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  1. # code1
  2. def filter(img, func, ksize, strides=1):
  3.     height,width = img.shape
  4.     f_height,f_width = ksize
  5.     new_height = height - f_height + 1
  6.     new_width = width - f_width + 1
  7.  
  8.     new_img = np.zeros((new_height,new_width))
  9.  
  10.     for i in range(new_height):
  11.         for j in range(new_width):
  12.             patch = img[i:i+f_height,j:j+f_width]
  13.             new_img[i][j] = func(patch)
  14.  
  15.     return new_img
  16.      
  17. # code2
  18. def relative_median_and_center_diff(patch, in_the_boundary, rectangle, center_point):
  19.         mask = patch == 255
  20.         mask[center_point] = True
  21.         masked_patch = np.ma.array(patch, mask=mask)
  22.         count = masked_patch.count()
  23.         if count <= 1:
  24.             return 0
  25.         else:
  26.             return patch[center_point]/(np.ma.median(masked_patch)+1)
  27.      
  28. # code3
  29. def filter(img, func, ksize, strides=1):
  30.     height,width = img.shape
  31.     f_height,f_width = ksize
  32.     new_height = height - f_height + 1
  33.     new_width = width - f_width + 1
  34.  
  35.     new_img = np.zeros((new_height,new_width))
  36.  
  37.     from skimage.util.shape import view_as_windows
  38.     patches = view_as_windows(img, (f_height,f_width))
  39.  
  40.     for i in range(new_height):
  41.         for j in range(new_width):
  42.             patch = patches[i,j]
  43.             new_img[i][j] = func(patch)
  44.  
  45.     return new_img
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