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48 lines
1.8 KiB
48 lines
1.8 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import torch |
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from ultralytics.yolo.engine.predictor import BasePredictor |
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from ultralytics.yolo.engine.results import Results |
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from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, ops |
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class DetectionPredictor(BasePredictor): |
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def postprocess(self, preds, img, orig_imgs): |
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"""Postprocesses 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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for i, pred in enumerate(preds): |
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orig_img = orig_imgs[i] if isinstance(orig_imgs, list) else orig_imgs |
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if not isinstance(orig_imgs, torch.Tensor): |
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pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape) |
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path = self.batch[0] |
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img_path = path[i] if isinstance(path, list) else path |
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results.append(Results(orig_img=orig_img, path=img_path, names=self.model.names, boxes=pred)) |
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return results |
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def predict(cfg=DEFAULT_CFG, use_python=False): |
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"""Runs YOLO model inference on input image(s).""" |
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model = cfg.model or 'yolov8n.pt' |
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source = cfg.source if cfg.source is not None else ROOT / 'assets' if (ROOT / 'assets').exists() \ |
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else 'https://ultralytics.com/images/bus.jpg' |
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args = dict(model=model, source=source) |
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if use_python: |
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from ultralytics import YOLO |
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YOLO(model)(**args) |
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else: |
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predictor = DetectionPredictor(overrides=args) |
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predictor.predict_cli() |
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if __name__ == '__main__': |
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predict()
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