SkalskiP
e416ef4c0e
Refactored import paths for test utility methods across multiple test files to align with a recent package restructure. Enriched 'read_txt_file' functionality in 'file.py' allowing to optionally skip empty lines, enhancing flexibility for various use-cases. Added tests for the new 'read_txt_file' behavior in a new 'test_file.py'. Renamed 'utils.py' in test package to 'test_utils.py' for better differentiation from the util package in the source code.
196 lines
7.7 KiB
Python
196 lines
7.7 KiB
Python
from contextlib import ExitStack as DoesNotRaise
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from test.test_utils import mock_detections
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from typing import List, Optional
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import numpy as np
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import pytest
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from supervision import DetectionDataset
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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={
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"image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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={
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"image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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={
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"image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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={
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"image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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={
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"image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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={
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"image-3.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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={
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"image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-3.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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={
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"image-1.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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={
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"image-2.png": np.zeros((100, 100, 3), dtype=np.uint8),
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"image-3.png": np.zeros((100, 100, 3), dtype=np.uint8),
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},
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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: Optional[DetectionDataset],
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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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