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42 lines
1.6 KiB
42 lines
1.6 KiB
from ultralytics.tracker import BYTETracker, BOTSORT
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from ultralytics.yolo.utils.checks import check_requirements, check_yaml
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from ultralytics.yolo.utils import IterableSimpleNamespace, yaml_load
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import torch
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TRACKER_MAP = {"bytetrack": BYTETracker, "botsort": BOTSORT}
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check_requirements('lap') # for linear_assignment
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def on_predict_start(predictor):
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tracker = check_yaml(predictor.args.tracker)
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cfg = IterableSimpleNamespace(**yaml_load(tracker))
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assert cfg.tracker_type in ["bytetrack", "botsort"], \
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f"Only support 'bytetrack' and 'botsort' for now, but got '{cfg.tracker_type}'"
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trackers = []
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for _ in range(predictor.dataset.bs):
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tracker = TRACKER_MAP[cfg.tracker_type](args=cfg, frame_rate=30)
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trackers.append(tracker)
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predictor.trackers = trackers
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def on_predict_postprocess_end(predictor):
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bs = predictor.dataset.bs
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im0s = predictor.batch[2]
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im0s = im0s if isinstance(im0s, list) else [im0s]
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for i in range(bs):
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det = predictor.results[i].boxes.cpu().numpy()
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if len(det) == 0:
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continue
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tracks = predictor.trackers[i].update(det, im0s[i])
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if len(tracks) == 0:
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continue
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predictor.results[i].update(boxes=torch.as_tensor(tracks[:, :-1]))
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if predictor.results[i].masks is not None:
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idx = tracks[:, -1].tolist()
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predictor.results[i].masks = predictor.results[i].masks[idx]
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def register_tracker(model):
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model.add_callback("on_predict_start", on_predict_start)
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model.add_callback("on_predict_postprocess_end", on_predict_postprocess_end)
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