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358 lines
14 KiB
Python
358 lines
14 KiB
Python
import numpy as np
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import pytest
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from supervision.detection.compact_mask import CompactMask
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from supervision.detection.core import Detections
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from supervision.metrics.core import AveragingMethod, MetricTarget
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from supervision.metrics.recall import Recall
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from tests.helpers import assert_almost_equal
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class TestRecall:
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@pytest.fixture
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def predictions_multiple_classes(self):
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return Detections(
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xyxy=np.array(
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[
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[10, 10, 50, 50], # class 0, matches first target
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[200, 200, 240, 240], # class 1, matches target
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],
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dtype=np.float32,
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),
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confidence=np.array([0.9, 0.8]),
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class_id=np.array([0, 1]),
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)
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@pytest.fixture
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def targets_multiple_classes(self):
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return Detections(
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xyxy=np.array(
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[
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[10, 10, 50, 50], # class 0, matched
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[60, 60, 100, 100], # class 0, missed
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[200, 200, 240, 240], # class 1, matched
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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, 1]),
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)
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def test_initialization_default(self):
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"""Test that Recall can be initialized with default parameters"""
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metric = Recall()
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assert metric._metric_target == MetricTarget.BOXES
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assert metric.averaging_method == AveragingMethod.WEIGHTED
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assert metric._predictions_list == []
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assert metric._targets_list == []
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def test_initialization_custom(self):
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"""Test that Recall can be initialized with custom parameters"""
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metric = Recall(
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metric_target=MetricTarget.MASKS,
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averaging_method=AveragingMethod.MACRO,
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)
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assert metric._metric_target == MetricTarget.MASKS
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assert metric.averaging_method == AveragingMethod.MACRO
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def test_mask_content_preserves_compact_mask(self) -> None:
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"""CompactMask inputs stay compact for mask IoU."""
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dense_mask = np.zeros((1, 4, 5), dtype=bool)
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dense_mask[0, 1:3, 1:4] = True
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xyxy = np.array([[1, 1, 4, 3]], dtype=np.float64)
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compact_mask = CompactMask.from_dense(
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dense_mask, xyxy=xyxy, image_shape=dense_mask.shape[1:]
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)
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detections = Detections(xyxy=xyxy, mask=compact_mask)
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metric = Recall(metric_target=MetricTarget.MASKS)
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content = metric._detections_content(detections)
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assert content is compact_mask
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def test_compute_with_compact_mask_matches_dense(self) -> None:
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"""Recall.compute() yields same recall_at_50 for CompactMask and dense."""
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masks = np.zeros((1, 50, 50), dtype=bool)
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masks[0, 10:20, 10:20] = True
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xyxy = np.array([[10, 10, 19, 19]], dtype=np.float64)
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cm = CompactMask.from_dense(masks, xyxy, image_shape=(50, 50))
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det_dense = Detections(
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xyxy=xyxy, mask=masks, confidence=np.array([0.9]), class_id=np.array([0])
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)
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det_compact = Detections(
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xyxy=xyxy, mask=cm, confidence=np.array([0.9]), class_id=np.array([0])
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)
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metric = Recall(metric_target=MetricTarget.MASKS)
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r_dense = metric.update(det_dense, det_dense).compute()
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metric.reset()
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r_compact = metric.update(det_compact, det_compact).compute()
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assert r_dense.recall_at_50 == pytest.approx(r_compact.recall_at_50)
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def test_reset(self, dummy_prediction):
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"""Test that reset() clears all stored data"""
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metric = Recall()
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# Add some dummy data
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metric.update(dummy_prediction, dummy_prediction)
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# Verify data was added
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assert len(metric._predictions_list) == 1
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assert len(metric._targets_list) == 1
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# Reset and verify lists are empty
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metric.reset()
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assert metric._predictions_list == []
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assert metric._targets_list == []
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def test_perfect_match(self, detections_50_50, targets_50_50):
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"""Test recall with perfect matching predictions and targets"""
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metric = Recall()
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result = metric.update(detections_50_50, targets_50_50).compute()
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# Perfect match should give recall = 1.0
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# TP = 1, FN = 0 -> recall = TP / (TP + FN) = 1 / 1 = 1.0
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assert result.recall_at_50 == 1.0
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assert result.recall_at_75 == 1.0
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assert len(result.matched_classes) == 1
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assert result.matched_classes[0] == 0
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def test_no_overlap(self, predictions_no_overlap, targets_no_overlap):
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"""Test recall with predictions that don't overlap with targets"""
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metric = Recall()
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result = metric.update(predictions_no_overlap, targets_no_overlap).compute()
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# No overlap means no TP, only FN
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# TP = 0, FN = 1 -> recall = TP / (TP + FN) = 0 / 1 = 0.0
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assert result.recall_at_50 == 0.0
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assert result.recall_at_75 == 0.0
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def test_empty_predictions(self, targets_50_50):
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"""Test recall with empty predictions but existing targets"""
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predictions = Detections.empty()
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metric = Recall()
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result = metric.update(predictions, targets_50_50).compute()
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# No predictions means TP = 0, FN = 1 -> recall = 0 / 1 = 0.0
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assert result.recall_at_50 == 0.0
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assert result.recall_at_75 == 0.0
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def test_empty_targets(self, detections_50_50):
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"""Test recall with predictions but no targets"""
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targets = Detections.empty()
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metric = Recall()
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result = metric.update(detections_50_50, targets).compute()
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# No targets means TP = 0, FN = 0 -> recall = 0 / 0 = 0
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assert result.recall_at_50 == 0.0
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assert result.recall_at_75 == 0.0
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def test_medium_bucket_scores_target_matched_small_prediction(self) -> None:
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"""Medium-object recall keeps valid matches even if the prediction is small."""
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predictions = Detections(
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xyxy=np.array([[0, 0, 31, 31]], dtype=np.float32),
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confidence=np.array([0.9], dtype=np.float32),
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class_id=np.array([0]),
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)
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targets = Detections(
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xyxy=np.array([[0, 0, 32, 32]], dtype=np.float32),
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class_id=np.array([0]),
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)
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result = Recall().update(predictions, targets).compute()
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assert result.medium_objects is not None
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assert result.medium_objects.recall_at_50 == 1.0
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def test_single_class_missed_detections(
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self, detections_50_50, targets_two_objects_class_0
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):
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"""Test recall calculation with some missed detections"""
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metric = Recall()
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result = metric.update(detections_50_50, targets_two_objects_class_0).compute()
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# TP = 1 (first target matched), FN = 1 (second target missed)
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# recall = TP / (TP + FN) = 1 / 2 = 0.5
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assert_almost_equal(result.recall_at_50, 0.5)
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assert_almost_equal(result.recall_at_75, 0.5)
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def test_multiple_classes(
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self, predictions_multiple_classes, targets_multiple_classes
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):
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"""Test recall calculation for multiple classes"""
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metric = Recall()
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result = metric.update(
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predictions_multiple_classes, targets_multiple_classes
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).compute()
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# Class 0: TP=1, FN=1 -> recall=0.5 (weight=2)
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# Class 1: TP=1, FN=0 -> recall=1.0 (weight=1)
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# Weighted avg: (2*0.5 + 1*1.0) / (2+1) = 2.0/3 = 0.6667
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expected_recall = (2 * 0.5 + 1 * 1.0) / (2 + 1)
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assert_almost_equal(result.recall_at_50, expected_recall)
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assert_almost_equal(result.recall_at_75, expected_recall)
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assert len(result.matched_classes) == 2
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assert 0 in result.matched_classes
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assert 1 in result.matched_classes
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def test_different_iou_thresholds(self, predictions_iou_064, targets_iou_064):
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"""Test recall at different IoU thresholds"""
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metric = Recall()
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result = metric.update(predictions_iou_064, targets_iou_064).compute()
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# IoU = 0.64 > 0.5 but < 0.75
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# Should match at IoU 0.5 but not at 0.75
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assert result.recall_at_50 == 1.0 # TP=1, FN=0
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assert result.recall_at_75 == 0.0 # TP=0, FN=1
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def test_confidence_ranking(self, predictions_confidence_ranking, targets_50_50):
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"""Test that higher confidence predictions are preferred for matching"""
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metric = Recall()
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result = metric.update(predictions_confidence_ranking, targets_50_50).compute()
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# Target should be matched (by higher confidence prediction)
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# TP = 1, FN = 0 -> recall = 1.0
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assert result.recall_at_50 == 1.0
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def test_multiple_predictions_one_target(
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self, predictions_confidence_ranking, targets_50_50
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):
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"""Test recall when multiple predictions compete for one target"""
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metric = Recall()
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result = metric.update(predictions_confidence_ranking, targets_50_50).compute()
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# Target should be matched exactly once
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# TP = 1, FN = 0 -> recall = 1.0
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assert result.recall_at_50 == 1.0
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def test_list_inputs(
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self, detections_50_50, targets_50_50, prediction_class_1, target_class_1
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):
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"""Test recall with list inputs"""
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metric = Recall()
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result = metric.update(
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[detections_50_50, prediction_class_1], [targets_50_50, target_class_1]
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).compute()
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# Perfect matches for both
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assert result.recall_at_50 == 1.0
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assert result.recall_at_75 == 1.0
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def test_mismatched_list_lengths(self, detections_50_50, targets_50_50):
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"""Test that mismatched prediction/target list lengths raise error"""
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metric = Recall()
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# Should raise ValueError for mismatched lengths
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with pytest.raises(ValueError, match="number of predictions"):
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metric.update([detections_50_50], [targets_50_50, targets_50_50])
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@pytest.mark.parametrize(
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"missing_attribute",
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["predictions_class_id", "targets_class_id", "predictions_confidence"],
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)
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def test_compute_value_error_for_missing_required_fields(
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self, missing_attribute
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) -> None:
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"""Test compute raises ValueError when required fields are missing."""
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metric = Recall()
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boxes = np.array([[10, 10, 50, 50]], dtype=np.float32)
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class_id = np.array([0], dtype=np.int32)
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confidence = np.array([0.9], dtype=np.float32)
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predictions = Detections(
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xyxy=boxes,
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confidence=confidence,
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class_id=class_id,
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)
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targets = Detections(
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xyxy=boxes,
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class_id=class_id,
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)
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if missing_attribute == "predictions_class_id":
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predictions = Detections(
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xyxy=boxes,
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confidence=confidence,
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)
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elif missing_attribute == "targets_class_id":
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targets = Detections(xyxy=boxes)
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else:
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predictions = Detections(
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xyxy=boxes,
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class_id=class_id,
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)
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with pytest.raises(ValueError, match="Recall metric requires"):
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metric.update(predictions, targets).compute()
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@pytest.mark.parametrize(
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"averaging_method",
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[AveragingMethod.MACRO, AveragingMethod.MICRO, AveragingMethod.WEIGHTED],
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)
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def test_averaging_methods(self, averaging_method, detections_50_50, targets_50_50):
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"""Test different averaging methods"""
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metric = Recall(averaging_method=averaging_method)
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result = metric.update(detections_50_50, targets_50_50).compute()
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# Perfect match should give 1.0 regardless of averaging method
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assert result.recall_at_50 == 1.0
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assert result.averaging_method == averaging_method
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def test_macro_averaging(self):
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"""Test MACRO averaging with specific example"""
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# Class 0: 1/2 targets matched -> recall = 0.5
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# Class 1: 1/1 targets matched -> recall = 1.0
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# Macro average: (0.5 + 1.0) / 2 = 0.75
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predictions = Detections(
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xyxy=np.array(
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[
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[10, 10, 50, 50], # matches class 0 target 1
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[200, 200, 240, 240], # matches class 1 target
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],
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dtype=np.float32,
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),
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confidence=np.array([0.9, 0.8]),
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class_id=np.array([0, 1]),
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)
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targets = Detections(
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xyxy=np.array(
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[
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[10, 10, 50, 50], # class 0, matched
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[60, 60, 100, 100], # class 0, missed
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[200, 200, 240, 240], # class 1, matched
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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, 1]),
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)
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metric = Recall(averaging_method=AveragingMethod.MACRO)
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result = metric.update(predictions, targets).compute()
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# Macro average: (0.5 + 1.0) / 2 = 0.75
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assert result.recall_at_50 == 0.75
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def test_greedy_matching_two_valid_pairs(self):
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"""Greedy matching finds both TPs; np.unique style missed the second pair.
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IoU matrix: [[1.0, 0.667], [0.333, 0.538]]. At iou>=0.5 the optimal
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assignment is T0<->P0 and T1<->P1 (2 TPs, recall=1.0).
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"""
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preds = Detections(
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xyxy=np.array([[40, 60, 380, 470], [108, 60, 448, 470]], dtype=np.float32),
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confidence=np.array([0.95, 0.90]),
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class_id=np.array([0, 0]),
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)
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targets = Detections(
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xyxy=np.array([[40, 60, 380, 470], [210, 60, 550, 470]], dtype=np.float32),
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class_id=np.array([0, 0]),
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)
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result = Recall().update(preds, targets).compute()
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assert result.recall_at_50 == 1.0
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