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import torch |
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from ultralytics.engine.results import Results |
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from ultralytics.models.yolo.detect.predict import DetectionPredictor |
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from ultralytics.utils import DEFAULT_CFG, ops |
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class OBBPredictor(DetectionPredictor): |
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""" |
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A class extending the DetectionPredictor class for prediction based on an Oriented Bounding Box (OBB) model. |
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Example: |
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```python |
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from ultralytics.utils import ASSETS |
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from ultralytics.models.yolo.obb import OBBPredictor |
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args = dict(model='yolov8n-obb.pt', source=ASSETS) |
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predictor = OBBPredictor(overrides=args) |
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predictor.predict_cli() |
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``` |
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""" |
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None): |
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"""Initializes OBBPredictor with optional model and data configuration overrides.""" |
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super().__init__(cfg, overrides, _callbacks) |
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self.args.task = "obb" |
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def postprocess(self, preds, img, orig_imgs): |
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"""Post-processes predictions and returns a list of Results objects.""" |
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preds = ops.non_max_suppression( |
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preds, |
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self.args.conf, |
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self.args.iou, |
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agnostic=self.args.agnostic_nms, |
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max_det=self.args.max_det, |
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nc=len(self.model.names), |
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classes=self.args.classes, |
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rotated=True, |
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) |
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if not isinstance(orig_imgs, list): |
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orig_imgs = ops.convert_torch2numpy_batch(orig_imgs) |
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results = [] |
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for pred, orig_img, img_path in zip(preds, orig_imgs, self.batch[0]): |
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rboxes = ops.regularize_rboxes(torch.cat([pred[:, :4], pred[:, -1:]], dim=-1)) |
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rboxes[:, :4] = ops.scale_boxes(img.shape[2:], rboxes[:, :4], orig_img.shape, xywh=True) |
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obb = torch.cat([rboxes, pred[:, 4:6]], dim=-1) |
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results.append(Results(orig_img, path=img_path, names=self.model.names, obb=obb)) |
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return results |
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