test.safe_run();
}
#endif*/ // CV_SSE2
+
+TEST(Features2d_BruteForceDescriptorMatcher_knnMatch, regression)
+{
+ const int sz = 100;
+ const int k = 3;
+
+ Ptr<DescriptorExtractor> ext = DescriptorExtractor::create("SURF");
+ ASSERT_TRUE(ext != NULL);
+
+ Ptr<FeatureDetector> det = FeatureDetector::create("SURF");
+ //"%YAML:1.0\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n"
+ ASSERT_TRUE(det != NULL);
+
+ Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
+ ASSERT_TRUE(matcher != NULL);
+
+ Mat imgT(sz, sz, CV_8U, Scalar(255));
+ line(imgT, Point(20, sz/2), Point(sz-21, sz/2), Scalar(100), 2);
+ line(imgT, Point(sz/2, 20), Point(sz/2, sz-21), Scalar(100), 2);
+ vector<KeyPoint> kpT;
+ kpT.push_back( KeyPoint(50, 50, 16, 0, 20000, 1, -1) );
+ kpT.push_back( KeyPoint(42, 42, 16, 160, 10000, 1, -1) );
+ Mat descT;
+ ext->compute(imgT, kpT, descT);
+
+ Mat imgQ(sz, sz, CV_8U, Scalar(255));
+ line(imgQ, Point(30, sz/2), Point(sz-31, sz/2), Scalar(100), 3);
+ line(imgQ, Point(sz/2, 30), Point(sz/2, sz-31), Scalar(100), 3);
+ vector<KeyPoint> kpQ;
+ det->detect(imgQ, kpQ);
+ Mat descQ;
+ ext->compute(imgQ, kpQ, descQ);
+
+ vector<vector<DMatch> > matches;
+
+ matcher->knnMatch(descQ, descT, matches, k);
+
+ //cout << "\nBest " << k << " matches to " << descT.rows << " train desc-s." << endl;
+ ASSERT_EQ(descQ.rows, matches.size());
+ for(int i = 0; i<matches.size(); i++)
+ {
+ //cout << "\nmatches[" << i << "].size()==" << matches[i].size() << endl;
+ ASSERT_TRUE(min(k, descT.rows) >= matches[i].size());
+ for(int j = 0; j<matches[i].size(); j++)
+ {
+ //cout << "\t" << matches[i][j].queryIdx << " -> " << matches[i][j].trainIdx << endl;
+ ASSERT_EQ(matches[i][j].queryIdx, i);
+ }
+ }
+}