我一直在尝试为维基文章中提供的中值过滤器实现算法：http://en.wikipedia.org/wiki/Median_filter#2D_median_filter_pseudo_code
据我所知，我知道我所实施的是正确的。但是，当我查看结果时，我似乎无法获得类似于OpenCV中median blur函数生成的输出的输出。目前，我并不关心通过使用共享内存或纹理内存来加速我的代码。我想先把事情搞定。输入图像的大小为1024 x 256像素。
我做错了什么？我的代码中是否存在线程泄漏？我知道我应该使用共享内存来阻止全局读取，因为目前我正在从全局内存中读取很多数据。
http://snag.gy/OkXzP.jpg - 第一个图像是输入，第二个图像是我的算法结果，第三个图像是openCV medianblur函数结果。理想情况下，我希望我的算法输出与medianblur函数相同的结果。
这是我写的所有代码：
内核实施
#include "cuda.h"
#include "cuda_runtime_api.h"
#include "device_launch_parameters.h"
#include "device_functions.h"
#include "highgui.h"
//#include "opencv2/core/imgproc.hpp"
//#include "opencv2/core/gpu.hpp"
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
// includes, project
#include "cufft.h"
#include "cublas_v2.h"
#include "CUDA_wrapper.h" // contains only func_prototype for function take_input()
// define the threads and grids for CUDA
#define BLOCK_ROWS 32
#define BLOCK_COLS 16
// define kernel dimensions
#define KERNEL_DIMENSION 3
#define MEDIAN_DIMENSION 3
#define MEDIAN_LENGTH 9
// this is the error checking part for CUDA
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
// create two vars for the rows and cols of the image
int d_imgRows;
int d_imgCols;
__global__ void FilterKernel (unsigned short *d_input_img, unsigned short *d_output_img, int d_iRows, int d_iCols)
{
unsigned short window[BLOCK_ROWS*BLOCK_COLS][KERNEL_DIMENSION*KERNEL_DIMENSION];
unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;
unsigned int tid = threadIdx.y*blockDim.y+threadIdx.x;
if(x>d_iCols || y>d_iRows)
return;
window[tid][0]= (y==0||x==0) ? 0.0f : d_input_img[(y-1)*d_iCols+(x-1)];
window[tid][1]= (y==0) ? 0.0f : d_input_img[(y-1)*d_iCols+x];
window[tid][2]= (y==0||x==d_iCols-1) ? 0.0f : d_input_img[(y-1)*d_iCols+(x+1)];
window[tid][3]= (x==0) ? 0.0f : d_input_img[y*d_iCols+(x-1)];
window[tid][4]= d_input_img[y*d_iCols+x];
window[tid][5]= (x==d_iCols-1) ? 0.0f : d_input_img[y*d_iCols+(x+1)];
window[tid][6]= (y==d_iRows-1||x==0) ? 0.0f : d_input_img[(y+1)*d_iCols+(x-1)];
window[tid][7]= (y==d_iRows-1) ? 0.0f : d_input_img[(y+1)*d_iCols+x];
window[tid][8]= (y==d_iRows-1||x==d_iCols-1) ? 0.0f : d_input_img[(y+1)*d_iCols+(x+1)];
__syncthreads();
// Order elements
for (unsigned int j=0; j<9; ++j)
{
// Find position of minimum element
int min=j;
for (unsigned int l=j+1; l<9; ++l)
if (window[tid][l] < window[tid][min])
min=l;
// Put found minimum element in its place
const unsigned char temp=window[tid][j];
window[tid][j]=window[tid][min];
window[tid][min]=temp;
__syncthreads();
}
d_output_img[y*d_iCols + x] = (window[tid][4]);
}
void take_input(const cv::Mat& input, const cv::Mat& output)
{
unsigned short *device_input;
unsigned short *device_output;
size_t d_ipimgSize = input.step * input.rows;
size_t d_opimgSize = output.step * output.rows;
gpuErrchk( cudaMalloc( (void**) &device_input, d_ipimgSize) );
gpuErrchk( cudaMalloc( (void**) &device_output, d_opimgSize) );
gpuErrchk( cudaMemcpy(device_input, input.data, d_ipimgSize, cudaMemcpyHostToDevice) );
dim3 Threads(BLOCK_ROWS, BLOCK_COLS); // 512 threads per block
dim3 Blocks((input.cols + Threads.x - 1)/Threads.x, (input.rows + Threads.y - 1)/Threads.y);
//int check = (input.cols + Threads.x - 1)/Threads.x;
//printf( "blockx %d", check);
FilterKernel <<< Blocks, Threads >>> (device_input, device_output, input.rows, input.cols);
gpuErrchk(cudaDeviceSynchronize());
gpuErrchk( cudaMemcpy(output.data, device_output, d_opimgSize, cudaMemcpyDeviceToHost) );
//printf( "num_rows_cuda %d", num_rows);
//printf("\n");
gpuErrchk(cudaFree(device_input));
gpuErrchk(cudaFree(device_output));
}
主要功能
#pragma once
#include<iostream>
#include<opencv2/core/core.hpp>
#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<opencv2/gpu/gpu.hpp>
#include <CUDA_wrapper.h>
using std::cout;
using std::endl;
int main()
{
//Read the image from harddisk, into a cv::Mat
//IplImage *img=cvLoadImage("image.jpg");
//cv::Mat input(img);
cv::Mat input = cv::imread("C:/Users/OCT/Documents/Visual Studio 2008/Projects/MedianFilter/MedianFilter/pic1.bmp",CV_LOAD_IMAGE_GRAYSCALE);
//IplImage* input = cvLoadImage("G:/Research/CUDA/Trials/OCTFilter/Debug/pic1.bmp");
if(input.empty())
{
cout<<"Image Not Found"<<endl;
getchar();
return -1;
}
cv::Mat output(input.rows,input.cols,CV_8UC1);
// store the different details of the input image like img_data, rows, cols in variables
int Rows = input.rows;
int Cols = input.cols;
unsigned char* Data = input.data;
cout<<"image rows "<<Rows<<endl;
cout<<"image cols "<<Cols<<endl;
cout<<"\n"<<endl;
cout<<"data "<<(int)Data<<endl;
cv::waitKey(0);
// call the device function to take the image as input
take_input(input, output);
cv::Mat dest;
medianBlur ( input, dest, 3 );
//Show the input and output
cv::imshow("Input",input);
cv::imshow("Output",output);
cv::imshow("Median blur",dest);
//Wait for key press
cv::waitKey();
}
答案 0 :(得分：3)
我相信你的“内核实现”文件中存在各种错误和不必要的复杂性。
您可能会有更好的运气：
$ cat t376.cu
#include <stdlib.h>
#include <stdio.h>
#define DCOLS 1024
#define DROWS 256
typedef struct {
size_t step;
size_t rows;
size_t cols;
unsigned char *data;
} mat;
// define the threads and grids for CUDA
#define BLOCK_ROWS 32
#define BLOCK_COLS 16
// define kernel dimensions
#define MEDIAN_LENGTH 9
// this is the error checking part for CUDA
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
__global__ void FilterKernel (unsigned char *d_input_img, unsigned char *d_output_img, int d_iRows, int d_iCols)
{
unsigned int row = blockIdx.y*blockDim.y + threadIdx.y;
unsigned int col = blockIdx.x*blockDim.x + threadIdx.x;
unsigned char window[MEDIAN_LENGTH];
if(col>=d_iCols || row>=d_iRows)
return;
window[0]= (row==0||col==0) ? 0 : d_input_img[(row-1)*d_iCols+(col-1)];
window[1]= (row==0) ? 0 : d_input_img[(row-1)*d_iCols+col];
window[2]= (row==0||col==d_iCols-1) ? 0 : d_input_img[(row-1)*d_iCols+(col+1)];
window[3]= (col==0) ? 0 : d_input_img[row*d_iCols+(col-1)];
window[4]= d_input_img[row*d_iCols+col];
window[5]= (col==d_iCols-1) ? 0 : d_input_img[row*d_iCols+(col+1)];
window[6]= (row==d_iRows-1||col==0) ? 0 : d_input_img[(row+1)*d_iCols+(col-1)];
window[7]= (row==d_iRows-1) ? 0 : d_input_img[(row+1)*d_iCols+col];
window[8]= (row==d_iRows-1||col==d_iCols-1) ? 0 : d_input_img[(row+1)*d_iCols+(col+1)];
// Order elements
for (unsigned int j=0; j<5; ++j)
{
// Find position of minimum element
unsigned char temp = window[j];
unsigned int idx = j;
for (unsigned int l=j+1; l<9; ++l)
if (window[l] < temp){ idx=l; temp = window[l];}
// Put found minimum element in its place
window[idx] = window[j];
window[j] = temp;
}
d_output_img[row*d_iCols + col] = (window[4]);
}
void take_input(const mat& input, const mat& output)
{
unsigned char *device_input;
unsigned char *device_output;
size_t d_ipimgSize = input.step * input.rows;
size_t d_opimgSize = output.step * output.rows;
gpuErrchk( cudaMalloc( (void**) &device_input, d_ipimgSize) );
gpuErrchk( cudaMalloc( (void**) &device_output, d_opimgSize) );
gpuErrchk( cudaMemcpy(device_input, input.data, d_ipimgSize, cudaMemcpyHostToDevice) );
dim3 Threads(BLOCK_COLS, BLOCK_ROWS); // 512 threads per block
dim3 Blocks((input.cols + Threads.x - 1)/Threads.x, (input.rows + Threads.y - 1)/Threads.y);
//int check = (input.cols + Threads.x - 1)/Threads.x;
//printf( "blockx %d", check);
FilterKernel <<< Blocks, Threads >>> (device_input, device_output, input.rows, input.cols);
gpuErrchk(cudaDeviceSynchronize());
gpuErrchk(cudaGetLastError());
gpuErrchk( cudaMemcpy(output.data, device_output, d_opimgSize, cudaMemcpyDeviceToHost) );
//printf( "num_rows_cuda %d", num_rows);
//printf("\n");
gpuErrchk(cudaFree(device_input));
gpuErrchk(cudaFree(device_output));
}
int main(){
mat input_im, output_im;
input_im.rows = DROWS;
input_im.cols = DCOLS;
input_im.step = input_im.cols;
input_im.data = (unsigned char *)malloc(input_im.step*input_im.rows);
output_im.rows = DROWS;
output_im.cols = DCOLS;
output_im.step = input_im.cols;
output_im.data = (unsigned char *)malloc(output_im.step*output_im.rows);
for (int i = 0; i < DCOLS*DROWS; i++) {
output_im.data[i] = 0;
input_im.data[i] = 0;
int temp = (i%DCOLS);
if (temp == 5) input_im.data[i] = 20;
if ((temp > 5) && (temp < 15)) input_im.data[i] = 40;
if (temp == 15) input_im.data[i] = 20;
}
take_input(input_im, output_im);
for (int i = 2*DCOLS; i < DCOLS*(DROWS-2); i++)
if (input_im.data[i] != output_im.data[i]) {printf("mismatch at %d, input: %d, output: %d\n", i, (int)input_im.data[i], (int)output_im.data[i]); return 1;}
printf("Success\n");
return 0;
}
$ nvcc -o t376 t376.cu
$ ./t376
Success
$
一些注释：
window的使用不必要地复杂。请注意，您拥有它的方式，每个线程将实例化其自己的window本地副本，独立于其他线程，并且对其他线程不可见。 （也许你打算在这里使用共享内存？但我离题了。）unsigned char替换了像素数据类型
x和y令人困惑，因此我将其更改为row和col，这似乎不那么令人困惑。window数据以及改进的排序例程之外，我不会花太多时间进行优化。 编辑：在阅读您的评论后仍然无法正常工作时，您的OpenCV代码似乎未正确设置，无法为单个通道提供8位灰度（CV_8UC1）图像 和来自您的take_input功能。问题出在这一行：
cv::Mat input = cv::imread("C:/Users/OCT/Documents/Visual Studio 2008/Projects/MedianFilter/MedianFilter/pic1.bmp",1);
传递给1的{{1}}参数指定RGB图像加载。请参阅imread documentation：
imread如果您指定Now we call the imread function which loads the image name specified by the first argument (argv[1]). The second argument specifies the format in what we want the image. This may be:
CV_LOAD_IMAGE_UNCHANGED (<0) loads the image as is (including the alpha channel if present)
CV_LOAD_IMAGE_GRAYSCALE ( 0) loads the image as an intensity one
CV_LOAD_IMAGE_COLOR (>0) loads the image in the RGB format
而不是CV_LOAD_IMAGE_GRAYSCALE，则可能会更好运。
否则，您应该研究如何加载图片，使其最终成为1类型。
但如果您按原样将CV_8UC1传递给input，那肯定无效。
答案 1 :(得分：3)
在CUDA 6.0中，NPP库现在包括所有数据类型和像素格式的中值滤波器的实现。因此，如果您只想要一个功能中值过滤器例程，您可以调用它。如果您需要帮助调试内核，请参阅所有其他答案......