72 lines
2.4 KiB
Markdown
72 lines
2.4 KiB
Markdown
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# Documentation
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- Class name: MiDaS-DepthMapPreprocessor
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- Category: ControlNet Preprocessors/Normal and Depth Estimators
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- Output node: False
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MiDaS深度图节点采用MiDaS模型对输入图像进行预处理,生成深度图。这一过程增强了图像的深度感知,为后续处理或可视化提供基础。
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# Input types
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## Required
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- image
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- image参数是用于生成深度图的输入图像,是深度估计的主要数据来源。
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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敏感度,影响深度图对距离和深度的感知。
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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,有助于在深度图中区分前景和背景元素。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- resolution
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- resolution参数指定生成深度图的分辨率,影响输出的细节和质量。
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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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- 输出是一张表示深度图的图像,其中像素强度对应从摄像机视角估计的深度。
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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:
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- [PreviewImage](../../Comfy/Nodes/PreviewImage.md)
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- [CR Multi-ControlNet Stack](../../ComfyUI_Comfyroll_CustomNodes/Nodes/CR Multi-ControlNet Stack.md)
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- [ControlNetApplyAdvanced](../../Comfy/Nodes/ControlNetApplyAdvanced.md)
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- Reroute
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- [Control Net Stacker](../../efficiency-nodes-comfyui/Nodes/Control Net Stacker.md)
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## Source code
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```python
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class MIDAS_Depth_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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# Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py
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model = MidasDetector.from_pretrained().to(model_management.get_torch_device())
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out = common_annotator_call(model, image, resolution=resolution, a=a, bg_th=bg_threshold)
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del model
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return (out, )
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```
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