected = LandmarkDetector::RotationMatrix2Euler(corrected_rotation);
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return cv::Vec6d(vec_trans[0], vec_trans[1], vec_trans[2], euler_corrected[0], euler_corrected[1], euler_corrected[2]);
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}
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else
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{
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return cv::Vec6d(0,0,0,0,0,0);
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}
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}
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// If landmark detection in video succeeded create a template for use in simple tracking
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void UpdateTemplate(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model)
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{
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cv::Rect bounding_box;
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clnf_model.pdm.CalcBoundingBox(bounding_box, clnf_model.params_global, clnf_model.params_local);
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// Make sure the box is not out of bounds
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bounding_box = bounding_box & cv::Rect(0, 0, grayscale_image.cols, grayscale_image.rows);
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clnf_model.face_template = grayscale_image(bounding_box).clone();
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}
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// This method uses basic template matching in order to allow for better tracking of fast moving faces
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void CorrectGlobalParametersVideo(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, const FaceModelParameters& params)
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{
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cv::Rect init_box;
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clnf_model.pdm.CalcBoundingBox(init_box, clnf_model.params_global, clnf_model.params_local);
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cv::Rect roi(init_box.x - init_box.width/2, init_box.y - init_box.height/2, init_box.width * 2, init_box.height * 2);
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roi = roi & cv::Rect(0, 0, grayscale_image.cols, grayscale_image.rows);
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int off_x = roi.x;
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int off_y = roi.y;
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double scaling = params.face_template_scale / clnf_model.params_global[0];
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cv::Mat_<uchar> image;
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if(scaling < 1)
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{
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cv::resize(clnf_model.face_template, clnf_model.face_template, cv::Size(), scaling, scaling);
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cv::resize(grayscale_image(roi), image, cv::Size(), scaling, scaling);
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}
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else
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{
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scaling = 1;
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image = grayscale_image(roi).clone();
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}
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// Resizing the template
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cv::Mat corr_out;
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cv::matchTemplate(image, clnf_model.face_template, corr_out, CV_TM_CCOEFF_NORMED);
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// Actually matching it
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//double min, max;
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int max_loc[2];
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cv::minMaxIdx(corr_out, NULL, NULL, NULL, max_loc);
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cv::Rect_<double> out_bbox(max_loc[1]/scaling + off_x, max_loc[0]/scaling + off_y, clnf_model.face_template.rows / scaling, clnf_model.face_template.cols / scaling);
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double shift_x = out_bbox.x - (double)init_box.x;
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double shift_y = out_bbox.y - (double)init_box.y;
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clnf_model.params_global[4] = clnf_model.params_global[4] + shift_x;
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clnf_model.params_global[5] = clnf_model.params_global[5] + shift_y;
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}
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bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, CLNF& clnf_model, FaceModelParameters& params)
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{
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// First need to decide if the landmarks should be "detected" or "tracked"
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// Detected means running face detection and a larger search area, tracked means initialising from previous step
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// and using a smaller search area
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// Indicating that this is a first detection in video sequence or after restart
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bool initial_detection = !clnf_model.tracking_initialised;
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// Only do it if there was a face detection at all
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if(clnf_model.tracking_initialised)
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|
{
|
|||
|
|
|
|||
|
|
// The area of interest search size will depend if the previous track was successful
|
|||
|
|
if(!clnf_model.detection_success)
|
|||
|
|
{
|
|||
|
|
params.window_sizes_current = params.window_sizes_init;
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
params.window_sizes_current = params.window_sizes_small;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Before the expensive landmark detection step apply a quick template tracking approach
|
|||
|
|
if(params.use_face_template && !clnf_model.face_template.empty() && clnf_model.detection_success)
|
|||
|
|
{
|
|||
|
|
CorrectGlobalParametersVideo(grayscale_image, clnf_model, params);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
bool track_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);
|
|||
|
|
if(!track_success)
|
|||
|
|
{
|
|||
|
|
// Make a record that tracking failed
|
|||
|
|
clnf_model.failures_in_a_row++;
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
// indicate that tracking is a success
|
|||
|
|
clnf_model.failures_in_a_row = -1;
|
|||
|
|
UpdateTemplate(grayscale_image, clnf_model);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// This is used for both detection (if it the tracking has not been initialised yet) or if the tracking failed (however we do this every n frames, for speed)
|
|||
|
|
// This also has the effect of an attempt to reinitialise just after the tracking has failed, which is useful during large motions
|
|||
|
|
if((!clnf_model.tracking_initialised && (clnf_model.failures_in_a_row + 1) % (params.reinit_video_every * 6) == 0)
|
|||
|
|
|| (clnf_model.tracking_initialised && !clnf_model.detection_success && params.reinit_video_every > 0 && clnf_model.failures_in_a_row % params.reinit_video_every == 0))
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
cv::Rect_<double> bounding_box;
|
|||
|
|
|
|||
|
|
// If the face detector has not been initialised read it in
|
|||
|
|
if(clnf_model.face_detector_HAAR.empty())
|
|||
|
|
{
|
|||
|
|
clnf_model.face_detector_HAAR.load(params.face_detector_location);
|
|||
|
|
clnf_model.face_detector_location = params.face_detector_location;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
cv::Point preference_det(-1, -1);
|
|||
|
|
if(clnf_model.preference_det.x != -1 && clnf_model.preference_det.y != -1)
|
|||
|
|
{
|
|||
|
|
preference_det.x = clnf_model.preference_det.x * grayscale_image.cols;
|
|||
|
|
preference_det.y = clnf_model.preference_det.y * grayscale_image.rows;
|
|||
|
|
clnf_model.preference_det = cv::Point(-1, -1);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
bool face_detection_success;
|
|||
|
|
if(params.curr_face_detector == FaceModelParameters::HOG_SVM_DETECTOR)
|
|||
|
|
{
|
|||
|
|
double confidence;
|
|||
|
|
face_detection_success = LandmarkDetector::DetectSingleFaceHOG(bounding_box, grayscale_image, clnf_model.face_detector_HOG, confidence, preference_det);
|
|||
|
|
}
|
|||
|
|
else if(params.curr_face_detector == FaceModelParameters::HAAR_DETECTOR)
|
|||
|
|
{
|
|||
|
|
face_detection_success = LandmarkDetector::DetectSingleFace(bounding_box, grayscale_image, clnf_model.face_detector_HAAR, preference_det);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Attempt to detect landmarks using the detected face (if unseccessful the detection will be ignored)
|
|||
|
|
if(face_detection_success)
|
|||
|
|
{
|
|||
|
|
// Indicate that tracking has started as a face was detected
|
|||
|
|
clnf_model.tracking_initialised = true;
|
|||
|
|
|
|||
|
|
// Keep track of old model values so that they can be restored if redetection fails
|
|||
|
|
cv::Vec6d params_global_init = clnf_model.params_global;
|
|||
|
|
cv::Mat_<double> params_local_init = clnf_model.params_local.clone();
|
|||
|
|
double likelihood_init = clnf_model.model_likelihood;
|
|||
|
|
cv::Mat_<double> detected_landmarks_init = clnf_model.detected_landmarks.clone();
|
|||
|
|
cv::Mat_<double> landmark_likelihoods_init = clnf_model.landmark_likelihoods.clone();
|
|||
|
|
|
|||
|
|
// Use the detected bounding box and empty local parameters
|
|||
|
|
clnf_model.params_local.setTo(0);
|
|||
|
|
clnf_model.pdm.CalcParams(clnf_model.params_global, bounding_box, clnf_model.params_local);
|
|||
|
|
|
|||
|
|
// Make sure the search size is large
|
|||
|
|
params.window_sizes_current = params.window_sizes_init;
|
|||
|
|
|
|||
|
|
// Do the actual landmark detection (and keep it only if successful)
|
|||
|
|
bool landmark_detection_success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);
|
|||
|
|
|
|||
|
|
// If landmark reinitialisation unsucessful continue from previous estimates
|
|||
|
|
// if it's initial detection however, do not care if it was successful as the validator might be wrong, so continue trackig
|
|||
|
|
// regardless
|
|||
|
|
if(!initial_detection && !landmark_detection_success)
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
// Restore previous estimates
|
|||
|
|
clnf_model.params_global = params_global_init;
|
|||
|
|
clnf_model.params_local = params_local_init.clone();
|
|||
|
|
clnf_model.pdm.CalcShape2D(clnf_model.detected_landmarks, clnf_model.params_local, clnf_model.params_global);
|
|||
|
|
clnf_model.model_likelihood = likelihood_init;
|
|||
|
|
clnf_model.detected_landmarks = detected_landmarks_init.clone();
|
|||
|
|
clnf_model.landmark_likelihoods = landmark_likelihoods_init.clone();
|
|||
|
|
|
|||
|
|
return false;
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
clnf_model.failures_in_a_row = -1;
|
|||
|
|
UpdateTemplate(grayscale_image, clnf_model);
|
|||
|
|
return true;
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// if the model has not been initialised yet class it as a failure
|
|||
|
|
if(!clnf_model.tracking_initialised)
|
|||
|
|
{
|
|||
|
|
clnf_model.failures_in_a_row++;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// un-initialise the tracking
|
|||
|
|
if( clnf_model.failures_in_a_row > 100)
|
|||
|
|
{
|
|||
|
|
clnf_model.tracking_initialised = false;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return clnf_model.detection_success;
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> &depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
|
|||
|
|
{
|
|||
|
|
if(bounding_box.width > 0)
|
|||
|
|
{
|
|||
|
|
// calculate the local and global parameters from the generated 2D shape (mapping from the 2D to 3D because camera params are unknown)
|
|||
|
|
clnf_model.params_local.setTo(0);
|
|||
|
|
clnf_model.pdm.CalcParams(clnf_model.params_global, bounding_box, clnf_model.params_local);
|
|||
|
|
|
|||
|
|
// indicate that face was detected so initialisation is not necessary
|
|||
|
|
clnf_model.tracking_initialised = true;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return DetectLandmarksInVideo(grayscale_image, depth_image, clnf_model, params);
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
|
|||
|
|
{
|
|||
|
|
return DetectLandmarksInVideo(grayscale_image, cv::Mat_<float>(), clnf_model, params);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
bool LandmarkDetector::DetectLandmarksInVideo(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
|
|||
|
|
{
|
|||
|
|
return DetectLandmarksInVideo(grayscale_image, cv::Mat_<float>(), clnf_model, params);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
//================================================================================================================
|
|||
|
|
// Landmark detection in image, need to provide an image and optionally CLNF model together with parameters (default values work well)
|
|||
|
|
// Optionally can provide a bounding box in which detection is performed (this is useful if multiple faces are to be detected in images)
|
|||
|
|
//================================================================================================================
|
|||
|
|
|
|||
|
|
// This is the one where the actual work gets done, other DetectLandmarksInImage calls lead to this one
|
|||
|
|
bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
// Can have multiple hypotheses
|
|||
|
|
vector<cv::Vec3d> rotation_hypotheses;
|
|||
|
|
|
|||
|
|
if(params.multi_view)
|
|||
|
|
{
|
|||
|
|
// Try out different orientation initialisations
|
|||
|
|
// It is possible to add other orientation hypotheses easilly by just pushing to this vector
|
|||
|
|
rotation_hypotheses.push_back(cv::Vec3d(0,0,0));
|
|||
|
|
rotation_hypotheses.push_back(cv::Vec3d(0,0.5236,0));
|
|||
|
|
rotation_hypotheses.push_back(cv::Vec3d(0,-0.5236,0));
|
|||
|
|
rotation_hypotheses.push_back(cv::Vec3d(0.5236,0,0));
|
|||
|
|
rotation_hypotheses.push_back(cv::Vec3d(-0.5236,0,0));
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
// Assume the face is close to frontal
|
|||
|
|
rotation_hypotheses.push_back(cv::Vec3d(0,0,0));
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Use the initialisation size for the landmark detection
|
|||
|
|
params.window_sizes_current = params.window_sizes_init;
|
|||
|
|
|
|||
|
|
// Store the current best estimate
|
|||
|
|
double best_likelihood;
|
|||
|
|
cv::Vec6d best_global_parameters;
|
|||
|
|
cv::Mat_<double> best_local_parameters;
|
|||
|
|
cv::Mat_<double> best_detected_landmarks;
|
|||
|
|
cv::Mat_<double> best_landmark_likelihoods;
|
|||
|
|
bool best_success;
|
|||
|
|
|
|||
|
|
// The hierarchical model parameters
|
|||
|
|
vector<double> best_likelihood_h(clnf_model.hierarchical_models.size());
|
|||
|
|
vector<cv::Vec6d> best_global_parameters_h(clnf_model.hierarchical_models.size());
|
|||
|
|
vector<cv::Mat_<double>> best_local_parameters_h(clnf_model.hierarchical_models.size());
|
|||
|
|
vector<cv::Mat_<double>> best_detected_landmarks_h(clnf_model.hierarchical_models.size());
|
|||
|
|
vector<cv::Mat_<double>> best_landmark_likelihoods_h(clnf_model.hierarchical_models.size());
|
|||
|
|
|
|||
|
|
for(size_t hypothesis = 0; hypothesis < rotation_hypotheses.size(); ++hypothesis)
|
|||
|
|
{
|
|||
|
|
// Reset the potentially set clnf_model parameters
|
|||
|
|
clnf_model.params_local.setTo(0.0);
|
|||
|
|
|
|||
|
|
for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
|
|||
|
|
{
|
|||
|
|
clnf_model.hierarchical_models[part].params_local.setTo(0.0);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// calculate the local and global parameters from the generated 2D shape (mapping from the 2D to 3D because camera params are unknown)
|
|||
|
|
clnf_model.pdm.CalcParams(clnf_model.params_global, bounding_box, clnf_model.params_local, rotation_hypotheses[hypothesis]);
|
|||
|
|
|
|||
|
|
bool success = clnf_model.DetectLandmarks(grayscale_image, depth_image, params);
|
|||
|
|
|
|||
|
|
if(hypothesis == 0 || best_likelihood < clnf_model.model_likelihood)
|
|||
|
|
{
|
|||
|
|
best_likelihood = clnf_model.model_likelihood;
|
|||
|
|
best_global_parameters = clnf_model.params_global;
|
|||
|
|
best_local_parameters = clnf_model.params_local.clone();
|
|||
|
|
best_detected_landmarks = clnf_model.detected_landmarks.clone();
|
|||
|
|
best_landmark_likelihoods = clnf_model.landmark_likelihoods.clone();
|
|||
|
|
best_success = success;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
|
|||
|
|
{
|
|||
|
|
if (hypothesis == 0 || best_likelihood < clnf_model.hierarchical_models[part].model_likelihood)
|
|||
|
|
{
|
|||
|
|
best_likelihood_h[part] = clnf_model.hierarchical_models[part].model_likelihood;
|
|||
|
|
best_global_parameters_h[part] = clnf_model.hierarchical_models[part].params_global;
|
|||
|
|
best_local_parameters_h[part] = clnf_model.hierarchical_models[part].params_local.clone();
|
|||
|
|
best_detected_landmarks_h[part] = clnf_model.hierarchical_models[part].detected_landmarks.clone();
|
|||
|
|
best_landmark_likelihoods_h[part] = clnf_model.hierarchical_models[part].landmark_likelihoods.clone();
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Store the best estimates in the clnf_model
|
|||
|
|
clnf_model.model_likelihood = best_likelihood;
|
|||
|
|
clnf_model.params_global = best_global_parameters;
|
|||
|
|
clnf_model.params_local = best_local_parameters.clone();
|
|||
|
|
clnf_model.detected_landmarks = best_detected_landmarks.clone();
|
|||
|
|
clnf_model.detection_success = best_success;
|
|||
|
|
clnf_model.landmark_likelihoods = best_landmark_likelihoods.clone();
|
|||
|
|
|
|||
|
|
for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
|
|||
|
|
{
|
|||
|
|
clnf_model.hierarchical_models[part].params_global = best_global_parameters_h[part];
|
|||
|
|
clnf_model.hierarchical_models[part].params_local = best_local_parameters_h[part].clone();
|
|||
|
|
clnf_model.hierarchical_models[part].detected_landmarks = best_detected_landmarks_h[part].clone();
|
|||
|
|
clnf_model.hierarchical_models[part].landmark_likelihoods = best_landmark_likelihoods_h[part].clone();
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
return best_success;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Mat_<float> depth_image, CLNF& clnf_model, FaceModelParameters& params)
|
|||
|
|
{
|
|||
|
|
|
|||
|
|
cv::Rect_<double> bounding_box;
|
|||
|
|
|
|||
|
|
// If the face detector has not been initialised read it in
|
|||
|
|
if(clnf_model.face_detector_HAAR.empty())
|
|||
|
|
{
|
|||
|
|
clnf_model.face_detector_HAAR.load(params.face_detector_location);
|
|||
|
|
clnf_model.face_detector_location = params.face_detector_location;
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Detect the face first
|
|||
|
|
if(params.curr_face_detector == FaceModelParameters::HOG_SVM_DETECTOR)
|
|||
|
|
{
|
|||
|
|
double confidence;
|
|||
|
|
LandmarkDetector::DetectSingleFaceHOG(bounding_box, grayscale_image, clnf_model.face_detector_HOG, confidence);
|
|||
|
|
}
|
|||
|
|
else if(params.curr_face_detector == FaceModelParameters::HAAR_DETECTOR)
|
|||
|
|
{
|
|||
|
|
LandmarkDetector::DetectSingleFace(bounding_box, grayscale_image, clnf_model.face_detector_HAAR);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
if(bounding_box.width == 0)
|
|||
|
|
{
|
|||
|
|
return false;
|
|||
|
|
}
|
|||
|
|
else
|
|||
|
|
{
|
|||
|
|
return DetectLandmarksInImage(grayscale_image, depth_image, bounding_box, clnf_model, params);
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
// Versions not using depth images
|
|||
|
|
bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, const cv::Rect_<double> bounding_box, CLNF& clnf_model, FaceModelParameters& params)
|
|||
|
|
{
|
|||
|
|
return DetectLandmarksInImage(grayscale_image, cv::Mat_<float>(), bounding_box, clnf_model, params);
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
bool LandmarkDetector::DetectLandmarksInImage(const cv::Mat_<uchar> &grayscale_image, CLNF& clnf_model, FaceModelParameters& params)
|
|||
|
|
{
|
|||
|
|
return DetectLandmarksInImage(grayscale_image, cv::Mat_<float>(), clnf_model, params);
|
|||
|
|
}
|
|||
|
|
|