Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

moved crossCorr (as NPP_Staging wrapper) into public GPU module part from the internal matchTemplate files

Browse Source
pull/13383/head
Alexey Spizhevoy 14 years ago
parent f9bcef9003
commit fef06c25b5
5 changed files with 166 additions and 141 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 5
      modules/gpu/include/opencv2/gpu/gpu.hpp
  2. 28
      modules/gpu/src/cuda/imgproc.cu
  3. 24
      modules/gpu/src/cuda/match_template.cu
  4. 131
      modules/gpu/src/imgproc_gpu.cpp
  5. 119
      modules/gpu/src/match_template.cpp

5
modules/gpu/include/opencv2/gpu/gpu.hpp
Unescape View File

@ -628,6 +628,11 @@ namespace cv
//! computes minimum eigen value of 2x2 derivative covariation matrix at each pixel - the cornerness criteria
CV_EXPORTS void cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, int ksize, int borderType=BORDER_REFLECT101);
//! computes cross-correlation of two images using FFT
//! supports source images of 32FC1 type only
//! result matrix will have 32FC1 type
CV_EXPORTS void crossCorr(const GpuMat& image, const GpuMat& templ, GpuMat& result);
//! 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);

28
modules/gpu/src/cuda/imgproc.cu
Unescape View File

@ -40,6 +40,7 @@
//
//M*/
#include <cufft.h>
#include "internal_shared.hpp"
#include "opencv2/gpu/device/border_interpolate.hpp"
@ -749,5 +750,32 @@ namespace cv { namespace gpu { namespace imgproc
cudaSafeCall(cudaThreadSynchronize());
}
//////////////////////////////////////////////////////////////////////////
// multiplyAndNormalizeSpects
__global__ void multiplyAndNormalizeSpectsKernel(
int n, float scale, const cufftComplex* a,
const cufftComplex* b, cufftComplex* c)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
if (x < n)
{
cufftComplex v = cuCmulf(a[x], cuConjf(b[x]));
c[x] = make_cuFloatComplex(cuCrealf(v) * scale, cuCimagf(v) * scale);
}
}
// Performs per-element multiplication and normalization of two spectrums
void multiplyAndNormalizeSpects(int n, float scale, const cufftComplex* a,
const cufftComplex* b, cufftComplex* c)
{
dim3 threads(256);
dim3 grid(divUp(n, threads.x));
multiplyAndNormalizeSpectsKernel<<<grid, threads>>>(n, scale, a, b, c);
cudaSafeCall(cudaThreadSynchronize());
}
}}}

24
modules/gpu/src/cuda/match_template.cu
Unescape View File

@ -40,7 +40,6 @@
//
//M*/
#include <cufft.h>
#include "internal_shared.hpp"
#include "opencv2/gpu/device/vecmath.hpp"
@ -256,29 +255,6 @@ void matchTemplateNaive_SQDIFF_8U(const DevMem2D image, const DevMem2D templ,
}
__global__ void multiplyAndNormalizeSpectsKernel(
int n, float scale, const cufftComplex* a,
const cufftComplex* b, cufftComplex* c)
{
int x = blockIdx.x * blockDim.x + threadIdx.x;
if (x < n)
{
cufftComplex v = cuCmulf(a[x], cuConjf(b[x]));
c[x] = make_cuFloatComplex(cuCrealf(v) * scale, cuCimagf(v) * scale);
}
}
void multiplyAndNormalizeSpects(int n, float scale, const cufftComplex* a,
const cufftComplex* b, cufftComplex* c)
{
dim3 threads(256);
dim3 grid(divUp(n, threads.x));
multiplyAndNormalizeSpectsKernel<<<grid, threads>>>(n, scale, a, b, c);
cudaSafeCall(cudaThreadSynchronize());
}
template <int cn>
__global__ void matchTemplatePreparedKernel_SQDIFF_8U(
int w, int h, const PtrStep_<unsigned long long> image_sqsum,

131
modules/gpu/src/imgproc_gpu.cpp
Unescape View File

@ -41,6 +41,7 @@
//M*/
#include "precomp.hpp"
#include <utility>
using namespace cv;
using namespace cv::gpu;
@ -73,6 +74,7 @@ void cv::gpu::histRange(const GpuMat&, GpuMat&, const GpuMat&) { throw_nogpu();
void cv::gpu::histRange(const GpuMat&, GpuMat*, const GpuMat*) { throw_nogpu(); }
void cv::gpu::cornerHarris(const GpuMat&, GpuMat&, int, int, double, int) { throw_nogpu(); }
void cv::gpu::cornerMinEigenVal(const GpuMat&, GpuMat&, int, int, int) { throw_nogpu(); }
void cv::gpu::crossCorr(const GpuMat&, const GpuMat&, GpuMat&) { throw_nogpu(); }
#else /* !defined (HAVE_CUDA) */
@ -1062,6 +1064,135 @@ void cv::gpu::cornerMinEigenVal(const GpuMat& src, GpuMat& dst, int blockSize, i
imgproc::cornerMinEigenVal_caller(blockSize, Dx, Dy, dst, gpuBorderType);
}
//////////////////////////////////////////////////////////////////////////////
// crossCorr
namespace
{
// Estimates optimal block size
void crossCorrOptBlockSize(int w, int h, int tw, int th, int& bw, int& bh)
{
int major, minor;
getComputeCapability(getDevice(), major, minor);
int scale = 40;
int bh_min = 1024;
int bw_min = 1024;
// Check whether we use Fermi generation or newer GPU
if (major >= 2)
{
bh_min = 2048;
bw_min = 2048;
}
bw = std::max(tw * scale, bw_min);
bh = std::max(th * scale, bh_min);
bw = std::min(bw, w);
bh = std::min(bh, h);
}
}
namespace cv { namespace gpu { namespace imgproc
{
void multiplyAndNormalizeSpects(int n, float scale, const cufftComplex* a,
const cufftComplex* b, cufftComplex* c);
}}}
void cv::gpu::crossCorr(const GpuMat& image, const GpuMat& templ, GpuMat& result)
{
CV_Assert(image.type() == CV_32F);
CV_Assert(templ.type() == CV_32F);
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
Size block_size;
crossCorrOptBlockSize(result.cols, result.rows, templ.cols, templ.rows,
block_size.width, block_size.height);
Size dft_size;
dft_size.width = getOptimalDFTSize(block_size.width + templ.cols - 1);
dft_size.height = getOptimalDFTSize(block_size.width + templ.rows - 1);
block_size.width = std::min(dft_size.width - templ.cols + 1, result.cols);
block_size.height = std::min(dft_size.height - templ.rows + 1, result.rows);
cufftReal* image_data;
cufftReal* templ_data;
cufftReal* result_data;
cudaSafeCall(cudaMalloc((void**)&image_data, sizeof(cufftReal) * dft_size.area()));
cudaSafeCall(cudaMalloc((void**)&templ_data, sizeof(cufftReal) * dft_size.area()));
cudaSafeCall(cudaMalloc((void**)&result_data, sizeof(cufftReal) * dft_size.area()));
int spect_len = dft_size.height * (dft_size.width / 2 + 1);
cufftComplex* image_spect;
cufftComplex* templ_spect;
cufftComplex* result_spect;
cudaSafeCall(cudaMalloc((void**)&image_spect, sizeof(cufftComplex) * spect_len));
cudaSafeCall(cudaMalloc((void**)&templ_spect, sizeof(cufftComplex) * spect_len));
cudaSafeCall(cudaMalloc((void**)&result_spect, sizeof(cufftComplex) * spect_len));
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));
GpuMat templ_roi(templ.size(), CV_32S, templ.data, templ.step);
GpuMat templ_block(dft_size, CV_32S, templ_data, dft_size.width * sizeof(cufftReal));
copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0,
templ_block.cols - templ_roi.cols, 0);
cufftSafeCall(cufftExecR2C(planR2C, templ_data, templ_spect));
GpuMat image_block(dft_size, CV_32S, image_data, dft_size.width * sizeof(cufftReal));
// Process all blocks of the result matrix
for (int y = 0; y < result.rows; y += block_size.height)
{
for (int x = 0; x < result.cols; x += block_size.width)
{
// Locate ROI in the source matrix
Size image_roi_size;
image_roi_size.width = std::min(x + dft_size.width, image.cols) - x;
image_roi_size.height = std::min(y + dft_size.height, image.rows) - y;
GpuMat image_roi(image_roi_size, CV_32S, (void*)(image.ptr<float>(y) + x), image.step);
// Make source image block continous
copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows, 0,
image_block.cols - image_roi.cols, 0);
cufftSafeCall(cufftExecR2C(planR2C, image_data, image_spect));
imgproc::multiplyAndNormalizeSpects(spect_len, 1.f / dft_size.area(),
image_spect, templ_spect, result_spect);
cufftSafeCall(cufftExecC2R(planC2R, result_spect, result_data));
// Copy result block into appropriate part of the result matrix.
// We can't compute it inplace as the result of the CUFFT transforms
// is always continous, while the result matrix and its blocks can have gaps.
Size result_roi_size;
result_roi_size.width = std::min(x + block_size.width, result.cols) - x;
result_roi_size.height = std::min(y + block_size.height, result.rows) - y;
GpuMat result_roi(result_roi_size, CV_32F, (void*)(result.ptr<float>(y) + x), result.step);
GpuMat result_block(result_roi_size, CV_32F, result_data, dft_size.width * sizeof(cufftReal));
result_block.copyTo(result_roi);
}
}
cufftSafeCall(cufftDestroy(planR2C));
cufftSafeCall(cufftDestroy(planC2R));
cudaSafeCall(cudaFree(image_spect));
cudaSafeCall(cudaFree(templ_spect));
cudaSafeCall(cudaFree(result_spect));
cudaSafeCall(cudaFree(image_data));
cudaSafeCall(cudaFree(templ_data));
cudaSafeCall(cudaFree(result_data));
}
#endif /* !defined (HAVE_CUDA) */

119
modules/gpu/src/match_template.cpp
Unescape View File

@ -41,7 +41,6 @@
//M*/
#include "precomp.hpp"
#include <utility>
using namespace cv;
using namespace cv::gpu;
@ -54,9 +53,6 @@ void cv::gpu::matchTemplate(const GpuMat&, const GpuMat&, GpuMat&, int) { throw_
namespace cv { namespace gpu { namespace imgproc
{
void multiplyAndNormalizeSpects(int n, float scale, const cufftComplex* a,
const cufftComplex* b, cufftComplex* c);
void matchTemplateNaive_CCORR_8U(
const DevMem2D image, const DevMem2D templ, DevMem2Df result, int cn);
@ -147,7 +143,7 @@ namespace cv { namespace gpu { namespace imgproc
DevMem2Df result);
void normalize_8U(int w, int h, const DevMem2D_<unsigned long long> image_sqsum,
unsigned int templ_sqsum, DevMem2Df result, int cn);
unsigned int templ_sqsum, DevMem2Df result, int cn);
void extractFirstChannel_32F(const DevMem2D image, DevMem2Df result, int cn);
}}}
@ -155,11 +151,6 @@ namespace cv { namespace gpu { namespace imgproc
namespace
{
// Estimates optimal blocks size for FFT method
void estimateBlockSize(int w, int h, int tw, int th, int& bw, int& bh);
// Performs FFT-based cross-correlation
void crossCorr_32F(const GpuMat& image, const GpuMat& templ, GpuMat& result);
// Evaluates optimal template's area threshold. If
// template's area is less than the threshold, we use naive match
@ -178,110 +169,6 @@ namespace
void matchTemplate_CCOFF_NORMED_8U(const GpuMat& image, const GpuMat& templ, GpuMat& result);
void estimateBlockSize(int w, int h, int tw, int th, int& bw, int& bh)
{
int major, minor;
getComputeCapability(getDevice(), major, minor);
int scale = 40;
int bh_min = 1024;
int bw_min = 1024;
if (major >= 2) // Fermi generation or newer
{
bh_min = 2048;
bw_min = 2048;
}
bw = std::max(tw * scale, bw_min);
bh = std::max(th * scale, bh_min);
bw = std::min(bw, w);
bh = std::min(bh, h);
}
void crossCorr_32F(const GpuMat& image, const GpuMat& templ, GpuMat& result)
{
CV_Assert(image.type() == CV_32F);
CV_Assert(templ.type() == CV_32F);
result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);
Size block_size;
estimateBlockSize(result.cols, result.rows, templ.cols, templ.rows,
block_size.width, block_size.height);
Size dft_size;
dft_size.width = getOptimalDFTSize(block_size.width + templ.cols - 1);
dft_size.height = getOptimalDFTSize(block_size.width + templ.rows - 1);
block_size.width = std::min(dft_size.width - templ.cols + 1, result.cols);
block_size.height = std::min(dft_size.height - templ.rows + 1, result.rows);
cufftReal* image_data;
cufftReal* templ_data;
cufftReal* result_data;
cudaSafeCall(cudaMalloc((void**)&image_data, sizeof(cufftReal) * dft_size.area()));
cudaSafeCall(cudaMalloc((void**)&templ_data, sizeof(cufftReal) * dft_size.area()));
cudaSafeCall(cudaMalloc((void**)&result_data, sizeof(cufftReal) * dft_size.area()));
int spect_len = dft_size.height * (dft_size.width / 2 + 1);
cufftComplex* image_spect;
cufftComplex* templ_spect;
cufftComplex* result_spect;
cudaSafeCall(cudaMalloc((void**)&image_spect, sizeof(cufftComplex) * spect_len));
cudaSafeCall(cudaMalloc((void**)&templ_spect, sizeof(cufftComplex) * spect_len));
cudaSafeCall(cudaMalloc((void**)&result_spect, sizeof(cufftComplex) * spect_len));
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));
GpuMat templ_roi(templ.size(), CV_32S, templ.data, templ.step);
GpuMat templ_block(dft_size, CV_32S, templ_data, dft_size.width * sizeof(cufftReal));
copyMakeBorder(templ_roi, templ_block, 0, templ_block.rows - templ_roi.rows, 0,
templ_block.cols - templ_roi.cols, 0);
cufftSafeCall(cufftExecR2C(planR2C, templ_data, templ_spect));
GpuMat image_block(dft_size, CV_32S, image_data, dft_size.width * sizeof(cufftReal));
for (int y = 0; y < result.rows; y += block_size.height)
{
for (int x = 0; x < result.cols; x += block_size.width)
{
Size image_roi_size;
image_roi_size.width = min(x + dft_size.width, image.cols) - x;
image_roi_size.height = min(y + dft_size.height, image.rows) - y;
GpuMat image_roi(image_roi_size, CV_32S, (void*)(image.ptr<float>(y) + x), image.step);
copyMakeBorder(image_roi, image_block, 0, image_block.rows - image_roi.rows, 0,
image_block.cols - image_roi.cols, 0);
cufftSafeCall(cufftExecR2C(planR2C, image_data, image_spect));
imgproc::multiplyAndNormalizeSpects(spect_len, 1.f / dft_size.area(),
image_spect, templ_spect, result_spect);
cufftSafeCall(cufftExecC2R(planC2R, result_spect, result_data));
Size result_roi_size;
result_roi_size.width = min(x + block_size.width, result.cols) - x;
result_roi_size.height = min(y + block_size.height, result.rows) - y;
GpuMat result_roi(result_roi_size, CV_32F, (void*)(result.ptr<float>(y) + x), result.step);
GpuMat result_block(result_roi_size, CV_32F, result_data, dft_size.width * sizeof(cufftReal));
result_block.copyTo(result_roi);
}
}
cufftSafeCall(cufftDestroy(planR2C));
cufftSafeCall(cufftDestroy(planC2R));
cudaSafeCall(cudaFree(image_spect));
cudaSafeCall(cudaFree(templ_spect));
cudaSafeCall(cudaFree(result_spect));
cudaSafeCall(cudaFree(image_data));
cudaSafeCall(cudaFree(templ_data));
cudaSafeCall(cudaFree(result_data));
}
int getTemplateThreshold(int method, int depth)
{
switch (method)
@ -309,7 +196,7 @@ namespace
}
GpuMat result_;
crossCorr_32F(image.reshape(1), templ.reshape(1), result_);
crossCorr(image.reshape(1), templ.reshape(1), result_);
imgproc::extractFirstChannel_32F(result_, result, image.channels());
}
@ -541,5 +428,3 @@ void cv::gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& re
}
#endif

Write Preview
Loading…
Cancel
Save