#define SHOW_DEBUG_LOG true
#if CV_MAJOR_VERSION==2
-#define OrbCreate new ORB(4000)
+#define OrbCreate new cv::ORB(4000)
#elif CV_MAJOR_VERSION==3
-#define OrbCreate ORB::create(4000)
-#define AKazeCreate AKAZE::create()
+#define OrbCreate cv::ORB::create(4000)
+#define AKazeCreate cv::AKAZE::create()
#endif
-using namespace cv;
using namespace std;
int testno_for_make_filename = 0;
{
public:
string transname;
- void(*transfunc)(float, const Mat&, Mat&);
+ void(*transfunc)(float, const cv::Mat&, cv::Mat&);
float from, to, step;
- testparam(string _transname, void(*_transfunc)(float, const Mat&, Mat&), float _from, float _to, float _step) :
+ testparam(string _transname, void(*_transfunc)(float, const cv::Mat&, cv::Mat&), float _from, float _to, float _step) :
transname(_transname),
transfunc(_transfunc),
from(_from),
// --------------------------------------------------------------------------------------
// from matching_to_many_images.cpp
// --------------------------------------------------------------------------------------
-int maskMatchesByTrainImgIdx(const vector<DMatch>& matches, int trainImgIdx, vector<char>& mask)
+int maskMatchesByTrainImgIdx(const vector<cv::DMatch>& matches, int trainImgIdx, vector<char>& mask)
{
int matchcnt = 0;
mask.resize(matches.size());
return matchcnt;
}
-int calcHomographyAndInlierCount(const vector<KeyPoint>& query_kp, const vector<KeyPoint>& train_kp, const vector<DMatch>& match, vector<char> &mask, Mat &homography)
+int calcHomographyAndInlierCount(const vector<cv::KeyPoint>& query_kp, const vector<cv::KeyPoint>& train_kp, const vector<cv::DMatch>& match, vector<char> &mask, cv::Mat &homography)
{
// make query and current train image keypoint pairs
std::vector<cv::Point2f> srcPoints, dstPoints;
}
// calc homography
vector<uchar> inlierMask;
- homography = findHomography(srcPoints, dstPoints, RANSAC, 3.0, inlierMask);
+ homography = findHomography(srcPoints, dstPoints, cv::RANSAC, 3.0, inlierMask);
// update outlier mask
int j = 0;
return inlierCnt;
}
-void drawDetectedRectangle(Mat& imgResult, const Mat& homography, const Mat& imgQuery)
+void drawDetectedRectangle(cv::Mat& imgResult, const cv::Mat& homography, const cv::Mat& imgQuery)
{
- std::vector<Point2f> query_corners(4);
- query_corners[0] = Point(0, 0);
- query_corners[1] = Point(imgQuery.cols, 0);
- query_corners[2] = Point(imgQuery.cols, imgQuery.rows);
- query_corners[3] = Point(0, imgQuery.rows);
- std::vector<Point2f> train_corners(4);
+ std::vector<cv::Point2f> query_corners(4);
+ query_corners[0] = cv::Point(0, 0);
+ query_corners[1] = cv::Point(imgQuery.cols, 0);
+ query_corners[2] = cv::Point(imgQuery.cols, imgQuery.rows);
+ query_corners[3] = cv::Point(0, imgQuery.rows);
+ std::vector<cv::Point2f> train_corners(4);
perspectiveTransform(query_corners, train_corners, homography);
- line(imgResult, train_corners[0] + query_corners[1], train_corners[1] + query_corners[1], Scalar(0, 255, 0), 4);
- line(imgResult, train_corners[1] + query_corners[1], train_corners[2] + query_corners[1], Scalar(0, 255, 0), 4);
- line(imgResult, train_corners[2] + query_corners[1], train_corners[3] + query_corners[1], Scalar(0, 255, 0), 4);
- line(imgResult, train_corners[3] + query_corners[1], train_corners[0] + query_corners[1], Scalar(0, 255, 0), 4);
+ line(imgResult, train_corners[0] + query_corners[1], train_corners[1] + query_corners[1], cv::Scalar(0, 255, 0), 4);
+ line(imgResult, train_corners[1] + query_corners[1], train_corners[2] + query_corners[1], cv::Scalar(0, 255, 0), 4);
+ line(imgResult, train_corners[2] + query_corners[1], train_corners[3] + query_corners[1], cv::Scalar(0, 255, 0), 4);
+ line(imgResult, train_corners[3] + query_corners[1], train_corners[0] + query_corners[1], cv::Scalar(0, 255, 0), 4);
}
// --------------------------------------------------------------------------------------
}TrainInfo;
TrainInfo transImgAndTrain(
- Feature2D *fe,
- DescriptorMatcher *matcher,
+ cv::Feature2D *fe,
+ cv::DescriptorMatcher *matcher,
const string &matchername,
- const Mat& imgQuery, const vector<KeyPoint>& query_kp, const Mat& query_desc,
- const vector<Mat>& imgOutliers, const vector<vector<KeyPoint> >& outliers_kp, const vector<Mat>& outliers_desc, const int totalOutlierDescCnt,
+ const cv::Mat& imgQuery, const vector<cv::KeyPoint>& query_kp, const cv::Mat& query_desc,
+ const vector<cv::Mat>& imgOutliers, const vector<vector<cv::KeyPoint> >& outliers_kp, const vector<cv::Mat>& outliers_desc, const int totalOutlierDescCnt,
const float t, const testparam *tp,
const int testno, const bool bVerboseOutput, const bool bSaveDrawMatches)
{
TrainInfo ti;
// transform query image
- Mat imgTransform;
+ cv::Mat imgTransform;
(tp->transfunc)(t, imgQuery, imgTransform);
// extract kp and compute desc from transformed query image
- vector<KeyPoint> trans_query_kp;
- Mat trans_query_desc;
+ vector<cv::KeyPoint> trans_query_kp;
+ cv::Mat trans_query_desc;
#if CV_MAJOR_VERSION==2
- (*fe)(imgTransform, Mat(), trans_query_kp, trans_query_desc);
+ (*fe)(imgTransform, cv::Mat(), trans_query_kp, trans_query_desc);
#elif CV_MAJOR_VERSION==3
fe->detectAndCompute(imgTransform, Mat(), trans_query_kp, trans_query_desc);
#endif
// add&train transformed query desc and outlier desc
matcher->clear();
- matcher->add(vector<Mat>(1, trans_query_desc));
- double s = (double)getTickCount();
+ matcher->add(vector<cv::Mat>(1, trans_query_desc));
+ double s = (double)cv::getTickCount();
matcher->train();
- ti.traintime = 1000.0*((double)getTickCount() - s) / getTickFrequency();
+ ti.traintime = 1000.0*((double)cv::getTickCount() - s) / cv::getTickFrequency();
ti.traindesccnt = trans_query_desc.rows;
#if defined(TRAIN_WITH_OUTLIER_IMAGES)
// same as matcher->add(outliers_desc); matcher->train();
for (unsigned int i = 0; i < outliers_desc.size(); ++i)
{
- matcher->add(vector<Mat>(1, outliers_desc[i]));
- s = (double)getTickCount();
+ matcher->add(vector<cv::Mat>(1, outliers_desc[i]));
+ s = (double)cv::getTickCount();
matcher->train();
- ti.traintime += 1000.0*((double)getTickCount() - s) / getTickFrequency();
+ ti.traintime += 1000.0*((double)cv::getTickCount() - s) / cv::getTickFrequency();
}
ti.traindesccnt += totalOutlierDescCnt;
#endif
// matching
- vector<DMatch> match;
- s = (double)getTickCount();
+ vector<cv::DMatch> match;
+ s = (double)cv::getTickCount();
matcher->match(query_desc, match);
- ti.matchtime = 1000.0*((double)getTickCount() - s) / getTickFrequency();
+ ti.matchtime = 1000.0*((double)cv::getTickCount() - s) / cv::getTickFrequency();
// prepare a directory and variables for save matching images
vector<char> mask;
- Mat imgResult;
+ cv::Mat imgResult;
const char resultDir[] = "result";
if (bSaveDrawMatches)
{
// save query vs transformed query matching image with detected rectangle
matchcnt = maskMatchesByTrainImgIdx(match, (int)0, mask);
// calc homography and inlier
- Mat homography;
+ cv::Mat homography;
int inlierCnt = calcHomographyAndInlierCount(query_kp, trans_query_kp, match, mask, homography);
ti.accuracy = (double)inlierCnt / (double)mask.size()*100.0;
- drawMatches(imgQuery, query_kp, imgTransform, trans_query_kp, match, imgResult, Scalar::all(-1), Scalar::all(128), mask, DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
+ drawMatches(imgQuery, query_kp, imgTransform, trans_query_kp, match, imgResult, cv::Scalar::all(-1), cv::Scalar::all(128), mask, cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
if (inlierCnt)
{
// draw detected rectangle
}
// draw status
sprintf(buff, "%s accuracy:%-3.2f%% %d descriptors training time:%-3.2fms matching :%-3.2fms", matchername.c_str(), ti.accuracy, ti.traindesccnt, ti.traintime, ti.matchtime);
- putText(imgResult, buff, Point(0, 12), FONT_HERSHEY_PLAIN, 0.8, Scalar(0., 0., 255.));
+ putText(imgResult, buff, cv::Point(0, 12), cv::FONT_HERSHEY_PLAIN, 0.8, cv::Scalar(0., 0., 255.));
sprintf(buff, "%s/res%03d_%s_%s%.1f_inlier.png", resultDir, testno, matchername.c_str(), tp->transname.c_str(), t);
if (bSaveDrawMatches && !imwrite(buff, imgResult)) cout << "Image " << buff << " can not be saved (may be because directory " << resultDir << " does not exist)." << endl;
for (unsigned int i = 0; i <imgOutliers.size(); ++i)
{
matchcnt = maskMatchesByTrainImgIdx(match, (int)i + 1, mask);
- drawMatches(imgQuery, query_kp, imgOutliers[i], outliers_kp[i], match, imgResult, Scalar::all(-1), Scalar::all(128), mask);// , DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
+ drawMatches(imgQuery, query_kp, imgOutliers[i], outliers_kp[i], match, imgResult, cv::Scalar::all(-1), cv::Scalar::all(128), mask);// , DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
sprintf(buff, "query_num:%d train_num:%d matched:%d %d descriptors training time:%-3.2fms matching :%-3.2fms", (int)query_kp.size(), (int)outliers_kp[i].size(), matchcnt, ti.traindesccnt, ti.traintime, ti.matchtime);
- putText(imgResult, buff, Point(0, 12), FONT_HERSHEY_PLAIN, 0.8, Scalar(0., 0., 255.));
+ putText(imgResult, buff, cv::Point(0, 12), cv::FONT_HERSHEY_PLAIN, 0.8, cv::Scalar(0., 0., 255.));
sprintf(buff, "%s/res%03d_%s_%s%.1f_outlier%02d.png", resultDir, testno, matchername.c_str(), tp->transname.c_str(), t, i);
if (bSaveDrawMatches && !imwrite(buff, imgResult)) cout << "Image " << buff << " can not be saved (may be because directory " << resultDir << " does not exist)." << endl;
}
testparam *tp;
double target_accuracy_margin_from_bfmatcher;
- Feature2D* fe; // feature detector extractor
-
- DescriptorMatcher* bfmatcher; // brute force matcher for accuracy of reference
- DescriptorMatcher* flmatcher; // flann matcher to test
- Mat imgQuery; // query image
- vector<Mat> imgOutliers; // outlier image
- vector<KeyPoint> query_kp; // query key points detect from imgQuery
- Mat query_desc; // query descriptors extract from imgQuery
- vector<vector<KeyPoint> > outliers_kp;
- vector<Mat> outliers_desc;
+ cv::Feature2D* fe; // feature detector extractor
+
+ cv::DescriptorMatcher* bfmatcher; // brute force matcher for accuracy of reference
+ cv::DescriptorMatcher* flmatcher; // flann matcher to test
+ cv::Mat imgQuery; // query image
+ vector<cv::Mat> imgOutliers; // outlier image
+ vector<cv::KeyPoint> query_kp; // query key points detect from imgQuery
+ cv::Mat query_desc; // query descriptors extract from imgQuery
+ vector<vector<cv::KeyPoint> > outliers_kp;
+ vector<cv::Mat> outliers_desc;
int totalOutlierDescCnt;
string flmatchername;
//
// constructor
//
- CV_FeatureDetectorMatcherBaseTest(testparam* _tp, double _accuracy_margin, Feature2D* _fe, DescriptorMatcher *_flmatcher, string _flmatchername, int norm_type_for_bfmatcher) :
+ CV_FeatureDetectorMatcherBaseTest(testparam* _tp, double _accuracy_margin, cv::Feature2D* _fe, cv::DescriptorMatcher *_flmatcher, string _flmatchername, int norm_type_for_bfmatcher) :
tp(_tp),
target_accuracy_margin_from_bfmatcher(_accuracy_margin),
fe(_fe),
srand((unsigned int)time(0));
#endif
// create brute force matcher for accuracy of reference
- bfmatcher = new BFMatcher(norm_type_for_bfmatcher);
+ bfmatcher = new cv::BFMatcher(norm_type_for_bfmatcher);
}
//
{
// load query image
string strQueryFile = string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.png";
- imgQuery = imread(strQueryFile, 0);
+ imgQuery = cv::imread(strQueryFile, 0);
if (imgQuery.empty())
{
ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", strQueryFile.c_str());
for (unsigned int i = 0; i < sizeof(outliers) / sizeof(char*); i++)
{
string strOutlierFile = string(cvtest::TS::ptr()->get_data_path()) + "shared/" + outliers[i];
- Mat imgOutlier = imread(strOutlierFile, 0);
+ cv::Mat imgOutlier = cv::imread(strOutlierFile, 0);
if (imgQuery.empty())
{
ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", strOutlierFile.c_str());
// extract and compute keypoints and descriptors from query image
#if CV_MAJOR_VERSION==2
- (*fe)(imgQuery, Mat(), query_kp, query_desc);
+ (*fe)(imgQuery, cv::Mat(), query_kp, query_desc);
#elif CV_MAJOR_VERSION==3
fe->detectAndCompute(imgQuery, Mat(), query_kp, query_desc);
#endif
// extract and compute keypoints and descriptors from outlier images
fe->detect(imgOutliers, outliers_kp);
- ((DescriptorExtractor*)fe)->compute(imgOutliers, outliers_kp, outliers_desc);
+ ((cv::DescriptorExtractor*)fe)->compute(imgOutliers, outliers_kp, outliers_desc);
totalOutlierDescCnt = 0;
for (unsigned int i = 0; i < outliers_desc.size(); ++i) totalOutlierDescCnt += outliers_desc[i].rows;
// --------------------------------------------------------------------------------------
// Transform Functions
// --------------------------------------------------------------------------------------
-static void rotate(float deg, const Mat& src, Mat& dst)
+static void rotate(float deg, const cv::Mat& src, cv::Mat& dst)
{
- warpAffine(src, dst, getRotationMatrix2D(Point2f(src.cols / 2.0f, src.rows / 2.0f), deg, 1), src.size(), INTER_CUBIC);
+ cv::warpAffine(src, dst, getRotationMatrix2D(cv::Point2f(src.cols / 2.0f, src.rows / 2.0f), deg, 1), src.size(), cv::INTER_CUBIC);
}
-static void scale(float scale, const Mat& src, Mat& dst)
+static void scale(float scale, const cv::Mat& src, cv::Mat& dst)
{
- resize(src, dst, Size((int)(src.cols*scale), (int)(src.rows*scale)), INTER_AREA);
+ cv::resize(src, dst, cv::Size((int)(src.cols*scale), (int)(src.rows*scale)), cv::INTER_CUBIC);
}
-static void blur(float k, const Mat& src, Mat& dst)
+static void blur(float k, const cv::Mat& src, cv::Mat& dst)
{
- GaussianBlur(src, dst, Size((int)k, (int)k), 0);
+ GaussianBlur(src, dst, cv::Size((int)k, (int)k), 0);
}
// --------------------------------------------------------------------------------------
TEST(BlurredQueryFlannBasedLshShortKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 16, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 16, 2));
testparam tp("blurred", blur, 1.0f, 11.0f, 2.0f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, SHORT_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 16, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, SHORT_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 16, 2)", cv::NORM_HAMMING);
test.safe_run();
}
TEST(BlurredQueryFlannBasedLshMiddleKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 24, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 24, 2));
testparam tp("blurred", blur, 1.0f, 11.0f, 2.0f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 24, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 24, 2)", cv::NORM_HAMMING);
test.safe_run();
}
TEST(BlurredQueryFlannBasedLshLongKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 31, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 31, 2));
testparam tp("blurred", blur, 1.0f, 11.0f, 2.0f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, LONG_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 31, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, LONG_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 31, 2)", cv::NORM_HAMMING);
test.safe_run();
}
TEST(ScaledQueryFlannBasedLshShortKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 16, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 16, 2));
testparam tp("scaled", scale, 0.5f, 1.5f, 0.1f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, SHORT_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 16, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, SHORT_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 16, 2)", cv::NORM_HAMMING);
test.safe_run();
}
TEST(ScaledQueryFlannBasedLshMiddleKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 24, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 24, 2));
testparam tp("scaled", scale, 0.5f, 1.5f, 0.1f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 24, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 24, 2)", cv::NORM_HAMMING);
test.safe_run();
}
TEST(ScaledQueryFlannBasedLshLongKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 31, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 31, 2));
testparam tp("scaled", scale, 0.5f, 1.5f, 0.1f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, LONG_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 31, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, LONG_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 31, 2)", cv::NORM_HAMMING);
test.safe_run();
}
TEST(RotatedQueryFlannBasedLshShortKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 16, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 16, 2));
testparam tp("rotated", rotate, 0.0f, 359.0f, 30.0f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, SHORT_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 16, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, SHORT_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 16, 2)", cv::NORM_HAMMING);
test.safe_run();
}
TEST(RotatedQueryFlannBasedLshMiddleKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 24, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 24, 2));
testparam tp("rotated", rotate, 0.0f, 359.0f, 30.0f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 24, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 24, 2)", cv::NORM_HAMMING);
test.safe_run();
}
TEST(RotatedQueryFlannBasedLshLongKeyMatcherAdditionalTrainTest, accuracy)
{
- Ptr<Feature2D> fe = OrbCreate;
- Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 31, 2));
+ cv::Ptr<cv::Feature2D> fe = OrbCreate;
+ cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 31, 2));
testparam tp("rotated", rotate, 0.0f, 359.0f, 30.0f);
- CV_FeatureDetectorMatcherBaseTest test(&tp, LONG_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 31, 2)", NORM_HAMMING);
+ CV_FeatureDetectorMatcherBaseTest test(&tp, LONG_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 31, 2)", cv::NORM_HAMMING);
test.safe_run();
}
projects / platform / upstream / opencv.git / commitdiff