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opencv/modules/gpu/src/match_template.cpp
OpenCV Buildbot 04384a71e4 Normalize line endings and whitespace
2012-10-17 15:32:23 +04:00

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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) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage 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 "precomp.hpp"
using namespace cv;
using namespace cv::gpu;
using namespace std;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
void cv::gpu::matchTemplate(const GpuMat&, const GpuMat&, GpuMat&, int, Stream&) { throw_nogpu(); }
#else
namespace cv { namespace gpu { namespace device
{
namespace match_template
{
void matchTemplateNaive_CCORR_8U(const PtrStepSzb image, const PtrStepSzb templ, PtrStepSzf result, int cn, cudaStream_t stream);
void matchTemplateNaive_CCORR_32F(const PtrStepSzb image, const PtrStepSzb templ, PtrStepSzf result, int cn, cudaStream_t stream);
void matchTemplateNaive_SQDIFF_8U(const PtrStepSzb image, const PtrStepSzb templ, PtrStepSzf result, int cn, cudaStream_t stream);
void matchTemplateNaive_SQDIFF_32F(const PtrStepSzb image, const PtrStepSzb templ, PtrStepSzf result, int cn, cudaStream_t stream);
void matchTemplatePrepared_SQDIFF_8U(int w, int h, const PtrStepSz<unsigned long long> image_sqsum, unsigned long long templ_sqsum, PtrStepSzf result,
int cn, cudaStream_t stream);
void matchTemplatePrepared_SQDIFF_NORMED_8U(int w, int h, const PtrStepSz<unsigned long long> image_sqsum, unsigned long long templ_sqsum, PtrStepSzf result,
int cn, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_8U(int w, int h, const PtrStepSz<unsigned int> image_sum, unsigned int templ_sum, PtrStepSzf result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_8UC2(
int w, int h,
const PtrStepSz<unsigned int> image_sum_r,
const PtrStepSz<unsigned int> image_sum_g,
unsigned int templ_sum_r,
unsigned int templ_sum_g,
PtrStepSzf result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_8UC3(
int w, int h,
const PtrStepSz<unsigned int> image_sum_r,
const PtrStepSz<unsigned int> image_sum_g,
const PtrStepSz<unsigned int> image_sum_b,
unsigned int templ_sum_r,
unsigned int templ_sum_g,
unsigned int templ_sum_b,
PtrStepSzf result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_8UC4(
int w, int h,
const PtrStepSz<unsigned int> image_sum_r,
const PtrStepSz<unsigned int> image_sum_g,
const PtrStepSz<unsigned int> image_sum_b,
const PtrStepSz<unsigned int> image_sum_a,
unsigned int templ_sum_r,
unsigned int templ_sum_g,
unsigned int templ_sum_b,
unsigned int templ_sum_a,
PtrStepSzf result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_NORMED_8U(
int w, int h, const PtrStepSz<unsigned int> image_sum,
const PtrStepSz<unsigned long long> image_sqsum,
unsigned int templ_sum, unsigned long long templ_sqsum,
PtrStepSzf result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_NORMED_8UC2(
int w, int h,
const PtrStepSz<unsigned int> image_sum_r, const PtrStepSz<unsigned long long> image_sqsum_r,
const PtrStepSz<unsigned int> image_sum_g, const PtrStepSz<unsigned long long> image_sqsum_g,
unsigned int templ_sum_r, unsigned long long templ_sqsum_r,
unsigned int templ_sum_g, unsigned long long templ_sqsum_g,
PtrStepSzf result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_NORMED_8UC3(
int w, int h,
const PtrStepSz<unsigned int> image_sum_r, const PtrStepSz<unsigned long long> image_sqsum_r,
const PtrStepSz<unsigned int> image_sum_g, const PtrStepSz<unsigned long long> image_sqsum_g,
const PtrStepSz<unsigned int> image_sum_b, const PtrStepSz<unsigned long long> image_sqsum_b,
unsigned int templ_sum_r, unsigned long long templ_sqsum_r,
unsigned int templ_sum_g, unsigned long long templ_sqsum_g,
unsigned int templ_sum_b, unsigned long long templ_sqsum_b,
PtrStepSzf result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_NORMED_8UC4(
int w, int h,
const PtrStepSz<unsigned int> image_sum_r, const PtrStepSz<unsigned long long> image_sqsum_r,
const PtrStepSz<unsigned int> image_sum_g, const PtrStepSz<unsigned long long> image_sqsum_g,
const PtrStepSz<unsigned int> image_sum_b, const PtrStepSz<unsigned long long> image_sqsum_b,
const PtrStepSz<unsigned int> image_sum_a, const PtrStepSz<unsigned long long> image_sqsum_a,
unsigned int templ_sum_r, unsigned long long templ_sqsum_r,
unsigned int templ_sum_g, unsigned long long templ_sqsum_g,
unsigned int templ_sum_b, unsigned long long templ_sqsum_b,
unsigned int templ_sum_a, unsigned long long templ_sqsum_a,
PtrStepSzf result, cudaStream_t stream);
void normalize_8U(int w, int h, const PtrStepSz<unsigned long long> image_sqsum,
unsigned long long templ_sqsum, PtrStepSzf result, int cn, cudaStream_t stream);
void extractFirstChannel_32F(const PtrStepSzb image, PtrStepSzf result, int cn, cudaStream_t stream);
}
}}}
using namespace ::cv::gpu::device::match_template;
namespace
{
// 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)
int getTemplateThreshold(int method, int depth)
{
switch (method)
{
case CV_TM_CCORR:
if (depth == CV_32F) return 250;
if (depth == CV_8U) return 300;
break;
case CV_TM_SQDIFF:
if (depth == CV_8U) return 300;
break;
}
CV_Error(CV_StsBadArg, "getTemplateThreshold: unsupported match template mode");
return 0;
}
void matchTemplate_CCORR_32F(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
if (templ.size().area() < getTemplateThreshold(CV_TM_CCORR, CV_32F))
{
matchTemplateNaive_CCORR_32F(image, templ, result, image.channels(), StreamAccessor::getStream(stream));
return;
}
ConvolveBuf convolve_buf;
convolve_buf.user_block_size = buf.user_block_size;
if (image.channels() == 1)
convolve(image.reshape(1), templ.reshape(1), result, true, convolve_buf, stream);
else
{
GpuMat result_;
convolve(image.reshape(1), templ.reshape(1), result_, true, convolve_buf, stream);
extractFirstChannel_32F(result_, result, image.channels(), StreamAccessor::getStream(stream));
}
}
void matchTemplate_CCORR_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
if (templ.size().area() < getTemplateThreshold(CV_TM_CCORR, CV_8U))
{
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
matchTemplateNaive_CCORR_8U(image, templ, result, image.channels(), StreamAccessor::getStream(stream));
return;
}
if (stream)
{
stream.enqueueConvert(image, buf.imagef, CV_32F);
stream.enqueueConvert(templ, buf.templf, CV_32F);
}
else
{
image.convertTo(buf.imagef, CV_32F);
templ.convertTo(buf.templf, CV_32F);
}
matchTemplate_CCORR_32F(buf.imagef, buf.templf, result, buf, stream);
}
void matchTemplate_CCORR_NORMED_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
matchTemplate_CCORR_8U(image, templ, result, buf, stream);
buf.image_sqsums.resize(1);
sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
normalize_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));
}
void matchTemplate_SQDIFF_32F(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
(void)buf;
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
matchTemplateNaive_SQDIFF_32F(image, templ, result, image.channels(), StreamAccessor::getStream(stream));
}
void matchTemplate_SQDIFF_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
if (templ.size().area() < getTemplateThreshold(CV_TM_SQDIFF, CV_8U))
{
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
matchTemplateNaive_SQDIFF_8U(image, templ, result, image.channels(), StreamAccessor::getStream(stream));
return;
}
buf.image_sqsums.resize(1);
sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
matchTemplate_CCORR_8U(image, templ, result, buf, stream);
matchTemplatePrepared_SQDIFF_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));
}
void matchTemplate_SQDIFF_NORMED_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
buf.image_sqsums.resize(1);
sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];
matchTemplate_CCORR_8U(image, templ, result, buf, stream);
matchTemplatePrepared_SQDIFF_NORMED_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));
}
void matchTemplate_CCOFF_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
matchTemplate_CCORR_8U(image, templ, result, buf, stream);
if (image.channels() == 1)
{
buf.image_sums.resize(1);
integral(image, buf.image_sums[0], stream);
unsigned int templ_sum = (unsigned int)sum(templ)[0];
matchTemplatePrepared_CCOFF_8U(templ.cols, templ.rows, buf.image_sums[0], templ_sum, result, StreamAccessor::getStream(stream));
}
else
{
split(image, buf.images);
buf.image_sums.resize(buf.images.size());
for (int i = 0; i < image.channels(); ++i)
integral(buf.images[i], buf.image_sums[i], stream);
Scalar templ_sum = sum(templ);
switch (image.channels())
{
case 2:
matchTemplatePrepared_CCOFF_8UC2(
templ.cols, templ.rows, buf.image_sums[0], buf.image_sums[1],
(unsigned int)templ_sum[0], (unsigned int)templ_sum[1],
result, StreamAccessor::getStream(stream));
break;
case 3:
matchTemplatePrepared_CCOFF_8UC3(
templ.cols, templ.rows, buf.image_sums[0], buf.image_sums[1], buf.image_sums[2],
(unsigned int)templ_sum[0], (unsigned int)templ_sum[1], (unsigned int)templ_sum[2],
result, StreamAccessor::getStream(stream));
break;
case 4:
matchTemplatePrepared_CCOFF_8UC4(
templ.cols, templ.rows, buf.image_sums[0], buf.image_sums[1], buf.image_sums[2], buf.image_sums[3],
(unsigned int)templ_sum[0], (unsigned int)templ_sum[1], (unsigned int)templ_sum[2],
(unsigned int)templ_sum[3], result, StreamAccessor::getStream(stream));
break;
default:
CV_Error(CV_StsBadArg, "matchTemplate: unsupported number of channels");
}
}
}
void matchTemplate_CCOFF_NORMED_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
if (stream)
{
stream.enqueueConvert(image, buf.imagef, CV_32F);
stream.enqueueConvert(templ, buf.templf, CV_32F);
}
else
{
image.convertTo(buf.imagef, CV_32F);
templ.convertTo(buf.templf, CV_32F);
}
matchTemplate_CCORR_32F(buf.imagef, buf.templf, result, buf, stream);
if (image.channels() == 1)
{
buf.image_sums.resize(1);
integral(image, buf.image_sums[0], stream);
buf.image_sqsums.resize(1);
sqrIntegral(image, buf.image_sqsums[0], stream);
unsigned int templ_sum = (unsigned int)sum(templ)[0];
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ)[0];
matchTemplatePrepared_CCOFF_NORMED_8U(
templ.cols, templ.rows, buf.image_sums[0], buf.image_sqsums[0],
templ_sum, templ_sqsum, result, StreamAccessor::getStream(stream));
}
else
{
split(image, buf.images);
buf.image_sums.resize(buf.images.size());
buf.image_sqsums.resize(buf.images.size());
for (int i = 0; i < image.channels(); ++i)
{
integral(buf.images[i], buf.image_sums[i], stream);
sqrIntegral(buf.images[i], buf.image_sqsums[i], stream);
}
Scalar templ_sum = sum(templ);
Scalar templ_sqsum = sqrSum(templ);
switch (image.channels())
{
case 2:
matchTemplatePrepared_CCOFF_NORMED_8UC2(
templ.cols, templ.rows,
buf.image_sums[0], buf.image_sqsums[0],
buf.image_sums[1], buf.image_sqsums[1],
(unsigned int)templ_sum[0], (unsigned long long)templ_sqsum[0],
(unsigned int)templ_sum[1], (unsigned long long)templ_sqsum[1],
result, StreamAccessor::getStream(stream));
break;
case 3:
matchTemplatePrepared_CCOFF_NORMED_8UC3(
templ.cols, templ.rows,
buf.image_sums[0], buf.image_sqsums[0],
buf.image_sums[1], buf.image_sqsums[1],
buf.image_sums[2], buf.image_sqsums[2],
(unsigned int)templ_sum[0], (unsigned long long)templ_sqsum[0],
(unsigned int)templ_sum[1], (unsigned long long)templ_sqsum[1],
(unsigned int)templ_sum[2], (unsigned long long)templ_sqsum[2],
result, StreamAccessor::getStream(stream));
break;
case 4:
matchTemplatePrepared_CCOFF_NORMED_8UC4(
templ.cols, templ.rows,
buf.image_sums[0], buf.image_sqsums[0],
buf.image_sums[1], buf.image_sqsums[1],
buf.image_sums[2], buf.image_sqsums[2],
buf.image_sums[3], buf.image_sqsums[3],
(unsigned int)templ_sum[0], (unsigned long long)templ_sqsum[0],
(unsigned int)templ_sum[1], (unsigned long long)templ_sqsum[1],
(unsigned int)templ_sum[2], (unsigned long long)templ_sqsum[2],
(unsigned int)templ_sum[3], (unsigned long long)templ_sqsum[3],
result, StreamAccessor::getStream(stream));
break;
default:
CV_Error(CV_StsBadArg, "matchTemplate: unsupported number of channels");
}
}
}
}
void cv::gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream& stream)
{
MatchTemplateBuf buf;
matchTemplate(image, templ, result, method, buf, stream);
}
void cv::gpu::matchTemplate(
const GpuMat& image, const GpuMat& templ, GpuMat& result, int method,
MatchTemplateBuf &buf, Stream& stream)
{
CV_Assert(image.type() == templ.type());
CV_Assert(image.cols >= templ.cols && image.rows >= templ.rows);
typedef void (*Caller)(const GpuMat&, const GpuMat&, GpuMat&, MatchTemplateBuf&, Stream& stream);
static const Caller callers8U[] = { ::matchTemplate_SQDIFF_8U, ::matchTemplate_SQDIFF_NORMED_8U,
::matchTemplate_CCORR_8U, ::matchTemplate_CCORR_NORMED_8U,
::matchTemplate_CCOFF_8U, ::matchTemplate_CCOFF_NORMED_8U };
static const Caller callers32F[] = { ::matchTemplate_SQDIFF_32F, 0,
::matchTemplate_CCORR_32F, 0, 0, 0 };
const Caller* callers = 0;
switch (image.depth())
{
case CV_8U: callers = callers8U; break;
case CV_32F: callers = callers32F; break;
default: CV_Error(CV_StsBadArg, "matchTemplate: unsupported data type");
}
Caller caller = callers[method];
CV_Assert(caller);
caller(image, templ, result, buf, stream);
}
#endif
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