How to extract R,G,B values with numpy into seperate arrays
If you want it to use in OpenCV
way then you may use cv2.split()
, keeping in mind channels of your image:
b, g, r = cv2.split(image) # For BGR image
b, g, r, a = cv2.split(image) # for BGRA image
Or if you may like direct numpy format then you may use directly [which seems to be more efficient as per comments of @igaurav]
b, g, r = image[:, :, 0], image[:, :, 1], image[:, :, 2] # For RGB image
b, g, r, a = image[:, :, 0], image[:, :, 1], image[:, :, 2], image[:, :, 3] # for BGRA image
You may use np.shape[2]
to check the number of channels in the given image.
dsplit
it.
import numpy as np
def channelSplit(image):
return np.dsplit(image,image.shape[-1])
[B,G,R]=channelSplit(image)
This works for RGB or RGBA images.
This works for me:
def split_channels(im: np.ndarray):
assert len(im.shape) == 3 and im.shape[-1] == 3
return np.squeeze(np.split(im, im.shape[-1], -1), axis=-1)
Note that, the np.split
itself is not enough, which will leave you a (M, N, 1)
image. But if you want to have (M, N)
, then the squeeze
works.
You can remove the assert
if you have other cases.