126 lines
3.5 KiB
Markdown
126 lines
3.5 KiB
Markdown
# Documentation
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- Class name: RestoreCropBox
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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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将被CropByMask裁切后的图片恢复到原图。
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# Input types
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## Required
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- background_image
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- 背景图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- croped_image
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- 裁切后的图片。如果中间经过放大处理，恢复前需将尺寸还原。
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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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- 反转遮罩。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- crop_box
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- 裁剪框。
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- Comfy dtype: BOX
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- Python dtype: tuple
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## Optional
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- croped_mask
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- 裁剪后的遮罩。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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# Output types
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- image
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- 图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 遮罩。
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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: CPU
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# Source code
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```python
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class RestoreCropBox:
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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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"background_image": ("IMAGE", ),
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"croped_image": ("IMAGE",),
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"invert_mask": ("BOOLEAN", {"default": False}), # 反转mask#
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"crop_box": ("BOX",),
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},
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"optional": {
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"croped_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 = 'restore_crop_box'
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CATEGORY = '😺dzNodes/LayerUtility'
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def restore_crop_box(self, background_image, croped_image, invert_mask, crop_box,
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croped_mask=None
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):
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b_images = []
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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 b in background_image:
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b_images.append(torch.unsqueeze(b, 0))
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for l in croped_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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else:
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l_masks.append(Image.new('L', size=m.size, color='white'))
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if croped_mask is not None:
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if croped_mask.dim() == 2:
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croped_mask = torch.unsqueeze(croped_mask, 0)
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l_masks = []
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for m in croped_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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max_batch = max(len(b_images), len(l_images), len(l_masks))
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for i in range(max_batch):
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background_image = b_images[i] if i < len(b_images) else b_images[-1]
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croped_image = l_images[i] if i < len(l_images) else l_images[-1]
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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_canvas = tensor2pil(background_image).convert('RGB')
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_layer = tensor2pil(croped_image).convert('RGB')
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ret_mask = Image.new('L', size=_canvas.size, color='black')
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_canvas.paste(_layer, box=tuple(crop_box), mask=_mask)
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ret_mask.paste(_mask, box=tuple(crop_box))
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ret_images.append(pil2tensor(_canvas))
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ret_masks.append(image2mask(ret_mask))
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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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``` |