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Open Source Computer Vision Library
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399 lines
15 KiB
399 lines
15 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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// 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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using namespace cv; |
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using namespace cv::gpu; |
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using namespace std; |
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struct CV_GpuMulSpectrumsTest: cvtest::BaseTest |
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{ |
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CV_GpuMulSpectrumsTest() {} |
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void run(int) |
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{ |
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test(0); |
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testConj(0); |
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testScaled(0); |
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testScaledConj(0); |
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test(DFT_ROWS); |
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testConj(DFT_ROWS); |
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testScaled(DFT_ROWS); |
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testScaledConj(DFT_ROWS); |
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} |
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void gen(int cols, int rows, Mat& mat) |
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{ |
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RNG rng; |
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mat.create(rows, cols, CV_32FC2); |
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rng.fill(mat, RNG::UNIFORM, Scalar::all(0.f), Scalar::all(10.f)); |
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} |
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bool cmp(const Mat& gold, const Mat& mine, float max_err=1e-3f) |
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{ |
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if (gold.size() != mine.size()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad sizes: gold: %d d%, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows); |
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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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if (gold.type() != mine.type()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad types: gold=%d, mine=%d\n", gold.type(), mine.type()); |
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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 < gold.rows; ++i) |
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{ |
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for (int j = 0; j < gold.cols * 2; ++j) |
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{ |
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float gold_ = gold.at<float>(i, j); |
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float mine_ = mine.at<float>(i, j); |
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if (fabs(gold_ - mine_) > max_err) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j, i, gold_, mine_); |
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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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} |
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} |
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return true; |
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} |
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bool cmpScaled(const Mat& gold, const Mat& mine, float scale, float max_err=1e-3f) |
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{ |
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if (gold.size() != mine.size()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad sizes: gold: %d d%, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows); |
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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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if (gold.type() != mine.type()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad types: gold=%d, mine=%d\n", gold.type(), mine.type()); |
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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 < gold.rows; ++i) |
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{ |
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for (int j = 0; j < gold.cols * 2; ++j) |
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{ |
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float gold_ = gold.at<float>(i, j) * scale; |
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float mine_ = mine.at<float>(i, j); |
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if (fabs(gold_ - mine_) > max_err) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j, i, gold_, mine_); |
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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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} |
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} |
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return true; |
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} |
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void test(int flags) |
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{ |
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int cols = 1 + rand() % 100, rows = 1 + rand() % 1000; |
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Mat a, b; |
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gen(cols, rows, a); |
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gen(cols, rows, b); |
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Mat c_gold; |
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mulSpectrums(a, b, c_gold, flags, false); |
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GpuMat d_c; |
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mulSpectrums(GpuMat(a), GpuMat(b), d_c, flags, false); |
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if (!cmp(c_gold, Mat(d_c))) |
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ts->printf(cvtest::TS::CONSOLE, "test failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags); |
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} |
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void testConj(int flags) |
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{ |
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int cols = 1 + rand() % 100, rows = 1 + rand() % 1000; |
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Mat a, b; |
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gen(cols, rows, a); |
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gen(cols, rows, b); |
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Mat c_gold; |
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mulSpectrums(a, b, c_gold, flags, true); |
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GpuMat d_c; |
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mulSpectrums(GpuMat(a), GpuMat(b), d_c, flags, true); |
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if (!cmp(c_gold, Mat(d_c))) |
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ts->printf(cvtest::TS::CONSOLE, "testConj failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags); |
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} |
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void testScaled(int flags) |
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{ |
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int cols = 1 + rand() % 100, rows = 1 + rand() % 1000; |
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Mat a, b; |
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gen(cols, rows, a); |
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gen(cols, rows, b); |
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float scale = 1.f / a.size().area(); |
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Mat c_gold; |
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mulSpectrums(a, b, c_gold, flags, false); |
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GpuMat d_c; |
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mulAndScaleSpectrums(GpuMat(a), GpuMat(b), d_c, flags, scale, false); |
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if (!cmpScaled(c_gold, Mat(d_c), scale)) |
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ts->printf(cvtest::TS::CONSOLE, "testScaled failed: cols=%d, rows=%d, flags=%d\n", cols, rows, flags); |
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} |
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void testScaledConj(int flags) |
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{ |
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int cols = 1 + rand() % 100, rows = 1 + rand() % 1000; |
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Mat a, b; |
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gen(cols, rows, a); |
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gen(cols, rows, b); |
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float scale = 1.f / a.size().area(); |
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Mat c_gold; |
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mulSpectrums(a, b, c_gold, flags, true); |
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GpuMat d_c; |
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mulAndScaleSpectrums(GpuMat(a), GpuMat(b), d_c, flags, scale, true); |
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if (!cmpScaled(c_gold, Mat(d_c), scale)) |
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ts->printf(cvtest::TS::CONSOLE, "testScaledConj failed: cols=%d, rows=%d, flags=%D\n", cols, rows, flags); |
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} |
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} CV_GpuMulSpectrumsTest_inst; |
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struct CV_GpuDftTest: cvtest::BaseTest |
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{ |
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CV_GpuDftTest() {} |
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void run(int) |
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{ |
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srand(0); |
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int cols = 2 + rand() % 100, rows = 2 + rand() % 100; |
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for (int i = 0; i < 2; ++i) |
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{ |
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bool inplace = i != 0; |
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testC2C("no flags", cols, rows, 0, inplace); |
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testC2C("no flags 0 1", cols, rows + 1, 0, inplace); |
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testC2C("no flags 1 0", cols, rows + 1, 0, inplace); |
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testC2C("no flags 1 1", cols + 1, rows, 0, inplace); |
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testC2C("DFT_INVERSE", cols, rows, DFT_INVERSE, inplace); |
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testC2C("DFT_ROWS", cols, rows, DFT_ROWS, inplace); |
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testC2C("single col", 1, rows, 0, inplace); |
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testC2C("single row", cols, 1, 0, inplace); |
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testC2C("single col inversed", 1, rows, DFT_INVERSE, inplace); |
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testC2C("single row inversed", cols, 1, DFT_INVERSE, inplace); |
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testC2C("single row DFT_ROWS", cols, 1, DFT_ROWS, inplace); |
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testC2C("size 1 2", 1, 2, 0, inplace); |
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testC2C("size 2 1", 2, 1, 0, inplace); |
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} |
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testR2CThenC2R("sanity", cols, rows); |
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testR2CThenC2R("sanity 0 1", cols, rows + 1); |
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testR2CThenC2R("sanity 1 0", cols + 1, rows); |
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testR2CThenC2R("sanity 1 1", cols + 1, rows + 1); |
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testR2CThenC2R("single col", 1, rows); |
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testR2CThenC2R("single col 1", 1, rows + 1); |
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testR2CThenC2R("single row", cols, 1); |
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testR2CThenC2R("single row 1", cols + 1, 1); |
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testR2CThenC2R("sanity", cols, rows, true); |
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testR2CThenC2R("sanity 0 1", cols, rows + 1, true); |
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testR2CThenC2R("sanity 1 0", cols + 1, rows, true); |
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testR2CThenC2R("sanity 1 1", cols + 1, rows + 1, true); |
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testR2CThenC2R("single row", cols, 1, true); |
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testR2CThenC2R("single row 1", cols + 1, 1, true); |
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} |
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void gen(int cols, int rows, int cn, Mat& mat) |
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{ |
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RNG rng(1); |
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mat.create(rows, cols, CV_MAKETYPE(CV_32F, cn)); |
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rng.fill(mat, RNG::UNIFORM, Scalar::all(0.f), Scalar::all(10.f)); |
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} |
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bool cmp(const Mat& gold, const Mat& mine, float max_err=1e-3f) |
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{ |
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if (gold.size() != mine.size()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad sizes: gold: %d %d, mine: %d %d\n", gold.cols, gold.rows, mine.cols, mine.rows); |
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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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if (gold.depth() != mine.depth()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad depth: gold=%d, mine=%d\n", gold.depth(), mine.depth()); |
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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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if (gold.channels() != mine.channels()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad channel count: gold=%d, mine=%d\n", gold.channels(), mine.channels()); |
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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 < gold.rows; ++i) |
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{ |
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for (int j = 0; j < gold.cols * gold.channels(); ++j) |
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{ |
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float gold_ = gold.at<float>(i, j); |
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float mine_ = mine.at<float>(i, j); |
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if (fabs(gold_ - mine_) > max_err) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad values at %d %d: gold=%f, mine=%f\n", j / gold.channels(), i, gold_, mine_); |
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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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} |
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} |
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return true; |
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} |
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void testC2C(const std::string& hint, int cols, int rows, int flags, bool inplace=false) |
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{ |
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Mat a; |
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gen(cols, rows, 2, a); |
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Mat b_gold; |
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dft(a, b_gold, flags); |
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GpuMat d_b; |
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GpuMat d_b_data; |
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if (inplace) |
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{ |
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d_b_data.create(1, a.size().area(), CV_32FC2); |
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d_b = GpuMat(a.rows, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); |
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} |
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dft(GpuMat(a), d_b, Size(cols, rows), flags); |
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bool ok = true; |
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if (ok && inplace && d_b.ptr() != d_b_data.ptr()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "unnecessary reallocation was done\n"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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ok = false; |
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} |
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if (ok && d_b.depth() != CV_32F) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad depth: %d\n", d_b.depth()); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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ok = false; |
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} |
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if (ok && d_b.channels() != 2) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad channel count: %d\n", d_b.channels()); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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ok = false; |
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} |
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if (ok) ok = cmp(b_gold, Mat(d_b), rows * cols * 1e-4f); |
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if (!ok) |
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ts->printf(cvtest::TS::CONSOLE, "testC2C failed: hint=%s, cols=%d, rows=%d, flags=%d, inplace=%d\n", |
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hint.c_str(), cols, rows, flags, inplace); |
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} |
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void testR2CThenC2R(const std::string& hint, int cols, int rows, bool inplace=false) |
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{ |
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Mat a; |
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gen(cols, rows, 1, a); |
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bool ok = true; |
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GpuMat d_b, d_c; |
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GpuMat d_b_data, d_c_data; |
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if (inplace) |
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{ |
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if (a.cols == 1) |
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{ |
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d_b_data.create(1, (a.rows / 2 + 1) * a.cols, CV_32FC2); |
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d_b = GpuMat(a.rows / 2 + 1, a.cols, CV_32FC2, d_b_data.ptr(), a.cols * d_b_data.elemSize()); |
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} |
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else |
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{ |
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d_b_data.create(1, a.rows * (a.cols / 2 + 1), CV_32FC2); |
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d_b = GpuMat(a.rows, a.cols / 2 + 1, CV_32FC2, d_b_data.ptr(), (a.cols / 2 + 1) * d_b_data.elemSize()); |
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} |
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d_c_data.create(1, a.size().area(), CV_32F); |
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d_c = GpuMat(a.rows, a.cols, CV_32F, d_c_data.ptr(), a.cols * d_c_data.elemSize()); |
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} |
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dft(GpuMat(a), d_b, Size(cols, rows), 0); |
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dft(d_b, d_c, Size(cols, rows), DFT_REAL_OUTPUT | DFT_SCALE); |
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if (ok && inplace && d_b.ptr() != d_b_data.ptr()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "unnecessary reallocation was done for b\n"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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ok = false; |
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} |
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if (ok && inplace && d_c.ptr() != d_c_data.ptr()) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "unnecessary reallocation was done for c\n"); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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ok = false; |
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} |
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if (ok && d_c.depth() != CV_32F) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad depth: %d\n", d_c.depth()); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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ok = false; |
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} |
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if (ok && d_c.channels() != 1) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "bad channel count: %d\n", d_c.channels()); |
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT); |
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ok = false; |
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} |
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if (ok) ok = cmp(a, Mat(d_c), rows * cols * 1e-5f); |
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if (!ok) |
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ts->printf(cvtest::TS::CONSOLE, "testR2CThenC2R failed: hint=%s, cols=%d, rows=%d, inplace=%d\n", |
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hint.c_str(), cols, rows, inplace); |
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} |
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}; |
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TEST(dft, accuracy) { CV_GpuDftTest test; test.safe_run(); }
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