ultralytics/models/yolo/obb/predict.py

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

import torch

from ultralytics.engine.results import Results
from ultralytics.models.yolo.detect.predict import DetectionPredictor
from ultralytics.utils import DEFAULT_CFG, ops


class OBBPredictor(DetectionPredictor):
    """
    A class extending the DetectionPredictor class for prediction based on an Oriented Bounding Box (OBB) model.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.obb import OBBPredictor

        args = dict(model="yolo11n-obb.pt", source=ASSETS)
        predictor = OBBPredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        """Initializes OBBPredictor with optional model and data configuration overrides."""
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = "obb"

    def construct_result(self, pred, img, orig_img, img_path):
        """
        Constructs the result object from the prediction.

        Args:
            pred (torch.Tensor): The predicted bounding boxes, scores, and rotation angles.
            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.

        Returns:
            (Results): The result object containing the original image, image path, class names, and oriented bounding boxes.
        """
        rboxes = ops.regularize_rboxes(torch.cat([pred[:, :4], pred[:, -1:]], dim=-1))
        rboxes[:, :4] = ops.scale_boxes(img.shape[2:], rboxes[:, :4], orig_img.shape, xywh=True)
        obb = torch.cat([rboxes, pred[:, 4:6]], dim=-1)
        return Results(orig_img, path=img_path, names=self.model.names, obb=obb)