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
183
184
185
186
187
188
189
190
191
192
193
194
// 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 "face_alignmentimpl.hpp"
#include <vector>
using namespace std;
namespace cv{
namespace face{
FacemarkKazemi::~FacemarkKazemi(){}
FacemarkKazemiImpl:: ~FacemarkKazemiImpl(){}
unsigned long FacemarkKazemiImpl::left(unsigned long index){
return 2*index+1;
}
unsigned long FacemarkKazemiImpl::right(unsigned long index){
return 2*index+2;
}
bool FacemarkKazemiImpl::setFaceDetector(FN_FaceDetector f, void* userData){
faceDetector = f;
faceDetectorData = userData;
//printf("face detector is configured\n");
return true;
}
bool FacemarkKazemiImpl::getFaces(InputArray image, OutputArray faces)
{
CV_Assert(faceDetector);
return faceDetector(image, faces, faceDetectorData);
}
FacemarkKazemiImpl::FacemarkKazemiImpl(const FacemarkKazemi::Params& parameters) :
faceDetector(NULL),
faceDetectorData(NULL)
{
minmeanx=8000.0;
maxmeanx=0.0;
minmeany=8000.0;
maxmeany=0.0;
isModelLoaded =false;
params = parameters;
}
FacemarkKazemi::Params::Params(){
//These variables are used for training data
//These are initialised as described in the research paper
//referenced above
cascade_depth = 15;
tree_depth = 5;
num_trees_per_cascade_level = 500;
learning_rate = float(0.1);
oversampling_amount = 20;
num_test_coordinates = 500;
lambda = float(0.1);
num_test_splits = 20;
}
bool FacemarkKazemiImpl::convertToActual(Rect r,Mat &warp){
Point2f srcTri[3],dstTri[3];
srcTri[0]=Point2f(0,0);
srcTri[1]=Point2f(1,0);
srcTri[2]=Point2f(0,1);
dstTri[0]=Point2f((float)r.x,(float)r.y);
dstTri[1]=Point2f((float)r.x+r.width,(float)r.y);
dstTri[2]=Point2f((float)r.x,(float)r.y+(float)1.3*r.height);
warp=getAffineTransform(srcTri,dstTri);
return true;
}
bool FacemarkKazemiImpl::convertToUnit(Rect r,Mat &warp){
Point2f srcTri[3],dstTri[3];
dstTri[0]=Point2f(0,0);
dstTri[1]=Point2f(1,0);
dstTri[2]=Point2f(0,1);
srcTri[0]=Point2f((float)r.x,(float)r.y);
srcTri[1]=Point2f((float)r.x+r.width,(float)r.y);
srcTri[2]=Point2f((float)r.x,(float)r.y+(float)1.3*r.height);
warp=getAffineTransform(srcTri,dstTri);
return true;
}
bool FacemarkKazemiImpl::setMeanExtreme(){
if(meanshape.empty()){
String error_message = "Model not loaded properly.No mean shape found.Aborting...";
CV_Error(Error::StsBadArg, error_message);
}
for(size_t i=0;i<meanshape.size();i++){
if(meanshape[i].x>maxmeanx)
maxmeanx = meanshape[i].x;
if(meanshape[i].x<minmeanx)
minmeanx = meanshape[i].x;
if(meanshape[i].y>maxmeany)
maxmeany = meanshape[i].y;
if(meanshape[i].y<minmeany)
minmeany = meanshape[i].y;
}
return true;
}
bool FacemarkKazemiImpl::calcMeanShape (vector< vector<Point2f> >& trainlandmarks,vector<Mat>& trainimages,std::vector<Rect>& faces){
//clear the loaded meanshape
if(trainimages.empty()||trainlandmarks.size()!=trainimages.size()) {
// throw error if no data (or simply return -1?)
CV_Error(Error::StsBadArg, "Number of images is not equal to corresponding landmarks. Aborting...");
}
meanshape.clear();
vector<Mat> finalimages;
vector< vector<Point2f> > finallandmarks;
float xmean[200] = {0.0};
//array to store mean of y coordinates
float ymean[200] = {0.0};
size_t k=0;
//loop to calculate mean
Mat warp_mat,src,C,D;
vector<Rect> facesp;
Rect face;
for(size_t i = 0;i < trainimages.size();i++){
src = trainimages[i].clone();
//get bounding rectangle of image for reference
//function from facemark class
facesp.clear();
if(!getFaces(src,facesp)){
continue;
}
if(facesp.size()>1||facesp.empty())
continue;
face = facesp[0];
convertToUnit(face,warp_mat);
//loop to bring points to a common reference and adding
for(k=0;k<trainlandmarks[i].size();k++){
Point2f pt=trainlandmarks[i][k];
C = (Mat_<double>(3,1) << pt.x, pt.y, 1);
D = warp_mat*C;
pt.x = float(D.at<double>(0,0));
pt.y = float(D.at<double>(1,0));
trainlandmarks[i][k] = pt;
xmean[k] = xmean[k]+pt.x;
ymean[k] = ymean[k]+pt.y;
}
finalimages.push_back(trainimages[i]);
finallandmarks.push_back(trainlandmarks[i]);
faces.push_back(face);
}
//dividing by size to get mean and initialize meanshape
for(size_t i=0;i<k;i++){
xmean[i]=xmean[i]/finalimages.size();
ymean[i]=ymean[i]/finalimages.size();
if(xmean[i]>maxmeanx)
maxmeanx = xmean[i];
if(xmean[i]<minmeanx)
minmeanx = xmean[i];
if(ymean[i]>maxmeany)
maxmeany = ymean[i];
if(ymean[i]<minmeany)
minmeany = ymean[i];
meanshape.push_back(Point2f(xmean[i],ymean[i]));
}
trainimages.clear();
trainlandmarks.clear();
trainimages = finalimages;
trainlandmarks = finallandmarks;
finalimages.clear();
finallandmarks.clear();
return true;
}
bool FacemarkKazemiImpl::scaleData( vector< vector<Point2f> > & trainlandmarks,
vector<Mat> & trainimages ,Size s)
{
if(trainimages.empty()||trainimages.size()!=trainlandmarks.size()){
// throw error if no data (or simply return -1?)
CV_Error(Error::StsBadArg, "The data is not loaded properly by train function. Aborting...");
}
float scalex,scaley;
//scale all images and their landmarks according to input size
for(size_t i=0;i< trainimages.size();i++){
//calculating scale for x and y axis
scalex=float(s.width)/float(trainimages[i].cols);
scaley=float(s.height)/float(trainimages[i].rows);
resize(trainimages[i],trainimages[i],s,0,0,INTER_LINEAR_EXACT);
for (vector<Point2f>::iterator it = trainlandmarks[i].begin(); it != trainlandmarks[i].end(); it++) {
Point2f pt = (*it);
pt.x = pt.x*scalex;
pt.y = pt.y*scaley;
(*it) = pt;
}
}
return true;
}
Ptr<FacemarkKazemi> FacemarkKazemi::create(const FacemarkKazemi::Params ¶meters){
return Ptr<FacemarkKazemiImpl>(new FacemarkKazemiImpl(parameters));
}
Ptr<Facemark> createFacemarkKazemi() {
FacemarkKazemi::Params parameters;
return Ptr<FacemarkKazemiImpl>(new FacemarkKazemiImpl(parameters));
}
}//cv
}//face