62 lines
1.9 KiB
Python
62 lines
1.9 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, tensor2pil, pil2tensor
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from .imagefunc import color_adapter, chop_image, RGB2RGBA
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class ColorAdapter:
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def __init__(self):
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self.NODE_NAME = 'ColorAdapter'
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE", ), #
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"color_ref_image": ("IMAGE", ), #
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"opacity": ("INT", {"default": 75, "min": 0, "max": 100, "step": 1}), # 透明度
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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 = ("image",)
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FUNCTION = 'color_adapter'
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CATEGORY = '😺dzNodes/LayerColor'
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def color_adapter(self, image, color_ref_image, opacity):
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ret_images = []
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l_images = []
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r_images = []
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for l in image:
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l_images.append(torch.unsqueeze(l, 0))
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for r in color_ref_image:
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r_images.append(torch.unsqueeze(r, 0))
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for i in range(len(l_images)):
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_image = l_images[i]
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_ref = r_images[i] if len(ret_images) > i else r_images[-1]
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__image = tensor2pil(_image)
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_canvas = __image.convert('RGB')
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ret_image = color_adapter(_canvas, tensor2pil(_ref).convert('RGB'))
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ret_image = chop_image(_canvas, ret_image, blend_mode='normal', opacity=opacity)
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if __image.mode == 'RGBA':
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ret_image = RGB2RGBA(ret_image, __image.split()[-1])
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ret_images.append(pil2tensor(ret_image))
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log(f"{self.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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"LayerColor: ColorAdapter": ColorAdapter
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerColor: ColorAdapter": "LayerColor: ColorAdapter"
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} |