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from ..utils import common_annotator_call, define_preprocessor_inputs, INPUT |
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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 define_preprocessor_inputs( |
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detect_hand=INPUT.COMBO(["enable", "disable"]), |
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detect_body=INPUT.COMBO(["enable", "disable"]), |
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detect_face=INPUT.COMBO(["enable", "disable"]), |
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resolution=INPUT.RESOLUTION(), |
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scale_stick_for_xinsr_cn=INPUT.COMBO(["disable", "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="enable", detect_body="enable", detect_face="enable", scale_stick_for_xinsr_cn="disable", resolution=512, **kwargs): |
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from custom_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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scale_stick_for_xinsr_cn = scale_stick_for_xinsr_cn == "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, xinsr_stick_scaling=scale_stick_for_xinsr_cn, 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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} |
