Merge pull request #2149 from ElenaGvozdeva:ocl_matchTemplate
Andrey Pavlenko
11 years ago
committed by
OpenCV Buildbot
commit
ac3f06bc7f
4 changed files with 929 additions and 8 deletions
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#include "perf_precomp.hpp" |
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#include "opencv2/ts/ocl_perf.hpp" |
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#ifdef HAVE_OPENCL |
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namespace cvtest { |
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namespace ocl { |
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CV_ENUM(MethodType, TM_SQDIFF, TM_SQDIFF_NORMED, TM_CCORR, TM_CCORR_NORMED, TM_CCOEFF, TM_CCOEFF_NORMED) |
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typedef std::tr1::tuple<Size, Size, MethodType> ImgSize_TmplSize_Method_t; |
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typedef TestBaseWithParam<ImgSize_TmplSize_Method_t> ImgSize_TmplSize_Method; |
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OCL_PERF_TEST_P(ImgSize_TmplSize_Method, MatchTemplate, |
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::testing::Combine( |
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testing::Values(szSmall128, cv::Size(320, 240), |
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cv::Size(640, 480), cv::Size(800, 600), |
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cv::Size(1024, 768), cv::Size(1280, 1024)), |
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testing::Values(cv::Size(12, 12), cv::Size(28, 9), |
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cv::Size(8, 30), cv::Size(16, 16)), |
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MethodType::all() |
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) |
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) |
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{ |
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Size imgSz = get<0>(GetParam()); |
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Size tmplSz = get<1>(GetParam()); |
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int method = get<2>(GetParam()); |
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UMat img(imgSz, CV_8UC1); |
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UMat tmpl(tmplSz, CV_8UC1); |
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UMat result(imgSz - tmplSz + Size(1,1), CV_32F); |
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declare |
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.in(img, WARMUP_RNG) |
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.in(tmpl, WARMUP_RNG) |
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.out(result) |
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.time(30); |
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OCL_TEST_CYCLE() matchTemplate(img, tmpl, result, method); |
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bool isNormed = |
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method == TM_CCORR_NORMED || |
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method == TM_SQDIFF_NORMED || |
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method == TM_CCOEFF_NORMED; |
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double eps = isNormed ? 3e-2 |
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: 255 * 255 * tmpl.total() * 1e-4; |
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if (isNormed) |
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SANITY_CHECK(result,eps,ERROR_RELATIVE); |
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else |
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SANITY_CHECK(result, eps); |
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} |
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} |
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} |
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#endif // HAVE_OPENCL
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@ -0,0 +1,397 @@ |
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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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// 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 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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#define DATA_SIZE ((int)sizeof(type)) |
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#define ELEM_TYPE elem_type |
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#define ELEM_SIZE ((int)sizeof(elem_type)) |
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#define CN cn |
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#define SQSUMS_PTR(ox, oy) mad24(gidy + oy, img_sqsums_step, gidx*CN + img_sqsums_offset + ox*CN) |
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#define SUMS_PTR(ox, oy) mad24(gidy + oy, img_sums_step, gidx*CN + img_sums_offset + ox*CN) |
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inline float normAcc(float num, float denum) |
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{ |
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if(fabs(num) < denum) |
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{ |
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return num / denum; |
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} |
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if(fabs(num) < denum * 1.125f) |
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{ |
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return num > 0 ? 1 : -1; |
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} |
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return 0; |
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} |
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inline float normAcc_SQDIFF(float num, float denum) |
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{ |
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if(fabs(num) < denum) |
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{ |
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return num / denum; |
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} |
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if(fabs(num) < denum * 1.125f) |
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{ |
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return num > 0 ? 1 : -1; |
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} |
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return 1; |
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} |
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//////////////////////////////////////////CCORR///////////////////////////////////////////////////////////////////////// |
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__kernel void matchTemplate_Naive_CCORR (__global const uchar * img,int img_step,int img_offset, |
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__global const uchar * tpl,int tpl_step,int tpl_offset,int tpl_rows, int tpl_cols, |
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__global uchar * res,int res_step,int res_offset,int res_rows,int res_cols) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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int i,j; |
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float sum = 0; |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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for(i = 0; i < tpl_rows; i ++) |
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{ |
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__global const ELEM_TYPE * img_ptr = (__global const ELEM_TYPE *)(img + mad24(gidy + i, img_step, gidx*DATA_SIZE + img_offset)); |
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__global const ELEM_TYPE * tpl_ptr = (__global const ELEM_TYPE *)(tpl + mad24(i, tpl_step, tpl_offset)); |
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for(j = 0; j < tpl_cols; j ++) |
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#pragma unroll |
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for (int c = 0; c < CN; c++) |
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sum += (float)(img_ptr[j*CN+c] * tpl_ptr[j*CN+c]); |
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} |
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__global float * result = (__global float *)(res+res_idx); |
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*result = sum; |
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} |
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} |
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__kernel void matchTemplate_CCORR_NORMED ( __global const uchar * img_sqsums, int img_sqsums_step, int img_sqsums_offset, |
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__global uchar * res, int res_step, int res_offset, int res_rows, int res_cols, |
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int tpl_rows, int tpl_cols, ulong tpl_sqsum) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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img_sqsums_step /= sizeof(float); |
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img_sqsums_offset /= sizeof(float); |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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__global float * sqsum = (__global float*)(img_sqsums); |
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float image_sqsum_ = (float)( |
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(sqsum[SQSUMS_PTR(tpl_cols, tpl_rows)] - sqsum[SQSUMS_PTR(tpl_cols, 0)]) - |
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(sqsum[SQSUMS_PTR(0, tpl_rows)] - sqsum[SQSUMS_PTR(0, 0)])); |
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__global float * result = (__global float *)(res+res_idx); |
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*result = normAcc(*result, sqrt(image_sqsum_ * tpl_sqsum)); |
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} |
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} |
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////////////////////////////////////////////SQDIFF//////////////////////////////////////////////////////////////////////// |
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__kernel void matchTemplate_Naive_SQDIFF(__global const uchar * img,int img_step,int img_offset, |
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__global const uchar * tpl,int tpl_step,int tpl_offset,int tpl_rows, int tpl_cols, |
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__global uchar * res,int res_step,int res_offset,int res_rows,int res_cols) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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int i,j; |
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float delta; |
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float sum = 0; |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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for(i = 0; i < tpl_rows; i ++) |
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{ |
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__global const ELEM_TYPE * img_ptr = (__global const ELEM_TYPE *)(img + mad24(gidy + i, img_step, gidx*DATA_SIZE + img_offset)); |
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__global const ELEM_TYPE * tpl_ptr = (__global const ELEM_TYPE *)(tpl + mad24(i, tpl_step, tpl_offset)); |
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for(j = 0; j < tpl_cols; j ++) |
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#pragma unroll |
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for (int c = 0; c < CN; c++) |
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{ |
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delta = (float)(img_ptr[j*CN+c] - tpl_ptr[j*CN+c]); |
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sum += delta*delta; |
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} |
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} |
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__global float * result = (__global float *)(res+res_idx); |
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*result = sum; |
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} |
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} |
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__kernel void matchTemplate_SQDIFF_NORMED ( __global const uchar * img_sqsums, int img_sqsums_step, int img_sqsums_offset, |
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__global uchar * res, int res_step, int res_offset, int res_rows, int res_cols, |
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int tpl_rows, int tpl_cols, ulong tpl_sqsum) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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img_sqsums_step /= sizeof(float); |
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img_sqsums_offset /= sizeof(float); |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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__global float * sqsum = (__global float*)(img_sqsums); |
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float image_sqsum_ = (float)( |
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(sqsum[SQSUMS_PTR(tpl_cols, tpl_rows)] - sqsum[SQSUMS_PTR(tpl_cols, 0)]) - |
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(sqsum[SQSUMS_PTR(0, tpl_rows)] - sqsum[SQSUMS_PTR(0, 0)])); |
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__global float * result = (__global float *)(res+res_idx); |
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*result = normAcc_SQDIFF(image_sqsum_ - 2.f * result[0] + tpl_sqsum, sqrt(image_sqsum_ * tpl_sqsum)); |
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} |
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} |
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////////////////////////////////////////////CCOEFF///////////////////////////////////////////////////////////////// |
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__kernel void matchTemplate_Prepared_CCOEFF_C1 (__global const uchar * img_sums, int img_sums_step, int img_sums_offset, |
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__global uchar * res, int res_step, int res_offset, int res_rows, int res_cols, |
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int tpl_rows, int tpl_cols, float tpl_sum) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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img_sums_step /= ELEM_SIZE; |
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img_sums_offset /= ELEM_SIZE; |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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float image_sum_ = 0; |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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__global ELEM_TYPE* sum = (__global ELEM_TYPE*)(img_sums); |
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image_sum_ += (float)((sum[SUMS_PTR(tpl_cols, tpl_rows)] - sum[SUMS_PTR(tpl_cols, 0)])- |
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(sum[SUMS_PTR(0, tpl_rows)] - sum[SUMS_PTR(0, 0)])) * tpl_sum; |
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__global float * result = (__global float *)(res+res_idx); |
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*result -= image_sum_; |
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} |
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} |
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__kernel void matchTemplate_Prepared_CCOEFF_C2 (__global const uchar * img_sums, int img_sums_step, int img_sums_offset, |
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__global uchar * res, int res_step, int res_offset, int res_rows, int res_cols, |
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int tpl_rows, int tpl_cols, float tpl_sum_0,float tpl_sum_1) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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img_sums_step /= ELEM_SIZE; |
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img_sums_offset /= ELEM_SIZE; |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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float image_sum_ = 0; |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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__global ELEM_TYPE* sum = (__global ELEM_TYPE*)(img_sums); |
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image_sum_ += tpl_sum_0 * (float)((sum[SUMS_PTR(tpl_cols, tpl_rows)] - sum[SUMS_PTR(tpl_cols, 0)]) -(sum[SUMS_PTR(0, tpl_rows)] - sum[SUMS_PTR(0, 0)])); |
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image_sum_ += tpl_sum_1 * (float)((sum[SUMS_PTR(tpl_cols, tpl_rows)+1] - sum[SUMS_PTR(tpl_cols, 0)+1])-(sum[SUMS_PTR(0, tpl_rows)+1] - sum[SUMS_PTR(0, 0)+1])); |
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__global float * result = (__global float *)(res+res_idx); |
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*result -= image_sum_; |
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} |
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} |
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__kernel void matchTemplate_Prepared_CCOEFF_C4 (__global const uchar * img_sums, int img_sums_step, int img_sums_offset, |
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__global uchar * res, int res_step, int res_offset, int res_rows, int res_cols, |
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int tpl_rows, int tpl_cols, float tpl_sum_0,float tpl_sum_1,float tpl_sum_2,float tpl_sum_3) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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img_sums_step /= ELEM_SIZE; |
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img_sums_offset /= ELEM_SIZE; |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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float image_sum_ = 0; |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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__global ELEM_TYPE* sum = (__global ELEM_TYPE*)(img_sums); |
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int c_r = SUMS_PTR(tpl_cols, tpl_rows); |
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int c_o = SUMS_PTR(tpl_cols, 0); |
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int o_r = SUMS_PTR(0,tpl_rows); |
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int oo = SUMS_PTR(0, 0); |
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image_sum_ += tpl_sum_0 * (float)((sum[c_r] - sum[c_o]) -(sum[o_r] - sum[oo])); |
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image_sum_ += tpl_sum_1 * (float)((sum[c_r+1] - sum[c_o+1])-(sum[o_r+1] - sum[oo+1])); |
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image_sum_ += tpl_sum_2 * (float)((sum[c_r+2] - sum[c_o+2])-(sum[o_r+2] - sum[oo+2])); |
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image_sum_ += tpl_sum_3 * (float)((sum[c_r+3] - sum[c_o+3])-(sum[o_r+3] - sum[oo+3])); |
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__global float * result = (__global float *)(res+res_idx); |
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*result -= image_sum_; |
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} |
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} |
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__kernel void matchTemplate_CCOEFF_NORMED_C1 (__global const uchar * img_sums, int img_sums_step, int img_sums_offset, |
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__global const uchar * img_sqsums, int img_sqsums_step, int img_sqsums_offset, |
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__global uchar * res, int res_step, int res_offset, int res_rows, int res_cols, |
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int t_rows, int t_cols, float weight, float tpl_sum, float tpl_sqsum) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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img_sums_offset /= ELEM_SIZE; |
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img_sums_step /= ELEM_SIZE; |
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img_sqsums_step /= sizeof(float); |
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img_sqsums_offset /= sizeof(float); |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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__global ELEM_TYPE* sum = (__global ELEM_TYPE*)(img_sums); |
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__global float * sqsum = (__global float*)(img_sqsums); |
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float image_sum_ = (float)((sum[SUMS_PTR(t_cols, t_rows)] - sum[SUMS_PTR(t_cols, 0)]) - |
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(sum[SUMS_PTR(0, t_rows)] - sum[SUMS_PTR(0, 0)])); |
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float image_sqsum_ = (float)((sqsum[SQSUMS_PTR(t_cols, t_rows)] - sqsum[SQSUMS_PTR(t_cols, 0)]) - |
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(sqsum[SQSUMS_PTR(0, t_rows)] - sqsum[SQSUMS_PTR(0, 0)])); |
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__global float * result = (__global float *)(res+res_idx); |
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*result = normAcc((*result) - image_sum_ * tpl_sum, |
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sqrt(tpl_sqsum * (image_sqsum_ - weight * image_sum_ * image_sum_))); |
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} |
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} |
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__kernel void matchTemplate_CCOEFF_NORMED_C2 (__global const uchar * img_sums, int img_sums_step, int img_sums_offset, |
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__global const uchar * img_sqsums, int img_sqsums_step, int img_sqsums_offset, |
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__global uchar * res, int res_step, int res_offset, int res_rows, int res_cols, |
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int t_rows, int t_cols, float weight, float tpl_sum_0, float tpl_sum_1, float tpl_sqsum) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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img_sums_offset /= ELEM_SIZE; |
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img_sums_step /= ELEM_SIZE; |
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img_sqsums_step /= sizeof(float); |
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img_sqsums_offset /= sizeof(float); |
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int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
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float sum_[2]; |
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float sqsum_[2]; |
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if(gidx < res_cols && gidy < res_rows) |
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{ |
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__global ELEM_TYPE* sum = (__global ELEM_TYPE*)(img_sums); |
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__global float * sqsum = (__global float*)(img_sqsums); |
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sum_[0] = (float)((sum[SUMS_PTR(t_cols, t_rows)] - sum[SUMS_PTR(t_cols, 0)])-(sum[SUMS_PTR(0, t_rows)] - sum[SUMS_PTR(0, 0)])); |
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sum_[1] = (float)((sum[SUMS_PTR(t_cols, t_rows)+1] - sum[SUMS_PTR(t_cols, 0)+1])-(sum[SUMS_PTR(0, t_rows)+1] - sum[SUMS_PTR(0, 0)+1])); |
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sqsum_[0] = (float)((sqsum[SQSUMS_PTR(t_cols, t_rows)] - sqsum[SQSUMS_PTR(t_cols, 0)])-(sqsum[SQSUMS_PTR(0, t_rows)] - sqsum[SQSUMS_PTR(0, 0)])); |
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sqsum_[1] = (float)((sqsum[SQSUMS_PTR(t_cols, t_rows)+1] - sqsum[SQSUMS_PTR(t_cols, 0)+1])-(sqsum[SQSUMS_PTR(0, t_rows)+1] - sqsum[SQSUMS_PTR(0, 0)+1])); |
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float num = sum_[0]*tpl_sum_0 + sum_[1]*tpl_sum_1; |
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float denum = sqrt( tpl_sqsum * (sqsum_[0] - weight * sum_[0]* sum_[0] + |
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sqsum_[1] - weight * sum_[1]* sum_[1])); |
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__global float * result = (__global float *)(res+res_idx); |
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*result = normAcc((*result) - num, denum); |
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} |
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} |
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__kernel void matchTemplate_CCOEFF_NORMED_C4 (__global const uchar * img_sums, int img_sums_step, int img_sums_offset, |
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__global const uchar * img_sqsums, int img_sqsums_step, int img_sqsums_offset, |
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__global uchar * res, int res_step, int res_offset, int res_rows, int res_cols, |
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int t_rows, int t_cols, float weight, |
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float tpl_sum_0,float tpl_sum_1,float tpl_sum_2,float tpl_sum_3, |
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float tpl_sqsum) |
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{ |
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int gidx = get_global_id(0); |
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int gidy = get_global_id(1); |
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img_sums_offset /= ELEM_SIZE; |
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img_sums_step /= ELEM_SIZE; |
||||
img_sqsums_step /= sizeof(float); |
||||
img_sqsums_offset /= sizeof(float); |
||||
|
||||
int res_idx = mad24(gidy, res_step, res_offset + gidx * (int)sizeof(float)); |
||||
|
||||
float sum_[4]; |
||||
float sqsum_[4]; |
||||
|
||||
if(gidx < res_cols && gidy < res_rows) |
||||
{ |
||||
__global ELEM_TYPE* sum = (__global ELEM_TYPE*)(img_sums); |
||||
__global float * sqsum = (__global float*)(img_sqsums); |
||||
|
||||
int c_r = SUMS_PTR(t_cols, t_rows); |
||||
int c_o = SUMS_PTR(t_cols, 0); |
||||
int o_r = SUMS_PTR(0, t_rows); |
||||
int o_o = SUMS_PTR(0, 0); |
||||
|
||||
sum_[0] = (float)((sum[c_r] - sum[c_o]) -(sum[o_r] - sum[o_o ])); |
||||
sum_[1] = (float)((sum[c_r+1] - sum[c_o+1])-(sum[o_r+1] - sum[o_o +1])); |
||||
sum_[2] = (float)((sum[c_r+2] - sum[c_o+2])-(sum[o_r+2] - sum[o_o +2])); |
||||
sum_[3] = (float)((sum[c_r+3] - sum[c_o+3])-(sum[o_r+3] - sum[o_o +3])); |
||||
|
||||
c_r = SQSUMS_PTR(t_cols, t_rows); |
||||
c_o = SQSUMS_PTR(t_cols, 0); |
||||
o_r = SQSUMS_PTR(0, t_rows); |
||||
o_o = SQSUMS_PTR(0, 0); |
||||
|
||||
sqsum_[0] = (float)((sqsum[c_r] - sqsum[c_o]) -(sqsum[o_r] - sqsum[o_o])); |
||||
sqsum_[1] = (float)((sqsum[c_r+1] - sqsum[c_o+1])-(sqsum[o_r+1] - sqsum[o_o+1])); |
||||
sqsum_[2] = (float)((sqsum[c_r+2] - sqsum[c_o+2])-(sqsum[o_r+2] - sqsum[o_o+2])); |
||||
sqsum_[3] = (float)((sqsum[c_r+3] - sqsum[c_o+3])-(sqsum[o_r+3] - sqsum[o_o+3])); |
||||
|
||||
float num = sum_[0]*tpl_sum_0 + sum_[1]*tpl_sum_1 + sum_[2]*tpl_sum_2 + sum_[3]*tpl_sum_3; |
||||
|
||||
float denum = sqrt( tpl_sqsum * ( |
||||
sqsum_[0] - weight * sum_[0]* sum_[0] + |
||||
sqsum_[1] - weight * sum_[1]* sum_[1] + |
||||
sqsum_[2] - weight * sum_[2]* sum_[2] + |
||||
sqsum_[3] - weight * sum_[3]* sum_[3] )); |
||||
|
||||
__global float * result = (__global float *)(res+res_idx); |
||||
*result = normAcc((*result) - num, denum); |
||||
} |
||||
} |
||||
@ -0,0 +1,128 @@ |
||||
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
|
||||
// Copyright (C) 2010-2012, Advanced Micro Devices, Inc., 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 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 "test_precomp.hpp" |
||||
#include "opencv2/ts/ocl_test.hpp" |
||||
#include "iostream" |
||||
#include "fstream" |
||||
|
||||
#ifdef HAVE_OPENCL |
||||
|
||||
namespace cvtest { |
||||
namespace ocl { |
||||
|
||||
/////////////////////////////////////////////matchTemplate//////////////////////////////////////////////////////////
|
||||
|
||||
PARAM_TEST_CASE(MatchTemplate, MatDepth, Channels, int, bool) |
||||
{ |
||||
int type; |
||||
int depth; |
||||
int method; |
||||
bool use_roi; |
||||
|
||||
TEST_DECLARE_INPUT_PARAMETER(image) |
||||
TEST_DECLARE_INPUT_PARAMETER(templ) |
||||
TEST_DECLARE_OUTPUT_PARAMETER(result) |
||||
|
||||
virtual void SetUp() |
||||
{ |
||||
type = CV_MAKE_TYPE(GET_PARAM(0), GET_PARAM(1)); |
||||
depth = GET_PARAM(0); |
||||
method = GET_PARAM(2); |
||||
use_roi = GET_PARAM(3); |
||||
} |
||||
|
||||
virtual void generateTestData() |
||||
{ |
||||
Size image_roiSize = randomSize(2, 100); |
||||
Size templ_roiSize = Size(randomInt(1, image_roiSize.width), randomInt(1, image_roiSize.height)); |
||||
Size result_roiSize = Size(image_roiSize.width - templ_roiSize.width + 1, |
||||
image_roiSize.height - templ_roiSize.height + 1); |
||||
|
||||
const double upValue = 256; |
||||
|
||||
Border imageBorder = randomBorder(0, use_roi ? MAX_VALUE : 0); |
||||
randomSubMat(image, image_roi, image_roiSize, imageBorder, type, -upValue, upValue); |
||||
|
||||
Border templBorder = randomBorder(0, use_roi ? MAX_VALUE : 0); |
||||
randomSubMat(templ, templ_roi, templ_roiSize, templBorder, type, -upValue, upValue); |
||||
|
||||
Border resultBorder = randomBorder(0, use_roi ? MAX_VALUE : 0); |
||||
randomSubMat(result, result_roi, result_roiSize, resultBorder, CV_32F, -upValue, upValue); |
||||
|
||||
UMAT_UPLOAD_INPUT_PARAMETER(image) |
||||
UMAT_UPLOAD_INPUT_PARAMETER(templ) |
||||
UMAT_UPLOAD_OUTPUT_PARAMETER(result) |
||||
} |
||||
|
||||
void Near(double threshold = 0.0) |
||||
{ |
||||
OCL_EXPECT_MATS_NEAR(result,threshold); |
||||
} |
||||
}; |
||||
|
||||
OCL_TEST_P(MatchTemplate, Mat) |
||||
{ |
||||
for (int j = 0; j < test_loop_times; j++) |
||||
{ |
||||
generateTestData(); |
||||
|
||||
OCL_OFF(cv::matchTemplate(image_roi,templ_roi,result_roi, method)); |
||||
OCL_ON(cv::matchTemplate(uimage_roi,utempl_roi,uresult_roi, method)); |
||||
|
||||
if (method == 0) |
||||
Near(10.0f); |
||||
else |
||||
Near(method % 2 == 1 ? 0.001f : 1.0f); |
||||
} |
||||
} |
||||
|
||||
OCL_INSTANTIATE_TEST_CASE_P(ImageProc, MatchTemplate, Combine( |
||||
Values(CV_8U, CV_32F), |
||||
Values(1, 2, 4), |
||||
Values(0,1,2,3,4,5), |
||||
Bool()) |
||||
); |
||||
} } // namespace cvtest::ocl
|
||||
|
||||
#endif |
||||
Loading…
Reference in new issue