693 lines
26 KiB
C++
693 lines
26 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// Intel License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000, Intel Corporation, all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's in binary form must reproduce the above copyright notice,
|
|
// this list of conditions and the following disclaimer in the documentation
|
|
// and/or other materials provided with the distribution.
|
|
//
|
|
// * The name of Intel Corporation may not be used to endorse or promote products
|
|
// derived from this software without specific prior written permission.
|
|
//
|
|
// This software is provided by the copyright holders and contributors "as is" and
|
|
// any express or implied warranties, including, but not limited to, the implied
|
|
// warranties of merchantability and fitness for a particular purpose are disclaimed.
|
|
// In no event shall the Intel Corporation or contributors be liable for any direct,
|
|
// indirect, incidental, special, exemplary, or consequential damages
|
|
// (including, but not limited to, procurement of substitute goods or services;
|
|
// loss of use, data, or profits; or business interruption) however caused
|
|
// and on any theory of liability, whether in contract, strict liability,
|
|
// or tort (including negligence or otherwise) arising in any way out of
|
|
// the use of this software, even if advised of the possibility of such damage.
|
|
//
|
|
//M*/
|
|
|
|
#include "test_precomp.hpp"
|
|
#include "opencv2/highgui/highgui.hpp"
|
|
|
|
using namespace std;
|
|
using namespace cv;
|
|
|
|
const string IMAGE_TSUKUBA = "/features2d/tsukuba.png";
|
|
const string IMAGE_BIKES = "/detectors_descriptors_evaluation/images_datasets/bikes/img1.png";
|
|
|
|
#define SHOW_DEBUG_LOG 0
|
|
|
|
static
|
|
Mat generateHomography(float angle)
|
|
{
|
|
// angle - rotation around Oz in degrees
|
|
float angleRadian = static_cast<float>(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);
|
|
H.at<float>(0,1) = -std::sin(angleRadian);
|
|
H.at<float>(1,0) = std::sin(angleRadian);
|
|
|
|
return H;
|
|
}
|
|
|
|
static
|
|
Mat rotateImage(const Mat& srcImage, float angle, Mat& dstImage, Mat& dstMask)
|
|
{
|
|
// 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) = static_cast<float>(-srcImage.cols/2);
|
|
LUShift.at<float>(1,2) = static_cast<float>(-srcImage.rows/2);
|
|
Mat RDShift = Mat::eye(3, 3, CV_32FC1); // right down
|
|
RDShift.at<float>(0,2) = diag/2;
|
|
RDShift.at<float>(1,2) = diag/2;
|
|
Size sz(cvRound(diag), cvRound(diag));
|
|
|
|
Mat srcMask(srcImage.size(), CV_8UC1, Scalar(255));
|
|
|
|
Mat H = RDShift * generateHomography(angle) * LUShift;
|
|
warpPerspective(srcImage, dstImage, H, sz);
|
|
warpPerspective(srcMask, dstMask, H, sz);
|
|
|
|
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, src[i].angle);
|
|
}
|
|
|
|
static
|
|
float calcCirclesIntersectArea(const Point2f& p0, float r0, const Point2f& p1, float r1)
|
|
{
|
|
float c = static_cast<float>(norm(p0 - p1)), sqr_c = c * c;
|
|
|
|
float sqr_r0 = r0 * r0;
|
|
float sqr_r1 = r1 * r1;
|
|
|
|
if(r0 + r1 <= c)
|
|
return 0;
|
|
|
|
float minR = std::min(r0, r1);
|
|
float maxR = std::max(r0, r1);
|
|
if(c + minR <= maxR)
|
|
return static_cast<float>(CV_PI * minR * minR);
|
|
|
|
float cos_halfA0 = (sqr_r0 + sqr_c - sqr_r1) / (2 * r0 * c);
|
|
float cos_halfA1 = (sqr_r1 + sqr_c - sqr_r0) / (2 * r1 * c);
|
|
|
|
float A0 = 2 * acos(cos_halfA0);
|
|
float A1 = 2 * acos(cos_halfA1);
|
|
|
|
return 0.5f * sqr_r0 * (A0 - sin(A0)) +
|
|
0.5f * sqr_r1 * (A1 - sin(A1));
|
|
}
|
|
|
|
static
|
|
float calcIntersectRatio(const Point2f& p0, float r0, const Point2f& p1, float r1)
|
|
{
|
|
float intersectArea = calcCirclesIntersectArea(p0, r0, p1, r1);
|
|
float unionArea = static_cast<float>(CV_PI) * (r0 * r0 + r1 * r1) - intersectArea;
|
|
return intersectArea / unionArea;
|
|
}
|
|
|
|
static
|
|
void matchKeyPoints(const vector<KeyPoint>& keypoints0, const Mat& H,
|
|
const vector<KeyPoint>& keypoints1,
|
|
vector<DMatch>& matches)
|
|
{
|
|
vector<Point2f> points0;
|
|
KeyPoint::convert(keypoints0, points0);
|
|
Mat points0t;
|
|
if(H.empty())
|
|
points0t = Mat(points0);
|
|
else
|
|
perspectiveTransform(Mat(points0), points0t, H);
|
|
|
|
matches.clear();
|
|
vector<uchar> usedMask(keypoints1.size(), 0);
|
|
for(int i0 = 0; i0 < static_cast<int>(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;
|
|
|
|
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 = static_cast<int>(i1);
|
|
}
|
|
}
|
|
|
|
matches.push_back(DMatch(i0, nearestPointIndex, maxIntersectRatio));
|
|
if(nearestPointIndex >= 0)
|
|
usedMask[nearestPointIndex] = 1;
|
|
}
|
|
}
|
|
|
|
class DetectorRotationInvarianceTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
DetectorRotationInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
|
float _minKeyPointMatchesRatio,
|
|
float _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, static_cast<float>(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;
|
|
};
|
|
|
|
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, static_cast<float>(angle), image1, mask1);
|
|
|
|
vector<KeyPoint> keypoints1;
|
|
rotateKeyPoints(keypoints0, H, static_cast<float>(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 minDescInliersRatio;
|
|
};
|
|
|
|
class DetectorScaleInvarianceTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
DetectorScaleInvarianceTest(const Ptr<FeatureDetector>& _featureDetector,
|
|
float _minKeyPointMatchesRatio,
|
|
float _minScaleInliersRatio) :
|
|
featureDetector(_featureDetector),
|
|
minKeyPointMatchesRatio(_minKeyPointMatchesRatio),
|
|
minScaleInliersRatio(_minScaleInliersRatio)
|
|
{
|
|
CV_Assert(!featureDetector.empty());
|
|
}
|
|
|
|
protected:
|
|
|
|
void run(int)
|
|
{
|
|
const string imageFilename = string(ts->get_data_path()) + IMAGE_BIKES;
|
|
|
|
// Read test data
|
|
Mat image0 = imread(imageFilename);
|
|
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");
|
|
|
|
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
|
|
{
|
|
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() > 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;
|
|
}
|
|
|
|
scaleKeyPoints(keypoints1, osiKeypoints1, scale);
|
|
|
|
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;
|
|
|
|
for(size_t m = 0; m < matches.size(); m++)
|
|
{
|
|
if(matches[m].distance < minIntersectRatio)
|
|
continue;
|
|
|
|
keyPointMatchesCount++;
|
|
|
|
// Check does this inlier have consistent sizes
|
|
const float maxSizeDiff = 0.8f;//0.9f; // grad
|
|
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))
|
|
scaleInliersCount++;
|
|
}
|
|
|
|
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->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
return;
|
|
}
|
|
|
|
if(keyPointMatchesCount)
|
|
{
|
|
float scaleInliersRatio = static_cast<float>(scaleInliersCount) / keyPointMatchesCount;
|
|
if(scaleInliersRatio < minScaleInliersRatio)
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "Incorrect scaleInliersRatio: curr = %f, min = %f.\n",
|
|
scaleInliersRatio, minScaleInliersRatio);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
|
return;
|
|
}
|
|
}
|
|
#if SHOW_DEBUG_LOG
|
|
std::cout << "keyPointMatchesRatio - " << keyPointMatchesRatio
|
|
<< " - scaleInliersRatio " << static_cast<float>(scaleInliersCount) / keyPointMatchesCount << std::endl;
|
|
#endif
|
|
}
|
|
ts->set_failed_test_info( cvtest::TS::OK );
|
|
}
|
|
|
|
Ptr<FeatureDetector> featureDetector;
|
|
float minKeyPointMatchesRatio;
|
|
float minScaleInliersRatio;
|
|
};
|
|
|
|
class DescriptorScaleInvarianceTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
DescriptorScaleInvarianceTest(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_BIKES;
|
|
|
|
// Read test data
|
|
Mat image0 = imread(imageFilename);
|
|
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");
|
|
Mat descriptors0;
|
|
descriptorExtractor->compute(image0, keypoints0, descriptors0);
|
|
|
|
BFMatcher bfmatcher(normType);
|
|
for(int scaleIdx = 1; scaleIdx <= 3; scaleIdx++)
|
|
{
|
|
float scale = 1.f + scaleIdx * 0.5f;
|
|
|
|
Mat image1;
|
|
resize(image0, image1, Size(), 1./scale, 1./scale);
|
|
|
|
vector<KeyPoint> keypoints1;
|
|
scaleKeyPoints(keypoints0, keypoints1, 1.0f/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++;
|
|
}
|
|
}
|
|
|
|
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;
|
|
};
|
|
|
|
// Tests registration
|
|
|
|
/*
|
|
* Detector's rotation invariance check
|
|
*/
|
|
|
|
TEST(Features2d_RotationInvariance_Detector_BRISK, regression)
|
|
{
|
|
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
|
|
0.32f,
|
|
0.76f);
|
|
test.safe_run();
|
|
}
|
|
|
|
TEST(Features2d_RotationInvariance_Detector_ORB, regression)
|
|
{
|
|
DetectorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
|
0.47f,
|
|
0.76f);
|
|
test.safe_run();
|
|
}
|
|
|
|
/*
|
|
* Descriptors's rotation invariance check
|
|
*/
|
|
|
|
TEST(Features2d_RotationInvariance_Descriptor_BRISK, regression)
|
|
{
|
|
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
|
|
Algorithm::create<DescriptorExtractor>("Feature2D.BRISK"),
|
|
NORM_HAMMING,
|
|
0.99f);
|
|
test.safe_run();
|
|
}
|
|
|
|
TEST(Features2d_RotationInvariance_Descriptor_ORB, regression)
|
|
{
|
|
DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
|
Algorithm::create<DescriptorExtractor>("Feature2D.ORB"),
|
|
NORM_HAMMING,
|
|
0.99f);
|
|
test.safe_run();
|
|
}
|
|
|
|
//TEST(Features2d_RotationInvariance_Descriptor_FREAK, regression)
|
|
//{
|
|
// DescriptorRotationInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.ORB"),
|
|
// Algorithm::create<DescriptorExtractor>("Feature2D.FREAK"),
|
|
// NORM_HAMMING,
|
|
// 0.f);
|
|
// test.safe_run();
|
|
//}
|
|
|
|
/*
|
|
* Detector's scale invariance check
|
|
*/
|
|
|
|
TEST(Features2d_ScaleInvariance_Detector_BRISK, regression)
|
|
{
|
|
DetectorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
|
|
0.08f,
|
|
0.49f);
|
|
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_BRISK, regression)
|
|
//{
|
|
// DescriptorScaleInvarianceTest test(Algorithm::create<FeatureDetector>("Feature2D.BRISK"),
|
|
// Algorithm::create<DescriptorExtractor>("Feature2D.BRISK"),
|
|
// NORM_HAMMING,
|
|
// 0.99f);
|
|
// test.safe_run();
|
|
//}
|
|
|
|
//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();
|
|
//}
|