Question

我试图用cuda实现多个黑色（0）和白色（255）图像侵蚀，我使用方形（5x5）结构元素。我实现的内核采取 unsigned char 数组缓冲区，其中存储 nImg 图像200X200 px。为了同时侵蚀多个图像，我使用3D结构制作网格：

每个块的尺寸为 strel （5x5）
网格 height = image_height / blockDim.y ， width = image_width / blockDim.x ， z = nImg

我尝试将其扩展为sample。

问题在于，如果我将一个线程块所考虑的像素存储到块的线程之间共享的共享缓冲区中; 为了允许快速内存访问，该算法无法正常工作。我尝试更改 bindex ，这对我来说是错误的，但我找不到解决方案。

有什么建议吗？

这是我的代码：

//strel size
#define STREL_W 5
#define STREL_H 5

// distance from the cente of strel to strel width or height
#define R (STREL_H/2)

//size of the 2D region that each block consider i.e all the  neighborns that each thread in a block consider
#define BLOCK_W (STREL_W+(2*R))
#define BLOCK_H (STREL_H+(2*R))

__global__ void erode_multiple_img_SM(unsigned char * buffer_in,
                                       unsigned char * buffer_out,
                                       int w,
                                       int h ){

    //array stored in shared memory,that contain all pixel neighborns that each thread in a block consider 
    __shared__ unsigned char fast_acc_arr[BLOCK_W*BLOCK_H];

    // map thread in a 3D structure 
    int col = blockIdx.x * STREL_W + threadIdx.x -R ;
    int row = blockIdx.y * STREL_H + threadIdx.y -R ;
    int plane = blockIdx.z * blockDim.z + threadIdx.z;

    // check if a foreground px of strel is not contain in a region of the image with size of strel (if only one px is not contain the image is eroded)
    bool is_contain = true;


    // clamp to edge of image
    col = max(0,col);
    col = min(col,w-1);

    row = max(0,row);
    row = min(row,h-1);

    //map each thread in one dim coord to map 3D structure(grid) with image buffer(1D) 
    unsigned int index =  (plane * h * w) + (row * w) + col;

    unsigned int bindex =  threadIdx.y * blockDim.y + threadIdx.x;


    //each thread copy its pixel of the block to shared memory (shared with thread of a block)
    fast_acc_arr[bindex] = buffer_in[index];

    __syncthreads();



    //the strel must be contain in image, thread.x and thread.y are the coords of the center of the mask that correspond to strel in image, and it must be contain in image
    if((threadIdx.x >= R) && (threadIdx.x < BLOCK_W-R) && (threadIdx.y >= R) && (threadIdx.y <BLOCK_H-R)){

        for(int dy=-R; dy<=R; dy++){
            if(is_contain == false)
            break;

            for (int dx = -R ; dx <= R; dx++) {

                //if only one element in mask is different from the value of strel el --> the strel is not contain in the mask --> the center of the mask is eroded (and it's no necessary to consider the other el of the mask this is the motivation of the break)
                if (fast_acc_arr[bindex + (dy * blockDim.x) + dx ] != 255 ){

                    buffer_out[index ] = 0;
                    is_contain = false;
                    break;
                 }
             }
         }
        // if the strel is contain into the image the the center is not eroded
        if(is_contain == true)
            buffer_out[index] = 255;
      }

}

这是我的内核设置：

 dim3 block(5,5,1);
 dim3 grid(200/(block.x),200/(block.y),nImg);

我的内核电话：

 erode_multiple_img_SM<<<grid,block>>>(dimage_src,dimage_dst,200,200);

我的图像输入和输出：

输入：输出（150 buff元素）：

没有共享内存的代码（低速）：

__global__ void erode_multiple_img(unsigned char * buffer_in,
                            unsigned char * buffer_out,
                            int w,int h ){



    int col = blockIdx.x * blockDim.x + threadIdx.x;
    int row = blockIdx.y * blockDim.y + threadIdx.y;
    int plane = blockIdx.z * blockDim.z +threadIdx.z;

    bool is_contain = true;
    col = max(0,col);
    col = min(col,w-1);

    row = max(0,row);
    row = min(row,h-1);



    for(int dy=-STREL_H/2; dy<=STREL_H/2; dy++){
        if(is_contain == false)
            break;
        for (int dx = -STREL_W/2 ; dx <= STREL_W/2; dx++) {
            if (buffer_in[(plane * h * w) +( row + dy) * w + (col + dx) ] !=255 ){
                buffer_out[(plane * h * w) + row * w + col ] = 0;
                is_contain = false;
                break;
            }
        }
    }
    if(is_contain == true)
        buffer_out[(plane * h * w) + row * w +col ] = 255;

}

更新的算法

我尝试按照samples进行卷积。我改变输入图像，现在 512x512大小，我写了那个算法：

#define STREL_SIZE 5


#define TILE_W 16
#define TILE_H 16

#define R (STREL_H/2)

#define BLOCK_W (TILE_W+(2*R))
#define BLOCK_H (TILE_H+(2*R))

__global__ void erode_multiple_img_SM_v2(unsigned char * buffer_in,
                            unsigned char * buffer_out,
                            int w,int h ){

    // Data cache: threadIdx.x , threadIdx.y
    __shared__ unsigned char data[TILE_W +STREL_SIZE ][TILE_W +STREL_SIZE ];

    // global mem address of this thread
    int col = blockIdx.x * blockDim.x + threadIdx.x;
    int row = blockIdx.y * blockDim.y + threadIdx.y;

    int plane = blockIdx.z * blockDim.z +threadIdx.z;

    int gLoc = (plane*h/w)+ row*w +col;

    bool is_contain = true;

    // load cache (32x32 shared memory, 16x16 threads blocks)
    // each threads loads four values from global memory into shared mem
    int x, y;   // image based coordinate



    if((col<w)&&(row<h)) {
        data[threadIdx.x][threadIdx.y]=buffer_in[gLoc];

    if (threadIdx.y > (h-STREL_SIZE))
        data[threadIdx.x][threadIdx.y + STREL_SIZE]=buffer_in[gLoc + STREL_SIZE];

    if (threadIdx.x >(w-STREL_SIZE))
        data[threadIdx.x + STREL_SIZE][threadIdx.y]=buffer_in[gLoc+STREL_SIZE];

    if ((threadIdx.x >(w-STREL_SIZE)) && (threadIdx.y > (h-STREL_SIZE)))
        data[threadIdx.x+STREL_SIZE][threadIdx.y+STREL_SIZE] =   buffer_in[gLoc+2*STREL_SIZE];
    //wait for all threads to finish read
     __syncthreads();


     //buffer_out[gLoc] = data[threadIdx.x][threadIdx.y];

      unsigned char min_value = 255;
      for(x=0;x<STREL_SIZE;x++){
          for(y=0;y<STREL_SIZE;y++){
              min_value = min( (data[threadIdx.x+x][threadIdx.y+y]) , min_value);
              }
          }
      buffer_out[gLoc]= min_value;
      }

}

我的内核设置现在是：

dim3 block(16,16);
dim3 grid(512/(block.x),512/(block.y),nImg);

输入：

输出：

似乎围裙的像素未在输出缓冲区中复制

Answer 1

您可能需要阅读以下链接以获取更多详细说明以及有关如何实现图像卷积CUDA内核函数的更好示例代码。

http://igm.univ-mlv.fr/~biri/Enseignement/MII2/Donnees/convolutionSeparable.pdf

https://www.evl.uic.edu/sjames/cs525/final.html

基本上使用大小为（5 x 5）的卷积滤波器并不意味着将线程块的大小设置为（5 x 5）。

通常，对于不可分离的卷积，您可以使用大小为（16 x 16）的线程块来计算输出图像上的（16 x 16）像素块。要实现这一点，您需要使用（16 x 16）线程协同读取输入图像中的（（2 + 16 + 2）x（2 + 16 + 2）像素块到共享内存。

cuda多图像侵蚀不起作用

1 个答案: