/j~VddlmZddlmZddlmZddlmZmZGddeZ dS)) annotations)Results)DetectionPredictor) DEFAULT_CFGopscBeZdZdZeddfd fd ZfdZdZdZxZ S) SegmentationPredictoraA 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="yolo26n-seg.pt", source=ASSETS) >>> predictor = SegmentationPredictor(overrides=args) >>> predictor.predict_cli() N _callbacks dict | Nonecft|||d|j_dS)aInitialize 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 (dict, optional): Dictionary of callback functions to be invoked during prediction. segmentN)super__init__argstask)selfcfg overridesr __class__s l/home/longshao/multi-rider-rag/.venv/lib/python3.11/site-packages/ultralytics/models/yolo/segment/predict.pyrzSegmentationPredictor.__init__"s. i444" ct|dtr|ddn|d}t|d|||S)a2Apply 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="yolo26n-seg.pt")) >>> results = predictor.postprocess(preds, img, orig_img) r)protos) isinstancetupler postprocess)rpredsimg orig_imgsrrs rrz!SegmentationPredictor.postprocess0sR"!+58U ; ;Iq!qww""58S)F"KKKrcZfdt||jd|DS)aBConstruct 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 (torch.Tensor): Prototype masks tensor with shape (B, C, H, W). Returns: (list[Results]): List of result objects containing the original images, image paths, class names, bounding boxes, and masks. c Lg|] \}}}}||||!S)construct_result).0predorig_imgimg_pathprotorrs r z;SegmentationPredictor.construct_results..QsG   /h%  ! !$Xx G G   rr)zipbatch)rrrr rs` ` rconstruct_resultsz'SegmentationPredictor.construct_resultsDsJ     36uiTUX^3_3_    rc |jddkrd}n|jjrztj|jdd|ddddf|j|ddddf<tj||ddddf|ddddf|jdd}n{tj||ddddf|ddddf|jddd}tj|jdd|ddddf|j|ddddf<|8|ddk}t|s||||}}t|||j j |ddddf| S) a'Construct a single result object from the prediction. Args: pred (torch.Tensor): The predicted bounding boxes, scores, and masks. img (torch.Tensor): The image after preprocessing. orig_img (np.ndarray): The original image before preprocessing. img_path (str): The path to the original image. proto (torch.Tensor): The prototype masks. Returns: (Results): Result object containing the original image, image path, class names, bounding boxes, and masks. rNT)upsample))pathnamesboxesmasks) shaper retina_masksr scale_boxesprocess_mask_native process_maskamaxallrmodelr6)rr&rr'r(r)r8keeps rr$z&SegmentationPredictor.construct_resultVs :a=A  EE Y # V/#)ABB-aaa!ehnUUDBQBK+E4122;QQQU X^\^]^\^M_``EE$UDABBKaaa!eciPQPRPRm^bcccE/#)ABB-aaa!ehnUUDBQBK  ::h''!+Dt99 6"4j%+exhdj6FdSTSTSTVXWXVXSXkafggggr)r r ) __name__ __module__ __qualname____doc__rrrr-r$ __classcell__)rs@rr r s.'$RV # # # # # # #LLLLL(   $hhhhhhhrr N) __future__rultralytics.engine.resultsr&ultralytics.models.yolo.detect.predictrultralytics.utilsrrr r#rrrKs#"""""......EEEEEE........eheheheheh.ehehehehehr