81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
import torch
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from PIL import Image
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from .imagefunc import log, tensor2pil, pil2tensor, image2mask, image_rotate_extend_with_alpha, RGB2RGBA
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class LayerMaskTransform:
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def __init__(self):
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self.NODE_NAME = 'LayerMaskTransform'
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@classmethod
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def INPUT_TYPES(self):
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mirror_mode = ['None', 'horizontal', 'vertical']
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method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
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return {
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"required": {
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"mask": ("MASK",), #
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"x": ("INT", {"default": 0, "min": -99999, "max": 99999, "step": 1}),
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"y": ("INT", {"default": 0, "min": -99999, "max": 99999, "step": 1}),
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"mirror": (mirror_mode,), # 镜像翻转
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"scale": ("FLOAT", {"default": 1, "min": 0.01, "max": 100, "step": 0.01}),
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"aspect_ratio": ("FLOAT", {"default": 1, "min": 0.01, "max": 100, "step": 0.01}),
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"rotate": ("FLOAT", {"default": 0, "min": -999999, "max": 999999, "step": 0.01}),
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"transform_method": (method_mode,),
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"anti_aliasing": ("INT", {"default": 2, "min": 0, "max": 16, "step": 1}),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("MASK",)
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RETURN_NAMES = ("mask",)
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FUNCTION = 'layer_mask_transform'
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CATEGORY = '😺dzNodes/LayerUtility'
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def layer_mask_transform(self, mask, x, y, mirror, scale, aspect_ratio, rotate,
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transform_method, anti_aliasing,
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):
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l_masks = []
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ret_masks = []
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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for m in mask:
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l_masks.append(torch.unsqueeze(m, 0))
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for i in range(len(l_masks)):
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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_mask = tensor2pil(_mask).convert('L')
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_mask_canvas = Image.new('L', size=_mask.size, color='black')
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orig_width = _mask.width
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orig_height = _mask.height
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target_layer_width = int(orig_width * scale)
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target_layer_height = int(orig_height * scale * aspect_ratio)
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# mirror
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if mirror == 'horizontal':
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_mask = _mask.transpose(Image.FLIP_LEFT_RIGHT)
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elif mirror == 'vertical':
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_mask = _mask.transpose(Image.FLIP_TOP_BOTTOM)
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# scale
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_mask = _mask.resize((target_layer_width, target_layer_height))
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# rotate
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_, _mask, _ = image_rotate_extend_with_alpha(_mask.convert('RGB'), rotate, _mask, transform_method, anti_aliasing)
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paste_x = (orig_width - _mask.width) // 2 + x
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paste_y = (orig_height - _mask.height) // 2 + y
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# composit layer
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_mask_canvas.paste(_mask, (paste_x, paste_y))
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ret_masks.append(image2mask(_mask_canvas))
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log(f"{self.NODE_NAME} Processed {len(l_masks)} mask(s).", message_type='finish')
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return (torch.cat(ret_masks, dim=0),)
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NODE_CLASS_MAPPINGS = {
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"LayerUtility: LayerMaskTransform": LayerMaskTransform
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"LayerUtility: LayerMaskTransform": "LayerUtility: LayerMaskTransform"
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} |