cuda纹理内存的使用

Posted 2020-10-15 逍遥一度，恣情江湖

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

 

 1 #include <opencv2\\opencv.hpp> 
 2 #include <iostream>
 3 #include <string>
 4 #include <cuda.h>
 5 #include <cuda_runtime.h>
 6 #include <device_launch_parameters.h>
 7 
 8 using namespace std;
 9 using namespace cv;
10 
11 //声明CUDA纹理
12 texture <uchar4, cudaTextureType2D, cudaReadModeNormalizedFloat> refTex1;
13 texture <uchar4, cudaTextureType2D, cudaReadModeNormalizedFloat> refTex2;
14 //声明CUDA数组
15 cudaArray* cuArray1;
16 cudaArray* cuArray2;
17 //通道数
18 cudaChannelFormatDesc cuDesc = cudaCreateChannelDesc<uchar4>();
19 
20 
21 __global__ void weightAddKerkel(uchar *pDstImgData, int imgHeight, int imgWidth,int channels)
22 {
23     const int tidx=blockDim.x*blockIdx.x+threadIdx.x;
24     const int tidy=blockDim.y*blockIdx.y+threadIdx.y;
25 
26     if (tidx<imgWidth && tidy<imgHeight)
27     {
28         float4 lenaBGR,moonBGR;
29         //使用tex2D函数采样纹理
30         lenaBGR=tex2D(refTex1, tidx, tidy);
31         moonBGR=tex2D(refTex2, tidx, tidy);
32 
33         int idx=(tidy*imgWidth+tidx)*channels;
34         float alpha=0.5;
35         pDstImgData[idx+0]=(alpha*lenaBGR.x+(1-alpha)*moonBGR.x)*255;
36         pDstImgData[idx+1]=(alpha*lenaBGR.y+(1-alpha)*moonBGR.y)*255;
37         pDstImgData[idx+2]=(alpha*lenaBGR.z+(1-alpha)*moonBGR.z)*255;
38         pDstImgData[idx+3]=0;
39     }
40 }
41 
42 void main()
43 {
44     Mat Lena=imread("data/lena.jpg");
45     Mat moon=imread("data/moon.jpg");
46     cvtColor(Lena, Lena, CV_BGR2BGRA);
47     cvtColor(moon, moon, CV_BGR2BGRA);
48     int imgWidth=Lena.cols;
49     int imgHeight=Lena.rows;
50     int channels=Lena.channels();
51 
52     //设置纹理属性
53     cudaError_t t;
54     refTex1.addressMode[0] = cudaAddressModeClamp;
55     refTex1.addressMode[1] = cudaAddressModeClamp;
56     refTex1.normalized = false;
57     refTex1.filterMode = cudaFilterModeLinear;
58     //绑定cuArray到纹理
59     cudaMallocArray(&cuArray1, &cuDesc, imgWidth, imgHeight);
60     t = cudaBindTextureToArray(refTex1, cuArray1);
61 
62     refTex2.addressMode[0] = cudaAddressModeClamp;
63     refTex2.addressMode[1] = cudaAddressModeClamp;
64     refTex2.normalized = false;
65     refTex2.filterMode = cudaFilterModeLinear;
66      cudaMallocArray(&cuArray2, &cuDesc, imgWidth, imgHeight);
67     t = cudaBindTextureToArray(refTex2, cuArray2);
68 
69     //拷贝数据到cudaArray
70     t=cudaMemcpyToArray(cuArray1, 0,0, Lena.data, imgWidth*imgHeight*sizeof(uchar)*channels, cudaMemcpyHostToDevice);
71     t=cudaMemcpyToArray(cuArray2, 0,0, moon.data, imgWidth*imgHeight*sizeof(uchar)*channels, cudaMemcpyHostToDevice);
72 
73     //输出图像
74     Mat dstImg=Mat::zeros(imgHeight, imgWidth, CV_8UC4);
75     uchar *pDstImgData=NULL;
76     t=cudaMalloc(&pDstImgData, imgHeight*imgWidth*sizeof(uchar)*channels);
77 
78     //核函数，实现两幅图像加权和
79     dim3 block(8,8);
80     dim3 grid( (imgWidth+block.x-1)/block.x, (imgHeight+block.y-1)/block.y );
81     weightAddKerkel<<<grid, block, 0>>>(pDstImgData, imgHeight, imgWidth, channels);
82     cudaThreadSynchronize();
83 
84     //从GPU拷贝输出数据到CPU
85     t=cudaMemcpy(dstImg.data, pDstImgData, imgWidth*imgHeight*sizeof(uchar)*channels, cudaMemcpyDeviceToHost);
86 
87     //显示
88     namedWindow("show");
89     imshow("show", dstImg);
90     waitKey(0);
91 }