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opencv/modules/ocl/src/match_template.cpp

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/*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.
//
// @Authors
// Peng Xiao, pengxiao@multicorewareinc.com
//
// 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 "precomp.hpp"
#include "opencl_kernels.hpp"
using namespace cv;
using namespace cv::ocl;
namespace cv
{
namespace ocl
{
void matchTemplate_SQDIFF(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void matchTemplate_SQDIFF_NORMED(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void convolve_32F(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void matchTemplate_CCORR(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void matchTemplate_CCORR_NORMED(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void matchTemplate_CCOFF(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void matchTemplate_CCOFF_NORMED(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf);
void matchTemplateNaive_SQDIFF(
const oclMat &image, const oclMat &templ, oclMat &result, int cn);
void matchTemplateNaive_CCORR(
const oclMat &image, const oclMat &templ, oclMat &result, int cn);
void extractFirstChannel_32F(
const oclMat &image, oclMat &result);
// Evaluates optimal template's area threshold. If
// template's area is less than the threshold, we use naive match
// template version, otherwise FFT-based (if available)
static bool useNaive(int , int , Size )
{
// FIXME!
// always use naive until convolve is imported
return true;
}
//////////////////////////////////////////////////////////////////////
// SQDIFF
void matchTemplate_SQDIFF(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf & buf)
{
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
if (useNaive(CV_TM_SQDIFF, image.depth(), templ.size()))
{
matchTemplateNaive_SQDIFF(image, templ, result, image.oclchannels());
return;
}
else
{
buf.image_sqsums.resize(1);
// TODO, add double support for ocl::integral
// use CPU integral temporarily
Mat sums, sqsums;
cv::integral(Mat(image.reshape(1)), sums, sqsums);
buf.image_sqsums[0] = sqsums;
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
matchTemplate_CCORR(image, templ, result, buf);
//port CUDA's matchTemplatePrepared_SQDIFF_8U
Context *clCxt = image.clCxt;
string kernelName = "matchTemplate_Prepared_SQDIFF";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[0].data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data));
args.push_back( make_pair( sizeof(cl_ulong), (void *)&templ_sqsum));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].step));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
const char * build_opt = image.oclchannels() == 4 ? "-D CN4" : "";
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U, build_opt);
}
}
void matchTemplate_SQDIFF_NORMED(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf)
{
matchTemplate_CCORR(image, templ, result, buf);
buf.image_sums.resize(1);
integral(image.reshape(1), buf.image_sums[0]);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
Context *clCxt = image.clCxt;
string kernelName = "matchTemplate_Prepared_SQDIFF_NORMED";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data));
args.push_back( make_pair( sizeof(cl_ulong), (void *)&templ_sqsum));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].step));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U);
}
void matchTemplateNaive_SQDIFF(
const oclMat &image, const oclMat &templ, oclMat &result, int)
{
CV_Assert((image.depth() == CV_8U && templ.depth() == CV_8U )
|| ((image.depth() == CV_32F && templ.depth() == CV_32F) && result.depth() == CV_32F)
);
CV_Assert(image.oclchannels() == templ.oclchannels() && (image.oclchannels() == 1 || image.oclchannels() == 4) && result.oclchannels() == 1);
CV_Assert(result.rows == image.rows - templ.rows + 1 && result.cols == image.cols - templ.cols + 1);
Context *clCxt = image.clCxt;
string kernelName = "matchTemplate_Naive_SQDIFF";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&image.data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&templ.data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.step));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.step));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
}
//////////////////////////////////////////////////////////////////////
// CCORR
void convolve_32F(
const oclMat &, const oclMat &, oclMat &, MatchTemplateBuf &)
{
CV_Error(-1, "convolve is not fully implemented yet");
}
void matchTemplate_CCORR(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf)
{
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
if (useNaive(CV_TM_CCORR, image.depth(), templ.size()))
{
matchTemplateNaive_CCORR(image, templ, result, image.oclchannels());
return;
}
else
{
if(image.depth() == CV_8U && templ.depth() == CV_8U)
{
image.convertTo(buf.imagef, CV_32F);
templ.convertTo(buf.templf, CV_32F);
convolve_32F(buf.imagef, buf.templf, result, buf);
}
else
{
convolve_32F(image, templ, result, buf);
}
}
}
void matchTemplate_CCORR_NORMED(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf)
{
matchTemplate_CCORR(image, templ, result, buf);
buf.image_sums.resize(1);
buf.image_sqsums.resize(1);
integral(image.reshape(1), buf.image_sums[0], buf.image_sqsums[0]);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
Context *clCxt = image.clCxt;
string kernelName = "normalizeKernel";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[0].data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data));
args.push_back( make_pair( sizeof(cl_ulong), (void *)&templ_sqsum));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].step));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, 1, CV_8U);
}
void matchTemplateNaive_CCORR(
const oclMat &image, const oclMat &templ, oclMat &result, int)
{
CV_Assert((image.depth() == CV_8U && templ.depth() == CV_8U )
|| ((image.depth() == CV_32F && templ.depth() == CV_32F) && result.depth() == CV_32F)
);
CV_Assert(image.oclchannels() == templ.oclchannels() && (image.oclchannels() == 1 || image.oclchannels() == 4) && result.oclchannels() == 1);
CV_Assert(result.rows == image.rows - templ.rows + 1 && result.cols == image.cols - templ.cols + 1);
Context *clCxt = image.clCxt;
string kernelName = "matchTemplate_Naive_CCORR";
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&image.data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&templ.data));
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.step));
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.step));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
}
//////////////////////////////////////////////////////////////////////
// CCOFF
void matchTemplate_CCOFF(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf)
{
CV_Assert(image.depth() == CV_8U && templ.depth() == CV_8U);
matchTemplate_CCORR(image, templ, result, buf);
Context *clCxt = image.clCxt;
string kernelName;
kernelName = "matchTemplate_Prepared_CCOFF";
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&image.rows) );
args.push_back( make_pair( sizeof(cl_int), (void *)&image.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows) );
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
Vec4f templ_sum = Vec4f::all(0);
// to be continued in the following section
if(image.oclchannels() == 1)
{
buf.image_sums.resize(1);
integral(image, buf.image_sums[0]);
templ_sum[0] = (float)sum(templ)[0] / templ.size().area();
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].step) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[0]) );
}
else
{
split(image, buf.images);
templ_sum = sum(templ) / templ.size().area();
buf.image_sums.resize(buf.images.size());
for(int i = 0; i < image.oclchannels(); i ++)
{
integral(buf.images[i], buf.image_sums[i]);
}
switch(image.oclchannels())
{
case 4:
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[1].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[2].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[3].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].step) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[0]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[1]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[2]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[3]) );
break;
default:
CV_Error(CV_StsBadArg, "matchTemplate: unsupported number of channels");
break;
}
}
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
}
void matchTemplate_CCOFF_NORMED(
const oclMat &image, const oclMat &templ, oclMat &result, MatchTemplateBuf &buf)
{
image.convertTo(buf.imagef, CV_32F);
templ.convertTo(buf.templf, CV_32F);
matchTemplate_CCORR(buf.imagef, buf.templf, result, buf);
float scale = 1.f / templ.size().area();
Context *clCxt = image.clCxt;
string kernelName;
kernelName = "matchTemplate_Prepared_CCOFF_NORMED";
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&image.rows) );
args.push_back( make_pair( sizeof(cl_int), (void *)&image.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.rows) );
args.push_back( make_pair( sizeof(cl_int), (void *)&templ.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
args.push_back( make_pair( sizeof(cl_float), (void *)&scale) );
Vec4f templ_sum = Vec4f::all(0);
Vec4f templ_sqsum = Vec4f::all(0);
// to be continued in the following section
if(image.oclchannels() == 1)
{
buf.image_sums.resize(1);
buf.image_sqsums.resize(1);
integral(image, buf.image_sums[0], buf.image_sqsums[0]);
templ_sum[0] = (float)sum(templ)[0];
templ_sqsum[0] = sqrSum(templ)[0];
templ_sqsum[0] -= scale * templ_sum[0] * templ_sum[0];
templ_sum[0] *= scale;
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].step) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[0].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].offset) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].step) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[0]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sqsum[0]) );
}
else
{
split(image, buf.images);
templ_sum = sum(templ);
templ_sqsum = sqrSum(templ);
templ_sqsum -= scale * templ_sum * templ_sum;
float templ_sqsum_sum = 0;
for(int i = 0; i < image.oclchannels(); i ++)
{
templ_sqsum_sum += templ_sqsum[i] - scale * templ_sum[i] * templ_sum[i];
}
templ_sum *= scale;
buf.image_sums.resize(buf.images.size());
buf.image_sqsums.resize(buf.images.size());
for(int i = 0; i < image.oclchannels(); i ++)
{
integral(buf.images[i], buf.image_sums[i], buf.image_sqsums[i]);
}
switch(image.oclchannels())
{
case 4:
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[0].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[1].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[2].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sums[3].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].offset) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sums[0].step) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[0].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[1].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[2].data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&buf.image_sqsums[3].data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].offset) );
args.push_back( make_pair( sizeof(cl_int), (void *)&buf.image_sqsums[0].step) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[0]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[1]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[2]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sum[3]) );
args.push_back( make_pair( sizeof(cl_float), (void *)&templ_sqsum_sum) );
break;
default:
CV_Error(CV_StsBadArg, "matchTemplate: unsupported number of channels");
break;
}
}
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, image.oclchannels(), image.depth());
}
void extractFirstChannel_32F(const oclMat &image, oclMat &result)
{
Context *clCxt = image.clCxt;
string kernelName;
kernelName = "extractFirstChannel";
size_t globalThreads[3] = {result.cols, result.rows, 1};
size_t localThreads[3] = {16, 16, 1};
vector< pair<size_t, const void *> > args;
args.push_back( make_pair( sizeof(cl_mem), (void *)&image.data) );
args.push_back( make_pair( sizeof(cl_mem), (void *)&result.data) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.rows) );
args.push_back( make_pair( sizeof(cl_int), (void *)&result.cols) );
args.push_back( make_pair( sizeof(cl_int), (void *)&image.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.offset));
args.push_back( make_pair( sizeof(cl_int), (void *)&image.step));
args.push_back( make_pair( sizeof(cl_int), (void *)&result.step));
openCLExecuteKernel(clCxt, &match_template, kernelName, globalThreads, localThreads, args, -1, -1);
}
}/*ocl*/
} /*cv*/
void cv::ocl::matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method)
{
MatchTemplateBuf buf;
matchTemplate(image, templ, result, method, buf);
}
void cv::ocl::matchTemplate(const oclMat &image, const oclMat &templ, oclMat &result, int method, MatchTemplateBuf &buf)
{
CV_Assert(image.type() == templ.type());
CV_Assert(image.cols >= templ.cols && image.rows >= templ.rows);
typedef void (*Caller)(const oclMat &, const oclMat &, oclMat &, MatchTemplateBuf &);
const Caller callers[] =
{
::matchTemplate_SQDIFF, ::matchTemplate_SQDIFF_NORMED,
::matchTemplate_CCORR, ::matchTemplate_CCORR_NORMED,
::matchTemplate_CCOFF, ::matchTemplate_CCOFF_NORMED
};
Caller caller = callers[method];
CV_Assert(caller);
caller(image, templ, result, buf);
}