99 lines
3.7 KiB
Markdown
99 lines
3.7 KiB
Markdown
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# Documentation
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- Class name: OpenposePreprocessor
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- Category: ControlNet Preprocessors/Faces and Poses Estimators
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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OpenPose预处理器节点旨在从图像中估计人体姿势。它利用OpenPose模型来检测和标注人体、面部和手部的各种关键点，提供全面的姿势估计。
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# Input types
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## Required
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- image
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- 用于姿势估计的输入图像。这是姿势检测算法主要操作的数据。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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## Optional
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- detect_hand
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- 决定是否在姿势估计过程中检测手部关键点。启用此选项可进行详细的手部姿势分析。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- detect_body
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- 控制姿势估计中的身体关键点检测。启用此功能可以分析整体身体姿势和动作。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- detect_face
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- 指定是否应检测面部关键点，以便在姿势估计中进行详细的面部表情和方向分析。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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- resolution
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- 处理前将输入图像调整到的分辨率。更高的分辨率可能会提高检测准确性，但会增加计算负担。
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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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- 带有标注关键点的输出图像，直观地展示姿势估计结果。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- pose_keypoint
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- 包含身体、面部和手部检测到的关键点的字典，支持详细的姿势分析。
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- Comfy dtype: POSE_KEYPOINT
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- Python dtype: List[Dict[str, Any]]
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- ui
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- 提供检测到的关键点的JSON表示，便于进一步分析或可视化姿势估计结果。
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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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- [ControlNetApply](../../Comfy/Nodes/ControlNetApply.md)
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- [ImageScaleToTotalPixels](../../Comfy/Nodes/ImageScaleToTotalPixels.md)
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- Reroute
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- [VHS_VideoCombine](../../ComfyUI-VideoHelperSuite/Nodes/VHS_VideoCombine.md)
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- MagicAnimate
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## Source code
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```python
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class OpenPose_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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detect_hand = (["enable", "disable"], {"default": "enable"}),
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detect_body = (["enable", "disable"], {"default": "enable"}),
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detect_face = (["enable", "disable"], {"default": "enable"})
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)
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RETURN_TYPES = ("IMAGE", "POSE_KEYPOINT")
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FUNCTION = "estimate_pose"
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CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators"
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def estimate_pose(self, image, detect_hand, detect_body, detect_face, resolution=512, **kwargs):
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from controlnet_aux.open_pose import OpenposeDetector
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detect_hand = detect_hand == "enable"
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detect_body = detect_body == "enable"
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detect_face = detect_face == "enable"
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model = OpenposeDetector.from_pretrained().to(model_management.get_torch_device())
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self.openpose_dicts = []
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def func(image, **kwargs):
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pose_img, openpose_dict = model(image, **kwargs)
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self.openpose_dicts.append(openpose_dict)
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return pose_img
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out = common_annotator_call(func, image, include_hand=detect_hand, include_face=detect_face, include_body=detect_body, image_and_json=True, resolution=resolution)
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del model
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return {
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'ui': { "openpose_json": [json.dumps(self.openpose_dicts, indent=4)] },
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"result": (out, self.openpose_dicts)
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}
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```
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