图像进行傅里叶变换

参考链接：

https://blog.csdn.net/wumu720123/article/details/89930745

https://blog.csdn.net/can3981132/article/details/52559402

代码：

# -*- coding: utf-8 -*-
import cv2
import numpy as np
from matplotlib import pyplot as plt
import os

def fuliye(img, save_name):
	#快速傅里叶变换算法得到频率分布
	f = np.fft.fft2(img)
	#默认结果中心点位置是在左上角,
	#调用fftshift()函数转移到中间位置
	fshift = np.fft.fftshift(f)       
	#fft结果是复数, 其绝对值结果是振幅
	fimg = np.log(np.abs(fshift))


	# # 1.高通滤波——把中心的低频信息都去掉，保留周围的高频信息
	# area=5
	# rows, cols = img.shape
	# crow, ccol = int(rows/2), int(cols/2)
	# fshift[crow-area:crow+area, ccol-area:ccol+area] = 0


	# 2.低通滤波——保留中心的低频信息，把周围的高频信息都去掉
	area=5
	rows, cols = img.shape
	crow,ccol = int(rows/2), int(cols/2)
	mask = np.zeros((rows, cols), np.uint8)
	mask[crow-area:crow+area, ccol-area:ccol+area] = 1
	fshift = fshift * mask

	#傅里叶逆变换
	ishift = np.fft.ifftshift(fshift)
	iimg = np.fft.ifft2(ishift)
	iimg = np.abs(iimg)

	cv2.imwrite(save_name, iimg)

path="data/data2/eyebrow/"
# path = "E:/data/ng/data/eyebrow/"
save="data/fuliye/test1/eyebrow3/"
for file in os.listdir(path):
	save_name=save+file
	file_name=path+file
	img=cv2.imread(file_name,0)
	# img=cv2.resize(img, (500,500))
	
	fuliye(img, save_name)
	print(file)