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Open Source Computer Vision Library
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233 lines
8.4 KiB
233 lines
8.4 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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#include <iostream> |
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#include <string> |
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using namespace cv; |
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using namespace cv::gpu; |
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struct CV_GpuMeanShiftTest : public cvtest::BaseTest |
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{ |
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CV_GpuMeanShiftTest() {} |
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void run(int) |
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{ |
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bool cc12_ok = TargetArchs::builtWith(FEATURE_SET_COMPUTE_12) && DeviceInfo().supports(FEATURE_SET_COMPUTE_12); |
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if (!cc12_ok) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "\nCompute capability 1.2 is required"); |
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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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int spatialRad = 30; |
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int colorRad = 30; |
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cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "meanshift/cones.png"); |
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cv::Mat img_template; |
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if (cv::gpu::TargetArchs::builtWith(cv::gpu::FEATURE_SET_COMPUTE_20) && |
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cv::gpu::DeviceInfo().supports(cv::gpu::FEATURE_SET_COMPUTE_20)) |
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img_template = cv::imread(std::string(ts->get_data_path()) + "meanshift/con_result.png"); |
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else |
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img_template = cv::imread(std::string(ts->get_data_path()) + "meanshift/con_result_CC1X.png"); |
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if (img.empty() || img_template.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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cv::Mat rgba; |
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cvtColor(img, rgba, CV_BGR2BGRA); |
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cv::gpu::GpuMat res; |
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cv::gpu::meanShiftFiltering( cv::gpu::GpuMat(rgba), res, spatialRad, colorRad ); |
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if (res.type() != CV_8UC4) |
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{ |
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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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cv::Mat result; |
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res.download(result); |
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uchar maxDiff = 0; |
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for (int j = 0; j < result.rows; ++j) |
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{ |
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const uchar* res_line = result.ptr<uchar>(j); |
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const uchar* ref_line = img_template.ptr<uchar>(j); |
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for (int i = 0; i < result.cols; ++i) |
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{ |
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for (int k = 0; k < 3; ++k) |
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{ |
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const uchar& ch1 = res_line[result.channels()*i + k]; |
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const uchar& ch2 = ref_line[img_template.channels()*i + k]; |
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uchar diff = static_cast<uchar>(abs(ch1 - ch2)); |
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if (maxDiff < diff) |
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maxDiff = diff; |
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} |
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} |
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} |
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if (maxDiff > 0) |
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{ |
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ts->printf(cvtest::TS::LOG, "\nMeanShift maxDiff = %d\n", maxDiff); |
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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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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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}; |
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TEST(meanShift, accuracy) { CV_GpuMeanShiftTest test; test.safe_run(); } |
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struct CV_GpuMeanShiftProcTest : public cvtest::BaseTest |
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{ |
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CV_GpuMeanShiftProcTest() {} |
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void run(int) |
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{ |
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bool cc12_ok = TargetArchs::builtWith(FEATURE_SET_COMPUTE_12) && DeviceInfo().supports(FEATURE_SET_COMPUTE_12); |
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if (!cc12_ok) |
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{ |
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ts->printf(cvtest::TS::CONSOLE, "\nCompute capability 1.2 is required"); |
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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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int spatialRad = 30; |
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int colorRad = 30; |
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cv::Mat img = cv::imread(std::string(ts->get_data_path()) + "meanshift/cones.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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cv::Mat rgba; |
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cvtColor(img, rgba, CV_BGR2BGRA); |
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cv::gpu::GpuMat h_rmap_filtered; |
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cv::gpu::meanShiftFiltering( cv::gpu::GpuMat(rgba), h_rmap_filtered, spatialRad, colorRad ); |
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cv::gpu::GpuMat d_rmap; |
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cv::gpu::GpuMat d_spmap; |
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cv::gpu::meanShiftProc( cv::gpu::GpuMat(rgba), d_rmap, d_spmap, spatialRad, colorRad ); |
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if (d_rmap.type() != CV_8UC4) |
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{ |
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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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cv::Mat rmap_filtered; |
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h_rmap_filtered.download(rmap_filtered); |
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cv::Mat rmap; |
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d_rmap.download(rmap); |
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uchar maxDiff = 0; |
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for (int j = 0; j < rmap_filtered.rows; ++j) |
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{ |
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const uchar* res_line = rmap_filtered.ptr<uchar>(j); |
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const uchar* ref_line = rmap.ptr<uchar>(j); |
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for (int i = 0; i < rmap_filtered.cols; ++i) |
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{ |
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for (int k = 0; k < 3; ++k) |
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{ |
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const uchar& ch1 = res_line[rmap_filtered.channels()*i + k]; |
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const uchar& ch2 = ref_line[rmap.channels()*i + k]; |
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uchar diff = static_cast<uchar>(abs(ch1 - ch2)); |
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if (maxDiff < diff) |
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maxDiff = diff; |
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} |
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} |
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} |
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if (maxDiff > 0) |
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{ |
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ts->printf(cvtest::TS::LOG, "\nMeanShiftProc maxDiff = %d\n", maxDiff); |
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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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cv::Mat spmap; |
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d_spmap.download(spmap); |
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cv::Mat spmap_template; |
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cv::FileStorage fs; |
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if (cv::gpu::TargetArchs::builtWith(cv::gpu::FEATURE_SET_COMPUTE_20) && |
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cv::gpu::DeviceInfo().supports(cv::gpu::FEATURE_SET_COMPUTE_20)) |
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fs.open(std::string(ts->get_data_path()) + "meanshift/spmap.yaml", cv::FileStorage::READ); |
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else |
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fs.open(std::string(ts->get_data_path()) + "meanshift/spmap_CC1X.yaml", cv::FileStorage::READ); |
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fs["spmap"] >> spmap_template; |
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for (int y = 0; y < spmap.rows; ++y) { |
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for (int x = 0; x < spmap.cols; ++x) { |
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cv::Point_<short> expected = spmap_template.at<cv::Point_<short> >(y, x); |
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cv::Point_<short> actual = spmap.at<cv::Point_<short> >(y, x); |
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int diff = (expected - actual).dot(expected - actual); |
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if (actual != expected) { |
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ts->printf(cvtest::TS::LOG, "\nMeanShiftProc SpMap is bad, diff=%d\n", diff); |
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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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} |
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} |
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ts->set_failed_test_info(cvtest::TS::OK); |
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} |
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}; |
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TEST(meanShiftProc, accuracy) { CV_GpuMeanShiftProcTest test; test.safe_run(); }
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