python 实时图像显示间隔_python图像处理——频率域增强

图像的傅里叶变换：

import chardet

import numpy as np

import cv2 as cv

import cv2

from PIL import Image

import sys

from matplotlib import pyplot as plt

img = cv2.imread('D:/1/4.jpg',0)

f = np.fft.fft2(img)

fshift = np.fft.fftshift(f)

magnitude_spectrum = 20*np.log(np.abs(fshift))

rows, cols = img.shape

crow,ccol = int(rows/2) , int(cols/2)

fshift[crow-30:crow+30, ccol-30:ccol+30] = 0

f_ishift = np.fft.ifftshift(fshift)

img_back = np.fft.ifft2(f_ishift)

img_back = np.abs(img_back)

plt.subplot(221),plt.imshow(img, cmap = 'gray')

plt.title('Input Image'), plt.xticks([]), plt.yticks([])

plt.subplot(222),plt.imshow(magnitude_spectrum, cmap = 'gray')

plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])

plt.subplot(223),plt.imshow(img_back) #恢复图像

plt.title('Result in JET'), plt.xticks([]), plt.yticks([])

plt.subplot(224),plt.imshow(img_back, cmap = 'gray')

plt.title('Image after HPF'), plt.xticks([]), plt.yticks([])

plt.show()

图像的噪声处理与去噪：

def add_noise(img):

rows,cols,dims=img.shape

for i in range(5000):

x=np.random.randint(0,rows)

y=np.random.randint(0,cols)

img[x,y,:]=1

#二值化处理，以128位界

def add1_noise(img):

rows,cols=img.shape

for i in range(rows):

for j in range(cols):

if (img[i,j]<=128):

img[i,j]=0

else:

img[i,j]=1

# 高斯噪声

def GaussieNoisy(image,sigma):

row,col,ch= image.shape

mean = 0

gauss = np.random.normal(mean,sigma,(row,col,ch))

gauss = gauss.reshape(row,col,ch)

noisy = image + gauss

return noisy.astype(np.uint8)

#椒盐噪声

def spNoisy(image,s_vs_p = 0.5,amount = 0.004):

row,col,ch = image.shape

out = np.copy(image)

num_salt = np.ceil(amount * image.size * s_vs_p)

coords = [np.random.randint(0, i - 1, int(num_salt)) for i in image.shape]

out[coords] = 1

num_pepper = np.ceil(amount* image.size * (1. - s_vs_p))

coords =