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5718d09e39
Found via `codespell`
1387 lines
37 KiB
C++
1387 lines
37 KiB
C++
/*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) 2013, OpenCV Foundation, 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 OpenCV Foundation 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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#include "opencv2/ts/ocl_test.hpp"
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using namespace opencv_test;
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using namespace testing;
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using namespace cv;
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namespace opencv_test {
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namespace ocl {
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#define UMAT_TEST_SIZES testing::Values(cv::Size(1, 1), cv::Size(1,128), cv::Size(128, 1), \
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cv::Size(128, 128), cv::Size(640, 480), cv::Size(751, 373), cv::Size(1200, 1200))
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/////////////////////////////// Basic Tests ////////////////////////////////
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PARAM_TEST_CASE(UMatBasicTests, int, int, Size, bool)
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{
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Mat a;
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UMat ua;
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int type;
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int depth;
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int cn;
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Size size;
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bool useRoi;
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Size roi_size;
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Rect roi;
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virtual void SetUp()
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{
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depth = GET_PARAM(0);
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cn = GET_PARAM(1);
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size = GET_PARAM(2);
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useRoi = GET_PARAM(3);
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type = CV_MAKE_TYPE(depth, cn);
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a = randomMat(size, type, -100, 100);
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a.copyTo(ua);
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int roi_shift_x = randomInt(0, size.width-1);
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int roi_shift_y = randomInt(0, size.height-1);
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roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
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roi = Rect(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
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}
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};
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TEST_P(UMatBasicTests, createUMat)
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{
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if(useRoi)
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{
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ua = UMat(ua, roi);
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}
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int dims = randomInt(2,6);
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int _sz[CV_MAX_DIM];
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for( int i = 0; i<dims; i++)
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{
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_sz[i] = randomInt(1,50);
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}
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int *sz = _sz;
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int new_depth = randomInt(CV_8S, CV_64F);
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int new_cn = randomInt(1,4);
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ua.create(dims, sz, CV_MAKE_TYPE(new_depth, new_cn));
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for(int i = 0; i<dims; i++)
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{
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ASSERT_EQ(ua.size[i], sz[i]);
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}
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ASSERT_EQ(ua.dims, dims);
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ASSERT_EQ(ua.type(), CV_MAKE_TYPE(new_depth, new_cn) );
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Size new_size = randomSize(1, 1000);
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ua.create(new_size, CV_MAKE_TYPE(new_depth, new_cn) );
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ASSERT_EQ( ua.size(), new_size);
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ASSERT_EQ(ua.type(), CV_MAKE_TYPE(new_depth, new_cn) );
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ASSERT_EQ( ua.dims, 2);
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}
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TEST_P(UMatBasicTests, swap)
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{
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Mat b = randomMat(size, type, -100, 100);
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UMat ub;
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b.copyTo(ub);
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if(useRoi)
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{
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ua = UMat(ua,roi);
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ub = UMat(ub,roi);
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}
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UMat uc = ua, ud = ub;
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swap(ua,ub);
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EXPECT_MAT_NEAR(ub,uc, 0);
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EXPECT_MAT_NEAR(ud, ua, 0);
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}
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TEST_P(UMatBasicTests, base)
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{
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const int align_mask = 3;
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roi.x &= ~align_mask;
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roi.y &= ~align_mask;
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roi.width = (roi.width + align_mask) & ~align_mask;
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roi &= Rect(0, 0, ua.cols, ua.rows);
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if(useRoi)
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{
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ua = UMat(ua,roi);
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}
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UMat ub = ua.clone();
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EXPECT_MAT_NEAR(ub,ua,0);
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ASSERT_EQ(ua.channels(), cn);
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ASSERT_EQ(ua.depth(), depth);
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ASSERT_EQ(ua.type(), type);
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ASSERT_EQ(ua.elemSize(), a.elemSize());
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ASSERT_EQ(ua.elemSize1(), a.elemSize1());
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ASSERT_EQ(ub.empty(), ub.cols*ub.rows == 0);
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ub.release();
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ASSERT_TRUE( ub.empty() );
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if(useRoi && a.size() != ua.size())
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{
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ASSERT_EQ(ua.isSubmatrix(), true);
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}
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else
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{
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ASSERT_EQ(ua.isSubmatrix(), false);
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}
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int dims = randomInt(2,6);
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int sz[CV_MAX_DIM];
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size_t total = 1;
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for(int i = 0; i<dims; i++)
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{
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sz[i] = randomInt(1,45);
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total *= (size_t)sz[i];
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}
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int new_type = CV_MAKE_TYPE(randomInt(CV_8S,CV_64F),randomInt(1,4));
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ub = UMat(dims, sz, new_type);
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ASSERT_EQ(ub.total(), total);
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}
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TEST_P(UMatBasicTests, copyTo)
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{
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int i;
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if(useRoi)
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{
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UMat roi_ua;
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Mat roi_a;
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roi_ua = UMat(ua, roi);
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roi_a = Mat(a, roi);
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roi_a.copyTo(roi_ua);
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EXPECT_MAT_NEAR(roi_a, roi_ua, 0);
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roi_ua.copyTo(roi_a);
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EXPECT_MAT_NEAR(roi_ua, roi_a, 0);
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roi_ua.copyTo(ua);
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EXPECT_MAT_NEAR(roi_ua, ua, 0);
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ua.copyTo(a);
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EXPECT_MAT_NEAR(ua, a, 0);
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}
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{
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UMat ub;
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ua.copyTo(ub);
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EXPECT_MAT_NEAR(ua, ub, 0);
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}
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{
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UMat ub;
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i = randomInt(0, ua.cols-1);
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a.col(i).copyTo(ub);
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EXPECT_MAT_NEAR(a.col(i), ub, 0);
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}
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{
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UMat ub;
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ua.col(i).copyTo(ub);
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EXPECT_MAT_NEAR(ua.col(i), ub, 0);
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}
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{
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Mat b;
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ua.col(i).copyTo(b);
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EXPECT_MAT_NEAR(ua.col(i), b, 0);
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}
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{
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UMat ub;
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i = randomInt(0, a.rows-1);
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ua.row(i).copyTo(ub);
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EXPECT_MAT_NEAR(ua.row(i), ub, 0);
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}
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{
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UMat ub;
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a.row(i).copyTo(ub);
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EXPECT_MAT_NEAR(a.row(i), ub, 0);
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}
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{
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Mat b;
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ua.row(i).copyTo(b);
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EXPECT_MAT_NEAR(ua.row(i), b, 0);
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}
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}
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TEST_P(UMatBasicTests, GetUMat)
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{
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if(useRoi)
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{
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a = Mat(a, roi);
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ua = UMat(ua,roi);
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}
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{
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UMat ub;
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ub = a.getUMat(ACCESS_RW);
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EXPECT_MAT_NEAR(ub, ua, 0);
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}
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{
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UMat u = a.getUMat(ACCESS_RW);
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{
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Mat b = u.getMat(ACCESS_RW);
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EXPECT_MAT_NEAR(b, a, 0);
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}
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}
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{
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Mat b;
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b = ua.getMat(ACCESS_RW);
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EXPECT_MAT_NEAR(b, a, 0);
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}
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{
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Mat m = ua.getMat(ACCESS_RW);
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{
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UMat ub = m.getUMat(ACCESS_RW);
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EXPECT_MAT_NEAR(ub, ua, 0);
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(UMat, UMatBasicTests, Combine(testing::Values(CV_8U, CV_64F), testing::Values(1, 2),
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testing::Values(cv::Size(1, 1), cv::Size(1, 128), cv::Size(128, 1), cv::Size(128, 128), cv::Size(640, 480)), Bool()));
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//////////////////////////////////////////////////////////////// Reshape ////////////////////////////////////////////////////////////////////////
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PARAM_TEST_CASE(UMatTestReshape, int, int, Size, bool)
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{
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Mat a;
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UMat ua, ub;
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int type;
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int depth;
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int cn;
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Size size;
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bool useRoi;
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Size roi_size;
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virtual void SetUp()
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{
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depth = GET_PARAM(0);
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cn = GET_PARAM(1);
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size = GET_PARAM(2);
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useRoi = GET_PARAM(3);
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type = CV_MAKE_TYPE(depth, cn);
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}
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};
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TEST_P(UMatTestReshape, reshape)
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{
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a = randomMat(size,type, -100, 100);
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a.copyTo(ua);
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if(useRoi)
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{
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int roi_shift_x = randomInt(0, size.width-1);
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int roi_shift_y = randomInt(0, size.height-1);
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roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
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Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
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ua = UMat(ua, roi).clone();
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a = Mat(a, roi).clone();
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}
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int nChannels = randomInt(1,4);
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if ((ua.cols*ua.channels()*ua.rows)%nChannels != 0)
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{
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EXPECT_ANY_THROW(ua.reshape(nChannels));
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}
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else
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{
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ub = ua.reshape(nChannels);
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ASSERT_EQ(ub.channels(),nChannels);
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ASSERT_EQ(ub.channels()*ub.cols*ub.rows, ua.channels()*ua.cols*ua.rows);
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EXPECT_MAT_NEAR(ua.reshape(nChannels), a.reshape(nChannels), 0);
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int new_rows = randomInt(1, INT_MAX);
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if ( ((int)ua.total()*ua.channels())%(new_rows*nChannels) != 0)
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{
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EXPECT_ANY_THROW (ua.reshape(nChannels, new_rows) );
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}
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else
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{
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EXPECT_NO_THROW ( ub = ua.reshape(nChannels, new_rows) );
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ASSERT_EQ(ub.channels(),nChannels);
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ASSERT_EQ(ub.rows, new_rows);
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ASSERT_EQ(ub.channels()*ub.cols*ub.rows, ua.channels()*ua.cols*ua.rows);
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EXPECT_MAT_NEAR(ua.reshape(nChannels,new_rows), a.reshape(nChannels,new_rows), 0);
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}
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new_rows = (int)ua.total()*ua.channels()/(nChannels*randomInt(1, size.width*size.height));
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if (new_rows == 0) new_rows = 1;
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int new_cols = (int)ua.total()*ua.channels()/(new_rows*nChannels);
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int sz[] = {new_rows, new_cols};
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if( ((int)ua.total()*ua.channels()) % (new_rows*new_cols) != 0 )
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{
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EXPECT_ANY_THROW( ua.reshape(nChannels, ua.dims, sz) );
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}
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else
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{
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EXPECT_NO_THROW ( ub = ua.reshape(nChannels, ua.dims, sz) );
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ASSERT_EQ(ub.channels(),nChannels);
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ASSERT_EQ(ub.rows, new_rows);
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ASSERT_EQ(ub.cols, new_cols);
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ASSERT_EQ(ub.channels()*ub.cols*ub.rows, ua.channels()*ua.cols*ua.rows);
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EXPECT_MAT_NEAR(ua.reshape(nChannels, ua.dims, sz), a.reshape(nChannels, a.dims, sz), 0);
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}
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}
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}
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INSTANTIATE_TEST_CASE_P(UMat, UMatTestReshape, Combine(OCL_ALL_DEPTHS, OCL_ALL_CHANNELS, UMAT_TEST_SIZES, Bool() ));
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static void check_ndim_shape(const cv::UMat &mat, int cn, int ndims, const int *sizes)
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{
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EXPECT_EQ(mat.channels(), cn);
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EXPECT_EQ(mat.dims, ndims);
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if (mat.dims != ndims)
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return;
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for (int i = 0; i < ndims; i++)
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EXPECT_EQ(mat.size[i], sizes[i]);
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}
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TEST(UMatTestReshape, reshape_ndims_2)
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{
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const cv::UMat A(8, 16, CV_8UC3);
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cv::UMat B;
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{
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int new_sizes_mask[] = { 0, 3, 4, 4 };
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int new_sizes_real[] = { 8, 3, 4, 4 };
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ASSERT_NO_THROW(B = A.reshape(1, 4, new_sizes_mask));
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check_ndim_shape(B, 1, 4, new_sizes_real);
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}
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{
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int new_sizes[] = { 16, 8 };
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ASSERT_NO_THROW(B = A.reshape(0, 2, new_sizes));
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check_ndim_shape(B, 3, 2, new_sizes);
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EXPECT_EQ(B.rows, new_sizes[0]);
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EXPECT_EQ(B.cols, new_sizes[1]);
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}
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{
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int new_sizes[] = { 2, 5, 1, 3 };
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cv::UMat A_sliced = A(cv::Range::all(), cv::Range(0, 15));
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ASSERT_ANY_THROW(A_sliced.reshape(4, 4, new_sizes));
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}
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}
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TEST(UMatTestReshape, reshape_ndims_4)
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{
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const int sizes[] = { 2, 6, 4, 12 };
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const cv::UMat A(4, sizes, CV_8UC3);
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cv::UMat B;
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{
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int new_sizes_mask[] = { 0, 864 };
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int new_sizes_real[] = { 2, 864 };
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ASSERT_NO_THROW(B = A.reshape(1, 2, new_sizes_mask));
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check_ndim_shape(B, 1, 2, new_sizes_real);
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EXPECT_EQ(B.rows, new_sizes_real[0]);
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EXPECT_EQ(B.cols, new_sizes_real[1]);
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}
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{
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int new_sizes_mask[] = { 4, 0, 0, 2, 3 };
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int new_sizes_real[] = { 4, 6, 4, 2, 3 };
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ASSERT_NO_THROW(B = A.reshape(0, 5, new_sizes_mask));
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check_ndim_shape(B, 3, 5, new_sizes_real);
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}
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{
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int new_sizes_mask[] = { 1, 1 };
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ASSERT_ANY_THROW(A.reshape(0, 2, new_sizes_mask));
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}
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{
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int new_sizes_mask[] = { 4, 6, 3, 3, 0 };
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ASSERT_ANY_THROW(A.reshape(0, 5, new_sizes_mask));
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}
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}
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////////////////////////////////////////////////////////////////// ROI testing ///////////////////////////////////////////////////////////////
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PARAM_TEST_CASE(UMatTestRoi, int, int, Size)
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{
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Mat a, roi_a;
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UMat ua, roi_ua;
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int type;
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int depth;
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int cn;
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Size size;
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Size roi_size;
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virtual void SetUp()
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{
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depth = GET_PARAM(0);
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cn = GET_PARAM(1);
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size = GET_PARAM(2);
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type = CV_MAKE_TYPE(depth, cn);
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}
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};
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TEST_P(UMatTestRoi, createRoi)
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{
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int roi_shift_x = randomInt(0, size.width-1);
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int roi_shift_y = randomInt(0, size.height-1);
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roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
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a = randomMat(size, type, -100, 100);
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Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
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roi_a = Mat(a, roi);
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a.copyTo(ua);
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roi_ua = UMat(ua, roi);
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EXPECT_MAT_NEAR(roi_a, roi_ua, 0);
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}
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TEST_P(UMatTestRoi, locateRoi)
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{
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int roi_shift_x = randomInt(0, size.width-1);
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int roi_shift_y = randomInt(0, size.height-1);
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roi_size = Size(size.width - roi_shift_x, size.height - roi_shift_y);
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a = randomMat(size, type, -100, 100);
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Rect roi(roi_shift_x, roi_shift_y, roi_size.width, roi_size.height);
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roi_a = Mat(a, roi);
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a.copyTo(ua);
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roi_ua = UMat(ua,roi);
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Size sz, usz;
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Point p, up;
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roi_a.locateROI(sz, p);
|
|
roi_ua.locateROI(usz, up);
|
|
ASSERT_EQ(sz, usz);
|
|
ASSERT_EQ(p, up);
|
|
}
|
|
|
|
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 = std::max(0, roi.x-adjLeft);
|
|
roi_shift_y = std::max(0, roi.y-adjTop);
|
|
Rect new_roi( roi_shift_x, roi_shift_y, std::min(roi.width+adjRight+adjLeft, size.width-roi_shift_x), std::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(OCL_ALL_DEPTHS, OCL_ALL_CHANNELS, UMAT_TEST_SIZES ));
|
|
|
|
TEST(UMatTestRoi, adjustRoiOverflow)
|
|
{
|
|
UMat m(15, 10, CV_32S);
|
|
UMat roi(m, cv::Range(2, 10), cv::Range(3,6));
|
|
int rowsInROI = roi.rows;
|
|
roi.adjustROI(1, 0, 0, 0);
|
|
|
|
ASSERT_EQ(roi.rows, rowsInROI + 1);
|
|
|
|
roi.adjustROI(-m.rows, -m.rows, 0, 0);
|
|
|
|
ASSERT_EQ(roi.rows, m.rows);
|
|
}
|
|
|
|
/////////////////////////////////////////////////////////////// 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);
|
|
}
|
|
};
|
|
|
|
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(OCL_ALL_DEPTHS, OCL_ALL_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);
|
|
}
|
|
};
|
|
|
|
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(OCL_ALL_DEPTHS, OCL_ALL_CHANNELS, UMAT_TEST_SIZES, Bool()));
|
|
|
|
|
|
/////////////////////////////////////////////////////////////// getUMat -> GetMat ///////////////////////////////////////////////////////////////////
|
|
|
|
PARAM_TEST_CASE(getUMat, int, int, Size, bool)
|
|
{
|
|
int type;
|
|
Size size;
|
|
|
|
virtual void SetUp()
|
|
{
|
|
int depth = GET_PARAM(0);
|
|
int cn = GET_PARAM(1);
|
|
size = GET_PARAM(2);
|
|
useOpenCL = GET_PARAM(3);
|
|
|
|
type = CV_MAKE_TYPE(depth, cn);
|
|
|
|
isOpenCL_enabled = cv::ocl::useOpenCL();
|
|
cv::ocl::setUseOpenCL(useOpenCL);
|
|
}
|
|
|
|
virtual void TearDown()
|
|
{
|
|
cv::ocl::setUseOpenCL(isOpenCL_enabled);
|
|
}
|
|
|
|
// UMat created from user allocated host memory (USE_HOST_PTR)
|
|
void custom_ptr_test(size_t align_base, size_t align_offset)
|
|
{
|
|
void* pData_allocated = new unsigned char [size.area() * CV_ELEM_SIZE(type) + (align_base + align_offset)];
|
|
void* pData = (char*)alignPtr(pData_allocated, (int)align_base) + align_offset;
|
|
size_t step = size.width * CV_ELEM_SIZE(type);
|
|
|
|
{
|
|
Mat m = Mat(size, type, pData, step);
|
|
m.setTo(cv::Scalar::all(2));
|
|
|
|
UMat u = m.getUMat(ACCESS_RW);
|
|
cv::add(u, cv::Scalar::all(2), u);
|
|
|
|
Mat d = u.getMat(ACCESS_READ);
|
|
|
|
Mat expected(m.size(), m.type(), cv::Scalar::all(4));
|
|
double norm = cvtest::norm(d, expected, NORM_INF);
|
|
|
|
EXPECT_EQ(0, norm);
|
|
}
|
|
|
|
delete[] (unsigned char*)pData_allocated;
|
|
}
|
|
|
|
private:
|
|
bool useOpenCL;
|
|
bool isOpenCL_enabled;
|
|
};
|
|
|
|
TEST_P(getUMat, custom_ptr_align_4Kb)
|
|
{
|
|
custom_ptr_test(4096, 0);
|
|
}
|
|
|
|
TEST_P(getUMat, custom_ptr_align_64b)
|
|
{
|
|
custom_ptr_test(4096, 64);
|
|
}
|
|
|
|
TEST_P(getUMat, custom_ptr_align_none)
|
|
{
|
|
custom_ptr_test(4096, cv::alignSize(CV_ELEM_SIZE(type), 4));
|
|
}
|
|
|
|
TEST_P(getUMat, self_allocated)
|
|
{
|
|
Mat m = Mat(size, type);
|
|
m.setTo(cv::Scalar::all(2));
|
|
|
|
UMat u = m.getUMat(ACCESS_RW);
|
|
cv::add(u, cv::Scalar::all(2), u);
|
|
|
|
Mat d = u.getMat(ACCESS_READ);
|
|
|
|
Mat expected(m.size(), m.type(), cv::Scalar::all(4));
|
|
double norm = cvtest::norm(d, expected, NORM_INF);
|
|
|
|
EXPECT_EQ(0, norm);
|
|
}
|
|
|
|
INSTANTIATE_TEST_CASE_P(UMat, getUMat, Combine(
|
|
Values(CV_8U, CV_64F), // depth
|
|
Values(1, 3), // channels
|
|
Values(cv::Size(1, 1), cv::Size(255, 255), cv::Size(256, 256)), // Size
|
|
Bool() // useOpenCL
|
|
));
|
|
|
|
|
|
|
|
///////////////////////////////////////////////////////////////// OpenCL ////////////////////////////////////////////////////////////////////////////
|
|
|
|
#ifdef HAVE_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;
|
|
}
|
|
#endif
|
|
|
|
class CV_UMatTest :
|
|
public cvtest::BaseTest
|
|
{
|
|
public:
|
|
CV_UMatTest() {}
|
|
~CV_UMatTest() {}
|
|
protected:
|
|
void run(int);
|
|
|
|
struct test_excep
|
|
{
|
|
test_excep(const string& _s=string("")) : s(_s) { }
|
|
string s;
|
|
};
|
|
|
|
bool TestUMat();
|
|
|
|
void checkDiff(const Mat& m1, const Mat& m2, const string& s)
|
|
{
|
|
if (cvtest::norm(m1, m2, NORM_INF) != 0)
|
|
throw test_excep(s);
|
|
}
|
|
void checkDiffF(const Mat& m1, const Mat& m2, const string& s)
|
|
{
|
|
if (cvtest::norm(m1, m2, NORM_INF) > 1e-5)
|
|
throw test_excep(s);
|
|
}
|
|
};
|
|
|
|
#define STR(a) STR2(a)
|
|
#define STR2(a) #a
|
|
|
|
#define CHECK_DIFF(a, b) checkDiff(a, b, "(" #a ") != (" #b ") at l." STR(__LINE__))
|
|
#define CHECK_DIFF_FLT(a, b) checkDiffF(a, b, "(" #a ") !=(eps) (" #b ") at l." STR(__LINE__))
|
|
|
|
|
|
bool CV_UMatTest::TestUMat()
|
|
{
|
|
try
|
|
{
|
|
Mat a(100, 100, CV_16SC2), b, c;
|
|
randu(a, Scalar::all(-100), Scalar::all(100));
|
|
Rect roi(1, 3, 5, 4);
|
|
Mat ra(a, roi), rb, rc, rc0;
|
|
UMat ua, ura, ub, urb, uc, urc;
|
|
a.copyTo(ua);
|
|
ua.copyTo(b);
|
|
CHECK_DIFF(a, b);
|
|
|
|
ura = ua(roi);
|
|
ura.copyTo(rb);
|
|
|
|
CHECK_DIFF(ra, rb);
|
|
|
|
ra += Scalar::all(1.f);
|
|
{
|
|
Mat temp = ura.getMat(ACCESS_RW);
|
|
temp += Scalar::all(1.f);
|
|
}
|
|
ra.copyTo(rb);
|
|
CHECK_DIFF(ra, rb);
|
|
|
|
b = a.clone();
|
|
ra = a(roi);
|
|
rb = b(roi);
|
|
randu(b, Scalar::all(-100), Scalar::all(100));
|
|
b.copyTo(ub);
|
|
urb = ub(roi);
|
|
|
|
/*std::cout << "==============================================\nbefore op (CPU):\n";
|
|
std::cout << "ra: " << ra << std::endl;
|
|
std::cout << "rb: " << rb << std::endl;*/
|
|
|
|
ra.copyTo(ura);
|
|
rb.copyTo(urb);
|
|
ra.release();
|
|
rb.release();
|
|
ura.copyTo(ra);
|
|
urb.copyTo(rb);
|
|
|
|
/*std::cout << "==============================================\nbefore op (GPU):\n";
|
|
std::cout << "ra: " << ra << std::endl;
|
|
std::cout << "rb: " << rb << std::endl;*/
|
|
|
|
cv::max(ra, rb, rc);
|
|
cv::max(ura, urb, urc);
|
|
urc.copyTo(rc0);
|
|
|
|
/*std::cout << "==============================================\nafter op:\n";
|
|
std::cout << "rc: " << rc << std::endl;
|
|
std::cout << "rc0: " << rc0 << std::endl;*/
|
|
|
|
CHECK_DIFF(rc0, rc);
|
|
|
|
{
|
|
UMat tmp = rc0.getUMat(ACCESS_WRITE);
|
|
cv::max(ura, urb, tmp);
|
|
}
|
|
CHECK_DIFF(rc0, rc);
|
|
|
|
ura.copyTo(urc);
|
|
cv::max(urc, urb, urc);
|
|
urc.copyTo(rc0);
|
|
CHECK_DIFF(rc0, rc);
|
|
|
|
rc = ra ^ rb;
|
|
cv::bitwise_xor(ura, urb, urc);
|
|
urc.copyTo(rc0);
|
|
|
|
/*std::cout << "==============================================\nafter op:\n";
|
|
std::cout << "ra: " << rc0 << std::endl;
|
|
std::cout << "rc: " << rc << std::endl;*/
|
|
|
|
CHECK_DIFF(rc0, rc);
|
|
|
|
rc = ra + rb;
|
|
cv::add(ura, urb, urc);
|
|
urc.copyTo(rc0);
|
|
|
|
CHECK_DIFF(rc0, rc);
|
|
|
|
cv::subtract(ra, Scalar::all(5), rc);
|
|
cv::subtract(ura, Scalar::all(5), urc);
|
|
urc.copyTo(rc0);
|
|
|
|
CHECK_DIFF(rc0, rc);
|
|
}
|
|
catch (const test_excep& e)
|
|
{
|
|
ts->printf(cvtest::TS::LOG, "%s\n", e.s.c_str());
|
|
ts->set_failed_test_info(cvtest::TS::FAIL_MISMATCH);
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
void CV_UMatTest::run( int /* start_from */)
|
|
{
|
|
printf("Use OpenCL: %s\nHave OpenCL: %s\n",
|
|
cv::ocl::useOpenCL() ? "TRUE" : "FALSE",
|
|
cv::ocl::haveOpenCL() ? "TRUE" : "FALSE" );
|
|
|
|
if (!TestUMat())
|
|
return;
|
|
|
|
ts->set_failed_test_info(cvtest::TS::OK);
|
|
}
|
|
|
|
TEST(Core_UMat, base) { CV_UMatTest test; test.safe_run(); }
|
|
|
|
TEST(Core_UMat, getUMat)
|
|
{
|
|
{
|
|
int a[3] = { 1, 2, 3 };
|
|
Mat m = Mat(1, 1, CV_32SC3, a);
|
|
UMat u = m.getUMat(ACCESS_READ);
|
|
EXPECT_NE((void*)NULL, u.u);
|
|
}
|
|
|
|
{
|
|
Mat m(10, 10, CV_8UC1), ref;
|
|
for (int y = 0; y < m.rows; ++y)
|
|
{
|
|
uchar * const ptr = m.ptr<uchar>(y);
|
|
for (int x = 0; x < m.cols; ++x)
|
|
ptr[x] = (uchar)(x + y * 2);
|
|
}
|
|
|
|
ref = m.clone();
|
|
Rect r(1, 1, 8, 8);
|
|
ref(r).setTo(17);
|
|
|
|
{
|
|
UMat um = m(r).getUMat(ACCESS_WRITE);
|
|
um.setTo(17);
|
|
}
|
|
|
|
double err = cvtest::norm(m, ref, NORM_INF);
|
|
if (err > 0)
|
|
{
|
|
std::cout << "m: " << std::endl << m << std::endl;
|
|
std::cout << "ref: " << std::endl << ref << std::endl;
|
|
}
|
|
EXPECT_EQ(0., err);
|
|
}
|
|
}
|
|
|
|
TEST(UMat, Sync)
|
|
{
|
|
UMat um(10, 10, CV_8UC1);
|
|
|
|
{
|
|
Mat m = um.getMat(ACCESS_WRITE);
|
|
m.setTo(cv::Scalar::all(17));
|
|
}
|
|
|
|
um.setTo(cv::Scalar::all(19));
|
|
|
|
EXPECT_EQ(0, cvtest::norm(um.getMat(ACCESS_READ), cv::Mat(um.size(), um.type(), 19), NORM_INF));
|
|
}
|
|
|
|
TEST(UMat, SyncTemp)
|
|
{
|
|
Mat m(10, 10, CV_8UC1);
|
|
|
|
{
|
|
UMat um = m.getUMat(ACCESS_WRITE);
|
|
|
|
{
|
|
Mat m2 = um.getMat(ACCESS_WRITE);
|
|
m2.setTo(cv::Scalar::all(17));
|
|
}
|
|
|
|
um.setTo(cv::Scalar::all(19));
|
|
|
|
EXPECT_EQ(0, cvtest::norm(um.getMat(ACCESS_READ), cv::Mat(um.size(), um.type(), 19), NORM_INF));
|
|
}
|
|
}
|
|
|
|
TEST(UMat, CopyToIfDeviceCopyIsObsolete)
|
|
{
|
|
UMat um(7, 2, CV_8UC1);
|
|
Mat m(um.size(), um.type());
|
|
m.setTo(Scalar::all(0));
|
|
|
|
{
|
|
// make obsolete device copy of UMat
|
|
Mat temp = um.getMat(ACCESS_WRITE);
|
|
temp.setTo(Scalar::all(10));
|
|
}
|
|
|
|
m.copyTo(um);
|
|
um.setTo(Scalar::all(17));
|
|
|
|
EXPECT_EQ(0, cvtest::norm(um.getMat(ACCESS_READ), Mat(um.size(), um.type(), 17), NORM_INF));
|
|
}
|
|
|
|
TEST(UMat, setOpenCL)
|
|
{
|
|
#ifndef HAVE_OPENCL
|
|
return; // test skipped
|
|
#else
|
|
// 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);
|
|
#endif
|
|
}
|
|
|
|
TEST(UMat, ReadBufferRect)
|
|
{
|
|
UMat m(1, 10000, CV_32FC2, Scalar::all(-1));
|
|
Mat t(1, 9000, CV_32FC2, Scalar::all(-200)), t2(1, 9000, CV_32FC2, Scalar::all(-1));
|
|
m.colRange(0, 9000).copyTo(t);
|
|
|
|
EXPECT_MAT_NEAR(t, t2, 0);
|
|
}
|
|
|
|
|
|
// Use iGPU or OPENCV_OPENCL_DEVICE=:CPU: to catch problem
|
|
TEST(UMat, synchronization_map_unmap)
|
|
{
|
|
class TestParallelLoopBody : public cv::ParallelLoopBody
|
|
{
|
|
UMat u_;
|
|
public:
|
|
TestParallelLoopBody(const UMat& u) : u_(u) { }
|
|
void operator() (const cv::Range& range) const
|
|
{
|
|
printf("range: %d, %d -- begin\n", range.start, range.end);
|
|
for (int i = 0; i < 10; i++)
|
|
{
|
|
printf("%d: %d map...\n", range.start, i);
|
|
Mat m = u_.getMat(cv::ACCESS_READ);
|
|
|
|
printf("%d: %d unmap...\n", range.start, i);
|
|
m.release();
|
|
}
|
|
printf("range: %d, %d -- end\n", range.start, range.end);
|
|
}
|
|
};
|
|
try
|
|
{
|
|
UMat u(1000, 1000, CV_32FC1, Scalar::all(0));
|
|
parallel_for_(cv::Range(0, 2), TestParallelLoopBody(u));
|
|
}
|
|
catch (const cv::Exception& e)
|
|
{
|
|
FAIL() << "Exception: " << e.what();
|
|
ADD_FAILURE();
|
|
}
|
|
catch (...)
|
|
{
|
|
FAIL() << "Exception!";
|
|
}
|
|
}
|
|
|
|
|
|
TEST(UMat, async_unmap)
|
|
{
|
|
for (int i = 0; i < 20; i++)
|
|
{
|
|
try
|
|
{
|
|
Mat m = Mat(1000, 1000, CV_8UC1, Scalar::all(0));
|
|
UMat u = m.getUMat(ACCESS_READ);
|
|
UMat dst;
|
|
cv::add(u, Scalar::all(0), dst); // start async operation
|
|
u.release();
|
|
m.release();
|
|
}
|
|
catch (const cv::Exception& e)
|
|
{
|
|
printf("i = %d... %s\n", i, e.what());
|
|
ADD_FAILURE();
|
|
}
|
|
catch (...)
|
|
{
|
|
printf("i = %d...\n", i);
|
|
ADD_FAILURE();
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
TEST(UMat, unmap_in_class)
|
|
{
|
|
class Logic
|
|
{
|
|
public:
|
|
Logic() {}
|
|
void processData(InputArray input)
|
|
{
|
|
Mat m = input.getMat();
|
|
{
|
|
Mat dst;
|
|
m.convertTo(dst, CV_32FC1);
|
|
// some additional CPU-based per-pixel processing into dst
|
|
intermediateResult = dst.getUMat(ACCESS_READ); // this violates lifetime of base(dst) / derived (intermediateResult) objects. Use copyTo?
|
|
std::cout << "data processed..." << std::endl;
|
|
} // problem is here: dst::~Mat()
|
|
std::cout << "leave ProcessData()" << std::endl;
|
|
}
|
|
UMat getResult() const { return intermediateResult; }
|
|
protected:
|
|
UMat intermediateResult;
|
|
};
|
|
try
|
|
{
|
|
Mat m = Mat(1000, 1000, CV_8UC1, Scalar::all(0));
|
|
Logic l;
|
|
l.processData(m);
|
|
UMat result = l.getResult();
|
|
}
|
|
catch (const cv::Exception& e)
|
|
{
|
|
printf("exception... %s\n", e.what());
|
|
ADD_FAILURE();
|
|
}
|
|
catch (...)
|
|
{
|
|
printf("exception... \n");
|
|
ADD_FAILURE();
|
|
}
|
|
}
|
|
|
|
|
|
TEST(UMat, map_unmap_counting)
|
|
{
|
|
if (!cv::ocl::useOpenCL())
|
|
{
|
|
std::cout << "OpenCL is not enabled. Skip test" << std::endl;
|
|
return;
|
|
}
|
|
std::cout << "Host memory: " << cv::ocl::Device::getDefault().hostUnifiedMemory() << std::endl;
|
|
Mat m(Size(10, 10), CV_8UC1, Scalar::all(0));
|
|
UMat um = m.getUMat(ACCESS_RW);
|
|
{
|
|
Mat d1 = um.getMat(ACCESS_RW);
|
|
Mat d2 = um.getMat(ACCESS_RW);
|
|
d1.release();
|
|
}
|
|
void* h = NULL;
|
|
EXPECT_NO_THROW(h = um.handle(ACCESS_RW));
|
|
std::cout << "Handle: " << h << std::endl;
|
|
}
|
|
|
|
|
|
///////////// oclCleanupCallback threadsafe check (#5062) /////////////////////
|
|
|
|
// Case 1: reuse of old src Mat in OCL pipe. Hard to catch!
|
|
OCL_TEST(UMat, DISABLED_OCL_ThreadSafe_CleanupCallback_1_VeryLongTest)
|
|
{
|
|
if (!cv::ocl::useOpenCL())
|
|
{
|
|
std::cout << "OpenCL is not enabled. Skip test" << std::endl;
|
|
return;
|
|
}
|
|
for (int j = 0; j < 100; j++)
|
|
{
|
|
const Size srcSize(320, 240);
|
|
const int type = CV_8UC1;
|
|
const int dtype = CV_16UC1;
|
|
|
|
Mat src(srcSize, type, Scalar::all(0));
|
|
Mat dst_ref(srcSize, dtype);
|
|
|
|
// Generate reference data as additional check
|
|
OCL_OFF(src.convertTo(dst_ref, dtype));
|
|
cv::ocl::setUseOpenCL(true); // restore OpenCL state
|
|
|
|
UMat dst(srcSize, dtype);
|
|
|
|
// Use multiple iterations to increase chance of data race catching
|
|
for(int k = 0; k < 10000; k++)
|
|
{
|
|
UMat tmpUMat = src.getUMat(ACCESS_RW);
|
|
tmpUMat.convertTo(dst, dtype);
|
|
::cv::ocl::finish(); // force kernel to complete to start cleanup sooner
|
|
}
|
|
|
|
EXPECT_MAT_NEAR(dst_ref, dst, 1);
|
|
printf(".\n"); fflush(stdout);
|
|
}
|
|
}
|
|
|
|
// Case 2: concurrent deallocation of UMatData between UMat and Mat deallocators. Hard to catch!
|
|
OCL_TEST(UMat, DISABLED_OCL_ThreadSafe_CleanupCallback_2_VeryLongTest)
|
|
{
|
|
if (!cv::ocl::useOpenCL())
|
|
{
|
|
std::cout << "OpenCL is not enabled. Skip test" << std::endl;
|
|
return;
|
|
}
|
|
for (int j = 0; j < 100; j++)
|
|
{
|
|
const Size srcSize(320, 240);
|
|
const int type = CV_8UC1;
|
|
const int dtype = CV_16UC1;
|
|
|
|
// This test is only relevant for OCL
|
|
UMat dst(srcSize, dtype);
|
|
|
|
// Use multiple iterations to increase chance of data race catching
|
|
for(int k = 0; k < 10000; k++)
|
|
{
|
|
Mat src(srcSize, type, Scalar::all(0)); // Declare src inside loop now to catch its destruction on stack
|
|
{
|
|
UMat tmpUMat = src.getUMat(ACCESS_RW);
|
|
tmpUMat.convertTo(dst, dtype);
|
|
}
|
|
::cv::ocl::finish(); // force kernel to complete to start cleanup sooner
|
|
}
|
|
printf(".\n"); fflush(stdout);
|
|
}
|
|
}
|
|
|
|
|
|
|
|
TEST(UMat, DISABLED_Test_same_behaviour_read_and_read)
|
|
{
|
|
bool exceptionDetected = false;
|
|
try
|
|
{
|
|
UMat u(Size(10, 10), CV_8UC1, Scalar::all(0));
|
|
Mat m = u.getMat(ACCESS_READ);
|
|
UMat dst;
|
|
cv::add(u, Scalar::all(1), dst);
|
|
}
|
|
catch (...)
|
|
{
|
|
exceptionDetected = true;
|
|
}
|
|
ASSERT_FALSE(exceptionDetected); // no data race, 2+ reads are valid
|
|
}
|
|
|
|
// VP: this test (and probably others from same_behaviour series) is not valid in my opinion.
|
|
TEST(UMat, DISABLED_Test_same_behaviour_read_and_write)
|
|
{
|
|
bool exceptionDetected = false;
|
|
try
|
|
{
|
|
UMat u(Size(10, 10), CV_8UC1, Scalar::all(0));
|
|
Mat m = u.getMat(ACCESS_READ);
|
|
cv::add(u, Scalar::all(1), u);
|
|
}
|
|
catch (...)
|
|
{
|
|
exceptionDetected = true;
|
|
}
|
|
ASSERT_TRUE(exceptionDetected); // data race
|
|
}
|
|
|
|
TEST(UMat, DISABLED_Test_same_behaviour_write_and_read)
|
|
{
|
|
bool exceptionDetected = false;
|
|
try
|
|
{
|
|
UMat u(Size(10, 10), CV_8UC1, Scalar::all(0));
|
|
Mat m = u.getMat(ACCESS_WRITE);
|
|
UMat dst;
|
|
cv::add(u, Scalar::all(1), dst);
|
|
}
|
|
catch (...)
|
|
{
|
|
exceptionDetected = true;
|
|
}
|
|
ASSERT_TRUE(exceptionDetected); // data race
|
|
}
|
|
|
|
TEST(UMat, DISABLED_Test_same_behaviour_write_and_write)
|
|
{
|
|
bool exceptionDetected = false;
|
|
try
|
|
{
|
|
UMat u(Size(10, 10), CV_8UC1, Scalar::all(0));
|
|
Mat m = u.getMat(ACCESS_WRITE);
|
|
cv::add(u, Scalar::all(1), u);
|
|
}
|
|
catch (...)
|
|
{
|
|
exceptionDetected = true;
|
|
}
|
|
ASSERT_TRUE(exceptionDetected); // data race
|
|
}
|
|
|
|
TEST(UMat, mat_umat_sync)
|
|
{
|
|
UMat u(10, 10, CV_8UC1, Scalar(1));
|
|
{
|
|
Mat m = u.getMat(ACCESS_RW).reshape(1);
|
|
m.setTo(Scalar(255));
|
|
}
|
|
|
|
UMat uDiff;
|
|
cv::compare(u, 255, uDiff, CMP_NE);
|
|
ASSERT_EQ(0, countNonZero(uDiff));
|
|
}
|
|
|
|
TEST(UMat, testTempObjects_UMat)
|
|
{
|
|
UMat u(10, 10, CV_8UC1, Scalar(1));
|
|
{
|
|
UMat u2 = u.getMat(ACCESS_RW).getUMat(ACCESS_RW);
|
|
u2.setTo(Scalar(255));
|
|
}
|
|
|
|
UMat uDiff;
|
|
cv::compare(u, 255, uDiff, CMP_NE);
|
|
ASSERT_EQ(0, countNonZero(uDiff));
|
|
}
|
|
|
|
TEST(UMat, testTempObjects_Mat)
|
|
{
|
|
Mat m(10, 10, CV_8UC1, Scalar(1));
|
|
{
|
|
Mat m2;
|
|
ASSERT_ANY_THROW({
|
|
// Below is unwrapped version of this invalid expression:
|
|
// m2 = m.getUMat(ACCESS_RW).getMat(ACCESS_RW)
|
|
UMat u = m.getUMat(ACCESS_RW);
|
|
m2 = u.getMat(ACCESS_RW);
|
|
u.release();
|
|
});
|
|
}
|
|
}
|
|
|
|
TEST(UMat, testWrongLifetime_UMat)
|
|
{
|
|
UMat u(10, 10, CV_8UC1, Scalar(1));
|
|
{
|
|
UMat u2 = u.getMat(ACCESS_RW).getUMat(ACCESS_RW);
|
|
u.release(); // base object
|
|
u2.release(); // derived object, should show warning message
|
|
}
|
|
}
|
|
|
|
TEST(UMat, testWrongLifetime_Mat)
|
|
{
|
|
Mat m(10, 10, CV_8UC1, Scalar(1));
|
|
{
|
|
UMat u = m.getUMat(ACCESS_RW);
|
|
Mat m2 = u.getMat(ACCESS_RW);
|
|
m.release(); // base object
|
|
m2.release(); // map of derived object
|
|
u.release(); // derived object, should show warning message
|
|
}
|
|
}
|
|
|
|
TEST(UMat, DISABLED_regression_5991)
|
|
{
|
|
int sz[] = {2,3,2};
|
|
UMat mat(3, sz, CV_32F, Scalar(1));
|
|
ASSERT_NO_THROW(mat.convertTo(mat, CV_8U));
|
|
EXPECT_EQ(sz[0], mat.size[0]);
|
|
EXPECT_EQ(sz[1], mat.size[1]);
|
|
EXPECT_EQ(sz[2], mat.size[2]);
|
|
EXPECT_EQ(0, cvtest::norm(mat.getMat(ACCESS_READ), Mat(3, sz, CV_8U, Scalar(1)), NORM_INF));
|
|
}
|
|
|
|
TEST(UMat, testTempObjects_Mat_issue_8693)
|
|
{
|
|
UMat srcUMat(3, 4, CV_32FC1);
|
|
Mat srcMat;
|
|
|
|
randu(srcUMat, -1.f, 1.f);
|
|
srcUMat.copyTo(srcMat);
|
|
|
|
reduce(srcUMat, srcUMat, 0, CV_REDUCE_SUM);
|
|
reduce(srcMat, srcMat, 0, CV_REDUCE_SUM);
|
|
|
|
srcUMat.convertTo(srcUMat, CV_64FC1);
|
|
srcMat.convertTo(srcMat, CV_64FC1);
|
|
|
|
EXPECT_EQ(0, cvtest::norm(srcUMat.getMat(ACCESS_READ), srcMat, NORM_INF));
|
|
}
|
|
|
|
} } // namespace opencv_test::ocl
|