feat: copy docs

2024-07-04 08:39:03 +08:00
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+# Documentation
+- Class name: Inference_Core_OpenposePreprocessor
+- Category: ControlNet Preprocessors/Faces and Poses Estimators
+- Output node: False
+- Repo Ref: https://github.com/comfyanonymous/ComfyUI
+
+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。
+
+# Input types
+## Required
+- image
+    - 用于姿势估计的输入图像，作为分析的主要数据源。
+    - Comfy dtype: IMAGE
+    - Python dtype: torch.Tensor
+
+## Optional
+- detect_hand
+    - 决定是否检测手部关键点，以增强姿势估计的细节。
+    - Comfy dtype: COMBO[STRING]
+    - Python dtype: str
+- detect_body
+    - 控制身体关键点的检测，构成姿势估计的核心。
+    - Comfy dtype: COMBO[STRING]
+    - Python dtype: str
+- detect_face
+    - 指定是否识别面部关键点，为姿势分析添加面部表情。
+    - Comfy dtype: COMBO[STRING]
+    - Python dtype: str
+- resolution
+    - 指定姿势估计过程的分辨率，影响输出的精度和比例。
+    - Comfy dtype: INT
+    - Python dtype: int
+
+# Output types
+- image
+    - 返回姿势图像，直观地表示估计的姿势。
+    - Comfy dtype: IMAGE
+    - Python dtype: torch.Tensor
+- pose_keypoint
+    - 提供姿势关键点数据，封装了有关估计姿势的详细信息。
+    - Comfy dtype: POSE_KEYPOINT
+    - Python dtype: List[Dict]
+- ui
+    - 提供检测到的姿势的JSON表示，对姿势估计提供详细的见解。
+
+
+## Usage tips
+- Infra type: `GPU`
+- Common nodes: unknown
+
+
+## Source code
+```python
+class OpenPose_Preprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return create_node_input_types(
+            detect_hand = (["enable", "disable"], {"default": "enable"}),
+            detect_body = (["enable", "disable"], {"default": "enable"}),
+            detect_face = (["enable", "disable"], {"default": "enable"})
+        )
+        
+    RETURN_TYPES = ("IMAGE", "POSE_KEYPOINT")
+    FUNCTION = "estimate_pose"
+
+    CATEGORY = "ControlNet Preprocessors/Faces and Poses Estimators"
+
+    def estimate_pose(self, image, detect_hand, detect_body, detect_face, resolution=512, **kwargs):
+        from controlnet_aux.open_pose import OpenposeDetector
+
+        detect_hand = detect_hand == "enable"
+        detect_body = detect_body == "enable"
+        detect_face = detect_face == "enable"
+
+        model = OpenposeDetector.from_pretrained().to(model_management.get_torch_device())        
+        self.openpose_dicts = []
+        def func(image, **kwargs):
+            pose_img, openpose_dict = model(image, **kwargs)
+            self.openpose_dicts.append(openpose_dict)
+            return pose_img
+        
+        out = common_annotator_call(func, image, include_hand=detect_hand, include_face=detect_face, include_body=detect_body, image_and_json=True, resolution=resolution)
+        del model
+        return {
+            'ui': { "openpose_json": [json.dumps(self.openpose_dicts, indent=4)] },
+            "result": (out, self.openpose_dicts)
+        }
+
+```