sustaining_gazes/exe/FaceLandmarkImg/FaceLandmarkImg.cpp
2017-11-08 21:13:02 +00:00

613 lines
20 KiB
C++

///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2017, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
// ACADEMIC OR NON-PROFIT ORGANIZATION NONCOMMERCIAL RESEARCH USE ONLY
//
// BY USING OR DOWNLOADING THE SOFTWARE, YOU ARE AGREEING TO THE TERMS OF THIS LICENSE AGREEMENT.
// IF YOU DO NOT AGREE WITH THESE TERMS, YOU MAY NOT USE OR DOWNLOAD THE SOFTWARE.
//
// License can be found in OpenFace-license.txt
// * Any publications arising from the use of this software, including but
// not limited to academic journal and conference publications, technical
// reports and manuals, must cite at least one of the following works:
//
// OpenFace: an open source facial behavior analysis toolkit
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency
// in IEEE Winter Conference on Applications of Computer Vision, 2016
//
// Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
// Erroll Wood, Tadas Baltrušaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, and Andreas Bulling
// in IEEE International. Conference on Computer Vision (ICCV), 2015
//
// Cross-dataset learning and person-speci?c normalisation for automatic Action Unit detection
// Tadas Baltrušaitis, Marwa Mahmoud, and Peter Robinson
// in Facial Expression Recognition and Analysis Challenge,
// IEEE International Conference on Automatic Face and Gesture Recognition, 2015
//
// Constrained Local Neural Fields for robust facial landmark detection in the wild.
// Tadas Baltrušaitis, Peter Robinson, and Louis-Philippe Morency.
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
//
///////////////////////////////////////////////////////////////////////////////
// FaceLandmarkImg.cpp : Defines the entry point for the console application for detecting landmarks in images.
#include "LandmarkCoreIncludes.h"
// System includes
#include <fstream>
// OpenCV includes
#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/imgproc.hpp>
// Boost includes
#include <filesystem.hpp>
#include <filesystem/fstream.hpp>
#include <dlib/image_processing/frontal_face_detector.h>
#include <tbb/tbb.h>
#include <FaceAnalyser.h>
#include <GazeEstimation.h>
#ifndef CONFIG_DIR
#define CONFIG_DIR "~"
#endif
using namespace std;
vector<string> get_arguments(int argc, char **argv)
{
vector<string> arguments;
for(int i = 0; i < argc; ++i)
{
arguments.push_back(string(argv[i]));
}
return arguments;
}
// TODO rem
void convert_to_grayscale(const cv::Mat& in, cv::Mat& out)
{
if(in.channels() == 3)
{
// Make sure it's in a correct format
if(in.depth() != CV_8U)
{
if(in.depth() == CV_16U)
{
cv::Mat tmp = in / 256;
tmp.convertTo(tmp, CV_8U);
cv::cvtColor(tmp, out, CV_BGR2GRAY);
}
}
else
{
cv::cvtColor(in, out, CV_BGR2GRAY);
}
}
else if(in.channels() == 4)
{
cv::cvtColor(in, out, CV_BGRA2GRAY);
}
else
{
if(in.depth() == CV_16U)
{
cv::Mat tmp = in / 256;
out = tmp.clone();
}
else if(in.depth() != CV_8U)
{
in.convertTo(out, CV_8U);
}
else
{
out = in.clone();
}
}
}
// Useful utility for creating directories for storing the output files
void create_directory_from_file(string output_path)
{
// Creating the right directory structure
// First get rid of the file
auto p = boost::filesystem::path(boost::filesystem::path(output_path).parent_path());
if (!p.empty() && !boost::filesystem::exists(p))
{
bool success = boost::filesystem::create_directories(p);
if (!success)
{
cout << "Failed to create a directory... " << p.string() << endl;
}
}
}
// This will only be accurate when camera parameters are accurate, useful for work on 3D data
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)
{
create_directory_from_file(outfeatures);
std::ofstream featuresFile;
featuresFile.open(outfeatures);
if (featuresFile.is_open())
{
int n = shape3D.cols;
featuresFile << "version: 1" << endl;
featuresFile << "npoints: " << n << endl;
featuresFile << "{" << endl;
for (int i = 0; i < n; ++i)
{
// Use matlab format, so + 1
featuresFile << shape3D.at<double>(i) << " " << shape3D.at<double>(i + n) << " " << shape3D.at<double>(i + 2*n) << endl;
}
featuresFile << "}" << endl;
// Do the pose and eye gaze if present as well
featuresFile << "pose: eul_x, eul_y, eul_z: " << endl;
featuresFile << "{" << endl;
featuresFile << pose[3] << " " << pose[4] << " " << pose[5] << endl;
featuresFile << "}" << endl;
// Do the pose and eye gaze if present as well
featuresFile << "gaze_vec: dir_x_1, dir_y_1, dir_z_1, dir_x_2, dir_y_2, dir_z_2: " << endl;
featuresFile << "{" << endl;
featuresFile << gaze0.x << " " << gaze0.y << " " << gaze0.z << " " << gaze1.x << " " << gaze1.y << " " << gaze1.z << endl;
featuresFile << "}" << endl;
featuresFile.close();
}
}
void write_out_landmarks(const string& outfeatures, const LandmarkDetector::CLNF& clnf_model, const cv::Vec6d& pose, const cv::Point3f& gaze0, const cv::Point3f& gaze1, const cv::Vec2d gaze_angle, std::vector<std::pair<std::string, double>> au_intensities, std::vector<std::pair<std::string, double>> au_occurences, bool output_gaze)
{
create_directory_from_file(outfeatures);
std::ofstream featuresFile;
featuresFile.open(outfeatures);
if (featuresFile.is_open())
{
int n = clnf_model.patch_experts.visibilities[0][0].rows;
featuresFile << "version: 2" << endl;
featuresFile << "npoints: " << n << endl;
featuresFile << "{" << endl;
for (int i = 0; i < n; ++i)
{
// Use matlab format, so + 1
featuresFile << clnf_model.detected_landmarks.at<double>(i) + 1 << " " << clnf_model.detected_landmarks.at<double>(i + n) + 1 << endl;
}
featuresFile << "}" << endl;
// Do the pose and eye gaze if present as well
featuresFile << "pose: eul_x, eul_y, eul_z: " << endl;
featuresFile << "{" << endl;
featuresFile << pose[3] << " " << pose[4] << " " << pose[5] << endl;
featuresFile << "}" << endl;
if(output_gaze)
{
featuresFile << "gaze: dir_x_1, dir_y_1, dir_z_1, dir_x_2, dir_y_2, dir_z_2: " << endl;
featuresFile << "{" << endl;
featuresFile << gaze0.x << " " << gaze0.y << " " << gaze0.z << " " << gaze1.x << " " << gaze1.y << " " << gaze1.z << endl;
featuresFile << "}" << endl;
featuresFile << "gaze: angle_x, angle_y: " << endl;
featuresFile << "{" << endl;
featuresFile << gaze_angle[0] << " " << gaze_angle[1] << endl;
featuresFile << "}" << endl;
std::vector<cv::Point2d> eye_landmark_points = LandmarkDetector::CalculateAllEyeLandmarks(clnf_model);
featuresFile << "eye_lmks: " << eye_landmark_points.size() << endl;
featuresFile << "{" << endl;
for (int i = 0; i < eye_landmark_points.size(); ++i)
{
// Use matlab format, so + 1
featuresFile << (eye_landmark_points[i].x + 1) << " " << (eye_landmark_points[i].y + 1) << endl;
}
featuresFile << "}" << endl;
}
// Do the au intensities
featuresFile << "au intensities: " << au_intensities.size() << endl;
featuresFile << "{" << endl;
for (size_t i = 0; i < au_intensities.size(); ++i)
{
// Use matlab format, so + 1
featuresFile << au_intensities[i].first << " " << au_intensities[i].second << endl;
}
featuresFile << "}" << endl;
// Do the au occurences
featuresFile << "au occurences: " << au_occurences.size() << endl;
featuresFile << "{" << endl;
for (size_t i = 0; i < au_occurences.size(); ++i)
{
// Use matlab format, so + 1
featuresFile << au_occurences[i].first << " " << au_occurences[i].second << endl;
}
featuresFile << "}" << endl;
featuresFile.close();
}
}
void create_display_image(const cv::Mat& orig, cv::Mat& display_image, LandmarkDetector::CLNF& clnf_model)
{
// Draw head pose if present and draw eye gaze as well
// preparing the visualisation image
display_image = orig.clone();
// Creating a display image
cv::Mat xs = clnf_model.detected_landmarks(cv::Rect(0, 0, 1, clnf_model.detected_landmarks.rows/2));
cv::Mat ys = clnf_model.detected_landmarks(cv::Rect(0, clnf_model.detected_landmarks.rows/2, 1, clnf_model.detected_landmarks.rows/2));
double min_x, max_x, min_y, max_y;
cv::minMaxLoc(xs, &min_x, &max_x);
cv::minMaxLoc(ys, &min_y, &max_y);
double width = max_x - min_x;
double height = max_y - min_y;
int minCropX = max((int)(min_x-width/3.0),0);
int minCropY = max((int)(min_y-height/3.0),0);
int widthCrop = min((int)(width*5.0/3.0), display_image.cols - minCropX - 1);
int heightCrop = min((int)(height*5.0/3.0), display_image.rows - minCropY - 1);
double scaling = 350.0/widthCrop;
// first crop the image
display_image = display_image(cv::Rect((int)(minCropX), (int)(minCropY), (int)(widthCrop), (int)(heightCrop)));
// now scale it
cv::resize(display_image.clone(), display_image, cv::Size(), scaling, scaling);
// Make the adjustments to points
xs = (xs - minCropX)*scaling;
ys = (ys - minCropY)*scaling;
cv::Mat shape = clnf_model.detected_landmarks.clone();
xs.copyTo(shape(cv::Rect(0, 0, 1, clnf_model.detected_landmarks.rows/2)));
ys.copyTo(shape(cv::Rect(0, clnf_model.detected_landmarks.rows/2, 1, clnf_model.detected_landmarks.rows/2)));
// Do the shifting for the hierarchical models as well
for (size_t part = 0; part < clnf_model.hierarchical_models.size(); ++part)
{
cv::Mat xs = clnf_model.hierarchical_models[part].detected_landmarks(cv::Rect(0, 0, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2));
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));
xs = (xs - minCropX)*scaling;
ys = (ys - minCropY)*scaling;
cv::Mat shape = clnf_model.hierarchical_models[part].detected_landmarks.clone();
xs.copyTo(shape(cv::Rect(0, 0, 1, clnf_model.hierarchical_models[part].detected_landmarks.rows / 2)));
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)));
}
LandmarkDetector::Draw(display_image, clnf_model);
}
int main (int argc, char **argv)
{
//Convert arguments to more convenient vector form
vector<string> arguments = get_arguments(argc, argv);
// Some initial parameters that can be overriden from command line
vector<string> files, output_images, output_landmark_locations, output_pose_locations;
// Bounding boxes for a face in each image (optional)
vector<cv::Rect_<double> > bounding_boxes;
LandmarkDetector::get_image_input_output_params(files, output_landmark_locations, output_pose_locations, output_images, bounding_boxes, arguments);
LandmarkDetector::FaceModelParameters det_parameters(arguments);
// No need to validate detections, as we're not doing tracking
det_parameters.validate_detections = false;
// Grab camera parameters if provided (only used for pose and eye gaze and are quite important for accurate estimates)
float fx = 0, fy = 0, cx = 0, cy = 0;
int device = -1;
LandmarkDetector::get_camera_params(device, fx, fy, cx, cy, arguments);
// If cx (optical axis centre) is undefined will use the image size/2 as an estimate
bool cx_undefined = false;
bool fx_undefined = false;
if (cx == 0 || cy == 0)
{
cx_undefined = true;
}
if (fx == 0 || fy == 0)
{
fx_undefined = true;
}
// The modules that are being used for tracking
cout << "Loading the model" << endl;
LandmarkDetector::CLNF clnf_model(det_parameters.model_location);
cout << "Model loaded" << endl;
cv::CascadeClassifier classifier(det_parameters.face_detector_location);
dlib::frontal_face_detector face_detector_hog = dlib::get_frontal_face_detector();
// Load facial feature extractor and AU analyser (make sure it is static)
FaceAnalysis::FaceAnalyserParameters face_analysis_params(arguments);
face_analysis_params.OptimizeForImages();
FaceAnalysis::FaceAnalyser face_analyser(face_analysis_params);
bool visualise = !det_parameters.quiet_mode;
// Do some image loading
for(size_t i = 0; i < files.size(); i++)
{
string file = files.at(i);
// Loading image
cv::Mat read_image = cv::imread(file, -1);
if (read_image.empty())
{
cout << "Could not read the input image" << endl;
return 1;
}
// Making sure the image is in uchar grayscale
cv::Mat_<uchar> grayscale_image;
convert_to_grayscale(read_image, grayscale_image);
// If optical centers are not defined just use center of image
if (cx_undefined)
{
cx = grayscale_image.cols / 2.0f;
cy = grayscale_image.rows / 2.0f;
}
// Use a rough guess-timate of focal length
if (fx_undefined)
{
fx = 500 * (grayscale_image.cols / 640.0);
fy = 500 * (grayscale_image.rows / 480.0);
fx = (fx + fy) / 2.0;
fy = fx;
}
// if no pose defined we just use a face detector
if(bounding_boxes.empty())
{
// Detect faces in an image
vector<cv::Rect_<double> > face_detections;
if(det_parameters.curr_face_detector == LandmarkDetector::FaceModelParameters::HOG_SVM_DETECTOR)
{
vector<double> confidences;
LandmarkDetector::DetectFacesHOG(face_detections, grayscale_image, face_detector_hog, confidences);
}
else
{
LandmarkDetector::DetectFaces(face_detections, grayscale_image, classifier);
}
// Detect landmarks around detected faces
int face_det = 0;
// perform landmark detection for every face detected
for(size_t face=0; face < face_detections.size(); ++face)
{
// if there are multiple detections go through them
bool success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, face_detections[face], clnf_model, det_parameters);
// Estimate head pose and eye gaze
cv::Vec6d headPose = LandmarkDetector::GetPose(clnf_model, fx, fy, cx, cy);
// Gaze tracking, absolute gaze direction
cv::Point3f gazeDirection0(0, 0, -1);
cv::Point3f gazeDirection1(0, 0, -1);
cv::Vec2d gazeAngle(0, 0);
if (success && det_parameters.track_gaze)
{
GazeAnalysis::EstimateGaze(clnf_model, gazeDirection0, fx, fy, cx, cy, true);
GazeAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false);
gazeAngle = GazeAnalysis::GetGazeAngle(gazeDirection0, gazeDirection1);
}
auto ActionUnits = face_analyser.PredictStaticAUs(read_image, clnf_model.detected_landmarks, 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, gazeAngle, ActionUnits.first, ActionUnits.second, det_parameters.track_gaze);
}
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::GetPose(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);
GazeAnalysis::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::GetPose(clnf_model, fx, fy, cx, cy);
// Gaze tracking, absolute gaze direction
cv::Point3f gazeDirection0(0, 0, -1);
cv::Point3f gazeDirection1(0, 0, -1);
cv::Vec2d gazeAngle(0, 0);
if (det_parameters.track_gaze)
{
GazeAnalysis::EstimateGaze(clnf_model, gazeDirection0, fx, fy, cx, cy, true);
GazeAnalysis::EstimateGaze(clnf_model, gazeDirection1, fx, fy, cx, cy, false);
gazeAngle = GazeAnalysis::GetGazeAngle(gazeDirection0, gazeDirection1);
}
auto ActionUnits = face_analyser.PredictStaticAUs(read_image, clnf_model.detected_landmarks, 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, gazeAngle, ActionUnits.first, ActionUnits.second, det_parameters.track_gaze);
}
// 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::GetPose(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);
GazeAnalysis::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;
}