mirror of
https://github.com/opencv/opencv.git
synced 2024-12-05 01:39:13 +08:00
318 lines
9.7 KiB
C++
318 lines
9.7 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
|
|
//
|
|
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
|
|
//
|
|
// By downloading, copying, installing or using the software you agree to this license.
|
|
// If you do not agree to this license, do not download, install,
|
|
// copy or use the software.
|
|
//
|
|
//
|
|
// License Agreement
|
|
// For Open Source Computer Vision Library
|
|
//
|
|
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
|
|
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
|
|
// Third party copyrights are property of their respective owners.
|
|
//
|
|
// Redistribution and use in source and binary forms, with or without modification,
|
|
// are permitted provided that the following conditions are met:
|
|
//
|
|
// * Redistribution's of source code must retain the above copyright notice,
|
|
// this list of conditions and the following disclaimer.
|
|
//
|
|
// * Redistribution's 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.
|
|
//
|
|
// * The name of the copyright holders may not 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 Intel Corporation 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.
|
|
//
|
|
//M*/
|
|
|
|
#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(); }
|
|
|
|
|
|
|