|hBddlmZddlmZddlmZmZGddeZy))Results)DetectionPredictor) DEFAULT_CFGopsc@eZdZdZeddffd ZfdZdZdZxZ S)SegmentationPredictora A class extending the DetectionPredictor class for prediction based on a segmentation model. This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the prediction results. Attributes: args (dict): Configuration arguments for the predictor. model (torch.nn.Module): The loaded YOLO segmentation model. batch (list): Current batch of images being processed. Methods: postprocess: Apply non-max suppression and process segmentation detections. construct_results: Construct a list of result objects from predictions. construct_result: Construct a single result object from a prediction. Examples: >>> from ultralytics.utils import ASSETS >>> from ultralytics.models.yolo.segment import SegmentationPredictor >>> args = dict(model="yolo11n-seg.pt", source=ASSETS) >>> predictor = SegmentationPredictor(overrides=args) >>> predictor.predict_cli() NcJt||||d|j_y)a Initialize the SegmentationPredictor with configuration, overrides, and callbacks. This class specializes in processing segmentation model outputs, handling both bounding boxes and masks in the prediction results. Args: cfg (dict): Configuration for the predictor. overrides (dict, optional): Configuration overrides that take precedence over cfg. _callbacks (list, optional): List of callback functions to be invoked during prediction. segmentN)super__init__argstask)selfcfg overrides _callbacks __class__s f/var/www/html/test/engine/venv/lib/python3.12/site-packages/ultralytics/models/yolo/segment/predict.pyr zSegmentationPredictor.__init__!s! i4" cpt|dtr|ddn|d}t| |d|||S)a; Apply non-max suppression and process segmentation detections for each image in the input batch. Args: preds (tuple): Model predictions, containing bounding boxes, scores, classes, and mask coefficients. img (torch.Tensor): Input image tensor in model format, with shape (B, C, H, W). orig_imgs (list | torch.Tensor | np.ndarray): Original image or batch of images. Returns: (list): List of Results objects containing the segmentation predictions for each image in the batch. Each Results object includes both bounding boxes and segmentation masks. Examples: >>> predictor = SegmentationPredictor(overrides=dict(model="yolo11n-seg.pt")) >>> results = predictor.postprocess(preds, img, orig_img) r)protos) isinstancetupler postprocess)rpredsimg orig_imgsrrs rrz!SegmentationPredictor.postprocess0sB$",E!He!<q"%(w"58S)F"KKrc t|||jd|Dcgc]\}}}}|j|||||c}}}}Scc}}}}w)a: Construct a list of result objects from the predictions. Args: preds (List[torch.Tensor]): List of predicted bounding boxes, scores, and masks. img (torch.Tensor): The image after preprocessing. orig_imgs (List[np.ndarray]): List of original images before preprocessing. protos (List[torch.Tensor]): List of prototype masks. Returns: (List[Results]): List of result objects containing the original images, image paths, class names, bounding boxes, and masks. r)zipbatchconstruct_result) rrrrrpredorig_imgimg_pathprotos rconstruct_resultsz'SegmentationPredictor.construct_resultsEsY 47uiTUX^3_  /h%  ! !$Xx G   s!A c t|sd}n|jjrxtj|j dd|ddddf|j |ddddf<tj ||ddddf|ddddf|j dd}nytj||ddddf|ddddf|j ddd}tj|j dd|ddddf|j |ddddf<||jddkD}||||}}t|||jj|ddddf| S) a. Construct a single result object from the prediction. Args: pred (np.ndarray): The predicted bounding boxes, scores, and masks. img (torch.Tensor): The image after preprocessing. orig_img (np.ndarray): The original image before preprocessing. img_path (str): The path to the original image. proto (torch.Tensor): The prototype masks. Returns: (Results): Result object containing the original image, image path, class names, bounding boxes, and masks. NT)upsample)rr)pathnamesboxesmasks) lenr retina_masksr scale_boxesshapeprocess_mask_native process_masksumrmodelr0)rr$rr%r&r'r2keeps rr#z&SegmentationPredictor.construct_resultXsZ4yE YY # #//#))AB-a!ehnnUDBQBK++E412;QU X^^\^]^M_`E$$UDABKa!eciiPQPRm^bcE//#))AB-a!ehnnUDBQBK  99X&*Dt*eDk%Dxhdjj6F6FdSTVXWXVXSXkafggr) __name__ __module__ __qualname____doc__rr rr(r# __classcell__)rs@rrrs'0'$4 #L* &hrrN)ultralytics.engine.resultsr&ultralytics.models.yolo.detect.predictrultralytics.utilsrrrrrrEs!/E.ih.ihr