Onuralp SEZER
212e69b5a4
- Updated type hints across multiple files to replace `Optional[X]` with `X | None` for better readability and consistency. - Added `from __future__ import annotations` to files to enable postponed evaluation of type annotations. - Adjusted function signatures and variable types in various modules including `polygon_zone.py`, `smoother.py`, `transformers.py`, `vlm.py`, and others. - Ensured compatibility with Python 3.10+ type hinting features. Signed-off-by: Onuralp SEZER <thunderbirdtr@gmail.com>
58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
from __future__ import annotations
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from typing import Any
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import numpy as np
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import numpy.typing as npt
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from supervision.detection.core import Detections
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from supervision.keypoint.core import KeyPoints
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def mock_detections(
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xyxy: npt.NDArray[np.float32],
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mask: list[np.ndarray] | None = None,
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confidence: list[float] | None = None,
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class_id: list[int] | None = None,
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tracker_id: list[int] | None = None,
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data: dict[str, list[Any]] | None = 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 mock_keypoints(
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xy: npt.NDArray[np.float32],
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confidence: list[float] | None = None,
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class_id: list[int] | None = None,
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data: dict[str, list[Any]] | None = None,
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) -> KeyPoints:
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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 KeyPoints(
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xy=np.array(xy, dtype=np.float32),
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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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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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