545 lines
18 KiB
C++
545 lines
18 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_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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}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
const double* src = _src.ptr<double>(i);
|
||
|
|
double* dst = _dst.ptr<double>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
double s = src[j];
|
||
|
|
dst[j] = s > thresh ? thresh : s;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
break;
|
||
|
|
case CV_THRESH_TOZERO:
|
||
|
|
for( i = 0; i < height; i++ )
|
||
|
|
{
|
||
|
|
if( depth == CV_8U )
|
||
|
|
{
|
||
|
|
const uchar* src = _src.ptr<uchar>(i);
|
||
|
|
uchar* dst = _dst.ptr<uchar>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
int s = src[j];
|
||
|
|
dst[j] = (uchar)(s > ithresh ? s : 0);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if( depth == CV_16S )
|
||
|
|
{
|
||
|
|
const short* src = _src.ptr<short>(i);
|
||
|
|
short* dst = _dst.ptr<short>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
int s = src[j];
|
||
|
|
dst[j] = (short)(s > ithresh ? s : 0);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if( depth == CV_16U )
|
||
|
|
{
|
||
|
|
const ushort* src = _src.ptr<ushort>(i);
|
||
|
|
ushort* dst = _dst.ptr<ushort>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
int s = src[j];
|
||
|
|
dst[j] = (ushort)(s > ithresh ? s : 0);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if( depth == CV_32F )
|
||
|
|
{
|
||
|
|
const float* src = _src.ptr<float>(i);
|
||
|
|
float* dst = _dst.ptr<float>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
float s = src[j];
|
||
|
|
dst[j] = s > thresh ? s : 0.f;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
const double* src = _src.ptr<double>(i);
|
||
|
|
double* dst = _dst.ptr<double>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
double s = src[j];
|
||
|
|
dst[j] = s > thresh ? s : 0.0;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
break;
|
||
|
|
case CV_THRESH_TOZERO_INV:
|
||
|
|
for( i = 0; i < height; i++ )
|
||
|
|
{
|
||
|
|
if( depth == CV_8U )
|
||
|
|
{
|
||
|
|
const uchar* src = _src.ptr<uchar>(i);
|
||
|
|
uchar* dst = _dst.ptr<uchar>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
int s = src[j];
|
||
|
|
dst[j] = (uchar)(s > ithresh ? 0 : s);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if( depth == CV_16S )
|
||
|
|
{
|
||
|
|
const short* src = _src.ptr<short>(i);
|
||
|
|
short* dst = _dst.ptr<short>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
int s = src[j];
|
||
|
|
dst[j] = (short)(s > ithresh ? 0 : s);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if( depth == CV_16U )
|
||
|
|
{
|
||
|
|
const ushort* src = _src.ptr<ushort>(i);
|
||
|
|
ushort* dst = _dst.ptr<ushort>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
int s = src[j];
|
||
|
|
dst[j] = (ushort)(s > ithresh ? 0 : s);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else if (depth == CV_32F)
|
||
|
|
{
|
||
|
|
const float* src = _src.ptr<float>(i);
|
||
|
|
float* dst = _dst.ptr<float>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
float s = src[j];
|
||
|
|
dst[j] = s > thresh ? 0.f : s;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
else
|
||
|
|
{
|
||
|
|
const double* src = _src.ptr<double>(i);
|
||
|
|
double* dst = _dst.ptr<double>(i);
|
||
|
|
for( j = 0; j < width_n; j++ )
|
||
|
|
{
|
||
|
|
double s = src[j];
|
||
|
|
dst[j] = s > thresh ? 0.0 : s;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
}
|
||
|
|
break;
|
||
|
|
default:
|
||
|
|
CV_Assert(0);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
void CV_ThreshTest::prepare_to_validation( int /*test_case_idx*/ )
|
||
|
|
{
|
||
|
|
test_threshold( test_mat[INPUT][0], test_mat[REF_OUTPUT][0],
|
||
|
|
thresh_val, max_val, thresh_type, extra_type );
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Imgproc_Threshold, accuracy) { CV_ThreshTest test; test.safe_run(); }
|
||
|
|
TEST(Imgproc_Threshold, accuracyOtsu) { CV_ThreshTest test(CV_THRESH_OTSU); test.safe_run(); }
|
||
|
|
|
||
|
|
BIGDATA_TEST(Imgproc_Threshold, huge)
|
||
|
|
{
|
||
|
|
Mat m(65000, 40000, CV_8U);
|
||
|
|
ASSERT_FALSE(m.isContinuous());
|
||
|
|
|
||
|
|
uint64 i, n = (uint64)m.rows*m.cols;
|
||
|
|
for( i = 0; i < n; i++ )
|
||
|
|
m.data[i] = (uchar)(i & 255);
|
||
|
|
|
||
|
|
cv::threshold(m, m, 127, 255, cv::THRESH_BINARY);
|
||
|
|
int nz = cv::countNonZero(m); // FIXIT 'int' is not enough here (overflow is possible with other inputs)
|
||
|
|
ASSERT_EQ((uint64)nz, n / 2);
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_16085)
|
||
|
|
{
|
||
|
|
Size sz(16, 16);
|
||
|
|
Mat input(sz, CV_32F, Scalar::all(2));
|
||
|
|
Mat result;
|
||
|
|
cv::threshold(input, result, 2.0, 0.0, THRESH_TOZERO);
|
||
|
|
EXPECT_EQ(0, cv::norm(result, NORM_INF));
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258)
|
||
|
|
{
|
||
|
|
Size sz(16, 16);
|
||
|
|
float val = nextafterf(16.0f, 0.0f); // 0x417fffff, all bits in mantissa are 1
|
||
|
|
Mat input(sz, CV_32F, Scalar::all(val));
|
||
|
|
Mat result;
|
||
|
|
cv::threshold(input, result, val, 0.0, THRESH_TOZERO);
|
||
|
|
EXPECT_EQ(0, cv::norm(result, NORM_INF));
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Min)
|
||
|
|
{
|
||
|
|
Size sz(16, 16);
|
||
|
|
float min_val = -std::numeric_limits<float>::max();
|
||
|
|
Mat input(sz, CV_32F, Scalar::all(min_val));
|
||
|
|
Mat result;
|
||
|
|
cv::threshold(input, result, min_val, 0.0, THRESH_TOZERO);
|
||
|
|
EXPECT_EQ(0, cv::norm(result, NORM_INF));
|
||
|
|
}
|
||
|
|
|
||
|
|
TEST(Imgproc_Threshold, regression_THRESH_TOZERO_IPP_21258_Max)
|
||
|
|
{
|
||
|
|
Size sz(16, 16);
|
||
|
|
float max_val = std::numeric_limits<float>::max();
|
||
|
|
Mat input(sz, CV_32F, Scalar::all(max_val));
|
||
|
|
Mat result;
|
||
|
|
cv::threshold(input, result, max_val, 0.0, THRESH_TOZERO);
|
||
|
|
EXPECT_EQ(0, cv::norm(result, NORM_INF));
|
||
|
|
}
|
||
|
|
|
||
|
|
}} // namespace
|