a yf @s*ddlmZddlmZmZd ddZdS) )Path)SAMYOLO yolov8x.ptsam_b.ptNc Cs@t|}t|}t|}|s.|j|jd}t|jddd||d|d}|D]}|jj }t |rR|jj }||j |dd|d} | dj j} tt|t|jjdd r} tt | D]T} | | } t | dkrqtt| | d  }| || d d |d qWd qR1s00YqRd S)a Automatically annotates 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 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'). output_dir (str | 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="yolov8n.pt", sam_model="mobile_sam.pt") Notes: - The function creates a new directory for output if not specified. - Annotation results are saved as text files with the same names as the input images. - Each line in the output text file represents a detected object with its class ID and segmentation points. Z_auto_annotate_labelsT)exist_okparents)streamdeviceF)Zbboxesverbosesaver rz.txtw  N)rrrparentstemmkdirboxesclsinttolistlenZxyxyZorig_imgmasksZxynopenpathrangemapstrZreshapewritejoin)dataZ det_modelZ sam_modelr output_dirZ det_resultsresultZ class_idsrZ sam_resultssegmentsfissegmentr*V/var/www/html/django/DPS/env/lib/python3.9/site-packages/ultralytics/data/annotator.py auto_annotates( " r,)rrrN)pathlibrZ ultralyticsrrr,r*r*r*r+s