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375 lines
11 KiB
C++
375 lines
11 KiB
C++
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/*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) 2010-2012, Institute Of Software Chinese Academy Of Science, all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// @Authors
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// Jia Haipeng, jiahaipeng95@gmail.com
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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 oclMaterials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "precomp.hpp"
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#ifdef HAVE_OPENCL
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using namespace cvtest;
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using namespace testing;
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using namespace std;
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PARAM_TEST_CASE(MergeTestBase, MatType, int)
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{
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int type;
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int channels;
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//src mat
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cv::Mat mat1;
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cv::Mat mat2;
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cv::Mat mat3;
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cv::Mat mat4;
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//dst mat
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cv::Mat dst;
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// set up roi
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int roicols;
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int roirows;
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int src1x;
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int src1y;
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int src2x;
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int src2y;
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int src3x;
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int src3y;
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int src4x;
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int src4y;
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int dstx;
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int dsty;
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//src mat with roi
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cv::Mat mat1_roi;
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cv::Mat mat2_roi;
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cv::Mat mat3_roi;
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cv::Mat mat4_roi;
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//dst mat with roi
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cv::Mat dst_roi;
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std::vector<cv::ocl::Info> oclinfo;
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//ocl dst mat for testing
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cv::ocl::oclMat gdst_whole;
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//ocl mat with roi
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cv::ocl::oclMat gmat1;
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cv::ocl::oclMat gmat2;
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cv::ocl::oclMat gmat3;
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cv::ocl::oclMat gmat4;
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cv::ocl::oclMat gdst;
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virtual void SetUp()
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{
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type = GET_PARAM(0);
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channels = GET_PARAM(1);
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cv::RNG &rng = TS::ptr()->get_rng();
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cv::Size size(MWIDTH, MHEIGHT);
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mat1 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
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mat2 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
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mat3 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
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mat4 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
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dst = randomMat(rng, size, CV_MAKETYPE(type, channels), 5, 16, false);
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int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
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CV_Assert(devnums > 0);
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//if you want to use undefault device, set it here
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//setDevice(oclinfo[0]);
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}
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void random_roi()
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{
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#ifdef RANDOMROI
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//randomize ROI
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cv::RNG &rng = TS::ptr()->get_rng();
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roicols = rng.uniform(1, mat1.cols);
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roirows = rng.uniform(1, mat1.rows);
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src1x = rng.uniform(0, mat1.cols - roicols);
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src1y = rng.uniform(0, mat1.rows - roirows);
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src2x = rng.uniform(0, mat2.cols - roicols);
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src2y = rng.uniform(0, mat2.rows - roirows);
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src3x = rng.uniform(0, mat3.cols - roicols);
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src3y = rng.uniform(0, mat3.cols - roirows);
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src4x = rng.uniform(0, mat4.rows - roicols);
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src4y = rng.uniform(0, mat4.rows - roirows);
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dstx = rng.uniform(0, dst.cols - roicols);
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dsty = rng.uniform(0, dst.rows - roirows);
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#else
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roicols = mat1.cols;
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roirows = mat1.rows;
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src1x = 0;
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src1y = 0;
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src2x = 0;
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src2y = 0;
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src3x = 0;
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src3y = 0;
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src4x = 0;
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src4y = 0;
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dstx = 0;
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dsty = 0;
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#endif
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mat1_roi = mat1(Rect(src1x, src1y, roicols, roirows));
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mat2_roi = mat2(Rect(src2x, src2y, roicols, roirows));
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mat3_roi = mat3(Rect(src3x, src3y, roicols, roirows));
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mat4_roi = mat4(Rect(src4x, src4y, roicols, roirows));
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dst_roi = dst(Rect(dstx, dsty, roicols, roirows));
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gdst_whole = dst;
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gdst = gdst_whole(Rect(dstx, dsty, roicols, roirows));
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gmat1 = mat1_roi;
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gmat2 = mat2_roi;
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gmat3 = mat3_roi;
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gmat4 = mat4_roi;
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}
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};
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struct Merge : MergeTestBase {};
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TEST_P(Merge, Accuracy)
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{
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for(int j = 0; j < LOOP_TIMES; j++)
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{
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random_roi();
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std::vector<cv::Mat> dev_src;
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dev_src.push_back(mat1_roi);
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dev_src.push_back(mat2_roi);
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dev_src.push_back(mat3_roi);
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dev_src.push_back(mat4_roi);
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std::vector<cv::ocl::oclMat> dev_gsrc;
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dev_gsrc.push_back(gmat1);
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dev_gsrc.push_back(gmat2);
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dev_gsrc.push_back(gmat3);
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dev_gsrc.push_back(gmat4);
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cv::merge(dev_src, dst_roi);
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cv::ocl::merge(dev_gsrc, gdst);
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cv::Mat cpu_dst;
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gdst_whole.download(cpu_dst);
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char sss[1024];
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sprintf(sss, "roicols=%d,roirows=%d,src1x =%d,src1y=%d,src2x =%d,src2y=%d,src3x =%d,src3y=%d,src4x =%d,src4y=%d,dstx=%d,dsty=%d", roicols, roirows, src1x, src1y, src2x , src2y, src3x , src3y, src4x , src4y, dstx, dsty);
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EXPECT_MAT_NEAR(dst, cpu_dst, 0.0, sss);
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}
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}
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PARAM_TEST_CASE(SplitTestBase, MatType, int)
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{
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int type;
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int channels;
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//src mat
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cv::Mat mat;
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//dstmat
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cv::Mat dst1;
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cv::Mat dst2;
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cv::Mat dst3;
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cv::Mat dst4;
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// set up roi
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int roicols;
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int roirows;
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int srcx;
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int srcy;
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int dst1x;
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int dst1y;
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int dst2x;
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int dst2y;
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int dst3x;
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int dst3y;
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int dst4x;
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int dst4y;
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//src mat with roi
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cv::Mat mat_roi;
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//dst mat with roi
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cv::Mat dst1_roi;
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cv::Mat dst2_roi;
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cv::Mat dst3_roi;
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cv::Mat dst4_roi;
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std::vector<cv::ocl::Info> oclinfo;
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//ocl dst mat for testing
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cv::ocl::oclMat gdst1_whole;
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cv::ocl::oclMat gdst2_whole;
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cv::ocl::oclMat gdst3_whole;
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cv::ocl::oclMat gdst4_whole;
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//ocl mat with roi
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cv::ocl::oclMat gmat;
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cv::ocl::oclMat gdst1;
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cv::ocl::oclMat gdst2;
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cv::ocl::oclMat gdst3;
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cv::ocl::oclMat gdst4;
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virtual void SetUp()
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{
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type = GET_PARAM(0);
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channels = GET_PARAM(1);
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cv::RNG &rng = TS::ptr()->get_rng();
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cv::Size size(MWIDTH, MHEIGHT);
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mat = randomMat(rng, size, CV_MAKETYPE(type, channels), 5, 16, false);
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dst1 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
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dst2 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
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dst3 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
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dst4 = randomMat(rng, size, CV_MAKETYPE(type, 1), 5, 16, false);
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int devnums = getDevice(oclinfo, OPENCV_DEFAULT_OPENCL_DEVICE);
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CV_Assert(devnums > 0);
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//if you want to use undefault device, set it here
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//setDevice(oclinfo[0]);
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}
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void random_roi()
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{
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#ifdef RANDOMROI
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//randomize ROI
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cv::RNG &rng = TS::ptr()->get_rng();
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roicols = rng.uniform(1, mat.cols);
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roirows = rng.uniform(1, mat.rows);
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srcx = rng.uniform(0, mat.cols - roicols);
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srcy = rng.uniform(0, mat.rows - roirows);
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dst1x = rng.uniform(0, dst1.cols - roicols);
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dst1y = rng.uniform(0, dst1.rows - roirows);
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dst2x = rng.uniform(0, dst2.cols - roicols);
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dst2y = rng.uniform(0, dst2.rows - roirows);
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dst3x = rng.uniform(0, dst3.cols - roicols);
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dst3y = rng.uniform(0, dst3.rows - roirows);
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dst4x = rng.uniform(0, dst4.cols - roicols);
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dst4y = rng.uniform(0, dst4.rows - roirows);
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#else
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roicols = mat.cols;
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roirows = mat.rows;
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srcx = 0;
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srcy = 0;
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dst1x = 0;
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dst1y = 0;
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dst2x = 0;
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dst2y = 0;
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dst3x = 0;
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dst3y = 0;
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dst4x = 0;
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dst4y = 0;
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#endif
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mat_roi = mat(Rect(srcx, srcy, roicols, roirows));
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dst1_roi = dst1(Rect(dst1x, dst1y, roicols, roirows));
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dst2_roi = dst2(Rect(dst2x, dst2y, roicols, roirows));
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dst3_roi = dst3(Rect(dst3x, dst3y, roicols, roirows));
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dst4_roi = dst4(Rect(dst4x, dst4y, roicols, roirows));
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gdst1_whole = dst1;
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gdst1 = gdst1_whole(Rect(dst1x, dst1y, roicols, roirows));
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gdst2_whole = dst2;
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gdst2 = gdst2_whole(Rect(dst2x, dst2y, roicols, roirows));
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gdst3_whole = dst3;
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gdst3 = gdst3_whole(Rect(dst3x, dst3y, roicols, roirows));
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gdst4_whole = dst4;
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gdst4 = gdst4_whole(Rect(dst4x, dst4y, roicols, roirows));
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gmat = mat_roi;
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}
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};
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struct Split : SplitTestBase {};
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TEST_P(Split, Accuracy)
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{
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for(int j = 0; j < LOOP_TIMES; j++)
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{
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random_roi();
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cv::Mat dev_dst[4] = {dst1_roi, dst2_roi, dst3_roi, dst4_roi};
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cv::ocl::oclMat dev_gdst[4] = {gdst1, gdst2, gdst3, gdst4};
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cv::split(mat_roi, dev_dst);
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cv::ocl::split(gmat, dev_gdst);
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cv::Mat cpu_dst1;
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cv::Mat cpu_dst2;
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cv::Mat cpu_dst3;
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cv::Mat cpu_dst4;
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gdst1_whole.download(cpu_dst1);
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gdst2_whole.download(cpu_dst2);
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gdst3_whole.download(cpu_dst3);
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gdst4_whole.download(cpu_dst4);
|
||
|
|
char sss[1024];
|
||
|
|
sprintf(sss, "roicols=%d,roirows=%d,dst1x =%d,dsty=%d,dst2x =%d,dst2y=%d,dst3x =%d,dst3y=%d,dst4x =%d,dst4y=%d,srcx=%d,srcy=%d", roicols, roirows, dst1x , dst1y, dst2x , dst2y, dst3x , dst3y, dst4x , dst4y, srcx, srcy);
|
||
|
|
|
||
|
|
EXPECT_MAT_NEAR(dst1, cpu_dst1, 0.0, sss);
|
||
|
|
EXPECT_MAT_NEAR(dst2, cpu_dst2, 0.0, sss);
|
||
|
|
EXPECT_MAT_NEAR(dst3, cpu_dst3, 0.0, sss);
|
||
|
|
EXPECT_MAT_NEAR(dst4, cpu_dst4, 0.0, sss);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
INSTANTIATE_TEST_CASE_P(SplitMerge, Merge, Combine(
|
||
|
|
Values(CV_8U, CV_32S, CV_32F), Values(1, 4)));
|
||
|
|
|
||
|
|
|
||
|
|
INSTANTIATE_TEST_CASE_P(SplitMerge, Split , Combine(
|
||
|
|
Values(CV_8U, CV_32S, CV_32F), Values(1, 4)));
|
||
|
|
|
||
|
|
|
||
|
|
#endif // HAVE_OPENCL
|