81 lines
2.5 KiB
Markdown
81 lines
2.5 KiB
Markdown
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# Documentation
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- Class name: FindComplementaryColor
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- Category: Bmad/CV/Color A.
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- Output node: False
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这个节点旨在基于给定的颜色字典和指定的功率因子，在图像中寻找与主色互补的颜色。它还可以选择性地应用蒙版来聚焦于图像的特定区域进行搜索。
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# Input types
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## Required
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- image
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- 需要寻找互补色的图像。它是分析的主要输入。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- color_dict
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- 一个将颜色名称映射到其RGB值的字典，用于识别和比较图像中的颜色。
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- Comfy dtype: COLOR_DICT
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- Python dtype: Dict[str, Tuple[int, int, int]]
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- power
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- 影响互补色选择的因子，用于调整算法对某些颜色的敏感度或偏好。
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- Comfy dtype: FLOAT
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- Python dtype: float
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## Optional
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- mask
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- 一个可选的蒙版，可以应用于图像以将互补色的搜索限制在特定区域。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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# Output types
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- color
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- 在图像中找到的互补色的RGB值。
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- Comfy dtype: COLOR
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- Python dtype: List[int]
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- string
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- 找到的互补色的名称，根据输入的颜色字典定义。
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- Comfy dtype: STRING
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- Python dtype: str
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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class FindComplementaryColor:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image": ("IMAGE",),
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"color_dict": ("COLOR_DICT",),
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"power": ("FLOAT", {"default": 0.5, "min": .01, "max": 10, "step": "0.01"}),
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},
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"optional":
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{
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"mask": ("IMAGE",)
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}
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}
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RETURN_TYPES = ("COLOR", "STRING",)
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FUNCTION = "find_color"
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CATEGORY = "Bmad/CV/Color A."
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def find_color(self, image, color_dict, power, mask=None):
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image = tensor2opencv(image, 3)
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if mask is not None:
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mask = tensor2opencv(mask, 1)
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# this is a quality of life feature, so that it is easier to run the node and test stuff
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# the behavior (img resize w/ lin. interpolation) can be avoided by setting up the data prior to this node
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if image.shape[0:2] != mask.shape[0:2]:
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print("FindComplementaryColor node will resize image to fit mask.")
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image = cv.resize(image, (mask.shape[1], mask.shape[0]), interpolation=cv.INTER_LINEAR)
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color = find_complementary_color(image, color_dict, mask, power)
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return (list(color_dict[color]), color,)
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```
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