81 lines
3.3 KiB
Python
81 lines
3.3 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, tensor2pil, pil2tensor, image2mask, expand_mask, mask_fix
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from .imagefunc import guided_filter_alpha, histogram_remap, mask_edge_detail ,RGB2RGBA
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class MaskEdgeUltraDetail:
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def __init__(self):
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self.NODE_NAME = 'MaskEdgeUltraDetail'
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@classmethod
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def INPUT_TYPES(cls):
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method_list = ['PyMatting', 'OpenCV-GuidedFilter']
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return {
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"required": {
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"image": ("IMAGE",),
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"mask": ("MASK",),
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"method": (method_list,),
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"mask_grow": ("INT", {"default": 0, "min": -999, "max": 999, "step": 1}),
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"fix_gap": ("INT", {"default": 0, "min": 0, "max": 32, "step": 1}),
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"fix_threshold": ("FLOAT", {"default": 0.75, "min": 0.01, "max": 0.99, "step": 0.01}),
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"detail_range": ("INT", {"default": 12, "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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},
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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 = "mask_edge_ultra_detail"
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CATEGORY = '😺dzNodes/LayerMask'
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def mask_edge_ultra_detail(self, image, mask, method, mask_grow, fix_gap, fix_threshold,
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detail_range, black_point, white_point,):
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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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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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for l in image:
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l_images.append(torch.unsqueeze(l, 0))
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for m in mask:
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l_masks.append(torch.unsqueeze(m, 0))
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if len(l_images) != len(l_masks) or tensor2pil(l_images[0]).size != tensor2pil(l_masks[0]).size:
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log(f"Error: {self.NODE_NAME} skipped, because mask does'nt match image.", message_type='error')
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return (image, mask,)
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for i in range(len(l_images)):
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_image = l_images[i]
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orig_image = tensor2pil(_image).convert('RGB')
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_image = pil2tensor(orig_image)
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_mask = l_masks[i]
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if mask_grow != 0:
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_mask = expand_mask(_mask, mask_grow, mask_grow//2)
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if fix_gap:
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_mask = mask_fix(_mask, 1, fix_gap, fix_threshold, fix_threshold)
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if method == 'OpenCV-GuidedFilter':
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_mask = guided_filter_alpha(_image, _mask, detail_range)
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_mask = tensor2pil(histogram_remap(_mask, black_point, white_point))
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else:
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_mask = tensor2pil(mask_edge_detail(_image, _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: MaskEdgeUltraDetail": MaskEdgeUltraDetail,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerMask: MaskEdgeUltraDetail": "LayerMask: MaskEdgeUltraDetail",
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}
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