o V hV @sddlmZddlmZmZmZddlmZmZ      dd ee efd e de de de de de de deee deee efdd fddZ d S))Path)ListOptionalUnion)SAMYOLO yolo11x.ptsam_b.pt??,Ndata det_model sam_modeldeviceconfiouimgszmax_detclasses output_dirreturnc  Cs6t|}t|}t|}| s|j|jd} t| jddd||d||||||d} | D]j} | jj } | r| jj } || j | dd|d}|dj j }tt| t| jjdd d d 0}t|D]#\}}|rtt|d  }|| |d d |dqeWdn1swYq.dS)a3 Automatically annotate images using a YOLO object detection model and a SAM segmentation model. This function processes images in a specified directory, detects objects using a YOLO model, and then generates segmentation masks using a SAM model. The resulting annotations are saved as text files. Args: data (str | Path): Path to a folder containing images to be annotated. det_model (str): Path or name of the pre-trained YOLO detection model. sam_model (str): Path or name of the pre-trained SAM segmentation model. device (str): Device to run the models on (e.g., 'cpu', 'cuda', '0'). conf (float): Confidence threshold for detection model. iou (float): IoU threshold for filtering overlapping boxes in detection results. imgsz (int): Input image resize dimension. max_det (int): Maximum number of detections per image. classes (List[int] | None): Filter predictions to specified class IDs, returning only relevant detections. output_dir (str | Path | None): Directory to save the annotated results. If None, a default directory is created. Examples: >>> from ultralytics.data.annotator import auto_annotate >>> auto_annotate(data="ultralytics/assets", det_model="yolo11n.pt", sam_model="mobile_sam.pt") _auto_annotate_labelsT)exist_okparents)streamrrrrrrF)bboxesverbosesaverrz.txtwzutf-8)encoding  N)rrrparentstemmkdirboxesclsinttolistxyxyorig_imgmasksxynopenpath enumerateanymapstrreshapewritejoin)rrrrrrrrrr det_resultsresult class_idsr) sam_resultssegmentsfissegmentrCN/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/data/annotator.py auto_annotate s2" &"rE) rr r r r r rNN) pathlibrtypingrrr ultralyticsrrr6floatr+rErCrCrCrDsF