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42 
43 #include "test_precomp.hpp"
44 
45 #ifdef HAVE_CUDA
46 
47 using namespace cvtest;
48 
49 /////////////////////////////////////////////////////////////////////////////////////////////////
50 // FAST
51 
52 namespace
53 {
54     IMPLEMENT_PARAM_CLASS(FAST_Threshold, int)
55     IMPLEMENT_PARAM_CLASS(FAST_NonmaxSuppression, bool)
56 }
57 
PARAM_TEST_CASE(FAST,cv::cuda::DeviceInfo,FAST_Threshold,FAST_NonmaxSuppression)58 PARAM_TEST_CASE(FAST, cv::cuda::DeviceInfo, FAST_Threshold, FAST_NonmaxSuppression)
59 {
60     cv::cuda::DeviceInfo devInfo;
61     int threshold;
62     bool nonmaxSuppression;
63 
64     virtual void SetUp()
65     {
66         devInfo = GET_PARAM(0);
67         threshold = GET_PARAM(1);
68         nonmaxSuppression = GET_PARAM(2);
69 
70         cv::cuda::setDevice(devInfo.deviceID());
71     }
72 };
73 
CUDA_TEST_P(FAST,Accuracy)74 CUDA_TEST_P(FAST, Accuracy)
75 {
76     cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE);
77     ASSERT_FALSE(image.empty());
78 
79     cv::Ptr<cv::cuda::FastFeatureDetector> fast = cv::cuda::FastFeatureDetector::create(threshold, nonmaxSuppression);
80 
81     if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
82     {
83         try
84         {
85             std::vector<cv::KeyPoint> keypoints;
86             fast->detect(loadMat(image), keypoints);
87         }
88         catch (const cv::Exception& e)
89         {
90             ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
91         }
92     }
93     else
94     {
95         std::vector<cv::KeyPoint> keypoints;
96         fast->detect(loadMat(image), keypoints);
97 
98         std::vector<cv::KeyPoint> keypoints_gold;
99         cv::FAST(image, keypoints_gold, threshold, nonmaxSuppression);
100 
101         ASSERT_KEYPOINTS_EQ(keypoints_gold, keypoints);
102     }
103 }
104 
105 INSTANTIATE_TEST_CASE_P(CUDA_Features2D, FAST, testing::Combine(
106     ALL_DEVICES,
107     testing::Values(FAST_Threshold(25), FAST_Threshold(50)),
108     testing::Values(FAST_NonmaxSuppression(false), FAST_NonmaxSuppression(true))));
109 
110 /////////////////////////////////////////////////////////////////////////////////////////////////
111 // ORB
112 
113 namespace
114 {
115     IMPLEMENT_PARAM_CLASS(ORB_FeaturesCount, int)
116     IMPLEMENT_PARAM_CLASS(ORB_ScaleFactor, float)
117     IMPLEMENT_PARAM_CLASS(ORB_LevelsCount, int)
118     IMPLEMENT_PARAM_CLASS(ORB_EdgeThreshold, int)
119     IMPLEMENT_PARAM_CLASS(ORB_firstLevel, int)
120     IMPLEMENT_PARAM_CLASS(ORB_WTA_K, int)
121     IMPLEMENT_PARAM_CLASS(ORB_PatchSize, int)
122     IMPLEMENT_PARAM_CLASS(ORB_BlurForDescriptor, bool)
123 }
124 
CV_ENUM(ORB_ScoreType,cv::ORB::HARRIS_SCORE,cv::ORB::FAST_SCORE)125 CV_ENUM(ORB_ScoreType, cv::ORB::HARRIS_SCORE, cv::ORB::FAST_SCORE)
126 
127 PARAM_TEST_CASE(ORB, cv::cuda::DeviceInfo, ORB_FeaturesCount, ORB_ScaleFactor, ORB_LevelsCount, ORB_EdgeThreshold, ORB_firstLevel, ORB_WTA_K, ORB_ScoreType, ORB_PatchSize, ORB_BlurForDescriptor)
128 {
129     cv::cuda::DeviceInfo devInfo;
130     int nFeatures;
131     float scaleFactor;
132     int nLevels;
133     int edgeThreshold;
134     int firstLevel;
135     int WTA_K;
136     int scoreType;
137     int patchSize;
138     bool blurForDescriptor;
139 
140     virtual void SetUp()
141     {
142         devInfo = GET_PARAM(0);
143         nFeatures = GET_PARAM(1);
144         scaleFactor = GET_PARAM(2);
145         nLevels = GET_PARAM(3);
146         edgeThreshold = GET_PARAM(4);
147         firstLevel = GET_PARAM(5);
148         WTA_K = GET_PARAM(6);
149         scoreType = GET_PARAM(7);
150         patchSize = GET_PARAM(8);
151         blurForDescriptor = GET_PARAM(9);
152 
153         cv::cuda::setDevice(devInfo.deviceID());
154     }
155 };
156 
CUDA_TEST_P(ORB,Accuracy)157 CUDA_TEST_P(ORB, Accuracy)
158 {
159     cv::Mat image = readImage("features2d/aloe.png", cv::IMREAD_GRAYSCALE);
160     ASSERT_FALSE(image.empty());
161 
162     cv::Mat mask(image.size(), CV_8UC1, cv::Scalar::all(1));
163     mask(cv::Range(0, image.rows / 2), cv::Range(0, image.cols / 2)).setTo(cv::Scalar::all(0));
164 
165     cv::Ptr<cv::cuda::ORB> orb =
166             cv::cuda::ORB::create(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel,
167                                   WTA_K, scoreType, patchSize, 20, blurForDescriptor);
168 
169     if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
170     {
171         try
172         {
173             std::vector<cv::KeyPoint> keypoints;
174             cv::cuda::GpuMat descriptors;
175             orb->detectAndComputeAsync(loadMat(image), loadMat(mask), keypoints, descriptors);
176         }
177         catch (const cv::Exception& e)
178         {
179             ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
180         }
181     }
182     else
183     {
184         std::vector<cv::KeyPoint> keypoints;
185         cv::cuda::GpuMat descriptors;
186         orb->detectAndCompute(loadMat(image), loadMat(mask), keypoints, descriptors);
187 
188         cv::Ptr<cv::ORB> orb_gold = cv::ORB::create(nFeatures, scaleFactor, nLevels, edgeThreshold, firstLevel, WTA_K, scoreType, patchSize);
189 
190         std::vector<cv::KeyPoint> keypoints_gold;
191         cv::Mat descriptors_gold;
192         orb_gold->detectAndCompute(image, mask, keypoints_gold, descriptors_gold);
193 
194         cv::BFMatcher matcher(cv::NORM_HAMMING);
195         std::vector<cv::DMatch> matches;
196         matcher.match(descriptors_gold, cv::Mat(descriptors), matches);
197 
198         int matchedCount = getMatchedPointsCount(keypoints_gold, keypoints, matches);
199         double matchedRatio = static_cast<double>(matchedCount) / keypoints.size();
200 
201         EXPECT_GT(matchedRatio, 0.35);
202     }
203 }
204 
205 INSTANTIATE_TEST_CASE_P(CUDA_Features2D, ORB,  testing::Combine(
206     ALL_DEVICES,
207     testing::Values(ORB_FeaturesCount(1000)),
208     testing::Values(ORB_ScaleFactor(1.2f)),
209     testing::Values(ORB_LevelsCount(4), ORB_LevelsCount(8)),
210     testing::Values(ORB_EdgeThreshold(31)),
211     testing::Values(ORB_firstLevel(0)),
212     testing::Values(ORB_WTA_K(2), ORB_WTA_K(3), ORB_WTA_K(4)),
213     testing::Values(ORB_ScoreType(cv::ORB::HARRIS_SCORE)),
214     testing::Values(ORB_PatchSize(31), ORB_PatchSize(29)),
215     testing::Values(ORB_BlurForDescriptor(false), ORB_BlurForDescriptor(true))));
216 
217 /////////////////////////////////////////////////////////////////////////////////////////////////
218 // BruteForceMatcher
219 
220 namespace
221 {
222     IMPLEMENT_PARAM_CLASS(DescriptorSize, int)
223     IMPLEMENT_PARAM_CLASS(UseMask, bool)
224 }
225 
PARAM_TEST_CASE(BruteForceMatcher,cv::cuda::DeviceInfo,NormCode,DescriptorSize,UseMask)226 PARAM_TEST_CASE(BruteForceMatcher, cv::cuda::DeviceInfo, NormCode, DescriptorSize, UseMask)
227 {
228     cv::cuda::DeviceInfo devInfo;
229     int normCode;
230     int dim;
231     bool useMask;
232 
233     int queryDescCount;
234     int countFactor;
235 
236     cv::Mat query, train;
237 
238     virtual void SetUp()
239     {
240         devInfo = GET_PARAM(0);
241         normCode = GET_PARAM(1);
242         dim = GET_PARAM(2);
243         useMask = GET_PARAM(3);
244 
245         cv::cuda::setDevice(devInfo.deviceID());
246 
247         queryDescCount = 300; // must be even number because we split train data in some cases in two
248         countFactor = 4; // do not change it
249 
250         cv::RNG& rng = cvtest::TS::ptr()->get_rng();
251 
252         cv::Mat queryBuf, trainBuf;
253 
254         // Generate query descriptors randomly.
255         // Descriptor vector elements are integer values.
256         queryBuf.create(queryDescCount, dim, CV_32SC1);
257         rng.fill(queryBuf, cv::RNG::UNIFORM, cv::Scalar::all(0), cv::Scalar::all(3));
258         queryBuf.convertTo(queryBuf, CV_32FC1);
259 
260         // Generate train decriptors as follows:
261         // copy each query descriptor to train set countFactor times
262         // and perturb some one element of the copied descriptors in
263         // in ascending order. General boundaries of the perturbation
264         // are (0.f, 1.f).
265         trainBuf.create(queryDescCount * countFactor, dim, CV_32FC1);
266         float step = 1.f / countFactor;
267         for (int qIdx = 0; qIdx < queryDescCount; qIdx++)
268         {
269             cv::Mat queryDescriptor = queryBuf.row(qIdx);
270             for (int c = 0; c < countFactor; c++)
271             {
272                 int tIdx = qIdx * countFactor + c;
273                 cv::Mat trainDescriptor = trainBuf.row(tIdx);
274                 queryDescriptor.copyTo(trainDescriptor);
275                 int elem = rng(dim);
276                 float diff = rng.uniform(step * c, step * (c + 1));
277                 trainDescriptor.at<float>(0, elem) += diff;
278             }
279         }
280 
281         queryBuf.convertTo(query, CV_32F);
282         trainBuf.convertTo(train, CV_32F);
283     }
284 };
285 
CUDA_TEST_P(BruteForceMatcher,Match_Single)286 CUDA_TEST_P(BruteForceMatcher, Match_Single)
287 {
288     cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
289             cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
290 
291     cv::cuda::GpuMat mask;
292     if (useMask)
293     {
294         mask.create(query.rows, train.rows, CV_8UC1);
295         mask.setTo(cv::Scalar::all(1));
296     }
297 
298     std::vector<cv::DMatch> matches;
299     matcher->match(loadMat(query), loadMat(train), matches, mask);
300 
301     ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
302 
303     int badCount = 0;
304     for (size_t i = 0; i < matches.size(); i++)
305     {
306         cv::DMatch match = matches[i];
307         if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor) || (match.imgIdx != 0))
308             badCount++;
309     }
310 
311     ASSERT_EQ(0, badCount);
312 }
313 
CUDA_TEST_P(BruteForceMatcher,Match_Collection)314 CUDA_TEST_P(BruteForceMatcher, Match_Collection)
315 {
316     cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
317             cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
318 
319     cv::cuda::GpuMat d_train(train);
320 
321     // make add() twice to test such case
322     matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
323     matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
324 
325     // prepare masks (make first nearest match illegal)
326     std::vector<cv::cuda::GpuMat> masks(2);
327     for (int mi = 0; mi < 2; mi++)
328     {
329         masks[mi] = cv::cuda::GpuMat(query.rows, train.rows/2, CV_8UC1, cv::Scalar::all(1));
330         for (int di = 0; di < queryDescCount/2; di++)
331             masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
332     }
333 
334     std::vector<cv::DMatch> matches;
335     if (useMask)
336         matcher->match(cv::cuda::GpuMat(query), matches, masks);
337     else
338         matcher->match(cv::cuda::GpuMat(query), matches);
339 
340     ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
341 
342     int badCount = 0;
343     int shift = useMask ? 1 : 0;
344     for (size_t i = 0; i < matches.size(); i++)
345     {
346         cv::DMatch match = matches[i];
347 
348         if ((int)i < queryDescCount / 2)
349         {
350             bool validQueryIdx = (match.queryIdx == (int)i);
351             bool validTrainIdx = (match.trainIdx == (int)i * countFactor + shift);
352             bool validImgIdx = (match.imgIdx == 0);
353             if (!validQueryIdx || !validTrainIdx || !validImgIdx)
354                 badCount++;
355         }
356         else
357         {
358             bool validQueryIdx = (match.queryIdx == (int)i);
359             bool validTrainIdx = (match.trainIdx == ((int)i - queryDescCount / 2) * countFactor + shift);
360             bool validImgIdx = (match.imgIdx == 1);
361             if (!validQueryIdx || !validTrainIdx || !validImgIdx)
362                 badCount++;
363         }
364     }
365 
366     ASSERT_EQ(0, badCount);
367 }
368 
CUDA_TEST_P(BruteForceMatcher,KnnMatch_2_Single)369 CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Single)
370 {
371     cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
372             cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
373 
374     const int knn = 2;
375 
376     cv::cuda::GpuMat mask;
377     if (useMask)
378     {
379         mask.create(query.rows, train.rows, CV_8UC1);
380         mask.setTo(cv::Scalar::all(1));
381     }
382 
383     std::vector< std::vector<cv::DMatch> > matches;
384     matcher->knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
385 
386     ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
387 
388     int badCount = 0;
389     for (size_t i = 0; i < matches.size(); i++)
390     {
391         if ((int)matches[i].size() != knn)
392             badCount++;
393         else
394         {
395             int localBadCount = 0;
396             for (int k = 0; k < knn; k++)
397             {
398                 cv::DMatch match = matches[i][k];
399                 if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
400                     localBadCount++;
401             }
402             badCount += localBadCount > 0 ? 1 : 0;
403         }
404     }
405 
406     ASSERT_EQ(0, badCount);
407 }
408 
CUDA_TEST_P(BruteForceMatcher,KnnMatch_3_Single)409 CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Single)
410 {
411     cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
412             cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
413 
414     const int knn = 3;
415 
416     cv::cuda::GpuMat mask;
417     if (useMask)
418     {
419         mask.create(query.rows, train.rows, CV_8UC1);
420         mask.setTo(cv::Scalar::all(1));
421     }
422 
423     std::vector< std::vector<cv::DMatch> > matches;
424     matcher->knnMatch(loadMat(query), loadMat(train), matches, knn, mask);
425 
426     ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
427 
428     int badCount = 0;
429     for (size_t i = 0; i < matches.size(); i++)
430     {
431         if ((int)matches[i].size() != knn)
432             badCount++;
433         else
434         {
435             int localBadCount = 0;
436             for (int k = 0; k < knn; k++)
437             {
438                 cv::DMatch match = matches[i][k];
439                 if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k) || (match.imgIdx != 0))
440                     localBadCount++;
441             }
442             badCount += localBadCount > 0 ? 1 : 0;
443         }
444     }
445 
446     ASSERT_EQ(0, badCount);
447 }
448 
CUDA_TEST_P(BruteForceMatcher,KnnMatch_2_Collection)449 CUDA_TEST_P(BruteForceMatcher, KnnMatch_2_Collection)
450 {
451     cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
452             cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
453 
454     const int knn = 2;
455 
456     cv::cuda::GpuMat d_train(train);
457 
458     // make add() twice to test such case
459     matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
460     matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
461 
462     // prepare masks (make first nearest match illegal)
463     std::vector<cv::cuda::GpuMat> masks(2);
464     for (int mi = 0; mi < 2; mi++ )
465     {
466         masks[mi] = cv::cuda::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
467         for (int di = 0; di < queryDescCount / 2; di++)
468             masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
469     }
470 
471     std::vector< std::vector<cv::DMatch> > matches;
472 
473     if (useMask)
474         matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn, masks);
475     else
476         matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn);
477 
478     ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
479 
480     int badCount = 0;
481     int shift = useMask ? 1 : 0;
482     for (size_t i = 0; i < matches.size(); i++)
483     {
484         if ((int)matches[i].size() != knn)
485             badCount++;
486         else
487         {
488             int localBadCount = 0;
489             for (int k = 0; k < knn; k++)
490             {
491                 cv::DMatch match = matches[i][k];
492                 {
493                     if ((int)i < queryDescCount / 2)
494                     {
495                         if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
496                             localBadCount++;
497                     }
498                     else
499                     {
500                         if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
501                             localBadCount++;
502                     }
503                 }
504             }
505             badCount += localBadCount > 0 ? 1 : 0;
506         }
507     }
508 
509     ASSERT_EQ(0, badCount);
510 }
511 
CUDA_TEST_P(BruteForceMatcher,KnnMatch_3_Collection)512 CUDA_TEST_P(BruteForceMatcher, KnnMatch_3_Collection)
513 {
514     cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
515             cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
516 
517     const int knn = 3;
518 
519     cv::cuda::GpuMat d_train(train);
520 
521     // make add() twice to test such case
522     matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
523     matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
524 
525     // prepare masks (make first nearest match illegal)
526     std::vector<cv::cuda::GpuMat> masks(2);
527     for (int mi = 0; mi < 2; mi++ )
528     {
529         masks[mi] = cv::cuda::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
530         for (int di = 0; di < queryDescCount / 2; di++)
531             masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
532     }
533 
534     std::vector< std::vector<cv::DMatch> > matches;
535 
536     if (useMask)
537         matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn, masks);
538     else
539         matcher->knnMatch(cv::cuda::GpuMat(query), matches, knn);
540 
541     ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
542 
543     int badCount = 0;
544     int shift = useMask ? 1 : 0;
545     for (size_t i = 0; i < matches.size(); i++)
546     {
547         if ((int)matches[i].size() != knn)
548             badCount++;
549         else
550         {
551             int localBadCount = 0;
552             for (int k = 0; k < knn; k++)
553             {
554                 cv::DMatch match = matches[i][k];
555                 {
556                     if ((int)i < queryDescCount / 2)
557                     {
558                         if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
559                             localBadCount++;
560                     }
561                     else
562                     {
563                         if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
564                             localBadCount++;
565                     }
566                 }
567             }
568             badCount += localBadCount > 0 ? 1 : 0;
569         }
570     }
571 
572     ASSERT_EQ(0, badCount);
573 }
574 
CUDA_TEST_P(BruteForceMatcher,RadiusMatch_Single)575 CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Single)
576 {
577     cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
578             cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
579 
580     const float radius = 1.f / countFactor;
581 
582     if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
583     {
584         try
585         {
586             std::vector< std::vector<cv::DMatch> > matches;
587             matcher->radiusMatch(loadMat(query), loadMat(train), matches, radius);
588         }
589         catch (const cv::Exception& e)
590         {
591             ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
592         }
593     }
594     else
595     {
596         cv::cuda::GpuMat mask;
597         if (useMask)
598         {
599             mask.create(query.rows, train.rows, CV_8UC1);
600             mask.setTo(cv::Scalar::all(1));
601         }
602 
603         std::vector< std::vector<cv::DMatch> > matches;
604         matcher->radiusMatch(loadMat(query), loadMat(train), matches, radius, mask);
605 
606         ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
607 
608         int badCount = 0;
609         for (size_t i = 0; i < matches.size(); i++)
610         {
611             if ((int)matches[i].size() != 1)
612                 badCount++;
613             else
614             {
615                 cv::DMatch match = matches[i][0];
616                 if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i*countFactor) || (match.imgIdx != 0))
617                     badCount++;
618             }
619         }
620 
621         ASSERT_EQ(0, badCount);
622     }
623 }
624 
CUDA_TEST_P(BruteForceMatcher,RadiusMatch_Collection)625 CUDA_TEST_P(BruteForceMatcher, RadiusMatch_Collection)
626 {
627     cv::Ptr<cv::cuda::DescriptorMatcher> matcher =
628             cv::cuda::DescriptorMatcher::createBFMatcher(normCode);
629 
630     const int n = 3;
631     const float radius = 1.f / countFactor * n;
632 
633     cv::cuda::GpuMat d_train(train);
634 
635     // make add() twice to test such case
636     matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(0, train.rows / 2)));
637     matcher->add(std::vector<cv::cuda::GpuMat>(1, d_train.rowRange(train.rows / 2, train.rows)));
638 
639     // prepare masks (make first nearest match illegal)
640     std::vector<cv::cuda::GpuMat> masks(2);
641     for (int mi = 0; mi < 2; mi++)
642     {
643         masks[mi] = cv::cuda::GpuMat(query.rows, train.rows / 2, CV_8UC1, cv::Scalar::all(1));
644         for (int di = 0; di < queryDescCount / 2; di++)
645             masks[mi].col(di * countFactor).setTo(cv::Scalar::all(0));
646     }
647 
648     if (!supportFeature(devInfo, cv::cuda::GLOBAL_ATOMICS))
649     {
650         try
651         {
652             std::vector< std::vector<cv::DMatch> > matches;
653             matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks);
654         }
655         catch (const cv::Exception& e)
656         {
657             ASSERT_EQ(cv::Error::StsNotImplemented, e.code);
658         }
659     }
660     else
661     {
662         std::vector< std::vector<cv::DMatch> > matches;
663 
664         if (useMask)
665             matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius, masks);
666         else
667             matcher->radiusMatch(cv::cuda::GpuMat(query), matches, radius);
668 
669         ASSERT_EQ(static_cast<size_t>(queryDescCount), matches.size());
670 
671         int badCount = 0;
672         int shift = useMask ? 1 : 0;
673         int needMatchCount = useMask ? n-1 : n;
674         for (size_t i = 0; i < matches.size(); i++)
675         {
676             if ((int)matches[i].size() != needMatchCount)
677                 badCount++;
678             else
679             {
680                 int localBadCount = 0;
681                 for (int k = 0; k < needMatchCount; k++)
682                 {
683                     cv::DMatch match = matches[i][k];
684                     {
685                         if ((int)i < queryDescCount / 2)
686                         {
687                             if ((match.queryIdx != (int)i) || (match.trainIdx != (int)i * countFactor + k + shift) || (match.imgIdx != 0) )
688                                 localBadCount++;
689                         }
690                         else
691                         {
692                             if ((match.queryIdx != (int)i) || (match.trainIdx != ((int)i - queryDescCount / 2) * countFactor + k + shift) || (match.imgIdx != 1) )
693                                 localBadCount++;
694                         }
695                     }
696                 }
697                 badCount += localBadCount > 0 ? 1 : 0;
698             }
699         }
700 
701         ASSERT_EQ(0, badCount);
702     }
703 }
704 
705 INSTANTIATE_TEST_CASE_P(CUDA_Features2D, BruteForceMatcher, testing::Combine(
706     ALL_DEVICES,
707     testing::Values(NormCode(cv::NORM_L1), NormCode(cv::NORM_L2)),
708     testing::Values(DescriptorSize(57), DescriptorSize(64), DescriptorSize(83), DescriptorSize(128), DescriptorSize(179), DescriptorSize(256), DescriptorSize(304)),
709     testing::Values(UseMask(false), UseMask(true))));
710 
711 #endif // HAVE_CUDA
712