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sustaining_gazes/lib/local/FaceAnalyser/src/Face_utils.cpp
Tadas Baltrusaitis 3a3b375197 Another attempt at a fix.
2017-10-25 19:13:47 +01:00

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///////////////////////////////////////////////////////////////////////////////
// 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.
//
///////////////////////////////////////////////////////////////////////////////
#include <Face_utils.h>
#include <PAW.h>
// OpenCV includes
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/calib3d.hpp>
// For FHOG visualisation
#include <dlib/opencv.h>
#include <dlib/image_processing/frontal_face_detector.h>
using namespace std;
namespace FaceAnalysis
{
// Pick only the more stable/rigid points under changes of expression
void extract_rigid_points(cv::Mat_<float>& source_points, cv::Mat_<float>& destination_points)
{
if(source_points.rows == 68)
{
cv::Mat_<float> tmp_source = source_points.clone();
source_points = cv::Mat_<float>();
// Push back the rigid points (some face outline, eyes, and nose)
source_points.push_back(tmp_source.row(1));
source_points.push_back(tmp_source.row(2));
source_points.push_back(tmp_source.row(3));
source_points.push_back(tmp_source.row(4));
source_points.push_back(tmp_source.row(12));
source_points.push_back(tmp_source.row(13));
source_points.push_back(tmp_source.row(14));
source_points.push_back(tmp_source.row(15));
source_points.push_back(tmp_source.row(27));
source_points.push_back(tmp_source.row(28));
source_points.push_back(tmp_source.row(29));
source_points.push_back(tmp_source.row(31));
source_points.push_back(tmp_source.row(32));
source_points.push_back(tmp_source.row(33));
source_points.push_back(tmp_source.row(34));
source_points.push_back(tmp_source.row(35));
source_points.push_back(tmp_source.row(36));
source_points.push_back(tmp_source.row(39));
source_points.push_back(tmp_source.row(40));
source_points.push_back(tmp_source.row(41));
source_points.push_back(tmp_source.row(42));
source_points.push_back(tmp_source.row(45));
source_points.push_back(tmp_source.row(46));
source_points.push_back(tmp_source.row(47));
cv::Mat_<float> tmp_dest = destination_points.clone();
destination_points = cv::Mat_<float>();
// Push back the rigid points
destination_points.push_back(tmp_dest.row(1));
destination_points.push_back(tmp_dest.row(2));
destination_points.push_back(tmp_dest.row(3));
destination_points.push_back(tmp_dest.row(4));
destination_points.push_back(tmp_dest.row(12));
destination_points.push_back(tmp_dest.row(13));
destination_points.push_back(tmp_dest.row(14));
destination_points.push_back(tmp_dest.row(15));
destination_points.push_back(tmp_dest.row(27));
destination_points.push_back(tmp_dest.row(28));
destination_points.push_back(tmp_dest.row(29));
destination_points.push_back(tmp_dest.row(31));
destination_points.push_back(tmp_dest.row(32));
destination_points.push_back(tmp_dest.row(33));
destination_points.push_back(tmp_dest.row(34));
destination_points.push_back(tmp_dest.row(35));
destination_points.push_back(tmp_dest.row(36));
destination_points.push_back(tmp_dest.row(39));
destination_points.push_back(tmp_dest.row(40));
destination_points.push_back(tmp_dest.row(41));
destination_points.push_back(tmp_dest.row(42));
destination_points.push_back(tmp_dest.row(45));
destination_points.push_back(tmp_dest.row(46));
destination_points.push_back(tmp_dest.row(47));
}
}
// Aligning a face to a common reference frame
void AlignFace(cv::Mat& aligned_face, const cv::Mat& frame, const cv::Mat_<float>& detected_landmarks, cv::Vec6d params_global, const PDM& pdm, bool rigid, float sim_scale, int out_width, int out_height)
{
// Will warp to scaled mean shape
cv::Mat_<float> similarity_normalised_shape = 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();
cv::Mat_<float> source_landmarks = detected_landmarks.reshape(1, 2).t();
cv::Mat_<float> destination_landmarks = similarity_normalised_shape.reshape(1, 2).t();
// Aligning only the more rigid points
if(rigid)
{
extract_rigid_points(source_landmarks, destination_landmarks);
}
// TODO rem the doubles here
cv::Matx22d scale_rot_matrix = AlignShapesWithScale(source_landmarks, destination_landmarks);
cv::Matx23d warp_matrix;
warp_matrix(0,0) = scale_rot_matrix(0,0);
warp_matrix(0,1) = scale_rot_matrix(0,1);
warp_matrix(1,0) = scale_rot_matrix(1,0);
warp_matrix(1,1) = scale_rot_matrix(1,1);
double tx = params_global[4];
double ty = params_global[5];
cv::Vec2d T(tx, ty);
T = scale_rot_matrix * T;
// Make sure centering is correct
warp_matrix(0,2) = -T(0) + out_width/2;
warp_matrix(1,2) = -T(1) + out_height/2;
cv::warpAffine(frame, aligned_face, warp_matrix, cv::Size(out_width, out_height), cv::INTER_LINEAR);
}
// Aligning a face to a common reference frame
void AlignFaceMask(cv::Mat& aligned_face, const cv::Mat& frame, const cv::Mat_<float>& detected_landmarks, cv::Vec6f params_global, const PDM& pdm, const cv::Mat_<int>& triangulation, bool rigid, float sim_scale, int out_width, int out_height)
{
// Will warp to scaled mean shape
cv::Mat_<float> similarity_normalised_shape = 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();
cv::Mat_<float> source_landmarks = detected_landmarks.reshape(1, 2).t();
cv::Mat_<float> destination_landmarks = similarity_normalised_shape.reshape(1, 2).t();
// Aligning only the more rigid points
if(rigid)
{
extract_rigid_points(source_landmarks, destination_landmarks);
}
cv::Matx22f scale_rot_matrix = AlignShapesWithScale(source_landmarks, destination_landmarks);
cv::Matx23f warp_matrix;
warp_matrix(0,0) = scale_rot_matrix(0,0);
warp_matrix(0,1) = scale_rot_matrix(0,1);
warp_matrix(1,0) = scale_rot_matrix(1,0);
warp_matrix(1,1) = scale_rot_matrix(1,1);
float tx = params_global[4];
float ty = params_global[5];
cv::Vec2f T(tx, ty);
T = scale_rot_matrix * T;
// Make sure centering is correct
warp_matrix(0,2) = -T(0) + out_width/2;
warp_matrix(1,2) = -T(1) + out_height/2;
cv::warpAffine(frame, aligned_face, warp_matrix, cv::Size(out_width, out_height), cv::INTER_LINEAR);
// Move the destination landmarks there as well
cv::Matx22f warp_matrix_2d(warp_matrix(0,0), warp_matrix(0,1), warp_matrix(1,0), warp_matrix(1,1));
destination_landmarks = cv::Mat(detected_landmarks.reshape(1, 2).t()) * cv::Mat(warp_matrix_2d).t();
destination_landmarks.col(0) = destination_landmarks.col(0) + warp_matrix(0,2);
destination_landmarks.col(1) = destination_landmarks.col(1) + warp_matrix(1,2);
// Move the eyebrows up to include more of upper face
destination_landmarks.at<float>(0,1) -= (30/0.7)*sim_scale;
destination_landmarks.at<float>(16,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(17,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(18,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(19,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(20,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(21,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(22,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(23,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(24,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(25,1) -= (30 / 0.7)*sim_scale;
destination_landmarks.at<float>(26,1) -= (30 / 0.7)*sim_scale;
destination_landmarks = cv::Mat(destination_landmarks.t()).reshape(1, 1).t();
FaceAnalysis::PAW paw(destination_landmarks, triangulation, 0, 0, aligned_face.cols-1, aligned_face.rows-1);
// Mask each of the channels (a bit of a roundabout way, but OpenCV 3.1 in debug mode doesn't seem to be able to handle a more direct way using split and merge)
vector<cv::Mat> aligned_face_channels(aligned_face.channels());
for (int c = 0; c < aligned_face.channels(); ++c)
{
cv::extractChannel(aligned_face, aligned_face_channels[c], c);
}
for(size_t i = 0; i < aligned_face_channels.size(); ++i)
{
cv::multiply(aligned_face_channels[i], paw.pixel_mask, aligned_face_channels[i], 1.0, CV_8U);
}
if(aligned_face.channels() == 3)
{
cv::Mat planes[] = { aligned_face_channels[0], aligned_face_channels[1], aligned_face_channels[2] };
cv::merge(planes, 3, aligned_face);
}
else
{
aligned_face = aligned_face_channels[0];
}
}
void Visualise_FHOG(const cv::Mat_<double>& descriptor, int num_rows, int num_cols, cv::Mat& visualisation)
{
// First convert to dlib format
dlib::array2d<dlib::matrix<float,31,1> > hog(num_rows, num_cols);
cv::MatConstIterator_<double> descriptor_it = 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)
{
hog[y][x](o) = *descriptor_it++;
}
}
}
// Draw the FHOG to OpenCV format
auto fhog_vis = dlib::draw_fhog(hog);
visualisation = dlib::toMat(fhog_vis).clone();
}
// Create a row vector Felzenszwalb HOG descriptor from a given image
void Extract_FHOG_descriptor(cv::Mat_<double>& descriptor, const cv::Mat& image, int& num_rows, int& num_cols, int cell_size)
{
dlib::array2d<dlib::matrix<float,31,1> > hog;
if(image.channels() == 1)
{
dlib::cv_image<uchar> dlib_warped_img(image);
dlib::extract_fhog_features(dlib_warped_img, hog, cell_size);
}
else
{
dlib::cv_image<dlib::bgr_pixel> dlib_warped_img(image);
dlib::extract_fhog_features(dlib_warped_img, hog, cell_size);
}
// Convert to a usable format
num_cols = hog.nc();
num_rows = hog.nr();
descriptor = cv::Mat_<double>(1, num_cols * num_rows * 31);
cv::MatIterator_<double> descriptor_it = 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)
{
*descriptor_it++ = (double)hog[y][x](o);
}
}
}
}
// Extract summary statistics (mean, stdev, min, max) from each dimension of a descriptor, each row is a descriptor
void ExtractSummaryStatistics(const cv::Mat_<double>& descriptors, cv::Mat_<double>& sum_stats, bool use_mean, bool use_stdev, bool use_max_min)
{
// Using four summary statistics at the moment
// Means, stds, mins, maxs
int num_stats = 0;
if(use_mean)
num_stats++;
if(use_stdev)
num_stats++;
if(use_max_min)
num_stats++;
sum_stats = cv::Mat_<double>(1, descriptors.cols * num_stats, 0.0);
for(int i = 0; i < descriptors.cols; ++i)
{
cv::Scalar mean, stdev;
cv::meanStdDev(descriptors.col(i), mean, stdev);
int add = 0;
if(use_mean)
{
sum_stats.at<double>(0, i*num_stats + add) = mean[0];
add++;
}
if(use_stdev)
{
sum_stats.at<double>(0, i*num_stats + add) = stdev[0];
add++;
}
if(use_max_min)
{
double min, max;
cv::minMaxIdx(descriptors.col(i), &min, &max);
sum_stats.at<double>(0, i*num_stats + add) = max - min;
add++;
}
}
}
void AddDescriptor(cv::Mat_<double>& descriptors, cv::Mat_<double> new_descriptor, int curr_frame, int num_frames_to_keep)
{
if(descriptors.empty())
{
descriptors = cv::Mat_<double>(num_frames_to_keep, new_descriptor.cols, 0.0);
}
int row_to_change = curr_frame % num_frames_to_keep;
new_descriptor.copyTo(descriptors.row(row_to_change));
}
//===========================================================================
// Point set and landmark manipulation functions
//===========================================================================
// Using Kabsch's algorithm for aligning shapes
//This assumes that align_from and align_to are already mean normalised
cv::Matx22f AlignShapesKabsch2D(const cv::Mat_<float>& align_from, const cv::Mat_<float>& align_to)
{
cv::SVD svd(align_from.t() * align_to);
// make sure no reflection is there
// corr ensures that we do only rotaitons and not reflections
float d = cv::determinant(svd.vt.t() * svd.u.t());
cv::Matx22f corr = cv::Matx22f::eye();
if (d > 0)
{
corr(1, 1) = 1;
}
else
{
corr(1, 1) = -1;
}
cv::Matx22f R;
cv::Mat(svd.vt.t()*cv::Mat(corr)*svd.u.t()).copyTo(R);
return R;
}
//=============================================================================
// Basically Kabsch's algorithm but also allows the collection of points to be different in scale from each other
cv::Matx22f AlignShapesWithScale(cv::Mat_<float>& src, cv::Mat_<float> dst)
{
int n = src.rows;
// First we mean normalise both src and dst
float mean_src_x = cv::mean(src.col(0))[0];
float mean_src_y = cv::mean(src.col(1))[0];
float mean_dst_x = cv::mean(dst.col(0))[0];
float mean_dst_y = cv::mean(dst.col(1))[0];
cv::Mat_<float> src_mean_normed = src.clone();
src_mean_normed.col(0) = src_mean_normed.col(0) - mean_src_x;
src_mean_normed.col(1) = src_mean_normed.col(1) - mean_src_y;
cv::Mat_<float> dst_mean_normed = dst.clone();
dst_mean_normed.col(0) = dst_mean_normed.col(0) - mean_dst_x;
dst_mean_normed.col(1) = dst_mean_normed.col(1) - mean_dst_y;
// Find the scaling factor of each
cv::Mat src_sq;
cv::pow(src_mean_normed, 2, src_sq);
cv::Mat dst_sq;
cv::pow(dst_mean_normed, 2, dst_sq);
float s_src = sqrt(cv::sum(src_sq)[0] / n);
float s_dst = sqrt(cv::sum(dst_sq)[0] / n);
src_mean_normed = src_mean_normed / s_src;
dst_mean_normed = dst_mean_normed / s_dst;
float s = s_dst / s_src;
// Get the rotation
cv::Matx22f R = AlignShapesKabsch2D(src_mean_normed, dst_mean_normed);
cv::Matx22f A;
cv::Mat(s * R).copyTo(A);
cv::Mat_<float> aligned = (cv::Mat(cv::Mat(A) * src.t())).t();
cv::Mat_<float> offset = dst - aligned;
float t_x = cv::mean(offset.col(0))[0];
float t_y = cv::mean(offset.col(1))[0];
return A;
}
//===========================================================================
// Visualisation functions
//===========================================================================
void Project(cv::Mat_<float>& dest, const cv::Mat_<float>& mesh, float fx, float fy, float cx, float cy)
{
dest = cv::Mat_<float>(mesh.rows, 2, 0.0);
int num_points = mesh.rows;
float X, Y, Z;
cv::Mat_<float>::const_iterator mData = mesh.begin();
cv::Mat_<float>::iterator projected = dest.begin();
for (int i = 0; i < num_points; i++)
{
// Get the points
X = *(mData++);
Y = *(mData++);
Z = *(mData++);
float x;
float y;
// if depth is 0 the projection is different
if (Z != 0)
{
x = ((X * fx / Z) + cx);
y = ((Y * fy / Z) + cy);
}
else
{
x = X;
y = Y;
}
// Project and store in dest matrix
(*projected++) = x;
(*projected++) = y;
}
}
//===========================================================================
// Angle representation conversion helpers
//===========================================================================
// Using the XYZ convention R = Rx * Ry * Rz, left-handed positive sign
cv::Matx33f Euler2RotationMatrix(const cv::Vec3f& eulerAngles)
{
cv::Matx33f rotation_matrix;
float s1 = sin(eulerAngles[0]);
float s2 = sin(eulerAngles[1]);
float s3 = sin(eulerAngles[2]);
float c1 = cos(eulerAngles[0]);
float c2 = cos(eulerAngles[1]);
float c3 = cos(eulerAngles[2]);
rotation_matrix(0, 0) = c2 * c3;
rotation_matrix(0, 1) = -c2 *s3;
rotation_matrix(0, 2) = s2;
rotation_matrix(1, 0) = c1 * s3 + c3 * s1 * s2;
rotation_matrix(1, 1) = c1 * c3 - s1 * s2 * s3;
rotation_matrix(1, 2) = -c2 * s1;
rotation_matrix(2, 0) = s1 * s3 - c1 * c3 * s2;
rotation_matrix(2, 1) = c3 * s1 + c1 * s2 * s3;
rotation_matrix(2, 2) = c1 * c2;
return rotation_matrix;
}
// Using the XYZ convention R = Rx * Ry * Rz, left-handed positive sign
cv::Vec3f RotationMatrix2Euler(const cv::Matx33f& rotation_matrix)
{
float q0 = sqrt(1 + rotation_matrix(0, 0) + rotation_matrix(1, 1) + rotation_matrix(2, 2)) / 2.0f;
float q1 = (rotation_matrix(2, 1) - rotation_matrix(1, 2)) / (4.0f*q0);
float q2 = (rotation_matrix(0, 2) - rotation_matrix(2, 0)) / (4.0f*q0);
float q3 = (rotation_matrix(1, 0) - rotation_matrix(0, 1)) / (4.0f*q0);
float t1 = 2.0f * (q0*q2 + q1*q3);
float yaw = asin(2.0 * (q0*q2 + q1*q3));
float pitch = atan2(2.0 * (q0*q1 - q2*q3), q0*q0 - q1*q1 - q2*q2 + q3*q3);
float roll = atan2(2.0 * (q0*q3 - q1*q2), q0*q0 + q1*q1 - q2*q2 - q3*q3);
return cv::Vec3f(pitch, yaw, roll);
}
cv::Vec3f Euler2AxisAngle(const cv::Vec3f& euler)
{
cv::Matx33f rotMatrix = Euler2RotationMatrix(euler);
cv::Vec3f axis_angle;
cv::Rodrigues(rotMatrix, axis_angle);
return axis_angle;
}
cv::Vec3f AxisAngle2Euler(const cv::Vec3f& axis_angle)
{
cv::Matx33f rotation_matrix;
cv::Rodrigues(axis_angle, rotation_matrix);
return RotationMatrix2Euler(rotation_matrix);
}
cv::Matx33f AxisAngle2RotationMatrix(const cv::Vec3f& axis_angle)
{
cv::Matx33f rotation_matrix;
cv::Rodrigues(axis_angle, rotation_matrix);
return rotation_matrix;
}
cv::Vec3f RotationMatrix2AxisAngle(const cv::Matx33f& rotation_matrix)
{
cv::Vec3f axis_angle;
cv::Rodrigues(rotation_matrix, axis_angle);
return axis_angle;
}
//============================================================================
// Matrix reading functionality
//============================================================================
// Reading in a matrix from a stream
void ReadMat(std::ifstream& stream, cv::Mat &output_mat)
{
// Read in the number of rows, columns and the data type
int row, col, type;
stream >> row >> col >> type;
output_mat = cv::Mat(row, col, type);
switch (output_mat.type())
{
case CV_64FC1:
{
cv::MatIterator_<double> begin_it = output_mat.begin<double>();
cv::MatIterator_<double> end_it = output_mat.end<double>();
while (begin_it != end_it)
{
stream >> *begin_it++;
}
}
break;
case CV_32FC1:
{
cv::MatIterator_<float> begin_it = output_mat.begin<float>();
cv::MatIterator_<float> end_it = output_mat.end<float>();
while (begin_it != end_it)
{
stream >> *begin_it++;
}
}
break;
case CV_32SC1:
{
cv::MatIterator_<int> begin_it = output_mat.begin<int>();
cv::MatIterator_<int> end_it = output_mat.end<int>();
while (begin_it != end_it)
{
stream >> *begin_it++;
}
}
break;
case CV_8UC1:
{
cv::MatIterator_<uchar> begin_it = output_mat.begin<uchar>();
cv::MatIterator_<uchar> end_it = output_mat.end<uchar>();
while (begin_it != end_it)
{
stream >> *begin_it++;
}
}
break;
default:
printf("ERROR(%s,%d) : Unsupported Matrix type %d!\n", __FILE__, __LINE__, output_mat.type()); abort();
}
}
void ReadMatBin(std::ifstream& stream, cv::Mat &output_mat)
{
// Read in the number of rows, columns and the data type
int row, col, type;
stream.read((char*)&row, 4);
stream.read((char*)&col, 4);
stream.read((char*)&type, 4);
output_mat = cv::Mat(row, col, type);
int size = output_mat.rows * output_mat.cols * output_mat.elemSize();
stream.read((char *)output_mat.data, size);
}
// Skipping lines that start with # (together with empty lines)
void SkipComments(std::ifstream& stream)
{
while (stream.peek() == '#' || stream.peek() == '\n' || stream.peek() == ' ' || stream.peek() == '\r')
{
std::string skipped;
std::getline(stream, skipped);
}
}
}
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