open source pkg v1
This commit is contained in:
Vijay Yadev
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pkg/OpenFace/exe/FaceLandmarkImg/FaceLandmarkImg.cpp
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pkg/OpenFace/exe/FaceLandmarkImg/FaceLandmarkImg.cpp
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///////////////////////////////////////////////////////////////////////////////
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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 2.0: Facial Behavior Analysis Toolkit
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// Tadas Baltrušaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency
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// in IEEE International Conference on Automatic Face and Gesture Recognition, 2018
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//
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// Convolutional experts constrained local model for facial landmark detection.
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// A. Zadeh, T. Baltrušaitis, and Louis-Philippe Morency,
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// in Computer Vision and Pattern Recognition Workshops, 2017.
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//
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// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
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// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
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// in IEEE International. Conference on Computer Vision (ICCV), 2015
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//
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// Cross-dataset learning and person-specific normalisation for automatic Action Unit detection
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// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
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// in Facial Expression Recognition and Analysis Challenge,
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// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
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//
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///////////////////////////////////////////////////////////////////////////////
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// FaceLandmarkImg.cpp : Defines the entry point for the console application for detecting landmarks in images.
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// dlib
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#include <dlib/image_processing/frontal_face_detector.h>
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#include "LandmarkCoreIncludes.h"
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#include <FaceAnalyser.h>
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#include <GazeEstimation.h>
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#include <ImageCapture.h>
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#include <Visualizer.h>
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#include <VisualizationUtils.h>
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#include <RecorderOpenFace.h>
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#include <RecorderOpenFaceParameters.h>
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#ifndef CONFIG_DIR
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#define CONFIG_DIR "~"
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#endif
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std::vector<std::string> get_arguments(int argc, char **argv)
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{
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std::vector<std::string> arguments;
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for (int i = 0; i < argc; ++i)
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{
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arguments.push_back(std::string(argv[i]));
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}
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return arguments;
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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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std::vector<std::string> arguments = get_arguments(argc, argv);
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// no arguments: output usage
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if (arguments.size() == 1)
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{
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std::cout << "For command line arguments see:" << std::endl;
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std::cout << " https://github.com/TadasBaltrusaitis/OpenFace/wiki/Command-line-arguments";
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return 0;
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}
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// Prepare for image reading
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Utilities::ImageCapture image_reader;
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// The sequence reader chooses what to open based on command line arguments provided
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if (!image_reader.Open(arguments))
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{
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std::cout << "Could not open any images" << std::endl;
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return 1;
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}
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// Load the models if images found
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LandmarkDetector::FaceModelParameters det_parameters(arguments);
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// The modules that are being used for tracking
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std::cout << "Loading the model" << std::endl;
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LandmarkDetector::CLNF face_model(det_parameters.model_location);
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if (!face_model.loaded_successfully)
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{
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std::cout << "ERROR: Could not load the landmark detector" << std::endl;
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return 1;
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}
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std::cout << "Model loaded" << std::endl;
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// Load facial feature extractor and AU analyser (make sure it is static)
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FaceAnalysis::FaceAnalyserParameters face_analysis_params(arguments);
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face_analysis_params.OptimizeForImages();
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FaceAnalysis::FaceAnalyser face_analyser(face_analysis_params);
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// If bounding boxes not provided, use a face detector
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cv::CascadeClassifier classifier(det_parameters.haar_face_detector_location);
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dlib::frontal_face_detector face_detector_hog = dlib::get_frontal_face_detector();
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LandmarkDetector::FaceDetectorMTCNN face_detector_mtcnn(det_parameters.mtcnn_face_detector_location);
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// If can't find MTCNN face detector, default to HOG one
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if (det_parameters.curr_face_detector == LandmarkDetector::FaceModelParameters::MTCNN_DETECTOR && face_detector_mtcnn.empty())
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{
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std::cout << "INFO: defaulting to HOG-SVM face detector" << std::endl;
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det_parameters.curr_face_detector = LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR;
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}
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// A utility for visualizing the results
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Utilities::Visualizer visualizer(arguments);
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cv::Mat rgb_image;
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rgb_image = image_reader.GetNextImage();
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if (!face_model.eye_model)
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{
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std::cout << "WARNING: no eye model found" << std::endl;
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}
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if (face_analyser.GetAUClassNames().size() == 0 && face_analyser.GetAUClassNames().size() == 0)
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{
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std::cout << "WARNING: no Action Unit models found" << std::endl;
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}
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std::cout << "Starting tracking" << std::endl;
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while (!rgb_image.empty())
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{
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Utilities::RecorderOpenFaceParameters recording_params(arguments, false, false,
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image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy);
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if (!face_model.eye_model)
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{
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recording_params.setOutputGaze(false);
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}
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Utilities::RecorderOpenFace open_face_rec(image_reader.name, recording_params, arguments);
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visualizer.SetImage(rgb_image, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy);
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// Making sure the image is in uchar grayscale (some face detectors use RGB, landmark detector uses grayscale)
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cv::Mat_<uchar> grayscale_image = image_reader.GetGrayFrame();
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// Detect faces in an image
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std::vector<cv::Rect_<float> > face_detections;
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if (image_reader.has_bounding_boxes)
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{
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face_detections = image_reader.GetBoundingBoxes();
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}
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else
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{
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if (det_parameters.curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR)
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{
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std::vector<float> confidences;
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LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, face_detector_hog, confidences);
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}
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else if (det_parameters.curr_face_detector == LandmarkDetector::FaceModelParameters::HAAR_DETECTOR)
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{
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LandmarkDetector::DetectFaces(face_detections, grayscale_image, classifier);
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}
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else
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{
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std::vector<float> confidences;
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LandmarkDetector::DetectFacesMTCNN(face_detections, rgb_image, face_detector_mtcnn, confidences);
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}
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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(rgb_image, face_detections[face], face_model, det_parameters, grayscale_image);
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// Estimate head pose and eye gaze
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cv::Vec6d pose_estimate = LandmarkDetector::GetPose(face_model, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy);
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// Gaze tracking, absolute gaze direction
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cv::Point3f gaze_direction0(0, 0, -1);
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cv::Point3f gaze_direction1(0, 0, -1);
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cv::Vec2f gaze_angle(0, 0);
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if (face_model.eye_model)
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{
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GazeAnalysis::EstimateGaze(face_model, gaze_direction0, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy, true);
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GazeAnalysis::EstimateGaze(face_model, gaze_direction1, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy, false);
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gaze_angle = GazeAnalysis::GetGazeAngle(gaze_direction0, gaze_direction1);
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}
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cv::Mat sim_warped_img;
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cv::Mat_<double> hog_descriptor; int num_hog_rows = 0, num_hog_cols = 0;
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// Perform AU detection and HOG feature extraction, as this can be expensive only compute it if needed by output or visualization
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if (recording_params.outputAlignedFaces() || recording_params.outputHOG() || recording_params.outputAUs() || visualizer.vis_align || visualizer.vis_hog)
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{
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face_analyser.PredictStaticAUsAndComputeFeatures(rgb_image, face_model.detected_landmarks);
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face_analyser.GetLatestAlignedFace(sim_warped_img);
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face_analyser.GetLatestHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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}
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// Displaying the tracking visualizations
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visualizer.SetObservationFaceAlign(sim_warped_img);
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visualizer.SetObservationHOG(hog_descriptor, num_hog_rows, num_hog_cols);
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visualizer.SetObservationLandmarks(face_model.detected_landmarks, 1.0, face_model.GetVisibilities()); // Set confidence to high to make sure we always visualize
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visualizer.SetObservationPose(pose_estimate, 1.0);
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visualizer.SetObservationGaze(gaze_direction0, gaze_direction1, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy), face_model.detection_certainty);
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visualizer.SetObservationActionUnits(face_analyser.GetCurrentAUsReg(), face_analyser.GetCurrentAUsClass());
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// Setting up the recorder output
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open_face_rec.SetObservationHOG(face_model.detection_success, hog_descriptor, num_hog_rows, num_hog_cols, 31); // The number of channels in HOG is fixed at the moment, as using FHOG
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open_face_rec.SetObservationActionUnits(face_analyser.GetCurrentAUsReg(), face_analyser.GetCurrentAUsClass());
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open_face_rec.SetObservationLandmarks(face_model.detected_landmarks, face_model.GetShape(image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy),
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face_model.params_global, face_model.params_local, face_model.detection_certainty, face_model.detection_success);
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open_face_rec.SetObservationPose(pose_estimate);
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open_face_rec.SetObservationGaze(gaze_direction0, gaze_direction1, gaze_angle, LandmarkDetector::CalculateAllEyeLandmarks(face_model), LandmarkDetector::Calculate3DEyeLandmarks(face_model, image_reader.fx, image_reader.fy, image_reader.cx, image_reader.cy));
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open_face_rec.SetObservationFaceAlign(sim_warped_img);
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open_face_rec.SetObservationFaceID(face);
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open_face_rec.WriteObservation();
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}
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if (face_detections.size() > 0)
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{
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visualizer.ShowObservation();
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}
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open_face_rec.SetObservationVisualization(visualizer.GetVisImage());
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open_face_rec.WriteObservationTracked();
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open_face_rec.Close();
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// Grabbing the next frame in the sequence
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rgb_image = image_reader.GetNextImage();
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}
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return 0;
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}
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