Fi~NddlmZddlmZddlmZddlmZmZGddeZ y)) annotations)Results)DetectionPredictor) DEFAULT_CFGopscBeZdZdZeddfdfd 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() NcJt||||d|j_y)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 overrides _callbacks __class__s i/var/www/pod-logistic/pod-ai/venv/lib/python3.12/site-packages/ultralytics/models/yolo/segment/predict.pyr zSegmentationPredictor.__init__"s! i4" cpt|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.postprocess0sB"!+58U ;q!qw"58S)F"KKrc t|||jd|Dcgc]\}}}}|j|||||c}}}}Scc}}}}w)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. r)zipbatchconstruct_result) rrrrrpredorig_imgimg_pathprotos rconstruct_resultsz'SegmentationPredictor.construct_resultsDsY47uiTUX^3_  /h%  ! !$Xx G   s!A c |jddk(rd}n|jjrxtj|jdd|ddddf|j|ddddf<tj ||ddddf|ddddf|jdd}nytj ||ddddf|ddddf|jddd}tj|jdd|ddddf|j|ddddf<|)|jddkD}t|s ||||}}t|||jj|ddddf| S) a'Construct a single result object from the prediction. Args: pred (torch.Tensor): The predicted bounding boxes, scores, and masks. img (torch.Tensor): The image after preprocessing. orig_img (np.ndarray): The original image before preprocessing. img_path (str): The path to the original image. proto (torch.Tensor): The prototype masks. Returns: (Results): Result object containing the original image, image path, class names, bounding boxes, and masks. rNT)upsample))pathnamesboxesmasks) shaper retina_masksr scale_boxesprocess_mask_native process_maskamaxallrmodelr1)rr$rr%r&r'r3keeps rr#z&SegmentationPredictor.construct_resultVsj ::a=A E YY # #//#))AB-a!ehnnUDBQBK++E412;QU X^^\^]^M_`E$$UDABKa!eciiPQPRm^bcE//#))AB-a!ehnnUDBQBK  ::h'!+Dt9"4j%+exhdjj6F6FdSTVXWXVXSXkafggr)rz dict | None) __name__ __module__ __qualname____doc__rr rr(r# __classcell__)rs@rr r s(.'$RV #L( $hrr N) __future__rultralytics.engine.resultsr&ultralytics.models.yolo.detect.predictrultralytics.utilsrrr rrrGs$#.E.eh.ehr