changed tests for rotation/scale invariance of descriptors
This commit is contained in:
Maria Dimashova
parent
ad7a6ec41f
commit
2556bb04f0
@ -53,7 +53,7 @@ const string IMAGE_BIKES = "/detectors_descriptors_evaluation/images_datasets/bi
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static
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Mat generateHomography(float angle)
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{
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// angle - rotation around Oz in degrees
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// angle - rotation around Oz in degrees
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float angleRadian = angle * CV_PI / 180.;
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Mat H = Mat::eye(3, 3, CV_32FC1);
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H.at<float>(0,0) = H.at<float>(1,1) = std::cos(angleRadian);
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@ -66,7 +66,7 @@ Mat generateHomography(float angle)
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static
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Mat rotateImage(const Mat& srcImage, float angle, Mat& dstImage, Mat& dstMask)
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{
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// angle - rotation around Oz in degrees
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// angle - rotation around Oz in degrees
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float diag = std::sqrt(static_cast<float>(srcImage.cols * srcImage.cols + srcImage.rows * srcImage.rows));
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Mat LUShift = Mat::eye(3, 3, CV_32FC1); // left up
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LUShift.at<float>(0,2) = -srcImage.cols/2;
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@ -85,6 +85,32 @@ Mat rotateImage(const Mat& srcImage, float angle, Mat& dstImage, Mat& dstMask)
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return H;
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}
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void rotateKeyPoints(const vector<KeyPoint>& src, const Mat& H, float angle, vector<KeyPoint>& dst)
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{
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// suppose that H is rotation given from rotateImage() and angle has value passed to rotateImage()
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vector<Point2f> srcCenters, dstCenters;
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KeyPoint::convert(src, srcCenters);
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perspectiveTransform(srcCenters, dstCenters, H);
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dst = src;
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for(size_t i = 0; i < dst.size(); i++)
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{
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dst[i].pt = dstCenters[i];
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float dstAngle = src[i].angle + angle;
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if(dstAngle >= 360.f)
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dstAngle -= 360.f;
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dst[i].angle = dstAngle;
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}
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}
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void scaleKeyPoints(const vector<KeyPoint>& src, vector<KeyPoint>& dst, float scale)
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{
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dst.resize(src.size());
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for(size_t i = 0; i < src.size(); i++)
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dst[i] = KeyPoint(src[i].pt.x * scale, src[i].pt.y * scale, src[i].size * scale);
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}
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static
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float calcCirclesIntersectArea(const Point2f& p0, float r0, const Point2f& p1, float r1)
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{
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@ -119,45 +145,45 @@ float calcIntersectRatio(const Point2f& p0, float r0, const Point2f& p1, float r
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return intersectArea / unionArea;
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}
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static
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static
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void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
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const vector<KeyPoint>& keypoints1,
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vector<DMatch>& matches)
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const vector<KeyPoint>& keypoints1,
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vector<DMatch>& matches)
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{
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vector<Point2f> points0;
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vector<Point2f> points0;
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KeyPoint::convert(keypoints0, points0);
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Mat points0t;
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if(H.empty())
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points0t = Mat(points0);
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else
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perspectiveTransform(Mat(points0), points0t, H);
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if(H.empty())
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points0t = Mat(points0);
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else
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perspectiveTransform(Mat(points0), points0t, H);
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matches.clear();
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vector<uchar> usedMask(keypoints1.size(), 0);
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for(size_t i0 = 0; i0 < keypoints0.size(); i0++)
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{
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int nearestPointIndex = -1;
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matches.clear();
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vector<uchar> usedMask(keypoints1.size(), 0);
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for(size_t i0 = 0; i0 < keypoints0.size(); i0++)
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{
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int nearestPointIndex = -1;
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float maxIntersectRatio = 0.f;
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const float r0 = 0.5f * keypoints0[i0].size;
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for(size_t i1 = 0; i1 < keypoints1.size(); i1++)
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{
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if(nearestPointIndex >= 0 && usedMask[i1])
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continue;
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if(nearestPointIndex >= 0 && usedMask[i1])
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continue;
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float r1 = 0.5f * keypoints1[i1].size;
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float r1 = 0.5f * keypoints1[i1].size;
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float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(i0), r0,
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keypoints1[i1].pt, r1);
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if(intersectRatio > maxIntersectRatio)
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{
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maxIntersectRatio = intersectRatio;
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nearestPointIndex = i1;
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maxIntersectRatio = intersectRatio;
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nearestPointIndex = i1;
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}
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}
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matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
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if(nearestPointIndex >= 0)
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usedMask[nearestPointIndex] = 1;
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}
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matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
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if(nearestPointIndex >= 0)
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usedMask[nearestPointIndex] = 1;
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}
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}
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class DetectorRotationInvarianceTest : public cvtest::BaseTest
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@ -166,134 +192,13 @@ public:
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DetectorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
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float _minKeyPointMatchesRatio,
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float _minAngleInliersRatio) :
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featureDetector(_featureDetector),
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minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
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minAngleInliersRatio(_minAngleInliersRatio)
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featureDetector(_featureDetector),
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minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
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minAngleInliersRatio(_minAngleInliersRatio)
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{
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CV_Assert(!featureDetector.empty());
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}
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protected:
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void run(int)
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{
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const string imageFilename = string(ts->get_data_path()) + IMAGE_TSUKUBA;
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// Read test data
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Mat image0 = imread(imageFilename), image1, mask1;
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if(image0.empty())
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{
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ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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vector<KeyPoint> keypoints0;
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featureDetector->detect(image0, keypoints0);
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if(keypoints0.size() < 15)
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CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
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const int maxAngle = 360, angleStep = 15;
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for(int angle = 0; angle < maxAngle; angle += angleStep)
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{
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Mat H = rotateImage(image0, angle, image1, mask1);
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vector<KeyPoint> keypoints1;
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featureDetector->detect(image1, keypoints1, mask1);
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vector<DMatch> matches;
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matchKeyPoints(keypoints0, H, keypoints1, matches);
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int angleInliersCount = 0;
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const float minIntersectRatio = 0.5f;
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int keyPointMatchesCount = 0;
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for(size_t m = 0; m < matches.size(); m++)
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{
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if(matches[m].distance < minIntersectRatio)
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continue;
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keyPointMatchesCount++;
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// Check does this inlier have consistent angles
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const float maxAngleDiff = 15.f; // grad
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float angle0 = keypoints0[matches[m].queryIdx].angle;
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float angle1 = keypoints1[matches[m].trainIdx].angle;
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if(angle0 == -1 || angle1 == -1)
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CV_Error(CV_StsBadArg, "Given FeatureDetector is not rotation invariant, it can not be tested here.\n");
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CV_Assert(angle0 >= 0.f && angle0 < 360.f);
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CV_Assert(angle1 >= 0.f && angle1 < 360.f);
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float rotAngle0 = angle0 + angle;
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if(rotAngle0 >= 360.f)
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rotAngle0 -= 360.f;
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float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
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angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
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CV_Assert(angleDiff >= 0.f);
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bool isAngleCorrect = angleDiff < maxAngleDiff;
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if(isAngleCorrect)
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angleInliersCount++;
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}
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float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
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if(keyPointMatchesRatio < minKeyPointMatchesRatio)
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{
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ts->printf(cvtest::TS::LOG, "Incorrect keyPointMatchesRatio: curr = %f, min = %f.\n",
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keyPointMatchesRatio, minKeyPointMatchesRatio);
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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return;
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}
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if(keyPointMatchesCount)
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{
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float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
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if(angleInliersRatio < minAngleInliersRatio)
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{
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ts->printf(cvtest::TS::LOG, "Incorrect angleInliersRatio: curr = %f, min = %f.\n",
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angleInliersRatio, minAngleInliersRatio);
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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return;
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}
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}
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#if SHOW_DEBUG_LOG
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std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
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<< " - angleInliersRatio " << static_cast<float>(angleInliersCount) / keyPointMatchesCount << std::endl;
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#endif
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}
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ts->set_failed_test_info( cvtest::TS::OK );
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}
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Ptr<FeatureDetector> featureDetector;
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float minKeyPointMatchesRatio;
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float minAngleInliersRatio;
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};
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void scaleKeyPoints(const vector<KeyPoint>& src, vector<KeyPoint>& dst, float scale)
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{
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dst.resize(src.size());
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for(size_t i = 0; i < src.size(); i++)
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dst[i] = KeyPoint(src[i].pt.x * scale, src[i].pt.y * scale, src[i].size * scale);
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}
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class DescriptorRotationInvarianceTest : public cvtest::BaseTest
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{
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public:
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DescriptorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
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const Ptr<DescriptorExtractor>& _descriptorExtractor,
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int _normType,
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float _minKeyPointMatchesRatio,
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float _minDescInliersRatio) :
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featureDetector(_featureDetector),
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descriptorExtractor(_descriptorExtractor),
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normType(_normType),
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minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
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minDescInliersRatio(_minDescInliersRatio)
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{
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CV_Assert(!featureDetector.empty());
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CV_Assert(!descriptorExtractor.empty());
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}
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protected:
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void run(int)
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@ -310,13 +215,9 @@ protected:
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}
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vector<KeyPoint> keypoints0;
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Mat descriptors0;
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featureDetector->detect(image0, keypoints0);
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if(keypoints0.size() < 15)
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CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
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descriptorExtractor->compute(image0, keypoints0, descriptors0);
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BFMatcher bfmatcher(normType);
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if(keypoints0.size() < 15)
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CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
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const int maxAngle = 360, angleStep = 15;
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for(int angle = 0; angle < maxAngle; angle += angleStep)
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@ -324,31 +225,42 @@ protected:
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Mat H = rotateImage(image0, angle, image1, mask1);
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vector<KeyPoint> keypoints1;
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Mat descriptors1;
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featureDetector->detect(image1, keypoints1, mask1);
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descriptorExtractor->compute(image1, keypoints1, descriptors1);
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vector<DMatch> descMatches;
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bfmatcher.match(descriptors0, descriptors1, descMatches);
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vector<DMatch> matches;
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matchKeyPoints(keypoints0, H, keypoints1, matches);
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vector<DMatch> keyPointMatches;
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matchKeyPoints(keypoints0, H, keypoints1, keyPointMatches);
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int angleInliersCount = 0;
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const float minIntersectRatio = 0.5f;
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int keyPointMatchesCount = 0;
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for(size_t m = 0; m < keyPointMatches.size(); m++)
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{
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if(keyPointMatches[m].distance >= minIntersectRatio)
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keyPointMatchesCount++;
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}
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int descInliersCount = 0;
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for(size_t m = 0; m < descMatches.size(); m++)
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const float minIntersectRatio = 0.5f;
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int keyPointMatchesCount = 0;
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for(size_t m = 0; m < matches.size(); m++)
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{
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int queryIdx = descMatches[m].queryIdx;
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if(keyPointMatches[queryIdx].distance >= minIntersectRatio &&
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descMatches[m].trainIdx == keyPointMatches[queryIdx].trainIdx)
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descInliersCount++;
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}
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if(matches[m].distance < minIntersectRatio)
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continue;
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keyPointMatchesCount++;
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// Check does this inlier have consistent angles
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const float maxAngleDiff = 15.f; // grad
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float angle0 = keypoints0[matches[m].queryIdx].angle;
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float angle1 = keypoints1[matches[m].trainIdx].angle;
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if(angle0 == -1 || angle1 == -1)
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CV_Error(CV_StsBadArg, "Given FeatureDetector is not rotation invariant, it can not be tested here.\n");
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CV_Assert(angle0 >= 0.f && angle0 < 360.f);
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CV_Assert(angle1 >= 0.f && angle1 < 360.f);
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float rotAngle0 = angle0 + angle;
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if(rotAngle0 >= 360.f)
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rotAngle0 -= 360.f;
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float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
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angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
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CV_Assert(angleDiff >= 0.f);
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bool isAngleCorrect = angleDiff < maxAngleDiff;
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if(isAngleCorrect)
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angleInliersCount++;
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}
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float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
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if(keyPointMatchesRatio < minKeyPointMatchesRatio)
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@ -359,31 +271,115 @@ protected:
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return;
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}
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if(keyPointMatchesCount)
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if(keyPointMatchesCount)
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{
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float descInliersRatio = static_cast<float>(descInliersCount) / keyPointMatchesCount;
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if(descInliersRatio < minDescInliersRatio)
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float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
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if(angleInliersRatio < minAngleInliersRatio)
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{
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ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
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descInliersRatio, minDescInliersRatio);
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ts->printf(cvtest::TS::LOG, "Incorrect angleInliersRatio: curr = %f, min = %f.\n",
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angleInliersRatio, minAngleInliersRatio);
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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return;
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}
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}
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#if SHOW_DEBUG_LOG
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std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
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<< " - descInliersRatio " << static_cast<float>(descInliersCount) / keyPointMatchesCount << std::endl;
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<< " - angleInliersRatio " << static_cast<float>(angleInliersCount) / keyPointMatchesCount << std::endl;
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#endif
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}
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ts->set_failed_test_info( cvtest::TS::OK );
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}
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Ptr<FeatureDetector> featureDetector;
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Ptr<DescriptorExtractor> descriptorExtractor;
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float minKeyPointMatchesRatio;
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float minAngleInliersRatio;
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};
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class DescriptorRotationInvarianceTest : public cvtest::BaseTest
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{
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public:
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DescriptorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
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const Ptr<DescriptorExtractor>& _descriptorExtractor,
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int _normType,
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float _minDescInliersRatio) :
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featureDetector(_featureDetector),
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descriptorExtractor(_descriptorExtractor),
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normType(_normType),
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minDescInliersRatio(_minDescInliersRatio)
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{
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CV_Assert(!featureDetector.empty());
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CV_Assert(!descriptorExtractor.empty());
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}
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protected:
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void run(int)
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{
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const string imageFilename = string(ts->get_data_path()) + IMAGE_TSUKUBA;
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// Read test data
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Mat image0 = imread(imageFilename), image1, mask1;
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if(image0.empty())
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{
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ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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vector<KeyPoint> keypoints0;
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Mat descriptors0;
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featureDetector->detect(image0, keypoints0);
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if(keypoints0.size() < 15)
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CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
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descriptorExtractor->compute(image0, keypoints0, descriptors0);
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BFMatcher bfmatcher(normType);
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const float minIntersectRatio = 0.5f;
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const int maxAngle = 360, angleStep = 15;
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for(int angle = 0; angle < maxAngle; angle += angleStep)
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{
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Mat H = rotateImage(image0, angle, image1, mask1);
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vector<KeyPoint> keypoints1;
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rotateKeyPoints(keypoints0, H, angle, keypoints1);
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Mat descriptors1;
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descriptorExtractor->compute(image1, keypoints1, descriptors1);
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vector<DMatch> descMatches;
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bfmatcher.match(descriptors0, descriptors1, descMatches);
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int descInliersCount = 0;
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for(size_t m = 0; m < descMatches.size(); m++)
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{
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const KeyPoint& transformed_p0 = keypoints1[descMatches[m].queryIdx];
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const KeyPoint& p1 = keypoints1[descMatches[m].trainIdx];
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if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
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p1.pt, 0.5f * p1.size) >= minIntersectRatio)
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{
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descInliersCount++;
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}
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}
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float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
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if(descInliersRatio < minDescInliersRatio)
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{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
|
||||
descInliersRatio, minDescInliersRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
#if SHOW_DEBUG_LOG
|
||||
std::cout << "descInliersRatio " << static_cast<float>(descInliersCount) / keypoints0.size() << std::endl;
|
||||
#endif
|
||||
}
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
Ptr<FeatureDetector> featureDetector;
|
||||
Ptr<DescriptorExtractor> descriptorExtractor;
|
||||
int normType;
|
||||
float minKeyPointMatchesRatio;
|
||||
float minDescInliersRatio;
|
||||
|
||||
float minDescInliersRatio;
|
||||
};
|
||||
|
||||
class DetectorScaleInvarianceTest : public cvtest::BaseTest
|
||||
@ -392,9 +388,9 @@ public:
|
||||
DetectorScaleInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
||||
float _minKeyPointMatchesRatio,
|
||||
float _minScaleInliersRatio) :
|
||||
featureDetector(_featureDetector),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minScaleInliersRatio(_minScaleInliersRatio)
|
||||
featureDetector(_featureDetector),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minScaleInliersRatio(_minScaleInliersRatio)
|
||||
{
|
||||
CV_Assert(!featureDetector.empty());
|
||||
}
|
||||
@ -403,7 +399,7 @@ protected:
|
||||
|
||||
void run(int)
|
||||
{
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
||||
|
||||
// Read test data
|
||||
Mat image0 = imread(imageFilename);
|
||||
@ -417,58 +413,59 @@ protected:
|
||||
vector<KeyPoint> keypoints0;
|
||||
featureDetector->detect(image0, keypoints0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
|
||||
for(int scale = 2; scale <= 4; scale++)
|
||||
for(float scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
|
||||
{
|
||||
Mat image1;
|
||||
resize(image0, image1, Size(), 1./scale, 1./scale);
|
||||
float scale = 1.f + scaleIdx * 0.5f;
|
||||
Mat image1;
|
||||
resize(image0, image1, Size(), 1./scale, 1./scale);
|
||||
|
||||
vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
|
||||
featureDetector->detect(image1, keypoints1);
|
||||
if(keypoints1.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
if(keypoints1.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
|
||||
if(keypoints1.size() > keypoints0.size())
|
||||
{
|
||||
if(keypoints1.size() > keypoints0.size())
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Strange behavior of the detector. "
|
||||
"It gives more points count in an image of the smaller size.\n"
|
||||
"original size (%d, %d), keypoints count = %d\n"
|
||||
"reduced size (%d, %d), keypoints count = %d\n",
|
||||
image0.cols, image0.rows, keypoints0.size(),
|
||||
image1.cols, image1.rows, keypoints1.size());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
||||
"It gives more points count in an image of the smaller size.\n"
|
||||
"original size (%d, %d), keypoints count = %d\n"
|
||||
"reduced size (%d, %d), keypoints count = %d\n",
|
||||
image0.cols, image0.rows, keypoints0.size(),
|
||||
image1.cols, image1.rows, keypoints1.size());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
||||
return;
|
||||
}
|
||||
|
||||
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
|
||||
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
|
||||
|
||||
vector<DMatch> matches;
|
||||
// image1 is query image (it's reduced image0)
|
||||
// image0 is train image
|
||||
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
|
||||
vector<DMatch> matches;
|
||||
// image1 is query image (it's reduced image0)
|
||||
// image0 is train image
|
||||
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
int scaleInliersCount = 0;
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
int scaleInliersCount = 0;
|
||||
|
||||
for(size_t m = 0; m < matches.size(); m++)
|
||||
for(size_t m = 0; m < matches.size(); m++)
|
||||
{
|
||||
if(matches[m].distance < minIntersectRatio)
|
||||
continue;
|
||||
if(matches[m].distance < minIntersectRatio)
|
||||
continue;
|
||||
|
||||
keyPointMatchesCount++;
|
||||
keyPointMatchesCount++;
|
||||
|
||||
// Check does this inlier have consistent sizes
|
||||
// Check does this inlier have consistent sizes
|
||||
const float maxSizeDiff = 0.8;//0.9f; // grad
|
||||
float size0 = keypoints0[matches[m].trainIdx].size;
|
||||
float size0 = keypoints0[matches[m].trainIdx].size;
|
||||
float size1 = osiKeypoints1[matches[m].queryIdx].size;
|
||||
CV_Assert(size0 > 0 && size1 > 0);
|
||||
if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
|
||||
CV_Assert(size0 > 0 && size1 > 0);
|
||||
if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
|
||||
scaleInliersCount++;
|
||||
}
|
||||
|
||||
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
|
||||
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
|
||||
if(keyPointMatchesRatio < minKeyPointMatchesRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect keyPointMatchesRatio: curr = %f, min = %f.\n",
|
||||
@ -477,9 +474,9 @@ protected:
|
||||
return;
|
||||
}
|
||||
|
||||
if(keyPointMatchesCount)
|
||||
if(keyPointMatchesCount)
|
||||
{
|
||||
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
|
||||
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
|
||||
if(scaleInliersRatio < minScaleInliersRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect scaleInliersRatio: curr = %f, min = %f.\n",
|
||||
@ -490,21 +487,8 @@ protected:
|
||||
}
|
||||
#if SHOW_DEBUG_LOG
|
||||
std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
|
||||
<< " - scaleInliersRatio " << static_cast<float>(scaleInliersCount) / keyPointMatchesCount << std::endl;
|
||||
<< " - scaleInliersRatio " << static_cast<float>(scaleInliersCount) / keyPointMatchesCount << std::endl;
|
||||
#endif
|
||||
/*vector<DMatch> filteredMatches;
|
||||
for(size_t i = 0; i < matches.size(); i++)
|
||||
{
|
||||
if(matches[i].distance >= minIntersectRatio)
|
||||
filteredMatches.push_back(matches[i]);
|
||||
}
|
||||
|
||||
Mat out;
|
||||
namedWindow("out", CV_WINDOW_NORMAL);
|
||||
drawMatches(image1, keypoints1, image0, keypoints0, filteredMatches, out,
|
||||
Scalar::all(-1), Scalar(-1), vector<char>(), DrawMatchesFlags::DEFAULT + DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
|
||||
imshow("out", out);
|
||||
waitKey();*/
|
||||
}
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
@ -518,25 +502,23 @@ class DescriptorScaleInvarianceTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
DescriptorScaleInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
||||
const Ptr<DescriptorExtractor>& _descriptorExtractor,
|
||||
int _normType,
|
||||
float _minKeyPointMatchesRatio,
|
||||
const Ptr<DescriptorExtractor>& _descriptorExtractor,
|
||||
int _normType,
|
||||
float _minDescInliersRatio) :
|
||||
featureDetector(_featureDetector),
|
||||
descriptorExtractor(_descriptorExtractor),
|
||||
normType(_normType),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minDescInliersRatio(_minDescInliersRatio)
|
||||
featureDetector(_featureDetector),
|
||||
descriptorExtractor(_descriptorExtractor),
|
||||
normType(_normType),
|
||||
minDescInliersRatio(_minDescInliersRatio)
|
||||
{
|
||||
CV_Assert(!featureDetector.empty());
|
||||
CV_Assert(!descriptorExtractor.empty());
|
||||
CV_Assert(!descriptorExtractor.empty());
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
void run(int)
|
||||
{
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
||||
|
||||
// Read test data
|
||||
Mat image0 = imread(imageFilename);
|
||||
@ -549,145 +531,126 @@ protected:
|
||||
|
||||
vector<KeyPoint> keypoints0;
|
||||
featureDetector->detect(image0, keypoints0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
Mat descriptors0;
|
||||
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
Mat descriptors0;
|
||||
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
||||
|
||||
BFMatcher bfmatcher(normType);
|
||||
for(int scale = 2; scale <= 4; scale++)
|
||||
BFMatcher bfmatcher(normType);
|
||||
for(float scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
|
||||
{
|
||||
Mat image1;
|
||||
resize(image0, image1, Size(), 1./scale, 1./scale);
|
||||
float scale = 1.f + scaleIdx * 0.5f;
|
||||
|
||||
vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
|
||||
featureDetector->detect(image1, keypoints1);
|
||||
if(keypoints1.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
if(keypoints1.size() > keypoints0.size() )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Strange behavior of the detector. "
|
||||
"It gives more points count in an image of the smaller size.\n"
|
||||
"original size (%d, %d), keypoints count = %d\n"
|
||||
"reduced size (%d, %d), keypoints count = %d\n",
|
||||
image0.cols, image0.rows, keypoints0.size(),
|
||||
image1.cols, image1.rows, keypoints1.size());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
||||
return;
|
||||
Mat image1;
|
||||
resize(image0, image1, Size(), 1./scale, 1./scale);
|
||||
|
||||
vector<KeyPoint> keypoints1;
|
||||
scaleKeyPoints(keypoints0, keypoints1, 1./scale);
|
||||
Mat descriptors1;
|
||||
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
||||
|
||||
vector<DMatch> descMatches;
|
||||
bfmatcher.match(descriptors0, descriptors1, descMatches);
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int descInliersCount = 0;
|
||||
for(size_t m = 0; m < descMatches.size(); m++)
|
||||
{
|
||||
const KeyPoint& transformed_p0 = keypoints0[descMatches[m].queryIdx];
|
||||
const KeyPoint& p1 = keypoints0[descMatches[m].trainIdx];
|
||||
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
|
||||
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
|
||||
{
|
||||
descInliersCount++;
|
||||
}
|
||||
}
|
||||
|
||||
Mat descriptors1;
|
||||
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
||||
|
||||
vector<DMatch> keyPointMatches, descMatches;
|
||||
// image1 is query image (it's reduced image0)
|
||||
// image0 is train image
|
||||
bfmatcher.match(descriptors1, descriptors0, descMatches);
|
||||
|
||||
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
|
||||
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, keyPointMatches);
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
for(size_t m = 0; m < keyPointMatches.size(); m++)
|
||||
{
|
||||
if(keyPointMatches[m].distance >= minIntersectRatio)
|
||||
keyPointMatchesCount++;
|
||||
}
|
||||
int descInliersCount = 0;
|
||||
for(size_t m = 0; m < descMatches.size(); m++)
|
||||
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
|
||||
if(descInliersRatio < minDescInliersRatio)
|
||||
{
|
||||
int queryIdx = descMatches[m].queryIdx;
|
||||
if(keyPointMatches[queryIdx].distance >= minIntersectRatio &&
|
||||
descMatches[m].trainIdx == keyPointMatches[queryIdx].trainIdx)
|
||||
descInliersCount++;
|
||||
}
|
||||
|
||||
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
|
||||
if(keyPointMatchesRatio < minKeyPointMatchesRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect keyPointMatchesRatio: curr = %f, min = %f.\n",
|
||||
keyPointMatchesRatio, minKeyPointMatchesRatio);
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
|
||||
descInliersRatio, minDescInliersRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
|
||||
if(keyPointMatchesCount)
|
||||
{
|
||||
float descInliersRatio = static_cast<float>(descInliersCount) / keyPointMatchesCount;
|
||||
if(descInliersRatio < minDescInliersRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
|
||||
descInliersRatio, minDescInliersRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
}
|
||||
#if SHOW_DEBUG_LOG
|
||||
std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
|
||||
<< " - descInliersRatio " << static_cast<float>(descInliersCount) / keyPointMatchesCount << std::endl;
|
||||
std::cout << "descInliersRatio " << static_cast<float>(descInliersCount) / keypoints0.size() << std::endl;
|
||||
#endif
|
||||
}
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
Ptr<FeatureDetector> featureDetector;
|
||||
Ptr<DescriptorExtractor> descriptorExtractor;
|
||||
int normType;
|
||||
Ptr<DescriptorExtractor> descriptorExtractor;
|
||||
int normType;
|
||||
float minKeyPointMatchesRatio;
|
||||
float minDescInliersRatio;
|
||||
};
|
||||
|
||||
// Tests registration
|
||||
|
||||
// Detector's rotation invariance check
|
||||
/*
|
||||
* Detector's rotation invariance check
|
||||
*/
|
||||
TEST(Features2d_RotationInvariance_Detector_ORB, regression)
|
||||
{
|
||||
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
0.45f,
|
||||
0.75f);
|
||||
0.47f,
|
||||
0.77f);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
// Descriptors's rotation invariance check
|
||||
/*
|
||||
* Descriptors's rotation invariance check
|
||||
*/
|
||||
TEST(Features2d_RotationInvariance_Descriptor_ORB, regression)
|
||||
{
|
||||
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
Algorithm::create<DescriptorExtractor>("Feature2D.ORB"),
|
||||
NORM_HAMMING,
|
||||
0.45f,
|
||||
0.53f);
|
||||
0.99f);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
// TODO: Uncomment test for FREAK when it will work; add test for scale invariance for FREAK
|
||||
//TEST(Features2d_RotationInvariance_Descriptor_FREAK, regression)
|
||||
//{
|
||||
// DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
// Algorithm::create<DescriptorExtractor>("Feature2D.FREAK"),
|
||||
// NORM_HAMMING(?),
|
||||
// 0.45f,
|
||||
// 0.?f);
|
||||
// DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
// Algorithm::create<DescriptorExtractor>("Feature2D.FREAK"),
|
||||
// NORM_HAMMING,
|
||||
// 0.f);
|
||||
// test.safe_run();
|
||||
//}
|
||||
|
||||
/* TODO: Why ORB has bad scale invariance in this tests?
|
||||
// Detector's scale invariance check
|
||||
TEST(Features2d_ScaleInvariance_Detector_ORB, regression)
|
||||
{
|
||||
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
0.13f,
|
||||
0.0f);
|
||||
test.safe_run();
|
||||
}
|
||||
/*
|
||||
* Detector's scale invariance check
|
||||
*/
|
||||
|
||||
// Descriptor's scale invariance check
|
||||
TEST(Features2d_ScaleInvariance_Descriptor_ORB, regression)
|
||||
{
|
||||
DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
Algorithm::create<DescriptorExtractor>("Feature2D.ORB"),
|
||||
NORM_HAMMING,
|
||||
0.13f,
|
||||
0.36f);
|
||||
test.safe_run();
|
||||
}*/
|
||||
//TEST(Features2d_ScaleInvariance_Detector_ORB, regression)
|
||||
//{
|
||||
// DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
// 0.22f,
|
||||
// 0.83f);
|
||||
// test.safe_run();
|
||||
//}
|
||||
|
||||
/*
|
||||
* Descriptor's scale invariance check
|
||||
*/
|
||||
|
||||
//TEST(Features2d_ScaleInvariance_Descriptor_ORB, regression)
|
||||
//{
|
||||
// DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
// Algorithm::create<DescriptorExtractor>("Feature2D.ORB"),
|
||||
// NORM_HAMMING,
|
||||
// 0.01f);
|
||||
// test.safe_run();
|
||||
//}
|
||||
|
||||
//TEST(Features2d_ScaleInvariance_Descriptor_FREAK, regression)
|
||||
//{
|
||||
// DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
||||
// Algorithm::create<DescriptorExtractor>("Feature2D.FREAK"),
|
||||
// NORM_HAMMING,
|
||||
// 0.01f);
|
||||
// test.safe_run();
|
||||
//}
|
||||
|
||||
@ -53,7 +53,7 @@ const string IMAGE_BIKES = "/detectors_descriptors_evaluation/images_datasets/bi
|
||||
static
|
||||
Mat generateHomography(float angle)
|
||||
{
|
||||
// angle - rotation around Oz in degrees
|
||||
// angle - rotation around Oz in degrees
|
||||
float angleRadian = angle * CV_PI / 180.;
|
||||
Mat H = Mat::eye(3, 3, CV_32FC1);
|
||||
H.at<float>(0,0) = H.at<float>(1,1) = std::cos(angleRadian);
|
||||
@ -66,7 +66,7 @@ Mat generateHomography(float angle)
|
||||
static
|
||||
Mat rotateImage(const Mat& srcImage, float angle, Mat& dstImage, Mat& dstMask)
|
||||
{
|
||||
// angle - rotation around Oz in degrees
|
||||
// angle - rotation around Oz in degrees
|
||||
float diag = std::sqrt(static_cast<float>(srcImage.cols * srcImage.cols + srcImage.rows * srcImage.rows));
|
||||
Mat LUShift = Mat::eye(3, 3, CV_32FC1); // left up
|
||||
LUShift.at<float>(0,2) = -srcImage.cols/2;
|
||||
@ -85,6 +85,32 @@ Mat rotateImage(const Mat& srcImage, float angle, Mat& dstImage, Mat& dstMask)
|
||||
return H;
|
||||
}
|
||||
|
||||
void rotateKeyPoints(const vector<KeyPoint>& src, const Mat& H, float angle, vector<KeyPoint>& dst)
|
||||
{
|
||||
// suppose that H is rotation given from rotateImage() and angle has value passed to rotateImage()
|
||||
vector<Point2f> srcCenters, dstCenters;
|
||||
KeyPoint::convert(src, srcCenters);
|
||||
|
||||
perspectiveTransform(srcCenters, dstCenters, H);
|
||||
|
||||
dst = src;
|
||||
for(size_t i = 0; i < dst.size(); i++)
|
||||
{
|
||||
dst[i].pt = dstCenters[i];
|
||||
float dstAngle = src[i].angle + angle;
|
||||
if(dstAngle >= 360.f)
|
||||
dstAngle -= 360.f;
|
||||
dst[i].angle = dstAngle;
|
||||
}
|
||||
}
|
||||
|
||||
void scaleKeyPoints(const vector<KeyPoint>& src, vector<KeyPoint>& dst, float scale)
|
||||
{
|
||||
dst.resize(src.size());
|
||||
for(size_t i = 0; i < src.size(); i++)
|
||||
dst[i] = KeyPoint(src[i].pt.x * scale, src[i].pt.y * scale, src[i].size * scale);
|
||||
}
|
||||
|
||||
static
|
||||
float calcCirclesIntersectArea(const Point2f& p0, float r0, const Point2f& p1, float r1)
|
||||
{
|
||||
@ -119,45 +145,45 @@ float calcIntersectRatio(const Point2f& p0, float r0, const Point2f& p1, float r
|
||||
return intersectArea / unionArea;
|
||||
}
|
||||
|
||||
static
|
||||
static
|
||||
void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
|
||||
const vector<KeyPoint>& keypoints1,
|
||||
vector<DMatch>& matches)
|
||||
const vector<KeyPoint>& keypoints1,
|
||||
vector<DMatch>& matches)
|
||||
{
|
||||
vector<Point2f> points0;
|
||||
vector<Point2f> points0;
|
||||
KeyPoint::convert(keypoints0, points0);
|
||||
Mat points0t;
|
||||
if(H.empty())
|
||||
points0t = Mat(points0);
|
||||
else
|
||||
perspectiveTransform(Mat(points0), points0t, H);
|
||||
if(H.empty())
|
||||
points0t = Mat(points0);
|
||||
else
|
||||
perspectiveTransform(Mat(points0), points0t, H);
|
||||
|
||||
matches.clear();
|
||||
vector<uchar> usedMask(keypoints1.size(), 0);
|
||||
for(size_t i0 = 0; i0 < keypoints0.size(); i0++)
|
||||
{
|
||||
int nearestPointIndex = -1;
|
||||
matches.clear();
|
||||
vector<uchar> usedMask(keypoints1.size(), 0);
|
||||
for(size_t i0 = 0; i0 < keypoints0.size(); i0++)
|
||||
{
|
||||
int nearestPointIndex = -1;
|
||||
float maxIntersectRatio = 0.f;
|
||||
const float r0 = 0.5f * keypoints0[i0].size;
|
||||
for(size_t i1 = 0; i1 < keypoints1.size(); i1++)
|
||||
{
|
||||
if(nearestPointIndex >= 0 && usedMask[i1])
|
||||
continue;
|
||||
if(nearestPointIndex >= 0 && usedMask[i1])
|
||||
continue;
|
||||
|
||||
float r1 = 0.5f * keypoints1[i1].size;
|
||||
float r1 = 0.5f * keypoints1[i1].size;
|
||||
float intersectRatio = calcIntersectRatio(points0t.at<Point2f>(i0), r0,
|
||||
keypoints1[i1].pt, r1);
|
||||
if(intersectRatio > maxIntersectRatio)
|
||||
{
|
||||
maxIntersectRatio = intersectRatio;
|
||||
nearestPointIndex = i1;
|
||||
maxIntersectRatio = intersectRatio;
|
||||
nearestPointIndex = i1;
|
||||
}
|
||||
}
|
||||
|
||||
matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
|
||||
if(nearestPointIndex >= 0)
|
||||
usedMask[nearestPointIndex] = 1;
|
||||
}
|
||||
matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
|
||||
if(nearestPointIndex >= 0)
|
||||
usedMask[nearestPointIndex] = 1;
|
||||
}
|
||||
}
|
||||
|
||||
class DetectorRotationInvarianceTest : public cvtest::BaseTest
|
||||
@ -166,134 +192,13 @@ public:
|
||||
DetectorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
||||
float _minKeyPointMatchesRatio,
|
||||
float _minAngleInliersRatio) :
|
||||
featureDetector(_featureDetector),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minAngleInliersRatio(_minAngleInliersRatio)
|
||||
featureDetector(_featureDetector),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minAngleInliersRatio(_minAngleInliersRatio)
|
||||
{
|
||||
CV_Assert(!featureDetector.empty());
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
void run(int)
|
||||
{
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_TSUKUBA;
|
||||
|
||||
// Read test data
|
||||
Mat image0 = imread(imageFilename), image1, mask1;
|
||||
if(image0.empty())
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
||||
return;
|
||||
}
|
||||
|
||||
vector<KeyPoint> keypoints0;
|
||||
featureDetector->detect(image0, keypoints0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
|
||||
const int maxAngle = 360, angleStep = 15;
|
||||
for(int angle = 0; angle < maxAngle; angle += angleStep)
|
||||
{
|
||||
Mat H = rotateImage(image0, angle, image1, mask1);
|
||||
|
||||
vector<KeyPoint> keypoints1;
|
||||
featureDetector->detect(image1, keypoints1, mask1);
|
||||
|
||||
vector<DMatch> matches;
|
||||
matchKeyPoints(keypoints0, H, keypoints1, matches);
|
||||
|
||||
int angleInliersCount = 0;
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
for(size_t m = 0; m < matches.size(); m++)
|
||||
{
|
||||
if(matches[m].distance < minIntersectRatio)
|
||||
continue;
|
||||
|
||||
keyPointMatchesCount++;
|
||||
|
||||
// Check does this inlier have consistent angles
|
||||
const float maxAngleDiff = 15.f; // grad
|
||||
float angle0 = keypoints0[matches[m].queryIdx].angle;
|
||||
float angle1 = keypoints1[matches[m].trainIdx].angle;
|
||||
if(angle0 == -1 || angle1 == -1)
|
||||
CV_Error(CV_StsBadArg, "Given FeatureDetector is not rotation invariant, it can not be tested here.\n");
|
||||
CV_Assert(angle0 >= 0.f && angle0 < 360.f);
|
||||
CV_Assert(angle1 >= 0.f && angle1 < 360.f);
|
||||
|
||||
float rotAngle0 = angle0 + angle;
|
||||
if(rotAngle0 >= 360.f)
|
||||
rotAngle0 -= 360.f;
|
||||
|
||||
float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
|
||||
angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
|
||||
CV_Assert(angleDiff >= 0.f);
|
||||
bool isAngleCorrect = angleDiff < maxAngleDiff;
|
||||
if(isAngleCorrect)
|
||||
angleInliersCount++;
|
||||
}
|
||||
|
||||
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
|
||||
if(keyPointMatchesRatio < minKeyPointMatchesRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect keyPointMatchesRatio: curr = %f, min = %f.\n",
|
||||
keyPointMatchesRatio, minKeyPointMatchesRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
|
||||
if(keyPointMatchesCount)
|
||||
{
|
||||
float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
|
||||
if(angleInliersRatio < minAngleInliersRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect angleInliersRatio: curr = %f, min = %f.\n",
|
||||
angleInliersRatio, minAngleInliersRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
}
|
||||
#if SHOW_DEBUG_LOG
|
||||
std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
|
||||
<< " - angleInliersRatio " << static_cast<float>(angleInliersCount) / keyPointMatchesCount << std::endl;
|
||||
#endif
|
||||
}
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
Ptr<FeatureDetector> featureDetector;
|
||||
float minKeyPointMatchesRatio;
|
||||
float minAngleInliersRatio;
|
||||
};
|
||||
|
||||
void scaleKeyPoints(const vector<KeyPoint>& src, vector<KeyPoint>& dst, float scale)
|
||||
{
|
||||
dst.resize(src.size());
|
||||
for(size_t i = 0; i < src.size(); i++)
|
||||
dst[i] = KeyPoint(src[i].pt.x * scale, src[i].pt.y * scale, src[i].size * scale);
|
||||
}
|
||||
|
||||
class DescriptorRotationInvarianceTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
DescriptorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
||||
const Ptr<DescriptorExtractor>& _descriptorExtractor,
|
||||
int _normType,
|
||||
float _minKeyPointMatchesRatio,
|
||||
float _minDescInliersRatio) :
|
||||
featureDetector(_featureDetector),
|
||||
descriptorExtractor(_descriptorExtractor),
|
||||
normType(_normType),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minDescInliersRatio(_minDescInliersRatio)
|
||||
{
|
||||
CV_Assert(!featureDetector.empty());
|
||||
CV_Assert(!descriptorExtractor.empty());
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
void run(int)
|
||||
@ -310,13 +215,9 @@ protected:
|
||||
}
|
||||
|
||||
vector<KeyPoint> keypoints0;
|
||||
Mat descriptors0;
|
||||
featureDetector->detect(image0, keypoints0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
||||
|
||||
BFMatcher bfmatcher(normType);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
|
||||
const int maxAngle = 360, angleStep = 15;
|
||||
for(int angle = 0; angle < maxAngle; angle += angleStep)
|
||||
@ -324,31 +225,42 @@ protected:
|
||||
Mat H = rotateImage(image0, angle, image1, mask1);
|
||||
|
||||
vector<KeyPoint> keypoints1;
|
||||
Mat descriptors1;
|
||||
featureDetector->detect(image1, keypoints1, mask1);
|
||||
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
||||
|
||||
vector<DMatch> descMatches;
|
||||
bfmatcher.match(descriptors0, descriptors1, descMatches);
|
||||
vector<DMatch> matches;
|
||||
matchKeyPoints(keypoints0, H, keypoints1, matches);
|
||||
|
||||
vector<DMatch> keyPointMatches;
|
||||
matchKeyPoints(keypoints0, H, keypoints1, keyPointMatches);
|
||||
int angleInliersCount = 0;
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
for(size_t m = 0; m < keyPointMatches.size(); m++)
|
||||
{
|
||||
if(keyPointMatches[m].distance >= minIntersectRatio)
|
||||
keyPointMatchesCount++;
|
||||
}
|
||||
int descInliersCount = 0;
|
||||
for(size_t m = 0; m < descMatches.size(); m++)
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
for(size_t m = 0; m < matches.size(); m++)
|
||||
{
|
||||
int queryIdx = descMatches[m].queryIdx;
|
||||
if(keyPointMatches[queryIdx].distance >= minIntersectRatio &&
|
||||
descMatches[m].trainIdx == keyPointMatches[queryIdx].trainIdx)
|
||||
descInliersCount++;
|
||||
}
|
||||
if(matches[m].distance < minIntersectRatio)
|
||||
continue;
|
||||
|
||||
keyPointMatchesCount++;
|
||||
|
||||
// Check does this inlier have consistent angles
|
||||
const float maxAngleDiff = 15.f; // grad
|
||||
float angle0 = keypoints0[matches[m].queryIdx].angle;
|
||||
float angle1 = keypoints1[matches[m].trainIdx].angle;
|
||||
if(angle0 == -1 || angle1 == -1)
|
||||
CV_Error(CV_StsBadArg, "Given FeatureDetector is not rotation invariant, it can not be tested here.\n");
|
||||
CV_Assert(angle0 >= 0.f && angle0 < 360.f);
|
||||
CV_Assert(angle1 >= 0.f && angle1 < 360.f);
|
||||
|
||||
float rotAngle0 = angle0 + angle;
|
||||
if(rotAngle0 >= 360.f)
|
||||
rotAngle0 -= 360.f;
|
||||
|
||||
float angleDiff = std::max(rotAngle0, angle1) - std::min(rotAngle0, angle1);
|
||||
angleDiff = std::min(angleDiff, static_cast<float>(360.f - angleDiff));
|
||||
CV_Assert(angleDiff >= 0.f);
|
||||
bool isAngleCorrect = angleDiff < maxAngleDiff;
|
||||
if(isAngleCorrect)
|
||||
angleInliersCount++;
|
||||
}
|
||||
|
||||
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints0.size();
|
||||
if(keyPointMatchesRatio < minKeyPointMatchesRatio)
|
||||
@ -359,31 +271,115 @@ protected:
|
||||
return;
|
||||
}
|
||||
|
||||
if(keyPointMatchesCount)
|
||||
if(keyPointMatchesCount)
|
||||
{
|
||||
float descInliersRatio = static_cast<float>(descInliersCount) / keyPointMatchesCount;
|
||||
if(descInliersRatio < minDescInliersRatio)
|
||||
float angleInliersRatio = static_cast<float>(angleInliersCount) / keyPointMatchesCount;
|
||||
if(angleInliersRatio < minAngleInliersRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
|
||||
descInliersRatio, minDescInliersRatio);
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect angleInliersRatio: curr = %f, min = %f.\n",
|
||||
angleInliersRatio, minAngleInliersRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
}
|
||||
#if SHOW_DEBUG_LOG
|
||||
std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
|
||||
<< " - descInliersRatio " << static_cast<float>(descInliersCount) / keyPointMatchesCount << std::endl;
|
||||
<< " - angleInliersRatio " << static_cast<float>(angleInliersCount) / keyPointMatchesCount << std::endl;
|
||||
#endif
|
||||
}
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
Ptr<FeatureDetector> featureDetector;
|
||||
Ptr<DescriptorExtractor> descriptorExtractor;
|
||||
float minKeyPointMatchesRatio;
|
||||
float minAngleInliersRatio;
|
||||
};
|
||||
|
||||
class DescriptorRotationInvarianceTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
DescriptorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
||||
const Ptr<DescriptorExtractor>& _descriptorExtractor,
|
||||
int _normType,
|
||||
float _minDescInliersRatio) :
|
||||
featureDetector(_featureDetector),
|
||||
descriptorExtractor(_descriptorExtractor),
|
||||
normType(_normType),
|
||||
minDescInliersRatio(_minDescInliersRatio)
|
||||
{
|
||||
CV_Assert(!featureDetector.empty());
|
||||
CV_Assert(!descriptorExtractor.empty());
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
void run(int)
|
||||
{
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_TSUKUBA;
|
||||
|
||||
// Read test data
|
||||
Mat image0 = imread(imageFilename), image1, mask1;
|
||||
if(image0.empty())
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", imageFilename.c_str());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
||||
return;
|
||||
}
|
||||
|
||||
vector<KeyPoint> keypoints0;
|
||||
Mat descriptors0;
|
||||
featureDetector->detect(image0, keypoints0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
||||
|
||||
BFMatcher bfmatcher(normType);
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
const int maxAngle = 360, angleStep = 15;
|
||||
for(int angle = 0; angle < maxAngle; angle += angleStep)
|
||||
{
|
||||
Mat H = rotateImage(image0, angle, image1, mask1);
|
||||
|
||||
vector<KeyPoint> keypoints1;
|
||||
rotateKeyPoints(keypoints0, H, angle, keypoints1);
|
||||
Mat descriptors1;
|
||||
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
||||
|
||||
vector<DMatch> descMatches;
|
||||
bfmatcher.match(descriptors0, descriptors1, descMatches);
|
||||
|
||||
int descInliersCount = 0;
|
||||
for(size_t m = 0; m < descMatches.size(); m++)
|
||||
{
|
||||
const KeyPoint& transformed_p0 = keypoints1[descMatches[m].queryIdx];
|
||||
const KeyPoint& p1 = keypoints1[descMatches[m].trainIdx];
|
||||
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
|
||||
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
|
||||
{
|
||||
descInliersCount++;
|
||||
}
|
||||
}
|
||||
|
||||
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
|
||||
if(descInliersRatio < minDescInliersRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
|
||||
descInliersRatio, minDescInliersRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
#if SHOW_DEBUG_LOG
|
||||
std::cout << "descInliersRatio " << static_cast<float>(descInliersCount) / keypoints0.size() << std::endl;
|
||||
#endif
|
||||
}
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
Ptr<FeatureDetector> featureDetector;
|
||||
Ptr<DescriptorExtractor> descriptorExtractor;
|
||||
int normType;
|
||||
float minKeyPointMatchesRatio;
|
||||
float minDescInliersRatio;
|
||||
|
||||
float minDescInliersRatio;
|
||||
};
|
||||
|
||||
class DetectorScaleInvarianceTest : public cvtest::BaseTest
|
||||
@ -392,9 +388,9 @@ public:
|
||||
DetectorScaleInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
||||
float _minKeyPointMatchesRatio,
|
||||
float _minScaleInliersRatio) :
|
||||
featureDetector(_featureDetector),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minScaleInliersRatio(_minScaleInliersRatio)
|
||||
featureDetector(_featureDetector),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minScaleInliersRatio(_minScaleInliersRatio)
|
||||
{
|
||||
CV_Assert(!featureDetector.empty());
|
||||
}
|
||||
@ -403,7 +399,7 @@ protected:
|
||||
|
||||
void run(int)
|
||||
{
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
||||
|
||||
// Read test data
|
||||
Mat image0 = imread(imageFilename);
|
||||
@ -417,58 +413,59 @@ protected:
|
||||
vector<KeyPoint> keypoints0;
|
||||
featureDetector->detect(image0, keypoints0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
|
||||
for(int scale = 2; scale <= 4; scale++)
|
||||
for(float scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
|
||||
{
|
||||
Mat image1;
|
||||
resize(image0, image1, Size(), 1./scale, 1./scale);
|
||||
float scale = 1.f + scaleIdx * 0.5f;
|
||||
Mat image1;
|
||||
resize(image0, image1, Size(), 1./scale, 1./scale);
|
||||
|
||||
vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
|
||||
featureDetector->detect(image1, keypoints1);
|
||||
if(keypoints1.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
if(keypoints1.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
|
||||
if(keypoints1.size() > keypoints0.size())
|
||||
{
|
||||
if(keypoints1.size() > keypoints0.size())
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Strange behavior of the detector. "
|
||||
"It gives more points count in an image of the smaller size.\n"
|
||||
"original size (%d, %d), keypoints count = %d\n"
|
||||
"reduced size (%d, %d), keypoints count = %d\n",
|
||||
image0.cols, image0.rows, keypoints0.size(),
|
||||
image1.cols, image1.rows, keypoints1.size());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
||||
"It gives more points count in an image of the smaller size.\n"
|
||||
"original size (%d, %d), keypoints count = %d\n"
|
||||
"reduced size (%d, %d), keypoints count = %d\n",
|
||||
image0.cols, image0.rows, keypoints0.size(),
|
||||
image1.cols, image1.rows, keypoints1.size());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
||||
return;
|
||||
}
|
||||
|
||||
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
|
||||
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
|
||||
|
||||
vector<DMatch> matches;
|
||||
// image1 is query image (it's reduced image0)
|
||||
// image0 is train image
|
||||
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
|
||||
vector<DMatch> matches;
|
||||
// image1 is query image (it's reduced image0)
|
||||
// image0 is train image
|
||||
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, matches);
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
int scaleInliersCount = 0;
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
int scaleInliersCount = 0;
|
||||
|
||||
for(size_t m = 0; m < matches.size(); m++)
|
||||
for(size_t m = 0; m < matches.size(); m++)
|
||||
{
|
||||
if(matches[m].distance < minIntersectRatio)
|
||||
continue;
|
||||
if(matches[m].distance < minIntersectRatio)
|
||||
continue;
|
||||
|
||||
keyPointMatchesCount++;
|
||||
keyPointMatchesCount++;
|
||||
|
||||
// Check does this inlier have consistent sizes
|
||||
// Check does this inlier have consistent sizes
|
||||
const float maxSizeDiff = 0.8;//0.9f; // grad
|
||||
float size0 = keypoints0[matches[m].trainIdx].size;
|
||||
float size0 = keypoints0[matches[m].trainIdx].size;
|
||||
float size1 = osiKeypoints1[matches[m].queryIdx].size;
|
||||
CV_Assert(size0 > 0 && size1 > 0);
|
||||
if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
|
||||
CV_Assert(size0 > 0 && size1 > 0);
|
||||
if(std::min(size0, size1) > maxSizeDiff * std::max(size0, size1))
|
||||
scaleInliersCount++;
|
||||
}
|
||||
|
||||
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
|
||||
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
|
||||
if(keyPointMatchesRatio < minKeyPointMatchesRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect keyPointMatchesRatio: curr = %f, min = %f.\n",
|
||||
@ -477,9 +474,9 @@ protected:
|
||||
return;
|
||||
}
|
||||
|
||||
if(keyPointMatchesCount)
|
||||
if(keyPointMatchesCount)
|
||||
{
|
||||
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
|
||||
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
|
||||
if(scaleInliersRatio < minScaleInliersRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect scaleInliersRatio: curr = %f, min = %f.\n",
|
||||
@ -490,7 +487,7 @@ protected:
|
||||
}
|
||||
#if SHOW_DEBUG_LOG
|
||||
std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
|
||||
<< " - scaleInliersRatio " << static_cast<float>(scaleInliersCount) / keyPointMatchesCount << std::endl;
|
||||
<< " - scaleInliersRatio " << static_cast<float>(scaleInliersCount) / keyPointMatchesCount << std::endl;
|
||||
#endif
|
||||
}
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
@ -505,25 +502,23 @@ class DescriptorScaleInvarianceTest : public cvtest::BaseTest
|
||||
{
|
||||
public:
|
||||
DescriptorScaleInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
||||
const Ptr<DescriptorExtractor>& _descriptorExtractor,
|
||||
int _normType,
|
||||
float _minKeyPointMatchesRatio,
|
||||
const Ptr<DescriptorExtractor>& _descriptorExtractor,
|
||||
int _normType,
|
||||
float _minDescInliersRatio) :
|
||||
featureDetector(_featureDetector),
|
||||
descriptorExtractor(_descriptorExtractor),
|
||||
normType(_normType),
|
||||
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
||||
minDescInliersRatio(_minDescInliersRatio)
|
||||
featureDetector(_featureDetector),
|
||||
descriptorExtractor(_descriptorExtractor),
|
||||
normType(_normType),
|
||||
minDescInliersRatio(_minDescInliersRatio)
|
||||
{
|
||||
CV_Assert(!featureDetector.empty());
|
||||
CV_Assert(!descriptorExtractor.empty());
|
||||
CV_Assert(!descriptorExtractor.empty());
|
||||
}
|
||||
|
||||
protected:
|
||||
|
||||
void run(int)
|
||||
{
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
||||
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
||||
|
||||
// Read test data
|
||||
Mat image0 = imread(imageFilename);
|
||||
@ -536,102 +531,71 @@ protected:
|
||||
|
||||
vector<KeyPoint> keypoints0;
|
||||
featureDetector->detect(image0, keypoints0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
Mat descriptors0;
|
||||
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
||||
if(keypoints0.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
Mat descriptors0;
|
||||
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
||||
|
||||
BFMatcher bfmatcher(normType);
|
||||
for(int scale = 2; scale <= 4; scale++)
|
||||
BFMatcher bfmatcher(normType);
|
||||
for(float scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
|
||||
{
|
||||
Mat image1;
|
||||
resize(image0, image1, Size(), 1./scale, 1./scale);
|
||||
float scale = 1.f + scaleIdx * 0.5f;
|
||||
|
||||
vector<KeyPoint> keypoints1, osiKeypoints1; // osi - original size image
|
||||
featureDetector->detect(image1, keypoints1);
|
||||
if(keypoints1.size() < 15)
|
||||
CV_Error(CV_StsAssert, "Detector gives too few points in a test image\n");
|
||||
if(keypoints1.size() > keypoints0.size() )
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Strange behavior of the detector. "
|
||||
"It gives more points count in an image of the smaller size.\n"
|
||||
"original size (%d, %d), keypoints count = %d\n"
|
||||
"reduced size (%d, %d), keypoints count = %d\n",
|
||||
image0.cols, image0.rows, keypoints0.size(),
|
||||
image1.cols, image1.rows, keypoints1.size());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
||||
return;
|
||||
Mat image1;
|
||||
resize(image0, image1, Size(), 1./scale, 1./scale);
|
||||
|
||||
vector<KeyPoint> keypoints1;
|
||||
scaleKeyPoints(keypoints0, keypoints1, 1./scale);
|
||||
Mat descriptors1;
|
||||
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
||||
|
||||
vector<DMatch> descMatches;
|
||||
bfmatcher.match(descriptors0, descriptors1, descMatches);
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int descInliersCount = 0;
|
||||
for(size_t m = 0; m < descMatches.size(); m++)
|
||||
{
|
||||
const KeyPoint& transformed_p0 = keypoints0[descMatches[m].queryIdx];
|
||||
const KeyPoint& p1 = keypoints0[descMatches[m].trainIdx];
|
||||
if(calcIntersectRatio(transformed_p0.pt, 0.5f * transformed_p0.size,
|
||||
p1.pt, 0.5f * p1.size) >= minIntersectRatio)
|
||||
{
|
||||
descInliersCount++;
|
||||
}
|
||||
}
|
||||
|
||||
Mat descriptors1;
|
||||
descriptorExtractor->compute(image1, keypoints1, descriptors1);
|
||||
|
||||
vector<DMatch> keyPointMatches, descMatches;
|
||||
// image1 is query image (it's reduced image0)
|
||||
// image0 is train image
|
||||
bfmatcher.match(descriptors1, descriptors0, descMatches);
|
||||
|
||||
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
|
||||
matchKeyPoints(osiKeypoints1, Mat(), keypoints0, keyPointMatches);
|
||||
|
||||
const float minIntersectRatio = 0.5f;
|
||||
int keyPointMatchesCount = 0;
|
||||
for(size_t m = 0; m < keyPointMatches.size(); m++)
|
||||
{
|
||||
if(keyPointMatches[m].distance >= minIntersectRatio)
|
||||
keyPointMatchesCount++;
|
||||
}
|
||||
int descInliersCount = 0;
|
||||
for(size_t m = 0; m < descMatches.size(); m++)
|
||||
float descInliersRatio = static_cast<float>(descInliersCount) / keypoints0.size();
|
||||
if(descInliersRatio < minDescInliersRatio)
|
||||
{
|
||||
int queryIdx = descMatches[m].queryIdx;
|
||||
if(keyPointMatches[queryIdx].distance >= minIntersectRatio &&
|
||||
descMatches[m].trainIdx == keyPointMatches[queryIdx].trainIdx)
|
||||
descInliersCount++;
|
||||
}
|
||||
|
||||
float keyPointMatchesRatio = static_cast<float>(keyPointMatchesCount) / keypoints1.size();
|
||||
if(keyPointMatchesRatio < minKeyPointMatchesRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect keyPointMatchesRatio: curr = %f, min = %f.\n",
|
||||
keyPointMatchesRatio, minKeyPointMatchesRatio);
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
|
||||
descInliersRatio, minDescInliersRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
|
||||
if(keyPointMatchesCount)
|
||||
{
|
||||
float descInliersRatio = static_cast<float>(descInliersCount) / keyPointMatchesCount;
|
||||
if(descInliersRatio < minDescInliersRatio)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Incorrect descInliersRatio: curr = %f, min = %f.\n",
|
||||
descInliersRatio, minDescInliersRatio);
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
return;
|
||||
}
|
||||
}
|
||||
#if SHOW_DEBUG_LOG
|
||||
std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
|
||||
<< " - descInliersRatio " << static_cast<float>(descInliersCount) / keyPointMatchesCount << std::endl;
|
||||
std::cout << "descInliersRatio " << static_cast<float>(descInliersCount) / keypoints0.size() << std::endl;
|
||||
#endif
|
||||
}
|
||||
ts->set_failed_test_info( cvtest::TS::OK );
|
||||
}
|
||||
|
||||
Ptr<FeatureDetector> featureDetector;
|
||||
Ptr<DescriptorExtractor> descriptorExtractor;
|
||||
int normType;
|
||||
Ptr<DescriptorExtractor> descriptorExtractor;
|
||||
int normType;
|
||||
float minKeyPointMatchesRatio;
|
||||
float minDescInliersRatio;
|
||||
};
|
||||
|
||||
// Tests registration
|
||||
|
||||
// Detector's rotation invariance check
|
||||
/*
|
||||
* Detector's rotation invariance check
|
||||
*/
|
||||
TEST(Features2d_RotationInvariance_Detector_SURF, regression)
|
||||
{
|
||||
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"),
|
||||
0.44f,
|
||||
0.45f,
|
||||
0.76f);
|
||||
test.safe_run();
|
||||
}
|
||||
@ -639,19 +603,20 @@ TEST(Features2d_RotationInvariance_Detector_SURF, regression)
|
||||
TEST(Features2d_RotationInvariance_Detector_SIFT, regression)
|
||||
{
|
||||
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
||||
0.64f,
|
||||
0.74f);
|
||||
0.75f,
|
||||
0.76f);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
// Descriptors's rotation invariance check
|
||||
/*
|
||||
* Descriptors's rotation invariance check
|
||||
*/
|
||||
TEST(Features2d_RotationInvariance_Descriptor_SURF, regression)
|
||||
{
|
||||
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"),
|
||||
Algorithm::create<DescriptorExtractor>("Feature2D.SURF"),
|
||||
NORM_L1,
|
||||
0.44f,
|
||||
0.63f);
|
||||
0.83f);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
@ -660,45 +625,46 @@ TEST(Features2d_RotationInvariance_Descriptor_SIFT, regression)
|
||||
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
||||
Algorithm::create<DescriptorExtractor>("Feature2D.SIFT"),
|
||||
NORM_L1,
|
||||
0.64f,
|
||||
0.72f);
|
||||
0.98f);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
// Detector's scale invariance check
|
||||
/*
|
||||
* Detector's scale invariance check
|
||||
*/
|
||||
TEST(Features2d_ScaleInvariance_Detector_SURF, regression)
|
||||
{
|
||||
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"),
|
||||
0.62f,
|
||||
0.68f);
|
||||
0.64f,
|
||||
0.84f);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST(Features2d_ScaleInvariance_Detector_SIFT, regression)
|
||||
{
|
||||
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
||||
0.59f,
|
||||
0.94f);
|
||||
0.69f,
|
||||
0.99f);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
// Descriptor's scale invariance check
|
||||
/*
|
||||
* Descriptor's scale invariance check
|
||||
*/
|
||||
TEST(Features2d_ScaleInvariance_Descriptor_SURF, regression)
|
||||
{
|
||||
DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SURF"),
|
||||
Algorithm::create<DescriptorExtractor>("Feature2D.SURF"),
|
||||
NORM_L1,
|
||||
0.62f,
|
||||
0.68f);
|
||||
0.61f);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST(Features2d_ScaleInvariance_Descriptor_SIFT, regression)
|
||||
{
|
||||
DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
||||
Algorithm::create<DescriptorExtractor>("Feature2D.SIFT"),
|
||||
NORM_L1,
|
||||
0.59f,
|
||||
0.78f);
|
||||
test.safe_run();
|
||||
}
|
||||
//TEST(Features2d_ScaleInvariance_Descriptor_SIFT, regression)
|
||||
//{
|
||||
// DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.SIFT"),
|
||||
// Algorithm::create<DescriptorExtractor>("Feature2D.SIFT"),
|
||||
// NORM_L1,
|
||||
// 0.14f);
|
||||
// test.safe_run();
|
||||
//}
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user