CHANNEL_SPLIT

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Return the RGBA channels of an image as 4 separate images for direct visualization. While the notion of "splitting an image into RGBA channels" is inherently tied to coloured pictures, this function will attempt to make sense of multiple input types. Should the input be of type 'Image', then the function will return the RGBA channels. Should the input be of type 'Matrix', meaning ideally a 3D 'numpy' array of size (L, M, 3) or (L, M, 4), then the function will return each channel respectively. Params: default : Image | Matrix The image to split. Returns: r : Image The red channel. g : Image The green channel. b : Image The blue channel. a : Image The alpha channel.

Python Code

from flojoy import flojoy, Image, Matrix
from typing import TypedDict
import numpy as np


class ChannelSplitOutput(TypedDict):
    r: Image
    g: Image
    b: Image
    a: Image


@flojoy
def CHANNEL_SPLIT(default: Image | Matrix) -> ChannelSplitOutput:
    """Return the RGBA channels of an image as 4 separate images for direct visualization.

    While the notion of "splitting an image into RGBA channels" is inherently tied to coloured pictures, this function will attempt to make sense of multiple input types.
    Should the input be of type 'Image', then the function will return the RGBA channels.
    Should the input be of type 'Matrix', meaning ideally a 3D 'numpy' array of size (L, M, 3) or (L, M, 4), then the function will return each channel respectively.

    Parameters
    ----------
    default : Image | Matrix
        The image to split.

    Returns
    -------
    r : Image
        The red channel.
    g : Image
        The green channel.
    b : Image
        The blue channel.
    a : Image
        The alpha channel.
    """

    try:
        if isinstance(default, Image):
            r = default.r
            g = default.g
            b = default.b
            a = default.a

        elif isinstance(default, Matrix):
            r = default.m[..., 0]
            g = default.m[..., 1]
            b = default.m[..., 2]
            a = np.zeros_like(r) if default.m.shape[-1] == 3 else default.m[..., 3]

            if default.m.shape[-1] != 3 or default.m.shape[-1] != 4:
                raise IndexError(
                    "Input array is not of sensible size to split channels"
                )
        else:
            raise TypeError("Unexpected type of the input argument.")

        zeros = np.zeros(r.shape, np.uint8)
        ones = 255 * np.ones(r.shape, np.uint8)

        return ChannelSplitOutput(
            r=Image(
                r=r,
                g=zeros,
                b=zeros,
                a=ones,
            ),
            g=Image(
                r=zeros,
                g=g,
                b=zeros,
                a=ones,
            ),
            b=Image(
                r=zeros,
                g=zeros,
                b=b,
                a=ones,
            ),
            a=Image(
                r=zeros,
                g=zeros,
                b=zeros,
                a=a,
            ),
        )
    except Exception as e:
        raise e

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This example shows the function of the CHANNEL_SPLIT node. This node takes an image and splits it into RGBA (Red Green Blue Alpha) layers (or channels).