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
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
#include "perf_precomp.hpp"
#include "opencv2/imgcodecs.hpp"
#include "opencv2/opencv_modules.hpp"
#include "opencv2/flann.hpp"
using namespace std;
using namespace cv;
using namespace perf;
using std::tr1::make_tuple;
using std::tr1::get;
typedef TestBaseWithParam<size_t> FeaturesFinderVec;
typedef TestBaseWithParam<string> match;
typedef std::tr1::tuple<string, int> matchVector_t;
typedef TestBaseWithParam<matchVector_t> matchVector;
#define NUMBER_IMAGES testing::Values(1, 5, 20)
#define SURF_MATCH_CONFIDENCE 0.65f
#define ORB_MATCH_CONFIDENCE 0.3f
#define WORK_MEGAPIX 0.6
#ifdef HAVE_OPENCV_XFEATURES2D
#define TEST_DETECTORS testing::Values("surf", "orb")
#else
#define TEST_DETECTORS testing::Values<string>("orb")
#endif
PERF_TEST_P(FeaturesFinderVec, ParallelFeaturesFinder, NUMBER_IMAGES)
{
Mat img = imread( getDataPath("stitching/a1.png") );
vector<Mat> imgs(GetParam(), img);
vector<detail::ImageFeatures> features(imgs.size());
Ptr<detail::FeaturesFinder> featuresFinder = makePtr<detail::OrbFeaturesFinder>();
TEST_CYCLE()
{
(*featuresFinder)(imgs, features);
}
SANITY_CHECK_NOTHING();
}
PERF_TEST_P(FeaturesFinderVec, SerialFeaturesFinder, NUMBER_IMAGES)
{
Mat img = imread( getDataPath("stitching/a1.png") );
vector<Mat> imgs(GetParam(), img);
vector<detail::ImageFeatures> features(imgs.size());
Ptr<detail::FeaturesFinder> featuresFinder = makePtr<detail::OrbFeaturesFinder>();
TEST_CYCLE()
{
for (size_t i = 0; i < imgs.size(); ++i)
(*featuresFinder)(imgs[i], features[i]);
}
SANITY_CHECK_NOTHING();
}
PERF_TEST_P( match, bestOf2Nearest, TEST_DETECTORS)
{
Mat img1, img1_full = imread( getDataPath("stitching/boat1.jpg") );
Mat img2, img2_full = imread( getDataPath("stitching/boat2.jpg") );
float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
resize(img1_full, img1, Size(), scale1, scale1);
resize(img2_full, img2, Size(), scale2, scale2);
Ptr<detail::FeaturesFinder> finder;
Ptr<detail::FeaturesMatcher> matcher;
if (GetParam() == "surf")
{
finder = makePtr<detail::SurfFeaturesFinder>();
matcher = makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
}
else if (GetParam() == "orb")
{
finder = makePtr<detail::OrbFeaturesFinder>();
matcher = makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE);
}
else
{
FAIL() << "Unknown 2D features type: " << GetParam();
}
detail::ImageFeatures features1, features2;
(*finder)(img1, features1);
(*finder)(img2, features2);
detail::MatchesInfo pairwise_matches;
declare.in(features1.descriptors, features2.descriptors);
while(next())
{
cvflann::seed_random(42);//for predictive FlannBasedMatcher
startTimer();
(*matcher)(features1, features2, pairwise_matches);
stopTimer();
matcher->collectGarbage();
}
Mat dist (pairwise_matches.H, Range::all(), Range(2, 3));
Mat R (pairwise_matches.H, Range::all(), Range(0, 2));
// separate transform matrix, use lower error on rotations
SANITY_CHECK(dist, 1., ERROR_ABSOLUTE);
SANITY_CHECK(R, .015, ERROR_ABSOLUTE);
}
PERF_TEST_P( matchVector, bestOf2NearestVectorFeatures, testing::Combine(
TEST_DETECTORS,
testing::Values(2, 4, 8))
)
{
Mat img1, img1_full = imread( getDataPath("stitching/boat1.jpg") );
Mat img2, img2_full = imread( getDataPath("stitching/boat2.jpg") );
float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
resize(img1_full, img1, Size(), scale1, scale1);
resize(img2_full, img2, Size(), scale2, scale2);
Ptr<detail::FeaturesFinder> finder;
Ptr<detail::FeaturesMatcher> matcher;
string detectorName = get<0>(GetParam());
int featuresVectorSize = get<1>(GetParam());
if (detectorName == "surf")
{
finder = makePtr<detail::SurfFeaturesFinder>();
matcher = makePtr<detail::BestOf2NearestMatcher>(false, SURF_MATCH_CONFIDENCE);
}
else if (detectorName == "orb")
{
finder = makePtr<detail::OrbFeaturesFinder>();
matcher = makePtr<detail::BestOf2NearestMatcher>(false, ORB_MATCH_CONFIDENCE);
}
else
{
FAIL() << "Unknown 2D features type: " << get<0>(GetParam());
}
detail::ImageFeatures features1, features2;
(*finder)(img1, features1);
(*finder)(img2, features2);
vector<detail::ImageFeatures> features;
vector<detail::MatchesInfo> pairwise_matches;
for(int i = 0; i < featuresVectorSize/2; i++)
{
features.push_back(features1);
features.push_back(features2);
}
declare.time(200);
while(next())
{
cvflann::seed_random(42);//for predictive FlannBasedMatcher
startTimer();
(*matcher)(features, pairwise_matches);
stopTimer();
matcher->collectGarbage();
}
size_t matches_count = 0;
for (size_t i = 0; i < pairwise_matches.size(); ++i)
{
if (pairwise_matches[i].src_img_idx < 0)
continue;
EXPECT_TRUE(pairwise_matches[i].matches.size() > 100);
EXPECT_FALSE(pairwise_matches[i].H.empty());
++matches_count;
}
EXPECT_TRUE(matches_count > 0);
SANITY_CHECK_NOTHING();
}
PERF_TEST_P( match, affineBestOf2Nearest, TEST_DETECTORS)
{
Mat img1, img1_full = imread( getDataPath("stitching/s1.jpg") );
Mat img2, img2_full = imread( getDataPath("stitching/s2.jpg") );
float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
resize(img1_full, img1, Size(), scale1, scale1);
resize(img2_full, img2, Size(), scale2, scale2);
Ptr<detail::FeaturesFinder> finder;
Ptr<detail::FeaturesMatcher> matcher;
if (GetParam() == "surf")
{
finder = makePtr<detail::SurfFeaturesFinder>();
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, SURF_MATCH_CONFIDENCE);
}
else if (GetParam() == "orb")
{
finder = makePtr<detail::OrbFeaturesFinder>();
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, ORB_MATCH_CONFIDENCE);
}
else
{
FAIL() << "Unknown 2D features type: " << GetParam();
}
detail::ImageFeatures features1, features2;
(*finder)(img1, features1);
(*finder)(img2, features2);
detail::MatchesInfo pairwise_matches;
declare.in(features1.descriptors, features2.descriptors);
while(next())
{
cvflann::seed_random(42);//for predictive FlannBasedMatcher
startTimer();
(*matcher)(features1, features2, pairwise_matches);
stopTimer();
matcher->collectGarbage();
}
// separate rotation and translation in transform matrix
Mat T (pairwise_matches.H, Range(0, 2), Range(2, 3));
Mat R (pairwise_matches.H, Range(0, 2), Range(0, 2));
Mat h (pairwise_matches.H, Range(2, 3), Range::all());
SANITY_CHECK(T, 5, ERROR_ABSOLUTE); // allow 5 pixels diff in translations
SANITY_CHECK(R, .01, ERROR_ABSOLUTE); // rotations must be more precise
// last row should be precisely (0, 0, 1) as it is just added for representation in homogeneous
// coordinates
EXPECT_DOUBLE_EQ(h.at<double>(0), 0.);
EXPECT_DOUBLE_EQ(h.at<double>(1), 0.);
EXPECT_DOUBLE_EQ(h.at<double>(2), 1.);
}
PERF_TEST_P( matchVector, affineBestOf2NearestVectorFeatures, testing::Combine(
TEST_DETECTORS,
testing::Values(2, 4, 8))
)
{
Mat img1, img1_full = imread( getDataPath("stitching/s1.jpg") );
Mat img2, img2_full = imread( getDataPath("stitching/s2.jpg") );
float scale1 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img1_full.total()));
float scale2 = (float)std::min(1.0, sqrt(WORK_MEGAPIX * 1e6 / img2_full.total()));
resize(img1_full, img1, Size(), scale1, scale1);
resize(img2_full, img2, Size(), scale2, scale2);
Ptr<detail::FeaturesFinder> finder;
Ptr<detail::FeaturesMatcher> matcher;
string detectorName = get<0>(GetParam());
int featuresVectorSize = get<1>(GetParam());
if (detectorName == "surf")
{
finder = makePtr<detail::SurfFeaturesFinder>();
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, SURF_MATCH_CONFIDENCE);
}
else if (detectorName == "orb")
{
finder = makePtr<detail::OrbFeaturesFinder>();
matcher = makePtr<detail::AffineBestOf2NearestMatcher>(false, false, ORB_MATCH_CONFIDENCE);
}
else
{
FAIL() << "Unknown 2D features type: " << get<0>(GetParam());
}
detail::ImageFeatures features1, features2;
(*finder)(img1, features1);
(*finder)(img2, features2);
vector<detail::ImageFeatures> features;
vector<detail::MatchesInfo> pairwise_matches;
for(int i = 0; i < featuresVectorSize/2; i++)
{
features.push_back(features1);
features.push_back(features2);
}
declare.time(200);
while(next())
{
cvflann::seed_random(42);//for predictive FlannBasedMatcher
startTimer();
(*matcher)(features, pairwise_matches);
stopTimer();
matcher->collectGarbage();
}
size_t matches_count = 0;
for (size_t i = 0; i < pairwise_matches.size(); ++i)
{
if (pairwise_matches[i].src_img_idx < 0)
continue;
EXPECT_TRUE(pairwise_matches[i].matches.size() > 400);
EXPECT_FALSE(pairwise_matches[i].H.empty());
++matches_count;
}
EXPECT_TRUE(matches_count > 0);
SANITY_CHECK_NOTHING();
}