471 lines
18 KiB
Python
471 lines
18 KiB
Python
from contextlib import ExitStack as DoesNotRaise
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from pathlib import Path
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import numpy as np
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import numpy.typing as npt
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import pytest
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from supervision import DetectionDataset, Detections
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from supervision.config import CLASS_NAME_DATA_FIELD
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from supervision.utils.internal import SupervisionWarnings
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from tests.helpers import _create_detections, create_yolo_dataset
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def _create_image(fill_value: int) -> npt.NDArray[np.uint8]:
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return np.full((4, 4, 3), fill_value, dtype=np.uint8)
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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": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": _create_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": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": _create_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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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": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": _create_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": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[1]
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),
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"image-2.png": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[2]
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),
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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": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": _create_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": _create_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": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[1]
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),
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"image-2.png": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[2]
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),
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"image-3.png": _create_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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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": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-2.png": _create_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": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"image-3.png": _create_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, match="not unique across datasets"),
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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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"""
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Verify that multiple DetectionDataset objects can be successfully merged.
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Ensures that multiple `DetectionDataset` objects can be merged into single dataset.
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This is vital for users who need to combine data from different sources or
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augment their datasets with additional labeled examples.
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"""
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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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class TestClassNamePopulation:
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"""Verify that DetectionDataset populates CLASS_NAME_DATA_FIELD on init."""
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def test_class_name_populated_on_init(self) -> None:
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"""Basic case: class_name data field is set from classes and class_id."""
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dataset = DetectionDataset(
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classes=["dog", "cat"],
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images=["img1.png"],
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annotations={
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"img1.png": _create_detections(
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xyxy=[[0, 0, 10, 10], [20, 20, 30, 30]],
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class_id=[0, 1],
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),
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},
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)
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annotation = dataset.annotations["img1.png"]
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assert CLASS_NAME_DATA_FIELD in annotation.data
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np.testing.assert_array_equal(
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annotation.data[CLASS_NAME_DATA_FIELD],
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np.array(["dog", "cat"]),
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)
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def test_class_name_with_empty_annotations(self) -> None:
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"""Empty Detections should not raise an error."""
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dataset = DetectionDataset(
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classes=["dog"],
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images=["img1.png"],
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annotations={"img1.png": Detections.empty()},
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)
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annotation = dataset.annotations["img1.png"]
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assert CLASS_NAME_DATA_FIELD in annotation.data
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assert len(annotation.data[CLASS_NAME_DATA_FIELD]) == 0
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def test_class_name_with_empty_classes(self) -> None:
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"""When classes is empty, class_name should not be populated."""
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dataset = DetectionDataset(
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classes=[],
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images=[],
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annotations={},
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)
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assert len(dataset.annotations) == 0
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def test_class_name_after_merge(self) -> None:
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"""After merging datasets, class_name must match remapped class_id."""
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ds1 = DetectionDataset(
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classes=["dog", "person"],
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images=["img1.png"],
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annotations={
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"img1.png": _create_detections(xyxy=[[0, 0, 10, 10]], class_id=[0]),
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},
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)
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ds2 = DetectionDataset(
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classes=["cat"],
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images=["img2.png"],
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annotations={
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"img2.png": _create_detections(xyxy=[[0, 0, 10, 10]], class_id=[0]),
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},
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)
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merged = DetectionDataset.merge([ds1, ds2])
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# merged.classes is ["cat", "dog", "person"]
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# ds1's dog (0) -> dog (1), ds2's cat (0) -> cat (0)
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ann1 = merged.annotations["img1.png"]
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assert CLASS_NAME_DATA_FIELD in ann1.data
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np.testing.assert_array_equal(
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ann1.data[CLASS_NAME_DATA_FIELD], np.array(["dog"])
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)
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ann2 = merged.annotations["img2.png"]
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assert CLASS_NAME_DATA_FIELD in ann2.data
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np.testing.assert_array_equal(
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ann2.data[CLASS_NAME_DATA_FIELD], np.array(["cat"])
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)
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def test_class_name_from_yolo(self, tmp_path: Path) -> None:
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"""Integration test: from_yolo should produce class_name data."""
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dataset_info = create_yolo_dataset(
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str(tmp_path), num_images=2, classes=["cat", "dog"]
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)
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dataset = DetectionDataset.from_yolo(
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images_directory_path=dataset_info["images_dir"],
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annotations_directory_path=dataset_info["labels_dir"],
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data_yaml_path=dataset_info["data_yaml_path"],
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)
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for _, annotation in dataset.annotations.items():
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if annotation.class_id is not None and len(annotation.class_id) > 0:
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assert CLASS_NAME_DATA_FIELD in annotation.data
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expected_names = np.array(dataset.classes)[annotation.class_id]
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np.testing.assert_array_equal(
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annotation.data[CLASS_NAME_DATA_FIELD], expected_names
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)
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class TestDetectionDatasetInMemoryImages:
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"""Verify DetectionDataset keeps dict-provided images in memory (DAT-01)."""
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@staticmethod
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def _build_dataset(
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images: dict[str, npt.NDArray[np.uint8]],
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) -> DetectionDataset:
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annotations = {
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path: _create_detections(xyxy=[[0, 0, 10, 10]], class_id=[0])
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for path in images
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}
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return DetectionDataset(classes=["dog"], images=images, annotations=annotations)
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def test_getitem_returns_in_memory_image(self) -> None:
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"""Indexing a dict-constructed dataset returns the in-memory array."""
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image = _create_image(fill_value=7)
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dataset = self._build_dataset({"imgX.jpg": image})
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image_path, loaded_image, _ = dataset[0]
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assert image_path == "imgX.jpg"
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np.testing.assert_array_equal(loaded_image, image)
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def test_len_counts_in_memory_images(self) -> None:
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"""`len` of a dict-constructed dataset equals the number of provided images."""
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images = {
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"img1.jpg": _create_image(fill_value=1),
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"img2.jpg": _create_image(fill_value=2),
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}
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dataset = self._build_dataset(images)
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assert len(dataset) == 2
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def test_merge_preserves_in_memory_pixel_access(self) -> None:
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"""Merging two in-memory datasets keeps pixel access via public __getitem__."""
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image_1 = _create_image(fill_value=10)
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image_2 = _create_image(fill_value=20)
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ds_1 = self._build_dataset({"img1.jpg": image_1})
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ds_2 = self._build_dataset({"img2.jpg": image_2})
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merged = DetectionDataset.merge([ds_1, ds_2])
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assert len(merged) == 2
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_, loaded_1, _ = merged[0]
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_, loaded_2, _ = merged[1]
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np.testing.assert_array_equal(loaded_1, image_1)
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np.testing.assert_array_equal(loaded_2, image_2)
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def test_iteration_yields_in_memory_images(self) -> None:
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"""Iteration yields (path, image, annotation) with correct pixels."""
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images = {
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"img1.jpg": _create_image(fill_value=1),
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"img2.jpg": _create_image(fill_value=2),
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}
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dataset = self._build_dataset(images)
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entries = list(dataset)
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assert [path for path, _, _ in entries] == ["img1.jpg", "img2.jpg"]
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for image_path, loaded_image, annotation in entries:
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np.testing.assert_array_equal(loaded_image, images[image_path])
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assert annotation is dataset.annotations[image_path]
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def test_dict_input_emits_deprecation_warning(self) -> None:
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"""Passing a dict of images emits the SupervisionWarnings deprecation notice."""
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with pytest.warns(SupervisionWarnings, match="deprecated"):
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self._build_dataset({"img1.jpg": _create_image(fill_value=3)})
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def test_eq_reflexive_in_memory(self) -> None:
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"""In-memory dataset equals itself (reflexive __eq__ via pixel comparison)."""
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images = {
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"img1.jpg": _create_image(fill_value=1),
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"img2.jpg": _create_image(fill_value=2),
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}
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dataset = self._build_dataset(images)
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assert dataset == dataset
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def test_eq_same_pixels_returns_true(self) -> None:
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"""Two in-memory datasets with identical images and annotations are equal."""
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images = {"img1.jpg": _create_image(fill_value=5)}
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ds_a = self._build_dataset(images)
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ds_b = self._build_dataset(dict(images))
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assert ds_a == ds_b
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def test_eq_different_pixels_returns_false(self) -> None:
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"""In-memory datasets with different pixel data are not equal."""
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ds_a = self._build_dataset({"img1.jpg": _create_image(fill_value=1)})
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ds_b = self._build_dataset({"img1.jpg": _create_image(fill_value=2)})
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assert ds_a != ds_b
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class TestDetectionDatasetExportCollisions:
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"""Regression tests for the basename-collision guard on export (DAT-04)."""
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def test_as_yolo_raises_on_same_basename_images(self, tmp_path: Path) -> None:
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"""Same-basename images from different directories must not overwrite."""
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dataset = DetectionDataset(
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classes=["cat"],
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images=["dir_a/img.png", "dir_b/img.png"],
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annotations={
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"dir_a/img.png": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"dir_b/img.png": _create_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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with pytest.raises(ValueError, match="both map to image file"):
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dataset.as_yolo(images_directory_path=str(tmp_path / "images"))
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def test_as_yolo_raises_on_same_stem_annotations(self, tmp_path: Path) -> None:
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"""Same-stem images must not overwrite annotations."""
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dataset = DetectionDataset(
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classes=["cat"],
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images=["dir_a/img.jpg", "dir_b/img.png"],
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annotations={
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"dir_a/img.jpg": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"dir_b/img.png": _create_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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with pytest.raises(ValueError, match="both map to YOLO annotation file"):
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dataset.as_yolo(
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images_directory_path=str(tmp_path / "images"),
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annotations_directory_path=str(tmp_path / "labels"),
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)
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def test_as_pascal_voc_raises_on_same_basename_images(self, tmp_path: Path) -> None:
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"""Same-basename images must not overwrite image files on export."""
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dataset = DetectionDataset(
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classes=["cat"],
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images=["dir_a/img.jpg", "dir_b/img.jpg"],
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annotations={
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"dir_a/img.jpg": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"dir_b/img.jpg": _create_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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with pytest.raises(ValueError, match="both map to image file"):
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dataset.as_pascal_voc(
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images_directory_path=str(tmp_path / "images"),
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)
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def test_as_pascal_voc_raises_on_same_stem_annotations(
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self, tmp_path: Path
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) -> None:
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"""Same-stem images must not overwrite annotations."""
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dataset = DetectionDataset(
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classes=["cat"],
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images=["dir_a/img.jpg", "dir_b/img.png"],
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annotations={
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"dir_a/img.jpg": _create_detections(
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xyxy=[[0, 0, 10, 10]], class_id=[0]
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),
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"dir_b/img.png": _create_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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with pytest.raises(ValueError, match="both map to Pascal VOC annotation file"):
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dataset.as_pascal_voc(
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annotations_directory_path=str(tmp_path / "annotations"),
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)
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