How To Extract R,G,B Values With Numpy Into Seperate Arrays

Suppose I have a image with some dimension (1920, 1080, 3) , I want to extract out R,G,B values into separate arrays R , G, B . I tried to do it like for i in range(image.shape[0])

Solution 1:

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.

Solution 2:

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.

Solution 3:

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.