5.1 KiB
5.1 KiB
Documentation
- Class name: DropShadow
- Category: 😺dzNodes/LayerStyle
- Output node: False
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
生成阴影。
Input types
Required
-
background_image
- 背景图像。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
layer_image
- 层图像的遮罩，阴影按此生成。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
invert_mask
- 反转mask
- Comfy dtype: BOOLEAN
- Python dtype: bool
-
blend_mode
- 阴影的混合模式。
- Comfy dtype: STRING
- Python dtype: str
-
opacity
- 透明度
- Comfy dtype: INT
- Python dtype: int
-
distance_x
- 阴影的水平方向偏移量。
- Comfy dtype: INT
- Python dtype: int
-
distance_y
- 阴影的垂直方向偏移量。
- Comfy dtype: INT
- Python dtype: int
-
grow
- 阴影的扩张。
- Comfy dtype: INT
- Python dtype: int
-
blur
- 阴影的模糊。
- Comfy dtype: INT
- Python dtype: int
-
shadow_color
- 阴影的颜色。
- Comfy dtype: STRING
- Python dtype: str
Optional
- layer_mask
- 层的遮罩。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
Output types
- image
- 生成的图像。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
Usage tips
- Infra type: GPU
Source code
class DropShadow:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"background_image": ("IMAGE", ), #
"layer_image": ("IMAGE",), #
"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
"blend_mode": (chop_mode,), # 混合模式
"opacity": ("INT", {"default": 50, "min": 0, "max": 100, "step": 1}), # 透明度
"distance_x": ("INT", {"default": 25, "min": -9999, "max": 9999, "step": 1}), # x_偏移
"distance_y": ("INT", {"default": 25, "min": -9999, "max": 9999, "step": 1}), # y_偏移
"grow": ("INT", {"default": 6, "min": -9999, "max": 9999, "step": 1}), # 扩张
"blur": ("INT", {"default": 18, "min": 0, "max": 100, "step": 1}), # 模糊
"shadow_color": ("STRING", {"default": "#000000"}), # 背景颜色
},
"optional": {
"layer_mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE",)
RETURN_NAMES = ("image",)
FUNCTION = 'drop_shadow'
CATEGORY = '😺dzNodes/LayerStyle'
def drop_shadow(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: {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]
# preprocess
_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: {NODE_NAME} mask mismatch, dropped!", message_type='warning')
if distance_x != 0 or distance_y != 0:
__mask = shift_image(_mask, distance_x, distance_y) # 位移
shadow_mask = expand_mask(image2mask(__mask), grow, blur) #扩张，模糊
# 合成阴影
alpha = tensor2pil(shadow_mask).convert('L')
_shadow = chop_image(_canvas, shadow_color, blend_mode, opacity)
_canvas.paste(_shadow, mask=alpha)
# 合成layer
_canvas.paste(_layer, mask=_mask)
ret_images.append(pil2tensor(_canvas))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0),)