58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
from .imagefunc import *
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NODE_NAME = 'ImageCombineAlpha'
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class ImageCombineAlpha:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(self):
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channel_mode = ['RGBA', 'YCbCr', 'LAB', 'HSV']
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return {
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"required": {
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"RGB_image": ("IMAGE", ), #
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"mask": ("MASK",), #
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("IMAGE",)
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RETURN_NAMES = ("RGBA_image",)
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FUNCTION = 'image_combine_alpha'
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CATEGORY = '😺dzNodes/LayerUtility'
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def image_combine_alpha(self, RGB_image, mask):
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ret_images = []
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input_images = []
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input_masks = []
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for i in RGB_image:
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input_images.append(torch.unsqueeze(i, 0))
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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for m in mask:
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input_masks.append(torch.unsqueeze(m, 0))
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max_batch = max(len(input_images), len(input_masks))
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for i in range(max_batch):
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_image = input_images[i] if i < len(input_images) else input_images[-1]
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_mask = input_masks[i] if i < len(input_masks) else input_masks[-1]
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r, g, b, _ = image_channel_split(tensor2pil(_image).convert('RGB'), 'RGB')
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ret_image = image_channel_merge((r, g, b, tensor2pil(_mask).convert('L')), 'RGBA')
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ret_images.append(pil2tensor(ret_image))
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: ImageCombineAlpha": ImageCombineAlpha
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: ImageCombineAlpha": "LayerUtility: ImageCombineAlpha"
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} |