现在在用Opencv进行图像处理，不是很熟。原因在于对图像处理的基础知识本身就不是很熟。
为了能够顺利的将毕业设计完成，现在是恶补图像处理的知识，也充分利用网上的一些资源对图像处理的一些算法进行总结
下面是从网上下载过来的图像低通滤波。进行调试时出现错误，进而一步一步分析。最终实现了用Opencv进行低通滤波的效果。
贴代码：
#include "stdafx.h"
#include "StdAfx.h"
#include "cxcore.h"
#include "cv.h"
#include "highgui.h"
double D0=30;
void ILPF(CvMat* src, const double D0)
{
int i, j;
int state = -1;
double tempD;
long width, height;
width = src->width;
height = src->height;
long x, y;
x = width / 2;
y = height / 2;
CvMat* H_mat;
H_mat = cvCreateMat(src->height,src->width, CV_64FC2);
for(i = 0; i < height; i++)
{
for(j = 0; j < width; j++)
{
if(i > y && j > x)
{
state = 3;
}
else if(i > y)
{
state = 1;
}
else if(j > x)
{
state = 2;
}
else
{
state = 0;
}
switch(state)
{
case 0:
tempD = (double) (i * i + j * j);tempD = sqrt(tempD);break;
case 1:
tempD = (double) ((height - i) * (height - i) + j * j);tempD = sqrt(tempD);break;
case 2:
tempD = (double) (i * i + (width - j) * (width - j));tempD = sqrt(tempD);break;
case 3:
tempD = (double) ((height - i) * (height - i) + (width - j) * (width - j));tempD = sqrt(tempD);break;
default:
break;
}
//二维高斯低通滤波器传递函数
/*tempD = exp(-0.5 * pow(tempD / D0, 2));
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2] = tempD;
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;*/
//衰减系数为2的二维指数低通滤波器传递函数
/* tempD = exp(-pow(tempD / D0, 2));
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2] = tempD;
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;*/
//2阶巴特沃思低通滤波器传递函数
tempD = 1 / (1 + pow(tempD / D0, 2 * 2));
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2] = tempD;
((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;
//二维理想低通滤波器传递函数
// if(tempD <= D0)
// {
// ((double*)(H_mat->data.ptr + H_mat->step * i))[j *2] = 1.0;
// //((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;
// }
// else
// {
// ((double*)(H_mat->data.ptr + H_mat->step * i))[j*2 ] = 0.0;
// //((double*)(H_mat->data.ptr + H_mat->step * i))[j * 2 + 1] = 0.0;
// }
}
}
cvMulSpectrums(src, H_mat, src, CV_DXT_ROWS);
cvReleaseMat(&H_mat);
}
int main(int argc, char ** argv)
{
const char* filename = argc >=2 ? argv[1] : "test1.bmp";
IplImage * im;
IplImage * realInput;
IplImage * imaginaryInput;
IplImage * complexInput;
int dft_M, dft_N;
CvMat* dft_A, tmp, *dft_B;
IplImage * image_Re;
IplImage * image_Im;
double m, M;
im = cvLoadImage( filename, CV_LOAD_IMAGE_GRAYSCALE );
if( !im )
return -1;
realInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 1);
imaginaryInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 1);
complexInput = cvCreateImage( cvGetSize(im), IPL_DEPTH_64F, 2);
cvScale(im, realInput, 1.0, 0.0);//做线性变换，[0,1]
cvZero(imaginaryInput);//图像清零
cvMerge(realInput, imaginaryInput, NULL, NULL, complexInput);//把四通道dst0，dst1，dst2,dst3合为dst。
dft_M = cvGetOptimalDFTSize( im->height - 1 );//对于给定的矢量尺寸返回最优DFT尺寸
dft_N = cvGetOptimalDFTSize( im->width - 1 );
dft_B = cvCreateMat( dft_M, dft_N, CV_64FC2 );//创建矩阵
dft_A = cvCreateMat( dft_M, dft_N, CV_64FC2 );
cvZero(dft_A);//原来代码中，没有这句话。
cvZero(dft_B);
image_Re = cvCreateImage( cvSize(dft_N, dft_M), IPL_DEPTH_64F, 1);
image_Im = cvCreateImage( cvSize(dft_N, dft_M), IPL_DEPTH_64F, 1);
cvGetSubRect( dft_A,&tmp, cvRect(0,0, im->width, im->height));//返回输入的图像或矩阵的矩形数组子集的矩阵头
cvCopy( complexInput, &tmp, NULL );
cvDFT( dft_A, dft_A, CV_DXT_FORWARD, complexInput->height );
ILPF(dft_A, D0);
cvDFT( dft_A, dft_A, CV_DXT_INVERSE , complexInput->height );///逆变换 二维傅立叶变换
cvNamedWindow("win");
cvNamedWindow("magnitude");
cvShowImage("win", im);
cvSplit( dft_A, image_Re, image_Im, 0, 0 );
cvMinMaxLoc(image_Re, &m, &M, NULL, NULL, NULL);//查找数组和子数组的全局最小值和最大值
cvScale(image_Re, image_Re, 1.0/(M-m), 1.0*(-m)/(M-m));
cvShowImage("magnitude", image_Re);
cvNamedWindow("image_im");
cvShowImage("image_im",image_Im);
cvWaitKey(-1);
return 0;
}
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