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from ultralytics.engine.predictor import BasePredictor |
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from ultralytics.engine.results import Results |
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from ultralytics.utils import ops |
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class DetectionPredictor(BasePredictor): |
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""" |
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A class extending the BasePredictor class for prediction based on a detection 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.detect import DetectionPredictor |
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args = dict(model='yolov8n.pt', source=ASSETS) |
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predictor = DetectionPredictor(overrides=args) |
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predictor.predict_cli() |
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``` |
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""" |
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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(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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classes=self.args.classes) |
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results = [] |
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is_list = isinstance(orig_imgs, list) |
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for i, pred in enumerate(preds): |
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orig_img = orig_imgs[i] if is_list else orig_imgs |
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if is_list: |
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) |
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img_path = self.batch[0][i] |
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results.append(Results(orig_img, path=img_path, names=self.model.names, boxes=pred)) |
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return results |
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