test_affine_feature.cpp
6.97 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
183
184
185
// 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 "test_precomp.hpp"
// #define GENERATE_DATA // generate data in debug mode
namespace opencv_test { namespace {
#ifndef GENERATE_DATA
static bool isSimilarKeypoints( const KeyPoint& p1, const KeyPoint& p2 )
{
const float maxPtDif = 1.f;
const float maxSizeDif = 1.f;
const float maxAngleDif = 2.f;
const float maxResponseDif = 0.1f;
float dist = (float)cv::norm( p1.pt - p2.pt );
return (dist < maxPtDif &&
fabs(p1.size - p2.size) < maxSizeDif &&
abs(p1.angle - p2.angle) < maxAngleDif &&
abs(p1.response - p2.response) < maxResponseDif &&
(p1.octave & 0xffff) == (p2.octave & 0xffff) // do not care about sublayers and class_id
);
}
#endif
TEST(Features2d_AFFINE_FEATURE, regression)
{
Mat image = imread(cvtest::findDataFile("features2d/tsukuba.png"));
string xml = cvtest::TS::ptr()->get_data_path() + "asift/regression_cpp.xml.gz";
ASSERT_FALSE(image.empty());
Mat gray;
cvtColor(image, gray, COLOR_BGR2GRAY);
// Default ASIFT generates too large descriptors. This test uses small maxTilt to suppress the size of testdata.
Ptr<AffineFeature> ext = AffineFeature::create(SIFT::create(), 2, 0, 1.4142135623730951f, 144.0f);
Mat mpt, msize, mangle, mresponse, moctave, mclass_id;
#ifdef GENERATE_DATA
// calculate
vector<KeyPoint> calcKeypoints;
Mat calcDescriptors;
ext->detectAndCompute(gray, Mat(), calcKeypoints, calcDescriptors, false);
// create keypoints XML
FileStorage fs(xml, FileStorage::WRITE);
ASSERT_TRUE(fs.isOpened()) << xml;
std::cout << "Creating keypoints XML..." << std::endl;
mpt = Mat(calcKeypoints.size(), 2, CV_32F);
msize = Mat(calcKeypoints.size(), 1, CV_32F);
mangle = Mat(calcKeypoints.size(), 1, CV_32F);
mresponse = Mat(calcKeypoints.size(), 1, CV_32F);
moctave = Mat(calcKeypoints.size(), 1, CV_32S);
mclass_id = Mat(calcKeypoints.size(), 1, CV_32S);
for( size_t i = 0; i < calcKeypoints.size(); i++ )
{
const KeyPoint& key = calcKeypoints[i];
mpt.at<float>(i, 0) = key.pt.x;
mpt.at<float>(i, 1) = key.pt.y;
msize.at<float>(i, 0) = key.size;
mangle.at<float>(i, 0) = key.angle;
mresponse.at<float>(i, 0) = key.response;
moctave.at<int>(i, 0) = key.octave;
mclass_id.at<int>(i, 0) = key.class_id;
}
fs << "keypoints_pt" << mpt;
fs << "keypoints_size" << msize;
fs << "keypoints_angle" << mangle;
fs << "keypoints_response" << mresponse;
fs << "keypoints_octave" << moctave;
fs << "keypoints_class_id" << mclass_id;
// create descriptor XML
fs << "descriptors" << calcDescriptors;
fs.release();
#else
const float badCountsRatio = 0.01f;
const float badDescriptorDist = 1.0f;
const float maxBadKeypointsRatio = 0.15f;
const float maxBadDescriptorRatio = 0.15f;
// read keypoints
vector<KeyPoint> validKeypoints;
Mat validDescriptors;
FileStorage fs(xml, FileStorage::READ);
ASSERT_TRUE(fs.isOpened()) << xml;
fs["keypoints_pt"] >> mpt;
ASSERT_EQ(mpt.type(), CV_32F);
fs["keypoints_size"] >> msize;
ASSERT_EQ(msize.type(), CV_32F);
fs["keypoints_angle"] >> mangle;
ASSERT_EQ(mangle.type(), CV_32F);
fs["keypoints_response"] >> mresponse;
ASSERT_EQ(mresponse.type(), CV_32F);
fs["keypoints_octave"] >> moctave;
ASSERT_EQ(moctave.type(), CV_32S);
fs["keypoints_class_id"] >> mclass_id;
ASSERT_EQ(mclass_id.type(), CV_32S);
validKeypoints.resize(mpt.rows);
for( int i = 0; i < (int)validKeypoints.size(); i++ )
{
validKeypoints[i].pt.x = mpt.at<float>(i, 0);
validKeypoints[i].pt.y = mpt.at<float>(i, 1);
validKeypoints[i].size = msize.at<float>(i, 0);
validKeypoints[i].angle = mangle.at<float>(i, 0);
validKeypoints[i].response = mresponse.at<float>(i, 0);
validKeypoints[i].octave = moctave.at<int>(i, 0);
validKeypoints[i].class_id = mclass_id.at<int>(i, 0);
}
// read descriptors
fs["descriptors"] >> validDescriptors;
fs.release();
// calc and compare keypoints
vector<KeyPoint> calcKeypoints;
ext->detectAndCompute(gray, Mat(), calcKeypoints, noArray(), false);
float countRatio = (float)validKeypoints.size() / (float)calcKeypoints.size();
ASSERT_LT(countRatio, 1 + badCountsRatio) << "Bad keypoints count ratio.";
ASSERT_GT(countRatio, 1 - badCountsRatio) << "Bad keypoints count ratio.";
int badPointCount = 0, commonPointCount = max((int)validKeypoints.size(), (int)calcKeypoints.size());
for( size_t v = 0; v < validKeypoints.size(); v++ )
{
int nearestIdx = -1;
float minDist = std::numeric_limits<float>::max();
float angleDistOfNearest = std::numeric_limits<float>::max();
for( size_t c = 0; c < calcKeypoints.size(); c++ )
{
if( validKeypoints[v].class_id != calcKeypoints[c].class_id )
continue;
float curDist = (float)cv::norm( calcKeypoints[c].pt - validKeypoints[v].pt );
if( curDist < minDist )
{
minDist = curDist;
nearestIdx = (int)c;
angleDistOfNearest = abs( calcKeypoints[c].angle - validKeypoints[v].angle );
}
else if( curDist == minDist ) // the keypoints whose positions are same but angles are different
{
float angleDist = abs( calcKeypoints[c].angle - validKeypoints[v].angle );
if( angleDist < angleDistOfNearest )
{
nearestIdx = (int)c;
angleDistOfNearest = angleDist;
}
}
}
if( nearestIdx == -1 || !isSimilarKeypoints( validKeypoints[v], calcKeypoints[nearestIdx] ) )
badPointCount++;
}
float badKeypointsRatio = (float)badPointCount / (float)commonPointCount;
std::cout << "badKeypointsRatio: " << badKeypointsRatio << std::endl;
ASSERT_LT( badKeypointsRatio , maxBadKeypointsRatio ) << "Bad accuracy!";
// Calc and compare descriptors. This uses validKeypoints for extraction.
Mat calcDescriptors;
ext->detectAndCompute(gray, Mat(), validKeypoints, calcDescriptors, true);
int dim = validDescriptors.cols;
int badDescriptorCount = 0;
L1<float> distance;
for( int i = 0; i < (int)validKeypoints.size(); i++ )
{
float dist = distance( validDescriptors.ptr<float>(i), calcDescriptors.ptr<float>(i), dim );
if( dist > badDescriptorDist )
badDescriptorCount++;
}
float badDescriptorRatio = (float)badDescriptorCount / (float)validKeypoints.size();
std::cout << "badDescriptorRatio: " << badDescriptorRatio << std::endl;
ASSERT_LT( badDescriptorRatio, maxBadDescriptorRatio ) << "Too many descriptors mismatched.";
#endif
}
}} // namespace