defauto_annotate(data,det_model="yolov8x.pt",sam_model="sam_b.pt",device="",output_dir=None):""" Automatically annotates images using a YOLO object detection model and a SAM segmentation model. Args: data (str): Path to a folder containing images to be annotated. det_model (str, optional): Pre-trained YOLO detection model. Defaults to 'yolov8x.pt'. sam_model (str, optional): Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. device (str, optional): Device to run the models on. Defaults to an empty string (CPU or GPU, if available). output_dir (str | None | optional): Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. Example: ```python from ultralytics.data.annotator import auto_annotate auto_annotate(data='ultralytics/assets', det_model='yolov8n.pt', sam_model='mobile_sam.pt') ``` """det_model=YOLO(det_model)sam_model=SAM(sam_model)data=Path(data)ifnotoutput_dir:output_dir=data.parent/f"{data.stem}_auto_annotate_labels"Path(output_dir).mkdir(exist_ok=True,parents=True)det_results=det_model(data,stream=True,device=device)forresultindet_results:class_ids=result.boxes.cls.int().tolist()# noqaiflen(class_ids):boxes=result.boxes.xyxy# Boxes object for bbox outputssam_results=sam_model(result.orig_img,bboxes=boxes,verbose=False,save=False,device=device)segments=sam_results[0].masks.xyn# noqawithopen(f"{Path(output_dir)/Path(result.path).stem}.txt","w")asf:foriinrange(len(segments)):s=segments[i]iflen(s)==0:continuesegment=map(str,segments[i].reshape(-1).tolist())f.write(f"{class_ids[i]} "+" ".join(segment)+"\n")