LayerUtility: ImageOpacity¶
Documentation¶
- Class name:
LayerUtility: ImageOpacity
- Category:
😺dzNodes/LayerUtility
- Output node:
False
The ImageOpacity node is designed to adjust the opacity of an image, optionally applying an inversion mask to selectively modify the transparency across the image. It provides a way to dynamically control the visual appearance of images by altering their transparency levels.
Input types¶
Required¶
image
- The 'image' parameter represents the input image to which the opacity adjustment will be applied. It is essential for defining the base visual content that will undergo transparency modification.
- Comfy dtype:
IMAGE
- Python dtype:
IMAGE
opacity
- The 'opacity' parameter specifies the degree of transparency to apply to the image, with a range from 0 (completely transparent) to 100 (fully opaque). It allows for fine-tuning the visibility of the image.
- Comfy dtype:
INT
- Python dtype:
INT
invert_mask
- The 'invert_mask' parameter determines whether to invert the mask applied to the image, enabling selective opacity adjustments across different regions of the image.
- Comfy dtype:
BOOLEAN
- Python dtype:
BOOLEAN
Optional¶
mask
- The 'mask' parameter, when provided, specifies a mask to apply to the image for targeted opacity adjustments. It is optional and used for more complex transparency effects.
- Comfy dtype:
MASK
- Python dtype:
MASK
Output types¶
image
- Comfy dtype:
IMAGE
- The modified image with adjusted opacity.
- Python dtype:
IMAGE
- Comfy dtype:
mask
- Comfy dtype:
MASK
- The mask used for opacity adjustment, if any was applied.
- Python dtype:
MASK
- Comfy dtype:
Usage tips¶
- Infra type:
CPU
- Common nodes: unknown
Source code¶
class ImageOpacity:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"image": ("IMAGE", ), #
"opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}), # 透明度
"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
},
"optional": {
"mask": ("MASK",), #
}
}
RETURN_TYPES = ("IMAGE", "MASK",)
RETURN_NAMES = ("image", "mask",)
FUNCTION = 'image_opacity'
CATEGORY = '😺dzNodes/LayerUtility'
def image_opacity(self, image, opacity, invert_mask,
mask=None,
):
ret_images = []
ret_masks = []
l_images = []
l_masks = []
for l in image:
l_images.append(torch.unsqueeze(l, 0))
m = tensor2pil(l)
if m.mode == 'RGBA':
l_masks.append(m.split()[-1])
else:
l_masks.append(Image.new('L', size=m.size, color='white'))
if mask is not None:
if mask.dim() == 2:
mask = torch.unsqueeze(mask, 0)
l_masks = []
for m in mask:
if invert_mask:
m = 1 - m
l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
max_batch = max(len(l_images), len(l_masks))
for i in range(max_batch):
_image = l_images[i] if i < len(l_images) else l_images[-1]
_image = tensor2pil(_image)
_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
if invert_mask:
_color = Image.new("L", _image.size, color=('white'))
_mask = ImageChops.invert(_mask)
else:
_color = Image.new("L", _image.size, color=('black'))
alpha = 1 - opacity / 100.0
ret_mask = Image.blend(_mask, _color, alpha)
R, G, B, = _image.convert('RGB').split()
if invert_mask:
ret_mask = ImageChops.invert(ret_mask)
ret_image = Image.merge('RGBA', (R, G, B, ret_mask))
ret_images.append(pil2tensor(ret_image))
ret_masks.append(image2mask(ret_mask))
log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)