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63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
"""Tests for supervision.metrics.utils.object_size — mask pixel-count path."""
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from __future__ import annotations
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import numpy as np
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import pytest
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from supervision.metrics.utils.object_size import (
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SIZE_THRESHOLDS,
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ObjectSizeCategory,
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get_mask_size_category,
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)
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def _reference_mask_size_category(mask: np.ndarray) -> np.ndarray:
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"""Reference categorization using a plain ``np.sum`` area."""
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areas = np.sum(mask, axis=(1, 2))
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result = np.full(areas.shape, ObjectSizeCategory.ANY.value)
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sm, lg = SIZE_THRESHOLDS
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result[areas < sm] = ObjectSizeCategory.SMALL.value
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result[(areas >= sm) & (areas < lg)] = ObjectSizeCategory.MEDIUM.value
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result[areas >= lg] = ObjectSizeCategory.LARGE.value
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return result
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class TestGetMaskSizeCategory:
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"""Verify the count_nonzero-based mask area categorization."""
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@pytest.mark.parametrize(
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("n", "h", "w", "seed"),
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[
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pytest.param(0, 8, 8, 1, id="empty"),
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pytest.param(1, 40, 40, 2, id="single"),
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pytest.param(6, 64, 64, 3, id="batch"),
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pytest.param(4, 200, 200, 4, id="across-thresholds"),
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],
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)
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def test_matches_sum_based_reference(
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self, n: int, h: int, w: int, seed: int
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) -> None:
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"""Categories match a plain ``np.sum(axis=(1, 2))`` reference."""
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rng = np.random.default_rng(seed)
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mask = rng.random((n, h, w)) < rng.random()
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np.testing.assert_array_equal(
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get_mask_size_category(mask), _reference_mask_size_category(mask)
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)
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def test_threshold_boundaries(self) -> None:
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"""Pixel counts on each SMALL/MEDIUM/LARGE boundary land in the right bin."""
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sm, lg = SIZE_THRESHOLDS
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mask = np.zeros((3, 100, 100), dtype=bool)
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mask[0].flat[: sm - 1] = True # below SMALL threshold -> SMALL
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mask[1].flat[:sm] = True # exactly SMALL threshold -> MEDIUM
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mask[2].flat[:lg] = True # exactly LARGE threshold -> LARGE
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np.testing.assert_array_equal(
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get_mask_size_category(mask),
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[
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ObjectSizeCategory.SMALL.value,
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ObjectSizeCategory.MEDIUM.value,
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ObjectSizeCategory.LARGE.value,
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],
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)
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