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Open Source Computer Vision Library
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295 lines
13 KiB
295 lines
13 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 GpuMaterials 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 bpied warranties, including, but not limited to, the bpied |
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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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//#define SHOW_TIME |
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#ifdef SHOW_TIME |
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#include <ctime> |
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#define F(x) x |
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#else |
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#define F(x) |
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#endif |
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using namespace cv; |
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using namespace std; |
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struct CV_GpuMatchTemplateTest: cvtest::BaseTest |
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{ |
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CV_GpuMatchTemplateTest() {} |
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void run(int) |
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{ |
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bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) && |
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gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE); |
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if (!double_ok) |
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{ |
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// For sqrIntegral |
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ts->printf(cvtest::TS::CONSOLE, "\nCode and device double support is required (CC >= 1.3)"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_GENERIC); |
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return; |
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} |
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Mat image, templ; |
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Mat dst_gold; |
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gpu::GpuMat dst; |
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int n, m, h, w; |
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F(clock_t t;) |
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RNG& rng = ts->get_rng(); |
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for (int cn = 1; cn <= 4; ++cn) |
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{ |
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F(ts->printf(cvtest::TS::CONSOLE, "cn: %d\n", cn);) |
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for (int i = 0; i <= 0; ++i) |
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{ |
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n = rng.uniform(30, 100); |
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m = rng.uniform(30, 100); |
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h = rng.uniform(5, n - 1); |
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w = rng.uniform(5, m - 1); |
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gen(image, n, m, CV_8U, cn); |
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gen(templ, h, w, CV_8U, cn); |
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F(t = clock();) |
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matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF); |
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F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;) |
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F(cout << "cpu:" << clock() - t << endl;) |
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F(t = clock();) |
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gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF); |
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F(cout << "gpu_block: " << clock() - t << endl;) |
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if (!check(dst_gold, Mat(dst), 5 * h * w * 1e-4f, "SQDIFF 8U")) return; |
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gen(image, n, m, CV_8U, cn); |
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gen(templ, h, w, CV_8U, cn); |
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F(t = clock();) |
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matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF_NORMED); |
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F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;) |
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F(cout << "cpu:" << clock() - t << endl;) |
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F(t = clock();) |
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gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF_NORMED); |
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F(cout << "gpu_block: " << clock() - t << endl;) |
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if (!check(dst_gold, Mat(dst), h * w * 1e-5f, "SQDIFF_NOREMD 8U")) return; |
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gen(image, n, m, CV_8U, cn); |
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gen(templ, h, w, CV_8U, cn); |
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F(t = clock();) |
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matchTemplate(image, templ, dst_gold, CV_TM_CCORR); |
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F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;) |
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F(cout << "cpu:" << clock() - t << endl;) |
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F(t = clock();) |
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gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR); |
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F(cout << "gpu_block: " << clock() - t << endl;) |
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if (!check(dst_gold, Mat(dst), 5 * h * w * cn * cn * 1e-5f, "CCORR 8U")) return; |
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gen(image, n, m, CV_8U, cn); |
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gen(templ, h, w, CV_8U, cn); |
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F(t = clock();) |
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matchTemplate(image, templ, dst_gold, CV_TM_CCORR_NORMED); |
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F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;) |
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F(cout << "cpu:" << clock() - t << endl;) |
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F(t = clock();) |
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gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR_NORMED); |
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F(cout << "gpu_block: " << clock() - t << endl;) |
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if (!check(dst_gold, Mat(dst), h * w * 1e-6f, "CCORR_NORMED 8U")) return; |
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gen(image, n, m, CV_8U, cn); |
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gen(templ, h, w, CV_8U, cn); |
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F(t = clock();) |
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matchTemplate(image, templ, dst_gold, CV_TM_CCOEFF); |
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F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;) |
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F(cout << "cpu:" << clock() - t << endl;) |
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F(t = clock();) |
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gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCOEFF); |
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F(cout << "gpu_block: " << clock() - t << endl;) |
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if (!check(dst_gold, Mat(dst), 5 * h * w * cn * cn * 1e-5f, "CCOEFF 8U")) return; |
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gen(image, n, m, CV_8U, cn); |
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gen(templ, h, w, CV_8U, cn); |
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F(t = clock();) |
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matchTemplate(image, templ, dst_gold, CV_TM_CCOEFF_NORMED); |
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F(cout << "depth: 8U cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;) |
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F(cout << "cpu:" << clock() - t << endl;) |
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F(t = clock();) |
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gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCOEFF_NORMED); |
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F(cout << "gpu_block: " << clock() - t << endl;) |
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if (!check(dst_gold, Mat(dst), h * w * 1e-6f, "CCOEFF_NORMED 8U")) return; |
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gen(image, n, m, CV_32F, cn); |
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gen(templ, h, w, CV_32F, cn); |
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F(t = clock();) |
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matchTemplate(image, templ, dst_gold, CV_TM_SQDIFF); |
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F(cout << "depth: 32F cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;) |
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F(cout << "cpu:" << clock() - t << endl;) |
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F(t = clock();) |
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gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_SQDIFF); |
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F(cout << "gpu_block: " << clock() - t << endl;) |
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if (!check(dst_gold, Mat(dst), 0.25f * h * w * 1e-5f, "SQDIFF 32F")) return; |
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gen(image, n, m, CV_32F, cn); |
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gen(templ, h, w, CV_32F, cn); |
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F(t = clock();) |
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matchTemplate(image, templ, dst_gold, CV_TM_CCORR); |
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F(cout << "depth: 32F cn: " << cn << " n: " << n << " m: " << m << " w: " << w << " h: " << h << endl;) |
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F(cout << "cpu:" << clock() - t << endl;) |
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F(t = clock();) |
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gpu::matchTemplate(gpu::GpuMat(image), gpu::GpuMat(templ), dst, CV_TM_CCORR); |
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F(cout << "gpu_block: " << clock() - t << endl;) |
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if (!check(dst_gold, Mat(dst), 0.25f * h * w * 1e-5f, "CCORR 32F")) return; |
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} |
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} |
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} |
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void gen(Mat& a, int rows, int cols, int depth, int cn) |
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{ |
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RNG rng; |
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a.create(rows, cols, CV_MAKETYPE(depth, cn)); |
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if (depth == CV_8U) |
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rng.fill(a, RNG::UNIFORM, Scalar::all(1), Scalar::all(10)); |
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else if (depth == CV_32F) |
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rng.fill(a, RNG::UNIFORM, Scalar::all(0.001f), Scalar::all(1.f)); |
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} |
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bool check(const Mat& a, const Mat& b, float max_err, const string& method="") |
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{ |
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if (a.size() != b.size()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad size, method=%s\n", method.c_str()); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return false; |
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} |
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//for (int i = 0; i < a.rows; ++i) |
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//{ |
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// for (int j = 0; j < a.cols; ++j) |
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// { |
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// float a_ = a.at<float>(i, j); |
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// float b_ = b.at<float>(i, j); |
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// if (fabs(a_ - b_) > max_err) |
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// { |
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// ts->printf(cvtest::TS::CONSOLE, "a=%f, b=%f, i=%d, j=%d\n", a_, b_, i, j); |
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// cin.get(); |
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// } |
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// } |
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//} |
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float err = (float)norm(a, b, NORM_INF); |
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if (err > max_err) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad accuracy: %f, method=%s\n", err, method.c_str()); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return false; |
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} |
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return true; |
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} |
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}; |
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TEST(matchTemplate, accuracy) { CV_GpuMatchTemplateTest test; test.safe_run(); } |
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struct CV_GpuMatchTemplateFindPatternInBlackTest: cvtest::BaseTest |
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{ |
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CV_GpuMatchTemplateFindPatternInBlackTest() {} |
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void run(int) |
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{ |
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bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) && |
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gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE); |
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if (!double_ok) |
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{ |
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// For sqrIntegral |
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ts->printf(cvtest::TS::CONSOLE, "\nCode and device double support is required (CC >= 1.3)"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_GENERIC); |
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return; |
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} |
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Mat image = imread(std::string(ts->get_data_path()) + "matchtemplate/black.png"); |
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if (image.empty()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "can't open file '%s'", (std::string(ts->get_data_path()) |
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+ "matchtemplate/black.png").c_str()); |
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ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA); |
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return; |
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} |
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Mat pattern = imread(std::string(ts->get_data_path()) + "matchtemplate/cat.png"); |
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if (pattern.empty()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "can't open file '%s'", (std::string(ts->get_data_path()) |
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+ "matchtemplate/cat.png").c_str()); |
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ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA); |
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return; |
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} |
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gpu::GpuMat d_image(image); |
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gpu::GpuMat d_pattern(pattern); |
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gpu::GpuMat d_result; |
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double maxValue; |
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Point maxLoc; |
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Point maxLocGold(284, 12); |
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gpu::matchTemplate(d_image, d_pattern, d_result, CV_TM_CCOEFF_NORMED); |
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gpu::minMaxLoc(d_result, NULL, &maxValue, NULL, &maxLoc ); |
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if (maxLoc != maxLocGold) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad match (CV_TM_CCOEFF_NORMED): %d %d, must be at: %d %d", |
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maxLoc.x, maxLoc.y, maxLocGold.x, maxLocGold.y); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return; |
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} |
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gpu::matchTemplate(d_image, d_pattern, d_result, CV_TM_CCORR_NORMED); |
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gpu::minMaxLoc(d_result, NULL, &maxValue, NULL, &maxLoc ); |
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if (maxLoc != maxLocGold) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad match (CV_TM_CCORR_NORMED): %d %d, must be at: %d %d", |
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maxLoc.x, maxLoc.y, maxLocGold.x, maxLocGold.y); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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return; |
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} |
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} |
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}; |
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TEST(matchTemplate, find_pattern_in_black) { CV_GpuMatchTemplateFindPatternInBlackTest test; test.safe_run(); }
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