| import torch
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| from PIL import Image
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| from .imagefunc import log, tensor2pil, pil2tensor, image2mask, mask2image
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| from .imagefunc import chop_image, chop_mode, shift_image, expand_mask
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|
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| class DropShadow:
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|
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| def __init__(self):
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| self.NODE_NAME = 'DropShadow'
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|
|
| @classmethod
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| def INPUT_TYPES(self):
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|
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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}),
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| "blend_mode": (chop_mode,),
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| "opacity": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}),
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| "distance_x": ("INT", {"default": 25, "min": -9999, "max": 9999, "step": 1}),
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| "distance_y": ("INT", {"default": 25, "min": -9999, "max": 9999, "step": 1}),
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| "grow": ("INT", {"default": 6, "min": -9999, "max": 9999, "step": 1}),
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| "blur": ("INT", {"default": 18, "min": 0, "max": 100, "step": 1}),
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| "shadow_color": ("STRING", {"default": "#000000"}),
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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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|
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| RETURN_TYPES = ("IMAGE",)
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| RETURN_NAMES = ("image",)
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| FUNCTION = 'drop_shadow'
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| CATEGORY = '😺dzNodes/LayerStyle'
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|
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| def drop_shadow(self, background_image, layer_image,
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| invert_mask, blend_mode, opacity, distance_x, distance_y,
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| grow, blur, shadow_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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|
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| max_batch = max(len(b_images), len(l_images), len(l_masks))
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| distance_x = -distance_x
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| distance_y = -distance_y
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| shadow_color = Image.new("RGB", tensor2pil(l_images[0]).size, color=shadow_color)
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|
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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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| _canvas = tensor2pil(background_image).convert('RGB')
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| _layer = tensor2pil(layer_image)
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|
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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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|
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| if distance_x != 0 or distance_y != 0:
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| __mask = shift_image(_mask, distance_x, distance_y)
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| shadow_mask = expand_mask(image2mask(__mask), grow, blur)
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|
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| alpha = tensor2pil(shadow_mask).convert('L')
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| _shadow = chop_image(_canvas, shadow_color, blend_mode, opacity)
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| _canvas.paste(_shadow, mask=alpha)
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|
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| _canvas.paste(_layer, mask=_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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|
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|
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| NODE_CLASS_MAPPINGS = {
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| "LayerStyle: DropShadow": DropShadow
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| }
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|
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| NODE_DISPLAY_NAME_MAPPINGS = {
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| "LayerStyle: DropShadow": "LayerStyle: DropShadow"
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| } |