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from ..utils import common_annotator_call, annotator_ckpts_path, HF_MODEL_NAME, DWPOSE_MODEL_NAME, create_node_input_types
import comfy.model_management as model_management
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",)
FUNCTION = "estimate_pose"
CATEGORY = "ControlNet Preprocessors/Faces and Poses"
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"
self.openpose_json = None
model = OpenposeDetector.from_pretrained(HF_MODEL_NAME, cache_dir=annotator_ckpts_path).to(model_management.get_torch_device())
def cb(image, **kwargs):
result = model(image, **kwargs)
self.openpose_json = result[1]
return result[0]
out = common_annotator_call(cb, 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": [self.openpose_json] },
"result": (out, )
}
NODE_CLASS_MAPPINGS = {
"OpenposePreprocessor": OpenPose_Preprocessor,
}
NODE_DISPLAY_NAME_MAPPINGS = {
"OpenposePreprocessor": "OpenPose Pose Recognition",
} |