141 lines
3.6 KiB
Markdown
141 lines
3.6 KiB
Markdown
# Documentation
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- Class name: ExtendCanvas
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- Category: 😺dzNodes/LayerUtility
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- Output node: False
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- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
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扩展画布。
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# Input types
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## Required
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- image
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- 图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- invert_mask
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- 反转mask
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- top
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- 顶部扩展。
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- Comfy dtype: INT
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- Python dtype: int
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- bottom
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- 底部扩展。
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- Comfy dtype: INT
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- Python dtype: int
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- left
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- 左侧扩展。
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- Comfy dtype: INT
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- Python dtype: int
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- right
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- 右侧扩展。
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- Comfy dtype: INT
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- Python dtype: int
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- color
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- 颜色。
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- Comfy dtype: COLOR
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- Python dtype: str
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# Output types
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- image
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- 生成的图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 生成的mask。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: GPU
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# Source code
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```
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class ExtendCanvas:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"image": ("IMAGE", ),
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"invert_mask": ("BOOLEAN", {"default": True}), # 反转mask
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"top": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
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"bottom": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
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"left": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
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"right": ("INT", {"default": 0, "min": 0, "max": 99999, "step": 1}),
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"color": ("COLOR", {"default": "#000000"},),
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},
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"optional": {
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"mask": ("MASK",), #
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask")
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FUNCTION = 'extend_canvas'
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CATEGORY = '😺dzNodes/LayerUtility'
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def extend_canvas(self, image, invert_mask,
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top, bottom, left, right, color,
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mask=None,
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):
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l_images = []
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l_masks = []
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ret_images = []
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ret_masks = []
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for l in 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 mask is not None:
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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l_masks = []
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for m in 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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else:
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if len(l_masks) == 0:
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l_masks.append(Image.new('L', size=tensor2pil(l_images[0]).size, color='white'))
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max_batch = max(len(l_images), len(l_masks))
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for i in range(max_batch):
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_image = l_images[i] if i < len(l_images) else l_images[-1]
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_image = tensor2pil(_image).convert('RGB')
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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width = _image.width + left + right
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height = _image.height + top + bottom
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_canvas = Image.new('RGB', (width, height), color)
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_mask_canvas = Image.new('L', (width, height), "black")
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_canvas.paste(_image, box=(left,top))
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_mask_canvas.paste(_mask.convert('L'), box=(left, top))
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ret_images.append(pil2tensor(_canvas))
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ret_masks.append(image2mask(_mask_canvas))
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log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
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return (torch.cat(ret_images, dim=0), torch.cat(ret_masks, dim=0),)
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``` |