基于opencv的一维Fourier变换

此程序主要实现了fft、ifft计算。原本是用于cpu雷达信号脉冲压缩的C++实现。程序调试后无错误，可以运行，程序需要读取磁盘中的.dat文件实现数据导入。

调试环境：VS2015+opencv

//2016.11.2 @Xidian
//DSP雷达信号处理2
//调用opencv中dft()函数实现fft和ifft
#include
#include
#include
using namespace std;
using namespace cv;
const int N = 7680;
const int M = 8192;
void main()
{


    double count1 = getTickCount();
    //-------------------对回波信号echo进行8192点FFT-----------------   
    FILE* fp;
    fp=fopen ("echo.dat", "r");
    float temp[7680 * 2] = {0};
    vector<float> echo;  //
    for (int i = 0; i < 7680*2; i++)
    {
        fscanf(fp, "%f", &temp[i]);
        echo.push_back(temp[i]);
    }
    fclose(fp);
    //echo信号的实部、虚部
    Mat_<float> echo_real = Mat::zeros(1, 8192, CV_32F);
    Mat_<float> echo_img = Mat::zeros(1, 8192, CV_32F);
    vector<float>hh[2];
    for (int i = 0; i < 7680 * 2; i += 2)
    {
        hh[0].push_back(echo[i]);
        hh[1].push_back(echo[i + 1]);
    }
    for (int i = 0; i < 7680; i++)
    {
        echo_real.at<float>(i) = hh[0][i];
        echo_img.at<float>(i) = hh[1][i];
    }
    //将echo的实、虚部组合为复数complex
    vectorfloat>> data;
    data.push_back(echo_real);
    data.push_back(echo_img);
    Mat complex;
    merge(data, complex);
    //计算FFT
    dft(complex, complex);
    //将echo信号的8192点fft写入.dat文件
    vectorfloat>> echo_fft;
    split(complex, echo_fft);
    FILE* fp1;
    FILE* fp2;
    fp1 = fopen("echo_fft_real.dat", "w");
    fp2 = fopen("echo_fft_img.dat", "w");
    for (int i = 0; i < 8192; i++)
    {
        //将echo进行FFT后的实部写入文本
        fprintf(fp1, "%f\n", echo_fft[0].at<float>(i));
        //将echo进行FFT后的虚部写入文本
        fprintf(fp2, "%f\n", echo_fft[1].at<float>(i));
    }

    //-----------------对脉压系数coeff进行8192点FFT-----------------

    //Mat_ coeff_real = Mat::zeros(1, 8192,CV_32F);  //coeff的实部
    //Mat_ coeff_img = Mat::zeros(1, 8192, CV_32F);  //coeff的虚部
    //Mat_ coeff_complex;                              //coeff的复数
    //Mat_ coeff_fft = Mat::zeros(1, 8192, CV_32F);  //coeff的8192点FFT
    //
    //FILE* fp3;
    //fp3 = fopen("coeff.dat", "r");
    //float temp3[7680 * 2];
    //for (int i = 0; i <7680*2; i++)
    //{ //读取coeff.dat文本数据
    //  fscanf(fp3, "%f", &temp3[i]);
    //}
    //vector GG[2];
    //for (int i = 0; i < 7680*2; i+=2)
    //{
    //  GG[0].push_back(temp3[i]);
    //  GG[1].push_back(temp3[i + 1]);
    //}
    //for (int i = 0; i < 7680; i++)
    //{
    //  coeff_real.at(i) = GG[0][i];
    //  coeff_img.at(i) = GG[1][i];
    //}
    //vector>coeff;
    //coeff.push_back(coeff_real);
    //coeff.push_back(coeff_img);
    组合复数
    //merge(coeff, coeff_complex);
    FFT计算
    //dft(coeff_complex, coeff_complex);

    //

    //----------------------读取coeff_fft并保存为复数---------------------------
    Mat_<float> coeff_fftReal2 = Mat::zeros(1, 8192,CV_32F);  //coeff_fft的实部
    Mat_<float> coeff_fftImg2 = Mat::zeros(1, 8192, CV_32F);  //coeff_fft的虚部
    Mat coeff_fftComplex2;                            //coeff_fft的复数

    FILE* fp4;
    fp4 = fopen("coeff_fft.dat", "r");
    float temp4[7680 * 2] = {0};
    for (int i = 0; i < 7680*2; i++)
    {
        fscanf(fp4, "%f", &temp4[i]);
    }
    vector<float> JJ[2];
    for (int i = 0; i < 7680*2; i+=2)
    {
        JJ[0].push_back(temp4[i]);
        JJ[1].push_back(temp4[i+1]);
    }
    for (int i = 0; i < 7680; i++)
    {
        coeff_fftReal2.at<float>(i) = JJ[0][i];
        coeff_fftImg2.at<float>(i) = JJ[1][i];
    }
    vectorfloat>>coeff_fft;
    coeff_fft.push_back(coeff_fftReal2);
    coeff_fft.push_back(coeff_fftImg2);
    //组合为复数
    merge(coeff_fft, coeff_fftComplex2);

    //------------------频域相乘-----------------------------
    Mat_<float> pc_freq0Img;
    Mat_<float> pc_freq0Real;
    Mat pc_freq0Complex;
    vectorfloat>> pc_freq0;

    for (int i = 0; i < 8192; i++)
    {
        Vec2f a = complex.at(i);
        Vec2f b = coeff_fftComplex2.at(i);
        float real = a[0] * b[0] - a[1] * b[1];   
        float img = a[0] * b[1] + a[1] * b[0];
        img = -1.0*img;
    /*  cout << "real=" << real << endl;
        cout << "imag=" << img << endl;*/
        pc_freq0Real.push_back(real);
        pc_freq0Img.push_back(img);   //共轭

    }

    //组合为复数
    pc_freq0.push_back(pc_freq0Real);
    pc_freq0.push_back(pc_freq0Img);
    merge(pc_freq0, pc_freq0Complex);

    /*cout << pc_freq0Complex << endl;*/
    //---------------------IFFT计算-----------------------
    //直接计算ifft
    dft(pc_freq0Complex, pc_freq0Complex, DFT_SCALE);  //???与matlab计算结果相差太大
    /*cout << pc_freq0Complex << endl;*/


    //用fft计算ifft
    //step1 计算fft
    //dft(pc_freq0Complex, pc_freq0Complex);
    step2 共轭、乘以1/N
    //for (int i = 0; i < 8192; i++)
    //{
    //  Vec2f bb = pc_freq0Complex.at(i);
    //  float real2 = bb[0];
    //  float imag2 = bb[1];
    //  imag2 = imag2*(-1.0);
    //  pc_freq0Complex.at(i) = (1.0/8192.0)*Vec2f(real2, imag2);
    //}
    //
    //cout << pc_freq0Complex << endl;

    //------------------计算结果写入.dat文件(包含暂态点)------------------------
    FILE* fp5;
    FILE* fp6;
    fp5 = fopen("pc_freq1_real.dat","w");
    fp6 = fopen("pc_freq1_imag.dat","w");
    for (int i = 0; i < 8192; i++)
    {
        Vec2f c = pc_freq0Complex.at(i);
        /*cout << "pc_freq0.real=" << c[0]<fprintf(fp5, "%f\n",c[0]);
        fprintf(fp6, "%f\n", c[1]);
    }
    fclose(fp5);
    fclose(fp6);
    double count2 = getTickCount();
    double freq = getTickFrequency();
    double time = (count2 - count1) / freq;
    cout << "运行时间=" << time << endl;
    cout << "Successful!" << endl;
    system("PAUSE");

}