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>
169 lines
6.4 KiB
Python
169 lines
6.4 KiB
Python
from __future__ import annotations
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from contextlib import ExitStack as DoesNotRaise
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import pytest
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from supervision import DetectionDataset
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from test.test_utils import mock_detections
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@pytest.mark.parametrize(
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"dataset_list, expected_result, exception",
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[
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(
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[],
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DetectionDataset(classes=[], images=[], annotations={}),
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DoesNotRaise(),
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), # empty dataset list
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(
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[DetectionDataset(classes=[], images=[], annotations={})],
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DetectionDataset(classes=[], images=[], annotations={}),
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DoesNotRaise(),
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), # single empty dataset
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(
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[
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DetectionDataset(classes=["dog", "person"], images=[], annotations={}),
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DetectionDataset(classes=["dog", "person"], images=[], annotations={}),
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],
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DetectionDataset(classes=["dog", "person"], images=[], annotations={}),
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DoesNotRaise(),
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), # two datasets; no images and annotations, the same classes
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(
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[
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DetectionDataset(classes=["dog", "person"], images=[], annotations={}),
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DetectionDataset(classes=["cat"], images=[], annotations={}),
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],
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DetectionDataset(
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classes=["cat", "dog", "person"], images=[], annotations={}
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),
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DoesNotRaise(),
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), # two datasets; no images and annotations, different classes
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(
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[
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DetectionDataset(
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classes=["dog", "person"],
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images=["image-1.png", "image-2.png"],
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annotations={
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"image-1.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[1]
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),
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},
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),
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DetectionDataset(classes=[], images=[], annotations={}),
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],
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DetectionDataset(
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classes=["dog", "person"],
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images=["image-1.png", "image-2.png"],
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annotations={
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"image-1.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[0]),
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"image-2.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[1]),
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},
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),
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DoesNotRaise(),
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), # two datasets; images and annotations, the same classes
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(
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[
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DetectionDataset(
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classes=["dog", "person"],
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images=["image-1.png", "image-2.png"],
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annotations={
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"image-1.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[1]
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),
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},
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),
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DetectionDataset(classes=["cat"], images=[], annotations={}),
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],
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DetectionDataset(
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classes=["cat", "dog", "person"],
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images=["image-1.png", "image-2.png"],
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annotations={
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"image-1.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[1]),
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"image-2.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[2]),
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},
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),
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DoesNotRaise(),
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), # two datasets; images and annotations, different classes
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(
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[
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DetectionDataset(
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classes=["dog", "person"],
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images=["image-1.png", "image-2.png"],
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annotations={
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"image-1.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[1]
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),
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},
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),
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DetectionDataset(
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classes=["cat"],
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images=["image-3.png"],
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annotations={
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"image-3.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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},
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),
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],
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DetectionDataset(
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classes=["cat", "dog", "person"],
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images=["image-1.png", "image-2.png", "image-3.png"],
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annotations={
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"image-1.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[1]),
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"image-2.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[2]),
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"image-3.png": mock_detections(xyxy=[[0, 0, 10, 10]], class_id=[0]),
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},
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),
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DoesNotRaise(),
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), # two datasets; images and annotations, different classes
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(
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[
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DetectionDataset(
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classes=["dog", "person"],
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images=["image-1.png", "image-2.png"],
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annotations={
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"image-1.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[1]
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),
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},
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),
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DetectionDataset(
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classes=["dog", "person"],
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images=["image-2.png", "image-3.png"],
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annotations={
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"image-2.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-3.png": mock_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[1]
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),
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},
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),
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],
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None,
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pytest.raises(ValueError),
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),
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],
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)
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def test_dataset_merge(
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dataset_list: list[DetectionDataset],
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expected_result: DetectionDataset | None,
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exception: Exception,
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) -> None:
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with exception:
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result = DetectionDataset.merge(dataset_list=dataset_list)
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assert result == expected_result
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