644 lines
21 KiB
C++
644 lines
21 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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// * 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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// FaceLandmarkImg.cpp : Defines the entry point for the console application for detecting landmarks in images.
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#include "LandmarkCoreIncludes.h"
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// System includes
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#include <fstream>
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// OpenCV includes
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#include <opencv2/core/core.hpp>
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#include <opencv2/highgui/highgui.hpp>
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#include <opencv2/imgproc.hpp>
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// Boost includes
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#include <filesystem.hpp>
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#include <filesystem/fstream.hpp>
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#include <dlib/image_processing/frontal_face_detector.h>
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#include <tbb/tbb.h>
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#include <FaceAnalyser.h>
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#include <GazeEstimation.h>
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#ifndef CONFIG_DIR
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#define CONFIG_DIR "~"
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#endif
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using namespace std;
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vector<string> get_arguments(int argc, char **argv)
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{
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vector<string> arguments;
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for(int i = 0; i < argc; ++i)
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{
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arguments.push_back(string(argv[i]));
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}
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return arguments;
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}
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void convert_to_grayscale(const cv::Mat& in, cv::Mat& out)
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{
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if(in.channels() == 3)
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{
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// Make sure it's in a correct format
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if(in.depth() != CV_8U)
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{
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if(in.depth() == CV_16U)
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{
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cv::Mat tmp = in / 256;
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tmp.convertTo(tmp, CV_8U);
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cv::cvtColor(tmp, out, CV_BGR2GRAY);
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}
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}
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else
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{
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cv::cvtColor(in, out, CV_BGR2GRAY);
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}
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}
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else if(in.channels() == 4)
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{
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cv::cvtColor(in, out, CV_BGRA2GRAY);
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}
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else
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{
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if(in.depth() == CV_16U)
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{
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cv::Mat tmp = in / 256;
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out = tmp.clone();
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}
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else if(in.depth() != CV_8U)
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{
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in.convertTo(out, CV_8U);
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}
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else
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{
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out = in.clone();
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}
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}
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}
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// Useful utility for creating directories for storing the output files
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void create_directory_from_file(string output_path)
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{
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// Creating the right directory structure
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// First get rid of the file
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auto p = boost::filesystem::path(boost::filesystem::path(output_path).parent_path());
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if (!p.empty() && !boost::filesystem::exists(p))
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{
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bool success = boost::filesystem::create_directories(p);
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if (!success)
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{
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cout << "Failed to create a directory... " << p.string() << endl;
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}
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}
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}
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// This will only be accurate when camera parameters are accurate, useful for work on 3D data
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void write_out_pose_landmarks(const string& outfeatures, const cv::Mat_<double>& shape3D, const cv::Vec6d& pose, const cv::Point3f& gaze0, const cv::Point3f& gaze1)
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{
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create_directory_from_file(outfeatures);
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std::ofstream featuresFile;
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featuresFile.open(outfeatures);
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if (featuresFile.is_open())
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{
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int n = shape3D.cols;
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featuresFile << "version: 1" << endl;
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featuresFile << "npoints: " << n << endl;
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featuresFile << "{" << endl;
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for (int i = 0; i < n; ++i)
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{
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// Use matlab format, so + 1
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featuresFile << shape3D.at<double>(i) << " " << shape3D.at<double>(i + n) << " " << shape3D.at<double>(i + 2*n) << endl;
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}
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featuresFile << "}" << endl;
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// Do the pose and eye gaze if present as well
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featuresFile << "pose: eul_x, eul_y, eul_z: " << endl;
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featuresFile << "{" << endl;
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featuresFile << pose[3] << " " << pose[4] << " " << pose[5] << endl;
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featuresFile << "}" << endl;
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// Do the pose and eye gaze if present as well
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featuresFile << "gaze: dir_x_1, dir_y_1, dir_z_1, dir_x_2, dir_y_2, dir_z_2: " << endl;
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featuresFile << "{" << endl;
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featuresFile << gaze0.x << " " << gaze0.y << " " << gaze0.z << " " << gaze1.x << " " << gaze1.y << " " << gaze1.z << endl;
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featuresFile << "}" << endl;
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featuresFile.close();
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}
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}
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void write_out_landmarks(const string& outfeatures, const LandmarkDetector::CLNF& clnf_model, const cv::Vec6d& pose, const cv::Point3f& gaze0, const cv::Point3f& gaze1, std::vector<std::pair<std::string, double>> au_intensities, std::vector<std::pair<std::string, double>> au_occurences)
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{
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create_directory_from_file(outfeatures);
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std::ofstream featuresFile;
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featuresFile.open(outfeatures);
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if (featuresFile.is_open())
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{
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int n = clnf_model.patch_experts.visibilities[0][0].rows;
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featuresFile << "version: 1" << endl;
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featuresFile << "npoints: " << n << endl;
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featuresFile << "{" << endl;
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for (int i = 0; i < n; ++i)
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{
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// Use matlab format, so + 1
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featuresFile << clnf_model.detected_landmarks.at<double>(i) + 1 << " " << clnf_model.detected_landmarks.at<double>(i + n) + 1 << endl;
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}
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featuresFile << "}" << endl;
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// Do the pose and eye gaze if present as well
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featuresFile << "pose: eul_x, eul_y, eul_z: " << endl;
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featuresFile << "{" << endl;
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featuresFile << pose[3] << " " << pose[4] << " " << pose[5] << endl;
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featuresFile << "}" << endl;
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// Do the pose and eye gaze if present as well
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featuresFile << "gaze: dir_x_1, dir_y_1, dir_z_1, dir_x_2, dir_y_2, dir_z_2: " << endl;
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featuresFile << "{" << endl;
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featuresFile << gaze0.x << " " << gaze0.y << " " << gaze0.z << " " << gaze1.x << " " << gaze1.y << " " << gaze1.z << endl;
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featuresFile << "}" << endl;
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// Do the au intensities
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featuresFile << "au intensities: " << au_intensities.size() << endl;
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featuresFile << "{" << endl;
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for (size_t i = 0; i < au_intensities.size(); ++i)
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{
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// Use matlab format, so + 1
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featuresFile << au_intensities[i].first << " " << au_intensities[i].second << endl;
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}
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featuresFile << "}" << endl;
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// Do the au occurences
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featuresFile << "au occurences: " << au_occurences.size() << endl;
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featuresFile << "{" << endl;
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for (size_t i = 0; i < au_occurences.size(); ++i)
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{
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// Use matlab format, so + 1
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featuresFile << au_occurences[i].first << " " << au_occurences[i].second << endl;
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}
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featuresFile << "}" << endl;
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featuresFile.close();
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}
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}
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void create_display_image(const cv::Mat& orig, cv::Mat& display_image, LandmarkDetector::CLNF& clnf_model)
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{
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// Draw head pose if present and draw eye gaze as well
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// preparing the visualisation image
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display_image = orig.clone();
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// Creating a display image
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cv::Mat xs = clnf_model.detected_landmarks(cv::Rect(0, 0, 1, clnf_model.detected_landmarks.rows/2));
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cv::Mat ys = clnf_model.detected_landmarks(cv::Rect(0, clnf_model.detected_landmarks.rows/2, 1, clnf_model.detected_landmarks.rows/2));
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double min_x, max_x, min_y, max_y;
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cv::minMaxLoc(xs, &min_x, &max_x);
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cv::minMaxLoc(ys, &min_y, &max_y);
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double width = max_x - min_x;
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double height = max_y - min_y;
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int minCropX = max((int)(min_x-width/3.0),0);
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int minCropY = max((int)(min_y-height/3.0),0);
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int widthCrop = min((int)(width*5.0/3.0), display_image.cols - minCropX - 1);
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int heightCrop = min((int)(height*5.0/3.0), display_image.rows - minCropY - 1);
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double scaling = 350.0/widthCrop;
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// first crop the image
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display_image = display_image(cv::Rect((int)(minCropX), (int)(minCropY), (int)(widthCrop), (int)(heightCrop)));
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// now scale it
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cv::resize(display_image.clone(), display_image, cv::Size(), scaling, scaling);
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// Make the adjustments to points
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xs = (xs - minCropX)*scaling;
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ys = (ys - minCropY)*scaling;
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cv::Mat shape = clnf_model.detected_landmarks.clone();
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xs.copyTo(shape(cv::Rect(0, 0, 1, clnf_model.detected_landmarks.rows/2)));
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ys.copyTo(shape(cv::Rect(0, clnf_model.detected_landmarks.rows/2, 1, clnf_model.detected_landmarks.rows/2)));
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// Do the shifting for the hierarchical models as well
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for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
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{
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cv::Mat xs = clnf_model.hierarchical_models[part].detected_landmarks(cv::Rect(0, 0, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2));
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cv::Mat ys = clnf_model.hierarchical_models[part].detected_landmarks(cv::Rect(0, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2));
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xs = (xs - minCropX)*scaling;
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ys = (ys - minCropY)*scaling;
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cv::Mat shape = clnf_model.hierarchical_models[part].detected_landmarks.clone();
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xs.copyTo(shape(cv::Rect(0, 0, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2)));
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ys.copyTo(shape(cv::Rect(0, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2)));
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}
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LandmarkDetector::Draw(display_image, clnf_model);
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}
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int main (int argc, char **argv)
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{
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//Convert arguments to more convenient vector form
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vector<string> arguments = get_arguments(argc, argv);
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// Search paths
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boost::filesystem::path config_path = boost::filesystem::path(CONFIG_DIR);
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boost::filesystem::path parent_path = boost::filesystem::path(arguments[0]).parent_path();
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// Some initial parameters that can be overriden from command line
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vector<string> files, depth_files, output_images, output_landmark_locations, output_pose_locations;
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// Bounding boxes for a face in each image (optional)
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vector<cv::Rect_<double> > bounding_boxes;
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LandmarkDetector::get_image_input_output_params(files, depth_files, output_landmark_locations, output_pose_locations, output_images, bounding_boxes, arguments);
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LandmarkDetector::FaceModelParameters det_parameters(arguments);
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// No need to validate detections, as we're not doing tracking
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det_parameters.validate_detections = false;
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// Grab camera parameters if provided (only used for pose and eye gaze and are quite important for accurate estimates)
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float fx = 0, fy = 0, cx = 0, cy = 0;
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int device = -1;
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LandmarkDetector::get_camera_params(device, fx, fy, cx, cy, arguments);
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// If cx (optical axis centre) is undefined will use the image size/2 as an estimate
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bool cx_undefined = false;
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bool fx_undefined = false;
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if (cx == 0 || cy == 0)
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{
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cx_undefined = true;
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}
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if (fx == 0 || fy == 0)
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{
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fx_undefined = true;
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}
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// The modules that are being used for tracking
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cout << "Loading the model" << endl;
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LandmarkDetector::CLNF clnf_model(det_parameters.model_location);
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cout << "Model loaded" << endl;
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cv::CascadeClassifier classifier(det_parameters.face_detector_location);
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dlib::frontal_face_detector face_detector_hog = dlib::get_frontal_face_detector();
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// Loading the AU prediction models
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string au_loc = "AU_predictors/AU_all_static.txt";
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boost::filesystem::path au_loc_path = boost::filesystem::path(au_loc);
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if (boost::filesystem::exists(au_loc_path))
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{
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au_loc = au_loc_path.string();
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}
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else if (boost::filesystem::exists(parent_path/au_loc_path))
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{
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au_loc = (parent_path/au_loc_path).string();
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}
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else if (boost::filesystem::exists(config_path/au_loc_path))
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{
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au_loc = (config_path/au_loc_path).string();
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}
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else
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{
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cout << "Can't find AU prediction files, exiting" << endl;
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return 1;
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}
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// Used for image masking for AUs
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string tri_loc;
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boost::filesystem::path tri_loc_path = boost::filesystem::path("model/tris_68_full.txt");
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if (boost::filesystem::exists(tri_loc_path))
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{
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tri_loc = tri_loc_path.string();
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}
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else if (boost::filesystem::exists(parent_path/tri_loc_path))
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{
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tri_loc = (parent_path/tri_loc_path).string();
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}
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else if (boost::filesystem::exists(config_path/tri_loc_path))
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{
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tri_loc = (config_path/tri_loc_path).string();
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}
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else
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{
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cout << "Can't find triangulation files, exiting" << endl;
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return 1;
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}
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FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), 0.7, 112, 112, au_loc, tri_loc);
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bool visualise = !det_parameters.quiet_mode;
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// Do some image loading
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for(size_t i = 0; i < files.size(); i++)
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{
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string file = files.at(i);
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// Loading image
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cv::Mat read_image = cv::imread(file, -1);
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if (read_image.empty())
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{
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cout << "Could not read the input image" << endl;
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return 1;
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}
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// Loading depth file if exists (optional)
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cv::Mat_<float> depth_image;
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if(depth_files.size() > 0)
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{
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string dFile = depth_files.at(i);
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cv::Mat dTemp = cv::imread(dFile, -1);
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dTemp.convertTo(depth_image, CV_32F);
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}
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// Making sure the image is in uchar grayscale
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cv::Mat_<uchar> grayscale_image;
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convert_to_grayscale(read_image, grayscale_image);
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// If optical centers are not defined just use center of image
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if (cx_undefined)
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{
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cx = grayscale_image.cols / 2.0f;
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cy = grayscale_image.rows / 2.0f;
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}
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// Use a rough guess-timate of focal length
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if (fx_undefined)
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{
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fx = 500 * (grayscale_image.cols / 640.0);
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fy = 500 * (grayscale_image.rows / 480.0);
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fx = (fx + fy) / 2.0;
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fy = fx;
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}
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// if no pose defined we just use a face detector
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if(bounding_boxes.empty())
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{
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// Detect faces in an image
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vector<cv::Rect_<double> > face_detections;
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if(det_parameters.curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR)
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{
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vector<double> confidences;
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LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, face_detector_hog, confidences);
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}
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else
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{
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LandmarkDetector::DetectFaces(face_detections, grayscale_image, classifier);
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}
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// Detect landmarks around detected faces
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int face_det = 0;
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// perform landmark detection for every face detected
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for(size_t face=0; face < face_detections.size(); ++face)
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{
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// if there are multiple detections go through them
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bool success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, depth_image, face_detections[face], clnf_model, det_parameters);
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// Estimate head pose and eye gaze
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cv::Vec6d headPose = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy);
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// Gaze tracking, absolute gaze direction
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cv::Point3f gazeDirection0(0, 0, -1);
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cv::Point3f gazeDirection1(0, 0, -1);
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if (success && det_parameters.track_gaze)
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{
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FaceAnalysis::EstimateGaze(clnf_model, gazeDirection0, fx, fy, cx, cy, true);
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FaceAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false);
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|
|
}
|
|
|
|
auto ActionUnits = face_analyser.PredictStaticAUs(read_image, clnf_model, false);
|
|
|
|
// Writing out the detected landmarks (in an OS independent manner)
|
|
if(!output_landmark_locations.empty())
|
|
{
|
|
char name[100];
|
|
// append detection number (in case multiple faces are detected)
|
|
sprintf(name, "_det_%d", face_det);
|
|
|
|
// Construct the output filename
|
|
boost::filesystem::path slash("/");
|
|
std::string preferredSlash = slash.make_preferred().string();
|
|
|
|
boost::filesystem::path out_feat_path(output_landmark_locations.at(i));
|
|
boost::filesystem::path dir = out_feat_path.parent_path();
|
|
boost::filesystem::path fname = out_feat_path.filename().replace_extension("");
|
|
boost::filesystem::path ext = out_feat_path.extension();
|
|
string outfeatures = dir.string() + preferredSlash + fname.string() + string(name) + ext.string();
|
|
write_out_landmarks(outfeatures, clnf_model, headPose, gazeDirection0, gazeDirection1, ActionUnits.first, ActionUnits.second);
|
|
}
|
|
|
|
if (!output_pose_locations.empty())
|
|
{
|
|
char name[100];
|
|
// append detection number (in case multiple faces are detected)
|
|
sprintf(name, "_det_%d", face_det);
|
|
|
|
// Construct the output filename
|
|
boost::filesystem::path slash("/");
|
|
std::string preferredSlash = slash.make_preferred().string();
|
|
|
|
boost::filesystem::path out_pose_path(output_pose_locations.at(i));
|
|
boost::filesystem::path dir = out_pose_path.parent_path();
|
|
boost::filesystem::path fname = out_pose_path.filename().replace_extension("");
|
|
boost::filesystem::path ext = out_pose_path.extension();
|
|
string outfeatures = dir.string() + preferredSlash + fname.string() + string(name) + ext.string();
|
|
write_out_pose_landmarks(outfeatures, clnf_model.GetShape(fx, fy, cx, cy), headPose, gazeDirection0, gazeDirection1);
|
|
|
|
}
|
|
|
|
if (det_parameters.track_gaze)
|
|
{
|
|
cv::Vec6d pose_estimate_to_draw = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy);
|
|
|
|
// Draw it in reddish if uncertain, blueish if certain
|
|
LandmarkDetector::DrawBox(read_image, pose_estimate_to_draw, cv::Scalar(255.0, 0, 0), 3, fx, fy, cx, cy);
|
|
FaceAnalysis::DrawGaze(read_image, clnf_model, gazeDirection0, gazeDirection1, fx, fy, cx, cy);
|
|
}
|
|
|
|
// displaying detected landmarks
|
|
cv::Mat display_image;
|
|
create_display_image(read_image, display_image, clnf_model);
|
|
|
|
if(visualise && success)
|
|
{
|
|
imshow("colour", display_image);
|
|
cv::waitKey(1);
|
|
}
|
|
|
|
// Saving the display images (in an OS independent manner)
|
|
if(!output_images.empty() && success)
|
|
{
|
|
string outimage = output_images.at(i);
|
|
if(!outimage.empty())
|
|
{
|
|
char name[100];
|
|
sprintf(name, "_det_%d", face_det);
|
|
|
|
boost::filesystem::path slash("/");
|
|
std::string preferredSlash = slash.make_preferred().string();
|
|
|
|
// append detection number
|
|
boost::filesystem::path out_feat_path(outimage);
|
|
boost::filesystem::path dir = out_feat_path.parent_path();
|
|
boost::filesystem::path fname = out_feat_path.filename().replace_extension("");
|
|
boost::filesystem::path ext = out_feat_path.extension();
|
|
outimage = dir.string() + preferredSlash + fname.string() + string(name) + ext.string();
|
|
create_directory_from_file(outimage);
|
|
bool write_success = cv::imwrite(outimage, display_image);
|
|
|
|
if (!write_success)
|
|
{
|
|
cout << "Could not output a processed image" << endl;
|
|
return 1;
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if(success)
|
|
{
|
|
face_det++;
|
|
}
|
|
|
|
}
|
|
}
|
|
else
|
|
{
|
|
// Have provided bounding boxes
|
|
LandmarkDetector::DetectLandmarksInImage(grayscale_image, bounding_boxes[i], clnf_model, det_parameters);
|
|
|
|
// Estimate head pose and eye gaze
|
|
cv::Vec6d headPose = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy);
|
|
|
|
// Gaze tracking, absolute gaze direction
|
|
cv::Point3f gazeDirection0(0, 0, -1);
|
|
cv::Point3f gazeDirection1(0, 0, -1);
|
|
|
|
if (det_parameters.track_gaze)
|
|
{
|
|
FaceAnalysis::EstimateGaze(clnf_model, gazeDirection0, fx, fy, cx, cy, true);
|
|
FaceAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false);
|
|
}
|
|
|
|
auto ActionUnits = face_analyser.PredictStaticAUs(read_image, clnf_model, false);
|
|
|
|
// Writing out the detected landmarks
|
|
if(!output_landmark_locations.empty())
|
|
{
|
|
string outfeatures = output_landmark_locations.at(i);
|
|
write_out_landmarks(outfeatures, clnf_model, headPose, gazeDirection0, gazeDirection1, ActionUnits.first, ActionUnits.second);
|
|
}
|
|
|
|
// Writing out the detected landmarks
|
|
if (!output_pose_locations.empty())
|
|
{
|
|
string outfeatures = output_pose_locations.at(i);
|
|
write_out_pose_landmarks(outfeatures, clnf_model.GetShape(fx, fy, cx, cy), headPose, gazeDirection0, gazeDirection1);
|
|
}
|
|
|
|
// displaying detected stuff
|
|
cv::Mat display_image;
|
|
|
|
if (det_parameters.track_gaze)
|
|
{
|
|
cv::Vec6d pose_estimate_to_draw = LandmarkDetector::GetCorrectedPoseWorld(clnf_model, fx, fy, cx, cy);
|
|
|
|
// Draw it in reddish if uncertain, blueish if certain
|
|
LandmarkDetector::DrawBox(read_image, pose_estimate_to_draw, cv::Scalar(255.0, 0, 0), 3, fx, fy, cx, cy);
|
|
FaceAnalysis::DrawGaze(read_image, clnf_model, gazeDirection0, gazeDirection1, fx, fy, cx, cy);
|
|
}
|
|
|
|
create_display_image(read_image, display_image, clnf_model);
|
|
|
|
if(visualise)
|
|
{
|
|
imshow("colour", display_image);
|
|
cv::waitKey(1);
|
|
}
|
|
|
|
if(!output_images.empty())
|
|
{
|
|
string outimage = output_images.at(i);
|
|
if(!outimage.empty())
|
|
{
|
|
create_directory_from_file(outimage);
|
|
bool write_success = imwrite(outimage, display_image);
|
|
|
|
if (!write_success)
|
|
{
|
|
cout << "Could not output a processed image" << endl;
|
|
return 1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|