def estimate_pose(self, image, detect_hand, detect_body, detect_face, resolution=512, bbox_detector="yolox_l.onnx", pose_estimator="dw-ll_ucoco_384.onnx", **kwargs): if bbox_detector == "yolox_l.onnx": yolo_repo = DWPOSE_MODEL_NAME elif "yolox" in bbox_detector: yolo_rep...
DWPOSE_MODEL_NAME if pose_estimator == "dw-ll_ucoco_384.onnx" else "hr16/UnJIT-DWPose", pose_repo, yolo_repo, cache_dir=annotator_ckpts_path, det_filename=bbox_detector, pose_filename=pose_estimator ) detect_hand = detect_hand == "enable" detect_body = detect_body == "enable"...
def estimate_pose(self, image, detect_hand="enable", detect_body="enable", detect_face="enable", resolution=512, bbox_detector="yolox_l.onnx", pose_estimator="dw-ll_ucoco_384.onnx", **kwargs): def estimate_pose(self, image, detect_hand="enable", detect_body="enable", detect...
This is mainly because HED or PIDI estimator tend to hide a corrupted greyscale version of original image inside the soft edge map and the previous model HED 1.0 is over-fitted to restore that hidden corrupted image rather than perform boundary-aware diffusion. The training of Soft Edge 1.1 ...