CUDA加速添加高斯噪声

[悠扬侠]

小弟最近在学习CUDA，尝试加速添加高斯噪声这一过程，但是始终不知道错误原因，一运行就全黑了，所以想请教各位大佬，万分感谢。



// ConsoleApplication1.cpp : 定义控制台应用程序的入口点。
//

#include "stdafx.h"
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include "Windows.h"
#include <math.h>
#include <iostream>
#include <cxcore.h>
#include <opencv.hpp>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
double getRandGaussVal_device(double noisefactor);

#define BLOCKDIM_X 16
#define BLOCKDIM_Y 16

#define GRIDDIM_X 256
#define GRIDDIM_Y 256

int g_iGaussList[1000];//RGB化的高斯值
__global__ void kernel_Gaussianweiguang(/*int width,int height,*/uchar* sensorBuffer,double* data_to_device);
void GenerateGaussNosieList(const int iNum,const int val);

int _tmain(int argc, _TCHAR* argv[])
{
int iTemp_host=getRandGaussVal_device(1);
double *data_to_device,data_device[1]={iTemp_host};
uchar* sensorBuffer=new uchar[1024*768*3];

cudaMalloc((void**)&data_to_device,sizeof(double));
cudaMemcpy(data_to_device,data_device,sizeof(double),cudaMemcpyHostToDevice);///由主机复制到设备


//分配显存空间
int size1=1024*768*3*sizeof(unsigned char);
unsigned char *d_src_imgbuf;
cudaMalloc((void**)&d_src_imgbuf, size1);


//把数据从Host传到Device
cudaMemcpy(d_src_imgbuf, sensorBuffer, size1, cudaMemcpyHostToDevice);



Mat src=imread("Chrysanthemum.jpg");
Mat dst;

uchar* Temp=sensorBuffer;
uchar* Temp1=sensorBuffer;
uchar* tem=sensorBuffer;
int i=0;
for(int r=0;r<src.rows;r++)
{
uchar* data=src.ptr<uchar>(r);
for(int c=0;c<src.cols*3;c++)
{
sensorBuffer[r*src.cols*3+c]=data[c];
}
}

int bx = ceil((double)dst.rows/BLOCKDIM_X); //网格和块的分配
int by = ceil((double)dst.cols/BLOCKDIM_Y);
if(bx > GRIDDIM_X) bx = GRIDDIM_X;
if(by > GRIDDIM_Y) by = GRIDDIM_Y;
dim3 grid(bx,by);//网格的结构
dim3 block(BLOCKDIM_X,BLOCKDIM_Y);//块的结构

GenerateGaussNosieList(1000,20);

int iTemp=0;int i1=0;
/*for(int r=0;r<src.rows;r++)
for(int c=0;c<src.cols*3;c++)
{
Temp[r*src.cols*3+c]=Temp[r*src.cols*3+c]+getRandGaussVal_device(1);
}*/
kernel_Gaussianweiguang<<<grid,block>>>(d_src_imgbuf,data_to_device);
cudaMemcpy(tem, d_src_imgbuf,size1, cudaMemcpyDeviceToHost);//数据传回主机端

Mat src1(src.size(),src.type());
for(int c=0;c<src.rows;c++)
for(int r=0;r<src.cols*3;r++)
{
src1.at<uchar>(c,r)=Temp1[c*src.cols*3+r];
}
imshow("a",src1);waitKey(0);
cudaFree(d_src_imgbuf);
cudaFree(data_to_device);


}
void GenerateGaussNosieList(const int iNum,const int val)
{
for(int i = 0; i < iNum; i++)
{
static bool hasSpare = false;
static double rand1, rand2;
double result;
if(hasSpare)
{
hasSpare = false;
result = sqrt(rand1) * sin(rand2);
}
hasSpare = true;
rand1 = rand() / ((double) RAND_MAX);
if(rand1 < 1e-100) rand1 = 1e-100;
rand1 = -2 * log(rand1);
rand2 = (rand() / ((double) RAND_MAX)) * 2*CV_PI;
result = sqrt(rand1) * cos(rand2);
g_iGaussList[i] = (int)((float)result*(float)val);
cout<<g_iGaussList[i]<<endl;
}
int itest = 0;
}
double getRandGaussVal_device(double noisefactor)
{
double i = 0;
int iRand = 1000*rand()/RAND_MAX;
i = g_iGaussList[iRand]*noisefactor;
return i;
}
__global__ void kernel_Gaussianweiguang(/*int width,int height,*/uchar* d_src_imgbuf,double* data_to_device)
{


uchar* temp=d_src_imgbuf;

int ncols=1024*3;

const int tix=blockDim.x*blockIdx.x+threadIdx.x;
const int tiy=blockDim.y*blockIdx.y+threadIdx.y;
const int threadTotalX=blockDim.x*gridDim.x;
const int threadTotalY=blockDim.y*gridDim.y;
for (int i=tix;i<768;i+=threadTotalX)
{

for (int j=tiy;j<ncols;j+=threadTotalY)
{

temp[i*ncols+j]=temp[i*ncols+j]+*data_to_device;

}
}
}

[赵4老师]

代码功能归根结底不是别人帮自己看或讲解或注释出来的；而是被自己静下心来花足够长的时间和精力亲自动手单步或设断点或对执行到某步获得的中间结果显示或写到日志文件中一步一步分析出来的。 提醒：再牛×的老师也无法代替学生自己领悟和上厕所！ 单步调试和设断点调试（VS IDE中编译连接通过以后，按F10或F11键单步执行，按Shift+F11退出当前函数；在某行按F9设断点后按F5执行停在该断点处。）是程序员必须掌握的技能之一。