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face_recognize.cpp
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face_recognize.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "precomp.hpp"
#include "opencv2/core.hpp"
#ifdef HAVE_OPENCV_DNN
#include "opencv2/dnn.hpp"
#endif
#include <algorithm>
namespace cv
{
#ifdef HAVE_OPENCV_DNN
class FaceRecognizerSFImpl : public FaceRecognizerSF
{
public:
FaceRecognizerSFImpl(const String& model, const String& config, int backend_id, int target_id)
{
net = dnn::readNet(model, config);
CV_Assert(!net.empty());
net.setPreferableBackend(backend_id);
net.setPreferableTarget(target_id);
};
void alignCrop(InputArray _src_img, InputArray _face_mat, OutputArray _aligned_img) const override
{
Mat face_mat = _face_mat.getMat();
float src_point[5][2];
for (int row = 0; row < 5; ++row)
{
for(int col = 0; col < 2; ++col)
{
src_point[row][col] = face_mat.at<float>(0, row*2+col+4);
}
}
Mat warp_mat = getSimilarityTransformMatrix(src_point);
warpAffine(_src_img, _aligned_img, warp_mat, Size(112, 112), INTER_LINEAR);
};
void feature(InputArray _aligned_img, OutputArray _face_feature) override
{
Mat inputBolb = dnn::blobFromImage(_aligned_img, 1, Size(112, 112), Scalar(0, 0, 0), true, false);
net.setInput(inputBolb);
net.forward(_face_feature);
};
double match(InputArray _face_feature1, InputArray _face_feature2, int dis_type) const override
{
Mat face_feature1 = _face_feature1.getMat(), face_feature2 = _face_feature2.getMat();
normalize(face_feature1, face_feature1);
normalize(face_feature2, face_feature2);
if(dis_type == DisType::FR_COSINE){
return sum(face_feature1.mul(face_feature2))[0];
}else if(dis_type == DisType::FR_NORM_L2){
return norm(face_feature1, face_feature2);
}else{
throw std::invalid_argument("invalid parameter " + std::to_string(dis_type));
}
};
private:
Mat getSimilarityTransformMatrix(float src[5][2]) const {
float dst[5][2] = { {38.2946f, 51.6963f}, {73.5318f, 51.5014f}, {56.0252f, 71.7366f}, {41.5493f, 92.3655f}, {70.7299f, 92.2041f} };
float avg0 = (src[0][0] + src[1][0] + src[2][0] + src[3][0] + src[4][0]) / 5;
float avg1 = (src[0][1] + src[1][1] + src[2][1] + src[3][1] + src[4][1]) / 5;
//Compute mean of src and dst.
float src_mean[2] = { avg0, avg1 };
float dst_mean[2] = { 56.0262f, 71.9008f };
//Subtract mean from src and dst.
float src_demean[5][2];
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < 5; j++)
{
src_demean[j][i] = src[j][i] - src_mean[i];
}
}
float dst_demean[5][2];
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < 5; j++)
{
dst_demean[j][i] = dst[j][i] - dst_mean[i];
}
}
double A00 = 0.0, A01 = 0.0, A10 = 0.0, A11 = 0.0;
for (int i = 0; i < 5; i++)
A00 += dst_demean[i][0] * src_demean[i][0];
A00 = A00 / 5;
for (int i = 0; i < 5; i++)
A01 += dst_demean[i][0] * src_demean[i][1];
A01 = A01 / 5;
for (int i = 0; i < 5; i++)
A10 += dst_demean[i][1] * src_demean[i][0];
A10 = A10 / 5;
for (int i = 0; i < 5; i++)
A11 += dst_demean[i][1] * src_demean[i][1];
A11 = A11 / 5;
Mat A = (Mat_<double>(2, 2) << A00, A01, A10, A11);
double d[2] = { 1.0, 1.0 };
double detA = A00 * A11 - A01 * A10;
if (detA < 0)
d[1] = -1;
double T[3][3] = { {1.0, 0.0, 0.0}, {0.0, 1.0, 0.0}, {0.0, 0.0, 1.0} };
Mat s, u, vt, v;
SVD::compute(A, s, u, vt);
double smax = s.ptr<double>(0)[0]>s.ptr<double>(1)[0] ? s.ptr<double>(0)[0] : s.ptr<double>(1)[0];
double tol = smax * 2 * FLT_MIN;
int rank = 0;
if (s.ptr<double>(0)[0]>tol)
rank += 1;
if (s.ptr<double>(1)[0]>tol)
rank += 1;
double arr_u[2][2] = { {u.ptr<double>(0)[0], u.ptr<double>(0)[1]}, {u.ptr<double>(1)[0], u.ptr<double>(1)[1]} };
double arr_vt[2][2] = { {vt.ptr<double>(0)[0], vt.ptr<double>(0)[1]}, {vt.ptr<double>(1)[0], vt.ptr<double>(1)[1]} };
double det_u = arr_u[0][0] * arr_u[1][1] - arr_u[0][1] * arr_u[1][0];
double det_vt = arr_vt[0][0] * arr_vt[1][1] - arr_vt[0][1] * arr_vt[1][0];
if (rank == 1)
{
if ((det_u*det_vt) > 0)
{
Mat uvt = u*vt;
T[0][0] = uvt.ptr<double>(0)[0];
T[0][1] = uvt.ptr<double>(0)[1];
T[1][0] = uvt.ptr<double>(1)[0];
T[1][1] = uvt.ptr<double>(1)[1];
}
else
{
double temp = d[1];
d[1] = -1;
Mat D = (Mat_<double>(2, 2) << d[0], 0.0, 0.0, d[1]);
Mat Dvt = D*vt;
Mat uDvt = u*Dvt;
T[0][0] = uDvt.ptr<double>(0)[0];
T[0][1] = uDvt.ptr<double>(0)[1];
T[1][0] = uDvt.ptr<double>(1)[0];
T[1][1] = uDvt.ptr<double>(1)[1];
d[1] = temp;
}
}
else
{
Mat D = (Mat_<double>(2, 2) << d[0], 0.0, 0.0, d[1]);
Mat Dvt = D*vt;
Mat uDvt = u*Dvt;
T[0][0] = uDvt.ptr<double>(0)[0];
T[0][1] = uDvt.ptr<double>(0)[1];
T[1][0] = uDvt.ptr<double>(1)[0];
T[1][1] = uDvt.ptr<double>(1)[1];
}
double var1 = 0.0;
for (int i = 0; i < 5; i++)
var1 += src_demean[i][0] * src_demean[i][0];
var1 = var1 / 5;
double var2 = 0.0;
for (int i = 0; i < 5; i++)
var2 += src_demean[i][1] * src_demean[i][1];
var2 = var2 / 5;
double scale = 1.0 / (var1 + var2)* (s.ptr<double>(0)[0] * d[0] + s.ptr<double>(1)[0] * d[1]);
double TS[2];
TS[0] = T[0][0] * src_mean[0] + T[0][1] * src_mean[1];
TS[1] = T[1][0] * src_mean[0] + T[1][1] * src_mean[1];
T[0][2] = dst_mean[0] - scale*TS[0];
T[1][2] = dst_mean[1] - scale*TS[1];
T[0][0] *= scale;
T[0][1] *= scale;
T[1][0] *= scale;
T[1][1] *= scale;
Mat transform_mat = (Mat_<double>(2, 3) << T[0][0], T[0][1], T[0][2], T[1][0], T[1][1], T[1][2]);
return transform_mat;
}
private:
dnn::Net net;
};
#endif
Ptr<FaceRecognizerSF> FaceRecognizerSF::create(const String& model, const String& config, int backend_id, int target_id)
{
#ifdef HAVE_OPENCV_DNN
return makePtr<FaceRecognizerSFImpl>(model, config, backend_id, target_id);
#else
CV_UNUSED(model); CV_UNUSED(config); CV_UNUSED(backend_id); CV_UNUSED(target_id);
CV_Error(cv::Error::StsNotImplemented, "cv::FaceRecognizerSF requires enabled 'dnn' module");
#endif
}
} // namespace cv