1. 理想低通滤波

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#include<iostream>
#include<opencv2/opencv.hpp>
#include"mydft.h"
#include"Salt.h"

using namespace std;
using namespace cv;

int main(){
    Mat img,img_gray,img_out,img_transform;
    img = imread("/home/v/home.png");
    if (img.empty()){
        cout<<"can't open image"<<endl;
        return -1;
    }

    imshow("img",img);
    cvtColor(img,img_gray,COLOR_BGR2GRAY);

    Salt(img_gray,10000);
    imshow("img_gray",img_gray);

    //1. 傅里变换，img_out为可显示的频谱图，img_transform为傅里叶变换的复数结果
    mydft(img_gray,img_out,img_transform);
    imshow("img_out",img_out);

    //2. 理想低通滤波
    Mat plances[] = {Mat_<float>(img_out),Mat::zeros(img_out.size(),CV_32F)};
    split(img_transform,plances); // 分离通道，获取实部和虚部
    Mat img_tansform_real = plances[0];
    Mat img_tansform_imag = plances[1];

    int core_x = img_tansform_real.rows/2;
    int core_y = img_tansform_real.cols/2;
    int r =  80; // 滤波半径

    for(int i=0;i<img_tansform_real.rows;i++){
        for(int j=0;j<img_tansform_real.cols;j++){
            //距离中心的距离大于设置半径r的点所在值设为0
            if((i-core_x)*(i-core_x)+(j-core_y)*(j-core_y)>r*r){
                img_tansform_real.at<float>(i,j) = 0;
                img_tansform_imag.at<float>(i,j) = 0;
            }
        }
    }

    plances[0] = img_tansform_real;
    plances[1] = img_tansform_imag;
    Mat img_transform_ilpf; // 定义理想低通滤波矩阵
    merge(plances,2,img_transform_ilpf);

    //3. 傅里叶变换
    Mat iDft[] = {Mat_<float>(img_out),Mat::zeros(img_out.size(),CV_32F)};

    idft(img_transform_ilpf,img_transform_ilpf); //傅里叶变换
    split(img_transform_ilpf,iDft); // 傅里叶变换
    magnitude(iDft[0],iDft[1],img_out); // 计算复数的幅值
    normalize(iDft[0],iDft[0],0,1,NORM_MINMAX); // 归一化处理
    imshow("idft",iDft[0]); // 显示逆变换图像
    
    waitKey(0);
    return 0;
}