小俊的数字人生 https://bbs.21ic.com/?293300 [收藏] [复制] [RSS]

日志

[原创]简单的CUDA程序:图像二值化处理

已有 2192 次阅读2009-7-28 16:43 |个人分类:高性能计算|系统分类:兴趣爱好| 二值化

常规的C++实现方法(假设图像为R8G8B8格式,每个像素4字节):








int x, y;
unsigned long offset = 0, p;
unsigned char r, g, b, mi, ma;


for(y = 0; y < HEIGHT; y ++)
for(x = 0; x < WIDTH; x ++)
{
    p = ((unsigned long *)in)[offset];


    b = ((unsigned char *)(&p))[0];
    g = ((unsigned char *)(&p))[1];
    r = ((unsigned char *)(&p))[2];


    mi = min(r, min(g, b));
    ma = max(r, max(g, b));


    if(((unsigned short)ma + (unsigned short)mi) > THRESHOLD * 2)
        out[offset] = 255;
    else
        out[offset] = 0;
    offset ++;
}
 


用CUDA的实现方法:


1、每个像素一个线程,各像素并行计算,互不干扰。每个线程块256个线程。
2、包含kernel代码和host代码。kernel代码在GPU运行,host代码在CPU运行。
3、kernel运行时,原始图像数据和结果都是保存在显存中。host代码负责分配内存和复制数据。


源代码:








#define THREAD_N    256
#define THRESHOLD    127


__global__ static void binarizeKernel(unsigned char *in, unsigned char *out)
{
    const unsigned long offset = (blockIdx.x * THREAD_N + threadIdx.x);
    unsigned long p = ((unsigned long *)in)[offset];


    unsigned char b = ((unsigned char *)(&p))[0];
    unsigned char g = ((unsigned char *)(&p))[1];
    unsigned char r = ((unsigned char *)(&p))[2];


    unsigned char mi = __min(r, __min(g, b));
    unsigned char ma = __max(r, __max(g, b));


    out[offset] = (((unsigned short)ma + (unsigned short)mi) > THRESHOLD * 2) ? 255 : 0;
}


extern "C" float binarizeCall(unsigned char *in, unsigned char *out, int w, int h)
{
    unsigned char *device_src = 0;
    unsigned char *device_dest = 0;


    cudaMalloc((void **)&device_src, w * h * 3);
    cudaMalloc((void **)&device_dest, w * h);


    unsigned int timer = 0;
    cutCreateTimer(&timer);


    cudaMemcpy(device_src, in, w * h * 3, cudaMemcpyHostToDevice);


    cutStartTimer(timer);


    binarizeKernel<<<w * h / THREAD_N, THREAD_N>>>(device_src, device_dest);
    cudaThreadSynchronize();


    cutStopTimer(timer);


    cudaMemcpy(out, device_dest, w * h, cudaMemcpyDeviceToHost);


    float ms = cutGetTimerValue(timer);
    cutDeleteTimer(timer);


    cudaFree(device_dest);
    cudaFree(device_src);


    return ms;
}
 


运行速度比较(CPU用QX6600 2.4GHz单线程,GPU用Quadro FX5800,GT200核心)


图像大小1280×800(1M像素):CPU耗时5.5ms,GPU耗时0.16ms。
图像大小3264×2448(8M像素):CPU耗时41.2ms,GPU耗时0.83ms。


用ION平台的ATOM 230 CPU和集成GPU比较:


图像大小1280×800(1M像素):CPU耗时17.3ms,GPU耗时1.8~2.2ms。


结论:


1、GT200在做高度并行的图形处理运算时,速度可以达到2.4GHz CPU单线程时的30~50倍,即使是ION平台的集成GPU也比普通的CPU单线程时要快。


2、ION的ATOM CPU性能太差。


 


路过

鸡蛋

鲜花

握手

雷人

评论 (0 个评论)