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instead of "using namespace cv" put all the tests into cv:: namespace.
This commit is contained in:
ippei ito
parent
140051b18c
commit
b8c00854fe
@ -62,13 +62,12 @@
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#define SHOW_DEBUG_LOG true
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#if CV_MAJOR_VERSION==2
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#define OrbCreate new ORB(4000)
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#define OrbCreate new cv::ORB(4000)
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#elif CV_MAJOR_VERSION==3
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#define OrbCreate ORB::create(4000)
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#define AKazeCreate AKAZE::create()
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#define OrbCreate cv::ORB::create(4000)
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#define AKazeCreate cv::AKAZE::create()
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#endif
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using namespace cv;
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using namespace std;
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int testno_for_make_filename = 0;
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@ -80,9 +79,9 @@ class testparam
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{
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public:
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string transname;
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void(*transfunc)(float, const Mat&, Mat&);
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void(*transfunc)(float, const cv::Mat&, cv::Mat&);
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float from, to, step;
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testparam(string _transname, void(*_transfunc)(float, const Mat&, Mat&), float _from, float _to, float _step) :
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testparam(string _transname, void(*_transfunc)(float, const cv::Mat&, cv::Mat&), float _from, float _to, float _step) :
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transname(_transname),
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transfunc(_transfunc),
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from(_from),
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@ -94,7 +93,7 @@ public:
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// --------------------------------------------------------------------------------------
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// from matching_to_many_images.cpp
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// --------------------------------------------------------------------------------------
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int maskMatchesByTrainImgIdx(const vector<DMatch>& matches, int trainImgIdx, vector<char>& mask)
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int maskMatchesByTrainImgIdx(const vector<cv::DMatch>& matches, int trainImgIdx, vector<char>& mask)
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{
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int matchcnt = 0;
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mask.resize(matches.size());
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@ -110,7 +109,7 @@ int maskMatchesByTrainImgIdx(const vector<DMatch>& matches, int trainImgIdx, vec
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return matchcnt;
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}
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int calcHomographyAndInlierCount(const vector<KeyPoint>& query_kp, const vector<KeyPoint>& train_kp, const vector<DMatch>& match, vector<char> &mask, Mat &homography)
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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)
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{
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// make query and current train image keypoint pairs
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std::vector<cv::Point2f> srcPoints, dstPoints;
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@ -124,7 +123,7 @@ int calcHomographyAndInlierCount(const vector<KeyPoint>& query_kp, const vector<
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}
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// calc homography
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vector<uchar> inlierMask;
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homography = findHomography(srcPoints, dstPoints, RANSAC, 3.0, inlierMask);
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homography = findHomography(srcPoints, dstPoints, cv::RANSAC, 3.0, inlierMask);
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// update outlier mask
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int j = 0;
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@ -152,19 +151,19 @@ int calcHomographyAndInlierCount(const vector<KeyPoint>& query_kp, const vector<
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return inlierCnt;
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}
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void drawDetectedRectangle(Mat& imgResult, const Mat& homography, const Mat& imgQuery)
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void drawDetectedRectangle(cv::Mat& imgResult, const cv::Mat& homography, const cv::Mat& imgQuery)
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{
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std::vector<Point2f> query_corners(4);
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query_corners[0] = Point(0, 0);
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query_corners[1] = Point(imgQuery.cols, 0);
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query_corners[2] = Point(imgQuery.cols, imgQuery.rows);
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query_corners[3] = Point(0, imgQuery.rows);
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std::vector<Point2f> train_corners(4);
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std::vector<cv::Point2f> query_corners(4);
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query_corners[0] = cv::Point(0, 0);
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query_corners[1] = cv::Point(imgQuery.cols, 0);
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query_corners[2] = cv::Point(imgQuery.cols, imgQuery.rows);
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query_corners[3] = cv::Point(0, imgQuery.rows);
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std::vector<cv::Point2f> train_corners(4);
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perspectiveTransform(query_corners, train_corners, homography);
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line(imgResult, train_corners[0] + query_corners[1], train_corners[1] + query_corners[1], Scalar(0, 255, 0), 4);
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line(imgResult, train_corners[1] + query_corners[1], train_corners[2] + query_corners[1], Scalar(0, 255, 0), 4);
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line(imgResult, train_corners[2] + query_corners[1], train_corners[3] + query_corners[1], Scalar(0, 255, 0), 4);
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line(imgResult, train_corners[3] + query_corners[1], train_corners[0] + query_corners[1], Scalar(0, 255, 0), 4);
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line(imgResult, train_corners[0] + query_corners[1], train_corners[1] + query_corners[1], cv::Scalar(0, 255, 0), 4);
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line(imgResult, train_corners[1] + query_corners[1], train_corners[2] + query_corners[1], cv::Scalar(0, 255, 0), 4);
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line(imgResult, train_corners[2] + query_corners[1], train_corners[3] + query_corners[1], cv::Scalar(0, 255, 0), 4);
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line(imgResult, train_corners[3] + query_corners[1], train_corners[0] + query_corners[1], cv::Scalar(0, 255, 0), 4);
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}
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// --------------------------------------------------------------------------------------
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@ -179,55 +178,55 @@ typedef struct tagTrainInfo
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}TrainInfo;
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TrainInfo transImgAndTrain(
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Feature2D *fe,
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DescriptorMatcher *matcher,
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cv::Feature2D *fe,
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cv::DescriptorMatcher *matcher,
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const string &matchername,
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const Mat& imgQuery, const vector<KeyPoint>& query_kp, const Mat& query_desc,
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const vector<Mat>& imgOutliers, const vector<vector<KeyPoint> >& outliers_kp, const vector<Mat>& outliers_desc, const int totalOutlierDescCnt,
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const cv::Mat& imgQuery, const vector<cv::KeyPoint>& query_kp, const cv::Mat& query_desc,
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const vector<cv::Mat>& imgOutliers, const vector<vector<cv::KeyPoint> >& outliers_kp, const vector<cv::Mat>& outliers_desc, const int totalOutlierDescCnt,
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const float t, const testparam *tp,
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const int testno, const bool bVerboseOutput, const bool bSaveDrawMatches)
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{
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TrainInfo ti;
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// transform query image
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Mat imgTransform;
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cv::Mat imgTransform;
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(tp->transfunc)(t, imgQuery, imgTransform);
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// extract kp and compute desc from transformed query image
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vector<KeyPoint> trans_query_kp;
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Mat trans_query_desc;
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vector<cv::KeyPoint> trans_query_kp;
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cv::Mat trans_query_desc;
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#if CV_MAJOR_VERSION==2
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(*fe)(imgTransform, Mat(), trans_query_kp, trans_query_desc);
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(*fe)(imgTransform, cv::Mat(), trans_query_kp, trans_query_desc);
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#elif CV_MAJOR_VERSION==3
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fe->detectAndCompute(imgTransform, Mat(), trans_query_kp, trans_query_desc);
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#endif
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// add&train transformed query desc and outlier desc
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matcher->clear();
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matcher->add(vector<Mat>(1, trans_query_desc));
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double s = (double)getTickCount();
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matcher->add(vector<cv::Mat>(1, trans_query_desc));
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double s = (double)cv::getTickCount();
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matcher->train();
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ti.traintime = 1000.0*((double)getTickCount() - s) / getTickFrequency();
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ti.traintime = 1000.0*((double)cv::getTickCount() - s) / cv::getTickFrequency();
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ti.traindesccnt = trans_query_desc.rows;
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#if defined(TRAIN_WITH_OUTLIER_IMAGES)
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// same as matcher->add(outliers_desc); matcher->train();
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for (unsigned int i = 0; i < outliers_desc.size(); ++i)
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{
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matcher->add(vector<Mat>(1, outliers_desc[i]));
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s = (double)getTickCount();
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matcher->add(vector<cv::Mat>(1, outliers_desc[i]));
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s = (double)cv::getTickCount();
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matcher->train();
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ti.traintime += 1000.0*((double)getTickCount() - s) / getTickFrequency();
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ti.traintime += 1000.0*((double)cv::getTickCount() - s) / cv::getTickFrequency();
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}
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ti.traindesccnt += totalOutlierDescCnt;
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#endif
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// matching
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vector<DMatch> match;
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s = (double)getTickCount();
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vector<cv::DMatch> match;
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s = (double)cv::getTickCount();
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matcher->match(query_desc, match);
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ti.matchtime = 1000.0*((double)getTickCount() - s) / getTickFrequency();
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ti.matchtime = 1000.0*((double)cv::getTickCount() - s) / cv::getTickFrequency();
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// prepare a directory and variables for save matching images
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vector<char> mask;
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Mat imgResult;
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cv::Mat imgResult;
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const char resultDir[] = "result";
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if (bSaveDrawMatches)
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{
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@ -241,10 +240,10 @@ TrainInfo transImgAndTrain(
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// save query vs transformed query matching image with detected rectangle
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matchcnt = maskMatchesByTrainImgIdx(match, (int)0, mask);
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// calc homography and inlier
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Mat homography;
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cv::Mat homography;
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int inlierCnt = calcHomographyAndInlierCount(query_kp, trans_query_kp, match, mask, homography);
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ti.accuracy = (double)inlierCnt / (double)mask.size()*100.0;
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drawMatches(imgQuery, query_kp, imgTransform, trans_query_kp, match, imgResult, Scalar::all(-1), Scalar::all(128), mask, DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
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drawMatches(imgQuery, query_kp, imgTransform, trans_query_kp, match, imgResult, cv::Scalar::all(-1), cv::Scalar::all(128), mask, cv::DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
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if (inlierCnt)
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{
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// draw detected rectangle
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@ -252,7 +251,7 @@ TrainInfo transImgAndTrain(
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}
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// draw status
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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);
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putText(imgResult, buff, Point(0, 12), FONT_HERSHEY_PLAIN, 0.8, Scalar(0., 0., 255.));
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putText(imgResult, buff, cv::Point(0, 12), cv::FONT_HERSHEY_PLAIN, 0.8, cv::Scalar(0., 0., 255.));
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sprintf(buff, "%s/res%03d_%s_%s%.1f_inlier.png", resultDir, testno, matchername.c_str(), tp->transname.c_str(), t);
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if (bSaveDrawMatches && !imwrite(buff, imgResult)) cout << "Image " << buff << " can not be saved (may be because directory " << resultDir << " does not exist)." << endl;
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@ -261,9 +260,9 @@ TrainInfo transImgAndTrain(
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for (unsigned int i = 0; i <imgOutliers.size(); ++i)
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{
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matchcnt = maskMatchesByTrainImgIdx(match, (int)i + 1, mask);
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drawMatches(imgQuery, query_kp, imgOutliers[i], outliers_kp[i], match, imgResult, Scalar::all(-1), Scalar::all(128), mask);// , DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
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drawMatches(imgQuery, query_kp, imgOutliers[i], outliers_kp[i], match, imgResult, cv::Scalar::all(-1), cv::Scalar::all(128), mask);// , DrawMatchesFlags::DRAW_RICH_KEYPOINTS);
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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);
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putText(imgResult, buff, Point(0, 12), FONT_HERSHEY_PLAIN, 0.8, Scalar(0., 0., 255.));
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putText(imgResult, buff, cv::Point(0, 12), cv::FONT_HERSHEY_PLAIN, 0.8, cv::Scalar(0., 0., 255.));
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sprintf(buff, "%s/res%03d_%s_%s%.1f_outlier%02d.png", resultDir, testno, matchername.c_str(), tp->transname.c_str(), t, i);
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if (bSaveDrawMatches && !imwrite(buff, imgResult)) cout << "Image " << buff << " can not be saved (may be because directory " << resultDir << " does not exist)." << endl;
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}
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@ -285,16 +284,16 @@ private:
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testparam *tp;
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double target_accuracy_margin_from_bfmatcher;
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Feature2D* fe; // feature detector extractor
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cv::Feature2D* fe; // feature detector extractor
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DescriptorMatcher* bfmatcher; // brute force matcher for accuracy of reference
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DescriptorMatcher* flmatcher; // flann matcher to test
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Mat imgQuery; // query image
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vector<Mat> imgOutliers; // outlier image
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vector<KeyPoint> query_kp; // query key points detect from imgQuery
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Mat query_desc; // query descriptors extract from imgQuery
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vector<vector<KeyPoint> > outliers_kp;
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vector<Mat> outliers_desc;
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cv::DescriptorMatcher* bfmatcher; // brute force matcher for accuracy of reference
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cv::DescriptorMatcher* flmatcher; // flann matcher to test
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cv::Mat imgQuery; // query image
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vector<cv::Mat> imgOutliers; // outlier image
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vector<cv::KeyPoint> query_kp; // query key points detect from imgQuery
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cv::Mat query_desc; // query descriptors extract from imgQuery
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vector<vector<cv::KeyPoint> > outliers_kp;
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vector<cv::Mat> outliers_desc;
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int totalOutlierDescCnt;
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string flmatchername;
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@ -304,7 +303,7 @@ public:
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//
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// constructor
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//
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CV_FeatureDetectorMatcherBaseTest(testparam* _tp, double _accuracy_margin, Feature2D* _fe, DescriptorMatcher *_flmatcher, string _flmatchername, int norm_type_for_bfmatcher) :
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CV_FeatureDetectorMatcherBaseTest(testparam* _tp, double _accuracy_margin, cv::Feature2D* _fe, cv::DescriptorMatcher *_flmatcher, string _flmatchername, int norm_type_for_bfmatcher) :
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tp(_tp),
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target_accuracy_margin_from_bfmatcher(_accuracy_margin),
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fe(_fe),
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@ -316,7 +315,7 @@ public:
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srand((unsigned int)time(0));
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#endif
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// create brute force matcher for accuracy of reference
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bfmatcher = new BFMatcher(norm_type_for_bfmatcher);
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bfmatcher = new cv::BFMatcher(norm_type_for_bfmatcher);
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}
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//
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@ -326,7 +325,7 @@ public:
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{
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// load query image
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string strQueryFile = string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.png";
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imgQuery = imread(strQueryFile, 0);
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imgQuery = cv::imread(strQueryFile, 0);
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if (imgQuery.empty())
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{
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ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", strQueryFile.c_str());
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@ -339,7 +338,7 @@ public:
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for (unsigned int i = 0; i < sizeof(outliers) / sizeof(char*); i++)
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{
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string strOutlierFile = string(cvtest::TS::ptr()->get_data_path()) + "shared/" + outliers[i];
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Mat imgOutlier = imread(strOutlierFile, 0);
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cv::Mat imgOutlier = cv::imread(strOutlierFile, 0);
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if (imgQuery.empty())
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{
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ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", strOutlierFile.c_str());
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@ -351,13 +350,13 @@ public:
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// extract and compute keypoints and descriptors from query image
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#if CV_MAJOR_VERSION==2
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(*fe)(imgQuery, Mat(), query_kp, query_desc);
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(*fe)(imgQuery, cv::Mat(), query_kp, query_desc);
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#elif CV_MAJOR_VERSION==3
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fe->detectAndCompute(imgQuery, Mat(), query_kp, query_desc);
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#endif
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// extract and compute keypoints and descriptors from outlier images
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fe->detect(imgOutliers, outliers_kp);
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((DescriptorExtractor*)fe)->compute(imgOutliers, outliers_kp, outliers_desc);
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((cv::DescriptorExtractor*)fe)->compute(imgOutliers, outliers_kp, outliers_desc);
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totalOutlierDescCnt = 0;
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for (unsigned int i = 0; i < outliers_desc.size(); ++i) totalOutlierDescCnt += outliers_desc[i].rows;
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@ -438,17 +437,17 @@ public:
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// --------------------------------------------------------------------------------------
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// Transform Functions
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// --------------------------------------------------------------------------------------
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static void rotate(float deg, const Mat& src, Mat& dst)
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static void rotate(float deg, const cv::Mat& src, cv::Mat& dst)
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{
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warpAffine(src, dst, getRotationMatrix2D(Point2f(src.cols / 2.0f, src.rows / 2.0f), deg, 1), src.size(), INTER_CUBIC);
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cv::warpAffine(src, dst, getRotationMatrix2D(cv::Point2f(src.cols / 2.0f, src.rows / 2.0f), deg, 1), src.size(), cv::INTER_CUBIC);
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}
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static void scale(float scale, const Mat& src, Mat& dst)
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static void scale(float scale, const cv::Mat& src, cv::Mat& dst)
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{
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resize(src, dst, Size((int)(src.cols*scale), (int)(src.rows*scale)), INTER_AREA);
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cv::resize(src, dst, cv::Size((int)(src.cols*scale), (int)(src.rows*scale)), cv::INTER_CUBIC);
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}
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static void blur(float k, const Mat& src, Mat& dst)
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static void blur(float k, const cv::Mat& src, cv::Mat& dst)
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{
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GaussianBlur(src, dst, Size((int)k, (int)k), 0);
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GaussianBlur(src, dst, cv::Size((int)k, (int)k), 0);
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}
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// --------------------------------------------------------------------------------------
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@ -460,75 +459,75 @@ static void blur(float k, const Mat& src, Mat& dst)
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TEST(BlurredQueryFlannBasedLshShortKeyMatcherAdditionalTrainTest, accuracy)
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{
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Ptr<Feature2D> fe = OrbCreate;
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Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 16, 2));
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cv::Ptr<cv::Feature2D> fe = OrbCreate;
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cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 16, 2));
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testparam tp("blurred", blur, 1.0f, 11.0f, 2.0f);
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CV_FeatureDetectorMatcherBaseTest test(&tp, SHORT_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 16, 2)", NORM_HAMMING);
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CV_FeatureDetectorMatcherBaseTest test(&tp, SHORT_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 16, 2)", cv::NORM_HAMMING);
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test.safe_run();
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}
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TEST(BlurredQueryFlannBasedLshMiddleKeyMatcherAdditionalTrainTest, accuracy)
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{
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Ptr<Feature2D> fe = OrbCreate;
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Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 24, 2));
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cv::Ptr<cv::Feature2D> fe = OrbCreate;
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cv::Ptr<cv::FlannBasedMatcher> fl = cv::makePtr<cv::FlannBasedMatcher>(cv::makePtr<cv::flann::LshIndexParams>(1, 24, 2));
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testparam tp("blurred", blur, 1.0f, 11.0f, 2.0f);
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CV_FeatureDetectorMatcherBaseTest test(&tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 24, 2)", NORM_HAMMING);
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CV_FeatureDetectorMatcherBaseTest test(&tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, fe, fl, "FlannLsh(1, 24, 2)", cv::NORM_HAMMING);
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test.safe_run();
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}
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TEST(BlurredQueryFlannBasedLshLongKeyMatcherAdditionalTrainTest, accuracy)
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{
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Ptr<Feature2D> fe = OrbCreate;
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Ptr<FlannBasedMatcher> fl = makePtr<FlannBasedMatcher>(makePtr<flann::LshIndexParams>(1, 31, 2));
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cv::Ptr<cv::Feature2D> fe = OrbCreate;
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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();
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user