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Open Source Computer Vision Library
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235 lines
10 KiB
235 lines
10 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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// Intel License Agreement |
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// For Open Source Computer Vision Library |
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// |
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// Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 <iostream> |
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#include <limits> |
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#include "test_precomp.hpp" |
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#define CHECK(pred, err) if (!(pred)) { \ |
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ts->printf(cvtest::TS::CONSOLE, "Fail: \"%s\" at line: %d\n", #pred, __LINE__); \ |
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ts->set_failed_test_info(err); \ |
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return; } |
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using namespace cv; |
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using namespace std; |
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struct CV_GpuBitwiseTest: public cvtest::BaseTest |
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{ |
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CV_GpuBitwiseTest() {} |
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void run(int) |
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{ |
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int rows, cols; |
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bool double_ok = gpu::TargetArchs::builtWith(gpu::NATIVE_DOUBLE) && |
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gpu::DeviceInfo().supports(gpu::NATIVE_DOUBLE); |
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int depth_end = double_ok ? CV_64F : CV_32F; |
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for (int depth = CV_8U; depth <= depth_end; ++depth) |
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for (int cn = 1; cn <= 4; ++cn) |
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for (int attempt = 0; attempt < 3; ++attempt) |
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{ |
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rows = 1 + rand() % 100; |
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cols = 1 + rand() % 100; |
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test_bitwise_not(rows, cols, CV_MAKETYPE(depth, cn)); |
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test_bitwise_or(rows, cols, CV_MAKETYPE(depth, cn)); |
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test_bitwise_and(rows, cols, CV_MAKETYPE(depth, cn)); |
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test_bitwise_xor(rows, cols, CV_MAKETYPE(depth, cn)); |
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} |
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} |
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void test_bitwise_not(int rows, int cols, int type) |
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{ |
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Mat src(rows, cols, type); |
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RNG rng; |
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for (int i = 0; i < src.rows; ++i) |
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{ |
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Mat row(1, src.cols * src.elemSize(), CV_8U, src.ptr(i)); |
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rng.fill(row, RNG::UNIFORM, Scalar(0), Scalar(255)); |
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} |
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Mat dst_gold = ~src; |
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gpu::GpuMat mask(src.size(), CV_8U); |
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mask.setTo(Scalar(1)); |
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gpu::GpuMat dst; |
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gpu::bitwise_not(gpu::GpuMat(src), dst); |
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CHECK(dst_gold.size() == dst.size(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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CHECK(dst_gold.type() == dst.type(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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Mat dsth(dst); |
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for (int i = 0; i < dst_gold.rows; ++i) |
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CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, cvtest::TS::FAIL_INVALID_OUTPUT); |
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dst.setTo(Scalar::all(0)); |
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gpu::bitwise_not(gpu::GpuMat(src), dst, mask); |
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CHECK(dst_gold.size() == dst.size(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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CHECK(dst_gold.type() == dst.type(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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dsth = dst; |
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for (int i = 0; i < dst_gold.rows; ++i) |
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CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, cvtest::TS::FAIL_INVALID_OUTPUT) |
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} |
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void test_bitwise_or(int rows, int cols, int type) |
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{ |
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Mat src1(rows, cols, type); |
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Mat src2(rows, cols, type); |
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RNG rng; |
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for (int i = 0; i < src1.rows; ++i) |
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{ |
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Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i)); |
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rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255)); |
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Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i)); |
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rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255)); |
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} |
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Mat dst_gold = src1 | src2; |
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gpu::GpuMat dst = gpu::GpuMat(src1) | gpu::GpuMat(src2); |
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CHECK(dst_gold.size() == dst.size(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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CHECK(dst_gold.type() == dst.type(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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Mat dsth(dst); |
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for (int i = 0; i < dst_gold.rows; ++i) |
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CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, cvtest::TS::FAIL_INVALID_OUTPUT) |
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Mat mask(src1.size(), CV_8U); |
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randu(mask, Scalar(0), Scalar(255)); |
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Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0)); |
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gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0)); |
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bitwise_or(src1, src2, dst_gold2, mask); |
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gpu::bitwise_or(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask)); |
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CHECK(dst_gold2.size() == dst2.size(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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CHECK(dst_gold2.type() == dst2.type(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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dsth = dst2; |
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for (int i = 0; i < dst_gold.rows; ++i) |
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CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, cvtest::TS::FAIL_INVALID_OUTPUT) |
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} |
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void test_bitwise_and(int rows, int cols, int type) |
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{ |
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Mat src1(rows, cols, type); |
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Mat src2(rows, cols, type); |
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RNG rng; |
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for (int i = 0; i < src1.rows; ++i) |
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{ |
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Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i)); |
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rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255)); |
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Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i)); |
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rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255)); |
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} |
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Mat dst_gold = src1 & src2; |
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gpu::GpuMat dst = gpu::GpuMat(src1) & gpu::GpuMat(src2); |
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CHECK(dst_gold.size() == dst.size(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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CHECK(dst_gold.type() == dst.type(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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Mat dsth(dst); |
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for (int i = 0; i < dst_gold.rows; ++i) |
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CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, cvtest::TS::FAIL_INVALID_OUTPUT) |
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Mat mask(src1.size(), CV_8U); |
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randu(mask, Scalar(0), Scalar(255)); |
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Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0)); |
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gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0)); |
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bitwise_and(src1, src2, dst_gold2, mask); |
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gpu::bitwise_and(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask)); |
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CHECK(dst_gold2.size() == dst2.size(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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CHECK(dst_gold2.type() == dst2.type(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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dsth = dst2; |
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for (int i = 0; i < dst_gold.rows; ++i) |
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CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, cvtest::TS::FAIL_INVALID_OUTPUT) |
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} |
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void test_bitwise_xor(int rows, int cols, int type) |
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{ |
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Mat src1(rows, cols, type); |
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Mat src2(rows, cols, type); |
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RNG rng; |
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for (int i = 0; i < src1.rows; ++i) |
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{ |
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Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i)); |
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rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255)); |
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Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i)); |
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rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255)); |
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} |
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Mat dst_gold = src1 ^ src2; |
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gpu::GpuMat dst = gpu::GpuMat(src1) ^ gpu::GpuMat(src2); |
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CHECK(dst_gold.size() == dst.size(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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CHECK(dst_gold.type() == dst.type(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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Mat dsth(dst); |
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for (int i = 0; i < dst_gold.rows; ++i) |
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CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, cvtest::TS::FAIL_INVALID_OUTPUT) |
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Mat mask(src1.size(), CV_8U); |
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randu(mask, Scalar(0), Scalar(255)); |
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Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0)); |
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gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0)); |
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bitwise_xor(src1, src2, dst_gold2, mask); |
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gpu::bitwise_xor(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask)); |
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CHECK(dst_gold2.size() == dst2.size(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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CHECK(dst_gold2.type() == dst2.type(), cvtest::TS::FAIL_INVALID_OUTPUT); |
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dsth = dst2; |
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for (int i = 0; i < dst_gold.rows; ++i) |
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CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, cvtest::TS::FAIL_INVALID_OUTPUT) |
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} |
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}; |
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TEST(BitwiseOperations, accuracy) { CV_GpuBitwiseTest test; test.safe_run(); }
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