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967 lines
32 KiB
C++
967 lines
32 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 <cmath>
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#include <limits>
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#include "test_precomp.hpp"
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using namespace cv;
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using namespace std;
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using namespace gpu;
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class CV_GpuImageProcTest : public cvtest::BaseTest
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{
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public:
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virtual ~CV_GpuImageProcTest() {}
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protected:
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void run(int);
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int test8UC1 (const Mat& img);
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int test8UC4 (const Mat& img);
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int test32SC1(const Mat& img);
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int test32FC1(const Mat& img);
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virtual int test(const Mat& img) = 0;
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int CheckNorm(const Mat& m1, const Mat& m2);
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// Checks whether two images are similar enough using normalized
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// cross-correlation as an error measure
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int CheckSimilarity(const Mat& m1, const Mat& m2, float max_err=1e-3f);
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};
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int CV_GpuImageProcTest::test8UC1(const Mat& img)
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{
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cv::Mat img_C1;
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cvtColor(img, img_C1, CV_BGR2GRAY);
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return test(img_C1);
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}
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int CV_GpuImageProcTest::test8UC4(const Mat& img)
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{
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cv::Mat img_C4;
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cvtColor(img, img_C4, CV_BGR2BGRA);
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return test(img_C4);
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}
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int CV_GpuImageProcTest::test32SC1(const Mat& img)
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{
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cv::Mat img_C1;
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cvtColor(img, img_C1, CV_BGR2GRAY);
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img_C1.convertTo(img_C1, CV_32S);
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return test(img_C1);
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}
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int CV_GpuImageProcTest::test32FC1(const Mat& img)
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{
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cv::Mat temp, img_C1;
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img.convertTo(temp, CV_32F, 1.f / 255.f);
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cvtColor(temp, img_C1, CV_BGR2GRAY);
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return test(img_C1);
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}
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int CV_GpuImageProcTest::CheckNorm(const Mat& m1, const Mat& m2)
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{
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double ret = norm(m1, m2, NORM_INF);
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if (ret < std::numeric_limits<double>::epsilon())
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{
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return cvtest::TS::OK;
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}
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else
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{
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ts->printf(cvtest::TS::LOG, "Norm: %f\n", ret);
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return cvtest::TS::FAIL_GENERIC;
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}
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}
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int CV_GpuImageProcTest::CheckSimilarity(const Mat& m1, const Mat& m2, float max_err)
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{
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Mat diff;
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cv::matchTemplate(m1, m2, diff, CV_TM_CCORR_NORMED);
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float err = abs(diff.at<float>(0, 0) - 1.f);
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if (err > max_err)
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return cvtest::TS::FAIL_INVALID_OUTPUT;
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return cvtest::TS::OK;
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}
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void CV_GpuImageProcTest::run( int )
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{
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//load image
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cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
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if (img.empty())
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{
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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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int testResult = cvtest::TS::OK;
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//run tests
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ts->printf(cvtest::TS::LOG, "\n========Start test 8UC1========\n");
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if (test8UC1(img) == cvtest::TS::OK)
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ts->printf(cvtest::TS::LOG, "SUCCESS\n");
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else
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{
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ts->printf(cvtest::TS::LOG, "FAIL\n");
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testResult = cvtest::TS::FAIL_GENERIC;
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}
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ts->printf(cvtest::TS::LOG, "\n========Start test 8UC4========\n");
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if (test8UC4(img) == cvtest::TS::OK)
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ts->printf(cvtest::TS::LOG, "SUCCESS\n");
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else
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{
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ts->printf(cvtest::TS::LOG, "FAIL\n");
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testResult = cvtest::TS::FAIL_GENERIC;
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}
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ts->printf(cvtest::TS::LOG, "\n========Start test 32SC1========\n");
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if (test32SC1(img) == cvtest::TS::OK)
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ts->printf(cvtest::TS::LOG, "SUCCESS\n");
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else
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{
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ts->printf(cvtest::TS::LOG, "FAIL\n");
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testResult = cvtest::TS::FAIL_GENERIC;
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}
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ts->printf(cvtest::TS::LOG, "\n========Start test 32FC1========\n");
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if (test32FC1(img) == cvtest::TS::OK)
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ts->printf(cvtest::TS::LOG, "SUCCESS\n");
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else
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{
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ts->printf(cvtest::TS::LOG, "FAIL\n");
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testResult = cvtest::TS::FAIL_GENERIC;
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}
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ts->set_failed_test_info(testResult);
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}
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////////////////////////////////////////////////////////////////////////////////
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// threshold
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struct CV_GpuImageThresholdTest : public CV_GpuImageProcTest
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{
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public:
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CV_GpuImageThresholdTest() {}
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int test(const Mat& img)
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{
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if (img.type() != CV_8UC1 && img.type() != CV_32FC1)
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{
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ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
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return cvtest::TS::OK;
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}
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const double maxVal = img.type() == CV_8UC1 ? 255 : 1.0;
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cv::RNG& rng = ts->get_rng();
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int res = cvtest::TS::OK;
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for (int type = THRESH_BINARY; type <= THRESH_TOZERO_INV; ++type)
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{
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const double thresh = rng.uniform(0.0, maxVal);
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cv::Mat cpuRes;
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cv::threshold(img, cpuRes, thresh, maxVal, type);
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GpuMat gpu1(img);
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GpuMat gpuRes;
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cv::gpu::threshold(gpu1, gpuRes, thresh, maxVal, type);
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if (CheckNorm(cpuRes, gpuRes) != cvtest::TS::OK)
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res = cvtest::TS::FAIL_GENERIC;
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}
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return res;
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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// resize
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struct CV_GpuNppImageResizeTest : public CV_GpuImageProcTest
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{
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CV_GpuNppImageResizeTest() {}
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int test(const Mat& img)
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{
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if (img.type() != CV_8UC1 && img.type() != CV_8UC4)
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{
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ts->printf(cvtest::TS::LOG, "Unsupported type\n");
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return cvtest::TS::OK;
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}
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int interpolations[] = {INTER_NEAREST, INTER_LINEAR, /*INTER_CUBIC,*/ /*INTER_LANCZOS4*/};
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const char* interpolations_str[] = {"INTER_NEAREST", "INTER_LINEAR", /*"INTER_CUBIC",*/ /*"INTER_LANCZOS4"*/};
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int interpolations_num = sizeof(interpolations) / sizeof(int);
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int test_res = cvtest::TS::OK;
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for (int i = 0; i < interpolations_num; ++i)
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{
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ts->printf(cvtest::TS::LOG, "Interpolation: %s\n", interpolations_str[i]);
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Mat cpu_res1, cpu_res2;
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cv::resize(img, cpu_res1, Size(), 2.0, 2.0, interpolations[i]);
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cv::resize(cpu_res1, cpu_res2, Size(), 0.5, 0.5, interpolations[i]);
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GpuMat gpu1(img), gpu_res1, gpu_res2;
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cv::gpu::resize(gpu1, gpu_res1, Size(), 2.0, 2.0, interpolations[i]);
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cv::gpu::resize(gpu_res1, gpu_res2, Size(), 0.5, 0.5, interpolations[i]);
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if (CheckSimilarity(cpu_res2, gpu_res2) != cvtest::TS::OK)
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test_res = cvtest::TS::FAIL_GENERIC;
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}
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return test_res;
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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// copyMakeBorder
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struct CV_GpuNppImageCopyMakeBorderTest : public CV_GpuImageProcTest
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{
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CV_GpuNppImageCopyMakeBorderTest() {}
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int test(const Mat& img)
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{
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if (img.type() != CV_8UC1 && img.type() != CV_8UC4 && img.type() != CV_32SC1)
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{
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ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
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return cvtest::TS::OK;
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}
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cv::RNG& rng = ts->get_rng();
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int top = rng.uniform(1, 10);
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int botton = rng.uniform(1, 10);
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int left = rng.uniform(1, 10);
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int right = rng.uniform(1, 10);
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cv::Scalar val(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
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Mat cpudst;
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cv::copyMakeBorder(img, cpudst, top, botton, left, right, BORDER_CONSTANT, val);
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GpuMat gpu1(img);
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GpuMat gpudst;
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cv::gpu::copyMakeBorder(gpu1, gpudst, top, botton, left, right, val);
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return CheckNorm(cpudst, gpudst);
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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// warpAffine
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struct CV_GpuNppImageWarpAffineTest : public CV_GpuImageProcTest
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{
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CV_GpuNppImageWarpAffineTest() {}
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int test(const Mat& img)
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{
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if (img.type() == CV_32SC1)
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{
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ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
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return cvtest::TS::OK;
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}
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static double reflect[2][3] = { {-1, 0, 0},
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{ 0, -1, 0} };
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reflect[0][2] = img.cols;
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reflect[1][2] = img.rows;
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Mat M(2, 3, CV_64F, (void*)reflect);
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int flags[] = {INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_NEAREST | WARP_INVERSE_MAP, INTER_LINEAR | WARP_INVERSE_MAP, INTER_CUBIC | WARP_INVERSE_MAP};
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const char* flags_str[] = {"INTER_NEAREST", "INTER_LINEAR", "INTER_CUBIC", "INTER_NEAREST | WARP_INVERSE_MAP", "INTER_LINEAR | WARP_INVERSE_MAP", "INTER_CUBIC | WARP_INVERSE_MAP"};
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int flags_num = sizeof(flags) / sizeof(int);
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int test_res = cvtest::TS::OK;
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for (int i = 0; i < flags_num; ++i)
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{
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ts->printf(cvtest::TS::LOG, "\nFlags: %s\n", flags_str[i]);
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Mat cpudst;
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cv::warpAffine(img, cpudst, M, img.size(), flags[i]);
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GpuMat gpu1(img);
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GpuMat gpudst;
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cv::gpu::warpAffine(gpu1, gpudst, M, gpu1.size(), flags[i]);
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// Check inner parts (ignoring 1 pixel width border)
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if (CheckSimilarity(cpudst.rowRange(1, cpudst.rows - 1).colRange(1, cpudst.cols - 1),
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gpudst.rowRange(1, gpudst.rows - 1).colRange(1, gpudst.cols - 1)) != cvtest::TS::OK)
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test_res = cvtest::TS::FAIL_GENERIC;
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}
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return test_res;
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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// warpPerspective
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struct CV_GpuNppImageWarpPerspectiveTest : public CV_GpuImageProcTest
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{
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CV_GpuNppImageWarpPerspectiveTest() {}
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int test(const Mat& img)
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{
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if (img.type() == CV_32SC1)
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{
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ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
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return cvtest::TS::OK;
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}
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static double reflect[3][3] = { { -1, 0, 0},
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{ 0, -1, 0},
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{ 0, 0, 1 }};
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reflect[0][2] = img.cols;
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reflect[1][2] = img.rows;
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Mat M(3, 3, CV_64F, (void*)reflect);
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int flags[] = {INTER_NEAREST, INTER_LINEAR, INTER_CUBIC, INTER_NEAREST | WARP_INVERSE_MAP, INTER_LINEAR | WARP_INVERSE_MAP, INTER_CUBIC | WARP_INVERSE_MAP};
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const char* flags_str[] = {"INTER_NEAREST", "INTER_LINEAR", "INTER_CUBIC", "INTER_NEAREST | WARP_INVERSE_MAP", "INTER_LINEAR | WARP_INVERSE_MAP", "INTER_CUBIC | WARP_INVERSE_MAP"};
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int flags_num = sizeof(flags) / sizeof(int);
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int test_res = cvtest::TS::OK;
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for (int i = 0; i < flags_num; ++i)
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{
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ts->printf(cvtest::TS::LOG, "\nFlags: %s\n", flags_str[i]);
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Mat cpudst;
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cv::warpPerspective(img, cpudst, M, img.size(), flags[i]);
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GpuMat gpu1(img);
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GpuMat gpudst;
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cv::gpu::warpPerspective(gpu1, gpudst, M, gpu1.size(), flags[i]);
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// Check inner parts (ignoring 1 pixel width border)
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if (CheckSimilarity(cpudst.rowRange(1, cpudst.rows - 1).colRange(1, cpudst.cols - 1),
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gpudst.rowRange(1, gpudst.rows - 1).colRange(1, gpudst.cols - 1)) != cvtest::TS::OK)
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test_res = cvtest::TS::FAIL_GENERIC;
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}
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return test_res;
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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// integral
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struct CV_GpuNppImageIntegralTest : public CV_GpuImageProcTest
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{
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CV_GpuNppImageIntegralTest() {}
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int test(const Mat& img)
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{
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if (img.type() != CV_8UC1)
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{
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ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
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return cvtest::TS::OK;
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}
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Mat cpusum;
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cv::integral(img, cpusum, CV_32S);
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GpuMat gpu1(img);
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GpuMat gpusum;
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cv::gpu::integral(gpu1, gpusum);
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return CheckNorm(cpusum, gpusum) == cvtest::TS::OK ? cvtest::TS::OK : cvtest::TS::FAIL_GENERIC;
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}
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};
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////////////////////////////////////////////////////////////////////////////////
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// Canny
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//struct CV_GpuNppImageCannyTest : public CV_GpuImageProcTest
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//{
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// CV_GpuNppImageCannyTest() : CV_GpuImageProcTest( "GPU-NppImageCanny", "Canny" ) {}
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//
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// int test(const Mat& img)
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// {
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// if (img.type() != CV_8UC1)
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// {
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// ts->printf(cvtest::TS::LOG, "\nUnsupported type\n");
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// return cvtest::TS::OK;
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// }
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//
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// const double threshold1 = 1.0, threshold2 = 10.0;
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//
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// Mat cpudst;
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// cv::Canny(img, cpudst, threshold1, threshold2);
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//
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// GpuMat gpu1(img);
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// GpuMat gpudst;
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// cv::gpu::Canny(gpu1, gpudst, threshold1, threshold2);
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//
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// return CheckNorm(cpudst, gpudst);
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// }
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//};
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////////////////////////////////////////////////////////////////////////////////
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// cvtColor
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class CV_GpuCvtColorTest : public cvtest::BaseTest
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{
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public:
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CV_GpuCvtColorTest() {}
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~CV_GpuCvtColorTest() {};
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protected:
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void run(int);
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int CheckNorm(const Mat& m1, const Mat& m2);
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};
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int CV_GpuCvtColorTest::CheckNorm(const Mat& m1, const Mat& m2)
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{
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float max_err = 1e-2f;
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Mat diff;
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cv::matchTemplate(m1, m2, diff, CV_TM_CCORR_NORMED);
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float err = abs(diff.at<float>(0, 0) - 1.f);
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if (err > max_err)
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return cvtest::TS::FAIL_INVALID_OUTPUT;
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return cvtest::TS::OK;
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}
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void CV_GpuCvtColorTest::run( int )
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{
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cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
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if (img.empty())
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{
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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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int testResult = cvtest::TS::OK;
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cv::Mat cpuRes;
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cv::gpu::GpuMat gpuImg(img), gpuRes;
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int codes[] = { CV_BGR2RGB, CV_RGB2BGRA, CV_BGRA2RGB,
|
|
CV_RGB2BGR555, CV_BGR5552BGR, CV_BGR2BGR565, CV_BGR5652RGB,
|
|
CV_RGB2YCrCb, CV_YCrCb2BGR, CV_BGR2YUV, CV_YUV2RGB,
|
|
CV_RGB2XYZ, CV_XYZ2BGR, CV_BGR2XYZ, CV_XYZ2RGB,
|
|
CV_RGB2HSV, CV_HSV2BGR, CV_BGR2HSV_FULL, CV_HSV2RGB_FULL,
|
|
CV_RGB2HLS, CV_HLS2BGR, CV_BGR2HLS_FULL, CV_HLS2RGB_FULL,
|
|
CV_RGB2GRAY, CV_GRAY2BGRA, CV_BGRA2GRAY,
|
|
CV_GRAY2BGR555, CV_BGR5552GRAY, CV_GRAY2BGR565, CV_BGR5652GRAY};
|
|
const char* codes_str[] = { "CV_BGR2RGB", "CV_RGB2BGRA", "CV_BGRA2RGB",
|
|
"CV_RGB2BGR555", "CV_BGR5552BGR", "CV_BGR2BGR565", "CV_BGR5652RGB",
|
|
"CV_RGB2YCrCb", "CV_YCrCb2BGR", "CV_BGR2YUV", "CV_YUV2RGB",
|
|
"CV_RGB2XYZ", "CV_XYZ2BGR", "CV_BGR2XYZ", "CV_XYZ2RGB",
|
|
"CV_RGB2HSV", "CV_HSV2RGB", "CV_BGR2HSV_FULL", "CV_HSV2RGB_FULL",
|
|
"CV_RGB2HLS", "CV_HLS2RGB", "CV_BGR2HLS_FULL", "CV_HLS2RGB_FULL",
|
|
"CV_RGB2GRAY", "CV_GRAY2BGRA", "CV_BGRA2GRAY",
|
|
"CV_GRAY2BGR555", "CV_BGR5552GRAY", "CV_GRAY2BGR565", "CV_BGR5652GRAY"};
|
|
int codes_num = sizeof(codes) / sizeof(int);
|
|
|
|
for (int i = 0; i < codes_num; ++i)
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "\n%s\n", codes_str[i]);
|
|
|
|
cv::cvtColor(img, cpuRes, codes[i]);
|
|
cv::gpu::cvtColor(gpuImg, gpuRes, codes[i]);
|
|
|
|
if (CheckNorm(cpuRes, gpuRes) == cvtest::TS::OK)
|
|
ts->printf(cvtest::TS::LOG, "\nSUCCESS\n");
|
|
else
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "\nFAIL\n");
|
|
testResult = cvtest::TS::FAIL_GENERIC;
|
|
}
|
|
|
|
img = cpuRes;
|
|
gpuImg = gpuRes;
|
|
}
|
|
|
|
ts->set_failed_test_info(testResult);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Histograms
|
|
class CV_GpuHistogramsTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_GpuHistogramsTest() {}
|
|
~CV_GpuHistogramsTest() {};
|
|
|
|
protected:
|
|
void run(int);
|
|
|
|
int CheckNorm(const Mat& m1, const Mat& m2)
|
|
{
|
|
double ret = norm(m1, m2, NORM_INF);
|
|
|
|
if (ret < std::numeric_limits<double>::epsilon())
|
|
{
|
|
return cvtest::TS::OK;
|
|
}
|
|
else
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "\nNorm: %f\n", ret);
|
|
return cvtest::TS::FAIL_GENERIC;
|
|
}
|
|
}
|
|
};
|
|
|
|
void CV_GpuHistogramsTest::run( int )
|
|
{
|
|
//load image
|
|
cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "stereobp/aloe-L.png");
|
|
|
|
if (img.empty())
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISSING_TEST_DATA);
|
|
return;
|
|
}
|
|
|
|
Mat hsv;
|
|
cv::cvtColor(img, hsv, CV_BGR2HSV);
|
|
|
|
int hbins = 30;
|
|
int histSize[] = {hbins};
|
|
|
|
float hranges[] = {0, 180};
|
|
const float* ranges[] = {hranges};
|
|
|
|
MatND hist;
|
|
|
|
int channels[] = {0};
|
|
calcHist(&hsv, 1, channels, Mat(), hist, 1, histSize, ranges);
|
|
|
|
GpuMat gpuHsv(hsv);
|
|
std::vector<GpuMat> srcs;
|
|
cv::gpu::split(gpuHsv, srcs);
|
|
GpuMat gpuHist;
|
|
histEven(srcs[0], gpuHist, hbins, (int)hranges[0], (int)hranges[1]);
|
|
|
|
Mat cpuHist = hist;
|
|
cpuHist = cpuHist.t();
|
|
cpuHist.convertTo(cpuHist, CV_32S);
|
|
|
|
ts->set_failed_test_info(CheckNorm(cpuHist, gpuHist));
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// Corner Harris feature detector
|
|
|
|
struct CV_GpuCornerHarrisTest: cvtest::BaseTest
|
|
{
|
|
CV_GpuCornerHarrisTest() {}
|
|
|
|
void run(int)
|
|
{
|
|
for (int i = 0; i < 5; ++i)
|
|
{
|
|
int rows = 25 + rand() % 300, cols = 25 + rand() % 300;
|
|
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
|
|
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, -1)) return;
|
|
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
|
|
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, -1)) return;
|
|
}
|
|
}
|
|
|
|
bool compareToCpuTest(int rows, int cols, int depth, int blockSize, int apertureSize)
|
|
{
|
|
RNG rng;
|
|
cv::Mat src(rows, cols, depth);
|
|
if (depth == CV_32F)
|
|
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(1));
|
|
else if (depth == CV_8U)
|
|
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(256));
|
|
|
|
double k = 0.1;
|
|
|
|
cv::Mat dst_gold;
|
|
cv::gpu::GpuMat dst;
|
|
cv::Mat dsth;
|
|
int borderType;
|
|
|
|
borderType = BORDER_REFLECT101;
|
|
cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType);
|
|
cv::gpu::cornerHarris(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, k, borderType);
|
|
|
|
dsth = dst;
|
|
for (int i = 0; i < dst.rows; ++i)
|
|
{
|
|
for (int j = 0; j < dst.cols; ++j)
|
|
{
|
|
float a = dst_gold.at<float>(i, j);
|
|
float b = dsth.at<float>(i, j);
|
|
if (fabs(a - b) > 1e-3f)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "%d %d %f %f %d\n", i, j, a, b, apertureSize);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
};
|
|
}
|
|
}
|
|
|
|
borderType = BORDER_REPLICATE;
|
|
cv::cornerHarris(src, dst_gold, blockSize, apertureSize, k, borderType);
|
|
cv::gpu::cornerHarris(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, k, borderType);
|
|
|
|
dsth = dst;
|
|
for (int i = 0; i < dst.rows; ++i)
|
|
{
|
|
for (int j = 0; j < dst.cols; ++j)
|
|
{
|
|
float a = dst_gold.at<float>(i, j);
|
|
float b = dsth.at<float>(i, j);
|
|
if (fabs(a - b) > 1e-3f)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "%d %d %f %f %d\n", i, j, a, b, apertureSize);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
};
|
|
}
|
|
}
|
|
return true;
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////
|
|
// Corner Min Eigen Val
|
|
|
|
struct CV_GpuCornerMinEigenValTest: cvtest::BaseTest
|
|
{
|
|
CV_GpuCornerMinEigenValTest() {}
|
|
|
|
void run(int)
|
|
{
|
|
for (int i = 0; i < 3; ++i)
|
|
{
|
|
int rows = 25 + rand() % 300, cols = 25 + rand() % 300;
|
|
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, -1)) return;
|
|
if (!compareToCpuTest(rows, cols, CV_32F, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
|
|
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, -1)) return;
|
|
if (!compareToCpuTest(rows, cols, CV_8U, 1 + rand() % 5, 1 + 2 * (rand() % 4))) return;
|
|
}
|
|
}
|
|
|
|
bool compareToCpuTest(int rows, int cols, int depth, int blockSize, int apertureSize)
|
|
{
|
|
RNG rng;
|
|
cv::Mat src(rows, cols, depth);
|
|
if (depth == CV_32F)
|
|
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(1));
|
|
else if (depth == CV_8U)
|
|
rng.fill(src, RNG::UNIFORM, cv::Scalar(0), cv::Scalar(256));
|
|
|
|
cv::Mat dst_gold;
|
|
cv::gpu::GpuMat dst;
|
|
cv::Mat dsth;
|
|
|
|
int borderType;
|
|
|
|
borderType = BORDER_REFLECT101;
|
|
cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType);
|
|
cv::gpu::cornerMinEigenVal(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, borderType);
|
|
|
|
dsth = dst;
|
|
for (int i = 0; i < dst.rows; ++i)
|
|
{
|
|
for (int j = 0; j < dst.cols; ++j)
|
|
{
|
|
float a = dst_gold.at<float>(i, j);
|
|
float b = dsth.at<float>(i, j);
|
|
if (fabs(a - b) > 1e-2f)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "%d %d %f %f %d %d\n", i, j, a, b, apertureSize, blockSize);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
};
|
|
}
|
|
}
|
|
|
|
borderType = BORDER_REPLICATE;
|
|
cv::cornerMinEigenVal(src, dst_gold, blockSize, apertureSize, borderType);
|
|
cv::gpu::cornerMinEigenVal(cv::gpu::GpuMat(src), dst, blockSize, apertureSize, borderType);
|
|
|
|
dsth = dst;
|
|
for (int i = 0; i < dst.rows; ++i)
|
|
{
|
|
for (int j = 0; j < dst.cols; ++j)
|
|
{
|
|
float a = dst_gold.at<float>(i, j);
|
|
float b = dsth.at<float>(i, j);
|
|
if (fabs(a - b) > 1e-2f)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "%d %d %f %f %d %d\n", i, j, a, b, apertureSize, blockSize);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return false;
|
|
};
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
};
|
|
|
|
struct CV_GpuColumnSumTest: cvtest::BaseTest
|
|
{
|
|
CV_GpuColumnSumTest() {}
|
|
|
|
void run(int)
|
|
{
|
|
int cols = 375;
|
|
int rows = 1072;
|
|
|
|
Mat src(rows, cols, CV_32F);
|
|
RNG rng(1);
|
|
rng.fill(src, RNG::UNIFORM, Scalar(0), Scalar(1));
|
|
|
|
GpuMat d_dst;
|
|
columnSum(GpuMat(src), d_dst);
|
|
|
|
Mat dst = d_dst;
|
|
for (int j = 0; j < src.cols; ++j)
|
|
{
|
|
float a = src.at<float>(0, j);
|
|
float b = dst.at<float>(0, j);
|
|
if (fabs(a - b) > 0.5f)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "big diff at %d %d: %f %f\n", 0, j, a, b);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return;
|
|
}
|
|
}
|
|
for (int i = 1; i < src.rows; ++i)
|
|
{
|
|
for (int j = 0; j < src.cols; ++j)
|
|
{
|
|
float a = src.at<float>(i, j) += src.at<float>(i - 1, j);
|
|
float b = dst.at<float>(i, j);
|
|
if (fabs(a - b) > 0.5f)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "big diff at %d %d: %f %f\n", i, j, a, b);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
struct CV_GpuNormTest : cvtest::BaseTest
|
|
{
|
|
CV_GpuNormTest() {}
|
|
|
|
void run(int)
|
|
{
|
|
RNG rng(0);
|
|
|
|
int rows = rng.uniform(1, 500);
|
|
int cols = rng.uniform(1, 500);
|
|
|
|
for (int cn = 1; cn <= 4; ++cn)
|
|
{
|
|
test(NORM_L1, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
|
|
test(NORM_L1, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_L1, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
|
|
test(NORM_L1, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_L1, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_L1, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
|
|
|
|
test(NORM_L2, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
|
|
test(NORM_L2, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_L2, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
|
|
test(NORM_L2, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_L2, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_L2, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
|
|
|
|
test(NORM_INF, rows, cols, CV_8U, cn, Scalar::all(0), Scalar::all(10));
|
|
test(NORM_INF, rows, cols, CV_8S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_INF, rows, cols, CV_16U, cn, Scalar::all(0), Scalar::all(10));
|
|
test(NORM_INF, rows, cols, CV_16S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_INF, rows, cols, CV_32S, cn, Scalar::all(-10), Scalar::all(10));
|
|
test(NORM_INF, rows, cols, CV_32F, cn, Scalar::all(0), Scalar::all(1));
|
|
}
|
|
}
|
|
|
|
void gen(Mat& mat, int rows, int cols, int type, Scalar low, Scalar high)
|
|
{
|
|
mat.create(rows, cols, type);
|
|
RNG rng(0);
|
|
rng.fill(mat, RNG::UNIFORM, low, high);
|
|
}
|
|
|
|
void test(int norm_type, int rows, int cols, int depth, int cn, Scalar low, Scalar high)
|
|
{
|
|
int type = CV_MAKE_TYPE(depth, cn);
|
|
|
|
Mat src;
|
|
gen(src, rows, cols, type, low, high);
|
|
|
|
double gold = norm(src, norm_type);
|
|
double mine = norm(GpuMat(src), norm_type);
|
|
|
|
if (abs(gold - mine) > 1e-3)
|
|
{
|
|
ts->printf(cvtest::TS::CONSOLE, "failed test: gold=%f, mine=%f, norm_type=%d, rows=%d, "
|
|
"cols=%d, depth=%d, cn=%d\n", gold, mine, norm_type, rows, cols, depth, cn);
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
}
|
|
}
|
|
};
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// reprojectImageTo3D
|
|
class CV_GpuReprojectImageTo3DTest : public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_GpuReprojectImageTo3DTest() {}
|
|
|
|
protected:
|
|
void run(int)
|
|
{
|
|
Mat disp(320, 240, CV_8UC1);
|
|
|
|
RNG& rng = ts->get_rng();
|
|
rng.fill(disp, RNG::UNIFORM, Scalar(5), Scalar(30));
|
|
|
|
Mat Q(4, 4, CV_32FC1);
|
|
rng.fill(Q, RNG::UNIFORM, Scalar(0.1), Scalar(1));
|
|
|
|
Mat cpures;
|
|
GpuMat gpures;
|
|
|
|
reprojectImageTo3D(disp, cpures, Q, false);
|
|
reprojectImageTo3D(GpuMat(disp), gpures, Q);
|
|
|
|
Mat temp = gpures;
|
|
|
|
for (int y = 0; y < cpures.rows; ++y)
|
|
{
|
|
const Vec3f* cpu_row = cpures.ptr<Vec3f>(y);
|
|
const Vec4f* gpu_row = temp.ptr<Vec4f>(y);
|
|
for (int x = 0; x < cpures.cols; ++x)
|
|
{
|
|
Vec3f a = cpu_row[x];
|
|
Vec4f b = gpu_row[x];
|
|
|
|
if (fabs(a[0] - b[0]) > 1e-5 || fabs(a[1] - b[1]) > 1e-5 || fabs(a[2] - b[2]) > 1e-5)
|
|
{
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
};
|
|
|
|
TEST(threshold, accuracy) { CV_GpuImageThresholdTest test; test.safe_run(); }
|
|
TEST(resize, accuracy) { CV_GpuNppImageResizeTest test; test.safe_run(); }
|
|
TEST(copyMakeBorder, accuracy) { CV_GpuNppImageCopyMakeBorderTest test; test.safe_run(); }
|
|
TEST(warpAffine, accuracy) { CV_GpuNppImageWarpAffineTest test; test.safe_run(); }
|
|
TEST(warpPerspective, accuracy) { CV_GpuNppImageWarpPerspectiveTest test; test.safe_run(); }
|
|
TEST(integral, accuracy) { CV_GpuNppImageIntegralTest test; test.safe_run(); }
|
|
TEST(cvtColor, accuracy) { CV_GpuCvtColorTest test; test.safe_run(); }
|
|
TEST(histograms, accuracy) { CV_GpuHistogramsTest test; test.safe_run(); }
|
|
TEST(cornerHearris, accuracy) { CV_GpuCornerHarrisTest test; test.safe_run(); }
|
|
TEST(minEigen, accuracy) { CV_GpuCornerMinEigenValTest test; test.safe_run(); }
|
|
TEST(columnSum, accuracy) { CV_GpuColumnSumTest test; test.safe_run(); }
|
|
TEST(norm, accuracy) { CV_GpuNormTest test; test.safe_run(); }
|
|
TEST(reprojectImageTo3D, accuracy) { CV_GpuReprojectImageTo3DTest test; test.safe_run(); }
|
|
|
|
TEST(downsample, accuracy_on_8U)
|
|
{
|
|
RNG& rng = cvtest::TS::ptr()->get_rng();
|
|
Size size(200 + cvtest::randInt(rng) % 1000, 200 + cvtest::randInt(rng) % 1000);
|
|
Mat src = cvtest::randomMat(rng, size, CV_8U, 0, 255, false);
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for (int k = 2; k <= 5; ++k)
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{
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GpuMat d_dst;
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downsample(GpuMat(src), d_dst, k);
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|
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Size dst_gold_size((src.cols + k - 1) / k, (src.rows + k - 1) / k);
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ASSERT_EQ(dst_gold_size.width, d_dst.cols)
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<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
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ASSERT_EQ(dst_gold_size.height, d_dst.rows)
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|
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
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|
|
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Mat dst = d_dst;
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for (int y = 0; y < dst.rows; ++y)
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for (int x = 0; x < dst.cols; ++x)
|
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ASSERT_EQ(src.at<uchar>(y * k, x * k), dst.at<uchar>(y, x))
|
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<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
|
|
}
|
|
}
|
|
|
|
TEST(downsample, accuracy_on_32F)
|
|
{
|
|
RNG& rng = cvtest::TS::ptr()->get_rng();
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Size size(200 + cvtest::randInt(rng) % 1000, 200 + cvtest::randInt(rng) % 1000);
|
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Mat src = cvtest::randomMat(rng, size, CV_32F, 0, 1, false);
|
|
|
|
for (int k = 2; k <= 5; ++k)
|
|
{
|
|
GpuMat d_dst;
|
|
downsample(GpuMat(src), d_dst, k);
|
|
|
|
Size dst_gold_size((src.cols + k - 1) / k, (src.rows + k - 1) / k);
|
|
ASSERT_EQ(dst_gold_size.width, d_dst.cols)
|
|
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
|
|
ASSERT_EQ(dst_gold_size.height, d_dst.rows)
|
|
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
|
|
|
|
Mat dst = d_dst;
|
|
for (int y = 0; y < dst.rows; ++y)
|
|
for (int x = 0; x < dst.cols; ++x)
|
|
ASSERT_FLOAT_EQ(src.at<float>(y * k, x * k), dst.at<float>(y, x))
|
|
<< "rows=" << size.height << ", cols=" << size.width << ", k=" << k;
|
|
}
|
|
}
|