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/*
* Copyright (C) 2010 The Android Open Source Project
*
* Licensed under the Apache License, Version 2.0 (the "License"); you may not
* use this file except in compliance with the License. You may obtain a copy of
* the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations under
* the License.
*/
#include "Fft.h"
#include <stdlib.h>
#include <math.h>
Fft::Fft(void) : mCosine(0), mSine(0), mFftSize(0), mFftTableSize(0) { }
Fft::~Fft(void) {
fftCleanup();
}
/* Construct a FFT table suitable to perform a DFT of size 2^power. */
void Fft::fftInit(int power) {
fftCleanup();
fftMakeTable(power);
}
void Fft::fftCleanup() {
delete [] mSine;
delete [] mCosine;
mSine = NULL;
mCosine = NULL;
mFftTableSize = 0;
mFftSize = 0;
}
/* z <- (10 * log10(x^2 + y^2)) for n elements */
int Fft::fftLogMag(float *x, float *y, float *z, int n) {
float *xp, *yp, *zp, t1, t2, ssq;
if(x && y && z && n) {
for(xp=x+n, yp=y+n, zp=z+n; zp > z;) {
t1 = *--xp;
t2 = *--yp;
ssq = (t1*t1)+(t2*t2);
*--zp = (ssq > 0.0)? 10.0 * log10((double)ssq) : -200.0;
}
return 1; //true
} else {
return 0; // false/fail
}
}
int Fft::fftMakeTable(int pow2) {
int lmx, lm;
float *c, *s;
double scl, arg;
mFftSize = 1 << pow2;
mFftTableSize = lmx = mFftSize/2;
mSine = new float[lmx];
mCosine = new float[lmx];
scl = (M_PI*2.0)/mFftSize;
for (s=mSine, c=mCosine, lm=0; lm<lmx; lm++ ) {
arg = scl * lm;
*s++ = sin(arg);
*c++ = cos(arg);
}
mBase = (mFftTableSize * 2)/mFftSize;
mPower2 = pow2;
return(mFftTableSize);
}
/* Compute the discrete Fourier transform of the 2**l complex sequence
* in x (real) and y (imaginary). The DFT is computed in place and the
* Fourier coefficients are returned in x and y.
*/
void Fft::fft( float *x, float *y ) {
float c, s, t1, t2;
int j1, j2, li, lix, i;
int lmx, lo, lixnp, lm, j, nv2, k=mBase, im, jm, l = mPower2;
for (lmx=mFftSize, lo=0; lo < l; lo++, k *= 2) {
lix = lmx;
lmx /= 2;
lixnp = mFftSize - lix;
for (i=0, lm=0; lm<lmx; lm++, i += k ) {
c = mCosine[i];
s = mSine[i];
for ( li = lixnp+lm, j1 = lm, j2 = lm+lmx; j1<=li;
j1+=lix, j2+=lix ) {
t1 = x[j1] - x[j2];
t2 = y[j1] - y[j2];
x[j1] += x[j2];
y[j1] += y[j2];
x[j2] = (c * t1) + (s * t2);
y[j2] = (c * t2) - (s * t1);
}
}
}
/* Now perform the bit reversal. */
j = 1;
nv2 = mFftSize/2;
for ( i=1; i < mFftSize; i++ ) {
if ( j < i ) {
jm = j-1;
im = i-1;
t1 = x[jm];
t2 = y[jm];
x[jm] = x[im];
y[jm] = y[im];
x[im] = t1;
y[im] = t2;
}
k = nv2;
while ( j > k ) {
j -= k;
k /= 2;
}
j += k;
}
}
/* Compute the discrete inverse Fourier transform of the 2**l complex
* sequence in x (real) and y (imaginary). The DFT is computed in
* place and the Fourier coefficients are returned in x and y. Note
* that this DOES NOT scale the result by the inverse FFT size.
*/
void Fft::ifft(float *x, float *y ) {
float c, s, t1, t2;
int j1, j2, li, lix, i;
int lmx, lo, lixnp, lm, j, nv2, k=mBase, im, jm, l = mPower2;
for (lmx=mFftSize, lo=0; lo < l; lo++, k *= 2) {
lix = lmx;
lmx /= 2;
lixnp = mFftSize - lix;
for (i=0, lm=0; lm<lmx; lm++, i += k ) {
c = mCosine[i];
s = - mSine[i];
for ( li = lixnp+lm, j1 = lm, j2 = lm+lmx; j1<=li;
j1+=lix, j2+=lix ) {
t1 = x[j1] - x[j2];
t2 = y[j1] - y[j2];
x[j1] += x[j2];
y[j1] += y[j2];
x[j2] = (c * t1) + (s * t2);
y[j2] = (c * t2) - (s * t1);
}
}
}
/* Now perform the bit reversal. */
j = 1;
nv2 = mFftSize/2;
for ( i=1; i < mFftSize; i++ ) {
if ( j < i ) {
jm = j-1;
im = i-1;
t1 = x[jm];
t2 = y[jm];
x[jm] = x[im];
y[jm] = y[im];
x[im] = t1;
y[im] = t2;
}
k = nv2;
while ( j > k ) {
j -= k;
k /= 2;
}
j += k;
}
}
int Fft::fftGetSize(void) { return mFftSize; }
int Fft::fftGetPower2(void) { return mPower2; }