94cfb7f290
* fix: ignore out-of-bucket detections in size-bucketed scoring * fix: honor area metadata in buckets - Prefer stored COCO area metadata before geometry, mask, or OBB fallbacks. - Add explicit-area, mask, and OBB regression coverage. - Align COCO, mAP, and changelog area semantics. --------- Co-authored-by: jirka <6035284+Borda@users.noreply.github.com> Co-authored-by: Codex <codex@openai.com>
107 lines
3.8 KiB
Python
107 lines
3.8 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.config import AREA_DATA_FIELD
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from supervision.detection.core import Detections
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from supervision.metrics.core import MetricTarget
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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_detection_size_category,
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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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def test_detection_size_category_prefers_explicit_area_metadata() -> None:
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"""Explicit area metadata overrides geometry for size categorization."""
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detections = Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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data={AREA_DATA_FIELD: np.array([2500.0], dtype=np.float32)},
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)
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result = get_detection_size_category(detections, MetricTarget.BOXES)
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np.testing.assert_array_equal(result, [ObjectSizeCategory.MEDIUM.value])
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def test_detection_size_category_falls_back_without_area_metadata() -> None:
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"""Geometry remains the fallback when explicit area metadata is absent."""
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detections = Detections(xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32))
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result = get_detection_size_category(detections, MetricTarget.BOXES)
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np.testing.assert_array_equal(result, [ObjectSizeCategory.SMALL.value])
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@pytest.mark.parametrize(
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"area_data",
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[
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pytest.param(np.array([[2500.0]], dtype=np.float32), id="two-dimensional"),
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],
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)
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def test_detection_size_category_rejects_invalid_area_metadata(
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area_data: np.ndarray,
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) -> None:
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"""Invalid area metadata is rejected instead of misaligning detections."""
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detections = Detections(
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xyxy=np.array([[0, 0, 10, 10]], dtype=np.float32),
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data={AREA_DATA_FIELD: area_data},
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)
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with pytest.raises(ValueError, match="area metadata"):
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get_detection_size_category(detections, MetricTarget.BOXES)
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