Python - opencv (六) 傅里叶变换

一. 傅里叶变换

概念

https://zhuanlan.zhihu.com/p/19763358

作用

高频:变化剧烈的灰度分量,如边界

低频:变化缓慢的灰度分量,如大海

高通滤波器:只保留高频,边界增强

低通滤波器:只保留低频,图片模糊

 

二. opencv的实现

傅里叶变换:

cv2.dft(), cv2.idft(), 需要先将图片转换为np.float32格式

得到的结果频率为0的部分会在左上角,通常要转换到中心位置,通过shift实现

cv2.dft()的结果是双通道的,需要转换为图像格式

 

 1 import numpy as np
 2 
 3 import cv2
 4 import matplotlib.pyplot as plt
 5 
 6 
 7 def add_to_plot(position, image, name):
 8     plt.subplot(position)
 9     plt.imshow(image, cmap='gray')
10     plt.title(name)
11     plt.xticks([])
12     plt.yticks([])
13 
14 
15 if __name__ == '__main__':
16     img = cv2.imread('../pics/6.jpg', 0)
17     img_float32 = np.float32(img)
18     dft = cv2.dft(img_float32, flags=cv2.DFT_COMPLEX_OUTPUT)
19     dft_shift = np.fft.fftshift(dft)
20 
21     magnitude_spectrum = 20 * np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1]))
22     add_to_plot(121, img, 'Input')
23     add_to_plot(122, magnitude_spectrum, 'Magnitude')
24     plt.show()

 

低通滤波:

 1 import numpy as np
 2 
 3 import cv2
 4 import matplotlib.pyplot as plt
 5 
 6 
 7 def add_to_plot(position, image, name):
 8     plt.subplot(position)
 9     plt.imshow(image, cmap='gray')
10     plt.title(name)
11     plt.xticks([])
12     plt.yticks([])
13 
14 
15 if __name__ == '__main__':
16     img = cv2.imread('../pics/6.jpg', 0)
17     img_float32 = np.float32(img)
18     dft = cv2.dft(img_float32, flags=cv2.DFT_COMPLEX_OUTPUT)
19     dft_shift = np.fft.fftshift(dft)
20 
21     rows, cols = img.shape
22     crow, ccol = int(rows/2), int(cols/2)
23 
24     # 低通
25     mask = np.zeros((rows, cols, 2), np.uint8)
26     mask[crow-30: crow+30, ccol-30: ccol+30] = 1
27 
28     # IDFT
29     f_shift = dft_shift * mask
30     f_ishift = np.fft.fftshift(f_shift)
31     img_back = cv2.idft(f_ishift)
32     img_back = cv2.magnitude(img_back[:,:,0], img_back[:,:,1])
33 
34     add_to_plot(121, img, 'Input')
35     add_to_plot(122, img_back, 'Magnitude')
36     plt.show()

效果:

 

 

高通滤波:

 1 import numpy as np
 2 
 3 import cv2
 4 import matplotlib.pyplot as plt
 5 
 6 
 7 def add_to_plot(position, image, name):
 8     plt.subplot(position)
 9     plt.imshow(image, cmap='gray')
10     plt.title(name)
11     plt.xticks([])
12     plt.yticks([])
13 
14 
15 if __name__ == '__main__':
16     img = cv2.imread('../pics/6.jpg', 0)
17     img_float32 = np.float32(img)
18     dft = cv2.dft(img_float32, flags=cv2.DFT_COMPLEX_OUTPUT)
19     dft_shift = np.fft.fftshift(dft)
20 
21     rows, cols = img.shape
22     crow, ccol = int(rows/2), int(cols/2)
23 
24     # 高通
25     mask = np.ones((rows, cols, 2), np.uint8)
26     mask[crow-30: crow+30, ccol-30: ccol+30] = 0
27 
28     # IDFT
29     f_shift = dft_shift * mask
30     f_ishift = np.fft.fftshift(f_shift)
31     img_back = cv2.idft(f_ishift)
32     img_back = cv2.magnitude(img_back[:,:,0], img_back[:,:,1])
33 
34     add_to_plot(121, img, 'Input')
35     add_to_plot(122, img_back, 'Magnitude')
36     plt.show()

效果:

 

 

 

posted @ 2021-07-19 20:56  Asp1rant  阅读(229)  评论(0编辑  收藏  举报