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opencv/modules/core/test/test_umat.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) 2013, OpenCV Foundation, 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 OpenCV Foundation 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"
#include "opencv2/core/ocl.hpp"
using namespace cvtest;
using namespace testing;
using namespace cv;
#define EXPECT_MAT_NEAR(mat1, mat2, eps) \
{ \
ASSERT_EQ(mat1.type(), mat2.type()); \
ASSERT_EQ(mat1.size(), mat2.size()); \
EXPECT_LE(cv::norm(mat1, mat2), eps); \
}\
////////////////////////////////////////////////////////////// Basic Tests /////////////////////////////////////////////////////////////////////
PARAM_TEST_CASE(UMatBasicTests, int, int, Size, bool)
{
Mat a;
UMat ua;
int type;
int depth;
int cn;
Size size;
bool useRoi;
Size roi_size;
Rect roi;
virtual void SetUp()
{
depth = GET_PARAM(0);
cn = GET_PARAM(1);
size = GET_PARAM(2);
useRoi = GET_PARAM(3);
type = CV_MAKE_TYPE(depth, cn);
a = randomMat(size, type, -100, 100);
a.copyTo(ua);
int roi_shift_x = randomInt(0, size.width-1);
int roi_shift_y = randomInt(0, size.height-1);
roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
roi = Rect(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
}
};
CORE_TEST_P(UMatBasicTests, createUMat)
{
if(useRoi)
{
ua = UMat(ua, roi);
}
int dims = randomInt(2,6);
int _sz[CV_MAX_DIM];
for( int i = 0; i<dims; i++)
{
_sz[i] = randomInt(1,50);
}
int *sz = _sz;
int new_depth = randomInt(CV_8S, CV_64F);
int new_cn = randomInt(1,4);
ua.create(dims, sz, CV_MAKE_TYPE(new_depth, new_cn));
for(int i = 0; i<dims; i++)
{
ASSERT_EQ(ua.size[i], sz[i]);
}
ASSERT_EQ(ua.dims, dims);
ASSERT_EQ(ua.type(), CV_MAKE_TYPE(new_depth, new_cn) );
Size new_size = randomSize(1, 1000);
ua.create(new_size, CV_MAKE_TYPE(new_depth, new_cn) );
ASSERT_EQ( ua.size(), new_size);
ASSERT_EQ(ua.type(), CV_MAKE_TYPE(new_depth, new_cn) );
ASSERT_EQ( ua.dims, 2);
}
CORE_TEST_P(UMatBasicTests, swap)
{
Mat b = randomMat(size, type, -100, 100);
UMat ub;
b.copyTo(ub);
if(useRoi)
{
ua = UMat(ua,roi);
ub = UMat(ub,roi);
}
UMat uc = ua, ud = ub;
swap(ua,ub);
EXPECT_MAT_NEAR(ub,uc, 0);
EXPECT_MAT_NEAR(ud, ua, 0);
}
CORE_TEST_P(UMatBasicTests, base)
{
if(useRoi)
{
ua = UMat(ua,roi);
}
UMat ub = ua.clone();
EXPECT_MAT_NEAR(ub,ua,0);
ASSERT_EQ(ua.channels(), cn);
ASSERT_EQ(ua.depth(), depth);
ASSERT_EQ(ua.type(), type);
ASSERT_EQ(ua.elemSize(), a.elemSize());
ASSERT_EQ(ua.elemSize1(), a.elemSize1());
ASSERT_EQ(ub.empty(), ub.cols*ub.rows == 0);
ub.release();
ASSERT_TRUE( ub.empty() );
if(useRoi && a.size() != ua.size())
{
ASSERT_EQ(ua.isSubmatrix(), true);
}
else
{
ASSERT_EQ(ua.isSubmatrix(), false);
}
int dims = randomInt(2,6);
int sz[CV_MAX_DIM];
size_t total = 1;
for(int i = 0; i<dims; i++)
{
sz[i] = randomInt(1,45);
total *= (size_t)sz[i];
}
int new_type = CV_MAKE_TYPE(randomInt(CV_8S,CV_64F),randomInt(1,4));
ub = UMat(dims, sz, new_type);
ASSERT_EQ(ub.total(), total);
}
CORE_TEST_P(UMatBasicTests, copyTo)
{
UMat roi_ua;
Mat roi_a;
int i;
if(useRoi)
{
roi_ua = UMat(ua, roi);
roi_a = Mat(a, roi);
roi_a.copyTo(roi_ua);
EXPECT_MAT_NEAR(roi_a, roi_ua, 0);
roi_ua.copyTo(roi_a);
EXPECT_MAT_NEAR(roi_ua, roi_a, 0);
roi_ua.copyTo(ua);
EXPECT_MAT_NEAR(roi_ua, ua, 0);
ua.copyTo(a);
EXPECT_MAT_NEAR(ua, a, 0);
}
{
UMat ub;
ua.copyTo(ub);
EXPECT_MAT_NEAR(ua, ub, 0);
}
{
UMat ub;
i = randomInt(0, ua.cols-1);
a.col(i).copyTo(ub);
EXPECT_MAT_NEAR(a.col(i), ub, 0);
}
{
UMat ub;
ua.col(i).copyTo(ub);
EXPECT_MAT_NEAR(ua.col(i), ub, 0);
}
{
Mat b;
ua.col(i).copyTo(b);
EXPECT_MAT_NEAR(ua.col(i), b, 0);
}
{
UMat ub;
i = randomInt(0, a.rows-1);
ua.row(i).copyTo(ub);
EXPECT_MAT_NEAR(ua.row(i), ub, 0);
}
{
UMat ub;
a.row(i).copyTo(ub);
EXPECT_MAT_NEAR(a.row(i), ub, 0);
}
{
Mat b;
ua.row(i).copyTo(b);
EXPECT_MAT_NEAR(ua.row(i), b, 0);
}
}
CORE_TEST_P(UMatBasicTests, DISABLED_GetUMat)
{
if(useRoi)
{
a = Mat(a, roi);
ua = UMat(ua,roi);
}
{
UMat ub;
ub = a.getUMat(ACCESS_RW);
EXPECT_MAT_NEAR(ub, ua, 0);
}
{
Mat b;
b = a.getUMat(ACCESS_RW).getMat(ACCESS_RW);
EXPECT_MAT_NEAR(b, a, 0);
}
{
Mat b;
b = ua.getMat(ACCESS_RW);
EXPECT_MAT_NEAR(b, a, 0);
}
{
UMat ub;
ub = ua.getMat(ACCESS_RW).getUMat(ACCESS_RW);
EXPECT_MAT_NEAR(ub, ua, 0);
}
}
INSTANTIATE_TEST_CASE_P(UMat, UMatBasicTests, Combine(testing::Values(CV_8U), testing::Values(1, 2),
testing::Values(cv::Size(1,1), cv::Size(1,128), cv::Size(128,1), cv::Size(128, 128), cv::Size(640,480)), Bool() ) );
//////////////////////////////////////////////////////////////// Reshape ////////////////////////////////////////////////////////////////////////
PARAM_TEST_CASE(UMatTestReshape, int, int, Size, bool)
{
Mat a;
UMat ua, ub;
int type;
int depth;
int cn;
Size size;
bool useRoi;
Size roi_size;
virtual void SetUp()
{
depth = GET_PARAM(0);
cn = GET_PARAM(1);
size = GET_PARAM(2);
useRoi = GET_PARAM(3);
type = CV_MAKE_TYPE(depth, cn);
}
};
CORE_TEST_P(UMatTestReshape, reshape)
{
a = randomMat(size,type, -100, 100);
a.copyTo(ua);
if(useRoi)
{
int roi_shift_x = randomInt(0, size.width-1);
int roi_shift_y = randomInt(0, size.height-1);
roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
ua = UMat(ua, roi).clone();
a = Mat(a, roi).clone();
}
int nChannels = randomInt(1,4);
if ((ua.cols*ua.channels()*ua.rows)%nChannels != 0)
{
EXPECT_ANY_THROW(ua.reshape(nChannels));
}
else
{
ub = ua.reshape(nChannels);
ASSERT_EQ(ub.channels(),nChannels);
ASSERT_EQ(ub.channels()*ub.cols*ub.rows, ua.channels()*ua.cols*ua.rows);
EXPECT_MAT_NEAR(ua.reshape(nChannels), a.reshape(nChannels), 0);
int new_rows = randomInt(1, INT_MAX);
if ( ((int)ua.total()*ua.channels())%(new_rows*nChannels) != 0)
{
EXPECT_ANY_THROW (ua.reshape(nChannels, new_rows) );
}
else
{
EXPECT_NO_THROW ( ub = ua.reshape(nChannels, new_rows) );
ASSERT_EQ(ub.channels(),nChannels);
ASSERT_EQ(ub.rows, new_rows);
ASSERT_EQ(ub.channels()*ub.cols*ub.rows, ua.channels()*ua.cols*ua.rows);
EXPECT_MAT_NEAR(ua.reshape(nChannels,new_rows), a.reshape(nChannels,new_rows), 0);
}
new_rows = (int)ua.total()*ua.channels()/(nChannels*randomInt(1, size.width*size.height));
if (new_rows == 0) new_rows = 1;
int new_cols = (int)ua.total()*ua.channels()/(new_rows*nChannels);
int sz[] = {new_rows, new_cols};
if( ((int)ua.total()*ua.channels()) % (new_rows*new_cols) != 0 )
{
EXPECT_ANY_THROW( ua.reshape(nChannels, ua.dims, sz) );
}
else
{
EXPECT_NO_THROW ( ub = ua.reshape(nChannels, ua.dims, sz) );
ASSERT_EQ(ub.channels(),nChannels);
ASSERT_EQ(ub.rows, new_rows);
ASSERT_EQ(ub.cols, new_cols);
ASSERT_EQ(ub.channels()*ub.cols*ub.rows, ua.channels()*ua.cols*ua.rows);
EXPECT_MAT_NEAR(ua.reshape(nChannels, ua.dims, sz), a.reshape(nChannels, a.dims, sz), 0);
}
}
}
INSTANTIATE_TEST_CASE_P(UMat, UMatTestReshape, Combine(UMAT_TEST_DEPTH, UMAT_TEST_CHANNELS, UMAT_TEST_SIZES, Bool() ));
////////////////////////////////////////////////////////////////// ROI testing ///////////////////////////////////////////////////////////////
PARAM_TEST_CASE(UMatTestRoi, int, int, Size)
{
Mat a, roi_a;
UMat ua, roi_ua;
int type;
int depth;
int cn;
Size size;
Size roi_size;
virtual void SetUp()
{
depth = GET_PARAM(0);
cn = GET_PARAM(1);
size = GET_PARAM(2);
type = CV_MAKE_TYPE(depth, cn);
}
};
CORE_TEST_P(UMatTestRoi, createRoi)
{
int roi_shift_x = randomInt(0, size.width-1);
int roi_shift_y = randomInt(0, size.height-1);
roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
a = randomMat(size, type, -100, 100);
Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
roi_a = Mat(a, roi);
a.copyTo(ua);
roi_ua = UMat(ua, roi);
EXPECT_MAT_NEAR(roi_a, roi_ua, 0);
}
CORE_TEST_P(UMatTestRoi, locateRoi)
{
int roi_shift_x = randomInt(0, size.width-1);
int roi_shift_y = randomInt(0, size.height-1);
roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
a = randomMat(size, type, -100, 100);
Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
roi_a = Mat(a, roi);
a.copyTo(ua);
roi_ua = UMat(ua,roi);
Size sz, usz;
Point p, up;
roi_a.locateROI(sz, p);
roi_ua.locateROI(usz, up);
ASSERT_EQ(sz, usz);
ASSERT_EQ(p, up);
}
CORE_TEST_P(UMatTestRoi, adjustRoi)
{
int roi_shift_x = randomInt(0, size.width-1);
int roi_shift_y = randomInt(0, size.height-1);
roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
a = randomMat(size, type, -100, 100);
Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
a.copyTo(ua);
roi_ua = UMat( ua, roi);
int adjLeft = randomInt(-(roi_ua.cols/2), (size.width-1)/2);
int adjRight = randomInt(-(roi_ua.cols/2), (size.width-1)/2);
int adjTop = randomInt(-(roi_ua.rows/2), (size.height-1)/2);
int adjBot = randomInt(-(roi_ua.rows/2), (size.height-1)/2);
roi_ua.adjustROI(adjTop, adjBot, adjLeft, adjRight);
roi_shift_x = max(0, roi.x-adjLeft);
roi_shift_y = max(0, roi.y-adjTop);
Rect new_roi( roi_shift_x, roi_shift_y, min(roi.width+adjRight+adjLeft, size.width-roi_shift_x), min(roi.height+adjBot+adjTop, size.height-roi_shift_y) );
UMat test_roi = UMat(ua, new_roi);
EXPECT_MAT_NEAR(roi_ua, test_roi, 0);
}
INSTANTIATE_TEST_CASE_P(UMat, UMatTestRoi, Combine(UMAT_TEST_DEPTH, UMAT_TEST_CHANNELS, UMAT_TEST_SIZES ));
/////////////////////////////////////////////////////////////// Size ////////////////////////////////////////////////////////////////////
PARAM_TEST_CASE(UMatTestSizeOperations, int, int, Size, bool)
{
Mat a, b, roi_a, roi_b;
UMat ua, ub, roi_ua, roi_ub;
int type;
int depth;
int cn;
Size size;
Size roi_size;
bool useRoi;
virtual void SetUp()
{
depth = GET_PARAM(0);
cn = GET_PARAM(1);
size = GET_PARAM(2);
useRoi = GET_PARAM(3);
type = CV_MAKE_TYPE(depth, cn);
}
};
CORE_TEST_P(UMatTestSizeOperations, copySize)
{
Size s = randomSize(1,300);
a = randomMat(size, type, -100, 100);
b = randomMat(s, type, -100, 100);
a.copyTo(ua);
b.copyTo(ub);
if(useRoi)
{
int roi_shift_x = randomInt(0, size.width-1);
int roi_shift_y = randomInt(0, size.height-1);
roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
ua = UMat(ua,roi);
roi_shift_x = randomInt(0, s.width-1);
roi_shift_y = randomInt(0, s.height-1);
roi_size = Size(s.width - roi_shift_x, s.height - roi_shift_y);
roi = Rect(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
ub = UMat(ub, roi);
}
ua.copySize(ub);
ASSERT_EQ(ua.size, ub.size);
}
INSTANTIATE_TEST_CASE_P(UMat, UMatTestSizeOperations, Combine(UMAT_TEST_DEPTH, UMAT_TEST_CHANNELS, UMAT_TEST_SIZES, Bool() ));
///////////////////////////////////////////////////////////////// UMat operations ////////////////////////////////////////////////////////////////////////////
PARAM_TEST_CASE(UMatTestUMatOperations, int, int, Size, bool)
{
Mat a, b;
UMat ua, ub;
int type;
int depth;
int cn;
Size size;
Size roi_size;
bool useRoi;
virtual void SetUp()
{
depth = GET_PARAM(0);
cn = GET_PARAM(1);
size = GET_PARAM(2);
useRoi = GET_PARAM(3);
type = CV_MAKE_TYPE(depth, cn);
}
};
CORE_TEST_P(UMatTestUMatOperations, diag)
{
a = randomMat(size, type, -100, 100);
a.copyTo(ua);
Mat new_diag;
if(useRoi)
{
int roi_shift_x = randomInt(0, size.width-1);
int roi_shift_y = randomInt(0, size.height-1);
roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
ua = UMat(ua,roi);
a = Mat(a, roi);
}
int n = randomInt(0, ua.cols-1);
ub = ua.diag(n);
b = a.diag(n);
EXPECT_MAT_NEAR(b, ub, 0);
new_diag = randomMat(Size(ua.rows, 1), type, -100, 100);
new_diag.copyTo(ub);
ua = cv::UMat::diag(ub);
EXPECT_MAT_NEAR(ua.diag(), new_diag.t(), 0);
}
INSTANTIATE_TEST_CASE_P(UMat, UMatTestUMatOperations, Combine(UMAT_TEST_DEPTH, UMAT_TEST_CHANNELS, UMAT_TEST_SIZES, Bool() ));
///////////////////////////////////////////////////////////////// OpenCL ////////////////////////////////////////////////////////////////////////////
TEST(UMat, BufferPoolGrowing)
{
#ifdef _DEBUG
const int ITERATIONS = 100;
#else
const int ITERATIONS = 200;
#endif
const Size sz(1920, 1080);
BufferPoolController* c = cv::ocl::getOpenCLAllocator()->getBufferPoolController();
if (c)
{
size_t oldMaxReservedSize = c->getMaxReservedSize();
c->freeAllReservedBuffers();
c->setMaxReservedSize(sz.area() * 10);
for (int i = 0; i < ITERATIONS; i++)
{
UMat um(Size(sz.width + i, sz.height + i), CV_8UC1);
UMat um2(Size(sz.width + 2 * i, sz.height + 2 * i), CV_8UC1);
}
c->setMaxReservedSize(oldMaxReservedSize);
c->freeAllReservedBuffers();
}
else
{
std::cout << "Skipped, no OpenCL" << std::endl;
}
}
TEST(UMat, setOpenCL)
{
// save the current state
bool useOCL = cv::ocl::useOpenCL();
Mat m = (Mat_<uchar>(3,3)<<0,1,2,3,4,5,6,7,8);
cv::ocl::setUseOpenCL(true);
UMat um1;
m.copyTo(um1);
cv::ocl::setUseOpenCL(false);
UMat um2;
m.copyTo(um2);
cv::ocl::setUseOpenCL(true);
countNonZero(um1);
countNonZero(um2);
um1.copyTo(um2);
EXPECT_MAT_NEAR(um1, um2, 0);
EXPECT_MAT_NEAR(um1, m, 0);
um2.copyTo(um1);
EXPECT_MAT_NEAR(um1, m, 0);
EXPECT_MAT_NEAR(um1, um2, 0);
cv::ocl::setUseOpenCL(false);
countNonZero(um1);
countNonZero(um2);
um1.copyTo(um2);
EXPECT_MAT_NEAR(um1, um2, 0);
EXPECT_MAT_NEAR(um1, m, 0);
um2.copyTo(um1);
EXPECT_MAT_NEAR(um1, um2, 0);
EXPECT_MAT_NEAR(um1, m, 0);
// reset state to the previous one
cv::ocl::setUseOpenCL(useOCL);
}