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d752bac43f
Change max distance at distanceTransform #24234 ### Pull Request Readiness Checklist resolves https://github.com/opencv/opencv/issues/23895 related: https://github.com/opencv/opencv/pull/12278 * DIST_MASK_3 and DIST_MASK_5 maximal distance increased from 8192 to 65533 +/- 1 * Fix squares processing at DIST_MASK_PRECISE * - [ ] TODO: Check with IPP ```cpp cv::Mat gray = cv::imread("opencv/samples/data/stuff.jpg", cv::ImreadModes::IMREAD_GRAYSCALE); cv::Mat gray_resize; cv::resize(gray, gray_resize, cv::Size(70000,70000), 0.0, 0.0, cv::INTER_LINEAR); gray_resize = gray_resize >= 100; cv::Mat dist; cv::distanceTransform(gray_resize, dist, cv::DIST_L2, cv::DIST_MASK_5, CV_32F); double minVal, maxVal; minMaxLoc(dist, &minVal, &maxVal); dist = 255 * (dist - minVal) / (maxVal - minVal); std::cout << minVal << " " << maxVal << std::endl; cv::Mat dist_resize; cv::resize(dist, dist_resize, cv::Size(1024,1024), 0.0, 0.0, cv::INTER_LINEAR); cv::String outfilePath = "test_mask_5.png"; cv::imwrite(outfilePath, dist_resize); ``` mask | 4.x | PR | ----------|--------------|-------------- DIST_MASK_3 | <img src="https://github.com/opencv/opencv/assets/25801568/23e5de76-a8ba-4eb8-ab03-fa55672834be" width="128"> | <img src="https://github.com/opencv/opencv/assets/25801568/e1149f6a-49d6-47bd-a2a8-20bb7e4dafa4" width="128"> | DIST_MASK_5 | <img src="https://github.com/opencv/opencv/assets/25801568/98aba29b-8865-4b9a-8066-669b16d175c9" width="128"> | <img src="https://github.com/opencv/opencv/assets/25801568/54f62ed2-9ef6-485f-bd63-48cc96accd7d" width="128"> | DIST_MASK_PRECISE | <img src="https://github.com/opencv/opencv/assets/25801568/c4d79451-fd7a-461f-98fc-13060c63f473" width="128"> | <img src="https://github.com/opencv/opencv/assets/25801568/b5bfcaf5-bc48-40ba-b8e3-d000e5ab48db" width="128">| See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request - [x] I agree to contribute to the project under Apache 2 License. - [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV - [x] The PR is proposed to the proper branch - [x] There is a reference to the original bug report and related work - [x] There is accuracy test, performance test and test data in opencv_extra repository, if applicable Patch to opencv_extra has the same branch name. - [x] The feature is well documented and sample code can be built with the project CMake
420 lines
13 KiB
C++
420 lines
13 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 <numeric>
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namespace opencv_test { namespace {
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class CV_DisTransTest : public cvtest::ArrayTest
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{
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public:
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CV_DisTransTest();
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protected:
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void get_test_array_types_and_sizes( int test_case_idx, vector<vector<Size> >& sizes, vector<vector<int> >& types );
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double get_success_error_level( int test_case_idx, int i, int j );
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void run_func();
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void prepare_to_validation( int );
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void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
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int prepare_test_case( int test_case_idx );
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int mask_size;
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int dist_type;
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int fill_labels;
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float mask[3];
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};
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CV_DisTransTest::CV_DisTransTest()
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{
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test_array[INPUT].push_back(NULL);
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test_array[OUTPUT].push_back(NULL);
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test_array[OUTPUT].push_back(NULL);
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test_array[REF_OUTPUT].push_back(NULL);
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test_array[REF_OUTPUT].push_back(NULL);
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optional_mask = false;
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element_wise_relative_error = true;
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}
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void CV_DisTransTest::get_test_array_types_and_sizes( int test_case_idx,
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vector<vector<Size> >& sizes, vector<vector<int> >& types )
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{
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RNG& rng = ts->get_rng();
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cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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types[INPUT][0] = CV_8UC1;
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types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_32FC1;
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types[OUTPUT][1] = types[REF_OUTPUT][1] = CV_32SC1;
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if( cvtest::randInt(rng) & 1 )
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{
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mask_size = 3;
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}
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else
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{
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mask_size = 5;
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}
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dist_type = cvtest::randInt(rng) % 3;
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dist_type = dist_type == 0 ? CV_DIST_C : dist_type == 1 ? CV_DIST_L1 : CV_DIST_L2;
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// for now, check only the "labeled" distance transform mode
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fill_labels = 0;
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if( !fill_labels )
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sizes[OUTPUT][1] = sizes[REF_OUTPUT][1] = cvSize(0,0);
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}
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double CV_DisTransTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
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{
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Size sz = test_mat[INPUT][0].size();
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return dist_type == CV_DIST_C || dist_type == CV_DIST_L1 ? 0 : 0.01*MAX(sz.width, sz.height);
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}
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void CV_DisTransTest::get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high )
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{
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cvtest::ArrayTest::get_minmax_bounds( i, j, type, low, high );
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if( i == INPUT && CV_MAT_DEPTH(type) == CV_8U )
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{
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low = Scalar::all(0);
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high = Scalar::all(10);
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}
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}
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int CV_DisTransTest::prepare_test_case( int test_case_idx )
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{
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int code = cvtest::ArrayTest::prepare_test_case( test_case_idx );
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if( code > 0 )
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{
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// the function's response to an "all-nonzeros" image is not determined,
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// so put at least one zero point
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Mat& mat = test_mat[INPUT][0];
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RNG& rng = ts->get_rng();
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int i = cvtest::randInt(rng) % mat.rows;
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int j = cvtest::randInt(rng) % mat.cols;
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mat.at<uchar>(i,j) = 0;
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}
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return code;
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}
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void CV_DisTransTest::run_func()
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{
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cvDistTransform( test_array[INPUT][0], test_array[OUTPUT][0], dist_type, mask_size,
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dist_type == CV_DIST_USER ? mask : 0, test_array[OUTPUT][1] );
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}
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static void
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cvTsDistTransform( const CvMat* _src, CvMat* _dst, int dist_type,
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int mask_size, float* _mask, CvMat* /*_labels*/ )
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{
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int i, j, k;
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int width = _src->cols, height = _src->rows;
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const float init_val = 1e6;
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float mask[3];
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CvMat* temp;
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int ofs[16] = {0};
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float delta[16];
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int tstep, count;
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CV_Assert( mask_size == 3 || mask_size == 5 );
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if( dist_type == CV_DIST_USER )
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memcpy( mask, _mask, sizeof(mask) );
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else if( dist_type == CV_DIST_C )
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{
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mask_size = 3;
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mask[0] = mask[1] = 1.f;
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}
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else if( dist_type == CV_DIST_L1 )
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{
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mask_size = 3;
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mask[0] = 1.f;
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mask[1] = 2.f;
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}
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else if( mask_size == 3 )
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{
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mask[0] = 0.955f;
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mask[1] = 1.3693f;
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}
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else
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{
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mask[0] = 1.0f;
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mask[1] = 1.4f;
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mask[2] = 2.1969f;
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}
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temp = cvCreateMat( height + mask_size-1, width + mask_size-1, CV_32F );
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tstep = temp->step / sizeof(float);
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if( mask_size == 3 )
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{
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count = 4;
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ofs[0] = -1; delta[0] = mask[0];
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ofs[1] = -tstep-1; delta[1] = mask[1];
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ofs[2] = -tstep; delta[2] = mask[0];
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ofs[3] = -tstep+1; delta[3] = mask[1];
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}
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else
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{
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count = 8;
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ofs[0] = -1; delta[0] = mask[0];
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ofs[1] = -tstep-2; delta[1] = mask[2];
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ofs[2] = -tstep-1; delta[2] = mask[1];
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ofs[3] = -tstep; delta[3] = mask[0];
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ofs[4] = -tstep+1; delta[4] = mask[1];
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ofs[5] = -tstep+2; delta[5] = mask[2];
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ofs[6] = -tstep*2-1; delta[6] = mask[2];
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ofs[7] = -tstep*2+1; delta[7] = mask[2];
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}
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for( i = 0; i < mask_size/2; i++ )
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{
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float* t0 = (float*)(temp->data.ptr + i*temp->step);
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float* t1 = (float*)(temp->data.ptr + (temp->rows - i - 1)*temp->step);
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for( j = 0; j < width + mask_size - 1; j++ )
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t0[j] = t1[j] = init_val;
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}
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for( i = 0; i < height; i++ )
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{
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uchar* s = _src->data.ptr + i*_src->step;
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float* tmp = (float*)(temp->data.ptr + temp->step*(i + (mask_size/2))) + (mask_size/2);
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for( j = 0; j < mask_size/2; j++ )
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tmp[-j-1] = tmp[j + width] = init_val;
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for( j = 0; j < width; j++ )
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{
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if( s[j] == 0 )
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tmp[j] = 0;
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else
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{
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float min_dist = init_val;
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for( k = 0; k < count; k++ )
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{
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float t = tmp[j+ofs[k]] + delta[k];
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if( min_dist > t )
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min_dist = t;
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}
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tmp[j] = min_dist;
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}
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}
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}
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for( i = height - 1; i >= 0; i-- )
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{
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float* d = (float*)(_dst->data.ptr + i*_dst->step);
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float* tmp = (float*)(temp->data.ptr + temp->step*(i + (mask_size/2))) + (mask_size/2);
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for( j = width - 1; j >= 0; j-- )
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{
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float min_dist = tmp[j];
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if( min_dist > mask[0] )
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{
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for( k = 0; k < count; k++ )
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{
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float t = tmp[j-ofs[k]] + delta[k];
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if( min_dist > t )
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min_dist = t;
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}
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tmp[j] = min_dist;
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}
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d[j] = min_dist;
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}
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}
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cvReleaseMat( &temp );
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}
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void CV_DisTransTest::prepare_to_validation( int /*test_case_idx*/ )
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{
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CvMat _input = cvMat(test_mat[INPUT][0]), _output = cvMat(test_mat[REF_OUTPUT][0]);
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cvTsDistTransform( &_input, &_output, dist_type, mask_size, mask, 0 );
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}
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TEST(Imgproc_DistanceTransform, accuracy) { CV_DisTransTest test; test.safe_run(); }
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BIGDATA_TEST(Imgproc_DistanceTransform, large_image_12218)
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{
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const int lls_maxcnt = 79992000; // labels's maximum count
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const int lls_mincnt = 1; // labels's minimum count
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int i, j, nz;
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Mat src(8000, 20000, CV_8UC1), dst, labels;
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for( i = 0; i < src.rows; i++ )
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for( j = 0; j < src.cols; j++ )
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src.at<uchar>(i, j) = (j > (src.cols / 2)) ? 0 : 255;
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distanceTransform(src, dst, labels, cv::DIST_L2, cv::DIST_MASK_3, DIST_LABEL_PIXEL);
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double scale = (double)lls_mincnt / (double)lls_maxcnt;
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labels.convertTo(labels, CV_32SC1, scale);
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Size size = labels.size();
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nz = cv::countNonZero(labels);
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EXPECT_EQ(nz, (size.height*size.width / 2));
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}
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TEST(Imgproc_DistanceTransform, wide_image_22732)
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{
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Mat src = Mat::zeros(1, 4099, CV_8U); // 4099 or larger used to be bad
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Mat dist(src.rows, src.cols, CV_32F);
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distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
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int nz = countNonZero(dist);
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EXPECT_EQ(nz, 0);
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}
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TEST(Imgproc_DistanceTransform, large_square_22732)
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{
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Mat src = Mat::zeros(8000, 8005, CV_8U), dist;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
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int nz = countNonZero(dist);
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EXPECT_EQ(dist.size(), src.size());
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EXPECT_EQ(dist.type(), CV_32F);
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EXPECT_EQ(nz, 0);
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Point p0(src.cols-1, src.rows-1);
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src.setTo(1);
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src.at<uchar>(p0) = 0;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
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EXPECT_EQ(dist.size(), src.size());
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EXPECT_EQ(dist.type(), CV_32F);
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bool first = true;
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int nerrs = 0;
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for (int y = 0; y < dist.rows; y++)
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for (int x = 0; x < dist.cols; x++) {
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float d = dist.at<float>(y, x);
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double dx = (double)(x - p0.x), dy = (double)(y - p0.y);
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float d0 = (float)sqrt(dx*dx + dy*dy);
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if (std::abs(d0 - d) > 1) {
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if (first) {
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printf("y=%d, x=%d. dist_ref=%.2f, dist=%.2f\n", y, x, d0, d);
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first = false;
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}
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nerrs++;
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}
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}
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EXPECT_EQ(0, nerrs) << "reference distance map is different from computed one at " << nerrs << " pixels\n";
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}
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BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_3x3)
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{
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Mat src = Mat::zeros(50000, 50000, CV_8U), dist;
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distanceTransform(src.col(0), dist, DIST_L2, DIST_MASK_3);
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int nz = countNonZero(dist);
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EXPECT_EQ(nz, 0);
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}
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BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_5x5)
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{
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Mat src = Mat::zeros(50000, 50000, CV_8U), dist;
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distanceTransform(src.col(0), dist, DIST_L2, DIST_MASK_5);
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int nz = countNonZero(dist);
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EXPECT_EQ(nz, 0);
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}
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BIGDATA_TEST(Imgproc_DistanceTransform, issue_23895_5x5_labels)
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{
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Mat src = Mat::zeros(50000, 50000, CV_8U), dist, labels;
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distanceTransform(src.col(0), dist, labels, DIST_L2, DIST_MASK_5);
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int nz = countNonZero(dist);
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EXPECT_EQ(nz, 0);
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}
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TEST(Imgproc_DistanceTransform, max_distance_3x3)
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{
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Mat src = Mat::ones(1, 70000, CV_8U), dist;
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src.at<uint8_t>(0, 0) = 0;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_3);
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double minVal, maxVal;
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minMaxLoc(dist, &minVal, &maxVal);
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EXPECT_GE(maxVal, 65533);
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}
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TEST(Imgproc_DistanceTransform, max_distance_5x5)
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{
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Mat src = Mat::ones(1, 70000, CV_8U), dist;
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src.at<uint8_t>(0, 0) = 0;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_5);
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double minVal, maxVal;
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minMaxLoc(dist, &minVal, &maxVal);
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EXPECT_GE(maxVal, 65533);
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}
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TEST(Imgproc_DistanceTransform, max_distance_5x5_labels)
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{
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Mat src = Mat::ones(1, 70000, CV_8U), dist, labels;
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src.at<uint8_t>(0, 0) = 0;
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distanceTransform(src, dist, labels, DIST_L2, DIST_MASK_5);
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double minVal, maxVal;
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minMaxLoc(dist, &minVal, &maxVal);
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EXPECT_GE(maxVal, 65533);
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}
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TEST(Imgproc_DistanceTransform, precise_long_dist)
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{
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static const int maxDist = 1 << 16;
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Mat src = Mat::ones(1, 70000, CV_8U), dist;
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src.at<uint8_t>(0, 0) = 0;
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distanceTransform(src, dist, DIST_L2, DIST_MASK_PRECISE, CV_32F);
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Mat expected(src.size(), CV_32F);
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std::iota(expected.begin<float>(), expected.end<float>(), 0.f);
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expected.colRange(maxDist, expected.cols).setTo(maxDist);
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EXPECT_EQ(cv::norm(expected, dist, NORM_INF), 0);
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}
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}} // namespace
|