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我正在尝试实现一种算法来验证 RGB 图像的 4 个邻域（上、下、左和右）像素，如果所有像素 RGB 值都相等，我将输出图像中的一个像素标记为 1，否则它将是0. 非向量化的实现是：

def set_border_interior(img):
  img_rows = img.shape[0]
  img_cols = img.shape[1]
  res = np.zeros((img_rows,img_cols))
  for row in xrange(1,img_rows-1):
      for col in xrange(1,img_cols-1):
          data_b = set()
          data_g = set()
          data_r = set()
          up = row - 1
          down = row + 1
          left = col - 1
          right = col + 1

          data_b.add(img.item(row,col,0))
          data_g.add(img.item(row,col,1))
          data_r.add(img.item(row,col,2))

          data_b.add(img.item(up,col,0))
          data_g.add(img.item(up,col,1))
          data_r.add(img.item(up,col,2))

          data_b.add(img.item(down,col,0))
          data_g.add(img.item(down,col,1))
          data_r.add(img.item(down,col,2))

          data_b.add(img.item(row,left,0))
          data_g.add(img.item(row,left,1))
          data_r.add(img.item(row,left,2))

          data_b.add(img.item(row,right,0))
          data_g.add(img.item(row,right,1))
          data_r.add(img.item(row,right,2))

          if (len(data_b) == 1) and (len(data_g) == 1) and (len(data_r) == 1):
              res.itemset(row,col, False)
          else:
              res.itemset(row,col, True)
  return res

这种非向量化的方式，但确实很慢（甚至使用 img.item 读取数据并使用 img.itemset 设置新值）。有没有更好的方法在 Numpy（或 scipy）中实现这一点？

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1 回答 1

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Leaving the border aside, where your function is not well defined anyway, you could do the following:

import numpy as np
import matplotlib.pyplot as plt

rows, cols = 480, 640
rgb_img = np.zeros((rows, cols, 3), dtype=np.uint8)

rgb_img[:rows//2, :cols//2] = 255

center_slice = rgb_img[1:-1, 1:-1]
left_slice = rgb_img[1:-1, :-2]
right_slice = rgb_img[1:-1, 2:]
up_slice = rgb_img[:-2, 1:-1]
down_slice = rgb_img[2:, 1:-1]

all_equal = (np.all(center_slice == left_slice, axis=-1) &
             np.all(center_slice == right_slice, axis=-1) &
             np.all(center_slice == up_slice, axis=-1) &
             np.all(center_slice == down_slice, axis=-1))

plt.subplot(211)
plt.imshow(rgb_img, interpolation='nearest')
plt.subplot(212)
plt.imshow(all_equal, interpolation='nearest')
plt.show()

enter image description here

于 2013-09-20T17:52:00.910 回答