1158 lines
36 KiB
Python
1158 lines
36 KiB
Python
from contextlib import ExitStack as DoesNotRaise
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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import pytest
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from supervision.config import CLASS_NAME_DATA_FIELD
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from supervision.detection.utils import (
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box_non_max_suppression,
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calculate_masks_centroids,
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clip_boxes,
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filter_polygons_by_area,
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get_data_item,
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mask_non_max_suppression,
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merge_data,
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move_boxes,
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process_roboflow_result,
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scale_boxes,
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)
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TEST_MASK = np.zeros((1, 1000, 1000), dtype=bool)
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TEST_MASK[:, 300:351, 200:251] = True
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@pytest.mark.parametrize(
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"predictions, iou_threshold, expected_result, exception",
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[
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(
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np.empty(shape=(0, 5)),
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0.5,
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np.array([]),
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DoesNotRaise(),
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), # single box with no category
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(
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np.array([[10.0, 10.0, 40.0, 40.0, 0.8]]),
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0.5,
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np.array([True]),
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DoesNotRaise(),
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), # single box with no category
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(
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np.array([[10.0, 10.0, 40.0, 40.0, 0.8, 0]]),
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0.5,
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np.array([True]),
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DoesNotRaise(),
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), # single box with category
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(
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np.array(
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[
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[10.0, 10.0, 40.0, 40.0, 0.8],
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[15.0, 15.0, 40.0, 40.0, 0.9],
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]
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),
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0.5,
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np.array([False, True]),
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DoesNotRaise(),
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), # two boxes with no category
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(
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np.array(
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[
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[10.0, 10.0, 40.0, 40.0, 0.8, 0],
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[15.0, 15.0, 40.0, 40.0, 0.9, 1],
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]
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),
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0.5,
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np.array([True, True]),
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DoesNotRaise(),
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), # two boxes with different category
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(
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np.array(
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[
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[10.0, 10.0, 40.0, 40.0, 0.8, 0],
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[15.0, 15.0, 40.0, 40.0, 0.9, 0],
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]
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),
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0.5,
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np.array([False, True]),
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DoesNotRaise(),
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), # two boxes with same category
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(
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np.array(
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[
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[0.0, 0.0, 30.0, 40.0, 0.8],
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[5.0, 5.0, 35.0, 45.0, 0.9],
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[10.0, 10.0, 40.0, 50.0, 0.85],
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]
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),
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0.5,
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np.array([False, True, False]),
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DoesNotRaise(),
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), # three boxes with no category
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(
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np.array(
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[
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[0.0, 0.0, 30.0, 40.0, 0.8, 0],
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[5.0, 5.0, 35.0, 45.0, 0.9, 1],
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[10.0, 10.0, 40.0, 50.0, 0.85, 2],
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]
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),
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0.5,
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np.array([True, True, True]),
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DoesNotRaise(),
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), # three boxes with same category
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(
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np.array(
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[
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[0.0, 0.0, 30.0, 40.0, 0.8, 0],
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[5.0, 5.0, 35.0, 45.0, 0.9, 0],
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[10.0, 10.0, 40.0, 50.0, 0.85, 1],
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]
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),
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0.5,
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np.array([False, True, True]),
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DoesNotRaise(),
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), # three boxes with different category
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],
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)
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def test_box_non_max_suppression(
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predictions: np.ndarray,
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iou_threshold: float,
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expected_result: Optional[np.ndarray],
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exception: Exception,
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) -> None:
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with exception:
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result = box_non_max_suppression(
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predictions=predictions, iou_threshold=iou_threshold
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)
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assert np.array_equal(result, expected_result)
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@pytest.mark.parametrize(
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"predictions, masks, iou_threshold, expected_result, exception",
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[
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(
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np.empty((0, 6)),
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np.empty((0, 5, 5)),
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0.5,
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np.array([]),
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DoesNotRaise(),
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), # empty predictions and masks
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(
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np.array([[0, 0, 0, 0, 0.8]]),
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np.array(
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[
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[
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[False, False, False, False, False],
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[False, True, True, True, False],
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[False, True, True, True, False],
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[False, True, True, True, False],
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[False, False, False, False, False],
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]
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]
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),
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0.5,
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np.array([True]),
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DoesNotRaise(),
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), # single mask with no category
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(
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np.array([[0, 0, 0, 0, 0.8, 0]]),
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np.array(
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[
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[
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[False, False, False, False, False],
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[False, True, True, True, False],
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[False, True, True, True, False],
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[False, True, True, True, False],
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[False, False, False, False, False],
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]
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]
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),
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0.5,
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np.array([True]),
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DoesNotRaise(),
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), # single mask with category
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(
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np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
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np.array(
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[
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[
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[False, False, False, False, False],
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[False, True, True, False, False],
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[False, True, True, False, False],
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[False, False, False, False, False],
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[False, False, False, False, False],
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],
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[
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[False, False, False, False, False],
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[False, False, False, False, False],
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[False, False, False, True, True],
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[False, False, False, True, True],
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[False, False, False, False, False],
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],
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]
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),
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0.5,
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np.array([True, True]),
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DoesNotRaise(),
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), # two masks non-overlapping with no category
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(
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np.array([[0, 0, 0, 0, 0.8], [0, 0, 0, 0, 0.9]]),
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np.array(
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[
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[
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[False, False, False, False, False],
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[False, True, True, True, False],
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[False, True, True, True, False],
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[False, True, True, True, False],
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[False, False, False, False, False],
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],
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[
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[False, False, False, False, False],
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[False, False, True, True, True],
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[False, False, True, True, True],
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[False, False, True, True, True],
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[False, False, False, False, False],
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],
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]
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),
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0.4,
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np.array([False, True]),
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DoesNotRaise(),
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), # two masks partially overlapping with no category
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(
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np.array([[0, 0, 0, 0, 0.8, 0], [0, 0, 0, 0, 0.9, 1]]),
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np.array(
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[
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[
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[False, False, False, False, False],
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[False, True, True, True, False],
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[False, True, True, True, False],
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[False, True, True, True, False],
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[False, False, False, False, False],
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],
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[
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[False, False, False, False, False],
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[False, False, True, True, True],
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[False, False, True, True, True],
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[False, False, True, True, True],
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[False, False, False, False, False],
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],
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]
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),
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0.5,
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np.array([True, True]),
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DoesNotRaise(),
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), # two masks partially overlapping with different category
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(
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np.array(
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[
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[0, 0, 0, 0, 0.8],
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[0, 0, 0, 0, 0.85],
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[0, 0, 0, 0, 0.9],
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]
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),
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np.array(
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[
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[
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[False, False, False, False, False],
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[False, True, True, False, False],
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[False, True, True, False, False],
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[False, False, False, False, False],
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[False, False, False, False, False],
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],
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[
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[False, False, False, False, False],
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[False, True, True, False, False],
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[False, True, True, False, False],
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[False, False, False, False, False],
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[False, False, False, False, False],
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],
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[
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[False, False, False, False, False],
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[False, False, False, True, True],
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[False, False, False, True, True],
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[False, False, False, False, False],
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[False, False, False, False, False],
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],
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]
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),
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0.5,
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np.array([False, True, True]),
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DoesNotRaise(),
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), # three masks with no category
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(
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np.array(
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[
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[0, 0, 0, 0, 0.8, 0],
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[0, 0, 0, 0, 0.85, 1],
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[0, 0, 0, 0, 0.9, 2],
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]
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),
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np.array(
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[
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[
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[False, False, False, False, False],
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[False, True, True, False, False],
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[False, True, True, False, False],
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[False, False, False, False, False],
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[False, False, False, False, False],
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],
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[
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[False, False, False, False, False],
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[False, True, True, False, False],
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[False, True, True, False, False],
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[False, True, True, False, False],
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[False, False, False, False, False],
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],
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[
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[False, False, False, False, False],
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[False, True, True, False, False],
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[False, True, True, False, False],
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[False, False, False, False, False],
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[False, False, False, False, False],
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],
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]
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),
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0.5,
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np.array([True, True, True]),
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DoesNotRaise(),
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), # three masks with different category
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],
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)
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def test_mask_non_max_suppression(
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predictions: np.ndarray,
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masks: np.ndarray,
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iou_threshold: float,
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expected_result: Optional[np.ndarray],
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exception: Exception,
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) -> None:
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with exception:
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result = mask_non_max_suppression(
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predictions=predictions, masks=masks, iou_threshold=iou_threshold
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)
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assert np.array_equal(result, expected_result)
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@pytest.mark.parametrize(
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"xyxy, resolution_wh, expected_result",
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[
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(
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np.empty(shape=(0, 4)),
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(1280, 720),
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np.empty(shape=(0, 4)),
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),
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(
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np.array([[1.0, 1.0, 1279.0, 719.0]]),
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(1280, 720),
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np.array([[1.0, 1.0, 1279.0, 719.0]]),
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),
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(
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np.array([[-1.0, 1.0, 1279.0, 719.0]]),
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(1280, 720),
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np.array([[0.0, 1.0, 1279.0, 719.0]]),
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),
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(
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np.array([[1.0, -1.0, 1279.0, 719.0]]),
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(1280, 720),
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np.array([[1.0, 0.0, 1279.0, 719.0]]),
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),
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(
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np.array([[1.0, 1.0, 1281.0, 719.0]]),
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(1280, 720),
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np.array([[1.0, 1.0, 1280.0, 719.0]]),
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),
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(
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np.array([[1.0, 1.0, 1279.0, 721.0]]),
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(1280, 720),
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np.array([[1.0, 1.0, 1279.0, 720.0]]),
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),
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],
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)
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def test_clip_boxes(
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xyxy: np.ndarray,
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resolution_wh: Tuple[int, int],
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expected_result: np.ndarray,
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) -> None:
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result = clip_boxes(xyxy=xyxy, resolution_wh=resolution_wh)
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assert np.array_equal(result, expected_result)
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|
|
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@pytest.mark.parametrize(
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"polygons, min_area, max_area, expected_result, exception",
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[
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(
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[np.array([[0, 0], [0, 10], [10, 10], [10, 0]])],
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None,
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None,
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[np.array([[0, 0], [0, 10], [10, 10], [10, 0]])],
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DoesNotRaise(),
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), # single polygon without area constraints
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(
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[np.array([[0, 0], [0, 10], [10, 10], [10, 0]])],
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50,
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None,
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[np.array([[0, 0], [0, 10], [10, 10], [10, 0]])],
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DoesNotRaise(),
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), # single polygon with min_area constraint
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(
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[np.array([[0, 0], [0, 10], [10, 10], [10, 0]])],
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None,
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50,
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[],
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DoesNotRaise(),
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), # single polygon with max_area constraint
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(
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[
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np.array([[0, 0], [0, 10], [10, 10], [10, 0]]),
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np.array([[0, 0], [0, 20], [20, 20], [20, 0]]),
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],
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200,
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None,
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[np.array([[0, 0], [0, 20], [20, 20], [20, 0]])],
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DoesNotRaise(),
|
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), # two polygons with min_area constraint
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(
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[
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np.array([[0, 0], [0, 10], [10, 10], [10, 0]]),
|
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np.array([[0, 0], [0, 20], [20, 20], [20, 0]]),
|
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],
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None,
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200,
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[np.array([[0, 0], [0, 10], [10, 10], [10, 0]])],
|
|
DoesNotRaise(),
|
|
), # two polygons with max_area constraint
|
|
(
|
|
[
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np.array([[0, 0], [0, 10], [10, 10], [10, 0]]),
|
|
np.array([[0, 0], [0, 20], [20, 20], [20, 0]]),
|
|
],
|
|
200,
|
|
200,
|
|
[],
|
|
DoesNotRaise(),
|
|
), # two polygons with both area constraints
|
|
(
|
|
[
|
|
np.array([[0, 0], [0, 10], [10, 10], [10, 0]]),
|
|
np.array([[0, 0], [0, 20], [20, 20], [20, 0]]),
|
|
],
|
|
100,
|
|
100,
|
|
[np.array([[0, 0], [0, 10], [10, 10], [10, 0]])],
|
|
DoesNotRaise(),
|
|
), # two polygons with min_area and
|
|
# max_area equal to the area of the first polygon
|
|
(
|
|
[
|
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np.array([[0, 0], [0, 10], [10, 10], [10, 0]]),
|
|
np.array([[0, 0], [0, 20], [20, 20], [20, 0]]),
|
|
],
|
|
400,
|
|
400,
|
|
[np.array([[0, 0], [0, 20], [20, 20], [20, 0]])],
|
|
DoesNotRaise(),
|
|
), # two polygons with min_area and
|
|
# max_area equal to the area of the second polygon
|
|
],
|
|
)
|
|
def test_filter_polygons_by_area(
|
|
polygons: List[np.ndarray],
|
|
min_area: Optional[float],
|
|
max_area: Optional[float],
|
|
expected_result: List[np.ndarray],
|
|
exception: Exception,
|
|
) -> None:
|
|
with exception:
|
|
result = filter_polygons_by_area(
|
|
polygons=polygons, min_area=min_area, max_area=max_area
|
|
)
|
|
assert len(result) == len(expected_result)
|
|
for result_polygon, expected_result_polygon in zip(result, expected_result):
|
|
assert np.array_equal(result_polygon, expected_result_polygon)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"roboflow_result, expected_result, exception",
|
|
[
|
|
(
|
|
{"predictions": [], "image": {"width": 1000, "height": 1000}},
|
|
(
|
|
np.empty((0, 4)),
|
|
np.empty(0),
|
|
np.empty(0),
|
|
None,
|
|
None,
|
|
{CLASS_NAME_DATA_FIELD: np.empty(0)},
|
|
),
|
|
DoesNotRaise(),
|
|
), # empty result
|
|
(
|
|
{
|
|
"predictions": [
|
|
{
|
|
"x": 200.0,
|
|
"y": 300.0,
|
|
"width": 50.0,
|
|
"height": 50.0,
|
|
"confidence": 0.9,
|
|
"class_id": 0,
|
|
"class": "person",
|
|
}
|
|
],
|
|
"image": {"width": 1000, "height": 1000},
|
|
},
|
|
(
|
|
np.array([[175.0, 275.0, 225.0, 325.0]]),
|
|
np.array([0.9]),
|
|
np.array([0]),
|
|
None,
|
|
None,
|
|
{CLASS_NAME_DATA_FIELD: np.array(["person"])},
|
|
),
|
|
DoesNotRaise(),
|
|
), # single correct object detection result
|
|
(
|
|
{
|
|
"predictions": [
|
|
{
|
|
"x": 200.0,
|
|
"y": 300.0,
|
|
"width": 50.0,
|
|
"height": 50.0,
|
|
"confidence": 0.9,
|
|
"class_id": 0,
|
|
"class": "person",
|
|
"tracker_id": 1,
|
|
},
|
|
{
|
|
"x": 500.0,
|
|
"y": 500.0,
|
|
"width": 100.0,
|
|
"height": 100.0,
|
|
"confidence": 0.8,
|
|
"class_id": 7,
|
|
"class": "truck",
|
|
"tracker_id": 2,
|
|
},
|
|
],
|
|
"image": {"width": 1000, "height": 1000},
|
|
},
|
|
(
|
|
np.array([[175.0, 275.0, 225.0, 325.0], [450.0, 450.0, 550.0, 550.0]]),
|
|
np.array([0.9, 0.8]),
|
|
np.array([0, 7]),
|
|
None,
|
|
np.array([1, 2]),
|
|
{CLASS_NAME_DATA_FIELD: np.array(["person", "truck"])},
|
|
),
|
|
DoesNotRaise(),
|
|
), # two correct object detection result
|
|
(
|
|
{
|
|
"predictions": [
|
|
{
|
|
"x": 200.0,
|
|
"y": 300.0,
|
|
"width": 50.0,
|
|
"height": 50.0,
|
|
"confidence": 0.9,
|
|
"class_id": 0,
|
|
"class": "person",
|
|
"points": [],
|
|
"tracker_id": None,
|
|
}
|
|
],
|
|
"image": {"width": 1000, "height": 1000},
|
|
},
|
|
(
|
|
np.empty((0, 4)),
|
|
np.empty(0),
|
|
np.empty(0),
|
|
None,
|
|
None,
|
|
{CLASS_NAME_DATA_FIELD: np.empty(0)},
|
|
),
|
|
DoesNotRaise(),
|
|
), # single incorrect instance segmentation result with no points
|
|
(
|
|
{
|
|
"predictions": [
|
|
{
|
|
"x": 200.0,
|
|
"y": 300.0,
|
|
"width": 50.0,
|
|
"height": 50.0,
|
|
"confidence": 0.9,
|
|
"class_id": 0,
|
|
"class": "person",
|
|
"points": [{"x": 200.0, "y": 300.0}, {"x": 250.0, "y": 300.0}],
|
|
}
|
|
],
|
|
"image": {"width": 1000, "height": 1000},
|
|
},
|
|
(
|
|
np.empty((0, 4)),
|
|
np.empty(0),
|
|
np.empty(0),
|
|
None,
|
|
None,
|
|
{CLASS_NAME_DATA_FIELD: np.empty(0)},
|
|
),
|
|
DoesNotRaise(),
|
|
), # single incorrect instance segmentation result with no enough points
|
|
(
|
|
{
|
|
"predictions": [
|
|
{
|
|
"x": 200.0,
|
|
"y": 300.0,
|
|
"width": 50.0,
|
|
"height": 50.0,
|
|
"confidence": 0.9,
|
|
"class_id": 0,
|
|
"class": "person",
|
|
"points": [
|
|
{"x": 200.0, "y": 300.0},
|
|
{"x": 250.0, "y": 300.0},
|
|
{"x": 250.0, "y": 350.0},
|
|
{"x": 200.0, "y": 350.0},
|
|
],
|
|
}
|
|
],
|
|
"image": {"width": 1000, "height": 1000},
|
|
},
|
|
(
|
|
np.array([[175.0, 275.0, 225.0, 325.0]]),
|
|
np.array([0.9]),
|
|
np.array([0]),
|
|
TEST_MASK,
|
|
None,
|
|
{CLASS_NAME_DATA_FIELD: np.array(["person"])},
|
|
),
|
|
DoesNotRaise(),
|
|
), # single incorrect instance segmentation result with no enough points
|
|
(
|
|
{
|
|
"predictions": [
|
|
{
|
|
"x": 200.0,
|
|
"y": 300.0,
|
|
"width": 50.0,
|
|
"height": 50.0,
|
|
"confidence": 0.9,
|
|
"class_id": 0,
|
|
"class": "person",
|
|
"points": [
|
|
{"x": 200.0, "y": 300.0},
|
|
{"x": 250.0, "y": 300.0},
|
|
{"x": 250.0, "y": 350.0},
|
|
{"x": 200.0, "y": 350.0},
|
|
],
|
|
},
|
|
{
|
|
"x": 500.0,
|
|
"y": 500.0,
|
|
"width": 100.0,
|
|
"height": 100.0,
|
|
"confidence": 0.8,
|
|
"class_id": 7,
|
|
"class": "truck",
|
|
"points": [],
|
|
},
|
|
],
|
|
"image": {"width": 1000, "height": 1000},
|
|
},
|
|
(
|
|
np.array([[175.0, 275.0, 225.0, 325.0]]),
|
|
np.array([0.9]),
|
|
np.array([0]),
|
|
TEST_MASK,
|
|
None,
|
|
{CLASS_NAME_DATA_FIELD: np.array(["person"])},
|
|
),
|
|
DoesNotRaise(),
|
|
), # two instance segmentation results - one correct, one incorrect
|
|
],
|
|
)
|
|
def test_process_roboflow_result(
|
|
roboflow_result: dict,
|
|
expected_result: Tuple[
|
|
np.ndarray, np.ndarray, np.ndarray, Optional[np.ndarray], np.ndarray
|
|
],
|
|
exception: Exception,
|
|
) -> None:
|
|
with exception:
|
|
result = process_roboflow_result(roboflow_result=roboflow_result)
|
|
assert np.array_equal(result[0], expected_result[0])
|
|
assert np.array_equal(result[1], expected_result[1])
|
|
assert np.array_equal(result[2], expected_result[2])
|
|
assert (result[3] is None and expected_result[3] is None) or (
|
|
np.array_equal(result[3], expected_result[3])
|
|
)
|
|
assert (result[4] is None and expected_result[4] is None) or (
|
|
np.array_equal(result[4], expected_result[4])
|
|
)
|
|
for key in result[5]:
|
|
if isinstance(result[5][key], np.ndarray):
|
|
assert np.array_equal(
|
|
result[5][key], expected_result[5][key]
|
|
), f"Mismatch in arrays for key {key}"
|
|
else:
|
|
assert (
|
|
result[5][key] == expected_result[5][key]
|
|
), f"Mismatch in non-array data for key {key}"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"xyxy, offset, expected_result, exception",
|
|
[
|
|
(
|
|
np.empty(shape=(0, 4)),
|
|
np.array([0, 0]),
|
|
np.empty(shape=(0, 4)),
|
|
DoesNotRaise(),
|
|
), # empty xyxy array
|
|
(
|
|
np.array([[0, 0, 10, 10]]),
|
|
np.array([0, 0]),
|
|
np.array([[0, 0, 10, 10]]),
|
|
DoesNotRaise(),
|
|
), # single box with zero offset
|
|
(
|
|
np.array([[0, 0, 10, 10]]),
|
|
np.array([10, 10]),
|
|
np.array([[10, 10, 20, 20]]),
|
|
DoesNotRaise(),
|
|
), # single box with non-zero offset
|
|
(
|
|
np.array([[0, 0, 10, 10], [0, 0, 10, 10]]),
|
|
np.array([10, 10]),
|
|
np.array([[10, 10, 20, 20], [10, 10, 20, 20]]),
|
|
DoesNotRaise(),
|
|
), # two boxes with non-zero offset
|
|
(
|
|
np.array([[0, 0, 10, 10], [0, 0, 10, 10]]),
|
|
np.array([-10, -10]),
|
|
np.array([[-10, -10, 0, 0], [-10, -10, 0, 0]]),
|
|
DoesNotRaise(),
|
|
), # two boxes with negative offset
|
|
],
|
|
)
|
|
def test_move_boxes(
|
|
xyxy: np.ndarray,
|
|
offset: np.ndarray,
|
|
expected_result: np.ndarray,
|
|
exception: Exception,
|
|
) -> None:
|
|
with exception:
|
|
result = move_boxes(xyxy=xyxy, offset=offset)
|
|
assert np.array_equal(result, expected_result)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"xyxy, factor, expected_result, exception",
|
|
[
|
|
(
|
|
np.empty(shape=(0, 4)),
|
|
2.0,
|
|
np.empty(shape=(0, 4)),
|
|
DoesNotRaise(),
|
|
), # empty xyxy array
|
|
(
|
|
np.array([[0, 0, 10, 10]]),
|
|
1.0,
|
|
np.array([[0, 0, 10, 10]]),
|
|
DoesNotRaise(),
|
|
), # single box with factor equal to 1.0
|
|
(
|
|
np.array([[0, 0, 10, 10]]),
|
|
2.0,
|
|
np.array([[-5, -5, 15, 15]]),
|
|
DoesNotRaise(),
|
|
), # single box with factor equal to 2.0
|
|
(
|
|
np.array([[0, 0, 10, 10]]),
|
|
0.5,
|
|
np.array([[2.5, 2.5, 7.5, 7.5]]),
|
|
DoesNotRaise(),
|
|
), # single box with factor equal to 0.5
|
|
(
|
|
np.array([[0, 0, 10, 10], [10, 10, 30, 30]]),
|
|
2.0,
|
|
np.array([[-5, -5, 15, 15], [0, 0, 40, 40]]),
|
|
DoesNotRaise(),
|
|
), # two boxes with factor equal to 2.0
|
|
],
|
|
)
|
|
def test_scale_boxes(
|
|
xyxy: np.ndarray,
|
|
factor: float,
|
|
expected_result: np.ndarray,
|
|
exception: Exception,
|
|
) -> None:
|
|
with exception:
|
|
result = scale_boxes(xyxy=xyxy, factor=factor)
|
|
assert np.array_equal(result, expected_result)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"masks, expected_result, exception",
|
|
[
|
|
(
|
|
np.array(
|
|
[
|
|
[
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
[0, 0, 0, 0],
|
|
]
|
|
]
|
|
),
|
|
np.array([[0, 0]]),
|
|
DoesNotRaise(),
|
|
), # single mask with all zeros
|
|
(
|
|
np.array(
|
|
[
|
|
[
|
|
[1, 1, 1, 1],
|
|
[1, 1, 1, 1],
|
|
[1, 1, 1, 1],
|
|
[1, 1, 1, 1],
|
|
]
|
|
]
|
|
),
|
|
np.array([[2, 2]]),
|
|
DoesNotRaise(),
|
|
), # single mask with all ones
|
|
(
|
|
np.array(
|
|
[
|
|
[
|
|
[0, 1, 1, 0],
|
|
[1, 1, 1, 1],
|
|
[1, 1, 1, 1],
|
|
[0, 1, 1, 0],
|
|
]
|
|
]
|
|
),
|
|
np.array([[2, 2]]),
|
|
DoesNotRaise(),
|
|
), # single mask with symmetric ones
|
|
(
|
|
np.array(
|
|
[
|
|
[
|
|
[0, 0, 0, 0],
|
|
[0, 0, 1, 1],
|
|
[0, 0, 1, 1],
|
|
[0, 0, 0, 0],
|
|
]
|
|
]
|
|
),
|
|
np.array([[3, 2]]),
|
|
DoesNotRaise(),
|
|
), # single mask with asymmetric ones
|
|
(
|
|
np.array(
|
|
[
|
|
[
|
|
[0, 1, 1, 0],
|
|
[1, 1, 1, 1],
|
|
[1, 1, 1, 1],
|
|
[0, 1, 1, 0],
|
|
],
|
|
[
|
|
[0, 0, 0, 0],
|
|
[0, 0, 1, 1],
|
|
[0, 0, 1, 1],
|
|
[0, 0, 0, 0],
|
|
],
|
|
]
|
|
),
|
|
np.array([[2, 2], [3, 2]]),
|
|
DoesNotRaise(),
|
|
), # two masks
|
|
],
|
|
)
|
|
def test_calculate_masks_centroids(
|
|
masks: np.ndarray,
|
|
expected_result: np.ndarray,
|
|
exception: Exception,
|
|
) -> None:
|
|
with exception:
|
|
result = calculate_masks_centroids(masks=masks)
|
|
assert np.array_equal(result, expected_result)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data_list, expected_result, exception",
|
|
[
|
|
(
|
|
[],
|
|
{},
|
|
DoesNotRaise(),
|
|
), # empty data list
|
|
(
|
|
[{}],
|
|
{},
|
|
DoesNotRaise(),
|
|
), # single empty data dict
|
|
(
|
|
[{}, {}],
|
|
{},
|
|
DoesNotRaise(),
|
|
), # two empty data dicts
|
|
(
|
|
[
|
|
{"test_1": []},
|
|
],
|
|
{"test_1": []},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and empty list values
|
|
(
|
|
[
|
|
{"test_1": np.array([])},
|
|
],
|
|
{"test_1": np.array([])},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and empty np.array values
|
|
(
|
|
[
|
|
{"test_1": [1, 2, 3]},
|
|
],
|
|
{"test_1": [1, 2, 3]},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and list values
|
|
(
|
|
[
|
|
{"test_1": []},
|
|
{"test_1": [3, 2, 1]},
|
|
],
|
|
{"test_1": [3, 2, 1]},
|
|
DoesNotRaise(),
|
|
), # two data dicts with the same field name and empty and list values
|
|
(
|
|
[
|
|
{"test_1": [1, 2, 3]},
|
|
{"test_1": [3, 2, 1]},
|
|
],
|
|
{"test_1": [1, 2, 3, 3, 2, 1]},
|
|
DoesNotRaise(),
|
|
), # two data dicts with the same field name and list values
|
|
(
|
|
[
|
|
{"test_1": [1, 2, 3]},
|
|
{"test_1": [3, 2, 1]},
|
|
{"test_1": [1, 2, 3]},
|
|
],
|
|
{"test_1": [1, 2, 3, 3, 2, 1, 1, 2, 3]},
|
|
DoesNotRaise(),
|
|
), # three data dicts with the same field name and list values
|
|
(
|
|
[
|
|
{"test_1": [1, 2, 3]},
|
|
{"test_2": [3, 2, 1]},
|
|
],
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # two data dicts with different field names
|
|
(
|
|
[
|
|
{"test_1": np.array([1, 2, 3])},
|
|
{"test_1": np.array([3, 2, 1])},
|
|
],
|
|
{"test_1": np.array([1, 2, 3, 3, 2, 1])},
|
|
DoesNotRaise(),
|
|
), # two data dicts with the same field name and np.array values as 1D arrays
|
|
(
|
|
[
|
|
{"test_1": np.array([[1, 2, 3]])},
|
|
{"test_1": np.array([[3, 2, 1]])},
|
|
],
|
|
{"test_1": np.array([[1, 2, 3], [3, 2, 1]])},
|
|
DoesNotRaise(),
|
|
), # two data dicts with the same field name and np.array values as 2D arrays
|
|
(
|
|
[
|
|
{"test_1": np.array([1, 2, 3]), "test_2": np.array(["a", "b", "c"])},
|
|
{"test_1": np.array([3, 2, 1]), "test_2": np.array(["c", "b", "a"])},
|
|
],
|
|
{
|
|
"test_1": np.array([1, 2, 3, 3, 2, 1]),
|
|
"test_2": np.array(["a", "b", "c", "c", "b", "a"]),
|
|
},
|
|
DoesNotRaise(),
|
|
), # two data dicts with the same field names and np.array values
|
|
(
|
|
[
|
|
{"test_1": [1, 2, 3], "test_2": np.array(["a", "b", "c"])},
|
|
{"test_1": [3, 2, 1], "test_2": np.array(["c", "b", "a"])},
|
|
],
|
|
{
|
|
"test_1": [1, 2, 3, 3, 2, 1],
|
|
"test_2": np.array(["a", "b", "c", "c", "b", "a"]),
|
|
},
|
|
DoesNotRaise(),
|
|
), # two data dicts with the same field names and mixed values
|
|
(
|
|
[
|
|
{"test_1": np.array([1, 2, 3])},
|
|
{"test_1": np.array([[3, 2, 1]])},
|
|
],
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # two data dicts with the same field name and 1D and 2D arrays values
|
|
(
|
|
[
|
|
{"test_1": np.array([1, 2, 3]), "test_2": np.array(["a", "b"])},
|
|
{"test_1": np.array([3, 2, 1]), "test_2": np.array(["c", "b", "a"])},
|
|
],
|
|
None,
|
|
pytest.raises(ValueError),
|
|
), # two data dicts with the same field name and different length arrays values
|
|
],
|
|
)
|
|
def test_merge_data(
|
|
data_list: List[Dict[str, Any]],
|
|
expected_result: Optional[Dict[str, Any]],
|
|
exception: Exception,
|
|
):
|
|
with exception:
|
|
result = merge_data(data_list=data_list)
|
|
for key in result:
|
|
if isinstance(result[key], np.ndarray):
|
|
assert np.array_equal(
|
|
result[key], expected_result[key]
|
|
), f"Mismatch in arrays for key {key}"
|
|
else:
|
|
assert (
|
|
result[key] == expected_result[key]
|
|
), f"Mismatch in non-array data for key {key}"
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"data, index, expected_result, exception",
|
|
[
|
|
({}, 0, {}, DoesNotRaise()), # empty data dict
|
|
(
|
|
{
|
|
"test_1": [1, 2, 3],
|
|
},
|
|
0,
|
|
{
|
|
"test_1": [1],
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and list values
|
|
(
|
|
{
|
|
"test_1": np.array([1, 2, 3]),
|
|
},
|
|
0,
|
|
{
|
|
"test_1": np.array([1]),
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and np.array values as 1D arrays
|
|
(
|
|
{
|
|
"test_1": [1, 2, 3],
|
|
},
|
|
slice(0, 2),
|
|
{
|
|
"test_1": [1, 2],
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and list values
|
|
(
|
|
{
|
|
"test_1": np.array([1, 2, 3]),
|
|
},
|
|
slice(0, 2),
|
|
{
|
|
"test_1": np.array([1, 2]),
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and np.array values as 1D arrays
|
|
(
|
|
{
|
|
"test_1": [1, 2, 3],
|
|
},
|
|
-1,
|
|
{
|
|
"test_1": [3],
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and list values
|
|
(
|
|
{
|
|
"test_1": np.array([1, 2, 3]),
|
|
},
|
|
-1,
|
|
{
|
|
"test_1": np.array([3]),
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and np.array values as 1D arrays
|
|
(
|
|
{
|
|
"test_1": [1, 2, 3],
|
|
},
|
|
[0, 2],
|
|
{
|
|
"test_1": [1, 3],
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and list values
|
|
(
|
|
{
|
|
"test_1": np.array([1, 2, 3]),
|
|
},
|
|
[0, 2],
|
|
{
|
|
"test_1": np.array([1, 3]),
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and np.array values as 1D arrays
|
|
(
|
|
{
|
|
"test_1": [1, 2, 3],
|
|
},
|
|
np.array([0, 2]),
|
|
{
|
|
"test_1": [1, 3],
|
|
},
|
|
DoesNotRaise(),
|
|
), # single data dict with a single field name and list values
|
|
(
|
|
{
|
|
"test_1": np.array([1, 2, 3]),
|
|
},
|
|
np.array([0, 2]),
|
|
{
|
|
"test_1": np.array([1, 3]),
|
|
},
|
|
DoesNotRaise(),
|
|
),
|
|
],
|
|
)
|
|
def test_get_data_item(
|
|
data: Dict[str, Any],
|
|
index: Any,
|
|
expected_result: Optional[Dict[str, Any]],
|
|
exception: Exception,
|
|
):
|
|
with exception:
|
|
result = get_data_item(data=data, index=index)
|
|
for key in result:
|
|
if isinstance(result[key], np.ndarray):
|
|
assert np.array_equal(
|
|
result[key], expected_result[key]
|
|
), f"Mismatch in arrays for key {key}"
|
|
else:
|
|
assert (
|
|
result[key] == expected_result[key]
|
|
), f"Mismatch in non-array data for key {key}"
|