首页 > 代码库 > 【视频处理】YV12ToARGB

【视频处理】YV12ToARGB

前面提到了YV12RGB的各种实现方法和优化方法,主要是CPU上的实现。本文主要介绍基于GPUYV12RGB的实现。

<!--[if !supportLists]-->1. <!--[endif]-->基于OpenGL的实现

利用OpenGL shader实现将YV12RGB,将YUV分量数据作为纹理数据,并构造YUVRGBshader代码,最终纹理数据在shader代码作用下,实现YV12RGB。该方法适合于将YV12RGB后直接显示,若YV12转化成RGB后,还需要进行图像处理操作,则利用OpenGL进行纹理数据的图像处理操作不方便。说明:由于本文着重于基于Cuda的实现,因而未验证基于OpenGL的代码实现。

具体资料可参考:

http://blog.csdn.net/xiaoguaihai/article/details/8672631

http://www.fourcc.org/source/YUV420P-OpenGL-GLSLang.c

<!--[if !supportLists]-->2. <!--[endif]-->基于Cuda的实现

YV12RGB的过程是逐一获取像素的YUV分量,然后通过转换公式计算得RGB。基于CUDA的实现关键在于两个步骤:YUV分量的获取,RGB的计算。YUV分量的获取与YUV的内存布局有关,RGB的计算公式一般是固定不变。具体的代码实现如下所示,主要参考NV12ToARGB.cu的代码,在该代码的基础上,保持RGB的计算方法不变,修改了YUV分量的获取方法。

#include "cuda.h"

#include "cuda_runtime_api.h"

 

#define COLOR_COMPONENT_BIT_SIZE 10

#define COLOR_COMPONENT_MASK     0x3FF

 

__constant__ float constHueColorSpaceMat[9]={1.1644f,0.0f,1.596f,1.1644f,-0.3918f,-0.813f,1.1644f,2.0172f,0.0f};

 

__device__ staticvoid YUV2RGB(constint* yuvi,float* red,float* green,float* blue)

{

    float luma, chromaCb, chromaCr;

 

    // Prepare for hue adjustment

    luma     =(float)yuvi[0];

    chromaCb =(float)((int)yuvi[1]-512.0f);

    chromaCr =(float)((int)yuvi[2]-512.0f);

 

   // Convert YUV To RGB with hue adjustment

   *red   =(luma     * constHueColorSpaceMat[0])+

            (chromaCb * constHueColorSpaceMat[1])+

            (chromaCr * constHueColorSpaceMat[2]);

 

   *green =(luma     * constHueColorSpaceMat[3])+

            (chromaCb * constHueColorSpaceMat[4])+

            (chromaCr * constHueColorSpaceMat[5]);

 

   *blue  =(luma     * constHueColorSpaceMat[6])+

            (chromaCb * constHueColorSpaceMat[7])+

            (chromaCr * constHueColorSpaceMat[8]);

}

 

__device__ staticint RGBA_pack_10bit(float red,float green,float blue,int alpha)

{

    int ARGBpixel =0;

 

    // Clamp final 10 bit results

    red   =::fmin(::fmax(red,   0.0f),1023.f);

    green =::fmin(::fmax(green,0.0f),1023.f);

    blue  =::fmin(::fmax(blue,  0.0f),1023.f);

 

    // Convert to 8 bit unsigned integers per color component

    ARGBpixel =(((int)blue  >>2)|

                (((int)green >>2)<<8)  |

                (((int)red   >>2)<<16)|

                (int)alpha);

 

    return ARGBpixel;

}

 

__global__ void YV12ToARGB_FourPixel(constunsignedchar* pYV12,unsignedint* pARGB,int width,int height)

{

    // Pad borders with duplicate pixels, and we multiply by 2 because we process 4 pixels per thread

    constint x = blockIdx.x *(blockDim.x <<1)+(threadIdx.x <<1);

    constint y = blockIdx.y *(blockDim.y <<1)+(threadIdx.y <<1);

 

    if((x +1)>= width ||(y +1)>= height)

       return;

 

    // Read 4 Luma components at a time

    int yuv101010Pel[4];

    yuv101010Pel[0]=(pYV12[y * width + x    ])<<2;

    yuv101010Pel[1]=(pYV12[y * width + x +1])<<2;

    yuv101010Pel[2]=(pYV12[(y +1)* width + x    ])<<2;

    yuv101010Pel[3]=(pYV12[(y +1)* width + x +1])<<2;

 

    constunsignedint vOffset = width * height;

    constunsignedint uOffset = vOffset +(vOffset >>2);

    constunsignedint vPitch = width >>1;

    constunsignedint uPitch = vPitch;

    constint x_chroma = x >>1;

    constint y_chroma = y >>1;

 

    int chromaCb = pYV12[uOffset + y_chroma * uPitch + x_chroma];      //U

    int chromaCr = pYV12[vOffset + y_chroma * vPitch + x_chroma];      //V

 

    yuv101010Pel[0]|=(chromaCb <<( COLOR_COMPONENT_BIT_SIZE       +2));

    yuv101010Pel[0]|=(chromaCr <<((COLOR_COMPONENT_BIT_SIZE <<1)+2));

    yuv101010Pel[1]|=(chromaCb <<( COLOR_COMPONENT_BIT_SIZE       +2));

    yuv101010Pel[1]|=(chromaCr <<((COLOR_COMPONENT_BIT_SIZE <<1)+2));

    yuv101010Pel[2]|=(chromaCb <<( COLOR_COMPONENT_BIT_SIZE       +2));

    yuv101010Pel[2]|=(chromaCr <<((COLOR_COMPONENT_BIT_SIZE <<1)+2));

    yuv101010Pel[3]|=(chromaCb <<( COLOR_COMPONENT_BIT_SIZE       +2));

    yuv101010Pel[3]|=(chromaCr <<((COLOR_COMPONENT_BIT_SIZE <<1)+2));

 

    // this steps performs the color conversion

    int yuvi[12];

    float red[4], green[4], blue[4];

 

    yuvi[0]=(yuv101010Pel[0]&   COLOR_COMPONENT_MASK    );

    yuvi[1]=((yuv101010Pel[0]>>  COLOR_COMPONENT_BIT_SIZE)       & COLOR_COMPONENT_MASK);

    yuvi[2]=((yuv101010Pel[0]>>(COLOR_COMPONENT_BIT_SIZE <<1))& COLOR_COMPONENT_MASK);

   

    yuvi[3]=(yuv101010Pel[1]&   COLOR_COMPONENT_MASK    );

    yuvi[4]=((yuv101010Pel[1]>>  COLOR_COMPONENT_BIT_SIZE)       & COLOR_COMPONENT_MASK);

    yuvi[5]=((yuv101010Pel[1]>>(COLOR_COMPONENT_BIT_SIZE <<1))& COLOR_COMPONENT_MASK);

   

    yuvi[6]=(yuv101010Pel[2]&   COLOR_COMPONENT_MASK    );

    yuvi[7]=((yuv101010Pel[2]>>  COLOR_COMPONENT_BIT_SIZE)       & COLOR_COMPONENT_MASK);

    yuvi[8]=((yuv101010Pel[2]>>(COLOR_COMPONENT_BIT_SIZE <<1))& COLOR_COMPONENT_MASK);

   

    yuvi[9]=(yuv101010Pel[3]&   COLOR_COMPONENT_MASK    );

    yuvi[10]=((yuv101010Pel[3]>>  COLOR_COMPONENT_BIT_SIZE)       & COLOR_COMPONENT_MASK);

    yuvi[11]=((yuv101010Pel[3]>>(COLOR_COMPONENT_BIT_SIZE <<1))& COLOR_COMPONENT_MASK);

 

    // YUV to RGB Transformation conversion

    YUV2RGB(&yuvi[0],&red[0],&green[0],&blue[0]);

    YUV2RGB(&yuvi[3],&red[1],&green[1],&blue[1]);

    YUV2RGB(&yuvi[6],&red[2],&green[2],&blue[2]);

    YUV2RGB(&yuvi[9],&red[3],&green[3],&blue[3]);

 

    pARGB[y * width + x     ]= RGBA_pack_10bit(red[0], green[0], blue[0],((int)0xff<<24));

    pARGB[y * width + x +1]= RGBA_pack_10bit(red[1], green[1], blue[1],((int)0xff<<24));

    pARGB[(y +1)* width + x     ]= RGBA_pack_10bit(red[2], green[2], blue[2],((int)0xff<<24));

    pARGB[(y +1)* width + x +1]= RGBA_pack_10bit(red[3], green[3], blue[3],((int)0xff<<24));

}

 

bool YV12ToARGB(unsignedchar* pYV12,unsignedchar* pARGB,int width,int height)

{

    unsignedchar* d_src;

    unsignedchar* d_dst;

    unsignedint srcMemSize =sizeof(unsignedchar)* width * height *3/2;

    unsignedint dstMemSize =sizeof(unsignedchar)* width * height *4;

 

    cudaMalloc((void**)&d_src,srcMemSize);

    cudaMalloc((void**)*d_dst,dstMemSize);

    cudaMemcpy(d_src,pYV12,srcMemSize,cudaMemcpyHostToDevice);

 

    dim3 block(32,8);

    int gridx =(width +2*block.x -1)/(2*block.x);

    int gridy =(height +2*block.y -1)/(2*block.y);

    dim3 grid(gridx,gridy);

 

    YV12ToARGB<<<grid,block>>>(d_src,(unsignedint*)d_dst,width,height);

    cudaMemcpy(pARGB,d_dst,dstMemSize,cudaMemcpyDeviceToHost);

    returntrue;

}

  线程内存访问示意图如下所示,每个线程访问4Y1U1V,最终转换得到4ARGB值。由于YV12属于YUV4:2:0采样,每四个Y共用一组UV分量,即Y(0,0)Y(0,1)Y(1,0)Y(1,1)共用V(0,0)U(0,0),如红色框标注所示。


<!--[endif]-->

<!--[if !supportLists]-->3. <!--[endif]-->基于Cuda的实现优化

优化主要关注于两个方面:单个线程处理像素粒度和数据传输。单个线程处理粒度分为:OnePixelPerThread,TwoPixelPerThread,FourPixelPerThread。数据传输优化主要采用Pageable MemoryPinned MemoryMapped Memory(Zero Copy)。经测试,不同实现版本的转换效率如下表所示,测试序列:1920*1080,时间统计包括内核函数执行时间和数据传输时间,单位为ms

 

OnePixel

TwoPixel

FourPixel

Pageable

6.91691

6.64319

6.2873

Pinned

5.31999

5.01890

4.71937

Mapped

3.39043

48.5298

23.8327

    由上表可知,不使用Mapped Memory(Zero Copy)时,单个线程处理像素的粒度越大,内核函数执行的时间越小,转换效率越好。使用Mapped Memory(Zero Copy)时,单线程处理单像素时,转换效率最好。

    单个线程处理四个像素时,内核函数执行时间最少;使用Pinned Memory会减少数据传输时间;使用Mapped Memory消除数据传输过程,但会增加内核函数执行时间,最终优化效果与内核函数访问内存的方式有关。建议使用Pinned Memory+FourPixelPerThread的优化版本。