Open Source Computer Vision Library https://opencv.org/
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#include "test_precomp.hpp"
#include <string>
#include <iostream>
#include <iterator>
#include <fstream>
#include <numeric>
#include <algorithm>
#include <iterator>
using namespace cv;
using namespace std;
class CV_DetectorsTest : public cvtest::BaseTest
{
public:
CV_DetectorsTest();
~CV_DetectorsTest();
protected:
void run(int);
template <class T> bool testDetector(const Mat& img, const T& detector, vector<KeyPoint>& expected);
void LoadExpected(const string& file, vector<KeyPoint>& out);
};
CV_DetectorsTest::CV_DetectorsTest()
{
}
CV_DetectorsTest::~CV_DetectorsTest() {}
void getRotation(const Mat& img, Mat& aff, Mat& out)
{
Point center(img.cols/2, img.rows/2);
aff = getRotationMatrix2D(center, 30, 1);
warpAffine( img, out, aff, img.size());
}
void getZoom(const Mat& img, Mat& aff, Mat& out)
{
const double mult = 1.2;
aff.create(2, 3, CV_64F);
double *data = aff.ptr<double>();
data[0] = mult; data[1] = 0; data[2] = 0;
data[3] = 0; data[4] = mult; data[5] = 0;
warpAffine( img, out, aff, img.size());
}
void getBlur(const Mat& img, Mat& aff, Mat& out)
{
aff.create(2, 3, CV_64F);
double *data = aff.ptr<double>();
data[0] = 1; data[1] = 0; data[2] = 0;
data[3] = 0; data[4] = 1; data[5] = 0;
GaussianBlur(img, out, Size(5, 5), 2);
}
void getBrightness(const Mat& img, Mat& aff, Mat& out)
{
aff.create(2, 3, CV_64F);
double *data = aff.ptr<double>();
data[0] = 1; data[1] = 0; data[2] = 0;
data[3] = 0; data[4] = 1; data[5] = 0;
add(img, Mat(img.size(), img.type(), Scalar(15)), out);
}
void showOrig(const Mat& img, const vector<KeyPoint>& orig_pts)
{
Mat img_color;
cvtColor(img, img_color, CV_GRAY2BGR);
for(size_t i = 0; i < orig_pts.size(); ++i)
circle(img_color, orig_pts[i].pt, (int)orig_pts[i].size/2, CV_RGB(0, 255, 0));
namedWindow("O"); imshow("O", img_color);
}
void show(const string& name, const Mat& new_img, const vector<KeyPoint>& new_pts, const vector<KeyPoint>& transf_pts)
{
Mat new_img_color;
cvtColor(new_img, new_img_color, CV_GRAY2BGR);
for(size_t i = 0; i < transf_pts.size(); ++i)
circle(new_img_color, transf_pts[i].pt, (int)transf_pts[i].size/2, CV_RGB(255, 0, 0));
for(size_t i = 0; i < new_pts.size(); ++i)
circle(new_img_color, new_pts[i].pt, (int)new_pts[i].size/2, CV_RGB(0, 0, 255));
namedWindow(name + "_T"); imshow(name + "_T", new_img_color);
}
struct WrapPoint
{
const double* R;
WrapPoint(const Mat& rmat) : R(rmat.ptr<double>()) { };
KeyPoint operator()(const KeyPoint& kp) const
{
KeyPoint res = kp;
res.pt.x = static_cast<float>(kp.pt.x * R[0] + kp.pt.y * R[1] + R[2]);
res.pt.y = static_cast<float>(kp.pt.x * R[3] + kp.pt.y * R[4] + R[5]);
return res;
}
};
struct sortByR { bool operator()(const KeyPoint& kp1, const KeyPoint& kp2) { return norm(kp1.pt) < norm(kp2.pt); } };
template <class T> bool CV_DetectorsTest::testDetector(const Mat& img, const T& detector, vector<KeyPoint>& exp)
{
vector<KeyPoint> orig_kpts;
detector(img, orig_kpts);
typedef void (*TransfFunc )(const Mat&, Mat&, Mat& FransfFunc);
const TransfFunc transfFunc[] = { getRotation, getZoom, getBlur, getBrightness };
//const string names[] = { "Rotation", "Zoom", "Blur", "Brightness" };
const size_t case_num = sizeof(transfFunc)/sizeof(transfFunc[0]);
vector<Mat> affs(case_num);
vector<Mat> new_imgs(case_num);
vector< vector<KeyPoint> > new_kpts(case_num);
vector< vector<KeyPoint> > transf_kpts(case_num);
//showOrig(img, orig_kpts);
for(size_t i = 0; i < case_num; ++i)
{
transfFunc[i](img, affs[i], new_imgs[i]);
detector(new_imgs[i], new_kpts[i]);
transform(orig_kpts.begin(), orig_kpts.end(), back_inserter(transf_kpts[i]), WrapPoint(affs[i]));
//show(names[i], new_imgs[i], new_kpts[i], transf_kpts[i]);
}
const float thres = 3;
const float nthres = 3;
vector<KeyPoint> result;
for(size_t i = 0; i < orig_kpts.size(); ++i)
{
const KeyPoint& okp = orig_kpts[i];
int foundCounter = 0;
for(size_t j = 0; j < case_num; ++j)
{
const KeyPoint& tkp = transf_kpts[j][i];
size_t k = 0;
for(; k < new_kpts[j].size(); ++k)
if (norm(new_kpts[j][k].pt - tkp.pt) < nthres && fabs(new_kpts[j][k].size - tkp.size) < thres)
break;
if (k != new_kpts[j].size())
++foundCounter;
}
if (foundCounter == (int)case_num)
result.push_back(okp);
}
sort(result.begin(), result.end(), sortByR());
sort(exp.begin(), exp.end(), sortByR());
if (result.size() != exp.size())
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return false;
}
int foundCounter1 = 0;
for(size_t i = 0; i < exp.size(); ++i)
{
const KeyPoint& e = exp[i];
size_t j = 0;
for(; j < result.size(); ++j)
{
const KeyPoint& r = result[i];
if (norm(r.pt-e.pt) < nthres && fabs(r.size - e.size) < thres)
break;
}
if (j != result.size())
++foundCounter1;
}
int foundCounter2 = 0;
for(size_t i = 0; i < result.size(); ++i)
{
const KeyPoint& r = result[i];
size_t j = 0;
for(; j < exp.size(); ++j)
{
const KeyPoint& e = exp[i];
if (norm(r.pt-e.pt) < nthres && fabs(r.size - e.size) < thres)
break;
}
if (j != exp.size())
++foundCounter2;
}
//showOrig(img, result); waitKey();
const float errorRate = 0.9f;
if (float(foundCounter1)/exp.size() < errorRate || float(foundCounter2)/result.size() < errorRate)
{
ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH);
return false;
}
return true;
}
struct SurfNoMaskWrap
{
const SURF& detector;
SurfNoMaskWrap(const SURF& surf) : detector(surf) {}
SurfNoMaskWrap& operator=(const SurfNoMaskWrap&);
void operator()(const Mat& img, vector<KeyPoint>& kpts) const { detector(img, Mat(), kpts); }
};
void CV_DetectorsTest::LoadExpected(const string& file, vector<KeyPoint>& out)
{
Mat mat_exp;
FileStorage fs(file, FileStorage::READ);
if (fs.isOpened())
{
read( fs["ResultVectorData"], mat_exp, Mat() );
out.resize(mat_exp.cols / sizeof(KeyPoint));
copy(mat_exp.ptr<KeyPoint>(), mat_exp.ptr<KeyPoint>() + out.size(), out.begin());
}
else
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA);
out.clear();
}
}
void CV_DetectorsTest::run( int /*start_from*/ )
{
Mat img = imread(string(ts->get_data_path()) + "shared/graffiti.png", 0);
if (img.empty())
{
ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA );
return;
}
Mat to_test(img.size() * 2, img.type(), Scalar(0));
Mat roi = to_test(Rect(img.rows/2, img.cols/2, img.cols, img.rows));
img.copyTo(roi);
GaussianBlur(to_test, to_test, Size(3, 3), 1.5);
vector<KeyPoint> exp;
LoadExpected(string(ts->get_data_path()) + "detectors/surf.xml", exp);
if (exp.empty())
return;
if (!testDetector(to_test, SurfNoMaskWrap(SURF(1536+512+512, 2)), exp))
return;
LoadExpected(string(ts->get_data_path()) + "detectors/star.xml", exp);
if (exp.empty())
return;
if (!testDetector(to_test, StarDetector(45, 30, 10, 8, 5), exp))
return;
ts->set_failed_test_info( cvtest::TS::OK);
}
TEST(Features2d_Detectors, regression) { CV_DetectorsTest test; test.safe_run(); }