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sustaining_gazes/exe/FeatureExtraction/FeatureExtraction.cpp

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Master commit of OpenFace.
2016-04-28 19:40:36 +00:00
///////////////////////////////////////////////////////////////////////////////
// Copyright (C) 2016, Carnegie Mellon University and University of Cambridge,
// all rights reserved.
//
// THIS SOFTWARE IS PROVIDED <20>AS IS<49> FOR ACADEMIC USE ONLY AND ANY EXPRESS
// OR IMPLIED WARRANTIES WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
// THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDERS OR CONTRIBUTORS
// BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY.
// OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION)
// HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
// STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
// ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
// POSSIBILITY OF SUCH DAMAGE.
//
// Notwithstanding the license granted herein, Licensee acknowledges that certain components
// of the Software may be covered by so-called <20>open source<63> software licenses (<28>Open Source
// Components<74>), which means any software licenses approved as open source licenses by the
// Open Source Initiative or any substantially similar licenses, including without limitation any
// license that, as a condition of distribution of the software licensed under such license,
// requires that the distributor make the software available in source code format. Licensor shall
// provide a list of Open Source Components for a particular version of the Software upon
// Licensee<65>s request. Licensee will comply with the applicable terms of such licenses and to
// the extent required by the licenses covering Open Source Components, the terms of such
// licenses will apply in lieu of the terms of this Agreement. To the extent the terms of the
// licenses applicable to Open Source Components prohibit any of the restrictions in this
// License Agreement with respect to such Open Source Component, such restrictions will not
// apply to such Open Source Component. To the extent the terms of the licenses applicable to
// Open Source Components require Licensor to make an offer to provide source code or
// related information in connection with the Software, such offer is hereby made. Any request
// for source code or related information should be directed to cl-face-tracker-distribution@lists.cam.ac.uk
// Licensee acknowledges receipt of notices for the Open Source Components for the initial
// delivery of the Software.
// * 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, 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 Baltru<72>aitis, Peter Robinson, and Louis-Philippe Morency.
// in IEEE Int. Conference on Computer Vision Workshops, 300 Faces in-the-Wild Challenge, 2013.
//
///////////////////////////////////////////////////////////////////////////////
// FeatureExtraction.cpp : Defines the entry point for the feature extraction console application.
// System includes
#include <fstream>
#include <sstream>
// OpenCV includes
#include <opencv2/videoio/videoio.hpp> // Video write
#include <opencv2/videoio/videoio_c.h> // Video write
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
// Boost includes
#include <filesystem.hpp>
#include <filesystem/fstream.hpp>
#include <boost/algorithm/string.hpp>
// Local includes
#include "LandmarkCoreIncludes.h"
#include <Face_utils.h>
#include <FaceAnalyser.h>
#include <GazeEstimation.h>
#define INFO_STREAM( stream ) \
std::cout << stream << std::endl
#define WARN_STREAM( stream ) \
std::cout << "Warning: " << stream << std::endl
#define ERROR_STREAM( stream ) \
std::cout << "Error: " << stream << std::endl
static void printErrorAndAbort( const std::string & error )
{
std::cout << error << std::endl;
}
#define FATAL_STREAM( stream ) \
printErrorAndAbort( std::string( "Fatal error: " ) + stream )
using namespace std;
using namespace boost::filesystem;
vector<string> get_arguments(int argc, char **argv)
{
vector<string> arguments;
// First argument is reserved for the name of the executable
for(int i = 0; i < argc; ++i)
{
arguments.push_back(string(argv[i]));
}
return arguments;
}
// 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 = path(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;
}
}
}
void create_directory(string output_path)
{
// Creating the right directory structure
auto p = path(output_path);
if(!boost::filesystem::exists(p))
{
bool success = boost::filesystem::create_directories(p);
if(!success)
{
cout << "Failed to create a directory..." << p.string() << endl;
}
}
}
void get_output_feature_params(vector<string> &output_similarity_aligned, vector<string> &output_hog_aligned_files, double &similarity_scale,
int &similarity_size, bool &grayscale, bool &rigid, bool& verbose,
bool &output_2D_landmarks, bool &output_3D_landmarks, bool &output_model_params, bool &output_pose, bool &output_AUs, bool &output_gaze,
vector<string> &arguments)
{
output_similarity_aligned.clear();
output_hog_aligned_files.clear();
bool* valid = new bool[arguments.size()];
for (size_t i = 0; i < arguments.size(); ++i)
{
valid[i] = true;
}
string input_root = "";
string output_root = "";
// First check if there is a root argument (so that videos and outputs could be defined more easilly)
for (size_t i = 0; i < arguments.size(); ++i)
{
if (arguments[i].compare("-root") == 0)
{
input_root = arguments[i + 1];
output_root = arguments[i + 1];
i++;
}
if (arguments[i].compare("-inroot") == 0)
{
input_root = arguments[i + 1];
i++;
}
if (arguments[i].compare("-outroot") == 0)
{
output_root = arguments[i + 1];
i++;
}
}
for (size_t i = 0; i < arguments.size(); ++i)
{
if (arguments[i].compare("-simalign") == 0)
{
output_similarity_aligned.push_back(output_root + arguments[i + 1]);
create_directory(output_root + arguments[i + 1]);
valid[i] = false;
valid[i + 1] = false;
i++;
}
else if (arguments[i].compare("-hogalign") == 0)
{
output_hog_aligned_files.push_back(output_root + arguments[i + 1]);
create_directory_from_file(output_root + arguments[i + 1]);
valid[i] = false;
valid[i + 1] = false;
i++;
}
else if (arguments[i].compare("-verbose") == 0)
{
verbose = true;
}
else if (arguments[i].compare("-rigid") == 0)
{
rigid = true;
}
else if (arguments[i].compare("-g") == 0)
{
grayscale = true;
valid[i] = false;
}
else if (arguments[i].compare("-simscale") == 0)
{
similarity_scale = stod(arguments[i + 1]);
valid[i] = false;
valid[i + 1] = false;
i++;
}
else if (arguments[i].compare("-simsize") == 0)
{
similarity_size = stoi(arguments[i + 1]);
valid[i] = false;
valid[i + 1] = false;
i++;
}
else if (arguments[i].compare("-no2Dfp") == 0)
{
output_2D_landmarks = false;
valid[i] = false;
}
else if (arguments[i].compare("-no3Dfp") == 0)
{
output_3D_landmarks = false;
valid[i] = false;
}
else if (arguments[i].compare("-noMparams") == 0)
{
output_model_params = false;
valid[i] = false;
}
else if (arguments[i].compare("-noPose") == 0)
{
output_pose = false;
valid[i] = false;
}
else if (arguments[i].compare("-noAUs") == 0)
{
output_AUs = false;
valid[i] = false;
}
else if (arguments[i].compare("-noGaze") == 0)
{
output_gaze = false;
valid[i] = false;
}
}
for (int i = arguments.size() - 1; i >= 0; --i)
{
if (!valid[i])
{
arguments.erase(arguments.begin() + i);
}
}
}
// Can process images via directories creating a separate output file per directory
void get_image_input_output_params_feats(vector<vector<string> > &input_image_files, bool& as_video, vector<string> &arguments)
{
bool* valid = new bool[arguments.size()];
for(size_t i = 0; i < arguments.size(); ++i)
{
valid[i] = true;
if (arguments[i].compare("-fdir") == 0)
{
// parse the -fdir directory by reading in all of the .png and .jpg files in it
path image_directory (arguments[i+1]);
try
{
// does the file exist and is it a directory
if (exists(image_directory) && is_directory(image_directory))
{
vector<path> file_in_directory;
copy(directory_iterator(image_directory), directory_iterator(), back_inserter(file_in_directory));
// Sort the images in the directory first
sort(file_in_directory.begin(), file_in_directory.end());
vector<string> curr_dir_files;
for (vector<path>::const_iterator file_iterator (file_in_directory.begin()); file_iterator != file_in_directory.end(); ++file_iterator)
{
// Possible image extension .jpg and .png
if(file_iterator->extension().string().compare(".jpg") == 0 || file_iterator->extension().string().compare(".png") == 0)
{
curr_dir_files.push_back(file_iterator->string());
}
}
input_image_files.push_back(curr_dir_files);
}
}
catch (const filesystem_error& ex)
{
cout << ex.what() << '\n';
}
valid[i] = false;
valid[i+1] = false;
i++;
}
else if (arguments[i].compare("-asvid") == 0)
{
as_video = true;
}
}
// Clear up the argument list
for(int i=arguments.size()-1; i >= 0; --i)
{
if(!valid[i])
{
arguments.erase(arguments.begin()+i);
}
}
}
void output_HOG_frame(std::ofstream* hog_file, bool good_frame, const cv::Mat_<double>& hog_descriptor, int num_rows, int num_cols)
{
// Using FHOGs, hence 31 channels
int num_channels = 31;
hog_file->write((char*)(&num_cols), 4);
hog_file->write((char*)(&num_rows), 4);
hog_file->write((char*)(&num_channels), 4);
// Not the best way to store a bool, but will be much easier to read it
float good_frame_float;
if(good_frame)
good_frame_float = 1;
else
good_frame_float = -1;
hog_file->write((char*)(&good_frame_float), 4);
cv::MatConstIterator_<double> descriptor_it = hog_descriptor.begin();
for(int y = 0; y < num_cols; ++y)
{
for(int x = 0; x < num_rows; ++x)
{
for(unsigned int o = 0; o < 31; ++o)
{
float hog_data = (float)(*descriptor_it++);
hog_file->write ((char*)&hog_data, 4);
}
}
}
}
// Some globals for tracking timing information for visualisation
double fps_tracker = -1.0;
int64 t0 = 0;
// Visualising the results
void visualise_tracking(cv::Mat& captured_image, const LandmarkDetector::CLNF& face_model, const LandmarkDetector::FaceModelParameters& det_parameters, cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, int frame_count, double fx, double fy, double cx, double cy)
{
// Drawing the facial landmarks on the face and the bounding box around it if tracking is successful and initialised
double detection_certainty = face_model.detection_certainty;
bool detection_success = face_model.detection_success;
double visualisation_boundary = 0.2;
// Only draw if the reliability is reasonable, the value is slightly ad-hoc
if (detection_certainty < visualisation_boundary)
{
LandmarkDetector::Draw(captured_image, face_model);
double vis_certainty = detection_certainty;
if (vis_certainty > 1)
vis_certainty = 1;
if (vis_certainty < -1)
vis_certainty = -1;
vis_certainty = (vis_certainty + 1) / (visualisation_boundary + 1);
// A rough heuristic for box around the face width
int thickness = (int)std::ceil(2.0* ((double)captured_image.cols) / 640.0);
cv::Vec6d pose_estimate_to_draw = LandmarkDetector::GetCorrectedPoseWorld(face_model, fx, fy, cx, cy);
// Draw it in reddish if uncertain, blueish if certain
LandmarkDetector::DrawBox(captured_image, pose_estimate_to_draw, cv::Scalar((1 - vis_certainty)*255.0, 0, vis_certainty * 255), thickness, fx, fy, cx, cy);
if (det_parameters.track_gaze && detection_success && face_model.eye_model)
{
FaceAnalysis::DrawGaze(captured_image, face_model, gazeDirection0, gazeDirection1, fx, fy, cx, cy);
}
}
// Work out the framerate
if (frame_count % 10 == 0)
{
double t1 = cv::getTickCount();
fps_tracker = 10.0 / (double(t1 - t0) / cv::getTickFrequency());
t0 = t1;
}
// Write out the framerate on the image before displaying it
char fpsC[255];
std::sprintf(fpsC, "%d", (int)fps_tracker);
string fpsSt("FPS:");
fpsSt += fpsC;
cv::putText(captured_image, fpsSt, cv::Point(10, 20), CV_FONT_HERSHEY_SIMPLEX, 0.5, CV_RGB(255, 0, 0));
if (!det_parameters.quiet_mode)
{
cv::namedWindow("tracking_result", 1);
cv::imshow("tracking_result", captured_image);
}
}
void prepareOutputFile(std::ofstream* output_file, bool output_2D_landmarks, bool output_3D_landmarks,
bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze,
int num_landmarks, int num_model_modes, vector<string> au_names_class, vector<string> au_names_reg)
{
*output_file << "frame, timestamp, confidence, success";
if (output_gaze)
{
*output_file << ", gaze_0_x, gaze_0_y, gaze_0_z, gaze_1_x, gaze_1_y, gaze_2_z";
}
if (output_pose)
{
*output_file << ", pose_Tx, pose_Ty, pose_Tz, pose_Rx, pose_Ry, pose_Rz";
}
if (output_2D_landmarks)
{
for (int i = 0; i < num_landmarks; ++i)
{
*output_file << ", x_" << i;
}
for (int i = 0; i < num_landmarks; ++i)
{
*output_file << ", y_" << i;
}
}
if (output_3D_landmarks)
{
for (int i = 0; i < num_landmarks; ++i)
{
*output_file << ", X_" << i;
}
for (int i = 0; i < num_landmarks; ++i)
{
*output_file << ", Y_" << i;
}
for (int i = 0; i < num_landmarks; ++i)
{
*output_file << ", Z_" << i;
}
}
// Outputting model parameters (rigid and non-rigid), the first parameters are the 6 rigid shape parameters, they are followed by the non rigid shape parameters
if (output_model_params)
{
*output_file << ", p_scale, p_rx, p_ry, p_rz, p_tx, p_ty";
for (int i = 0; i < num_model_modes; ++i)
{
*output_file << ", p_" << i;
}
}
if (output_AUs)
{
std::sort(au_names_reg.begin(), au_names_reg.end());
for (string reg_name : au_names_reg)
{
*output_file << ", " << reg_name << "_r";
}
std::sort(au_names_class.begin(), au_names_class.end());
for (string class_name : au_names_class)
{
*output_file << ", " << class_name << "_c";
}
}
*output_file << endl;
}
// Output all of the information into one file in one go (quite a few parameters, but simplifies the flow)
void outputAllFeatures(std::ofstream* output_file, bool output_2D_landmarks, bool output_3D_landmarks,
bool output_model_params, bool output_pose, bool output_AUs, bool output_gaze,
const LandmarkDetector::CLNF& face_model, int frame_count, double time_stamp, bool detection_success,
cv::Point3f gazeDirection0, cv::Point3f gazeDirection1, const cv::Vec6d& pose_estimate, double fx, double fy, double cx, double cy,
const FaceAnalysis::FaceAnalyser& face_analyser)
{
double confidence = 0.5 * (1 - face_model.detection_certainty);
*output_file << frame_count + 1 << ", " << time_stamp << ", " << confidence << ", " << detection_success;
// Output the estimated gaze
if (output_gaze)
{
*output_file << ", " << gazeDirection0.x << ", " << gazeDirection0.y << ", " << gazeDirection0.z
<< ", " << gazeDirection1.x << ", " << gazeDirection1.y << ", " << gazeDirection1.z;
}
// Output the estimated head pose
if (output_pose)
{
*output_file << ", " << pose_estimate[0] << ", " << pose_estimate[1] << ", " << pose_estimate[2]
<< ", " << pose_estimate[3] << ", " << pose_estimate[4] << ", " << pose_estimate[5];
}
// Output the detected 2D facial landmarks
if (output_2D_landmarks)
{
for (int i = 0; i < face_model.pdm.NumberOfPoints() * 2; ++i)
{
*output_file << ", " << face_model.detected_landmarks.at<double>(i);
}
}
// Output the detected 3D facial landmarks
if (output_3D_landmarks)
{
cv::Mat_<double> shape_3D = face_model.GetShape(fx, fy, cx, cy);
for (int i = 0; i < face_model.pdm.NumberOfPoints() * 3; ++i)
{
*output_file << ", " << shape_3D.at<double>(i);
}
}
if (output_model_params)
{
for (int i = 0; i < 6; ++i)
{
*output_file << ", " << face_model.params_global[i];
}
for (int i = 0; i < face_model.pdm.NumberOfModes(); ++i)
{
*output_file << ", " << face_model.params_local.at<double>(i, 0);
}
}
if (output_AUs)
{
auto aus_reg = face_analyser.GetCurrentAUsReg();
vector<string> au_reg_names = face_analyser.GetAURegNames();
std::sort(au_reg_names.begin(), au_reg_names.end());
// write out ar the correct index
for (string au_name : au_reg_names)
{
for (auto au_reg : aus_reg)
{
if (au_name.compare(au_reg.first) == 0)
{
*output_file << ", " << au_reg.second;
break;
}
}
}
if (aus_reg.size() == 0)
{
for (size_t p = 0; p < face_analyser.GetAURegNames().size(); ++p)
{
*output_file << ", 0";
}
}
auto aus_class = face_analyser.GetCurrentAUsClass();
vector<string> au_class_names = face_analyser.GetAUClassNames();
std::sort(au_class_names.begin(), au_class_names.end());
// write out ar the correct index
for (string au_name : au_class_names)
{
for (auto au_class: aus_class)
{
if (au_name.compare(au_class.first) == 0)
{
*output_file << ", " << au_class.second;
break;
}
}
}
if (aus_class.size() == 0)
{
for (size_t p = 0; p < face_analyser.GetAUClassNames().size(); ++p)
{
*output_file << ", 0";
}
}
}
*output_file << endl;
}
void post_process_output_file(FaceAnalysis::FaceAnalyser& face_analyser, string output_file);
int main (int argc, char **argv)
{
vector<string> arguments = get_arguments(argc, argv);
// Some initial parameters that can be overriden from command line
vector<string> input_files, depth_directories, output_files, tracked_videos_output;
LandmarkDetector::FaceModelParameters det_parameters(arguments);
// Always track gaze in feature extraction
det_parameters.track_gaze = true;
// Get the input output file parameters
// Indicates that rotation should be with respect to camera or world coordinates
bool use_world_coordinates;
LandmarkDetector::get_video_input_output_params(input_files, depth_directories, output_files, tracked_videos_output, use_world_coordinates, arguments);
bool video_input = true;
bool verbose = true;
bool images_as_video = false;
vector<vector<string> > input_image_files;
// Adding image support for reading in the files
if(input_files.empty())
{
vector<string> d_files;
vector<string> o_img;
vector<cv::Rect_<double>> bboxes;
get_image_input_output_params_feats(input_image_files, images_as_video, arguments);
if(!input_image_files.empty())
{
video_input = false;
}
}
// Grab camera parameters, if they are not defined (approximate values will be used)
float fx = 0, fy = 0, cx = 0, cy = 0;
int d = 0;
// Get camera parameters
LandmarkDetector::get_camera_params(d, 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
LandmarkDetector::CLNF face_model(det_parameters.model_location);
vector<string> output_similarity_align;
vector<string> output_hog_align_files;
double sim_scale = 0.7;
int sim_size = 112;
bool grayscale = false;
bool video_output = false;
bool rigid = false;
int num_hog_rows;
int num_hog_cols;
// By default output all parameters, but these can be turned off to get smaller files or slightly faster processing times
// use -no2Dfp, -no3Dfp, -noMparams, -noPose, -noAUs, -noGaze to turn them off
bool output_2D_landmarks = true;
bool output_3D_landmarks = true;
bool output_model_params = true;
bool output_pose = true;
bool output_AUs = true;
bool output_gaze = true;
get_output_feature_params(output_similarity_align, output_hog_align_files, sim_scale, sim_size, grayscale, rigid, verbose,
output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, arguments);
// Used for image masking
cv::Mat_<int> triangulation;
string tri_loc;
if(boost::filesystem::exists(path("model/tris_68_full.txt")))
{
std::ifstream triangulation_file("model/tris_68_full.txt");
LandmarkDetector::ReadMat(triangulation_file, triangulation);
tri_loc = "model/tris_68_full.txt";
}
else
{
path loc = path(arguments[0]).parent_path() / "model/tris_68_full.txt";
tri_loc = loc.string();
if(exists(loc))
{
std::ifstream triangulation_file(loc.string());
LandmarkDetector::ReadMat(triangulation_file, triangulation);
}
else
{
cout << "Can't find triangulation files, exiting" << endl;
return 0;
}
}
// Will warp to scaled mean shape
cv::Mat_<double> similarity_normalised_shape = face_model.pdm.mean_shape * sim_scale;
// Discard the z component
similarity_normalised_shape = similarity_normalised_shape(cv::Rect(0, 0, 1, 2*similarity_normalised_shape.rows/3)).clone();
// If multiple video files are tracked, use this to indicate if we are done
bool done = false;
int f_n = -1;
int curr_img = -1;
string au_loc;
if(boost::filesystem::exists(path("AU_predictors/AU_all_best.txt")))
{
au_loc = "AU_predictors/AU_all_best.txt";
}
else
{
path loc = path(arguments[0]).parent_path() / "AU_predictors/AU_all_best.txt";
if(exists(loc))
{
au_loc = loc.string();
}
else
{
cout << "Can't find AU prediction files, exiting" << endl;
return 0;
}
}
// Creating a face analyser that will be used for AU extraction
FaceAnalysis::FaceAnalyser face_analyser(vector<cv::Vec3d>(), 0.7, 112, 112, au_loc, tri_loc);
while(!done) // this is not a for loop as we might also be reading from a webcam
{
string current_file;
cv::VideoCapture video_capture;
cv::Mat captured_image;
int total_frames = -1;
int reported_completion = 0;
double fps_vid_in = -1.0;
if(video_input)
{
// We might specify multiple video files as arguments
if(input_files.size() > 0)
{
f_n++;
current_file = input_files[f_n];
}
else
{
// If we want to write out from webcam
f_n = 0;
}
// Do some grabbing
if( current_file.size() > 0 )
{
INFO_STREAM( "Attempting to read from file: " << current_file );
video_capture = cv::VideoCapture( current_file );
total_frames = (int)video_capture.get(CV_CAP_PROP_FRAME_COUNT);
fps_vid_in = video_capture.get(CV_CAP_PROP_FPS);
// Check if fps is nan or less than 0
if (fps_vid_in != fps_vid_in || fps_vid_in <= 0)
{
INFO_STREAM("FPS of the video file cannot be determined, assuming 30");
fps_vid_in = 30;
}
}
if (!video_capture.isOpened())
{
FATAL_STREAM("Failed to open video source, exiting");
return 1;
}
else
{
INFO_STREAM("Device or file opened");
}
video_capture >> captured_image;
}
else
{
f_n++;
curr_img++;
if(!input_image_files[f_n].empty())
{
string curr_img_file = input_image_files[f_n][curr_img];
captured_image = cv::imread(curr_img_file, -1);
}
else
{
FATAL_STREAM( "No .jpg or .png images in a specified drectory, exiting" );
return 1;
}
}
// If optical centers are not defined just use center of image
if(cx_undefined)
{
cx = captured_image.cols / 2.0f;
cy = captured_image.rows / 2.0f;
}
// Use a rough guess-timate of focal length
if (fx_undefined)
{
fx = 500 * (captured_image.cols / 640.0);
fy = 500 * (captured_image.rows / 480.0);
fx = (fx + fy) / 2.0;
fy = fx;
}
// Creating output files
std::ofstream output_file;
if (!output_files.empty())
{
output_file.open(output_files[f_n], ios_base::out);
prepareOutputFile(&output_file, output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze, face_model.pdm.NumberOfPoints(), face_model.pdm.NumberOfModes(), face_analyser.GetAUClassNames(), face_analyser.GetAURegNames());
}
// Saving the HOG features
std::ofstream hog_output_file;
if(!output_hog_align_files.empty())
{
hog_output_file.open(output_hog_align_files[f_n], ios_base::out | ios_base::binary);
}
// saving the videos
cv::VideoWriter writerFace;
if(!tracked_videos_output.empty())
{
writerFace = cv::VideoWriter(tracked_videos_output[f_n], CV_FOURCC('D', 'I', 'V', 'X'), fps_vid_in, captured_image.size(), true);
}
int frame_count = 0;
// This is useful for a second pass run (if want AU predictions)
vector<cv::Vec6d> params_global_video;
vector<bool> successes_video;
vector<cv::Mat_<double>> params_local_video;
vector<cv::Mat_<double>> detected_landmarks_video;
// Use for timestamping if using a webcam
int64 t_initial = cv::getTickCount();
bool visualise_hog = verbose;
// Timestamp in seconds of current processing
double time_stamp = 0;
INFO_STREAM( "Starting tracking");
while(!captured_image.empty())
{
// Grab the timestamp first
if (video_input)
{
time_stamp = (double)frame_count * (1.0 / fps_vid_in);
}
else
{
// if loading images assume 30fps
time_stamp = (double)frame_count * (1.0 / 30.0);
}
// Reading the images
cv::Mat_<uchar> grayscale_image;
if(captured_image.channels() == 3)
{
cvtColor(captured_image, grayscale_image, CV_BGR2GRAY);
}
else
{
grayscale_image = captured_image.clone();
}
// The actual facial landmark detection / tracking
bool detection_success;
if(video_input || images_as_video)
{
detection_success = LandmarkDetector::DetectLandmarksInVideo(grayscale_image, face_model, det_parameters);
}
else
{
detection_success = LandmarkDetector::DetectLandmarksInImage(grayscale_image, face_model, det_parameters);
}
// Gaze tracking, absolute gaze direction
cv::Point3f gazeDirection0(0, 0, -1);
cv::Point3f gazeDirection1(0, 0, -1);
if (det_parameters.track_gaze && detection_success && face_model.eye_model)
{
FaceAnalysis::EstimateGaze(face_model, gazeDirection0, fx, fy, cx, cy, true);
FaceAnalysis::EstimateGaze(face_model, gazeDirection1, fx, fy, cx, cy, false);
}
// Do face alignment
cv::Mat sim_warped_img;
cv::Mat_<double> hog_descriptor;
// But only if needed in output
if(!output_similarity_align.empty() || hog_output_file.is_open() || output_AUs)
{
face_analyser.AddNextFrame(captured_image, face_model, time_stamp, false, !det_parameters.quiet_mode);
face_analyser.GetLatestAlignedFace(sim_warped_img);
if(!det_parameters.quiet_mode)
{
cv::imshow("sim_warp", sim_warped_img);
}
if(hog_output_file.is_open())
{
FaceAnalysis::Extract_FHOG_descriptor(hog_descriptor, sim_warped_img, num_hog_rows, num_hog_cols);
if(visualise_hog && !det_parameters.quiet_mode)
{
cv::Mat_<double> hog_descriptor_vis;
FaceAnalysis::Visualise_FHOG(hog_descriptor, num_hog_rows, num_hog_cols, hog_descriptor_vis);
cv::imshow("hog", hog_descriptor_vis);
}
}
}
// Work out the pose of the head from the tracked model
cv::Vec6d pose_estimate;
if(use_world_coordinates)
{
pose_estimate = LandmarkDetector::GetCorrectedPoseWorld(face_model, fx, fy, cx, cy);
}
else
{
pose_estimate = LandmarkDetector::GetCorrectedPoseCamera(face_model, fx, fy, cx, cy);
}
if(hog_output_file.is_open())
{
output_HOG_frame(&hog_output_file, detection_success, hog_descriptor, num_hog_rows, num_hog_cols);
}
// Write the similarity normalised output
if(!output_similarity_align.empty())
{
if (sim_warped_img.channels() == 3 && grayscale)
{
cvtColor(sim_warped_img, sim_warped_img, CV_BGR2GRAY);
}
char name[100];
// output the frame number
std::sprintf(name, "frame_det_%06d.png", frame_count);
// Construct the output filename
boost::filesystem::path slash("/");
std::string preferredSlash = slash.make_preferred().string();
string out_file = output_similarity_align[f_n] + preferredSlash + string(name);
imwrite(out_file, sim_warped_img);
}
// Visualising the tracker
visualise_tracking(captured_image, face_model, det_parameters, gazeDirection0, gazeDirection1, frame_count, fx, fy, cx, cy);
// Output the landmarks, pose, gaze, parameters and AUs
outputAllFeatures(&output_file, output_2D_landmarks, output_3D_landmarks, output_model_params, output_pose, output_AUs, output_gaze,
face_model, frame_count, time_stamp, detection_success, gazeDirection0, gazeDirection1,
pose_estimate, fx, fy, cx, cy, face_analyser);
// output the tracked video
if(!tracked_videos_output.empty())
{
writerFace << captured_image;
}
if(video_input)
{
video_capture >> captured_image;
}
else
{
curr_img++;
if(curr_img < (int)input_image_files[f_n].size())
{
string curr_img_file = input_image_files[f_n][curr_img];
captured_image = cv::imread(curr_img_file, -1);
}
else
{
captured_image = cv::Mat();
}
}
// detect key presses
char character_press = cv::waitKey(1);
// restart the tracker
if(character_press == 'r')
{
face_model.Reset();
}
// quit the application
else if(character_press=='q')
{
return(0);
}
// Update the frame count
frame_count++;
if(total_frames != -1)
{
if((double)frame_count/(double)total_frames >= reported_completion / 10.0)
{
cout << reported_completion * 10 << "% ";
reported_completion = reported_completion + 1;
}
}
}
output_file.close();
if(output_files.size() > 0)
{
// If the video is long enough post-process it for AUs
if (output_AUs && frame_count > 100)
{
post_process_output_file(face_analyser, output_files[f_n]);
}
}
// Reset the models for the next video
face_analyser.Reset();
face_model.Reset();
frame_count = 0;
curr_img = -1;
if (total_frames != -1)
{
cout << endl;
}
// break out of the loop if done with all the files (or using a webcam)
if(f_n == input_files.size() -1 || input_files.empty())
{
done = true;
}
}
return 0;
}
// Allows for post processing of the AU signal
void post_process_output_file(FaceAnalysis::FaceAnalyser& face_analyser, string output_file)
{
vector<double> certainties;
vector<bool> successes;
vector<double> timestamps;
vector<std::pair<std::string, vector<double>>> predictions_reg;
vector<std::pair<std::string, vector<double>>> predictions_class;
// Construct the new values to overwrite the output file with
face_analyser.ExtractAllPredictionsOfflineReg(predictions_reg, certainties, successes, timestamps);
face_analyser.ExtractAllPredictionsOfflineClass(predictions_class, certainties, successes, timestamps);
int num_class = predictions_class.size();
int num_reg = predictions_reg.size();
// Extract the indices of writing out first
vector<string> au_reg_names = face_analyser.GetAURegNames();
std::sort(au_reg_names.begin(), au_reg_names.end());
vector<int> inds_reg;
// write out ar the correct index
for (string au_name : au_reg_names)
{
for (int i = 0; i < num_reg; ++i)
{
if (au_name.compare(predictions_reg[i].first) == 0)
{
inds_reg.push_back(i);
break;
}
}
}
vector<string> au_class_names = face_analyser.GetAUClassNames();
std::sort(au_class_names.begin(), au_class_names.end());
vector<int> inds_class;
// write out ar the correct index
for (string au_name : au_class_names)
{
for (int i = 0; i < num_class; ++i)
{
if (au_name.compare(predictions_class[i].first) == 0)
{
inds_class.push_back(i);
break;
}
}
}
// Read all of the output file in
vector<string> output_file_contents;
std::ifstream infile(output_file);
string line;
while (std::getline(infile, line))
output_file_contents.push_back(line);
infile.close();
// Read the header and find all _r and _c parts in a file and use their indices
std::vector<std::string> tokens;
boost::split(tokens, output_file_contents[0], boost::is_any_of(","));
int begin_ind = -1;
for (int i = 0; i < tokens.size(); ++i)
{
if (tokens[i].find("AU") != string::npos && begin_ind == -1)
{
begin_ind = i;
break;
}
}
int end_ind = begin_ind + num_class + num_reg;
// Now overwrite the whole file
std::ofstream outfile(output_file, ios_base::out);
// Write the header
outfile << output_file_contents[0].c_str() << endl;
// Write the contents
for (int i = 1; i < output_file_contents.size(); ++i)
{
std::vector<std::string> tokens;
boost::split(tokens, output_file_contents[i], boost::is_any_of(","));
outfile << tokens[0];
for (int t = 1; t < tokens.size(); ++t)
{
if (t >= begin_ind && t < end_ind)
{
if(t - begin_ind < num_reg)
{
outfile << "," << predictions_reg[inds_reg[t - begin_ind]].second[i - 1];
}
else
{
outfile << "," << predictions_class[inds_class[t - begin_ind - num_reg]].second[i - 1];
}
}
else
{
outfile << "," << tokens[t];
}
}
outfile << endl;
}
}
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