写一个cuda核函数，实现yuv图像的缩放

以下是一个简单的CUDA核函数，用于实现YUV图像的缩放。该函数使用了双线性插值算法进行缩放，假设输入的YUV图像为420格式，即Y、U、V分量的采样率分别为1：2：2。

__global__
void yuvResizeKernel(unsigned char* input_y, unsigned char* input_u, unsigned char* input_v, int input_width, int input_height, int input_pitch, 
                     unsigned char* output_y, unsigned char* output_u, unsigned char* output_v, int output_width, int output_height, int output_pitch) {
    int output_x = blockIdx.x * blockDim.x + threadIdx.x;
    int output_y = blockIdx.y * blockDim.y + threadIdx.y;
    if (output_x < output_width && output_y < output_height) {
        float input_x = output_x * (float)input_width / output_width;
        float input_y = output_y * (float)input_height / output_height;
        int input_x1 = floor(input_x);
        int input_x2 = ceil(input_x);
        int input_y1 = floor(input_y);
        int input_y2 = ceil(input_y);
        float dx = input_x - input_x1;
        float dy = input_y - input_y1;
        int input_idx1 = input_y1 * input_pitch + input_x1;
        int input_idx2 = input_y1 * input_pitch + input_x2;
        int input_idx3 = input_y2 * input_pitch + input_x1;
        int input_idx4 = input_y2 * input_pitch + input_x2;
        int output_idx = output_y * output_pitch + output_x;
        output_y[output_idx] = (1 - dy) * ((1 - dx) * input_y[input_idx1] + dx * input_y[input_idx2])
                             + dy * ((1 - dx) * input_y[input_idx3] + dx * input_y[input_idx4]);
        if (output_x % 2 == 0 && output_y % 2 == 0) {
            int input_u_idx = input_y1 / 2 * input_pitch / 2 + input_x1 / 2;
            int input_v_idx = input_y1 / 2 * input_pitch / 2 + input_x1 / 2;
            int output_u_idx = output_y / 2 * output_pitch / 2 + output_x / 2;
            output_u[output_u_idx] = (1 - dy) * ((1 - dx) * input_u[input_u_idx] + dx * input_u[input_u_idx + 1])
                                   + dy * ((1 - dx) * input_u[input_u_idx + input_pitch / 2] + dx * input_u[input_u_idx + input_pitch / 2 + 1]);
            output_v[output_v_idx] = (1 - dy) * ((1 - dx) * input_v[input_v_idx] + dx * input_v[input_v_idx + 1])
                                   + dy * ((1 - dx) * input_v[input_v_idx + input_pitch / 2] + dx * input_v[input_v_idx + input_pitch / 2 + 1]);
        }
    }
}

该函数的输入参数包括输入Y、U、V分量的指针input_y、input_u、input_v，输入图像的宽度、高度和行字节数input_width、input_height、input_pitch，输出Y、U、V分量的指针output_y、output_u、output_v，输出图像的宽度、高度和行字节数output_width、output_height、output_pitch。在主函数中，需要先将输入YUV图像从主机内存复制到设备内存中，然后调用该核函数进行缩放处理，最后将输出YUV图像从设备内存复制回主机内存。

void yuvResize(unsigned char* input_y, unsigned char* input_u, unsigned char* input_v, int input_width, int input_height, 
               unsigned char* output_y, unsigned char* output_u, unsigned char* output_v, int output_width, int output_height) {
    int input_pitch = input_width + (input_width % 2);
    int output_pitch = output_width + (output_width % 2);
    unsigned char* d_input_y, *d_input_u, *d_input_v;
    unsigned char* d_output_y, *d_output_u, *d_output_v;
    cudaMalloc(&d_input_y, input_pitch * input_height);
    cudaMalloc(&d_input_u, input_pitch / 2 * input_height / 2);
    cudaMalloc(&d_input_v, input_pitch / 2 * input_height / 2);
    cudaMalloc(&d_output_y, output_pitch * output_height);
    cudaMalloc(&d_output_u, output_pitch / 2 * output_height / 2);
    cudaMalloc(&d_output_v, output_pitch / 2 * output_height / 2);
    cudaMemcpy(d_input_y, input_y, input_pitch * input_height, cudaMemcpyHostToDevice);
    cudaMemcpy(d_input_u, input_u, input_pitch / 2 * input_height / 2, cudaMemcpyHostToDevice);
    cudaMemcpy(d_input_v, input_v, input_pitch / 2 * input_height / 2, cudaMemcpyHostToDevice);
    dim3 blockSize(32, 32);
    dim3 gridSize((output_width + blockSize.x - 1) / blockSize.x, (output_height + blockSize.y - 1) / blockSize.y);
    yuvResizeKernel<<<gridSize, blockSize>>>(d_input_y, d_input_u, d_input_v, input_width, input_height, input_pitch, 
                                             d_output_y, d_output_u, d_output_v, output_width, output_height, output_pitch);
    cudaMemcpy(output_y, d_output_y, output_pitch * output_height, cudaMemcpyDeviceToHost);
    cudaMemcpy(output_u, d_output_u, output_pitch / 2 * output_height / 2, cudaMemcpyDeviceToHost);
    cudaMemcpy(output_v, d_output_v, output_pitch / 2 * output_height / 2, cudaMemcpyDeviceToHost);
    cudaFree(d_input_y);
    cudaFree(d_input_u);
    cudaFree(d_input_v);
    cudaFree(d_output_y);
    cudaFree(d_output_u);
    cudaFree(d_output_v);
}

这里假设输入的YUV图像为420格式，即U和V分量的采样率为1：2。如果输入的YUV图像为其他格式，需要根据实际情况修改代码。