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
#include "perf_precomp.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/opencv_modules.hpp"
#include "opencv2/core/ocl.hpp"
using namespace std;
using namespace cv;
using namespace perf;
using std::tr1::tuple;
using std::tr1::get;
#define SURF_MATCH_CONFIDENCE 0.65f
#define ORB_MATCH_CONFIDENCE 0.3f
#define WORK_MEGAPIX 0.6
typedef TestBaseWithParam<string> stitch;
typedef TestBaseWithParam<tuple<string, string> > stitchDatasets;
#ifdef HAVE_OPENCV_XFEATURES2D
#define TEST_DETECTORS testing::Values("surf", "orb", "akaze")
#else
#define TEST_DETECTORS testing::Values("orb", "akaze")
#endif
#define AFFINE_DATASETS testing::Values("s", "budapest", "newspaper", "prague")
PERF_TEST_P(stitch, a123, TEST_DETECTORS)
{
Mat pano;
vector<Mat> imgs;
imgs.push_back( imread( getDataPath("stitching/a1.png") ) );
imgs.push_back( imread( getDataPath("stitching/a2.png") ) );
imgs.push_back( imread( getDataPath("stitching/a3.png") ) );
Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(GetParam());
Ptr<detail::FeaturesMatcher> featuresMatcher = GetParam() == "orb"
? makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE)
: makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
declare.time(30 * 20).iterations(20);
while(next())
{
Stitcher stitcher = Stitcher::createDefault();
stitcher.setFeaturesFinder(featuresFinder);
stitcher.setFeaturesMatcher(featuresMatcher);
stitcher.setWarper(makePtr<SphericalWarper>());
stitcher.setRegistrationResol(WORK_MEGAPIX);
startTimer();
stitcher.stitch(imgs, pano);
stopTimer();
}
EXPECT_NEAR(pano.size().width, 1182, 50);
EXPECT_NEAR(pano.size().height, 682, 30);
SANITY_CHECK_NOTHING();
}
PERF_TEST_P(stitch, b12, TEST_DETECTORS)
{
Mat pano;
vector<Mat> imgs;
imgs.push_back( imread( getDataPath("stitching/b1.png") ) );
imgs.push_back( imread( getDataPath("stitching/b2.png") ) );
Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(GetParam());
Ptr<detail::FeaturesMatcher> featuresMatcher = GetParam() == "orb"
? makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE)
: makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
declare.time(30 * 20).iterations(20);
while(next())
{
Stitcher stitcher = Stitcher::createDefault();
stitcher.setFeaturesFinder(featuresFinder);
stitcher.setFeaturesMatcher(featuresMatcher);
stitcher.setWarper(makePtr<SphericalWarper>());
stitcher.setRegistrationResol(WORK_MEGAPIX);
startTimer();
stitcher.stitch(imgs, pano);
stopTimer();
}
EXPECT_NEAR(pano.size().width, 1117, 50);
EXPECT_NEAR(pano.size().height, 642, 30);
SANITY_CHECK_NOTHING();
}
PERF_TEST_P(stitchDatasets, affine, testing::Combine(AFFINE_DATASETS, TEST_DETECTORS))
{
string dataset = get<0>(GetParam());
string detector = get<1>(GetParam());
Mat pano;
vector<Mat> imgs;
int width, height, allowed_diff = 10;
Ptr<detail::FeaturesFinder> featuresFinder = getFeatureFinder(detector);
if(dataset == "budapest")
{
imgs.push_back(imread(getDataPath("stitching/budapest1.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest2.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest3.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest4.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest5.jpg")));
imgs.push_back(imread(getDataPath("stitching/budapest6.jpg")));
width = 2313;
height = 1158;
// this dataset is big, the results between surf and orb differ slightly,
// but both are still good
allowed_diff = 27;
}
else if (dataset == "newspaper")
{
imgs.push_back(imread(getDataPath("stitching/newspaper1.jpg")));
imgs.push_back(imread(getDataPath("stitching/newspaper2.jpg")));
imgs.push_back(imread(getDataPath("stitching/newspaper3.jpg")));
imgs.push_back(imread(getDataPath("stitching/newspaper4.jpg")));
width = 1791;
height = 1136;
// we need to boost ORB number of features to be able to stitch this dataset
// SURF works just fine with default settings
if(detector == "orb")
featuresFinder = makePtr<detail::OrbFeaturesFinder>(Size(3,1), 3000);
}
else if (dataset == "prague")
{
imgs.push_back(imread(getDataPath("stitching/prague1.jpg")));
imgs.push_back(imread(getDataPath("stitching/prague2.jpg")));
width = 983;
height = 1759;
}
else // dataset == "s"
{
imgs.push_back(imread(getDataPath("stitching/s1.jpg")));
imgs.push_back(imread(getDataPath("stitching/s2.jpg")));
width = 1815;
height = 700;
}
declare.time(30 * 20).iterations(20);
while(next())
{
Ptr<Stitcher> stitcher = Stitcher::create(Stitcher::SCANS, false);
stitcher->setFeaturesFinder(featuresFinder);
if (cv::ocl::useOpenCL())
cv::theRNG() = cv::RNG(12345); // prevent fails of Windows OpenCL builds (see #8294)
startTimer();
stitcher->stitch(imgs, pano);
stopTimer();
}
EXPECT_NEAR(pano.size().width, width, allowed_diff);
EXPECT_NEAR(pano.size().height, height, allowed_diff);
SANITY_CHECK_NOTHING();
}