opencv/modules/gpu/src/cuda/match_template.cu

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
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// License Agreement
// For Open Source Computer Vision Library
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#include <cufft.h>
#include "internal_shared.hpp"
#include <iostream>
using namespace std;
using namespace cv::gpu;
namespace cv { namespace gpu { namespace imgproc {
texture<unsigned char, 2> imageTex_8U;
texture<unsigned char, 2> templTex_8U;
__global__ void matchTemplateKernel_8U_SQDIFF(int w, int h, DevMem2Df result)
{
int x = blockDim.x * blockIdx.x + threadIdx.x;
int y = blockDim.y * blockIdx.y + threadIdx.y;
if (x < result.cols && y < result.rows)
{
float sum = 0.f;
float delta;
for (int i = 0; i < h; ++i)
{
for (int j = 0; j < w; ++j)
{
delta = (float)tex2D(imageTex_8U, x + j, y + i) -
(float)tex2D(templTex_8U, j, i);
sum += delta * delta;
}
}
result.ptr(y)[x] = sum;
}
}
void matchTemplate_8U_SQDIFF(const DevMem2D image, const DevMem2D templ, DevMem2Df result)
{
dim3 threads(32, 8);
dim3 grid(divUp(image.cols - templ.cols + 1, threads.x),
divUp(image.rows - templ.rows + 1, threads.y));
cudaChannelFormatDesc desc = cudaCreateChannelDesc<unsigned char>();
cudaBindTexture2D(0, imageTex_8U, image.data, desc, image.cols, image.rows, image.step);
cudaBindTexture2D(0, templTex_8U, templ.data, desc, templ.cols, templ.rows, templ.step);
imageTex_8U.filterMode = cudaFilterModePoint;
templTex_8U.filterMode = cudaFilterModePoint;
matchTemplateKernel_8U_SQDIFF<<<grid, threads>>>(templ.cols, templ.rows, result);
cudaSafeCall(cudaThreadSynchronize());
cudaSafeCall(cudaUnbindTexture(imageTex_8U));
cudaSafeCall(cudaUnbindTexture(templTex_8U));
}
__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);
}
}}}