86 lines
2.7 KiB
Python
86 lines
2.7 KiB
Python
from .imagefunc import *
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NODE_NAME = 'ImageOpacity'
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class ImageOpacity:
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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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return {
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"required": {
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"image": ("IMAGE", ), #
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"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), # 透明度
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"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
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},
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"optional": {
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask",)
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FUNCTION = 'image_opacity'
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CATEGORY = '😺dzNodes/LayerUtility'
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OUTPUT_NODE = True
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def image_opacity(self, image, opacity, invert_mask,
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mask=None,
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):
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ret_images = []
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ret_masks = []
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l_images = []
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l_masks = []
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for l in image:
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l_images.append(torch.unsqueeze(l, 0))
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m = tensor2pil(l)
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if m.mode == 'RGBA':
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l_masks.append(m.split()[-1])
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else:
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l_masks.append(Image.new('L', size=m.size, color='white'))
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if mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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l_masks = []
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for m in mask:
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if invert_mask:
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m = 1 - m
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l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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max_batch = max(len(l_images), len(l_masks))
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for i in range(max_batch):
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_image = l_images[i] if i < len(l_images) else l_images[-1]
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_image = tensor2pil(_image)
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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if invert_mask:
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_color = Image.new("L", _image.size, color=('white'))
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_mask = ImageChops.invert(_mask)
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else:
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_color = Image.new("L", _image.size, color=('black'))
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alpha = 1 - opacity / 100.0
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ret_mask = Image.blend(_mask, _color, alpha)
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R, G, B, = _image.convert('RGB').split()
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if invert_mask:
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ret_mask = ImageChops.invert(ret_mask)
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ret_image = Image.merge('RGBA', (R, G, B, ret_mask))
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ret_images.append(pil2tensor(ret_image))
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ret_masks.append(image2mask(ret_mask))
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).")
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return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: ImageOpacity": ImageOpacity
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: ImageOpacity": "LayerUtility: ImageOpacity"
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} |