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Open Source Computer Vision Library
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337 lines
10 KiB
337 lines
10 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, all rights reserved. |
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// Third party copyrights are property of their respective owners. |
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// |
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// Redistribution and use in source and binary forms, with or without modification, |
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// are permitted provided that the following conditions are met: |
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// |
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// * Redistribution's of source code must retain the above copyright notice, |
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// this list of conditions and the following disclaimer. |
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// |
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// * Redistribution's in binary form must reproduce the above copyright notice, |
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// this list of conditions and the following disclaimer in the documentation |
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// and/or other materials provided with the distribution. |
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// |
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// * The name of Intel Corporation may not be used to endorse or promote products |
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// derived from this software without specific prior written permission. |
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// |
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// This software is provided by the copyright holders and contributors "as is" and |
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// any express or implied warranties, including, but not limited to, the implied |
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// warranties of merchantability and fitness for a particular purpose are disclaimed. |
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// In no event shall the Intel Corporation or contributors be liable for any direct, |
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// indirect, incidental, special, exemplary, or consequential damages |
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// (including, but not limited to, procurement of substitute goods or services; |
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// loss of use, data, or profits; or business interruption) however caused |
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// and on any theory of liability, whether in contract, strict liability, |
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// or tort (including negligence or otherwise) arising in any way out of |
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// the use of this software, even if advised of the possibility of such damage. |
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// |
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//M*/ |
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#include "precomp.hpp" |
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#include "opencv2/core/core_c.h" |
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namespace cvtest |
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{ |
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static const int default_test_case_count = 500; |
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static const int default_max_log_array_size = 9; |
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ArrayTest::ArrayTest() |
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{ |
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test_case_count = default_test_case_count; |
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iplimage_allowed = true; |
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cvmat_allowed = true; |
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optional_mask = false; |
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min_log_array_size = 0; |
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max_log_array_size = default_max_log_array_size; |
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element_wise_relative_error = true; |
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test_array.resize(MAX_ARR); |
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} |
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ArrayTest::~ArrayTest() |
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{ |
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clear(); |
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} |
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void ArrayTest::clear() |
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{ |
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for( size_t i = 0; i < test_array.size(); i++ ) |
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{ |
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for( size_t j = 0; j < test_array[i].size(); j++ ) |
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cvRelease( &test_array[i][j] ); |
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} |
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BaseTest::clear(); |
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} |
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int ArrayTest::read_params( const cv::FileStorage& fs ) |
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{ |
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int code = BaseTest::read_params( fs ); |
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if( code < 0 ) |
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return code; |
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read( find_param( fs, "min_log_array_size" ), min_log_array_size, min_log_array_size ); |
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read( find_param( fs, "max_log_array_size" ), max_log_array_size, max_log_array_size ); |
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read( find_param( fs, "test_case_count" ), test_case_count, test_case_count ); |
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test_case_count = cvRound( test_case_count*ts->get_test_case_count_scale() ); |
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min_log_array_size = clipInt( min_log_array_size, 0, 20 ); |
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max_log_array_size = clipInt( max_log_array_size, min_log_array_size, 20 ); |
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test_case_count = clipInt( test_case_count, 0, 100000 ); |
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return code; |
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} |
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void ArrayTest::get_test_array_types_and_sizes( int /*test_case_idx*/, 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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Size size; |
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double val; |
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size_t i, j; |
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val = randReal(rng) * (max_log_array_size - min_log_array_size) + min_log_array_size; |
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size.width = cvRound( exp(val*CV_LOG2) ); |
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val = randReal(rng) * (max_log_array_size - min_log_array_size) + min_log_array_size; |
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size.height = cvRound( exp(val*CV_LOG2) ); |
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for( i = 0; i < test_array.size(); i++ ) |
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{ |
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size_t sizei = test_array[i].size(); |
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for( j = 0; j < sizei; j++ ) |
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{ |
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sizes[i][j] = size; |
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types[i][j] = CV_8UC1; |
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} |
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} |
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} |
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static const unsigned int icvTsTypeToDepth[] = |
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{ |
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IPL_DEPTH_8U, IPL_DEPTH_8S, IPL_DEPTH_16U, IPL_DEPTH_16S, |
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IPL_DEPTH_32S, IPL_DEPTH_32F, IPL_DEPTH_64F |
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}; |
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int ArrayTest::prepare_test_case( int test_case_idx ) |
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{ |
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int code = 1; |
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size_t max_arr = test_array.size(); |
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vector<vector<Size> > sizes(max_arr); |
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vector<vector<Size> > whole_sizes(max_arr); |
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vector<vector<int> > types(max_arr); |
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size_t i, j; |
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RNG& rng = ts->get_rng(); |
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bool is_image = false; |
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for( i = 0; i < max_arr; i++ ) |
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{ |
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size_t sizei = std::max(test_array[i].size(), (size_t)1); |
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sizes[i].resize(sizei); |
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types[i].resize(sizei); |
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whole_sizes[i].resize(sizei); |
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} |
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get_test_array_types_and_sizes( test_case_idx, sizes, types ); |
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for( i = 0; i < max_arr; i++ ) |
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{ |
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size_t sizei = test_array[i].size(); |
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for( j = 0; j < sizei; j++ ) |
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{ |
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unsigned t = randInt(rng); |
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bool create_mask = true, use_roi = false; |
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CvSize size = cvSize(sizes[i][j]), whole_size = size; |
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CvRect roi = CV_STRUCT_INITIALIZER; |
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is_image = !cvmat_allowed ? true : iplimage_allowed ? (t & 1) != 0 : false; |
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create_mask = (t & 6) == 0; // ~ each of 3 tests will use mask |
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use_roi = (t & 8) != 0; |
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if( use_roi ) |
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{ |
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whole_size.width += randInt(rng) % 10; |
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whole_size.height += randInt(rng) % 10; |
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} |
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cvRelease( &test_array[i][j] ); |
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if( size.width > 0 && size.height > 0 && |
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types[i][j] >= 0 && (i != MASK || create_mask) ) |
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{ |
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if( use_roi ) |
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{ |
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roi.width = size.width; |
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roi.height = size.height; |
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if( whole_size.width > size.width ) |
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roi.x = randInt(rng) % (whole_size.width - size.width); |
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if( whole_size.height > size.height ) |
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roi.y = randInt(rng) % (whole_size.height - size.height); |
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} |
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if( is_image ) |
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{ |
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test_array[i][j] = cvCreateImage( whole_size, |
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icvTsTypeToDepth[CV_MAT_DEPTH(types[i][j])], CV_MAT_CN(types[i][j]) ); |
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if( use_roi ) |
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cvSetImageROI( (IplImage*)test_array[i][j], roi ); |
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} |
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else |
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{ |
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test_array[i][j] = cvCreateMat( whole_size.height, whole_size.width, types[i][j] ); |
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if( use_roi ) |
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{ |
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CvMat submat, *mat = (CvMat*)test_array[i][j]; |
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cvGetSubRect( test_array[i][j], &submat, roi ); |
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submat.refcount = mat->refcount; |
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*mat = submat; |
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} |
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} |
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} |
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} |
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} |
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test_mat.resize(test_array.size()); |
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for( i = 0; i < max_arr; i++ ) |
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{ |
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size_t sizei = test_array[i].size(); |
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test_mat[i].resize(sizei); |
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for( j = 0; j < sizei; j++ ) |
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{ |
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CvArr* arr = test_array[i][j]; |
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test_mat[i][j] = cv::cvarrToMat(arr); |
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if( !test_mat[i][j].empty() ) |
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fill_array( test_case_idx, (int)i, (int)j, test_mat[i][j] ); |
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} |
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} |
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return code; |
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} |
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void ArrayTest::get_minmax_bounds( int i, int /*j*/, int type, Scalar& low, Scalar& high ) |
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{ |
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double l, u; |
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int depth = CV_MAT_DEPTH(type); |
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if( i == MASK ) |
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{ |
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l = -2; |
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u = 2; |
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} |
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else if( depth < CV_32S ) |
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{ |
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l = getMinVal(type); |
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u = getMaxVal(type); |
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} |
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else |
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{ |
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u = depth == CV_32S ? 1000000 : 1000.; |
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l = -u; |
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} |
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low = Scalar::all(l); |
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high = Scalar::all(u); |
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} |
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void ArrayTest::fill_array( int /*test_case_idx*/, int i, int j, Mat& arr ) |
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{ |
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if( i == REF_INPUT_OUTPUT ) |
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cvtest::copy( test_mat[INPUT_OUTPUT][j], arr, Mat() ); |
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else if( i == INPUT || i == INPUT_OUTPUT || i == MASK ) |
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{ |
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Scalar low, high; |
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get_minmax_bounds( i, j, arr.type(), low, high ); |
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randUni( ts->get_rng(), arr, low, high ); |
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} |
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} |
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double ArrayTest::get_success_error_level( int /*test_case_idx*/, int i, int j ) |
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{ |
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int elem_depth = CV_MAT_DEPTH(cvGetElemType(test_array[i][j])); |
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assert( i == OUTPUT || i == INPUT_OUTPUT ); |
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return elem_depth < CV_32F ? 0 : elem_depth == CV_32F ? FLT_EPSILON*100: DBL_EPSILON*5000; |
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} |
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void ArrayTest::prepare_to_validation( int /*test_case_idx*/ ) |
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{ |
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assert(0); |
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} |
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int ArrayTest::validate_test_results( int test_case_idx ) |
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{ |
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static const char* arr_names[] = { "input", "input/output", "output", |
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"ref input/output", "ref output", |
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"temporary", "mask" }; |
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size_t i, j; |
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prepare_to_validation( test_case_idx ); |
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for( i = 0; i < 2; i++ ) |
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{ |
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int i0 = i == 0 ? OUTPUT : INPUT_OUTPUT; |
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int i1 = i == 0 ? REF_OUTPUT : REF_INPUT_OUTPUT; |
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size_t sizei = test_array[i0].size(); |
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assert( sizei == test_array[i1].size() ); |
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for( j = 0; j < sizei; j++ ) |
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{ |
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double err_level; |
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int code; |
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if( !test_array[i1][j] ) |
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continue; |
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err_level = get_success_error_level( test_case_idx, i0, (int)j ); |
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code = cmpEps2(ts, test_mat[i0][j], test_mat[i1][j], err_level, element_wise_relative_error, arr_names[i0]); |
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if (code == 0) continue; |
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for( i0 = 0; i0 < (int)test_array.size(); i0++ ) |
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{ |
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size_t sizei0 = test_array[i0].size(); |
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if( i0 == REF_INPUT_OUTPUT || i0 == OUTPUT || i0 == TEMP ) |
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continue; |
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for( i1 = 0; i1 < (int)sizei0; i1++ ) |
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{ |
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const Mat& arr = test_mat[i0][i1]; |
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if( !arr.empty() ) |
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{ |
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string sizestr = vec2str(", ", &arr.size[0], arr.dims); |
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ts->printf( TS::LOG, "%s array %d type=%sC%d, size=(%s)\n", |
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arr_names[i0], i1, getTypeName(arr.depth()), |
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arr.channels(), sizestr.c_str() ); |
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} |
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} |
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} |
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ts->set_failed_test_info( code ); |
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return code; |
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} |
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} |
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return 0; |
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} |
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} |
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/* End of file. */
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