image - Cuda 图像平均滤波器

Question

平均过滤器是线性类的加窗滤波器，用于平滑信号(图像)。该滤波器用作低通滤波器。过滤器背后的基本思想是对信号(图像)的任何元素取其邻域的平均值。

如果我们有 m x n矩阵，我们要应用大小为 k 的平均滤波器在它上面，然后对于矩阵中的每个点p:(i,j)该点的值将是正方形中所有点的平均值

该图是用于大小为 2 的滤波的 Square kernel ，黄色框是要平均的像素，所有网格是相邻像素的平方，像素的新值将是它们的平均值。
问题是这个算法很慢，特别是在大图像上，所以我想到了使用GPGPU .
现在的问题是 , 如果可能的话，如何在 cuda 中实现？

Answer 1

这是embarrassingly parallel的经典案例可以很容易地映射到 CUDA 框架的图像处理问题。平均滤波器被称为 Box Filter在图像处理领域。

最简单的方法是使用 CUDA 纹理进行过滤，因为边界条件可以很容易地通过纹理处理。

假设您在主机上分配了源和目标指针。该程序将是这样的。

分配足够大的内存来保存设备上的源图像和目标图像。

将源图像从主机复制到设备。

将源图像设备指针绑定(bind)到纹理。

指定适当的 block 大小和足够大的网格以覆盖图像的每个像素。

使用指定的网格和 block 大小启动过滤内核。

将结果复制回主机。

解绑贴图

免费设备指针。

盒式过滤器的示例实现

内核

texture<unsigned char, cudaTextureType2D> tex8u;

//Box Filter Kernel For Gray scale image with 8bit depth
__global__ void box_filter_kernel_8u_c1(unsigned char* output,const int width, const int height, const size_t pitch, const int fWidth, const int fHeight)
{
    int xIndex = blockIdx.x * blockDim.x + threadIdx.x;
    int yIndex = blockIdx.y * blockDim.y + threadIdx.y;

    const int filter_offset_x = fWidth/2;
    const int filter_offset_y = fHeight/2;

    float output_value = 0.0f;

    //Make sure the current thread is inside the image bounds
    if(xIndex<width && yIndex<height)
    {
        //Sum the window pixels
        for(int i= -filter_offset_x; i<=filter_offset_x; i++)
        {
            for(int j=-filter_offset_y; j<=filter_offset_y; j++)
            {
                //No need to worry about Out-Of-Range access. tex2D automatically handles it.
                output_value += tex2D(tex8u,xIndex + i,yIndex + j);
            }
        }

        //Average the output value
        output_value /= (fWidth * fHeight);

        //Write the averaged value to the output.
        //Transform 2D index to 1D index, because image is actually in linear memory
        int index = yIndex * pitch + xIndex;

        output[index] = static_cast<unsigned char>(output_value);
    }
}

包装函数:

void box_filter_8u_c1(unsigned char* CPUinput, unsigned char* CPUoutput, const int width, const int height, const int widthStep, const int filterWidth, const int filterHeight)
{

    /*
     * 2D memory is allocated as strided linear memory on GPU.
     * The terminologies "Pitch", "WidthStep", and "Stride" are exactly the same thing.
     * It is the size of a row in bytes.
     * It is not necessary that width = widthStep.
     * Total bytes occupied by the image = widthStep x height.
     */

    //Declare GPU pointer
    unsigned char *GPU_input, *GPU_output;

    //Allocate 2D memory on GPU. Also known as Pitch Linear Memory
    size_t gpu_image_pitch = 0;
    cudaMallocPitch<unsigned char>(&GPU_input,&gpu_image_pitch,width,height);
    cudaMallocPitch<unsigned char>(&GPU_output,&gpu_image_pitch,width,height);

    //Copy data from host to device.
    cudaMemcpy2D(GPU_input,gpu_image_pitch,CPUinput,widthStep,width,height,cudaMemcpyHostToDevice);

    //Bind the image to the texture. Now the kernel will read the input image through the texture cache.
    //Use tex2D function to read the image
    cudaBindTexture2D(NULL,tex8u,GPU_input,width,height,gpu_image_pitch);

    /*
     * Set the behavior of tex2D for out-of-range image reads.
     * cudaAddressModeBorder = Read Zero
     * cudaAddressModeClamp  = Read the nearest border pixel
     * We can skip this step. The default mode is Clamp.
     */
    tex8u.addressMode[0] = tex8u.addressMode[1] = cudaAddressModeBorder;

    /*
     * Specify a block size. 256 threads per block are sufficient.
     * It can be increased, but keep in mind the limitations of the GPU.
     * Older GPUs allow maximum 512 threads per block.
     * Current GPUs allow maximum 1024 threads per block
     */

    dim3 block_size(16,16);

    /*
     * Specify the grid size for the GPU.
     * Make it generalized, so that the size of grid changes according to the input image size
     */

    dim3 grid_size;
    grid_size.x = (width + block_size.x - 1)/block_size.x;  /*< Greater than or equal to image width */
    grid_size.y = (height + block_size.y - 1)/block_size.y; /*< Greater than or equal to image height */

    //Launch the kernel
    box_filter_kernel_8u_c1<<<grid_size,block_size>>>(GPU_output,width,height,gpu_image_pitch,filterWidth,filterHeight);

    //Copy the results back to CPU
    cudaMemcpy2D(CPUoutput,widthStep,GPU_output,gpu_image_pitch,width,height,cudaMemcpyDeviceToHost);

    //Release the texture
    cudaUnbindTexture(tex8u);

    //Free GPU memory
    cudaFree(GPU_input);
    cudaFree(GPU_output);
}

好消息是您不必自己实现过滤器。 CUDA 工具包带有免费的信号和图像处理库，名为 NVIDIA Performance Primitives aka NPP，由 NVIDIA 开发。 NPP 利用支持 CUDA 的 GPU 来加速处理。平均滤波器已经在 NPP 中实现。当前版本的 NPP (5.0) 支持 8 位、1 channel 和 4 channel 图像。
功能是:

nppiFilterBox_8u_C1R对于 1 个 channel 图像。

nppiFilterBox_8u_C4R对于 4 channel 图像。