132 lines
3.6 KiB
Python
132 lines
3.6 KiB
Python
import torch
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class AlphaChanelByMask:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"mask": ("MASK",),
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"method": (["default", "invert"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "alpha_chanel_by_mask"
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CATEGORY = "mask/alpha"
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def alpha_chanel_by_mask(self, images, mask, method):
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img_height, img_width = images[0, :, :, 0].shape
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mask_height, mask_width = mask.shape
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if img_height != mask_height or img_width != mask_width:
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raise ValueError(
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"[AlphaChanelByMask]: Size of images not equals size of mask. " +
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"Images: [" + str(img_width) + ", " + str(img_height) + "] - " +
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"Mask: [" + str(mask_width) + ", " + str(mask_height) + "]."
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)
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if method == "default":
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return (torch.stack([
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torch.stack((
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images[i, :, :, 0],
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images[i, :, :, 1],
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images[i, :, :, 2],
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1. - mask
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), dim=-1) for i in range(len(images))
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]),)
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else:
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return (torch.stack([
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torch.stack((
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images[i, :, :, 0],
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images[i, :, :, 1],
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images[i, :, :, 2],
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mask
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), dim=-1) for i in range(len(images))
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]),)
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class AlphaChanelAsMask:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"method": (["default", "invert"],),
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},
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "alpha_chanel_as_mask"
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CATEGORY = "mask/alpha"
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def alpha_chanel_as_mask(self, images, method):
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if images[0, 0, 0].shape[0] != 4:
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raise ValueError("Alpha chanel not exist.")
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if method == "default":
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return (1.0 - images[0, :, :, 3],)
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elif method == "invert":
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return (images[0, :, :, 3],)
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else:
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raise ValueError("Unexpected method.")
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class AlphaChanelRestore:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"method": (["default", "only_add", "only_restore"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "alpha_chanel_restore"
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CATEGORY = "mask/alpha"
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def alpha_chanel_restore(self, images, method):
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dimensions = images[0, 0, 0, :].shape[0]
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width, height = images[0, :, :, 0].shape
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alpha = torch.ones((width, height))
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def add():
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return (torch.stack([
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torch.stack((
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images[i, :, :, 0],
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images[i, :, :, 1],
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images[i, :, :, 2],
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alpha
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), dim=-1) for i in range(len(images))
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]),)
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def restore():
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images[:, :, :, 3] = alpha
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return (images,)
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if method == "default":
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return add() if dimensions != 4 else restore()
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elif method == "only_add":
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return add() if dimensions != 4 else (images,)
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else:
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return restore() if dimensions == 4 else (images,)
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NODE_CLASS_MAPPINGS = {
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"AlphaChanelByMask": AlphaChanelByMask,
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"AlphaChanelAsMask": AlphaChanelAsMask,
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"AlphaChanelRestore": AlphaChanelRestore
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}
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