151 lines
4.3 KiB
Markdown
151 lines
4.3 KiB
Markdown
# Documentation
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- Class name: ImageShift
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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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使图片产生位移。此节点支持位移接缝遮罩的输出，方便制作连续贴图。
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# Input types
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## Required
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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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- shift_x
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- X轴移动像素数。
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- Comfy dtype: INT
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- Python dtype: int
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- shift_y
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- Y轴移动像素数。
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- Comfy dtype: INT
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- Python dtype: int
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- cyclic
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- 是否循环重复。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- background_color
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- 背景颜色。
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- Comfy dtype: STRING
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- Python dtype: str
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- border_mask_width
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- 边框宽度。
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- Comfy dtype: INT
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- Python dtype: int
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- border_mask_blur
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- 边框模糊。
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- Comfy dtype: INT
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- Python dtype: int
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## Optional
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- mask
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- 蒙版。
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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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- 移动后的图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 移动后的蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- border_mask
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- 边框蒙版。
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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 ImageShift:
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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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"shift_x": ("INT", {"default": 256, "min": -9999, "max": 9999, "step": 1}),
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"shift_y": ("INT", {"default": 256, "min": -9999, "max": 9999, "step": 1}),
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"cyclic": ("BOOLEAN", {"default": True}), # 是否循环重复
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"background_color": ("STRING", {"default": "#000000"}),
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"border_mask_width": ("INT", {"default": 20, "min": 0, "max": 999, "step": 1}),
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"border_mask_blur": ("INT", {"default": 12, "min": 0, "max": 999, "step": 1}),
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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", "MASK",)
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RETURN_NAMES = ("image", "mask", "border_mask")
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FUNCTION = 'image_shift'
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CATEGORY = '😺dzNodes/LayerUtility'
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def image_shift(self, image, shift_x, shift_y,
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cyclic, background_color,
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border_mask_width, border_mask_blur,
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mask=None
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):
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ret_images = []
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ret_masks = []
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ret_border_masks = []
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l_images = []
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l_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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else:
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l_masks.append(Image.new('L', size=m.size, color='white'))
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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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l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
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shift_x, shift_y = -shift_x, -shift_y
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for i in range(len(l_images)):
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_image = l_images[i]
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_canvas = tensor2pil(_image).convert('RGB')
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_mask = l_masks[i] if len(l_masks) < i else l_masks[-1]
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_border = Image.new('L', size=_canvas.size, color='black')
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_border = draw_border(_border, border_width=border_mask_width, color='#FFFFFF')
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_border = _border.resize(_canvas.size)
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_canvas = shift_image(_canvas, shift_x, shift_y, background_color=background_color, cyclic=cyclic)
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_mask = shift_image(_mask, shift_x, shift_y, background_color='#000000', cyclic=cyclic)
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_border = shift_image(_border, shift_x, shift_y, background_color='#000000', cyclic=cyclic)
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_border = gaussian_blur(_border, border_mask_blur)
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ret_images.append(pil2tensor(_canvas))
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ret_masks.append(image2mask(_mask))
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ret_border_masks.append(image2mask(_border))
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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), torch.cat(ret_border_masks, dim=0),)
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``` |