积分图实现均值滤波的CUDA代码

Posted 2020-10-08 逍遥一度，恣情江湖

没想到我2010年买的笔记本显卡GT330M 竟然还能跑CUDA，果断小试了一把，环境为CUDA6.5+VS2012，写了一个积分图实现均值滤波。类似于OpenCV的blur()函数。

使用lena.jpg做测试，效果如下：

      

代码在此：

#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <opencv2\\opencv.hpp>

using namespace std;
using namespace cv;

 __global__ void rowAddKernel(float* pIntegImgLena,int* pPtsImg,int imgW,int imgH)
 {
     const int tidx=blockDim.x*blockIdx.x + threadIdx.x;
     if (tidx<imgW)
     {
         for (int j=1; j<imgH; j++)
         {
             pIntegImgLena[j*imgW+ tidx] +=pIntegImgLena[(j-1)*imgW+tidx];
             pPtsImg[j*imgW+ tidx] +=pPtsImg[(j-1)*imgW+ tidx];
         }
     }
 }

  __global__ void colAddKernel(float* pIntegImgLena,int* pPtsImg,int imgW,int imgH)
 {
     const int tidy=blockDim.y*blockIdx.y + threadIdx.y;
     if (tidy<imgH)
     {
         for (int i=1; i<imgW; i++)
         {
             pIntegImgLena[tidy*imgW+ i] +=pIntegImgLena[tidy*imgW+i-1];
             pPtsImg[tidy*imgW+ i] +=pPtsImg[tidy*imgW+ i-1];
         }
     }
 }

 __global__ void filterKernel(uchar* pImgLena,float* pIntegImgLena,int* pPtsImg,int imgW,int imgH,int win)
 {
     const int tidx=blockDim.x*blockIdx.x + threadIdx.x;
     const int tidy=blockDim.y*blockIdx.y + threadIdx.y;
     if (tidx<imgW && tidy<imgH)
     {
         int left=tidx-win;
         int right=tidx+win;
         int top=tidy-win;
         int bot=tidy+win;

         left=max(left, 0);
         right=min(right, imgW-1);
         top=max(top, 0);
         bot=min(bot, imgH-1);

         int id1=top*imgW+left;
         int id2=top*imgW+right;
         int id3=bot*imgW+left;
         int id4=bot*imgW+right;
         int cnt=pPtsImg[id4]+pPtsImg[id1]-pPtsImg[id2]-pPtsImg[id3];
         float sum=pIntegImgLena[id4]+pIntegImgLena[id1]-pIntegImgLena[id2]-pIntegImgLena[id3];

         float value=sum/cnt;

         pImgLena[tidy*imgW+tidx]=(uchar)value;
     }
 }

void main()
{
    //读取原图像
    string imgPath="data/lena.jpg";
    Mat imgLena=imread(imgPath, 0);
    int imgH=imgLena.rows;
    int imgW=imgLena.cols;
    namedWindow("lena");
    imshow("lena", imgLena);
    waitKey(0);
    //滤波后的lena
     Mat filterLena=imgLena.clone();
     filterLena.setTo(0);
    //积分图以及坐标索引图
    Mat integImgLena=Mat::zeros(imgLena.size(), CV_32FC1);
    Mat ptsImg=Mat::zeros(imgLena.size(), CV_32SC1);
    //积分图初始化
    imgLena.convertTo(imgLena, CV_32FC1);
    integImgLena=imgLena.clone();
    ptsImg.setTo(1);

    //分配内存
    uchar* pImgLena=NULL;
    float* pIntegImgLena=NULL;
    int* pPtsImg=NULL;
    cudaMalloc(&pImgLena, imgH*imgW*sizeof(uchar));
    cudaMalloc(&pIntegImgLena, imgH*imgW*sizeof(float));
    cudaMalloc(&pPtsImg, imgH*imgW*sizeof(int));

    //拷贝数据至GPU
    cudaMemcpy(pImgLena, imgLena.data,imgH*imgW*sizeof(uchar), cudaMemcpyHostToDevice);
    cudaMemcpy(pIntegImgLena, integImgLena.data,imgH*imgW*sizeof(float), cudaMemcpyHostToDevice);
    cudaMemcpy(pPtsImg, ptsImg.data,imgH*imgW*sizeof(int), cudaMemcpyHostToDevice);

    //按行求前缀和
    dim3 block(8,1);
    dim3 grid((imgW+block.x-1)/block.x,1);
    rowAddKernel<<<grid, block, 0>>>(pIntegImgLena, pPtsImg, imgW, imgH);
    cudaThreadSynchronize();
     //按列求前缀和
    block=dim3(1,8);
    grid=dim3(1,(imgH+block.y-1)/block.y);
    colAddKernel<<<grid, block, 0>>>(pIntegImgLena, pPtsImg, imgW, imgH);
     cudaThreadSynchronize();
    //滤波
    int win=3;
    block=dim3(8,8);
    grid=dim3((imgW+block.x-1)/block.x, (imgH+block.y-1)/block.y);
    filterKernel<<<grid, block, 0>>>(pImgLena,pIntegImgLena, pPtsImg, imgW, imgH, win);
    cudaThreadSynchronize();

    cudaMemcpy(filterLena.data, pImgLena, imgH*imgW*sizeof(uchar), cudaMemcpyDeviceToHost);

    cudaError err;
    err=cudaGetLastError();
    if (err!=cudaSuccess)
    {
        cout<<"err="<<err<<endl;
        getchar();
    }

    namedWindow("filterLena");
    imshow("filterLena", filterLena);
    waitKey(0);

    cudaFree(pImgLena);
    cudaFree(pIntegImgLena);
    cudaFree(pPtsImg);
}

View Code