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367 lines
11 KiB
C++
367 lines
11 KiB
C++
/*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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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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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#define VARNAME(A) #A
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using namespace std;
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using namespace cv;
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using namespace cvtest;
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namespace cvtest {
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//std::string generateVarList(int first,...)
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//{
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// vector<std::string> varname;
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//
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// va_list argp;
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// string s;
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// stringstream ss;
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// va_start(argp,first);
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// int i=first;
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// while(i!=-1)
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// {
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// ss<<i<<",";
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// i=va_arg(argp,int);
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// };
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// s=ss.str();
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// va_end(argp);
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// return s;
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//};
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//std::string generateVarList(int& p1,int& p2)
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//{
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// stringstream ss;
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// ss<<VARNAME(p1)<<":"<<src1x<<","<<VARNAME(p2)<<":"<<src1y;
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// return ss.str();
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//};
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cv::ocl::oclMat createMat_ocl(cv::RNG& rng, Size size, int type, bool useRoi)
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{
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Size size0 = size;
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if (useRoi)
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{
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size0.width += rng.uniform(5, 15);
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size0.height += rng.uniform(5, 15);
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}
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cv::ocl::oclMat d_m(size0, type);
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if (size0 != size)
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d_m = d_m(Rect((size0.width - size.width) / 2, (size0.height - size.height) / 2, size.width, size.height));
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return d_m;
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}
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cv::ocl::oclMat loadMat_ocl(cv::RNG& rng, const Mat& m, bool useRoi)
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{
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CV_Assert(m.type() == CV_8UC1 || m.type() == CV_8UC3);
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cv::ocl::oclMat d_m;
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d_m = createMat_ocl(rng, m.size(), m.type(), useRoi);
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Size ls;
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Point pt;
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d_m.locateROI(ls, pt);
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Rect roi(pt.x, pt.y, d_m.size().width, d_m.size().height);
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cv::ocl::oclMat m_ocl(m);
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cv::ocl::oclMat d_m_roi(d_m, roi);
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m_ocl.copyTo(d_m);
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return d_m;
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}
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vector<MatType> types(int depth_start, int depth_end, int cn_start, int cn_end)
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{
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vector<MatType> v;
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v.reserve((depth_end - depth_start + 1) * (cn_end - cn_start + 1));
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for (int depth = depth_start; depth <= depth_end; ++depth)
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{
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for (int cn = cn_start; cn <= cn_end; ++cn)
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{
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v.push_back(CV_MAKETYPE(depth, cn));
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}
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}
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return v;
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}
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const vector<MatType> &all_types()
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{
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static vector<MatType> v = types(CV_8U, CV_64F, 1, 4);
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return v;
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}
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Mat readImage(const string &fileName, int flags)
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{
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return imread(string(cvtest::TS::ptr()->get_data_path()) + fileName, flags);
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}
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Mat readImageType(const string &fname, int type)
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{
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Mat src = readImage(fname, CV_MAT_CN(type) == 1 ? IMREAD_GRAYSCALE : IMREAD_COLOR);
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if (CV_MAT_CN(type) == 4)
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{
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Mat temp;
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cvtColor(src, temp, cv::COLOR_BGR2BGRA);
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swap(src, temp);
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}
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src.convertTo(src, CV_MAT_DEPTH(type));
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return src;
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}
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double checkNorm(const Mat &m)
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{
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return norm(m, NORM_INF);
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}
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double checkNorm(const Mat &m1, const Mat &m2)
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{
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return norm(m1, m2, NORM_INF);
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}
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double checkSimilarity(const Mat &m1, const Mat &m2)
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{
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Mat diff;
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matchTemplate(m1, m2, diff, TM_CCORR_NORMED);
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return std::abs(diff.at<float>(0, 0) - 1.f);
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}
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/*
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void cv::ocl::PrintTo(const DeviceInfo& info, ostream* os)
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{
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(*os) << info.name();
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}
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*/
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void PrintTo(const Inverse &inverse, std::ostream *os)
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{
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if (inverse)
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(*os) << "inverse";
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else
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(*os) << "direct";
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}
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double checkRectSimilarity(Size sz, std::vector<Rect>& ob1, std::vector<Rect>& ob2)
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{
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double final_test_result = 0.0;
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size_t sz1 = ob1.size();
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size_t sz2 = ob2.size();
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if(sz1 != sz2)
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{
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return sz1 > sz2 ? (double)(sz1 - sz2) : (double)(sz2 - sz1);
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}
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else
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{
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if(sz1==0 && sz2==0)
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return 0;
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cv::Mat cpu_result(sz, CV_8UC1);
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cpu_result.setTo(0);
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for(vector<Rect>::const_iterator r = ob1.begin(); r != ob1.end(); r++)
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{
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cv::Mat cpu_result_roi(cpu_result, *r);
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cpu_result_roi.setTo(1);
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cpu_result.copyTo(cpu_result);
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}
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int cpu_area = cv::countNonZero(cpu_result > 0);
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cv::Mat gpu_result(sz, CV_8UC1);
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gpu_result.setTo(0);
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for(vector<Rect>::const_iterator r2 = ob2.begin(); r2 != ob2.end(); r2++)
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{
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cv::Mat gpu_result_roi(gpu_result, *r2);
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gpu_result_roi.setTo(1);
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gpu_result.copyTo(gpu_result);
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}
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cv::Mat result_;
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multiply(cpu_result, gpu_result, result_);
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int result = cv::countNonZero(result_ > 0);
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if(cpu_area!=0 && result!=0)
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final_test_result = 1.0 - (double)result/(double)cpu_area;
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else if(cpu_area==0 && result!=0)
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final_test_result = -1;
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}
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return final_test_result;
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}
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void showDiff(const Mat& src, const Mat& gold, const Mat& actual, double eps, bool alwaysShow)
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{
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Mat diff, diff_thresh;
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absdiff(gold, actual, diff);
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diff.convertTo(diff, CV_32F);
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threshold(diff, diff_thresh, eps, 255.0, cv::THRESH_BINARY);
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if (alwaysShow || cv::countNonZero(diff_thresh.reshape(1)) > 0)
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{
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#if 0
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std::cout << "Src: " << std::endl << src << std::endl;
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std::cout << "Reference: " << std::endl << gold << std::endl;
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std::cout << "OpenCL: " << std::endl << actual << std::endl;
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#endif
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namedWindow("src", WINDOW_NORMAL);
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namedWindow("gold", WINDOW_NORMAL);
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namedWindow("actual", WINDOW_NORMAL);
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namedWindow("diff", WINDOW_NORMAL);
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imshow("src", src);
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imshow("gold", gold);
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imshow("actual", actual);
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imshow("diff", diff);
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waitKey();
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}
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}
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namespace
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{
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bool keyPointsEquals(const cv::KeyPoint& p1, const cv::KeyPoint& p2)
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{
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const double maxPtDif = 1.0;
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const double maxSizeDif = 1.0;
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const double maxAngleDif = 2.0;
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const double maxResponseDif = 0.1;
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double dist = cv::norm(p1.pt - p2.pt);
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if (dist < maxPtDif &&
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fabs(p1.size - p2.size) < maxSizeDif &&
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abs(p1.angle - p2.angle) < maxAngleDif &&
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abs(p1.response - p2.response) < maxResponseDif &&
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p1.octave == p2.octave &&
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p1.class_id == p2.class_id)
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{
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return true;
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}
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return false;
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}
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struct KeyPointLess : std::binary_function<cv::KeyPoint, cv::KeyPoint, bool>
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{
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bool operator()(const cv::KeyPoint& kp1, const cv::KeyPoint& kp2) const
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{
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return kp1.pt.y < kp2.pt.y || (kp1.pt.y == kp2.pt.y && kp1.pt.x < kp2.pt.x);
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}
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};
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}
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testing::AssertionResult assertKeyPointsEquals(const char* gold_expr, const char* actual_expr, std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual)
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{
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if (gold.size() != actual.size())
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{
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return testing::AssertionFailure() << "KeyPoints size mistmach\n"
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<< "\"" << gold_expr << "\" : " << gold.size() << "\n"
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<< "\"" << actual_expr << "\" : " << actual.size();
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}
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std::sort(actual.begin(), actual.end(), KeyPointLess());
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std::sort(gold.begin(), gold.end(), KeyPointLess());
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for (size_t i = 0; i < gold.size(); ++i)
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{
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const cv::KeyPoint& p1 = gold[i];
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const cv::KeyPoint& p2 = actual[i];
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if (!keyPointsEquals(p1, p2))
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{
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return testing::AssertionFailure() << "KeyPoints differ at " << i << "\n"
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<< "\"" << gold_expr << "\" vs \"" << actual_expr << "\" : \n"
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<< "pt : " << testing::PrintToString(p1.pt) << " vs " << testing::PrintToString(p2.pt) << "\n"
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<< "size : " << p1.size << " vs " << p2.size << "\n"
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<< "angle : " << p1.angle << " vs " << p2.angle << "\n"
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<< "response : " << p1.response << " vs " << p2.response << "\n"
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<< "octave : " << p1.octave << " vs " << p2.octave << "\n"
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<< "class_id : " << p1.class_id << " vs " << p2.class_id;
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}
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}
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return ::testing::AssertionSuccess();
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}
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int getMatchedPointsCount(std::vector<cv::KeyPoint>& gold, std::vector<cv::KeyPoint>& actual)
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{
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std::sort(actual.begin(), actual.end(), KeyPointLess());
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std::sort(gold.begin(), gold.end(), KeyPointLess());
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int validCount = 0;
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size_t sz = std::min(gold.size(), actual.size());
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for (size_t i = 0; i < sz; ++i)
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{
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const cv::KeyPoint& p1 = gold[i];
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const cv::KeyPoint& p2 = actual[i];
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if (keyPointsEquals(p1, p2))
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++validCount;
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}
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return validCount;
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}
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int getMatchedPointsCount(const std::vector<cv::KeyPoint>& keypoints1, const std::vector<cv::KeyPoint>& keypoints2, const std::vector<cv::DMatch>& matches)
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{
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int validCount = 0;
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for (size_t i = 0; i < matches.size(); ++i)
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{
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const cv::DMatch& m = matches[i];
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const cv::KeyPoint& p1 = keypoints1[m.queryIdx];
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const cv::KeyPoint& p2 = keypoints2[m.trainIdx];
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if (keyPointsEquals(p1, p2))
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++validCount;
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}
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return validCount;
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}
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} // namespace cvtest
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