bandpass butterworth filter implementation in C++

Question

I am implementing an image analysis algorithm using openCV and c++, but I found out openCV doesnt have any function for Butterworth Bandpass filter officially. in my project I

Answer 1

There are code which could be found online implementing butterworth filter. If you use the source code to try to get result matching MATLAB results, there will be the same problem.Basically the result you got from the code hasn't been normalized, and in the source code there is a variable sff in bwhp.c. If you set that to 1, the problem will be easily solved. I recommend you to use this source code and the source code and usage could be found here

Answer 2

I finally found it. I just need to implement the following code from matlab source code to c++ . "the_mandrill" were right, I need to add the normalizing constant into the coefficient:

kern = exp(-j*w*(0:length(b)-1));
b = real(b*(kern*den(:))/(kern*b(:)));

EDIT: and here is the final edition, which the whole code will return numbers exactly equal to MATLAB :

double *ComputeNumCoeffs(int FilterOrder,double Lcutoff, double Ucutoff, double *DenC)
{
    double *TCoeffs;
    double *NumCoeffs;
    std::complex<double> *NormalizedKernel;
    double Numbers[11]={0,1,2,3,4,5,6,7,8,9,10};
    int i;

    NumCoeffs = (double *)calloc( 2*FilterOrder+1, sizeof(double) );
    if( NumCoeffs == NULL ) return( NULL );

    NormalizedKernel = (std::complex<double> *)calloc( 2*FilterOrder+1, sizeof(std::complex<double>) );
    if( NormalizedKernel == NULL ) return( NULL );

    TCoeffs = ComputeHP(FilterOrder);
    if( TCoeffs == NULL ) return( NULL );

    for( i = 0; i < FilterOrder; ++i)
    {
        NumCoeffs[2*i] = TCoeffs[i];
        NumCoeffs[2*i+1] = 0.0;
    }
    NumCoeffs[2*FilterOrder] = TCoeffs[FilterOrder];
    double cp[2];
    double Bw, Wn;
    cp[0] = 2*2.0*tan(PI * Lcutoff/ 2.0);
    cp[1] = 2*2.0*tan(PI * Ucutoff / 2.0);

    Bw = cp[1] - cp[0];
    //center frequency
    Wn = sqrt(cp[0]*cp[1]);
    Wn = 2*atan2(Wn,4);
    double kern;
    const std::complex<double> result = std::complex<double>(-1,0);

    for(int k = 0; k<11; k++)
    {
        NormalizedKernel[k] = std::exp(-sqrt(result)*Wn*Numbers[k]);
    }
    double b=0;
    double den=0;
    for(int d = 0; d<11; d++)
    {
        b+=real(NormalizedKernel[d]*NumCoeffs[d]);
        den+=real(NormalizedKernel[d]*DenC[d]);
    }
    for(int c = 0; c<11; c++)
    {
        NumCoeffs[c]=(NumCoeffs[c]*den)/b;
    }

    free(TCoeffs);
    return NumCoeffs;
}

Answer 3

I know this is a post on an old thread, and I would usually leave this as a comment, but I'm apparently not able to do that.

In any case, for people searching for similar code, I thought I would post the link from where this code originates (it also has C code for other types of Butterworth filter coefficients and some other cool signal processing code).

The code is located here: http://www.exstrom.com/journal/sigproc/

Additionally, I think there is a piece of code which calculates said scaling factor for you already.

/**********************************************************************
sf_bwbp - calculates the scaling factor for a butterworth bandpass filter.
The scaling factor is what the c coefficients must be multiplied by so
that the filter response has a maximum value of 1.
*/

double sf_bwbp( int n, double f1f, double f2f )
{
    int k;            // loop variables
    double ctt;       // cotangent of theta
    double sfr, sfi;  // real and imaginary parts of the scaling factor
    double parg;      // pole angle
    double sparg;     // sine of pole angle
    double cparg;     // cosine of pole angle
    double a, b, c;   // workspace variables

    ctt = 1.0 / tan(M_PI * (f2f - f1f) / 2.0);
    sfr = 1.0;
    sfi = 0.0;

    for( k = 0; k < n; ++k )
    {
        parg = M_PI * (double)(2*k+1)/(double)(2*n);
        sparg = ctt + sin(parg);
        cparg = cos(parg);
        a = (sfr + sfi)*(sparg - cparg);
        b = sfr * sparg;
        c = -sfi * cparg;
        sfr = b - c;
        sfi = a - b - c;
    }

    return( 1.0 / sfr );
}

Answer 4

I added the final edition of function ComputeNumCoeffs to the program and fix "FilterOrder" (k<11 to k<2*FiltOrd+1). Maybe it will save someone's time. f1=0.5Gz, f2=10Gz, fs=127Gz/2

In MatLab

a={1.000000000000000,-3.329746259105707, 4.180522138699884,-2.365540522960743,0.514875789136976};
b={0.041065495448784, 0.000000000000000,-0.082130990897568, 0.000000000000000,0.041065495448784};

Program:

#include <iostream>
#include <stdio.h>
#include <vector>
#include <math.h>

#include <complex>

using namespace std;

#define N 10 //The number of images which construct a time series for each pixel
#define PI 3.1415926535897932384626433832795

double *ComputeLP(int FilterOrder)
{
    double *NumCoeffs;
    int m;
    int i;

    NumCoeffs = (double *)calloc(FilterOrder+1, sizeof(double));
    if(NumCoeffs == NULL) return(NULL);

    NumCoeffs[0] = 1;
    NumCoeffs[1] = FilterOrder;
    m = FilterOrder/2;
    for(i=2; i <= m; ++i)
    {
     NumCoeffs[i] =(double) (FilterOrder-i+1)*NumCoeffs[i-1]/i;
     NumCoeffs[FilterOrder-i]= NumCoeffs[i];
    }
    NumCoeffs[FilterOrder-1] = FilterOrder;
    NumCoeffs[FilterOrder] = 1;

    return NumCoeffs;
}

double *ComputeHP(int FilterOrder)
{
    double *NumCoeffs;
    int i;

    NumCoeffs = ComputeLP(FilterOrder);
    if(NumCoeffs == NULL) return(NULL);

    for(i = 0; i <= FilterOrder; ++i)
     if(i % 2) NumCoeffs[i] = -NumCoeffs[i];

    return NumCoeffs;
}

double *TrinomialMultiply(int FilterOrder, double *b, double *c)
{
    int i, j;
    double *RetVal;

    RetVal = (double *)calloc(4 * FilterOrder, sizeof(double));
    if(RetVal == NULL) return(NULL);

    RetVal[2] = c[0];
    RetVal[3] = c[1];
    RetVal[0] = b[0];
    RetVal[1] = b[1];

    for(i = 1; i < FilterOrder; ++i)
    {
     RetVal[2*(2*i+1)] += c[2*i] * RetVal[2*(2*i-1)] - c[2*i+1] * RetVal[2*(2*i-1)+1];
     RetVal[2*(2*i+1)+1] += c[2*i] * RetVal[2*(2*i-1)+1] + c[2*i+1] * RetVal[2*(2*i-1)];

     for(j = 2*i; j > 1; --j)
     {
      RetVal[2*j] += b[2*i] * RetVal[2*(j-1)] - b[2*i+1] * RetVal[2*(j-1)+1] +
       c[2*i] * RetVal[2*(j-2)] - c[2*i+1] * RetVal[2*(j-2)+1];
      RetVal[2*j+1] += b[2*i] * RetVal[2*(j-1)+1] + b[2*i+1] * RetVal[2*(j-1)] +
       c[2*i] * RetVal[2*(j-2)+1] + c[2*i+1] * RetVal[2*(j-2)];
     }

     RetVal[2] += b[2*i] * RetVal[0] - b[2*i+1] * RetVal[1] + c[2*i];
     RetVal[3] += b[2*i] * RetVal[1] + b[2*i+1] * RetVal[0] + c[2*i+1];
     RetVal[0] += b[2*i];
     RetVal[1] += b[2*i+1];
    }
    return RetVal;
}

double *ComputeNumCoeffs(int FilterOrder,double Lcutoff, double Ucutoff, double *DenC)
{
    double *TCoeffs;
    double *NumCoeffs;
    std::complex<double> *NormalizedKernel;
    double Numbers[11]={0,1,2,3,4,5,6,7,8,9,10};
    int i;

    NumCoeffs = (double *)calloc(2*FilterOrder+1, sizeof(double));
    if(NumCoeffs == NULL) return(NULL);

    NormalizedKernel = (std::complex<double> *)calloc(2*FilterOrder+1, sizeof(std::complex<double>));
    if(NormalizedKernel == NULL) return(NULL);

    TCoeffs = ComputeHP(FilterOrder);
    if(TCoeffs == NULL) return(NULL);

    for(i = 0; i < FilterOrder; ++i)
    {
     NumCoeffs[2*i] = TCoeffs[i];
     NumCoeffs[2*i+1] = 0.0;
    }
    NumCoeffs[2*FilterOrder] = TCoeffs[FilterOrder];
    double cp[2];
    //double Bw;
    double Wn;
    cp[0] = 2*2.0*tan(PI * Lcutoff/ 2.0);
    cp[1] = 2*2.0*tan(PI * Ucutoff/2.0);

    //Bw = cp[1] - cp[0];
    //center frequency
    Wn = sqrt(cp[0]*cp[1]);
    Wn = 2*atan2(Wn,4);
    //double kern;
    const std::complex<double> result = std::complex<double>(-1,0);

    for(int k = 0; k<2*FilterOrder+1; k++)
    {
     NormalizedKernel[k] = std::exp(-sqrt(result)*Wn*Numbers[k]);
    }
    double b=0;
    double den=0;
    for(int d = 0; d<2*FilterOrder+1; d++)
    {
     b+=real(NormalizedKernel[d]*NumCoeffs[d]);
     den+=real(NormalizedKernel[d]*DenC[d]);
    }
    for(int c = 0; c<2*FilterOrder+1; c++)
    {
     NumCoeffs[c]=(NumCoeffs[c]*den)/b;
    }

    free(TCoeffs);
    return NumCoeffs;
}

double *ComputeDenCoeffs(int FilterOrder, double Lcutoff, double Ucutoff)
{
    int k;   // loop variables
    double theta;  // PI * (Ucutoff - Lcutoff)/2.0
    double cp;  // cosine of phi
    double st;  // sine of theta
    double ct;  // cosine of theta
    double s2t;  // sine of 2*theta
    double c2t;  // cosine 0f 2*theta
    double *RCoeffs;  // z^-2 coefficients
    double *TCoeffs;  // z^-1 coefficients
    double *DenomCoeffs;  // dk coefficients
    double PoleAngle;  // pole angle
    double SinPoleAngle;  // sine of pole angle
    double CosPoleAngle;  // cosine of pole angle
    double a;   // workspace variables

    cp = cos(PI * (Ucutoff + Lcutoff)/2.0);
    theta = PI * (Ucutoff - Lcutoff)/2.0;
    st = sin(theta);
    ct = cos(theta);
    s2t = 2.0*st*ct;  // sine of 2*theta
    c2t = 2.0*ct*ct - 1.0; // cosine of 2*theta

    RCoeffs = (double *)calloc(2 * FilterOrder, sizeof(double));
    TCoeffs = (double *)calloc(2 * FilterOrder, sizeof(double));

    for(k = 0; k < FilterOrder; ++k)
    {
     PoleAngle = PI * (double)(2*k+1)/(double)(2*FilterOrder);
     SinPoleAngle = sin(PoleAngle);
     CosPoleAngle = cos(PoleAngle);
     a = 1.0 + s2t*SinPoleAngle;
     RCoeffs[2*k] = c2t/a;
     RCoeffs[2*k+1] = s2t*CosPoleAngle/a;
     TCoeffs[2*k] = -2.0*cp*(ct+st*SinPoleAngle)/a;
     TCoeffs[2*k+1] = -2.0*cp*st*CosPoleAngle/a;
    }

    DenomCoeffs = TrinomialMultiply(FilterOrder, TCoeffs, RCoeffs);
    free(TCoeffs);
    free(RCoeffs);

    DenomCoeffs[1] = DenomCoeffs[0];
    DenomCoeffs[0] = 1.0;
    for(k = 3; k <= 2*FilterOrder; ++k)
     DenomCoeffs[k] = DenomCoeffs[2*k-2];


    return DenomCoeffs;
}

void filter(int ord, double *a, double *b, int np, double *x, double *y)
{
    int i,j;
    y[0]=b[0] * x[0];
    for (i=1;i<ord+1;i++)
    {
     y[i]=0.0;
     for (j=0;j<i+1;j++)
      y[i]=y[i]+b[j]*x[i-j];
     for (j=0;j<i;j++)
      y[i]=y[i]-a[j+1]*y[i-j-1];
    }
    for (i=ord+1;i<np+1;i++)
    {
     y[i]=0.0;
     for (j=0;j<ord+1;j++)
      y[i]=y[i]+b[j]*x[i-j];
     for (j=0;j<ord;j++)
      y[i]=y[i]-a[j+1]*y[i-j-1];
    }
}

int main(int argc, char *argv[])
{
    (void)argc;
    (void)argv;
    //Frequency bands is a vector of values - Lower Frequency Band and Higher Frequency Band

    //First value is lower cutoff and second value is higher cutoff
    //f1 = 0.5Gz f2=10Gz
    //fs=127Gz
    //Kotelnikov/2=Nyquist (127/2)
    double FrequencyBands[2] = {0.5/(127.0/2.0),10.0/(127.0/2.0)};//these values are as a ratio of f/fs, where fs is sampling rate, and f is cutoff frequency
    //and therefore should lie in the range [0 1]
    //Filter Order
    int FiltOrd = 2;//5;

    //Pixel Time Series
    /*int PixelTimeSeries[N];
    int outputSeries[N];
    */
    //Create the variables for the numerator and denominator coefficients
    double *DenC = 0;
    double *NumC = 0;
    //Pass Numerator Coefficients and Denominator Coefficients arrays into function, will return the same

    printf("\n");

    //is A in matlab function and the numbers are correct
    DenC = ComputeDenCoeffs(FiltOrd, FrequencyBands[0], FrequencyBands[1]);
    for(int k = 0; k<2*FiltOrd+1; k++)
    {
     printf("DenC is: %lf\n", DenC[k]);
    }

    printf("\n");

    NumC = ComputeNumCoeffs(FiltOrd,FrequencyBands[0],FrequencyBands[1],DenC);
    for(int k = 0; k<2*FiltOrd+1; k++)
    {
     printf("NumC is: %lf\n", NumC[k]);
    }


    double y[5];
    double x[5]={1,2,3,4,5};
    filter(5, DenC, NumC, 5, x, y);
    return 1;
}