LayerStyle: DropShadow¶
Documentation¶
- Class name:
LayerStyle: DropShadow - Category:
😺dzNodes/LayerStyle - Output node:
False
The DropShadow node applies a drop shadow effect to an image layer, enhancing its visual depth by simulating a shadow cast behind it. This node processes input images to add shadows based on specified parameters such as distance, direction, color, and blur, effectively creating a more dynamic and visually appealing composition.
Input types¶
Required¶
background_image- The background image over which the drop shadow effect will be applied. It serves as the canvas for the shadow and layer composition.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
layer_image- The image layer to which the drop shadow effect will be applied. This layer will appear to cast a shadow on the background image.
- Comfy dtype:
IMAGE - Python dtype:
torch.Tensor
invert_mask- A boolean flag to indicate whether the mask should be inverted. This affects how the shadow is applied relative to the mask, allowing for more control over the shadow's appearance.
- Comfy dtype:
BOOLEAN - Python dtype:
bool
blend_mode- Specifies the blending mode to be used when applying the shadow, affecting how the shadow merges with the layer.
- Comfy dtype:
COMBO[STRING] - Python dtype:
str
opacity- The opacity level of the drop shadow, determining how transparent the shadow appears over the background.
- Comfy dtype:
INT - Python dtype:
int
distance_x- The horizontal distance to offset the shadow from the layer, simulating the direction of light.
- Comfy dtype:
INT - Python dtype:
int
distance_y- The vertical distance to offset the shadow from the layer, further contributing to the illusion of depth.
- Comfy dtype:
INT - Python dtype:
int
grow- The amount by which the shadow expands beyond the original boundaries of the layer, making the shadow appear larger or smaller.
- Comfy dtype:
INT - Python dtype:
int
blur- The blur radius for the shadow, determining how sharp or soft the shadow edges are.
- Comfy dtype:
INT - Python dtype:
int
shadow_color- The color of the drop shadow, allowing customization to match the aesthetic of the composition.
- Comfy dtype:
STRING - Python dtype:
str
Optional¶
layer_mask- An optional mask that defines the area of the layer image where the drop shadow effect should be applied. It helps in fine-tuning the shadow effect by restricting it to specific parts of the layer image.
- Comfy dtype:
MASK - Python dtype:
torch.Tensor
Output types¶
image- Comfy dtype:
IMAGE - The resulting image after applying the drop shadow effect, combining the background, layer, and the generated shadow into a single image.
- Python dtype:
torch.Tensor
- Comfy dtype:
Usage tips¶
- Infra type:
GPU - Common nodes: unknown
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),)