55 lines
1.9 KiB
Python
55 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, image2mask, mask2image, RMBG, RGB2RGBA, mask_edge_detail
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class RemBgUltra:
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def __init__(self):
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self.NODE_NAME = 'RemBgUltra'
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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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"image": ("IMAGE",),
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"detail_range": ("INT", {"default": 8, "min": 1, "max": 256, "step": 1}),
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"black_point": ("FLOAT", {"default": 0.01, "min": 0.01, "max": 0.98, "step": 0.01}),
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"white_point": ("FLOAT", {"default": 0.99, "min": 0.02, "max": 0.99, "step": 0.01}),
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"process_detail": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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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 = "rembg_ultra"
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CATEGORY = '😺dzNodes/LayerMask'
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def rembg_ultra(self, image, detail_range, black_point, white_point, process_detail):
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ret_images = []
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ret_masks = []
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for i in image:
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i = torch.unsqueeze(i, 0)
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i = pil2tensor(tensor2pil(i).convert('RGB'))
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orig_image = tensor2pil(i).convert('RGB')
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_mask = RMBG(orig_image)
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if process_detail:
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_mask = tensor2pil(mask_edge_detail(i, pil2tensor(_mask), detail_range, black_point, white_point))
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ret_image = RGB2RGBA(orig_image, _mask.convert('L'))
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ret_images.append(pil2tensor(ret_image))
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ret_masks.append(image2mask(_mask))
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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), torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerMask: RemBgUltra": RemBgUltra,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: RemBgUltra": "LayerMask: RemBgUltra",
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}
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