/*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. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, 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 Intel Corporation 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 Intel Corporation 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 #include #include "gputest.hpp" #define CHECK(pred, err) if (!(pred)) { \ ts->printf(CvTS::CONSOLE, "Fail: \"%s\" at line: %d\n", #pred, __LINE__); \ ts->set_failed_test_info(err); \ return; } using namespace cv; using namespace std; struct CV_GpuBitwiseTest: public CvTest { CV_GpuBitwiseTest(): CvTest("GPU-BitwiseOpers", "bitwiseMatOperators") {} void run(int) { int rows, cols; for (int depth = CV_8U; depth <= CV_64F; ++depth) for (int cn = 1; cn <= 4; ++cn) for (int attempt = 0; attempt < 3; ++attempt) { rows = 1 + rand() % 100; cols = 1 + rand() % 100; test_bitwise_not(rows, cols, CV_MAKETYPE(depth, cn)); test_bitwise_or(rows, cols, CV_MAKETYPE(depth, cn)); test_bitwise_and(rows, cols, CV_MAKETYPE(depth, cn)); test_bitwise_xor(rows, cols, CV_MAKETYPE(depth, cn)); } } void test_bitwise_not(int rows, int cols, int type) { Mat src(rows, cols, type); RNG rng; for (int i = 0; i < src.rows; ++i) { Mat row(1, src.cols * src.elemSize(), CV_8U, src.ptr(i)); rng.fill(row, RNG::UNIFORM, Scalar(0), Scalar(255)); } Mat dst_gold = ~src; gpu::GpuMat mask(src.size(), CV_8U); mask.setTo(Scalar(1)); gpu::GpuMat dst; gpu::bitwise_not(gpu::GpuMat(src), dst); CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT); CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT); Mat dsth(dst); for (int i = 0; i < dst_gold.rows; ++i) CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT); dst.setTo(Scalar::all(0)); gpu::bitwise_not(gpu::GpuMat(src), dst, mask); CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT); CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT); dsth = dst; for (int i = 0; i < dst_gold.rows; ++i) CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT) } void test_bitwise_or(int rows, int cols, int type) { Mat src1(rows, cols, type); Mat src2(rows, cols, type); RNG rng; for (int i = 0; i < src1.rows; ++i) { Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i)); rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255)); Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i)); rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255)); } Mat dst_gold = src1 | src2; gpu::GpuMat dst = gpu::GpuMat(src1) | gpu::GpuMat(src2); CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT); CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT); Mat dsth(dst); for (int i = 0; i < dst_gold.rows; ++i) CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT) Mat mask(src1.size(), CV_8U); randu(mask, Scalar(0), Scalar(255)); Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0)); gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0)); bitwise_or(src1, src2, dst_gold2, mask); gpu::bitwise_or(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask)); CHECK(dst_gold2.size() == dst2.size(), CvTS::FAIL_INVALID_OUTPUT); CHECK(dst_gold2.type() == dst2.type(), CvTS::FAIL_INVALID_OUTPUT); dsth = dst2; for (int i = 0; i < dst_gold.rows; ++i) CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT) } void test_bitwise_and(int rows, int cols, int type) { Mat src1(rows, cols, type); Mat src2(rows, cols, type); RNG rng; for (int i = 0; i < src1.rows; ++i) { Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i)); rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255)); Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i)); rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255)); } Mat dst_gold = src1 & src2; gpu::GpuMat dst = gpu::GpuMat(src1) & gpu::GpuMat(src2); CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT); CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT); Mat dsth(dst); for (int i = 0; i < dst_gold.rows; ++i) CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT) Mat mask(src1.size(), CV_8U); randu(mask, Scalar(0), Scalar(255)); Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0)); gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0)); bitwise_and(src1, src2, dst_gold2, mask); gpu::bitwise_and(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask)); CHECK(dst_gold2.size() == dst2.size(), CvTS::FAIL_INVALID_OUTPUT); CHECK(dst_gold2.type() == dst2.type(), CvTS::FAIL_INVALID_OUTPUT); dsth = dst2; for (int i = 0; i < dst_gold.rows; ++i) CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT) } void test_bitwise_xor(int rows, int cols, int type) { Mat src1(rows, cols, type); Mat src2(rows, cols, type); RNG rng; for (int i = 0; i < src1.rows; ++i) { Mat row1(1, src1.cols * src1.elemSize(), CV_8U, src1.ptr(i)); rng.fill(row1, RNG::UNIFORM, Scalar(0), Scalar(255)); Mat row2(1, src2.cols * src2.elemSize(), CV_8U, src2.ptr(i)); rng.fill(row2, RNG::UNIFORM, Scalar(0), Scalar(255)); } Mat dst_gold = src1 ^ src2; gpu::GpuMat dst = gpu::GpuMat(src1) ^ gpu::GpuMat(src2); CHECK(dst_gold.size() == dst.size(), CvTS::FAIL_INVALID_OUTPUT); CHECK(dst_gold.type() == dst.type(), CvTS::FAIL_INVALID_OUTPUT); Mat dsth(dst); for (int i = 0; i < dst_gold.rows; ++i) CHECK(memcmp(dst_gold.ptr(i), dsth.ptr(i), dst_gold.cols * dst_gold.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT) Mat mask(src1.size(), CV_8U); randu(mask, Scalar(0), Scalar(255)); Mat dst_gold2(dst_gold.size(), dst_gold.type()); dst_gold2.setTo(Scalar::all(0)); gpu::GpuMat dst2(dst.size(), dst.type()); dst2.setTo(Scalar::all(0)); bitwise_xor(src1, src2, dst_gold2, mask); gpu::bitwise_xor(gpu::GpuMat(src1), gpu::GpuMat(src2), dst2, gpu::GpuMat(mask)); CHECK(dst_gold2.size() == dst2.size(), CvTS::FAIL_INVALID_OUTPUT); CHECK(dst_gold2.type() == dst2.type(), CvTS::FAIL_INVALID_OUTPUT); dsth = dst2; for (int i = 0; i < dst_gold.rows; ++i) CHECK(memcmp(dst_gold2.ptr(i), dsth.ptr(i), dst_gold2.cols * dst_gold2.elemSize()) == 0, CvTS::FAIL_INVALID_OUTPUT) } } gpu_bitwise_test;