标签：fftw
FFTW首页：http://www.fftw.org/测试demo
#include "fftw3.h"
#include <stdio.h>
#define N 8
int main()
{
int i;
fftw_complex *din,*out;
fftw_plan p;
din = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * N);
out = (fftw_complex*) fftw_malloc(sizeof(fftw_complex) * N);
if((din==NULL)||(out==NULL))
{
printf("Error:insufficient available memory\n");
}
else
{
for(i=0; i<N; i++)/*测试数据*/
{
din[i][0] = i+1;
din[i][1] = 0;
}
}
p = fftw_plan_dft_1d(N, din, out, FFTW_FORWARD,FFTW_ESTIMATE);
fftw_execute(p); /* repeat as needed */
fftw_destroy_plan(p);
fftw_cleanup();
for(i=0;i<N;i++)/*OUTPUT*/
{
printf("%f,%fi\n",din[i][0],din[i][1]);
}
printf("\n");
for(i=0;i<N;i++)/*OUTPUT*/
{
printf("%f,%fi\n",out[i][0],out[i][1]);
}
if(din!=NULL) fftw_free(din);
if(out!=NULL) fftw_free(out);
getchar();
return 0;
}结果输出如下：
IplImage *img1 = 0;
IplImage *img2 = 0;
uchar *img1_data;
uchar *img2_data;
fftw_complex *data_in;
fftw_complex *fft;
fftw_complex *ifft;
fftw_plan plan_f;
fftw_plan plan_b;
int width, height, step;
int i, j, k;
/* load original image */
img1 = cvLoadImage("lena.bmp", CV_LOAD_IMAGE_GRAYSCALE);
if (img1 == 0)
{
std::cout << "cannot load file" << std::endl;
return 0;
}
/* create new image for IFFT result */
img2 = cvCreateImage(cvSize(img1->width, img1->height), IPL_DEPTH_8U, 1);
/* get image properties */
width = img1->width;
height = img1->height;
step = img1->widthStep;
img1_data = (uchar *)img1->imageData;
img2_data = (uchar *)img2->imageData;
/* initialize arrays for fftw operations */
data_in = (fftw_complex *)fftw_malloc(sizeof(fftw_complex) * width * height);
fft = (fftw_complex *)fftw_malloc(sizeof(fftw_complex) * width * height);
ifft = (fftw_complex *)fftw_malloc(sizeof(fftw_complex) * width * height);
/* create plans */
plan_f = fftw_plan_dft_1d(width * height, data_in, fft, FFTW_FORWARD, FFTW_ESTIMATE);
plan_b = fftw_plan_dft_1d(width * height, fft, ifft, FFTW_BACKWARD, FFTW_ESTIMATE);
/* load img1's data to fftw input */
for (i = 0, k = 0; i < height; ++i)
{
for (j = 0; j < width; ++j)
{
data_in[k][0] = (double)img1_data[i * step + j];
data_in[k][1] = 0.0;
k++;
}
}
/* perform FFT */
fftw_execute(plan_f);
/* perform IFFT */
fftw_execute(plan_b);
/* normalize IFFT result */
for (i = 0; i < width * height; ++i)
{
ifft[i][0] /= (double)(width * height);
}
/* copy IFFT result to img2's data */
for (i = 0, k = 0; i < height; ++i)
{
for (j = 0; j < width; ++j)
{
img2_data[i * step + j] = (uchar)ifft[k++][0];
}
}
/* display images */
cvNamedWindow("original_image", CV_WINDOW_AUTOSIZE);
cvNamedWindow("IFFT", CV_WINDOW_AUTOSIZE);
cvShowImage("original_image", img1);
cvShowImage("IFFT", img2);
cvWaitKey(0);
/* free memory */
cvDestroyWindow("original_image");
cvDestroyWindow("IFFT");
cvReleaseImage(&img1);
cvReleaseImage(&img2);
fftw_destroy_plan(plan_f);
fftw_destroy_plan(plan_b);
fftw_free(data_in);
fftw_free(fft);
fftw_free(ifft);
标签：fftw
原文地址：http://blog.csdn.net/cyh706510441/article/details/46676123
