CUDA 图像进行均值滤波 CPU 和 GPU 对比

CUDA 图像进行均值滤波 CPU 和 GPU 对比

CUDA 图像进行均值滤波 CPU 和 GPU 对比

算法思想

均值滤波

算法核心

代码

结果对比

---

CUDA 图像进行均值滤波 CPU 和 GPU 对比

参考网上博文(感谢)以及结合自己的想法思路进行图像均值滤波，Markdown此博文是为了记录自己的学习历程。

小白上路，莫喷！！！仅是自己的学习历程，进步ing！！！

• 算法思想

• 均值滤波

均值滤波的理论在此就不赘述，可参考如下博文，里面有具体的理论讲解。

• 算法核心

个人思路：1、计算图像的行，按行进行前缀求和取均值。

                  2、根据步骤1的结果，按列进行前缀求和取均值。

                  3、步骤2就是所需的结果。

注：前缀求和个人对它的解释比如一维数组，窗口大小为3

------ [0, 1, 2, 3, 4, 5, 6, 7, 8]， 那么前缀求和就是sum0 = 0 + 1 + 2; sum1 = sum 0 - 0 + 3; sum 2 = sum1 - 1 + 4.........

• 代码

// cpuSmoothImg.cpp
#include <iostream>
#include <stdlib.h>
#include "cpuSmoothImg.h"
#include <memory.h>

void cpuSmoothImage(unsigned char* srcData, unsigned char* dstData, int width, int height, int winSize){
	// copy srcData to dstData
//	memcpy(dstData, srcData, width * height * sizeof(unsigned char));

	int nAnchor = 0;
	nAnchor = winSize / 2;

	for(int i = 0; i < height; i++){
		float rowPixel = 0;
		for(int j = 0; j < width - 2*nAnchor; j++){
			if(j == 0){
				for(int k = 0; k < winSize; k++){
					rowPixel += srcData[i * width + k];
				}
				dstData[i * width + j + nAnchor] = rowPixel / winSize;
			}
			else{
				rowPixel = rowPixel - srcData[i * width + j - 1] + srcData[i * width + j - 1 + winSize];
				dstData[i * width + j + nAnchor] = rowPixel / winSize;

			}
		}
	}

	unsigned char* cpyData = NULL;
	cpyData = (unsigned char*)malloc(height * width * sizeof(unsigned char));
	memcpy(cpyData, dstData, width * height * sizeof(unsigned char));

	for(int x = nAnchor; x < width - nAnchor; x++){
		float colPixel = 0;
		for(int y = 0; y < height - 2*nAnchor; y++){
			if(y == 0){
				for(int z = 0; z < winSize; z++){
					colPixel += cpyData[z * width + x];
				}
				dstData[(nAnchor + y) * width + x] = colPixel / winSize;

			}
			else{
				colPixel = colPixel - cpyData[(y - 1) * width + x] + cpyData[(y- 1 + winSize) * width + x];
				dstData[(nAnchor + y) * width + x] = colPixel / winSize;
			}
		}
	}

	free(cpyData);
}

解释：1、连续的图片数据，用一维数组表示；

           2、第一个嵌套for循环就是对行进行前缀求和，但得对第一个窗口的数据进行单独计算。

           3、第二个嵌套for循环就是对列进行前缀求和，但前提得开辟一块空间用于步骤1结果的保存。

其实有这种思想，再去进行GPU的也就不难，一样的思路！

// gpuSmoothImg.cu
#include "gpuSmoothImg.h"
#include <iostream>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <cuda_runtime_api.h>
#include <device_launch_parameters.h>

__global__ void rowAddKernel(int width, int height, int winSize, unsigned char* srcData, unsigned char* dstData){
	const int nAnchor = winSize / 2;
	float rowPixel = 0;
	int rowsId = threadIdx.y + blockIdx.y * blockDim.y;

	if(rowsId <= height){
		for(int i = 0; i < width - 2*nAnchor; i++){
			if(i == 0){
				for(int j = 0; j < winSize; j++){
					rowPixel += srcData[rowsId * width + j];
				}
				dstData[rowsId * width + i + nAnchor] = rowPixel / winSize;
			}
			else{
				rowPixel = rowPixel - srcData[rowsId * width + i - 1] + srcData[rowsId * width + i - 1 + winSize];
				dstData[rowsId * width + i + nAnchor] = rowPixel / winSize;
			}
		}
	}
	else{
		return;
	}
}

__global__ void colAddKernel(int width, int height, int winSize, unsigned char* srcData, unsigned char* dstData){
	const int nAnchor = winSize / 2;
	float colPixel = 0;
	int colsId = threadIdx.x + blockIdx.x * blockDim.x;

	if(colsId >= nAnchor && colsId < width -nAnchor){
		for(int m = 0; m < height - 2*nAnchor; m++){
			if(m == 0){
				for(int n = 0; n < winSize; n++){
					colPixel += srcData[n * width + colsId];
				}
				dstData[(nAnchor + m) * width + colsId] = colPixel / winSize;

			}
			else{
				colPixel = colPixel - srcData[(m - 1) * width + colsId] + srcData[(m - 1 + winSize) * width + colsId];
				dstData[(nAnchor + m) * width + colsId] = colPixel / winSize;
			}
		}
	}
	else{
		return;
	}
}

void gpuSmoothImage(int width, int height, int winSize, unsigned char* srcData, unsigned char* dstData, unsigned char* d_srcData, unsigned char* d_dstData){
	// allocate GPU memory
	size_t size = width * height * sizeof(unsigned char);
	unsigned char* d_cpyData = NULL;
	cudaMalloc((void**)&d_cpyData, size);

	cudaMemcpy(d_srcData, srcData, size, cudaMemcpyHostToDevice);
	cudaMemcpy(d_dstData, dstData, size, cudaMemcpyHostToDevice);


	cudaEvent_t d_start, d_end;
	cudaEventCreate(&d_start);
	cudaEventCreate(&d_end);
	cudaEventRecord(d_start, 0);

	// Divide into blocks and threads for rowAddKernel
	dim3 rowBlock(1, 128);
	dim3 rowGrid(1, (height + rowBlock.y - 1) / rowBlock.y);
	rowAddKernel<<<rowGrid, rowBlock>>>(width, height, winSize, d_srcData, d_dstData);
	cudaMemcpy(d_cpyData, d_dstData, size, cudaMemcpyDeviceToDevice);

	// Divide into blocks and threads for colsAddKernel
	dim3 colBlock(128, 1);
	dim3 colGrid((width + colBlock.x - 1) / colBlock.x, 1);
	colAddKernel<<<colGrid, colBlock>>>(width, height, winSize, d_cpyData, d_dstData);
	cudaMemcpy(dstData, d_dstData, size, cudaMemcpyDeviceToHost);

	cudaEventRecord(d_end, 0);
	cudaEventSynchronize(d_end);

	float gpuTime = 0.0;
	cudaEventElapsedTime(&gpuTime, d_start, d_end);
	printf(">>> Calculation time is: %f ms\n", gpuTime);

	cudaFree(d_cpyData);
}

简言之就是对一维数组进行按行列进行遍历！ 

• 结果对比

很明显可以看出，CPU计算一张图片需要25ms; GPU 只需要8ms就完成！

具体代码：meanFilter.zip_CUDA均值滤波-C++代码类资源-优快云下载

csdn必须要积分，我不知道怎么设置0积分，需要代码的可以评论区留下邮箱！

链接: https://pan.baidu.com/s/11fDgx8YB82tZH23tZszV1g?pwd=n5i6 提取码: n5i6 复制这段内容后打开百度网盘手机App，操作更方便哦