69 lines
2.3 KiB
Markdown
69 lines
2.3 KiB
Markdown
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# Documentation
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- Class name: MiDaS-NormalMapPreprocessor
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- Category: ControlNet Preprocessors/Normal and Depth Estimators
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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MiDaS-NormalMapPreprocessor 节点专门用于利用 MiDaS 模型从图像生成法线贴图。它预处理图像以增强深度估计，从而促进生成详细的法线贴图，这些贴图可用于 3D 建模和增强现实等各种应用。
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# Input types
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## Required
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- image
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- image 参数是将生成法线贴图的输入图像，它作为深度估计和法线贴图创建的基础。
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- Comfy dtype: IMAGE
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- Python dtype: numpy.ndarray
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## Optional
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- a
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- a 参数用于调整深度估计的强度，影响生成的法线贴图中的对比度和细节。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- bg_threshold
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- bg_threshold 参数设置背景分离的阈值，有助于在深度估计过程中将主体与背景隔离。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- resolution
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- resolution 参数指定执行深度估计和法线贴图生成的分辨率，影响输出的细节水平。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- image
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- 输出是一个图像，代表从输入图像生成的法线贴图，提供详细的表面方向信息。
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- Comfy dtype: IMAGE
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- Python dtype: numpy.ndarray
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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 MIDAS_Normal_Map_Preprocessor:
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@classmethod
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def INPUT_TYPES(s):
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return create_node_input_types(
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a = ("FLOAT", {"default": np.pi * 2.0, "min": 0.0, "max": np.pi * 5.0, "step": 0.05}),
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bg_threshold = ("FLOAT", {"default": 0.1, "min": 0, "max": 1, "step": 0.05})
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)
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "ControlNet Preprocessors/Normal and Depth Estimators"
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def execute(self, image, a, bg_threshold, resolution=512, **kwargs):
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from controlnet_aux.midas import MidasDetector
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model = MidasDetector.from_pretrained().to(model_management.get_torch_device())
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#Dirty hack :))
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cb = lambda image, **kargs: model(image, **kargs)[1]
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out = common_annotator_call(cb, image, resolution=resolution, a=a, bg_th=bg_threshold, depth_and_normal=True)
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del model
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return (out, )
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```
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