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vendor: OpenCV 5.0.0 snapshot at 755e50675d97db9b7d449d8bd6b09888646f6c6e

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2026-08-22 00:11:13 +08:00
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modules/xstereo/test/test_block_matching.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
class CV_BlockMatchingTest : public cvtest::BaseTest
{
public:
CV_BlockMatchingTest();
~CV_BlockMatchingTest();
protected:
void run(int /* idx */);
};
CV_BlockMatchingTest::CV_BlockMatchingTest(){}
CV_BlockMatchingTest::~CV_BlockMatchingTest(){}
static double errorLevel(const Mat &ideal, Mat &actual)
{
uint8_t *date, *harta;
harta = actual.data;
date = ideal.data;
int stride, h;
stride = (int)ideal.step;
h = ideal.rows;
int error = 0;
for (int i = 0; i < ideal.rows; i++)
{
for (int j = 0; j < ideal.cols; j++)
{
if (date[i * stride + j] != 0)
if (abs(date[i * stride + j] - harta[i * stride + j]) > 2 * 16)
{
error += 1;
}
}
}
return ((double)((error * 100) * 1.0) / (stride * h));
}
void CV_BlockMatchingTest::run(int )
{
Mat image1, image2, gt;
image1 = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im2.png", IMREAD_GRAYSCALE);
image2 = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im6.png", IMREAD_GRAYSCALE);
gt = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/disp2.png", IMREAD_GRAYSCALE);
if(image1.empty() || image2.empty() || gt.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(image1.rows != image2.rows || image1.cols != image2.cols || gt.cols != image1.cols || gt.rows != image1.rows)
{
ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
RNG range;
//set the parameters
int binary_descriptor_type = range.uniform(0,8);
int kernel_size, aggregation_window;
if(binary_descriptor_type == 0)
kernel_size = 5;
else if(binary_descriptor_type == 2 || binary_descriptor_type == 3)
kernel_size = 7;
else if(binary_descriptor_type == 1)
kernel_size = 11;
else
kernel_size = 9;
if(binary_descriptor_type == 3)
aggregation_window = 13;
else
aggregation_window = 11;
Mat test = Mat(image1.rows, image1.cols, CV_8UC1);
Ptr<StereoBinaryBM> sbm = StereoBinaryBM::create(16, kernel_size);
//we set the corresponding parameters
sbm->setPreFilterCap(31);
sbm->setMinDisparity(0);
sbm->setTextureThreshold(10);
sbm->setUniquenessRatio(0);
sbm->setSpeckleWindowSize(400);//speckle size
sbm->setSpeckleRange(200);
sbm->setDisp12MaxDiff(0);
sbm->setScalleFactor(16);//the scaling factor
sbm->setBinaryKernelType(binary_descriptor_type);//binary descriptor kernel
sbm->setAgregationWindowSize(aggregation_window);
//speckle removal algorithm the user can choose between the average speckle removal algorithm
//or the classical version that was implemented in open cv
sbm->setSpekleRemovalTechnique(CV_SPECKLE_REMOVAL_AVG_ALGORITHM);
sbm->setUsePrefilter(false);//pre-filter or not the images prior to making the transformations
//-- calculate the disparity image
sbm->compute(image1, image2, test);
if(test.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return;
}
if(errorLevel(gt,test) > 20)
{
ts->printf( cvtest::TS::LOG,
"Too big error\n");
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
return;
}
}
class CV_SGBlockMatchingTest : public cvtest::BaseTest
{
public:
CV_SGBlockMatchingTest();
~CV_SGBlockMatchingTest();
protected:
void run(int /* idx */);
};
CV_SGBlockMatchingTest::CV_SGBlockMatchingTest(){}
CV_SGBlockMatchingTest::~CV_SGBlockMatchingTest(){}
void CV_SGBlockMatchingTest::run(int )
{
Mat image1, image2, gt;
image1 = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im2.png", IMREAD_GRAYSCALE);
image2 = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im6.png", IMREAD_GRAYSCALE);
gt = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/disp2.png", IMREAD_GRAYSCALE);
ts->printf(cvtest::TS::LOG,(ts->get_data_path() + "stereomatching/datasets/tsukuba/im2.png").c_str());
if(image1.empty() || image2.empty() || gt.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(image1.rows != image2.rows || image1.cols != image2.cols || gt.cols != image1.cols || gt.rows != image1.rows)
{
ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
RNG range;
//set the parameters
int binary_descriptor_type = range.uniform(0,8);
int kernel_size;
if(binary_descriptor_type == 0)
kernel_size = 5;
else if(binary_descriptor_type == 2 || binary_descriptor_type == 3)
kernel_size = 7;
else if(binary_descriptor_type == 1)
kernel_size = 11;
else
kernel_size = 9;
Mat test = Mat(image1.rows, image1.cols, CV_8UC1);
Mat imgDisparity16S2 = Mat(image1.rows, image1.cols, CV_16S);
Ptr<StereoBinarySGBM> sgbm = StereoBinarySGBM::create(0, 16, kernel_size);
//setting the penalties for sgbm
sgbm->setP1(10);
sgbm->setP2(100);
sgbm->setMinDisparity(0);
sgbm->setNumDisparities(16);//set disparity number
sgbm->setUniquenessRatio(1);
sgbm->setSpeckleWindowSize(400);
sgbm->setSpeckleRange(200);
sgbm->setDisp12MaxDiff(1);
sgbm->setBinaryKernelType(binary_descriptor_type);//set the binary descriptor
sgbm->setSpekleRemovalTechnique(CV_SPECKLE_REMOVAL_AVG_ALGORITHM); //the avg speckle removal algorithm
sgbm->setSubPixelInterpolationMethod(CV_SIMETRICV_INTERPOLATION);// the SIMETRIC V interpolation method
sgbm->compute(image1, image2, imgDisparity16S2);
double minVal; double maxVal;
minMaxLoc(imgDisparity16S2, &minVal, &maxVal);
imgDisparity16S2.convertTo(test, CV_8UC1, 255 / (maxVal - minVal));
if(test.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return;
}
double error = errorLevel(gt,test);
if(error > 10)
{
ts->printf( cvtest::TS::LOG,
"Too big error\n");
ts->set_failed_test_info(cvtest::TS::FAIL_BAD_ACCURACY);
return;
}
}
TEST(block_matching_simple_test, accuracy) { CV_BlockMatchingTest test; test.safe_run(); }
TEST(SG_block_matching_simple_test, accuracy) { CV_SGBlockMatchingTest test; test.safe_run(); }
}} // namespace
modules/xstereo/test/test_descriptors.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
namespace opencv_test { namespace {
class CV_DescriptorBaseTest : public cvtest::BaseTest
{
public:
CV_DescriptorBaseTest();
~CV_DescriptorBaseTest();
protected:
virtual void imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2) = 0;
virtual void imageTransformation(const Mat &img1, Mat &out1) = 0;
void testROI(const Mat &img);
void testMonotonicity(const Mat &img, Mat &out);
void run(int );
Mat censusImage[2];
Mat censusImageSingle[2];
Mat left;
Mat right;
int kernel_size, descriptor_type;
};
//we test to see if the descriptor applied on a roi
//has the same value with the descriptor from the original image
//tested at the roi boundaries
void CV_DescriptorBaseTest::testROI(const Mat &img)
{
int pt, pb,w,h;
//initialize random values for the roi top and bottom
pt = rand() % 100;
pb = rand() % 100;
//calculate the new width and height
w = img.cols;
h = img.rows - pt - pb;
int start = pt + kernel_size / 2 + 1;
int stop = h - kernel_size/2 - 1;
//set the region of interest according to above values
Rect region_of_interest = Rect(0, pt, w, h);
Mat image_roi1 = img(region_of_interest);
Mat p1,p2;
//create 2 images where to put our output
p1.create(image_roi1.rows, image_roi1.cols, CV_32SC4);
p2.create(img.rows, img.cols, CV_32SC4);
imageTransformation(image_roi1,p1);
imageTransformation(img,p2);
int *roi_data = (int *)p1.data;
int *img_data = (int *)p2.data;
//verify result
for(int i = start; i < stop; i++)
{
for(int j = 0; j < w ; j++)
{
if(roi_data[(i - pt) * w + j] != img_data[(i) * w + j])
{
ts->printf(cvtest::TS::LOG, "Something wrong with ROI \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
return;
}
}
}
}
CV_DescriptorBaseTest::~CV_DescriptorBaseTest()
{
left.release();
right.release();
censusImage[0].release();
censusImage[1].release();
censusImageSingle[0].release();
censusImageSingle[1].release();
}
CV_DescriptorBaseTest::CV_DescriptorBaseTest()
{
//read 2 images from file
left = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im2.png", IMREAD_GRAYSCALE);
right = imread(ts->get_data_path() + "stereomatching/datasets/tsukuba/im6.png", IMREAD_GRAYSCALE);
if(left.empty() || right.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
ts->printf(cvtest::TS::LOG, "Data loaded \n");
}
//verify if we don't have an image with all pixels the same( except when all input pixels are equal)
void CV_DescriptorBaseTest::testMonotonicity(const Mat &img, Mat &out)
{
//verify if input data is correct
if(img.rows != out.rows || img.cols != out.cols || img.empty() || out.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output dimension \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
//verify that for an input image with different pxels the values of the
//output pixels are not the same
int same = 0;
uint8_t *data = img.data;
uint8_t val = data[1];
int stride = (int)img.step;
for(int i = 0 ; i < img.rows && !same; i++)
{
for(int j = 0; j < img.cols; j++)
{
if(val != data[i * stride + j])
{
same = 1;
break;
}
}
}
int value_descript = out.data[1];
int accept = 0;
uint8_t *outData = out.data;
for(int i = 0 ; i < img.rows && !accept; i++)
{
for(int j = 0; j < img.cols; j++)
{
//we verify for the output image if the iage pixels are not all the same of an input
//image with different pixels
if(value_descript != outData[i * stride + j] && same)
{
//if we found a value that is different we accept
accept = 1;
break;
}
}
}
if(accept == 1 && same == 0)
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ts->printf(cvtest::TS::LOG, "The image has all values the same \n");
return;
}
if(accept == 0 && same == 1)
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ts->printf(cvtest::TS::LOG, "For correct image we get all descriptor values the same \n");
return;
}
ts->set_failed_test_info(cvtest::TS::OK);
}
///////////////////////////////////
//census transform
class CV_CensusTransformTest: public CV_DescriptorBaseTest
{
public:
CV_CensusTransformTest();
protected:
void imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2);
void imageTransformation(const Mat &img1, Mat &out1);
};
CV_CensusTransformTest::CV_CensusTransformTest()
{
kernel_size = 11;
descriptor_type = CV_SPARSE_CENSUS;
}
void CV_CensusTransformTest::imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2)
{
//verify if input data is correct
if(img1.rows != out1.rows || img1.cols != out1.cols || img1.empty() || out1.empty()
|| img2.rows != out2.rows || img2.cols != out2.cols || img2.empty() || out2.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(kernel_size % 2 == 0)
{
ts->printf(cvtest::TS::LOG, "Wrong kernel size;Kernel should be odd \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
censusTransform(img1,img2,kernel_size,out1,out2,descriptor_type);
}
void CV_CensusTransformTest::imageTransformation(const Mat &img1, Mat &out1)
{
//verify if input data is correct
if(img1.rows != out1.rows || img1.cols != out1.cols || img1.empty() || out1.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(kernel_size % 2 == 0)
{
ts->printf(cvtest::TS::LOG, "Wrong kernel size;Kernel should be odd \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
censusTransform(img1,kernel_size,out1,descriptor_type);
}
//////////////////////////////////
//symetric census
class CV_SymetricCensusTest: public CV_DescriptorBaseTest
{
public:
CV_SymetricCensusTest();
protected:
void imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2);
void imageTransformation(const Mat &img1, Mat &out1);
};
CV_SymetricCensusTest::CV_SymetricCensusTest()
{
kernel_size = 7;
descriptor_type = CV_CS_CENSUS;
}
void CV_SymetricCensusTest::imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2)
{
//verify if input data is correct
if(img1.rows != out1.rows || img1.cols != out1.cols || img1.empty() || out1.empty()
|| img2.rows != out2.rows || img2.cols != out2.cols || img2.empty() || out2.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(kernel_size % 2 == 0)
{
ts->printf(cvtest::TS::LOG, "Wrong kernel size;Kernel should be odd \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
symetricCensusTransform(img1,img2,kernel_size,out1,out2,descriptor_type);
}
void CV_SymetricCensusTest::imageTransformation(const Mat &img1, Mat &out1)
{
//verify if input data is correct
if(img1.rows != out1.rows || img1.cols != out1.cols || img1.empty() || out1.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(kernel_size % 2 == 0)
{
ts->printf(cvtest::TS::LOG, "Wrong kernel size;Kernel should be odd \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
symetricCensusTransform(img1,kernel_size,out1,descriptor_type);
}
//////////////////////////////////
//modified census transform
class CV_ModifiedCensusTransformTest: public CV_DescriptorBaseTest
{
public:
CV_ModifiedCensusTransformTest();
protected:
void imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2);
void imageTransformation(const Mat &img1, Mat &out1);
};
CV_ModifiedCensusTransformTest::CV_ModifiedCensusTransformTest()
{
kernel_size = 9;
descriptor_type = CV_MODIFIED_CENSUS_TRANSFORM;
}
void CV_ModifiedCensusTransformTest::imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2)
{
//verify if input data is correct
if(img1.rows != out1.rows || img1.cols != out1.cols || img1.empty() || out1.empty()
|| img2.rows != out2.rows || img2.cols != out2.cols || img2.empty() || out2.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(kernel_size % 2 == 0)
{
ts->printf(cvtest::TS::LOG, "Wrong kernel size;Kernel should be odd \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
modifiedCensusTransform(img1,img2,kernel_size,out1,out2,descriptor_type);
}
void CV_ModifiedCensusTransformTest::imageTransformation(const Mat &img1, Mat &out1)
{
if(img1.rows != out1.rows || img1.cols != out1.cols || img1.empty() || out1.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(kernel_size % 2 == 0)
{
ts->printf(cvtest::TS::LOG, "Wrong kernel size;Kernel should be odd \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
modifiedCensusTransform(img1,kernel_size,out1,descriptor_type);
}
//////////////////////////////////
//star kernel census
class CV_StarKernelCensusTest: public CV_DescriptorBaseTest
{
public:
CV_StarKernelCensusTest();
protected:
void imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2);
void imageTransformation(const Mat &img1, Mat &out1);
};
CV_StarKernelCensusTest :: CV_StarKernelCensusTest()
{
kernel_size = 9;
descriptor_type = CV_STAR_KERNEL;
}
void CV_StarKernelCensusTest :: imageTransformation(const Mat &img1, const Mat &img2, Mat &out1, Mat &out2)
{
//verify if input data is correct
if(img1.rows != out1.rows || img1.cols != out1.cols || img1.empty() || out1.empty()
|| img2.rows != out2.rows || img2.cols != out2.cols || img2.empty() || out2.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(kernel_size % 2 == 0)
{
ts->printf(cvtest::TS::LOG, "Wrong kernel size;Kernel should be odd \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
starCensusTransform(img1,img2,kernel_size,out1,out2);
}
void CV_StarKernelCensusTest::imageTransformation(const Mat &img1, Mat &out1)
{
if(img1.rows != out1.rows || img1.cols != out1.cols || img1.empty() || out1.empty())
{
ts->printf(cvtest::TS::LOG, "Wrong input / output data \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
if(kernel_size % 2 == 0)
{
ts->printf(cvtest::TS::LOG, "Wrong kernel size;Kernel should be odd \n");
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
starCensusTransform(img1,kernel_size,out1);
}
void CV_DescriptorBaseTest::run(int )
{
if (left.empty() || right.empty())
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
ts->printf(cvtest::TS::LOG, "No input images detected\n");
return;
}
testROI(left);
censusImage[0].create(left.rows, left.cols, CV_32SC4);
censusImage[1].create(left.rows, left.cols, CV_32SC4);
censusImageSingle[0].create(left.rows, left.cols, CV_32SC4);
censusImageSingle[1].create(left.rows, left.cols, CV_32SC4);
censusImage[0].setTo(0);
censusImage[1].setTo(0);
censusImageSingle[0].setTo(0);
censusImageSingle[1].setTo(0);
imageTransformation(left, right, censusImage[0], censusImage[1]);
imageTransformation(left, censusImageSingle[0]);
imageTransformation(right, censusImageSingle[1]);
testMonotonicity(left,censusImage[0]);
testMonotonicity(right,censusImage[1]);
testMonotonicity(left,censusImageSingle[0]);
testMonotonicity(right,censusImageSingle[1]);
if (censusImage[0].empty() || censusImage[1].empty() || censusImageSingle[0].empty() || censusImageSingle[1].empty())
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ts->printf(cvtest::TS::LOG, "The descriptor images are empty \n");
return;
}
int *datl1 = (int *)censusImage[0].data;
int *datr1 = (int *)censusImage[1].data;
int *datl2 = (int *)censusImageSingle[0].data;
int *datr2 = (int *)censusImageSingle[1].data;
for(int i = 0; i < censusImage[0].rows - kernel_size/ 2; i++)
{
for(int j = 0; j < censusImage[0].cols; j++)
{
if(datl1[i * censusImage[0].cols + j] != datl2[i * censusImage[0].cols + j])
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ts->printf(cvtest::TS::LOG, "Mismatch for left images %d \n",descriptor_type);
return;
}
if(datr1[i * censusImage[0].cols + j] != datr2[i * censusImage[0].cols + j])
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_OUTPUT);
ts->printf(cvtest::TS::LOG, "Mismatch for right images %d \n",descriptor_type);
return;
}
}
}
int min = std::numeric_limits<int>::min();
int max = std::numeric_limits<int>::max();
//check if all values are between int min and int max and not NAN
if (0 != cvtest::check(censusImage[0], min, max, 0))
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return;
}
//check if all values are between int min and int max and not NAN
if (0 != cvtest::check(censusImage[1], min, max, 0))
{
ts->set_failed_test_info(cvtest::TS::FAIL_INVALID_TEST_DATA);
return ;
}
}
TEST(DISABLED_census_transform_testing, accuracy) { CV_CensusTransformTest test; test.safe_run(); }
TEST(DISABLED_symetric_census_testing, accuracy) { CV_SymetricCensusTest test; test.safe_run(); }
TEST(DISABLED_Dmodified_census_testing, accuracy) { CV_ModifiedCensusTransformTest test; test.safe_run(); }
TEST(DISABLED_Dstar_kernel_testing, accuracy) { CV_StarKernelCensusTest test; test.safe_run(); }
}} // namespace
modules/xstereo/test/test_main.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "test_precomp.hpp"
CV_TEST_MAIN("cv")
modules/xstereo/test/test_precomp.hpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#ifndef __OPENCV_TEST_PRECOMP_HPP__
#define __OPENCV_TEST_PRECOMP_HPP__
#include "opencv2/ts.hpp"
#include "opencv2/xstereo.hpp"
namespace opencv_test {
using namespace cv::stereo;
}
#endif
modules/xstereo/test/test_qds_matching.cpp
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// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
#include "test_precomp.hpp"
namespace opencv_test { namespace {
static float disparity_MAE(const Mat &reference, const Mat &estimation)
{
int elems=0;
float error=0;
for (int row=0; row< reference.rows; row++){
for (int col=0; col<reference.cols; col++){
float ref_val = reference.at<float>(row, col);
float estimated_val = estimation.at<float>(row, col);
// filter out pixels with unknown reference value and pixels whose disparity did not get estimated.
if (estimated_val == 0 || ref_val == 0 || std::isnan(estimated_val)){
continue;
}
else{
error+=abs(ref_val - estimated_val);
elems+=1;
}
}
}
return error/elems;
}
// void CV_QdsMatchingTest::run(int)
TEST(qds_getDisparity, accuracy)
{
//load data
Mat image1, image2, gt;
image1 = imread(cvtest::TS::ptr()->get_data_path() + "stereomatching/datasets/cones/im2.png", IMREAD_GRAYSCALE);
image2 = imread(cvtest::TS::ptr()->get_data_path() + "stereomatching/datasets/cones/im6.png", IMREAD_GRAYSCALE);
gt = imread(cvtest::TS::ptr()->get_data_path() + "stereomatching/datasets/cones/disp2.png", IMREAD_GRAYSCALE);
// reference scale factor is based on this https://github.com/opencv/opencv_extra/blob/master/testdata/cv/stereomatching/datasets/datasets.xml
gt.convertTo(gt, CV_32F);
gt =gt/4;
//test inputs
ASSERT_FALSE(image1.empty() || image2.empty() || gt.empty()) << "Issue with input data";
//configure disparity algorithm
cv::Size frameSize = image1.size();
Ptr<stereo::QuasiDenseStereo> qds_matcher = stereo::QuasiDenseStereo::create(frameSize);
//compute disparity
qds_matcher->process(image1, image2);
Mat outDisp = qds_matcher->getDisparity();
// test input output size consistency
ASSERT_EQ(gt.size(), outDisp.size()) << "Mismatch input/output dimensions";
ASSERT_LT(disparity_MAE(gt, outDisp),2) << "EPE should be 1.1053 for this sample/hyperparamters (Tested on version 4.5.1)";
}
}} // namespace