CUDA纹理参考的使用示例

记录个简单小例子帮助理解CUDA纹理参考的使用，其实和OpenGL纹理使用是一样的。
转载：https://www.cnblogs.com/riddick/p/7892663.html

CUDA纹理内存的访问速度比全局内存要快，因此处理图像数据时，使用纹理内存是一个提升性能的好方法。

贴一段自己写的简单的实现两幅图像加权和的代码，使用纹理内存实现。

输入：两幅图 lena, moon

  

输出：两幅图像加权和

#include <opencv2\opencv.hpp> 
#include <iostream>
#include <string>
#include <cuda.h>
#include <cuda_runtime.h>
#include <device_launch_parameters.h>

using namespace std;
using namespace cv;

//声明CUDA纹理
texture <uchar4, cudaTextureType2D, cudaReadModeNormalizedFloat> refTex1;
texture <uchar4, cudaTextureType2D, cudaReadModeNormalizedFloat> refTex2;
//声明CUDA数组
cudaArray* cuArray1;
cudaArray* cuArray2;
//通道数
cudaChannelFormatDesc cuDesc = cudaCreateChannelDesc<uchar4>();


__global__ void weightAddKerkel(uchar *pDstImgData, int imgHeight, int imgWidth,int channels)
{
    const int tidx=blockDim.x*blockIdx.x+threadIdx.x;
    const int tidy=blockDim.y*blockIdx.y+threadIdx.y;

    if (tidx<imgWidth && tidy<imgHeight)
    {
        float4 lenaBGR,moonBGR;
        //使用tex2D函数采样纹理
        lenaBGR=tex2D(refTex1, tidx, tidy);
        moonBGR=tex2D(refTex2, tidx, tidy);

        int idx=(tidy*imgWidth+tidx)*channels;
        float alpha=0.5;
        pDstImgData[idx+0]=(alpha*lenaBGR.x+(1-alpha)*moonBGR.x)*255;
        pDstImgData[idx+1]=(alpha*lenaBGR.y+(1-alpha)*moonBGR.y)*255;
        pDstImgData[idx+2]=(alpha*lenaBGR.z+(1-alpha)*moonBGR.z)*255;
        pDstImgData[idx+3]=0;
    }
}

void main()
{
    Mat Lena=imread("data/lena.jpg");
    Mat moon=imread("data/moon.jpg");
    cvtColor(Lena, Lena, CV_BGR2BGRA);
    cvtColor(moon, moon, CV_BGR2BGRA);
    int imgWidth=Lena.cols;
    int imgHeight=Lena.rows;
    int channels=Lena.channels();

    //设置纹理属性
    cudaError_t t;
    refTex1.addressMode[0] = cudaAddressModeClamp;
    refTex1.addressMode[1] = cudaAddressModeClamp;
    refTex1.normalized = false;
    refTex1.filterMode = cudaFilterModeLinear;
    //绑定cuArray到纹理
    cudaMallocArray(&cuArray1, &cuDesc, imgWidth, imgHeight);
    t = cudaBindTextureToArray(refTex1, cuArray1);

    refTex2.addressMode[0] = cudaAddressModeClamp;
    refTex2.addressMode[1] = cudaAddressModeClamp;
    refTex2.normalized = false;
    refTex2.filterMode = cudaFilterModeLinear;
     cudaMallocArray(&cuArray2, &cuDesc, imgWidth, imgHeight);
    t = cudaBindTextureToArray(refTex2, cuArray2);

    //拷贝数据到cudaArray
    t=cudaMemcpyToArray(cuArray1, 0,0, Lena.data, Width*imgHeight*sizeof(uchar)*channels, cudaMemcpyHostToDevice);
    t=cudaMemcpyToArray(cuArray2, 0,0, moon.data, Width*imgHeight*sizeof(uchar)*channels, cudaMemcpyHostToDevice);

    //输出图像
    Mat dstImg=Mat::zeros(imgHeight, imgWidth, CV_8UC4);
    uchar *pDstImgData=NULL;
    t=cudaMalloc(&pDstImgData, imgHeight*imgWidth*sizeof(uchar)*channels);

    //核函数，实现两幅图像加权和
    dim3 block(8,8);
    dim3 grid( (imgWidth+block.x-1)/block.x, (imgHeight+block.y-1)/block.y );
    weightAddKerkel<<<grid, block, 0>>>(pDstImgData, imgHeight, imgWidth, channels);
    cudaThreadSynchronize();

    //从GPU拷贝输出数据到CPU
    t=cudaMemcpy(dstImg.data, pDstImgData, imgWidth*imgHeight*sizeof(uchar)*channels, aMemcpyDeviceToHost);

    //显示
    namedWindow("show");
    imshow("show", dstImg);
    waitKey(0);
}