face_recognize.cpp
6.72 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
// 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/dnn.hpp"
#include <algorithm>
namespace cv
{
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();
face_feature1 /= norm(face_feature1);
face_feature2 /= norm(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;
};
Ptr<FaceRecognizerSF> FaceRecognizerSF::create(const String& model, const String& config, int backend_id, int target_id)
{
return makePtr<FaceRecognizerSFImpl>(model, config, backend_id, target_id);
}
} // namespace cv