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from ..utils import common_annotator_call, create_node_input_types
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import comfy.model_management as model_management
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import json
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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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NODE_CLASS_MAPPINGS = {
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"OpenposePreprocessor": OpenPose_Preprocessor,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"OpenposePreprocessor": "OpenPose Pose",
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} |
