improve tests
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Balthasar
parent
d7774381cc
commit
6e3001194d
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@ -1,5 +1,6 @@
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from __future__ import annotations
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import pytest
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import numpy as np
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from supervision.detection.core import Detections
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@ -9,191 +10,97 @@ from supervision.metrics.mean_average_precision import MeanAveragePrecision
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class TestMeanAveragePrecisionArea:
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"""Test area calculation in MeanAveragePrecision."""
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def test_area_calculated_from_bbox_when_data_empty(self):
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"""Test that area is calculated from bbox when data is empty (normal case)."""
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# Create detections with empty data (normal case)
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gt = Detections(
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xyxy=np.array(
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[
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[10, 10, 40, 40], # Small: 30x30 = 900
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[100, 100, 200, 150], # Medium: 100x50 = 5000
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[300, 300, 500, 400], # Large: 200x100 = 20000
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],
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dtype=np.float32,
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@pytest.mark.parametrize(
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"xyxy, expected_areas, expected_size_maps",
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[
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(
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np.array([
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[10, 10, 40, 40], # Small: 900
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[100, 100, 200, 150], # Medium: 5000
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[300, 300, 500, 400], # Large: 20000
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], dtype=np.float32),
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[900.0, 5000.0, 20000.0],
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{"small": True, "medium": True, "large": True}
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),
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class_id=np.array([0, 0, 0]),
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confidence=np.array([1.0, 1.0, 1.0]),
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(
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np.array([[0, 0, 10, 10]], dtype=np.float32), # Small: 100
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[100.0],
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{"small": True, "medium": False, "large": False}
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),
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(
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np.array([[0, 0, 50, 50]], dtype=np.float32), # Medium: 2500
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[2500.0],
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{"small": False, "medium": True, "large": False}
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),
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(
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np.array([[0, 0, 100, 100]], dtype=np.float32), # Large: 10000
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[10000.0],
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{"small": False, "medium": False, "large": True}
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),
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]
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)
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def test_area_calculation_and_size_specific_map(self, xyxy, expected_areas, expected_size_maps):
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"""Test area calculation and size-specific mAP functionality."""
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gt = Detections(
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xyxy=xyxy,
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class_id=np.arange(len(xyxy)),
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)
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pred = Detections(
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xyxy=gt.xyxy.copy(),
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class_id=gt.class_id.copy(),
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confidence=np.array([0.9, 0.9, 0.9]),
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confidence=np.full(len(xyxy), 0.9),
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)
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# Verify data is empty (normal case)
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assert gt.data == {}
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assert pred.data == {}
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# Create mAP metric and test area calculation
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map_metric = MeanAveragePrecision()
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map_metric.update([pred], [gt])
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# Check that areas were calculated correctly from bbox
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prepared_targets = map_metric._prepare_targets(map_metric._targets_list)
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areas = [ann["area"] for ann in prepared_targets["annotations"]]
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expected_areas = [900.0, 5000.0, 20000.0] # width * height for each bbox
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assert np.allclose(areas, expected_areas, rtol=1e-05, atol=1e-08), (
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f"Expected {expected_areas}, got {areas}"
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)
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# Verify mAP works correctly (no -1.0 for medium/large objects)
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result = map_metric.compute()
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assert result.medium_objects.map50 >= 0.0, (
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"Medium objects should have valid mAP"
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)
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assert result.large_objects.map50 >= 0.0, "Large objects should have valid mAP"
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def test_area_preserved_when_provided_in_data(self):
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"""Test that area from data is preserved when provided (COCO case)."""
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# Create detections with area in data (COCO style)
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gt = Detections(
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xyxy=np.array([[100, 100, 200, 150]], dtype=np.float32), # Would be 5000
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class_id=np.array([0]),
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confidence=np.array([1.0]),
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)
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# Add custom area to data (different from calculated)
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gt.data = {"area": np.array([3000.0])}
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pred = Detections(
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xyxy=gt.xyxy.copy(), class_id=gt.class_id.copy(), confidence=np.array([0.9])
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)
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pred.data = {"area": np.array([3000.0])}
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# Test area calculation
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map_metric = MeanAveragePrecision()
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map_metric.update([pred], [gt])
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# Check that provided area is used (not calculated)
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prepared_targets = map_metric._prepare_targets(map_metric._targets_list)
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used_area = prepared_targets["annotations"][0]["area"]
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assert np.allclose(used_area, 3000.0, rtol=1e-05, atol=1e-08), (
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f"Should use provided area 3000.0, got {used_area}"
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)
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# Verify it's different from what would be calculated
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calculated_area = (200 - 100) * (150 - 100) # 100 * 50 = 5000
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assert not np.allclose(used_area, calculated_area, rtol=1e-05, atol=1e-08), (
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"Should use provided area, not calculated"
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)
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def test_mixed_area_sources(self):
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"""Test mix of detections with and without area in data."""
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# Create detections where some have area in data, others don't
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gt1 = Detections(
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xyxy=np.array([[10, 10, 40, 40]], dtype=np.float32), # 900
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class_id=np.array([0]),
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)
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# No area in data - should be calculated
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gt2 = Detections(
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xyxy=np.array([[100, 100, 200, 150]], dtype=np.float32), # 5000
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class_id=np.array([1]),
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)
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# Add area in data - should be preserved
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gt2.data = {"area": np.array([3000.0])}
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pred1 = Detections(
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xyxy=gt1.xyxy.copy(),
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class_id=gt1.class_id.copy(),
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confidence=np.array([0.9]),
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)
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pred2 = Detections(
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xyxy=gt2.xyxy.copy(),
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class_id=gt2.class_id.copy(),
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confidence=np.array([0.8]),
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)
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pred2.data = {"area": np.array([3000.0])}
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# Test area calculation for mixed sources
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map_metric = MeanAveragePrecision()
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map_metric.update([pred1, pred2], [gt1, gt2])
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prepared_targets = map_metric._prepare_targets(map_metric._targets_list)
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areas = [ann["area"] for ann in prepared_targets["annotations"]]
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assert np.allclose(areas, expected_areas), f"Expected {expected_areas}, got {areas}"
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expected_areas = [900.0, 3000.0] # calculated, then provided
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assert np.allclose(areas, expected_areas, rtol=1e-05, atol=1e-08), (
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f"Expected {expected_areas}, got {areas}"
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)
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# Test size-specific mAP
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result = map_metric.compute()
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def test_size_specific_map_works_correctly(self):
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"""Test that size-specific mAP works correctly with area fix."""
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# Create detections with one object of each size
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if expected_size_maps["small"]:
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assert result.small_objects.map50 > 0.9, "Small objects should have high mAP"
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else:
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assert result.small_objects.map50 == -1.0, "Small objects should have no data"
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if expected_size_maps["medium"]:
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assert result.medium_objects.map50 > 0.9, "Medium objects should have high mAP"
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else:
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assert result.medium_objects.map50 == -1.0, "Medium objects should have no data"
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if expected_size_maps["large"]:
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assert result.large_objects.map50 > 0.9, "Large objects should have high mAP"
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else:
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assert result.large_objects.map50 == -1.0, "Large objects should have no data"
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def test_area_preserved_from_data(self):
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"""Test that area from data field is preserved (COCO case)."""
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gt = Detections(
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xyxy=np.array(
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[
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[10, 10, 40, 40], # Small: 30x30 = 900 < 1024
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[100, 100, 200, 150], # Medium: 100x50 = 5000 (1024 <= x < 9216)
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[300, 300, 500, 400], # Large: 200x100 = 20000 >= 9216
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],
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dtype=np.float32,
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),
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class_id=np.array([0, 0, 0]),
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xyxy=np.array([[100, 100, 200, 150]], dtype=np.float32), # Would calculate to 5000
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class_id=np.array([0]),
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)
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# Perfect predictions
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# Override with custom area
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gt.data = {"area": np.array([3000.0])}
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pred = Detections(
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xyxy=gt.xyxy.copy(),
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class_id=gt.class_id.copy(),
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confidence=np.array([0.9, 0.9, 0.9]),
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confidence=np.array([0.9]),
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)
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# Test mAP calculation
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pred.data = {"area": np.array([3000.0])}
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map_metric = MeanAveragePrecision()
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map_metric.update([pred], [gt])
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result = map_metric.compute()
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# All size categories should have valid results (not -1.0)
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assert result.small_objects.map50 >= 0.0, "Small objects should have valid mAP"
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assert result.medium_objects.map50 >= 0.0, (
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"Medium objects should have valid mAP"
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)
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assert result.large_objects.map50 >= 0.0, "Large objects should have valid mAP"
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# Perfect matches should yield high mAP for medium and large
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assert result.medium_objects.map50 > 0.9, (
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"Perfect medium matches should have high mAP"
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)
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assert result.large_objects.map50 > 0.9, (
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"Perfect large matches should have high mAP"
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)
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def test_area_uses_detections_property(self):
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"""Test that area calculation uses Detections.area property correctly."""
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# Create detection
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gt = Detections(
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xyxy=np.array([[100, 100, 200, 150]], dtype=np.float32),
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class_id=np.array([0]),
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)
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pred = Detections(
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xyxy=gt.xyxy.copy(), class_id=gt.class_id.copy(), confidence=np.array([0.9])
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)
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# Test that internal calculation matches Detections.area property
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map_metric = MeanAveragePrecision()
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map_metric.update([pred], [gt])
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prepared_targets = map_metric._prepare_targets(map_metric._targets_list)
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used_area = prepared_targets["annotations"][0]["area"]
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expected_area = gt.area[0]
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assert np.allclose(used_area, expected_area, rtol=1e-05, atol=1e-08), (
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f"Should use Detections.area property {expected_area}, got {used_area}"
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)
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assert np.allclose(used_area, 3000.0), f"Should use provided area 3000.0, got {used_area}"
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# Verify it's different from what would be calculated
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calculated_area = (200 - 100) * (150 - 100) # 100 * 50 = 5000
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assert not np.allclose(used_area, calculated_area), "Should use provided area, not calculated"
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