| import torch |
| import time |
| from PIL import Image |
| from .imagefunc import log, tensor2pil, pil2tensor, image2mask, mask2image |
| from .imagefunc import chop_image_v2, chop_mode_v2, shift_image, expand_mask |
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|
| class DropShadowV2: |
|
|
| def __init__(self): |
| self.NODE_NAME = 'DropShadowV2' |
|
|
| @classmethod |
| def INPUT_TYPES(self): |
|
|
| return { |
| "required": { |
| "background_image": ("IMAGE", ), |
| "layer_image": ("IMAGE",), |
| "invert_mask": ("BOOLEAN", {"default": True}), |
| "blend_mode": (chop_mode_v2,), |
| "opacity": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), |
| "distance_x": ("INT", {"default": 25, "min": -9999, "max": 9999, "step": 1}), |
| "distance_y": ("INT", {"default": 25, "min": -9999, "max": 9999, "step": 1}), |
| "grow": ("INT", {"default": 6, "min": -9999, "max": 9999, "step": 1}), |
| "blur": ("INT", {"default": 18, "min": 0, "max": 100, "step": 1}), |
| "shadow_color": ("STRING", {"default": "#000000"}), |
| }, |
| "optional": { |
| "layer_mask": ("MASK",), |
| } |
| } |
|
|
| RETURN_TYPES = ("IMAGE",) |
| RETURN_NAMES = ("image",) |
| FUNCTION = 'drop_shadow_v2' |
| CATEGORY = '😺dzNodes/LayerStyle' |
|
|
| def drop_shadow_v2(self, background_image, layer_image, |
| invert_mask, blend_mode, opacity, distance_x, distance_y, |
| grow, blur, shadow_color, |
| layer_mask=None |
| ): |
|
|
| b_images = [] |
| l_images = [] |
| l_masks = [] |
| ret_images = [] |
| for b in background_image: |
| b_images.append(torch.unsqueeze(b, 0)) |
| for l in layer_image: |
| l_images.append(torch.unsqueeze(l, 0)) |
| m = tensor2pil(l) |
| if m.mode == 'RGBA': |
| l_masks.append(m.split()[-1]) |
| if layer_mask is not None: |
| if layer_mask.dim() == 2: |
| layer_mask = torch.unsqueeze(layer_mask, 0) |
| l_masks = [] |
| for m in layer_mask: |
| if invert_mask: |
| m = 1 - m |
| l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L')) |
| if len(l_masks) == 0: |
| log(f"Error: {self.NODE_NAME} skipped, because the available mask is not found.", message_type='error') |
| return (background_image,) |
|
|
| max_batch = max(len(b_images), len(l_images), len(l_masks)) |
| distance_x = -distance_x |
| distance_y = -distance_y |
| shadow_color = Image.new("RGB", tensor2pil(l_images[0]).size, color=shadow_color) |
|
|
| for i in range(max_batch): |
| background_image = b_images[i] if i < len(b_images) else b_images[-1] |
| layer_image = l_images[i] if i < len(l_images) else l_images[-1] |
| _mask = l_masks[i] if i < len(l_masks) else l_masks[-1] |
|
|
| |
| _canvas = tensor2pil(background_image).convert('RGB') |
| _layer = tensor2pil(layer_image) |
|
|
| if _mask.size != _layer.size: |
| _mask = Image.new('L', _layer.size, 'white') |
| log(f"Warning: {self.NODE_NAME} mask mismatch, dropped!", message_type='warning') |
|
|
| if distance_x != 0 or distance_y != 0: |
| __mask = shift_image(_mask, distance_x, distance_y) |
| shadow_mask = expand_mask(image2mask(__mask), grow, blur) |
| |
| alpha = tensor2pil(shadow_mask).convert('L') |
| _shadow = chop_image_v2(_canvas, shadow_color, blend_mode, opacity) |
| _canvas.paste(_shadow, mask=alpha) |
| |
| _canvas.paste(_layer, mask=_mask) |
|
|
| ret_images.append(pil2tensor(_canvas)) |
|
|
| log(f"{self.NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish') |
| return (torch.cat(ret_images, dim=0),) |
|
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|
|
| NODE_CLASS_MAPPINGS = { |
| "LayerStyle: DropShadow V2": DropShadowV2 |
| } |
|
|
| NODE_DISPLAY_NAME_MAPPINGS = { |
| "LayerStyle: DropShadow V2": "LayerStyle: DropShadow V2" |
| } |