148 lines
4.6 KiB
Markdown
148 lines
4.6 KiB
Markdown
# Documentation
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- Class name: LayerMaskTransform
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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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与LayerImageTransform类似，这个节点用于单独对layer_mask进行变换，可改变大小，旋转，改变长宽比以及镜像翻转。
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# Input types
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## Required
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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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- x
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- 坐标x值。
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- Comfy dtype: INT
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- Python dtype: int
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- y
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- 坐标y值。
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- Comfy dtype: INT
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- Python dtype: int
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- mirror
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- 镜像翻转。提供2种翻转模式, 水平翻转和垂直翻转。
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- Comfy dtype: STRING_ONEOF
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- Python dtype: str
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- Options:
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- None
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- horizontal
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- vertical
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- scale
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- 图层放大倍数，1.0 表示原大。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- aspect_ratio
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- 图层长宽比。1.0 是原始比例，大于此值表示拉长，小于此值表示压扁。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- rotate
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- 图层旋转度数。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- transform_method
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- 用于图层放大和旋转的采样方法，包括lanczos、bicubic、hamming、bilinear、box和nearest。不同的采样方法会影响合成的画质和画面处理时间。
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- Comfy dtype: STRING_ONEOF
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- Python dtype: str
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- Options:
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- lanczos
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- bicubic
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- hamming
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- bilinear
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- box
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- nearest
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- anti_aliasing
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- 抗锯齿，范围从0-16，数值越大，锯齿越不明显。过高的数值将显著降低节点的处理速度。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- mask
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- 变换后的蒙版。
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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 LayerMaskTransform:
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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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mirror_mode = ['None', 'horizontal', 'vertical']
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method_mode = ['lanczos', 'bicubic', 'hamming', 'bilinear', 'box', 'nearest']
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return {
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"required": {
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"mask": ("MASK",), #
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"x": ("INT", {"default": 0, "min": -99999, "max": 99999, "step": 1}),
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"y": ("INT", {"default": 0, "min": -99999, "max": 99999, "step": 1}),
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"mirror": (mirror_mode,), # 镜像翻转
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"scale": ("FLOAT", {"default": 1, "min": 0.01, "max": 100, "step": 0.01}),
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"aspect_ratio": ("FLOAT", {"default": 1, "min": 0.01, "max": 100, "step": 0.01}),
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"rotate": ("FLOAT", {"default": 0, "min": -999999, "max": 999999, "step": 0.01}),
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"transform_method": (method_mode,),
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"anti_aliasing": ("INT", {"default": 2, "min": 0, "max": 16, "step": 1}),
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},
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"optional": {
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}
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}
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RETURN_TYPES = ("MASK",)
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RETURN_NAMES = ("mask",)
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FUNCTION = 'layer_mask_transform'
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CATEGORY = '😺dzNodes/LayerUtility'
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def layer_mask_transform(self, mask, x, y, mirror, scale, aspect_ratio, rotate,
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transform_method, anti_aliasing,
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):
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l_masks = []
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ret_masks = []
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if mask.dim() == 2:
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mask = torch.unsqueeze(mask, 0)
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for m in mask:
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l_masks.append(torch.unsqueeze(m, 0))
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for i in range(len(l_masks)):
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_mask = l_masks[i] if i < len(l_masks) else l_masks[-1]
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_mask = tensor2pil(_mask).convert('L')
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_mask_canvas = Image.new('L', size=_mask.size, color='black')
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orig_width = _mask.width
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orig_height = _mask.height
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target_layer_width = int(orig_width * scale)
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target_layer_height = int(orig_height * scale * aspect_ratio)
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# mirror
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if mirror == 'horizontal':
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_mask = _mask.transpose(Image.FLIP_LEFT_RIGHT)
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elif mirror == 'vertical':
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_mask = _mask.transpose(Image.FLIP_TOP_BOTTOM)
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# scale
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_mask = _mask.resize((target_layer_width, target_layer_height))
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# rotate
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_, _mask, _ = image_rotate_extend_with_alpha(_mask.convert('RGB'), rotate, _mask, transform_method, anti_aliasing)
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paste_x = (orig_width - _mask.width) // 2 + x
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paste_y = (orig_height - _mask.height) // 2 + y
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# composit layer
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_mask_canvas.paste(_mask, (paste_x, paste_y))
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ret_masks.append(image2mask(_mask_canvas))
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log(f"{NODE_NAME} Processed {len(l_masks)} mask(s).", message_type='finish')
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return (torch.cat(ret_masks, dim=0),)
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``` |