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instead of "using namespace cv" put all the tests into cv:: namespace.

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pull/3829/head
ippei ito 10 years ago
parent 140051b18c
commit b8c00854fe
1 changed files with 91 additions and 92 deletions
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  1. 183
      modules/features2d/test/test_lshindex_flannbased_matcher.cpp

183
modules/features2d/test/test_lshindex_flannbased_matcher.cpp
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@ -62,13 +62,12 @@
#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;
@ -80,9 +79,9 @@ class testparam
{
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),
@ -94,7 +93,7 @@ public:
// --------------------------------------------------------------------------------------
// 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());
@ -110,7 +109,7 @@ int maskMatchesByTrainImgIdx(const vector<DMatch>& matches, int trainImgIdx, vec
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;
@ -124,7 +123,7 @@ int calcHomographyAndInlierCount(const vector<KeyPoint>& query_kp, const vector<
}
// 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;
@ -152,19 +151,19 @@ int calcHomographyAndInlierCount(const vector<KeyPoint>& query_kp, const vector<
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);
}
// --------------------------------------------------------------------------------------
@ -179,55 +178,55 @@ typedef struct tagTrainInfo
}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)
{
@ -241,10 +240,10 @@ TrainInfo transImgAndTrain(
// 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
@ -252,7 +251,7 @@ TrainInfo transImgAndTrain(
}
// 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;
@ -261,9 +260,9 @@ TrainInfo transImgAndTrain(
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;
}
@ -285,16 +284,16 @@ private:
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;
@ -304,7 +303,7 @@ public:
//
// 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),
@ -316,7 +315,7 @@ public:
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);
}
//
@ -326,7 +325,7 @@ public:
{
// 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());
@ -339,7 +338,7 @@ public:
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());
@ -351,13 +350,13 @@ public:
// 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;
@ -438,17 +437,17 @@ public:
// --------------------------------------------------------------------------------------
// 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);
}
// --------------------------------------------------------------------------------------
@ -460,75 +459,75 @@ static void blur(float k, const Mat& src, Mat& dst)
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();
}

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