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317 lines
10 KiB
317 lines
10 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of the copyright holders may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "test_precomp.hpp" |
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#include <string> |
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#include <iostream> |
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#include <iterator> |
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#include <fstream> |
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#include <numeric> |
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#include <algorithm> |
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#include <iterator> |
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using namespace cv; |
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using namespace std; |
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class CV_DetectorsTest : public cvtest::BaseTest |
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{ |
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public: |
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CV_DetectorsTest(); |
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~CV_DetectorsTest(); |
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protected: |
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void run(int); |
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template <class T> bool testDetector(const Mat& img, const T& detector, vector<KeyPoint>& expected); |
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void LoadExpected(const string& file, vector<KeyPoint>& out); |
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}; |
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CV_DetectorsTest::CV_DetectorsTest() |
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{ |
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} |
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CV_DetectorsTest::~CV_DetectorsTest() {} |
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void getRotation(const Mat& img, Mat& aff, Mat& out) |
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{ |
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Point center(img.cols/2, img.rows/2); |
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aff = getRotationMatrix2D(center, 30, 1); |
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warpAffine( img, out, aff, img.size()); |
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} |
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void getZoom(const Mat& img, Mat& aff, Mat& out) |
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{ |
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const double mult = 1.2; |
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aff.create(2, 3, CV_64F); |
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double *data = aff.ptr<double>(); |
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data[0] = mult; data[1] = 0; data[2] = 0; |
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data[3] = 0; data[4] = mult; data[5] = 0; |
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warpAffine( img, out, aff, img.size()); |
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} |
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void getBlur(const Mat& img, Mat& aff, Mat& out) |
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{ |
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aff.create(2, 3, CV_64F); |
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double *data = aff.ptr<double>(); |
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data[0] = 1; data[1] = 0; data[2] = 0; |
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data[3] = 0; data[4] = 1; data[5] = 0; |
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GaussianBlur(img, out, Size(5, 5), 2); |
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} |
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void getBrightness(const Mat& img, Mat& aff, Mat& out) |
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{ |
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aff.create(2, 3, CV_64F); |
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double *data = aff.ptr<double>(); |
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data[0] = 1; data[1] = 0; data[2] = 0; |
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data[3] = 0; data[4] = 1; data[5] = 0; |
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add(img, Mat(img.size(), img.type(), Scalar(15)), out); |
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} |
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void showOrig(const Mat& img, const vector<KeyPoint>& orig_pts) |
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{ |
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Mat img_color; |
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cvtColor(img, img_color, CV_GRAY2BGR); |
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for(size_t i = 0; i < orig_pts.size(); ++i) |
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circle(img_color, orig_pts[i].pt, (int)orig_pts[i].size/2, CV_RGB(0, 255, 0)); |
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namedWindow("O"); imshow("O", img_color); |
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} |
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void show(const string& name, const Mat& new_img, const vector<KeyPoint>& new_pts, const vector<KeyPoint>& transf_pts) |
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{ |
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Mat new_img_color; |
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cvtColor(new_img, new_img_color, CV_GRAY2BGR); |
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for(size_t i = 0; i < transf_pts.size(); ++i) |
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circle(new_img_color, transf_pts[i].pt, (int)transf_pts[i].size/2, CV_RGB(255, 0, 0)); |
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for(size_t i = 0; i < new_pts.size(); ++i) |
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circle(new_img_color, new_pts[i].pt, (int)new_pts[i].size/2, CV_RGB(0, 0, 255)); |
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namedWindow(name + "_T"); imshow(name + "_T", new_img_color); |
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} |
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struct WrapPoint |
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{ |
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const double* R; |
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WrapPoint(const Mat& rmat) : R(rmat.ptr<double>()) { }; |
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KeyPoint operator()(const KeyPoint& kp) const |
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{ |
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KeyPoint res = kp; |
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res.pt.x = static_cast<float>(kp.pt.x * R[0] + kp.pt.y * R[1] + R[2]); |
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res.pt.y = static_cast<float>(kp.pt.x * R[3] + kp.pt.y * R[4] + R[5]); |
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return res; |
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} |
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}; |
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struct sortByR { bool operator()(const KeyPoint& kp1, const KeyPoint& kp2) { return norm(kp1.pt) < norm(kp2.pt); } }; |
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template <class T> bool CV_DetectorsTest::testDetector(const Mat& img, const T& detector, vector<KeyPoint>& exp) |
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{ |
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vector<KeyPoint> orig_kpts; |
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detector(img, orig_kpts); |
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typedef void (*TransfFunc )(const Mat&, Mat&, Mat& FransfFunc); |
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const TransfFunc transfFunc[] = { getRotation, getZoom, getBlur, getBrightness }; |
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//const string names[] = { "Rotation", "Zoom", "Blur", "Brightness" }; |
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const size_t case_num = sizeof(transfFunc)/sizeof(transfFunc[0]); |
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vector<Mat> affs(case_num); |
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vector<Mat> new_imgs(case_num); |
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vector< vector<KeyPoint> > new_kpts(case_num); |
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vector< vector<KeyPoint> > transf_kpts(case_num); |
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//showOrig(img, orig_kpts); |
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for(size_t i = 0; i < case_num; ++i) |
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{ |
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transfFunc[i](img, affs[i], new_imgs[i]); |
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detector(new_imgs[i], new_kpts[i]); |
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transform(orig_kpts.begin(), orig_kpts.end(), back_inserter(transf_kpts[i]), WrapPoint(affs[i])); |
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//show(names[i], new_imgs[i], new_kpts[i], transf_kpts[i]); |
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} |
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const float thres = 3; |
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const float nthres = 3; |
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vector<KeyPoint> result; |
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for(size_t i = 0; i < orig_kpts.size(); ++i) |
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{ |
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const KeyPoint& okp = orig_kpts[i]; |
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int foundCounter = 0; |
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for(size_t j = 0; j < case_num; ++j) |
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{ |
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const KeyPoint& tkp = transf_kpts[j][i]; |
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size_t k = 0; |
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for(; k < new_kpts[j].size(); ++k) |
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if (norm(new_kpts[j][k].pt - tkp.pt) < nthres && fabs(new_kpts[j][k].size - tkp.size) < thres) |
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break; |
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if (k != new_kpts[j].size()) |
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++foundCounter; |
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} |
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if (foundCounter == (int)case_num) |
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result.push_back(okp); |
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} |
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sort(result.begin(), result.end(), sortByR()); |
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sort(exp.begin(), exp.end(), sortByR()); |
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if (result.size() != exp.size()) |
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{ |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA); |
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return false; |
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} |
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int foundCounter1 = 0; |
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for(size_t i = 0; i < exp.size(); ++i) |
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{ |
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const KeyPoint& e = exp[i]; |
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size_t j = 0; |
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for(; j < result.size(); ++j) |
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{ |
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const KeyPoint& r = result[i]; |
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if (norm(r.pt-e.pt) < nthres && fabs(r.size - e.size) < thres) |
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break; |
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} |
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if (j != result.size()) |
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++foundCounter1; |
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} |
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int foundCounter2 = 0; |
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for(size_t i = 0; i < result.size(); ++i) |
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{ |
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const KeyPoint& r = result[i]; |
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size_t j = 0; |
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for(; j < exp.size(); ++j) |
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{ |
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const KeyPoint& e = exp[i]; |
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if (norm(r.pt-e.pt) < nthres && fabs(r.size - e.size) < thres) |
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break; |
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} |
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if (j != exp.size()) |
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++foundCounter2; |
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} |
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//showOrig(img, result); waitKey(); |
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const float errorRate = 0.9f; |
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if (float(foundCounter1)/exp.size() < errorRate || float(foundCounter2)/result.size() < errorRate) |
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{ |
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ts->set_failed_test_info( cvtest::TS::FAIL_MISMATCH); |
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return false; |
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} |
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return true; |
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} |
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struct SurfNoMaskWrap |
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{ |
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const SURF& detector; |
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SurfNoMaskWrap(const SURF& surf) : detector(surf) {} |
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SurfNoMaskWrap& operator=(const SurfNoMaskWrap&); |
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void operator()(const Mat& img, vector<KeyPoint>& kpts) const { detector(img, Mat(), kpts); } |
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}; |
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void CV_DetectorsTest::LoadExpected(const string& file, vector<KeyPoint>& out) |
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{ |
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Mat mat_exp; |
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FileStorage fs(file, FileStorage::READ); |
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if (fs.isOpened()) |
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{ |
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read( fs["ResultVectorData"], mat_exp, Mat() ); |
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out.resize(mat_exp.cols / sizeof(KeyPoint)); |
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copy(mat_exp.ptr<KeyPoint>(), mat_exp.ptr<KeyPoint>() + out.size(), out.begin()); |
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} |
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else |
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{ |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA); |
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out.clear(); |
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} |
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} |
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void CV_DetectorsTest::run( int /*start_from*/ ) |
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{ |
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Mat img = imread(string(ts->get_data_path()) + "shared/graffiti.png", 0); |
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if (img.empty()) |
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{ |
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ts->set_failed_test_info( cvtest::TS::FAIL_INVALID_TEST_DATA ); |
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return; |
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} |
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Mat to_test(img.size() * 2, img.type(), Scalar(0)); |
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Mat roi = to_test(Rect(img.rows/2, img.cols/2, img.cols, img.rows)); |
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img.copyTo(roi); |
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GaussianBlur(to_test, to_test, Size(3, 3), 1.5); |
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vector<KeyPoint> exp; |
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LoadExpected(string(ts->get_data_path()) + "detectors/surf.xml", exp); |
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if (exp.empty()) |
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return; |
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if (!testDetector(to_test, SurfNoMaskWrap(SURF(1536+512+512, true, false, 2)), exp)) |
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return; |
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LoadExpected(string(ts->get_data_path()) + "detectors/star.xml", exp); |
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if (exp.empty()) |
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return; |
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if (!testDetector(to_test, StarDetector(45, 30, 10, 8, 5), exp)) |
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return; |
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ts->set_failed_test_info( cvtest::TS::OK); |
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} |
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TEST(Features2d_Detectors, regression) { CV_DetectorsTest test; test.safe_run(); } |
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