o W h"@s<ddlmZddlmZddlmZmZGdddeZdS))Results)DetectionPredictor) DEFAULT_CFGopscsDeZdZdZeddffdd ZfddZddZd d ZZ 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: Applies non-max suppression and processes detections. construct_results: Constructs a list of result objects from predictions. construct_result: Constructs 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() Ncst|||d|j_dS)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. Defaults to Ultralytics DEFAULT_CFG. 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__[/var/www/vscode/kcb/lib/python3.10/site-packages/ultralytics/models/yolo/segment/predict.pyr !s zSegmentationPredictor.__init__cs:t|dtr |ddn|d}tj|d|||dS)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 postprocessr predsimg orig_imgsrrrrr0s"z!SegmentationPredictor.postprocesscs$fddt||jd|DS)a: Construct a list of result objects from the predictions. Args: preds (List[torch.Tensor]): List of predicted bounding boxes, scores, and masks. img (torch.Tensor): The image after preprocessing. orig_imgs (List[np.ndarray]): List of original images before preprocessing. protos (List[torch.Tensor]): List of prototype masks. Returns: (List[Results]): List of result objects containing the original images, image paths, class names, bounding boxes, and masks. c s&g|]\}}}}||||qSr)construct_result).0predorig_imgimg_pathprotorr rr Ss z;SegmentationPredictor.construct_results..r)zipbatchrrr$rconstruct_resultsEs z'SegmentationPredictor.construct_resultscCsRt|sd}n{|jjrFt|jdd|ddddf|j|ddddf<t||ddddf|ddddf|jdd}n:&z&SegmentationPredictor.construct_result) __name__ __module__ __qualname____doc__rr rr(r __classcell__rrrrrs  rN)ultralytics.engine.resultsr&ultralytics.models.yolo.detect.predictrultralytics.utilsrrrrrrrs