306 lines
9.4 KiB
C++
306 lines
9.4 KiB
C++
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/*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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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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}} // namespace
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