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| # Ultralytics YOLO 🚀, AGPL-3.0 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='yolov8n-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 postprocess(self, preds, img, orig_imgs): | |
| """Post-processes predictions and returns a list of Results objects.""" | |
| preds = ops.non_max_suppression( | |
| preds, | |
| self.args.conf, | |
| self.args.iou, | |
| agnostic=self.args.agnostic_nms, | |
| max_det=self.args.max_det, | |
| nc=len(self.model.names), | |
| classes=self.args.classes, | |
| rotated=True, | |
| ) | |
| if not isinstance(orig_imgs, list): # input images are a torch.Tensor, not a list | |
| orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) | |
| results = [] | |
| for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]): | |
| 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) | |
| # xywh, r, conf, cls | |
| obb = torch.cat([rboxes, pred[:, 4:6]], dim=-1) | |
| results.append(Results(orig_img, path=img_path, names=self.model.names, obb=obb)) | |
| return results | |