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337 lines
12 KiB
C++
337 lines
12 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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using namespace cv;
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using namespace std;
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class CV_TemplMatchTest : public cvtest::ArrayTest
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{
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public:
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CV_TemplMatchTest();
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protected:
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int read_params( CvFileStorage* fs );
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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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void get_minmax_bounds( int i, int j, int type, Scalar& low, Scalar& high );
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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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int max_template_size;
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int method;
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bool test_cpp;
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};
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CV_TemplMatchTest::CV_TemplMatchTest()
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{
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test_array[INPUT].push_back(NULL);
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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[REF_OUTPUT].push_back(NULL);
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element_wise_relative_error = false;
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max_template_size = 100;
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method = 0;
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test_cpp = false;
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}
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int CV_TemplMatchTest::read_params( CvFileStorage* fs )
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{
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int code = cvtest::ArrayTest::read_params( fs );
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if( code < 0 )
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return code;
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max_template_size = cvReadInt( find_param( fs, "max_template_size" ), max_template_size );
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max_template_size = cvtest::clipInt( max_template_size, 1, 100 );
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return code;
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}
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void CV_TemplMatchTest::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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int depth = CV_MAT_DEPTH(type);
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if( depth == CV_32F )
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{
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low = Scalar::all(-10.);
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high = Scalar::all(10.);
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}
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}
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void CV_TemplMatchTest::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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int depth = cvtest::randInt(rng) % 2, cn = cvtest::randInt(rng) & 1 ? 3 : 1;
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cvtest::ArrayTest::get_test_array_types_and_sizes( test_case_idx, sizes, types );
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depth = depth == 0 ? CV_8U : CV_32F;
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types[INPUT][0] = types[INPUT][1] = CV_MAKETYPE(depth,cn);
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types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_32FC1;
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sizes[INPUT][1].width = cvtest::randInt(rng)%MIN(sizes[INPUT][1].width,max_template_size) + 1;
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sizes[INPUT][1].height = cvtest::randInt(rng)%MIN(sizes[INPUT][1].height,max_template_size) + 1;
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sizes[OUTPUT][0].width = sizes[INPUT][0].width - sizes[INPUT][1].width + 1;
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sizes[OUTPUT][0].height = sizes[INPUT][0].height - sizes[INPUT][1].height + 1;
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sizes[REF_OUTPUT][0] = sizes[OUTPUT][0];
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method = cvtest::randInt(rng)%6;
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test_cpp = (cvtest::randInt(rng) & 256) == 0;
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}
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double CV_TemplMatchTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ )
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{
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if( test_mat[INPUT][1].depth() == CV_8U ||
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(method >= CV_TM_CCOEFF && test_mat[INPUT][1].cols*test_mat[INPUT][1].rows <= 2) )
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return 1e-2;
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else
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return 1e-3;
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}
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void CV_TemplMatchTest::run_func()
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{
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if(!test_cpp)
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cvMatchTemplate( test_array[INPUT][0], test_array[INPUT][1], test_array[OUTPUT][0], method );
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else
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{
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cv::Mat _out = cv::cvarrToMat(test_array[OUTPUT][0]);
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cv::matchTemplate(cv::cvarrToMat(test_array[INPUT][0]), cv::cvarrToMat(test_array[INPUT][1]), _out, method);
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}
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}
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static void cvTsMatchTemplate( const CvMat* img, const CvMat* templ, CvMat* result, int method )
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{
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int i, j, k, l;
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int depth = CV_MAT_DEPTH(img->type), cn = CV_MAT_CN(img->type);
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int width_n = templ->cols*cn, height = templ->rows;
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int a_step = img->step / CV_ELEM_SIZE(img->type & CV_MAT_DEPTH_MASK);
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int b_step = templ->step / CV_ELEM_SIZE(templ->type & CV_MAT_DEPTH_MASK);
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CvScalar b_mean, b_sdv;
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double b_denom = 1., b_sum2 = 0;
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int area = templ->rows*templ->cols;
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cvAvgSdv(templ, &b_mean, &b_sdv);
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for( i = 0; i < cn; i++ )
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b_sum2 += (b_sdv.val[i]*b_sdv.val[i] + b_mean.val[i]*b_mean.val[i])*area;
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if( b_sdv.val[0]*b_sdv.val[0] + b_sdv.val[1]*b_sdv.val[1] +
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b_sdv.val[2]*b_sdv.val[2] + b_sdv.val[3]*b_sdv.val[3] < DBL_EPSILON &&
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method == CV_TM_CCOEFF_NORMED )
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{
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cvSet( result, cvScalarAll(1.) );
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return;
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}
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if( method & 1 )
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{
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b_denom = 0;
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if( method != CV_TM_CCOEFF_NORMED )
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{
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b_denom = b_sum2;
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}
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else
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{
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for( i = 0; i < cn; i++ )
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b_denom += b_sdv.val[i]*b_sdv.val[i]*area;
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}
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b_denom = sqrt(b_denom);
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if( b_denom == 0 )
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b_denom = 1.;
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}
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assert( CV_TM_SQDIFF <= method && method <= CV_TM_CCOEFF_NORMED );
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for( i = 0; i < result->rows; i++ )
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{
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for( j = 0; j < result->cols; j++ )
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{
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CvScalar a_sum = {{ 0, 0, 0, 0 }}, a_sum2 = {{ 0, 0, 0, 0 }};
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CvScalar ccorr = {{ 0, 0, 0, 0 }};
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double value = 0.;
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if( depth == CV_8U )
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{
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const uchar* a = img->data.ptr + i*img->step + j*cn;
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const uchar* b = templ->data.ptr;
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if( cn == 1 || method < CV_TM_CCOEFF )
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{
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for( k = 0; k < height; k++, a += a_step, b += b_step )
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for( l = 0; l < width_n; l++ )
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{
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ccorr.val[0] += a[l]*b[l];
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a_sum.val[0] += a[l];
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a_sum2.val[0] += a[l]*a[l];
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}
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}
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else
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{
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for( k = 0; k < height; k++, a += a_step, b += b_step )
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for( l = 0; l < width_n; l += 3 )
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{
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ccorr.val[0] += a[l]*b[l];
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ccorr.val[1] += a[l+1]*b[l+1];
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ccorr.val[2] += a[l+2]*b[l+2];
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a_sum.val[0] += a[l];
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a_sum.val[1] += a[l+1];
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a_sum.val[2] += a[l+2];
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a_sum2.val[0] += a[l]*a[l];
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a_sum2.val[1] += a[l+1]*a[l+1];
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a_sum2.val[2] += a[l+2]*a[l+2];
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}
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}
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}
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else
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{
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const float* a = (const float*)(img->data.ptr + i*img->step) + j*cn;
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const float* b = (const float*)templ->data.ptr;
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if( cn == 1 || method < CV_TM_CCOEFF )
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{
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for( k = 0; k < height; k++, a += a_step, b += b_step )
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for( l = 0; l < width_n; l++ )
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{
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ccorr.val[0] += a[l]*b[l];
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a_sum.val[0] += a[l];
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a_sum2.val[0] += a[l]*a[l];
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}
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}
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else
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{
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for( k = 0; k < height; k++, a += a_step, b += b_step )
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for( l = 0; l < width_n; l += 3 )
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{
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ccorr.val[0] += a[l]*b[l];
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ccorr.val[1] += a[l+1]*b[l+1];
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ccorr.val[2] += a[l+2]*b[l+2];
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a_sum.val[0] += a[l];
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a_sum.val[1] += a[l+1];
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a_sum.val[2] += a[l+2];
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a_sum2.val[0] += a[l]*a[l];
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a_sum2.val[1] += a[l+1]*a[l+1];
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a_sum2.val[2] += a[l+2]*a[l+2];
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}
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}
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}
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switch( method )
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{
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case CV_TM_CCORR:
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case CV_TM_CCORR_NORMED:
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value = ccorr.val[0];
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break;
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case CV_TM_SQDIFF:
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case CV_TM_SQDIFF_NORMED:
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value = (a_sum2.val[0] + b_sum2 - 2*ccorr.val[0]);
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break;
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default:
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value = (ccorr.val[0] - a_sum.val[0]*b_mean.val[0]+
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ccorr.val[1] - a_sum.val[1]*b_mean.val[1]+
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ccorr.val[2] - a_sum.val[2]*b_mean.val[2]);
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}
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if( method & 1 )
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{
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double denom;
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// calc denominator
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if( method != CV_TM_CCOEFF_NORMED )
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{
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denom = a_sum2.val[0] + a_sum2.val[1] + a_sum2.val[2];
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}
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else
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{
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denom = a_sum2.val[0] - (a_sum.val[0]*a_sum.val[0])/area;
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denom += a_sum2.val[1] - (a_sum.val[1]*a_sum.val[1])/area;
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denom += a_sum2.val[2] - (a_sum.val[2]*a_sum.val[2])/area;
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}
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denom = sqrt(MAX(denom,0))*b_denom;
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if( fabs(value) < denom )
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value /= denom;
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else if( fabs(value) < denom*1.125 )
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value = value > 0 ? 1 : -1;
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else
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value = method != CV_TM_SQDIFF_NORMED ? 0 : 1;
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}
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((float*)(result->data.ptr + result->step*i))[j] = (float)value;
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}
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}
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}
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void CV_TemplMatchTest::prepare_to_validation( int /*test_case_idx*/ )
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{
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CvMat _input = test_mat[INPUT][0], _templ = test_mat[INPUT][1];
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CvMat _output = test_mat[REF_OUTPUT][0];
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cvTsMatchTemplate( &_input, &_templ, &_output, method );
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//if( ts->get_current_test_info()->test_case_idx == 0 )
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/*{
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CvFileStorage* fs = cvOpenFileStorage( "_match_template.yml", 0, CV_STORAGE_WRITE );
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cvWrite( fs, "image", &test_mat[INPUT][0] );
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cvWrite( fs, "template", &test_mat[INPUT][1] );
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cvWrite( fs, "ref", &test_mat[REF_OUTPUT][0] );
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cvWrite( fs, "opencv", &test_mat[OUTPUT][0] );
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cvWriteInt( fs, "method", method );
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cvReleaseFileStorage( &fs );
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}*/
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if( method >= CV_TM_CCOEFF )
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{
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// avoid numerical stability problems in singular cases (when the results are near to 0)
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const double delta = 10.;
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test_mat[REF_OUTPUT][0] += Scalar::all(delta);
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test_mat[OUTPUT][0] += Scalar::all(delta);
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}
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}
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TEST(Imgproc_MatchTemplate, accuracy) { CV_TemplMatchTest test; test.safe_run(); }
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