103 lines
4.0 KiB
Python
103 lines
4.0 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, subtract_mask, chop_image_v2, chop_mode_v2
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class StrokeV2:
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def __init__(self):
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self.NODE_NAME = 'StorkeV2'
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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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"background_image": ("IMAGE", ), #
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"layer_image": ("IMAGE",), #
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"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
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"blend_mode": (chop_mode_v2,), # 混合模式
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"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), # 透明度
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"stroke_grow": ("INT", {"default": 0, "min": -999, "max": 999, "step": 1}), # 收缩值
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"stroke_width": ("INT", {"default": 8, "min": 0, "max": 999, "step": 1}), # 扩张值
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"blur": ("INT", {"default": 0, "min": 0, "max": 100, "step": 1}), # 模糊
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"stroke_color": ("STRING", {"default": "#FF0000"}), # 描边颜色
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},
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"optional": {
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"layer_mask": ("MASK",), #
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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 = 'stroke_v2'
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CATEGORY = '😺dzNodes/LayerStyle'
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def stroke_v2(self, background_image, layer_image,
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invert_mask, blend_mode, opacity,
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stroke_grow, stroke_width, blur, stroke_color,
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layer_mask=None
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):
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b_images = []
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l_images = []
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l_masks = []
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ret_images = []
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for b in background_image:
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b_images.append(torch.unsqueeze(b, 0))
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for l in layer_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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if layer_mask is not None:
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if layer_mask.dim() == 2:
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layer_mask = torch.unsqueeze(layer_mask, 0)
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l_masks = []
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for m in layer_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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if len(l_masks) == 0:
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log(f"Error: {self.NODE_NAME} skipped, because the available mask is not found.", message_type='error')
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return (background_image,)
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max_batch = max(len(b_images), len(l_images), len(l_masks))
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grow_offset = int(stroke_width / 2)
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inner_stroke = stroke_grow - grow_offset
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outer_stroke = inner_stroke + stroke_width
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for i in range(max_batch):
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background_image = b_images[i] if i < len(b_images) else b_images[-1]
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layer_image = l_images[i] if i < len(l_images) else l_images[-1]
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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# preprocess
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_canvas = tensor2pil(background_image).convert('RGB')
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_layer = tensor2pil(layer_image).convert('RGB')
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if _mask.size != _layer.size:
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_mask = Image.new('L', _layer.size, 'white')
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log(f"Warning: {self.NODE_NAME} mask mismatch, dropped!", message_type='warning')
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inner_mask = expand_mask(image2mask(_mask), inner_stroke, blur)
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outer_mask = expand_mask(image2mask(_mask), outer_stroke, blur)
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stroke_mask = subtract_mask(outer_mask, inner_mask)
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color_image = Image.new('RGB', size=_layer.size, color=stroke_color)
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blend_image = chop_image_v2(_layer, color_image, blend_mode, opacity)
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_canvas.paste(_layer, mask=_mask)
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_canvas.paste(blend_image, mask=tensor2pil(stroke_mask))
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ret_images.append(pil2tensor(_canvas))
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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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"LayerStyle: Stroke V2": StrokeV2
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerStyle: Stroke V2": "LayerStyle: Stroke V2"
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} |