python opencv How to realize high pass filtering and low pass filtering of images

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Complete code

Low pass filtering

import cv2import numpy as npimport matplotlib.pyplot as plt# cv2.imread() When reading an image , The default is read as RGB Images ,cv2.IMREAD_GRAYSCALE Will be read in the form of a grayscale image img = cv2.imread('./moon.jpg', flags = cv2.IMREAD_GRAYSCALE)  #  Divide the image by 255 It is to change the image to digital standard fioat32 data img1 = img/255 #  Fourier transform , Time domain ——> frequency domain dtf = cv2.dft(img1, flags = cv2.DFT_COMPLEX_OUTPUT)  #  Move the low frequency wave to the center dft_shift = np.fft.fftshift(dtf)  #  Low pass filtering h,w = img.shape#  The center point of the image is the location of the low-frequency wave h3, w2 = h//2, w//2  mask = np.zeros((h,w,2), dtype=np.uint8)#  Select length and width as 100 The low frequency part of the region is 1, The rest is 0mask[h3-50:h3+50,w2-50:w2+50] = 1  #  The low frequency part is reserved , The rest of the *0 Filtered out dft_shift*=mask  #  Inverse Fourier transform , frequency domain ——> Time domain ifft_shift2 = np.fft.ifftshift(dft_shift)  result = cv2.idft(ifft_shift2)#  Create a display window , Show the original plt.figure(figsize=(12,9))plt.subplot(121)plt.imshow(img, cmap = 'gray')#  Create a display window , Display the image after low-pass filtering plt.subplot(122)plt.imshow(result[:,:,0], cmap='gray')plt.show()

High pass filtering

The main difference between high pass filtering and low-pass filtering is , Low pass filtering is to retain the center of the low-frequency wave and remove the high-frequency wave , High pass filtering is to remove the low-frequency wave in the center and retain the high-frequency wave .

import cv2import numpy as npimport matplotlib.pyplot as plt# cv2.imread() When reading an image , The default is read as RGB Images ,cv2.IMREAD_GRAYSCALE Will be read in the form of a grayscale image img = cv2.imread('./moon.jpg', flags = cv2.IMREAD_GRAYSCALE)  #  Divide the image by 255 It is to change the image to digital standard fioat32 data img1 = img/255 #  Fourier transform , Time domain ——> frequency domain dtf = cv2.dft(img1, flags = cv2.DFT_COMPLEX_OUTPUT)  #  Move the low frequency wave to the center dft_shift = np.fft.fftshift(dtf)#  High pass filtering h,w = img.shape#  The center point of the image is the location of the low-frequency wave h3, w2 = h//2, w//2  #  Center point #  Select length and width as 100 The low frequency part of the region is 0, The other high-frequency parts are 1dft_shift[h3-5:h3+5,w2-5:w2+5] = 0#  Inverse Fourier transform , frequency domain ——> Time domain ifft_shift2 = np.fft.ifftshift(dft_shift)  result = cv2.idft(ifft_shift2)#  Create a display window , Show the original plt.figure(figsize=(12,9))plt.subplot(121)plt.imshow(img, cmap = 'gray')#  Create a display window , Display the image after low-pass filtering plt.subplot(122)plt.imshow(result[:,:,0], cmap='gray')plt.show()

Result display

Changing the size of the filtering area can change the filtering degree , You can modify the relevant parts of the code shown in the figure ：

Low pass filtering

High pass filtering

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