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#1713 Added the possibility of setting user_block_size manually for gpu::matchTemplate function (and gpu::convolve). Added a buffer param into these functions. Removed using of 2^n block sizes when it's not necessary.

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This commit is contained in:
Alexey Spizhevoy 2012-03-28 07:11:07 +00:00
parent d1423adbc7
commit c776bff95b
5 changed files with 223 additions and 189 deletions
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84
modules/gpu/doc/image_processing.rst
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@ -234,6 +234,35 @@ The source matrix should be continuous, otherwise reallocation and data copying
.. seealso:: :ocv:func:`dft`
gpu::ConvolveBuf
----------------
.. ocv:class:: gpu::ConvolveBuf
Class providing a memory buffer for :ocv:func:`gpu::convolve` function, plus it allows to adjust some specific parameters. ::
struct CV_EXPORTS ConvolveBuf
{
Size result_size;
Size block_size;
Size user_block_size;
Size dft_size;
int spect_len;
GpuMat image_spect, templ_spect, result_spect;
GpuMat image_block, templ_block, result_data;
void create(Size image_size, Size templ_size);
static Size estimateBlockSize(Size result_size, Size templ_size);
};
You can use field `user_block_size` to set specific block size for :ocv:func:`gpu::convolve` function. If you leave its default value `Size(0,0)` then automatic estimation of block size will be used (which is optimized for speed). By varying `user_block_size` you can reduce memory requirements at the cost of speed.
gpu::ConvolveBuf::create
------------------------
.. ocv:function:: ConvolveBuf::create(Size image_size, Size templ_size)
Constructs a buffer for :ocv:func:`gpu::convolve` function with respective arguments.
gpu::convolve
-----------------
@ -241,7 +270,7 @@ Computes a convolution (or cross-correlation) of two images.
.. ocv:function:: void gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr=false)
.. ocv:function:: void gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf)
.. ocv:function:: void gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream &stream = Stream::Null())
:param image: Source image. Only ``CV_32FC1`` images are supported for now.
@ -251,50 +280,36 @@ Computes a convolution (or cross-correlation) of two images.
:param ccorr: Flags to evaluate cross-correlation instead of convolution.
:param buf: Optional buffer to avoid extra memory allocations (for many calls with the same sizes).
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:class:`gpu::ConvolveBuf`.
:param stream: Stream for the asynchronous version.
.. seealso:: :ocv:func:`gpu::filter2D`
gpu::MatchTemplateBuf
---------------------
.. ocv:class:: gpu::MatchTemplateBuf
Class providing memory buffers for :ocv:func:`gpu::matchTemplate` function, plus it allows to adjust some specific parameters. ::
gpu::ConvolveBuf
----------------
.. ocv:class:: gpu::ConvolveBuf
Class providing a memory buffer for the :ocv:func:`gpu::convolve` function. ::
struct CV_EXPORTS ConvolveBuf
struct CV_EXPORTS MatchTemplateBuf
{
ConvolveBuf() {}
ConvolveBuf(Size image_size, Size templ_size)
{ create(image_size, templ_size); }
void create(Size image_size, Size templ_size);
private:
// Hidden
Size user_block_size;
GpuMat imagef, templf;
std::vector<GpuMat> images;
std::vector<GpuMat> image_sums;
std::vector<GpuMat> image_sqsums;
};
gpu::ConvolveBuf::ConvolveBuf
---------------------------------
The constructors.
.. ocv:function:: ConvolveBuf::ConvolveBuf()
Constructs an empty buffer that is properly resized after the first call of the :ocv:func:`gpu::convolve` function.
.. ocv:function:: ConvolveBuf::ConvolveBuf(Size image_size, Size templ_size)
Constructs a buffer for the :ocv:func:`gpu::convolve` function with respective arguments.
You can use field `user_block_size` to set specific block size for :ocv:func:`gpu::matchTemplate` function. If you leave its default value `Size(0,0)` then automatic estimation of block size will be used (which is optimized for speed). By varying `user_block_size` you can reduce memory requirements at the cost of speed.
gpu::matchTemplate
----------------------
Computes a proximity map for a raster template and an image where the template is searched for.
.. ocv:function:: void gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method)
.. ocv:function:: void gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream &stream = Stream::Null())
.. ocv:function:: void gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf &buf, Stream& stream = Stream::Null())
:param image: Source image. ``CV_32F`` and ``CV_8U`` depth images (1..4 channels) are supported for now.
@ -303,6 +318,10 @@ Computes a proximity map for a raster template and an image where the template i
:param result: Map containing comparison results ( ``CV_32FC1`` ). If ``image`` is *W x H* and ``templ`` is *w x h*, then ``result`` must be *W-w+1 x H-h+1*.
:param method: Specifies the way to compare the template with the image.
:param buf: Optional buffer to avoid extra memory allocations and to adjust some specific parameters. See :ocv:class:`gpu::MatchTemplateBuf`.
:param stream: Stream for the asynchronous version.
The following methods are supported for the ``CV_8U`` depth images for now:
@ -321,7 +340,6 @@ Computes a proximity map for a raster template and an image where the template i
.. seealso:: :ocv:func:`matchTemplate`
gpu::remap
--------------
Applies a generic geometrical transformation to an image.

42
modules/gpu/include/opencv2/gpu/gpu.hpp
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@ -716,36 +716,42 @@ CV_EXPORTS void mulAndScaleSpectrums(const GpuMat& a, const GpuMat& b, GpuMat& c
//! For complex-to-real transform it is assumed that the source matrix is packed in CUFFT's format.
CV_EXPORTS void dft(const GpuMat& src, GpuMat& dst, Size dft_size, int flags=0, Stream& stream = Stream::Null());
//! computes convolution (or cross-correlation) of two images using discrete Fourier transform
//! supports source images of 32FC1 type only
//! result matrix will have 32FC1 type
struct CV_EXPORTS ConvolveBuf;
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false);
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null());
struct CV_EXPORTS ConvolveBuf
{
ConvolveBuf() {}
ConvolveBuf(Size image_size, Size templ_size)
{ create(image_size, templ_size); }
void create(Size image_size, Size templ_size);
void create(Size image_size, Size templ_size, Size block_size);
private:
static Size estimateBlockSize(Size result_size, Size templ_size);
friend void convolve(const GpuMat&, const GpuMat&, GpuMat&, bool, ConvolveBuf&, Stream& stream);
Size result_size;
Size block_size;
Size user_block_size;
Size dft_size;
int spect_len;
GpuMat image_spect, templ_spect, result_spect;
GpuMat image_block, templ_block, result_data;
void create(Size image_size, Size templ_size);
static Size estimateBlockSize(Size result_size, Size templ_size);
};
//! computes convolution (or cross-correlation) of two images using discrete Fourier transform
//! supports source images of 32FC1 type only
//! result matrix will have 32FC1 type
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr = false);
CV_EXPORTS void convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result, bool ccorr, ConvolveBuf& buf, Stream& stream = Stream::Null());
struct CV_EXPORTS MatchTemplateBuf
{
Size user_block_size;
GpuMat imagef, templf;
std::vector<GpuMat> images;
std::vector<GpuMat> image_sums;
std::vector<GpuMat> image_sqsums;
};
//! computes the proximity map for the raster template and the image where the template is searched for
CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream& stream = Stream::Null());
CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream &stream = Stream::Null());
//! computes the proximity map for the raster template and the image where the template is searched for
CV_EXPORTS void matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, MatchTemplateBuf &buf, Stream& stream = Stream::Null());
//! smoothes the source image and downsamples it
CV_EXPORTS void pyrDown(const GpuMat& src, GpuMat& dst, Stream& stream = Stream::Null());

24
modules/gpu/src/cuda/match_template.cu
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@ -559,7 +559,7 @@ namespace cv { namespace gpu { namespace device
void matchTemplatePrepared_CCOFF_NORMED_8U(
int w, int h, const DevMem2D_<unsigned int> image_sum,
const DevMem2D_<unsigned long long> image_sqsum,
unsigned int templ_sum, unsigned int templ_sqsum,
unsigned int templ_sum, unsigned long long templ_sqsum,
DevMem2Df result, cudaStream_t stream)
{
dim3 threads(32, 8);
@ -618,8 +618,8 @@ namespace cv { namespace gpu { namespace device
int w, int h,
const DevMem2D_<unsigned int> image_sum_r, const DevMem2D_<unsigned long long> image_sqsum_r,
const DevMem2D_<unsigned int> image_sum_g, const DevMem2D_<unsigned long long> image_sqsum_g,
unsigned int templ_sum_r, unsigned int templ_sqsum_r,
unsigned int templ_sum_g, unsigned int templ_sqsum_g,
unsigned int templ_sum_r, unsigned long long templ_sqsum_r,
unsigned int templ_sum_g, unsigned long long templ_sqsum_g,
DevMem2Df result, cudaStream_t stream)
{
dim3 threads(32, 8);
@ -694,9 +694,9 @@ namespace cv { namespace gpu { namespace device
const DevMem2D_<unsigned int> image_sum_r, const DevMem2D_<unsigned long long> image_sqsum_r,
const DevMem2D_<unsigned int> image_sum_g, const DevMem2D_<unsigned long long> image_sqsum_g,
const DevMem2D_<unsigned int> image_sum_b, const DevMem2D_<unsigned long long> image_sqsum_b,
unsigned int templ_sum_r, unsigned int templ_sqsum_r,
unsigned int templ_sum_g, unsigned int templ_sqsum_g,
unsigned int templ_sum_b, unsigned int templ_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,
DevMem2Df result, cudaStream_t stream)
{
dim3 threads(32, 8);
@ -782,10 +782,10 @@ namespace cv { namespace gpu { namespace device
const DevMem2D_<unsigned int> image_sum_g, const DevMem2D_<unsigned long long> image_sqsum_g,
const DevMem2D_<unsigned int> image_sum_b, const DevMem2D_<unsigned long long> image_sqsum_b,
const DevMem2D_<unsigned int> image_sum_a, const DevMem2D_<unsigned long long> image_sqsum_a,
unsigned int templ_sum_r, unsigned int templ_sqsum_r,
unsigned int templ_sum_g, unsigned int templ_sqsum_g,
unsigned int templ_sum_b, unsigned int templ_sqsum_b,
unsigned int templ_sum_a, unsigned int templ_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,
DevMem2Df result, cudaStream_t stream)
{
dim3 threads(32, 8);
@ -822,7 +822,7 @@ namespace cv { namespace gpu { namespace device
template <int cn>
__global__ void normalizeKernel_8U(
int w, int h, const PtrStep<unsigned long long> image_sqsum,
unsigned int templ_sqsum, DevMem2Df result)
unsigned long long templ_sqsum, DevMem2Df result)
{
const int x = blockIdx.x * blockDim.x + threadIdx.x;
const int y = blockIdx.y * blockDim.y + threadIdx.y;
@ -837,7 +837,7 @@ namespace cv { namespace gpu { namespace device
}
void normalize_8U(int w, int h, const DevMem2D_<unsigned long long> image_sqsum,
unsigned int templ_sqsum, DevMem2Df result, int cn, cudaStream_t stream)
unsigned long long templ_sqsum, DevMem2Df result, int cn, cudaStream_t stream)
{
dim3 threads(32, 8);
dim3 grid(divUp(result.cols, threads.x), divUp(result.rows, threads.y));

37
modules/gpu/src/imgproc.cpp
View File

@ -1293,21 +1293,25 @@ void cv::gpu::ConvolveBuf::create(Size image_size, Size templ_size)
{
result_size = Size(image_size.width - templ_size.width + 1,
image_size.height - templ_size.height + 1);
create(image_size, templ_size, estimateBlockSize(result_size, templ_size));
}
void cv::gpu::ConvolveBuf::create(Size image_size, Size templ_size, Size block_size)
{
result_size = Size(image_size.width - templ_size.width + 1,
image_size.height - templ_size.height + 1);
this->block_size = block_size;
block_size = user_block_size;
if (user_block_size.width == 0 || user_block_size.height == 0)
block_size = estimateBlockSize(result_size, templ_size);
dft_size.width = 1 << int(ceil(std::log(block_size.width + templ_size.width - 1.) / std::log(2.)));
dft_size.height = 1 << int(ceil(std::log(block_size.height + templ_size.height - 1.) / std::log(2.)));
if (dft_size.width < 512) dft_size.width = 512;
if (dft_size.height < 512) dft_size.height = 512;
// CUFFT has hard-coded kernels for power-of-2 sizes (up to 8192),
// see CUDA Toolkit 4.1 CUFFT Library Programming Guide
if (dft_size.width > 8192)
dft_size.width = getOptimalDFTSize(block_size.width + templ_size.width - 1.);
if (dft_size.height > 8192)
dft_size.height = getOptimalDFTSize(block_size.height + templ_size.height - 1.);
// To avoid wasting time doing small DFTs
dft_size.width = std::max(dft_size.width, 512);
dft_size.height = std::max(dft_size.height, 512);
createContinuous(dft_size, CV_32F, image_block);
createContinuous(dft_size, CV_32F, templ_block);
createContinuous(dft_size, CV_32F, result_data);
@ -1317,17 +1321,18 @@ void cv::gpu::ConvolveBuf::create(Size image_size, Size templ_size, Size block_s
createContinuous(1, spect_len, CV_32FC2, templ_spect);
createContinuous(1, spect_len, CV_32FC2, result_spect);
this->block_size.width = std::min(dft_size.width - templ_size.width + 1, result_size.width);
this->block_size.height = std::min(dft_size.height - templ_size.height + 1, result_size.height);
// Use maximum result matrix block size for the estimated DFT block size
block_size.width = std::min(dft_size.width - templ_size.width + 1, result_size.width);
block_size.height = std::min(dft_size.height - templ_size.height + 1, result_size.height);
}
Size cv::gpu::ConvolveBuf::estimateBlockSize(Size result_size, Size templ_size)
Size cv::gpu::ConvolveBuf::estimateBlockSize(Size result_size, Size /*templ_size*/)
{
int width = (result_size.width + 2) / 3;
int height = (result_size.height + 2) / 3;
width = std::min(width, result_size.width);
height = std::min(height, result_size.height);
height = std::min(height, result_size.height);
return Size(width, height);
}
@ -1367,7 +1372,7 @@ void cv::gpu::convolve(const GpuMat& image, const GpuMat& templ, GpuMat& result,
cufftHandle planR2C, planC2R;
cufftSafeCall(cufftPlan2d(&planC2R, dft_size.height, dft_size.width, CUFFT_C2R));
cufftSafeCall(cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C));
cufftSafeCall(cufftPlan2d(&planR2C, dft_size.height, dft_size.width, CUFFT_R2C));
cufftSafeCall( cufftSetStream(planR2C, StreamAccessor::getStream(stream)) );
cufftSafeCall( cufftSetStream(planC2R, StreamAccessor::getStream(stream)) );

225
modules/gpu/src/match_template.cpp
View File

@ -101,23 +101,23 @@ namespace cv { namespace gpu { namespace device
void matchTemplatePrepared_CCOFF_NORMED_8U(
int w, int h, const DevMem2D_<unsigned int> image_sum,
const DevMem2D_<unsigned long long> image_sqsum,
unsigned int templ_sum, unsigned int templ_sqsum,
unsigned int templ_sum, unsigned long long templ_sqsum,
DevMem2Df result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_NORMED_8UC2(
int w, int h,
const DevMem2D_<unsigned int> image_sum_r, const DevMem2D_<unsigned long long> image_sqsum_r,
const DevMem2D_<unsigned int> image_sum_g, const DevMem2D_<unsigned long long> image_sqsum_g,
unsigned int templ_sum_r, unsigned int templ_sqsum_r,
unsigned int templ_sum_g, unsigned int templ_sqsum_g,
unsigned int templ_sum_r, unsigned long long templ_sqsum_r,
unsigned int templ_sum_g, unsigned long long templ_sqsum_g,
DevMem2Df result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_NORMED_8UC3(
int w, int h,
const DevMem2D_<unsigned int> image_sum_r, const DevMem2D_<unsigned long long> image_sqsum_r,
const DevMem2D_<unsigned int> image_sum_g, const DevMem2D_<unsigned long long> image_sqsum_g,
const DevMem2D_<unsigned int> image_sum_b, const DevMem2D_<unsigned long long> image_sqsum_b,
unsigned int templ_sum_r, unsigned int templ_sqsum_r,
unsigned int templ_sum_g, unsigned int templ_sqsum_g,
unsigned int templ_sum_b, unsigned int templ_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,
DevMem2Df result, cudaStream_t stream);
void matchTemplatePrepared_CCOFF_NORMED_8UC4(
int w, int h,
@ -125,14 +125,14 @@ namespace cv { namespace gpu { namespace device
const DevMem2D_<unsigned int> image_sum_g, const DevMem2D_<unsigned long long> image_sqsum_g,
const DevMem2D_<unsigned int> image_sum_b, const DevMem2D_<unsigned long long> image_sqsum_b,
const DevMem2D_<unsigned int> image_sum_a, const DevMem2D_<unsigned long long> image_sqsum_a,
unsigned int templ_sum_r, unsigned int templ_sqsum_r,
unsigned int templ_sum_g, unsigned int templ_sqsum_g,
unsigned int templ_sum_b, unsigned int templ_sqsum_b,
unsigned int templ_sum_a, unsigned int templ_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,
DevMem2Df result, cudaStream_t stream);
void normalize_8U(int w, int h, const DevMem2D_<unsigned long long> image_sqsum,
unsigned int templ_sqsum, DevMem2Df result, int cn, cudaStream_t stream);
unsigned long long templ_sqsum, DevMem2Df result, int cn, cudaStream_t stream);
void extractFirstChannel_32F(const DevMem2Db image, DevMem2Df result, int cn, cudaStream_t stream);
}
@ -146,20 +146,6 @@ 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);
void matchTemplate_CCORR_32F(const GpuMat& image, const GpuMat& templ, GpuMat& result);
void matchTemplate_CCORR_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result);
void matchTemplate_CCORR_NORMED_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result);
void matchTemplate_SQDIFF_32F(const GpuMat& image, const GpuMat& templ, GpuMat& result);
void matchTemplate_SQDIFF_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result);
void matchTemplate_SQDIFF_NORMED_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result);
void matchTemplate_CCOFF_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result);
void matchTemplate_CCOFF_NORMED_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result);
int getTemplateThreshold(int method, int depth)
{
switch (method)
@ -177,8 +163,9 @@ namespace
}
void matchTemplate_CCORR_32F(const GpuMat& image, const GpuMat& templ, GpuMat& result, Stream& stream)
{
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))
{
@ -186,14 +173,22 @@ namespace
return;
}
GpuMat result_;
ConvolveBuf buf;
convolve(image.reshape(1), templ.reshape(1), result_, true, buf, stream);
extractFirstChannel_32F(result_, result, image.channels(), StreamAccessor::getStream(stream));
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, Stream& 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))
{
@ -202,41 +197,43 @@ namespace
return;
}
GpuMat imagef, templf;
if (stream)
{
stream.enqueueConvert(image, imagef, CV_32F);
stream.enqueueConvert(templ, templf, CV_32F);
stream.enqueueConvert(image, buf.imagef, CV_32F);
stream.enqueueConvert(templ, buf.templf, CV_32F);
}
else
{
image.convertTo(imagef, CV_32F);
templ.convertTo(templf, CV_32F);
image.convertTo(buf.imagef, CV_32F);
templ.convertTo(buf.templf, CV_32F);
}
matchTemplate_CCORR_32F(imagef, templf, result, stream);
matchTemplate_CCORR_32F(buf.imagef, buf.templf, result, buf, stream);
}
void matchTemplate_CCORR_NORMED_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result, Stream& stream)
void matchTemplate_CCORR_NORMED_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
matchTemplate_CCORR_8U(image, templ, result, stream);
matchTemplate_CCORR_8U(image, templ, result, buf, stream);
GpuMat img_sqsum;
sqrIntegral(image.reshape(1), img_sqsum, stream);
buf.image_sqsums.resize(1);
sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);
unsigned int templ_sqsum = (unsigned int)sqrSum(templ.reshape(1))[0];
normalize_8U(templ.cols, templ.rows, img_sqsum, templ_sqsum, result, image.channels(), StreamAccessor::getStream(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, Stream& stream)
void matchTemplate_SQDIFF_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);
matchTemplateNaive_SQDIFF_32F(image, templ, result, image.channels(), StreamAccessor::getStream(stream));
}
void matchTemplate_SQDIFF_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result, Stream& 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))
{
@ -245,48 +242,48 @@ namespace
return;
}
GpuMat img_sqsum;
sqrIntegral(image.reshape(1), img_sqsum, 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, stream);
matchTemplatePrepared_SQDIFF_8U(templ.cols, templ.rows, img_sqsum, templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));
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, Stream& stream)
void matchTemplate_SQDIFF_NORMED_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
GpuMat img_sqsum;
sqrIntegral(image.reshape(1), img_sqsum, 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, stream);
matchTemplatePrepared_SQDIFF_NORMED_8U(templ.cols, templ.rows, img_sqsum, templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));
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, Stream& stream)
void matchTemplate_CCOFF_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
matchTemplate_CCORR_8U(image, templ, result, stream);
matchTemplate_CCORR_8U(image, templ, result, buf, stream);
if (image.channels() == 1)
{
GpuMat image_sum;
integral(image, image_sum, stream);
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, image_sum, templ_sum, result, StreamAccessor::getStream(stream));
matchTemplatePrepared_CCOFF_8U(templ.cols, templ.rows, buf.image_sums[0], templ_sum, result, StreamAccessor::getStream(stream));
}
else
{
vector<GpuMat> images;
vector<GpuMat> image_sums(image.channels());
split(image, images);
split(image, buf.images);
buf.image_sums.resize(buf.images.size());
for (int i = 0; i < image.channels(); ++i)
integral(images[i], image_sums[i], stream);
integral(buf.images[i], buf.image_sums[i], stream);
Scalar templ_sum = sum(templ);
@ -294,19 +291,19 @@ namespace
{
case 2:
matchTemplatePrepared_CCOFF_8UC2(
templ.cols, templ.rows, image_sums[0], image_sums[1],
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, image_sums[0], image_sums[1], image_sums[2],
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, image_sums[0], image_sums[1], image_sums[2], image_sums[3],
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;
@ -317,46 +314,45 @@ namespace
}
void matchTemplate_CCOFF_NORMED_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result, Stream& stream)
void matchTemplate_CCOFF_NORMED_8U(
const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)
{
GpuMat imagef, templf;
if (stream)
{
stream.enqueueConvert(image, imagef, CV_32F);
stream.enqueueConvert(templ, templf, CV_32F);
stream.enqueueConvert(image, buf.imagef, CV_32F);
stream.enqueueConvert(templ, buf.templf, CV_32F);
}
else
{
image.convertTo(imagef, CV_32F);
templ.convertTo(templf, CV_32F);
image.convertTo(buf.imagef, CV_32F);
templ.convertTo(buf.templf, CV_32F);
}
matchTemplate_CCORR_32F(imagef, templf, result, stream);
matchTemplate_CCORR_32F(buf.imagef, buf.templf, result, buf, stream);
if (image.channels() == 1)
{
GpuMat image_sum, image_sqsum;
integral(image, image_sum, stream);
sqrIntegral(image, image_sqsum, stream);
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 int templ_sqsum = (unsigned int)sqrSum(templ)[0];
unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ)[0];
matchTemplatePrepared_CCOFF_NORMED_8U(
templ.cols, templ.rows, image_sum, image_sqsum,
templ.cols, templ.rows, buf.image_sums[0], buf.image_sqsums[0],
templ_sum, templ_sqsum, result, StreamAccessor::getStream(stream));
}
else
{
vector<GpuMat> images;
vector<GpuMat> image_sums(image.channels());
vector<GpuMat> image_sqsums(image.channels());
split(image, images);
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(images[i], image_sums[i], stream);
sqrIntegral(images[i], image_sqsums[i], stream);
integral(buf.images[i], buf.image_sums[i], stream);
sqrIntegral(buf.images[i], buf.image_sqsums[i], stream);
}
Scalar templ_sum = sum(templ);
@ -367,34 +363,34 @@ namespace
case 2:
matchTemplatePrepared_CCOFF_NORMED_8UC2(
templ.cols, templ.rows,
image_sums[0], image_sqsums[0],
image_sums[1], image_sqsums[1],
(unsigned int)templ_sum[0], (unsigned int)templ_sqsum[0],
(unsigned int)templ_sum[1], (unsigned int)templ_sqsum[1],
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,
image_sums[0], image_sqsums[0],
image_sums[1], image_sqsums[1],
image_sums[2], image_sqsums[2],
(unsigned int)templ_sum[0], (unsigned int)templ_sqsum[0],
(unsigned int)templ_sum[1], (unsigned int)templ_sqsum[1],
(unsigned int)templ_sum[2], (unsigned int)templ_sqsum[2],
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,
image_sums[0], image_sqsums[0],
image_sums[1], image_sqsums[1],
image_sums[2], image_sqsums[2],
image_sums[3], image_sqsums[3],
(unsigned int)templ_sum[0], (unsigned int)templ_sqsum[0],
(unsigned int)templ_sum[1], (unsigned int)templ_sqsum[1],
(unsigned int)templ_sum[2], (unsigned int)templ_sqsum[2],
(unsigned int)templ_sum[3], (unsigned int)templ_sqsum[3],
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:
@ -406,16 +402,25 @@ namespace
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&, Stream& stream);
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,
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,
static const Caller callers32F[] = { ::matchTemplate_SQDIFF_32F, 0,
::matchTemplate_CCORR_32F, 0, 0, 0 };
const Caller* callers = 0;
@ -428,7 +433,7 @@ void cv::gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& re
Caller caller = callers[method];
CV_Assert(caller);
caller(image, templ, result, stream);
caller(image, templ, result, buf, stream);
}
#endif