def test_metrics_bad_confidence(self):
# [2 2 10 20; 80 80 30 40]
true1 = BoundingBox(1, 'A', (2, 2), (12, 22))
true2 = BoundingBox(1, 'A', (80, 80), (110, 120))
labels = [true1, true2]
# [4 4 10 20; 50 50 30 10; 90 90 40 50];
pred1 = BoundingBox(1, 'A', (4, 4), (14, 24), confidence=0.1)
pred2 = BoundingBox(1, 'A', (50, 50), (80, 60), confidence=0.1)
pred3 = BoundingBox(1, 'A', (80, 80), (110, 120), confidence=0.1)
predictions = [pred1, pred2, pred3]
det = Detection(labels=labels, predictions=predictions)
pr, re, _ = det.metrics()
self.assertEqual(pr, 0.0)
self.assertEqual(re, 0.0)
pr, re, _ = det.metrics(confidence_threshold=0.1)
self.assertEqual(pr, 2.0 / 3.0)
self.assertEqual(re, 1.000)
def test_precision_one_class(self):
# [2 2 10 20; 80 80 30 40]
true1 = BoundingBox(1, 'A', (2, 2), (12, 22))
true2 = BoundingBox(1, 'A', (80, 80), (110, 120))
labels = [true1, true2]
# [4 4 10 20; 50 50 30 10; 90 90 40 50];
pred1 = BoundingBox(1, 'A', (4, 4), (14, 24))
pred2 = BoundingBox(1, 'A', (50, 50), (80, 60))
pred3 = BoundingBox(1, 'A', (80, 80), (110, 120))
predictions = [pred1, pred2, pred3]
det = Detection(labels=labels, predictions=predictions)
pr, re, _ = det.metrics()
self.assertEqual(pr, 2.0 / 3.0)
self.assertEqual(re, 1.000)
def test_precision_bad_predicts_stream(self):
det = Detection()
det.add_label(BoundingBox(1, 'A', (2, 2), (12, 22)))
det.add_label(1, 'A', 80, 80, 110, 120)
det.add_label(BoundingBox(1, 'A', (20, 20), (30, 30)))
# [4 4 10 20; 50 50 30 10; 90 90 40 50];
pred1 = BoundingBox(1, 'A', (4, 4), (14, 24))
pred2 = BoundingBox(1, 'A', (50, 50), (80, 60))
pred3 = BoundingBox(1, 'B', (80, 80), (110, 120))
det.add_prediction(BoundingBox(1, 'A', (4, 4), (14, 24)))
det.add_prediction(1, 'A', 50, 50, 80, 60)
det.add_prediction(1, 'B', 80, 80, 110, 120)
pr, re, _ = det.metrics()
self.assertEqual(pr, 1.0 / 3.0)
self.assertEqual(re, 1.0 / 3.0)
from detection import Detection
if __name__ == "__main__":
det = Detection()
det.from_csv(label_filepath='example/labels.csv',
pred_filepath='example/preds.csv')
precision, recall, fscore = det.metrics()
print('Precision: {}\nRecall: {}\nfscore: {}'.format(
precision, recall, fscore))
