FiBddlmZddlmZddlmZmZGddeZy))Any)Results) BaseSolutionSolutionResultsc8eZdZdZdeddffd ZdefdZxZS)InstanceSegmentationa4A class to manage instance segmentation in images or video streams. This class extends the BaseSolution class and provides functionality for performing instance segmentation, including drawing segmented masks with bounding boxes and labels. Attributes: model (str): The segmentation model to use for inference. line_width (int): Width of the bounding box and text lines. names (dict[int, str]): Dictionary mapping class indices to class names. clss (list[int]): List of detected class indices. track_ids (list[int]): List of track IDs for detected instances. masks (list[np.ndarray]): List of segmentation masks for detected instances. show_conf (bool): Whether to display confidence scores. show_labels (bool): Whether to display class labels. show_boxes (bool): Whether to display bounding boxes. Methods: process: Process the input image to perform instance segmentation and annotate results. extract_tracks: Extract tracks including bounding boxes, classes, and masks from model predictions. Examples: >>> segmenter = InstanceSegmentation() >>> frame = cv2.imread("frame.jpg") >>> results = segmenter.process(frame) >>> print(f"Total segmented instances: {results.total_tracks}") kwargsreturnNc |jdd|d<t|di||jjdd|_|jjdd|_|jjdd|_y) a%Initialize the InstanceSegmentation class for detecting and annotating segmented instances. Args: **kwargs (Any): Keyword arguments passed to the BaseSolution parent class including: - model (str): Model name or path, defaults to "yolo26n-seg.pt". modelzyolo26n-seg.pt show_confT show_labels show_boxesN)getsuper__init__CFGr rr)selfr __class__s m/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/solutions/instance_segmentation.pyrzInstanceSegmentation.__init__%sm!**W.>?w "6"k4888<< t<((,,|T:c|j|t|jdd|_|j|jj d|}nt |d|j|jj|jj}|j|j|j|j|jd}|j|t!|t#|j$S)aPerform instance segmentation on the input image and annotate the results. Args: im0 (np.ndarray): The input image for segmentation. Returns: (SolutionResults): Object containing the annotated image and total number of tracked instances. Examples: >>> segmenter = InstanceSegmentation() >>> frame = cv2.imread("image.jpg") >>> summary = segmenter.process(frame) >>> print(summary) masksNzRNo masks detected! Ensure you're using a supported Ultralytics segmentation model.)pathnamesboxesrinstance) line_widthrconflabels color_mode)plot_im total_tracks)extract_tracksgetattrtracksrLOGGERwarningrr track_datadataplotrrr rdisplay_outputrlen track_ids)rim0r#resultss rprocesszInstanceSegmentation.process3s C T[['48  ::  KK   t uGcDJJdooFZFZbfblblbqbqrGll??oo^^''% #G G$wS=PQQr) __name__ __module__ __qualname____doc__rrrr2 __classcell__)rs@rrr s)6 ; ; ;#Ro#RrrN)typingrultralytics.engine.resultsrultralytics.solutions.solutionsrrrrrrr;s .IMR<MRr