236 lines
8.7 KiB
C++
236 lines
8.7 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_BlockMatchingTest : public cvtest::BaseTest
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{
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public:
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CV_BlockMatchingTest();
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~CV_BlockMatchingTest();
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protected:
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void run(int /* idx */);
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};
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CV_BlockMatchingTest::CV_BlockMatchingTest(){}
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CV_BlockMatchingTest::~CV_BlockMatchingTest(){}
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static double errorLevel(const Mat &ideal, Mat &actual)
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{
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uint8_t *date, *harta;
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harta = actual.data;
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date = ideal.data;
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int stride, h;
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stride = (int)ideal.step;
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h = ideal.rows;
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int error = 0;
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for (int i = 0; i < ideal.rows; i++)
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{
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for (int j = 0; j < ideal.cols; j++)
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{
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if (date[i * stride + j] != 0)
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if (abs(date[i * stride + j] - harta[i * stride + j]) > 2 * 16)
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{
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error += 1;
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}
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}
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}
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return ((double)((error * 100) * 1.0) / (stride * h));
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}
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void CV_BlockMatchingTest::run(int )
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{
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Mat image1, image2, gt;
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image1 = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im2.png", IMREAD_GRAYSCALE);
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image2 = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im6.png", IMREAD_GRAYSCALE);
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gt = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/disp2.png", IMREAD_GRAYSCALE);
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if(image1.empty() || image2.empty() || gt.empty())
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{
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ts->printf(cvtest::TS::LOG, "Wrong input data \n");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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if(image1.rows != image2.rows || image1.cols != image2.cols || gt.cols != image1.cols || gt.rows != image1.rows)
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{
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ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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RNG range;
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//set the parameters
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int binary_descriptor_type = range.uniform(0,8);
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int kernel_size, aggregation_window;
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if(binary_descriptor_type == 0)
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kernel_size = 5;
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else if(binary_descriptor_type == 2 || binary_descriptor_type == 3)
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kernel_size = 7;
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else if(binary_descriptor_type == 1)
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kernel_size = 11;
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else
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kernel_size = 9;
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if(binary_descriptor_type == 3)
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aggregation_window = 13;
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else
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aggregation_window = 11;
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Mat test = Mat(image1.rows, image1.cols, CV_8UC1);
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Ptr<StereoBinaryBM> sbm = StereoBinaryBM::create(16, kernel_size);
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//we set the corresponding parameters
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sbm->setPreFilterCap(31);
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sbm->setMinDisparity(0);
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sbm->setTextureThreshold(10);
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sbm->setUniquenessRatio(0);
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sbm->setSpeckleWindowSize(400);//speckle size
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sbm->setSpeckleRange(200);
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sbm->setDisp12MaxDiff(0);
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sbm->setScalleFactor(16);//the scaling factor
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sbm->setBinaryKernelType(binary_descriptor_type);//binary descriptor kernel
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sbm->setAgregationWindowSize(aggregation_window);
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//speckle removal algorithm the user can choose between the average speckle removal algorithm
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//or the classical version that was implemented in open cv
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sbm->setSpekleRemovalTechnique(CV_SPECKLE_REMOVAL_AVG_ALGORITHM);
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sbm->setUsePrefilter(false);//pre-filter or not the images prior to making the transformations
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//-- calculate the disparity image
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sbm->compute(image1, image2, test);
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if(test.empty())
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{
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ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return;
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}
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if(errorLevel(gt,test) > 20)
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{
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ts->printf( cvtest::TS::LOG,
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"Too big error\n");
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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return;
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}
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}
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class CV_SGBlockMatchingTest : public cvtest::BaseTest
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{
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public:
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CV_SGBlockMatchingTest();
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~CV_SGBlockMatchingTest();
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protected:
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void run(int /* idx */);
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};
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CV_SGBlockMatchingTest::CV_SGBlockMatchingTest(){}
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CV_SGBlockMatchingTest::~CV_SGBlockMatchingTest(){}
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void CV_SGBlockMatchingTest::run(int )
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{
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Mat image1, image2, gt;
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image1 = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im2.png", IMREAD_GRAYSCALE);
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image2 = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im6.png", IMREAD_GRAYSCALE);
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gt = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/disp2.png", IMREAD_GRAYSCALE);
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if(image1.empty() || image2.empty() || gt.empty())
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{
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ts->printf(cvtest::TS::LOG, "Wrong input data \n");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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if(image1.rows != image2.rows || image1.cols != image2.cols || gt.cols != image1.cols || gt.rows != image1.rows)
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{
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ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
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return;
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}
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RNG range;
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//set the parameters
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int binary_descriptor_type = range.uniform(0,8);
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int kernel_size;
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if(binary_descriptor_type == 0)
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kernel_size = 5;
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else if(binary_descriptor_type == 2 || binary_descriptor_type == 3)
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kernel_size = 7;
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else if(binary_descriptor_type == 1)
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kernel_size = 11;
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else
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kernel_size = 9;
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Mat test = Mat(image1.rows, image1.cols, CV_8UC1);
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Mat imgDisparity16S2 = Mat(image1.rows, image1.cols, CV_16S);
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Ptr<StereoBinarySGBM> sgbm = StereoBinarySGBM::create(0, 16, kernel_size);
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//setting the penalties for sgbm
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sgbm->setP1(10);
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sgbm->setP2(100);
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sgbm->setMinDisparity(0);
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sgbm->setNumDisparities(16);//set disparity number
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sgbm->setUniquenessRatio(1);
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sgbm->setSpeckleWindowSize(400);
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sgbm->setSpeckleRange(200);
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sgbm->setDisp12MaxDiff(1);
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sgbm->setBinaryKernelType(binary_descriptor_type);//set the binary descriptor
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sgbm->setSpekleRemovalTechnique(CV_SPECKLE_REMOVAL_AVG_ALGORITHM); //the avg speckle removal algorithm
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sgbm->setSubPixelInterpolationMethod(CV_SIMETRICV_INTERPOLATION);// the SIMETRIC V interpolation method
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sgbm->compute(image1, image2, imgDisparity16S2);
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double minVal; double maxVal;
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minMaxLoc(imgDisparity16S2, &minVal, &maxVal);
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imgDisparity16S2.convertTo(test, CV_8UC1, 255 / (maxVal - minVal));
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if(test.empty())
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{
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ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
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ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
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return;
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}
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double error = errorLevel(gt,test);
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if(error > 10)
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{
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ts->printf( cvtest::TS::LOG,
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"Too big error\n");
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ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
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return;
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}
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}
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TEST(block_matching_simple_test, accuracy) { CV_BlockMatchingTest test; test.safe_run(); }
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TEST(SG_block_matching_simple_test, accuracy) { CV_SGBlockMatchingTest test; test.safe_run(); }
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}} // namespace
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