/*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/ts/ocl_test.hpp" using namespace cvtest; using namespace testing; using namespace cv; namespace cvtest { namespace ocl { #define UMAT_TEST_SIZES testing::Values(cv::Size(1, 1), cv::Size(1,128), cv::Size(128, 1), \ cv::Size(128, 128), cv::Size(640, 480), cv::Size(751, 373), cv::Size(1200, 1200)) /////////////////////////////// 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); } }; 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); } 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); } TEST_P(UMatBasicTests, base) { const int align_mask = 3; roi.x &= ~align_mask; roi.y &= ~align_mask; roi.width = (roi.width + align_mask) & ~align_mask; roi &= Rect(0, 0, ua.cols, ua.rows); 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); } TEST_P(UMatBasicTests, DISABLED_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); } } 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); } }; TEST_P(UMatTestReshape, DISABLED_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(OCL_ALL_DEPTHS, OCL_ALL_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); } }; 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); } 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); } 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 )); /////////////////////////////////////////////////////////////// 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())); ///////////////////////////////////////////////////////////////// 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; } 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, 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) { // 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); } 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, DISABLED_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); parallel_for_(cv::Range(0, 2), TestParallelLoopBody(u)); } catch (const cv::Exception& e) { FAIL() << "Exception: " << e.what(); ADD_FAILURE(); } catch (...) { FAIL() << "Exception!"; } } } } // namespace cvtest::ocl TEST(UMat, DISABLED_bug_with_unmap) { for (int i = 0; i < 20; i++) { try { Mat m = Mat(1000, 1000, CV_8UC1); UMat u = m.getUMat(ACCESS_READ); UMat dst; 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, DISABLED_bug_with_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); 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); 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, Test_same_behaviour_read_and_read) { bool exceptionDetected = false; try { UMat u(Size(10, 10), CV_8UC1); Mat m = u.getMat(ACCESS_READ); UMat dst; 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); Mat m = u.getMat(ACCESS_READ); 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); Mat m = u.getMat(ACCESS_WRITE); UMat dst; 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); Mat m = u.getMat(ACCESS_WRITE); add(u, Scalar::all(1), u); } catch (...) { exceptionDetected = true; } ASSERT_TRUE(exceptionDetected); // data race }