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Open Source Computer Vision Library
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201 lines
6.9 KiB
201 lines
6.9 KiB
/*M/////////////////////////////////////////////////////////////////////////////////////// |
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// |
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. |
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// |
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// By downloading, copying, installing or using the software you agree to this license. |
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// If you do not agree to this license, do not download, install, |
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// copy or use the software. |
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// |
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// |
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// License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved. |
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// Copyright (C) 2009, Willow Garage Inc., 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 the copyright holders 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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typedef testing::TestWithParam<std::tuple<int, Size> > HasNonZeroAllZeros; |
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TEST_P(HasNonZeroAllZeros, hasNonZeroAllZeros) |
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{ |
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const int type = std::get<0>(GetParam()); |
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const Size size = std::get<1>(GetParam()); |
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Mat m = Mat::zeros(size, type); |
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EXPECT_FALSE(hasNonZero(m)); |
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} |
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INSTANTIATE_TEST_CASE_P(Core, HasNonZeroAllZeros, |
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testing::Combine( |
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testing::Values(CV_8UC1, CV_8SC1, CV_16UC1, CV_16SC1, CV_32SC1, CV_32FC1, CV_64FC1), |
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testing::Values(Size(1, 1), Size(320, 240), Size(127, 113), Size(1, 113)) |
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) |
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); |
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typedef testing::TestWithParam<std::tuple<int, Size> > HasNonZeroNegZeros; |
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TEST_P(HasNonZeroNegZeros, hasNonZeroNegZeros) |
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{ |
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const int type = std::get<0>(GetParam()); |
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const Size size = std::get<1>(GetParam()); |
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Mat m = Mat(size, type); |
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m.setTo(Scalar::all(-0.)); |
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EXPECT_FALSE(hasNonZero(m)); |
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} |
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INSTANTIATE_TEST_CASE_P(Core, HasNonZeroNegZeros, |
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testing::Combine( |
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testing::Values(CV_32FC1, CV_64FC1), |
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testing::Values(Size(1, 1), Size(320, 240), Size(127, 113), Size(1, 113)) |
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) |
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); |
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typedef testing::TestWithParam<std::tuple<int, Size> > HasNonZeroLimitValues; |
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TEST_P(HasNonZeroLimitValues, hasNonZeroLimitValues) |
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{ |
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const int type = std::get<0>(GetParam()); |
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const Size size = std::get<1>(GetParam()); |
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Mat m = Mat(size, type); |
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m.setTo(Scalar::all(std::numeric_limits<double>::infinity())); |
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EXPECT_TRUE(hasNonZero(m)); |
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m.setTo(Scalar::all(-std::numeric_limits<double>::infinity())); |
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EXPECT_TRUE(hasNonZero(m)); |
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m.setTo(Scalar::all(std::numeric_limits<double>::quiet_NaN())); |
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EXPECT_TRUE(hasNonZero(m)); |
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m.setTo((CV_MAT_DEPTH(type) == CV_64F) ? Scalar::all(std::numeric_limits<double>::epsilon()) : Scalar::all(std::numeric_limits<float>::epsilon())); |
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EXPECT_TRUE(hasNonZero(m)); |
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m.setTo((CV_MAT_DEPTH(type) == CV_64F) ? Scalar::all(std::numeric_limits<double>::min()) : Scalar::all(std::numeric_limits<float>::min())); |
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EXPECT_TRUE(hasNonZero(m)); |
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m.setTo((CV_MAT_DEPTH(type) == CV_64F) ? Scalar::all(std::numeric_limits<double>::denorm_min()) : Scalar::all(std::numeric_limits<float>::denorm_min())); |
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EXPECT_TRUE(hasNonZero(m)); |
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} |
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INSTANTIATE_TEST_CASE_P(Core, HasNonZeroLimitValues, |
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testing::Combine( |
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testing::Values(CV_32FC1, CV_64FC1), |
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testing::Values(Size(1, 1), Size(320, 240), Size(127, 113), Size(1, 113)) |
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) |
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); |
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typedef testing::TestWithParam<std::tuple<int, Size> > HasNonZeroRandom; |
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TEST_P(HasNonZeroRandom, hasNonZeroRandom) |
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{ |
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const int type = std::get<0>(GetParam()); |
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const Size size = std::get<1>(GetParam()); |
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RNG& rng = theRNG(); |
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const size_t N = std::min(100, size.area()); |
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for(size_t i = 0 ; i<N ; ++i) |
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{ |
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const int nz_pos_x = rng.uniform(0, size.width); |
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const int nz_pos_y = rng.uniform(0, size.height); |
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Mat m = Mat::zeros(size, type); |
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Mat nzROI = Mat(m, Rect(nz_pos_x, nz_pos_y, 1, 1)); |
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nzROI.setTo(Scalar::all(1)); |
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EXPECT_TRUE(hasNonZero(m)); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(Core, HasNonZeroRandom, |
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testing::Combine( |
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testing::Values(CV_8UC1, CV_8SC1, CV_16UC1, CV_16SC1, CV_32SC1, CV_32FC1, CV_64FC1), |
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testing::Values(Size(1, 1), Size(320, 240), Size(127, 113), Size(1, 113)) |
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) |
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); |
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typedef testing::TestWithParam<tuple<int, int, bool> > HasNonZeroNd; |
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TEST_P(HasNonZeroNd, hasNonZeroNd) |
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{ |
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const int type = get<0>(GetParam()); |
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const int ndims = get<1>(GetParam()); |
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const bool continuous = get<2>(GetParam()); |
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RNG& rng = theRNG(); |
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const size_t N = 10; |
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for(size_t i = 0 ; i<N ; ++i) |
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{ |
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std::vector<size_t> steps(ndims); |
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std::vector<int> sizes(ndims); |
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size_t totalBytes = 1; |
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for(int dim = 0 ; dim<ndims ; ++dim) |
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{ |
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const bool isFirstDim = (dim == 0); |
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const bool isLastDim = (dim+1 == ndims); |
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const int length = rng.uniform(1, 64); |
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steps[dim] = (isLastDim ? 1 : static_cast<size_t>(length))*CV_ELEM_SIZE(type); |
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sizes[dim] = (isFirstDim || continuous) ? length : rng.uniform(1, length); |
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totalBytes *= steps[dim]*static_cast<size_t>(sizes[dim]); |
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} |
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std::vector<unsigned char> buffer(totalBytes); |
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void* data = buffer.data(); |
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Mat m = Mat(ndims, sizes.data(), type, data, steps.data()); |
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std::vector<Range> nzRange(ndims); |
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for(int dim = 0 ; dim<ndims ; ++dim) |
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{ |
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const int pos = rng.uniform(0, sizes[dim]); |
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nzRange[dim] = Range(pos, pos+1); |
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} |
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Mat nzROI = Mat(m, nzRange.data()); |
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nzROI.setTo(Scalar::all(1)); |
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const int nzCount = countNonZero(m); |
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EXPECT_EQ((nzCount>0), hasNonZero(m)); |
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} |
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} |
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INSTANTIATE_TEST_CASE_P(Core, HasNonZeroNd, |
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testing::Combine( |
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testing::Values(CV_8UC1), |
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testing::Values(2, 3), |
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testing::Values(true, false) |
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) |
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); |
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}} // namespace
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