35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
from typing import Any, Dict, List, Optional
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import numpy as np
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from supervision.detection.core import Detections
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def mock_detections(
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xyxy: List[List[float]],
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mask: Optional[List[np.ndarray]] = None,
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confidence: Optional[List[float]] = None,
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class_id: Optional[List[int]] = None,
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tracker_id: Optional[List[int]] = None,
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data: Optional[Dict[str, List[Any]]] = None,
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) -> Detections:
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def convert_data(data: Dict[str, List[Any]]):
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return {k: np.array(v) for k, v in data.items()}
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return Detections(
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xyxy=np.array(xyxy, dtype=np.float32),
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mask=(mask if mask is None else np.array(mask, dtype=bool)),
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confidence=(
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confidence if confidence is None else np.array(confidence, dtype=np.float32)
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),
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class_id=(class_id if class_id is None else np.array(class_id, dtype=int)),
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tracker_id=(
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tracker_id if tracker_id is None else np.array(tracker_id, dtype=int)
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),
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data=convert_data(data) if data else {},
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)
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def assert_almost_equal(actual, expected, tolerance=1e-5):
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assert abs(actual - expected) < tolerance, f"Expected {expected}, but got {actual}."
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