mirror of
https://github.com/opencv/opencv.git
synced 2024-11-25 03:30:34 +08:00
Test accuracy for Pull Request # 3829
This commit is contained in:
ippei ito
parent
7d665a4754
commit
7c740b6a31
523
modules/features2d/test/test_lshindex_flannbased_matcher.cpp
Normal file
523
modules/features2d/test/test_lshindex_flannbased_matcher.cpp
Normal file
@ -0,0 +1,523 @@
|
||||
/*
|
||||
/*********************************************************************
|
||||
* Software License Agreement (BSD License)
|
||||
*
|
||||
* Copyright (c) 2015, Willow Garage, Inc.
|
||||
* All rights reserved.
|
||||
*
|
||||
* Redistribution and use in source and binary forms, with or without
|
||||
* modification, are permitted provided that the following conditions
|
||||
* are met:
|
||||
*
|
||||
* * Redistributions of source code must retain the above copyright
|
||||
* notice, this list of conditions and the following disclaimer.
|
||||
* * Redistributions 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.
|
||||
* * Neither the name of the Willow Garage nor the names of its
|
||||
* contributors may 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
|
||||
* COPYRIGHT OWNER 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.
|
||||
*********************************************************************/
|
||||
|
||||
/*
|
||||
Authors: Ippei Ito
|
||||
|
||||
for OpenCV2.4/OpenCV3.0
|
||||
|
||||
Test for Pull Request # 3829
|
||||
https://github.com/Itseez/opencv/pull/3829
|
||||
|
||||
This test code creates brute force matcher for accuracy of reference, and the test target matcher.
|
||||
Then, add() and train() transformed query image descriptors, and some outlier images descriptors to both matchers.
|
||||
Then, compared with the query image by match() and findHomography() to detect outlier and calculate accuracy.
|
||||
And each drawMatches() images are saved, if SAVE_DRAW_MATCHES_IMAGES is true.
|
||||
Finally, compare accuracies between the brute force matcher and the test target matcher.
|
||||
|
||||
The lsh algorithm uses std::random_shuffle in lsh_index.h to make the random indexes table.
|
||||
So, in relation to default random seed value of the execution environment or by using "srand(time(0)) function",
|
||||
the match time and accuracy of the match results are different, each time the code ran.
|
||||
And the match time becomes late in relation to the number of the hash collision times.
|
||||
*/
|
||||
|
||||
#include "test_precomp.hpp"
|
||||
#include "opencv2/ts.hpp" // for FilePath::CreateFolder()
|
||||
#include <time.h> // for time()
|
||||
|
||||
// If defined, the match time and accuracy of the match results are a little different, each time the code ran.
|
||||
#define INIT_RANDOM_SEED
|
||||
|
||||
// If defined, some outlier images descriptors add() the matcher.
|
||||
#define TRAIN_WITH_OUTLIER_IMAGES
|
||||
|
||||
// If true, save drawMatches() images.
|
||||
#define SAVE_DRAW_MATCHES_IMAGES false
|
||||
|
||||
// if true, verbose output
|
||||
#define SHOW_DEBUG_LOG true
|
||||
|
||||
#if CV_MAJOR_VERSION==2
|
||||
#define OrbCreate new ORB(4000)
|
||||
#elif CV_MAJOR_VERSION==3
|
||||
#define OrbCreate ORB::create(4000)
|
||||
#define AKazeCreate AKAZE::create()
|
||||
#endif
|
||||
|
||||
using namespace cv;
|
||||
using namespace std;
|
||||
|
||||
int testno_for_make_filename = 0;
|
||||
|
||||
// --------------------------------------------------------------------------------------
|
||||
// Parameter class to transform query image
|
||||
// --------------------------------------------------------------------------------------
|
||||
class testparam
|
||||
{
|
||||
public:
|
||||
string transname;
|
||||
void(*transfunc)(float, const Mat&, Mat&);
|
||||
float from, to, step;
|
||||
testparam(string _transname, void(*_transfunc)(float, const Mat&, Mat&), float _from, float _to, float _step) :
|
||||
transname(_transname),
|
||||
transfunc(_transfunc),
|
||||
from(_from),
|
||||
to(_to),
|
||||
step(_step)
|
||||
{}
|
||||
};
|
||||
|
||||
// --------------------------------------------------------------------------------------
|
||||
// from matching_to_many_images.cpp
|
||||
// --------------------------------------------------------------------------------------
|
||||
int maskMatchesByTrainImgIdx(const vector<DMatch>& matches, int trainImgIdx, vector<char>& mask)
|
||||
{
|
||||
int matchcnt = 0;
|
||||
mask.resize(matches.size());
|
||||
fill(mask.begin(), mask.end(), 0);
|
||||
for (size_t i = 0; i < matches.size(); i++)
|
||||
{
|
||||
if (matches[i].imgIdx == trainImgIdx)
|
||||
{
|
||||
mask[i] = 1;
|
||||
matchcnt++;
|
||||
}
|
||||
}
|
||||
return matchcnt;
|
||||
}
|
||||
|
||||
int calcHomographyAndInlierCount(const vector<KeyPoint>& query_kp, const vector<KeyPoint>& train_kp, const vector<DMatch>& match, vector<char> &mask, Mat &homography)
|
||||
{
|
||||
// make query and current train image keypoint pairs
|
||||
std::vector<cv::Point2f> srcPoints, dstPoints;
|
||||
for (unsigned int i = 0; i < match.size(); ++i)
|
||||
{
|
||||
if (mask[i] != 0) // is current train image ?
|
||||
{
|
||||
srcPoints.push_back(query_kp[match[i].queryIdx].pt);
|
||||
dstPoints.push_back(train_kp[match[i].trainIdx].pt);
|
||||
}
|
||||
}
|
||||
// calc homography
|
||||
vector<uchar> inlierMask;
|
||||
homography = findHomography(srcPoints, dstPoints, RANSAC, 3.0, inlierMask);
|
||||
|
||||
// update outlier mask
|
||||
int j = 0;
|
||||
for (unsigned int i = 0; i < match.size(); ++i)
|
||||
{
|
||||
if (mask[i] != 0) // is current train image ?
|
||||
{
|
||||
if (inlierMask.size() == 0 || inlierMask[j] == 0) // is outlier ?
|
||||
{
|
||||
mask[i] = 0;
|
||||
}
|
||||
j++;
|
||||
}
|
||||
}
|
||||
|
||||
// count inlier
|
||||
int inlierCnt = 0;
|
||||
for (unsigned int i = 0; i < mask.size(); ++i)
|
||||
{
|
||||
if (mask[i] != 0)
|
||||
{
|
||||
inlierCnt++;
|
||||
}
|
||||
}
|
||||
return inlierCnt;
|
||||
}
|
||||
|
||||
void drawDetectedRectangle(Mat& imgResult, const Mat& homography, const 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);
|
||||
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);
|
||||
}
|
||||
|
||||
// --------------------------------------------------------------------------------------
|
||||
// transform query image, extract&compute, train, matching and save result image function
|
||||
// --------------------------------------------------------------------------------------
|
||||
typedef struct tagTrainInfo
|
||||
{
|
||||
int traindesccnt;
|
||||
double traintime;
|
||||
double matchtime;
|
||||
double accuracy;
|
||||
}TrainInfo;
|
||||
|
||||
TrainInfo transImgAndTrain(
|
||||
Feature2D *fe,
|
||||
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 float t, const testparam &tp,
|
||||
const int testno, const bool bVerboseOutput, const bool bSaveDrawMatches)
|
||||
{
|
||||
TrainInfo ti;
|
||||
|
||||
// transform query image
|
||||
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;
|
||||
#if CV_MAJOR_VERSION==2
|
||||
(*fe)(imgTransform, 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->train();
|
||||
ti.traintime = 1000.0*((double)getTickCount() - s) / 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->train();
|
||||
ti.traintime += 1000.0*((double)getTickCount() - s) / getTickFrequency();
|
||||
}
|
||||
ti.traindesccnt += totalOutlierDescCnt;
|
||||
#endif
|
||||
// matching
|
||||
vector<DMatch> match;
|
||||
s = (double)getTickCount();
|
||||
matcher->match(query_desc, match);
|
||||
ti.matchtime = 1000.0*((double)getTickCount() - s) / getTickFrequency();
|
||||
|
||||
// prepare a directory and variables for save matching images
|
||||
vector<char> mask;
|
||||
Mat imgResult;
|
||||
const char resultDir[] = "result";
|
||||
if (bSaveDrawMatches)
|
||||
{
|
||||
testing::internal::FilePath fp = testing::internal::FilePath(resultDir);
|
||||
fp.CreateFolder();
|
||||
}
|
||||
|
||||
char buff[2048];
|
||||
int matchcnt;
|
||||
|
||||
// save query vs transformed query matching image with detected rectangle
|
||||
matchcnt = maskMatchesByTrainImgIdx(match, (int)0, mask);
|
||||
// calc homography and inlier
|
||||
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);
|
||||
if (inlierCnt)
|
||||
{
|
||||
// draw detected rectangle
|
||||
drawDetectedRectangle(imgResult, homography, imgQuery);
|
||||
}
|
||||
// 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.));
|
||||
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;
|
||||
|
||||
#if defined(TRAIN_WITH_OUTLIER_IMAGES)
|
||||
// save query vs outlier matching image(s)
|
||||
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);
|
||||
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.));
|
||||
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;
|
||||
}
|
||||
#endif
|
||||
if (bVerboseOutput)
|
||||
{
|
||||
cout << tp.transname <<" image matching accuracy:" << ti.accuracy << "% " << ti.traindesccnt << " train:" << ti.traintime << "ms match:" << ti.matchtime << "ms" << endl;
|
||||
}
|
||||
|
||||
return ti;
|
||||
}
|
||||
|
||||
// --------------------------------------------------------------------------------------
|
||||
// Main Test Class
|
||||
// --------------------------------------------------------------------------------------
|
||||
class CV_FeatureDetectorMatcherBaseTest : public cvtest::BaseTest
|
||||
{
|
||||
private:
|
||||
|
||||
Ptr<DescriptorMatcher> bfmatcher; // brute force matcher for accuracy of reference
|
||||
Ptr<DescriptorMatcher> flmatcher; // flann matcher to test
|
||||
Ptr<Feature2D> fe; // feature detector extractor
|
||||
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;
|
||||
int totalOutlierDescCnt;
|
||||
|
||||
string flmatchername;
|
||||
testparam tp;
|
||||
double target_accuracy_margin_from_bfmatcher;
|
||||
|
||||
public:
|
||||
|
||||
//
|
||||
// constructor
|
||||
//
|
||||
CV_FeatureDetectorMatcherBaseTest(testparam _tp, double _accuracy_margin, Ptr<Feature2D> _fe, DescriptorMatcher *_flmatcher, string _flmatchername, int norm_type_for_bfmatcher) :
|
||||
tp(_tp),
|
||||
fe(_fe),
|
||||
flmatcher(_flmatcher),
|
||||
flmatchername(_flmatchername),
|
||||
target_accuracy_margin_from_bfmatcher(_accuracy_margin)
|
||||
{
|
||||
#if defined(INIT_RANDOM_SEED)
|
||||
// from test/test_eigen.cpp
|
||||
srand((unsigned int)time(0));
|
||||
#endif
|
||||
// create brute force matcher for accuracy of reference
|
||||
bfmatcher = makePtr<BFMatcher>(norm_type_for_bfmatcher);
|
||||
}
|
||||
|
||||
//
|
||||
// Main Test method
|
||||
//
|
||||
virtual void run(int)
|
||||
{
|
||||
// load query image
|
||||
string strQueryFile = string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.png";
|
||||
imgQuery = imread(strQueryFile, 0);
|
||||
if (imgQuery.empty())
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", strQueryFile.c_str());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
||||
return;
|
||||
}
|
||||
|
||||
// load outlier images
|
||||
char* outliers[] = { (char*)"baboon.png", (char*)"fruits.png", (char*)"airplane.png" };
|
||||
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);
|
||||
if (imgQuery.empty())
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Image %s can not be read.\n", strOutlierFile.c_str());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
|
||||
return;
|
||||
}
|
||||
imgOutliers.push_back(imgOutlier);
|
||||
}
|
||||
|
||||
// extract and compute keypoints and descriptors from query image
|
||||
#if CV_MAJOR_VERSION==2
|
||||
(*fe)(imgQuery, 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);
|
||||
totalOutlierDescCnt = 0;
|
||||
for (unsigned int i = 0; i < outliers_desc.size(); ++i) totalOutlierDescCnt += outliers_desc[i].rows;
|
||||
|
||||
if (SHOW_DEBUG_LOG)
|
||||
{
|
||||
cout << query_kp.size() << " keypoints extracted from query image." << endl;
|
||||
#if defined(TRAIN_WITH_OUTLIER_IMAGES)
|
||||
cout << totalOutlierDescCnt << " keypoints extracted from outlier image(s)." << endl;
|
||||
#endif
|
||||
}
|
||||
// compute brute force matcher accuracy for reference
|
||||
double totalTrainTime = 0.;
|
||||
double totalMatchTime = 0.;
|
||||
double totalAccuracy = 0.;
|
||||
int cnt = 0;
|
||||
for (float t = tp.from; t <= tp.to; t += tp.step, ++testno_for_make_filename, ++cnt)
|
||||
{
|
||||
if (SHOW_DEBUG_LOG) cout << "Test No." << testno_for_make_filename << " BFMatcher " << t;
|
||||
|
||||
TrainInfo ti = transImgAndTrain(fe, bfmatcher, "BFMatcher",
|
||||
imgQuery, query_kp, query_desc,
|
||||
imgOutliers, outliers_kp, outliers_desc,
|
||||
totalOutlierDescCnt,
|
||||
t, tp, testno_for_make_filename, SHOW_DEBUG_LOG, SAVE_DRAW_MATCHES_IMAGES);
|
||||
totalTrainTime += ti.traintime;
|
||||
totalMatchTime += ti.matchtime;
|
||||
totalAccuracy += ti.accuracy;
|
||||
}
|
||||
double bf_average_accuracy = totalAccuracy / cnt;
|
||||
if (SHOW_DEBUG_LOG)
|
||||
{
|
||||
cout << "total training time: " << totalTrainTime << "ms" << endl;
|
||||
cout << "total matching time: " << totalMatchTime << "ms" << endl;
|
||||
cout << "average accuracy:" << bf_average_accuracy << "%" << endl;
|
||||
}
|
||||
|
||||
// test the target matcher
|
||||
totalTrainTime = 0.;
|
||||
totalMatchTime = 0.;
|
||||
totalAccuracy = 0.;
|
||||
cnt = 0;
|
||||
for (float t = tp.from; t <= tp.to; t += tp.step, ++testno_for_make_filename, ++cnt)
|
||||
{
|
||||
if (SHOW_DEBUG_LOG) cout << "Test No." << testno_for_make_filename << " " << flmatchername << " " << t;
|
||||
|
||||
TrainInfo ti = transImgAndTrain(fe, flmatcher, flmatchername,
|
||||
imgQuery, query_kp, query_desc,
|
||||
imgOutliers, outliers_kp, outliers_desc,
|
||||
totalOutlierDescCnt,
|
||||
t, tp, testno_for_make_filename, SHOW_DEBUG_LOG, SAVE_DRAW_MATCHES_IMAGES);
|
||||
|
||||
totalTrainTime += ti.traintime;
|
||||
totalMatchTime += ti.matchtime;
|
||||
totalAccuracy += ti.accuracy;
|
||||
}
|
||||
double average_accuracy = totalAccuracy / cnt;
|
||||
double target_average_accuracy = bf_average_accuracy * target_accuracy_margin_from_bfmatcher;
|
||||
|
||||
if (SHOW_DEBUG_LOG)
|
||||
{
|
||||
cout << "total training time: " << totalTrainTime << "ms" << endl;
|
||||
cout << "total matching time: " << totalMatchTime << "ms" << endl;
|
||||
cout << "average accuracy:" << average_accuracy << "%" << endl;
|
||||
cout << "threshold of the target matcher average accuracy as error :" << target_average_accuracy << "%" << endl;
|
||||
cout << "accuracy degraded " << (100.0 - (average_accuracy / bf_average_accuracy *100.0)) << "% from BFMatcher.(lower percentage is better)" << endl;
|
||||
}
|
||||
// compare accuracies between the brute force matcher and the test target matcher
|
||||
if (average_accuracy < target_average_accuracy)
|
||||
{
|
||||
ts->printf(cvtest::TS::LOG, "Bad average accuracy %f < %f while test %s %s query\n", average_accuracy, target_average_accuracy, flmatchername, tp.transname.c_str());
|
||||
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
|
||||
}
|
||||
return;
|
||||
}
|
||||
|
||||
};
|
||||
|
||||
// --------------------------------------------------------------------------------------
|
||||
// Transform Functions
|
||||
// --------------------------------------------------------------------------------------
|
||||
static void rotate(float deg, const Mat& src, Mat& dst)
|
||||
{
|
||||
warpAffine(src, dst, getRotationMatrix2D(Point2f(src.cols / 2.0f, src.rows / 2.0f), deg, 1), src.size(), INTER_CUBIC);
|
||||
}
|
||||
static void scale(float scale, const Mat& src, Mat& dst)
|
||||
{
|
||||
resize(src, dst, Size((int)(src.cols*scale), (int)(src.rows*scale)), INTER_AREA);
|
||||
}
|
||||
static void blur(float k, const Mat& src, Mat& dst)
|
||||
{
|
||||
GaussianBlur(src, dst, Size((int)k, (int)k), 0);
|
||||
}
|
||||
|
||||
// --------------------------------------------------------------------------------------
|
||||
// Tests Registrations
|
||||
// --------------------------------------------------------------------------------------
|
||||
#define SHORT_LSH_KEY_ACCURACY_MARGIN 0.72 // The margin for FlannBasedMatcher. 28% degraded from BFMatcher(Actually, about 10..24% measured.lower percentage is better.) for lsh key size=16.
|
||||
#define MIDDLE_LSH_KEY_ACCURACY_MARGIN 0.72 // The margin for FlannBasedMatcher. 28% degraded from BFMatcher(Actually, about 7..24% measured.lower percentage is better.) for lsh key size=24.
|
||||
#define LONG_LSH_KEY_ACCURACY_MARGIN 0.90 // The margin for FlannBasedMatcher. 10% degraded from BFMatcher(Actually, about -29...7% measured.lower percentage is better.) for lsh key size=31.
|
||||
|
||||
TEST(BlurredQueryFlannBasedLshShortKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("blurred", blur, 1.0f, 11.0f, 2.0f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, SHORT_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 16, 2)), "FlannLsh(1, 16, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
TEST(BlurredQueryFlannBasedLshMiddleKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("blurred", blur, 1.0f, 11.0f, 2.0f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 24, 2)), "FlannLsh(1, 24, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
TEST(BlurredQueryFlannBasedLshLongKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("blurred", blur, 1.0f, 11.0f, 2.0f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, LONG_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 31, 2)), "FlannLsh(1, 31, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST(ScaledQueryFlannBasedLshShortKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("scaled", scale, 0.5f, 1.5f, 0.1f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, SHORT_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 16, 2)), "FlannLsh(1, 16, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
TEST(ScaledQueryFlannBasedLshMiddleKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("scaled", scale, 0.5f, 1.5f, 0.1f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 24, 2)), "FlannLsh(1, 24, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
TEST(ScaledQueryFlannBasedLshLongKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("scaled", scale, 0.5f, 1.5f, 0.1f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, LONG_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 31, 2)), "FlannLsh(1, 31, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
|
||||
TEST(RotatedQueryFlannBasedLshShortKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("rotated", rotate, 0.0f, 359.0f, 30.0f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, SHORT_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 16, 2)), "FlannLsh(1, 16, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
TEST(RotatedQueryFlannBasedLshMiddleKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("rotated", rotate, 0.0f, 359.0f, 30.0f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, MIDDLE_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 24, 2)), "FlannLsh(1, 24, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
TEST(RotatedQueryFlannBasedLshLongKeyMatcherAdditionalTrainTest, accuracy)
|
||||
{
|
||||
testparam tp("rotated", rotate, 0.0f, 359.0f, 30.0f);
|
||||
CV_FeatureDetectorMatcherBaseTest test(tp, LONG_LSH_KEY_ACCURACY_MARGIN, OrbCreate, new FlannBasedMatcher(makePtr<flann::LshIndexParams>(1, 31, 2)), "FlannLsh(1, 31, 2)", NORM_HAMMING);
|
||||
test.safe_run();
|
||||
}
|
||||
@ -14,6 +14,7 @@
|
||||
#include "opencv2/imgproc/imgproc_c.h"
|
||||
#include "opencv2/features2d/features2d.hpp"
|
||||
#include "opencv2/highgui/highgui.hpp"
|
||||
#include "opencv2/calib3d/calib3d.hpp"
|
||||
#include <iostream>
|
||||
|
||||
#endif
|
||||
|
||||
@ -9,7 +9,7 @@ set(OPENCV_MODULE_IS_PART_OF_WORLD FALSE)
|
||||
|
||||
ocv_warnings_disable(CMAKE_CXX_FLAGS -Wundef)
|
||||
|
||||
ocv_add_module(ts opencv_core opencv_features2d opencv_highgui opencv_imgproc opencv_video)
|
||||
ocv_add_module(ts opencv_core opencv_features2d opencv_highgui opencv_imgproc opencv_video opencv_calib3d)
|
||||
|
||||
ocv_glob_module_sources()
|
||||
ocv_module_include_directories()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user