使用 numpy 将图像分成通道

Question

我想分离我的图像通道。然后，我想对每一个应用 Otsu Thresholding，最后将它们合并在一起。但是，在我的代码的第 4 行，它给了我以下错误：

File "C:/Users/Berke/PycharmProjects/goruntu/main.py", line 28, in <module>
    image_channels = np.split(np.asarray(gradient_image), 3, axis=2)
File "C:\Users\Berke\PycharmProjects\goruntu\venv\lib\site-packages\numpy\lib\shape_base.py", line 846, in split
    N = ary.shape[axis]
IndexError: tuple index out of range

这是我的代码：

morph = mypic.copy()
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
myImage = cv2.morphologyEx(morph, cv2.MORPH_GRADIENT, kernel)
myImageChannels = np.split(np.asarray(gradient_image), 3, axis=2)
for channels in range(3):
  _, myImageChannels[channels] = cv2.threshold(myImageChannels[channels],
                                               0,
                                               255,
                                               cv2.THRESH_OTSU | cv2.THRESH_BINARY)

Answer 1

为什么不是更简单的方法？

import numpy as np
import cv2

original_image = cv2.imread(path) #expect [X,Y,3] shape
#or
original_image = np.asarray(gradient_image)

otsu_image = np.zeros(shape=original_image.shape)
for channel in range(3):
    _,otsu_image[:,:,channel]= cv2.threshold(original_image[:,:channel],0,255,cv2.THRESH_OTSU | cv2.THRESH_BINARY)

通过这个[:,:,channel] 索引选择，您基本上可以访问特定通道的图像层，而无需对图像进行任何特殊处理。您当然可以将阈值图像分配给该层，因为 1 个通道层与灰度图像具有相同的维度