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234 lines
8.4 KiB
C++
234 lines
8.4 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "test_precomp.hpp"
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#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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