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544 lines
18 KiB
544 lines
18 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 "test_precomp.hpp" |
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namespace opencv_test { namespace { |
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class CV_ThreshTest : public cvtest::ArrayTest |
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{ |
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public: |
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CV_ThreshTest(int test_type = 0); |
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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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int thresh_type; |
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double thresh_val; |
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double max_val; |
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int extra_type; |
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}; |
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CV_ThreshTest::CV_ThreshTest(int test_type) |
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{ |
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CV_Assert( (test_type & CV_THRESH_MASK) == 0 ); |
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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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optional_mask = false; |
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element_wise_relative_error = true; |
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extra_type = test_type; |
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// Reduce number of test with automated thresholding |
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if (extra_type != 0) |
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test_case_count = 250; |
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} |
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void CV_ThreshTest::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) % 5, cn = cvtest::randInt(rng) % 4 + 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 : depth == 1 ? CV_16S : depth == 2 ? CV_16U : depth == 3 ? CV_32F : CV_64F; |
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if ( extra_type == CV_THRESH_OTSU ) |
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{ |
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depth = cvtest::randInt(rng) % 2 == 0 ? CV_8U : CV_16U; |
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cn = 1; |
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} |
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types[INPUT][0] = types[OUTPUT][0] = types[REF_OUTPUT][0] = CV_MAKETYPE(depth,cn); |
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thresh_type = cvtest::randInt(rng) % 5; |
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if( depth == CV_8U ) |
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{ |
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thresh_val = (cvtest::randReal(rng)*350. - 50.); |
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max_val = (cvtest::randReal(rng)*350. - 50.); |
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if( cvtest::randInt(rng)%4 == 0 ) |
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max_val = 255.f; |
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} |
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else if( depth == CV_16S ) |
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{ |
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double min_val = SHRT_MIN-100.f; |
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max_val = SHRT_MAX+100.f; |
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thresh_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val); |
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max_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val); |
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if( cvtest::randInt(rng)%4 == 0 ) |
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max_val = (double)SHRT_MAX; |
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} |
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else if( depth == CV_16U ) |
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{ |
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double min_val = -100.f; |
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max_val = USHRT_MAX+100.f; |
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thresh_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val); |
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max_val = (cvtest::randReal(rng)*(max_val - min_val) + min_val); |
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if( cvtest::randInt(rng)%4 == 0 ) |
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max_val = (double)USHRT_MAX; |
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} |
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else |
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{ |
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thresh_val = (cvtest::randReal(rng)*1000. - 500.); |
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max_val = (cvtest::randReal(rng)*1000. - 500.); |
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} |
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} |
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double CV_ThreshTest::get_success_error_level( int /*test_case_idx*/, int /*i*/, int /*j*/ ) |
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{ |
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return FLT_EPSILON*10; |
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} |
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void CV_ThreshTest::run_func() |
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{ |
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cvThreshold( test_array[INPUT][0], test_array[OUTPUT][0], |
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thresh_val, max_val, thresh_type | extra_type); |
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} |
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static double compute_otsu_thresh(const Mat& _src) |
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{ |
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int depth = _src.depth(); |
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int width = _src.cols, height = _src.rows; |
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const int N = 65536; |
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std::vector<int> h(N, 0); |
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int i, j; |
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double mu = 0, scale = 1./(width*height); |
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for(i = 0; i < height; ++i) |
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{ |
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for(j = 0; j < width; ++j) |
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{ |
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const int val = depth == CV_16UC1 ? (int)_src.at<ushort>(i, j) : (int)_src.at<uchar>(i,j); |
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h[val]++; |
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} |
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} |
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for( i = 0; i < N; i++ ) |
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{ |
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mu += i*(double)h[i]; |
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} |
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mu *= scale; |
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double mu1 = 0, q1 = 0; |
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double max_sigma = 0, max_val = 0; |
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for( i = 0; i < N; i++ ) |
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{ |
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double p_i, q2, mu2, sigma; |
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p_i = h[i]*scale; |
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mu1 *= q1; |
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q1 += p_i; |
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q2 = 1. - q1; |
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if( std::min(q1,q2) < FLT_EPSILON || std::max(q1,q2) > 1. - FLT_EPSILON ) |
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continue; |
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mu1 = (mu1 + i*p_i)/q1; |
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mu2 = (mu - q1*mu1)/q2; |
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sigma = q1*q2*(mu1 - mu2)*(mu1 - mu2); |
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if( sigma > max_sigma ) |
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{ |
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max_sigma = sigma; |
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max_val = i; |
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} |
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} |
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return max_val; |
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} |
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static void test_threshold( const Mat& _src, Mat& _dst, |
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double thresh, double maxval, int thresh_type, int extra_type ) |
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{ |
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int i, j; |
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int depth = _src.depth(), cn = _src.channels(); |
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int width_n = _src.cols*cn, height = _src.rows; |
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int ithresh = cvFloor(thresh); |
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int imaxval, ithresh2; |
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if (extra_type == CV_THRESH_OTSU) |
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{ |
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thresh = compute_otsu_thresh(_src); |
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ithresh = cvFloor(thresh); |
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} |
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if( depth == CV_8U ) |
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{ |
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ithresh2 = saturate_cast<uchar>(ithresh); |
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imaxval = saturate_cast<uchar>(maxval); |
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} |
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else if( depth == CV_16S ) |
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{ |
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ithresh2 = saturate_cast<short>(ithresh); |
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imaxval = saturate_cast<short>(maxval); |
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} |
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else if( depth == CV_16U ) |
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{ |
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ithresh2 = saturate_cast<ushort>(ithresh); |
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imaxval = saturate_cast<ushort>(maxval); |
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} |
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else |
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{ |
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ithresh2 = cvRound(ithresh); |
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imaxval = cvRound(maxval); |
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} |
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CV_Assert( depth == CV_8U || depth == CV_16S || depth == CV_16U || depth == CV_32F || depth == CV_64F ); |
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switch( thresh_type ) |
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{ |
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case CV_THRESH_BINARY: |
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for( i = 0; i < height; i++ ) |
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{ |
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if( depth == CV_8U ) |
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{ |
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const uchar* src = _src.ptr<uchar>(i); |
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uchar* dst = _dst.ptr<uchar>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = (uchar)(src[j] > ithresh ? imaxval : 0); |
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} |
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else if( depth == CV_16S ) |
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{ |
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const short* src = _src.ptr<short>(i); |
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short* dst = _dst.ptr<short>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = (short)(src[j] > ithresh ? imaxval : 0); |
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} |
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else if( depth == CV_16U ) |
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{ |
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const ushort* src = _src.ptr<ushort>(i); |
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ushort* dst = _dst.ptr<ushort>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = (ushort)(src[j] > ithresh ? imaxval : 0); |
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} |
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else if( depth == CV_32F ) |
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{ |
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const float* src = _src.ptr<float>(i); |
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float* dst = _dst.ptr<float>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = (float)(src[j] > thresh ? maxval : 0.f); |
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} |
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else |
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{ |
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const double* src = _src.ptr<double>(i); |
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double* dst = _dst.ptr<double>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = src[j] > thresh ? maxval : 0.0; |
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} |
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} |
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break; |
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case CV_THRESH_BINARY_INV: |
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for( i = 0; i < height; i++ ) |
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{ |
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if( depth == CV_8U ) |
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{ |
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const uchar* src = _src.ptr<uchar>(i); |
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uchar* dst = _dst.ptr<uchar>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = (uchar)(src[j] > ithresh ? 0 : imaxval); |
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} |
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else if( depth == CV_16S ) |
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{ |
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const short* src = _src.ptr<short>(i); |
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short* dst = _dst.ptr<short>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = (short)(src[j] > ithresh ? 0 : imaxval); |
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} |
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else if( depth == CV_16U ) |
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{ |
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const ushort* src = _src.ptr<ushort>(i); |
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ushort* dst = _dst.ptr<ushort>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = (ushort)(src[j] > ithresh ? 0 : imaxval); |
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} |
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else if( depth == CV_32F ) |
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{ |
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const float* src = _src.ptr<float>(i); |
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float* dst = _dst.ptr<float>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = (float)(src[j] > thresh ? 0.f : maxval); |
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} |
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else |
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{ |
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const double* src = _src.ptr<double>(i); |
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double* dst = _dst.ptr<double>(i); |
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for( j = 0; j < width_n; j++ ) |
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dst[j] = src[j] > thresh ? 0.0 : maxval; |
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} |
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} |
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break; |
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case CV_THRESH_TRUNC: |
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for( i = 0; i < height; i++ ) |
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{ |
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if( depth == CV_8U ) |
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{ |
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const uchar* src = _src.ptr<uchar>(i); |
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uchar* dst = _dst.ptr<uchar>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (uchar)(s > ithresh ? ithresh2 : s); |
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} |
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} |
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else if( depth == CV_16S ) |
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{ |
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const short* src = _src.ptr<short>(i); |
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short* dst = _dst.ptr<short>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (short)(s > ithresh ? ithresh2 : s); |
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} |
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} |
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else if( depth == CV_16U ) |
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{ |
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const ushort* src = _src.ptr<ushort>(i); |
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ushort* dst = _dst.ptr<ushort>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (ushort)(s > ithresh ? ithresh2 : s); |
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} |
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} |
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else if( depth == CV_32F ) |
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{ |
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const float* src = _src.ptr<float>(i); |
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float* dst = _dst.ptr<float>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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float s = src[j]; |
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dst[j] = (float)(s > thresh ? thresh : s); |
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} |
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} |
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else |
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{ |
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const double* src = _src.ptr<double>(i); |
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double* dst = _dst.ptr<double>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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double s = src[j]; |
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dst[j] = s > thresh ? thresh : s; |
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} |
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} |
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} |
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break; |
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case CV_THRESH_TOZERO: |
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for( i = 0; i < height; i++ ) |
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{ |
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if( depth == CV_8U ) |
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{ |
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const uchar* src = _src.ptr<uchar>(i); |
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uchar* dst = _dst.ptr<uchar>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (uchar)(s > ithresh ? s : 0); |
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} |
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} |
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else if( depth == CV_16S ) |
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{ |
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const short* src = _src.ptr<short>(i); |
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short* dst = _dst.ptr<short>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (short)(s > ithresh ? s : 0); |
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} |
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} |
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else if( depth == CV_16U ) |
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{ |
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const ushort* src = _src.ptr<ushort>(i); |
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ushort* dst = _dst.ptr<ushort>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (ushort)(s > ithresh ? s : 0); |
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} |
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} |
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else if( depth == CV_32F ) |
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{ |
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const float* src = _src.ptr<float>(i); |
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float* dst = _dst.ptr<float>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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float s = src[j]; |
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dst[j] = s > thresh ? s : 0.f; |
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} |
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} |
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else |
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{ |
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const double* src = _src.ptr<double>(i); |
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double* dst = _dst.ptr<double>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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double s = src[j]; |
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dst[j] = s > thresh ? s : 0.0; |
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} |
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} |
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} |
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break; |
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case CV_THRESH_TOZERO_INV: |
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for( i = 0; i < height; i++ ) |
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{ |
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if( depth == CV_8U ) |
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{ |
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const uchar* src = _src.ptr<uchar>(i); |
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uchar* dst = _dst.ptr<uchar>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (uchar)(s > ithresh ? 0 : s); |
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} |
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} |
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else if( depth == CV_16S ) |
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{ |
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const short* src = _src.ptr<short>(i); |
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short* dst = _dst.ptr<short>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (short)(s > ithresh ? 0 : s); |
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} |
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} |
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else if( depth == CV_16U ) |
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{ |
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const ushort* src = _src.ptr<ushort>(i); |
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ushort* dst = _dst.ptr<ushort>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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int s = src[j]; |
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dst[j] = (ushort)(s > ithresh ? 0 : s); |
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} |
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} |
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else if (depth == CV_32F) |
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{ |
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const float* src = _src.ptr<float>(i); |
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float* dst = _dst.ptr<float>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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float s = src[j]; |
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dst[j] = s > thresh ? 0.f : s; |
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} |
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} |
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else |
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{ |
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const double* src = _src.ptr<double>(i); |
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double* dst = _dst.ptr<double>(i); |
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for( j = 0; j < width_n; j++ ) |
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{ |
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double s = src[j]; |
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dst[j] = s > thresh ? 0.0 : s; |
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} |
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} |
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} |
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break; |
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default: |
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CV_Assert(0); |
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} |
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} |
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void CV_ThreshTest::prepare_to_validation( int /*test_case_idx*/ ) |
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{ |
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test_threshold( test_mat[INPUT][0], test_mat[REF_OUTPUT][0], |
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thresh_val, max_val, thresh_type, extra_type ); |
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} |
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TEST(Imgproc_Threshold, accuracy) { CV_ThreshTest test; test.safe_run(); } |
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TEST(Imgproc_Threshold, accuracyOtsu) { CV_ThreshTest test(CV_THRESH_OTSU); test.safe_run(); } |
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BIGDATA_TEST(Imgproc_Threshold, huge) |
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{ |
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Mat m(65000, 40000, CV_8U); |
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ASSERT_FALSE(m.isContinuous()); |
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uint64 i, n = (uint64)m.rows*m.cols; |
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for( i = 0; i < n; i++ ) |
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m.data[i] = (uchar)(i & 255); |
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cv::threshold(m, m, 127, 255, cv::THRESH_BINARY); |
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int nz = cv::countNonZero(m); // FIXIT 'int' is not enough here (overflow is possible with other inputs) |
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ASSERT_EQ((uint64)nz, n / 2); |
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} |
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TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_16085) |
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{ |
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Size sz(16, 16); |
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Mat input(sz, CV_32F, Scalar::all(2)); |
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Mat result; |
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cv::threshold(input, result, 2.0, 0.0, THRESH_TOZERO); |
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EXPECT_EQ(0, cv::norm(result, NORM_INF)); |
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} |
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TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258) |
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{ |
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Size sz(16, 16); |
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float val = nextafterf(16.0f, 0.0f); // 0x417fffff, all bits in mantissa are 1 |
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Mat input(sz, CV_32F, Scalar::all(val)); |
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Mat result; |
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cv::threshold(input, result, val, 0.0, THRESH_TOZERO); |
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EXPECT_EQ(0, cv::norm(result, NORM_INF)); |
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} |
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TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Min) |
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{ |
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Size sz(16, 16); |
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float min_val = -std::numeric_limits<float>::max(); |
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Mat input(sz, CV_32F, Scalar::all(min_val)); |
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Mat result; |
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cv::threshold(input, result, min_val, 0.0, THRESH_TOZERO); |
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EXPECT_EQ(0, cv::norm(result, NORM_INF)); |
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} |
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TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Max) |
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{ |
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Size sz(16, 16); |
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float max_val = std::numeric_limits<float>::max(); |
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Mat input(sz, CV_32F, Scalar::all(max_val)); |
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Mat result; |
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cv::threshold(input, result, max_val, 0.0, THRESH_TOZERO); |
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EXPECT_EQ(0, cv::norm(result, NORM_INF)); |
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} |
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}} // namespace
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