89 lines
3.2 KiB
Python
89 lines
3.2 KiB
Python
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import unittest
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import torch
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from gluefactory.eval.utils import eval_matches_homography
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from gluefactory.geometry.homography import warp_points_torch
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class TestEvalUtils(unittest.TestCase):
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@staticmethod
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def default_pts():
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return torch.tensor(
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[
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[10.0, 10.0],
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[10.0, 20.0],
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[20.0, 20.0],
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[20.0, 10.0],
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]
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)
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@staticmethod
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def default_pred(kps0, kps1):
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return {
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"keypoints0": kps0,
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"keypoints1": kps1,
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"matches0": torch.arange(len(kps0)),
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"matching_scores0": torch.ones(len(kps1)),
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}
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def test_eval_matches_homography_trivial(self):
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data = {"H_0to1": torch.eye(3)}
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kps = self.default_pts()
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pred = self.default_pred(kps, kps)
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results = eval_matches_homography(data, pred)
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self.assertEqual(results["prec@1px"], 1)
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self.assertEqual(results["prec@3px"], 1)
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self.assertEqual(results["num_matches"], 4)
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self.assertEqual(results["num_keypoints"], 4)
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def test_eval_matches_homography_real(self):
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data = {"H_0to1": torch.tensor([[1.5, 0.2, 21], [-0.3, 1.6, 33], [0, 0, 1.0]])}
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kps0 = self.default_pts()
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kps1 = warp_points_torch(kps0, data["H_0to1"], inverse=False)
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pred = self.default_pred(kps0, kps1)
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results = eval_matches_homography(data, pred)
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self.assertEqual(results["prec@1px"], 1)
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self.assertEqual(results["prec@3px"], 1)
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def test_eval_matches_homography_real_outliers(self):
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data = {"H_0to1": torch.tensor([[1.5, 0.2, 21], [-0.3, 1.6, 33], [0, 0, 1.0]])}
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kps0 = self.default_pts()
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kps0 = torch.cat([kps0, torch.tensor([[5.0, 5.0]])])
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kps1 = warp_points_torch(kps0, data["H_0to1"], inverse=False)
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# Move one keypoint 1.5 pixels away in x and y
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kps1[-1] += 1.5
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pred = self.default_pred(kps0, kps1)
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results = eval_matches_homography(data, pred)
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self.assertAlmostEqual(results["prec@1px"], 0.8)
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self.assertAlmostEqual(results["prec@3px"], 1.0)
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def test_eval_matches_homography_batched(self):
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H0 = torch.tensor([[1.5, 0.2, 21], [-0.3, 1.6, 33], [0, 0, 1.0]])
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H1 = torch.tensor([[0.7, 0.1, -5], [-0.1, 0.65, 13], [0, 0, 1.0]])
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data = {"H_0to1": torch.stack([H0, H1])}
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kps0 = torch.stack([self.default_pts(), self.default_pts().flip(0)])
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kps1 = warp_points_torch(kps0, data["H_0to1"], inverse=False)
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# In the first element of the batch there is one outlier
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kps1[0, -1] += 5
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matches0 = torch.stack([torch.arange(4), torch.arange(4)])
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# In the second element of the batch there is only 2 matches
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matches0[1, :2] = -1
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pred = {
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"keypoints0": kps0,
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"keypoints1": kps1,
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"matches0": matches0,
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"matching_scores0": torch.ones_like(matches0),
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}
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results = eval_matches_homography(data, pred)
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self.assertAlmostEqual(results["prec@1px"][0], 0.75)
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self.assertAlmostEqual(results["prec@1px"][1], 1.0)
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self.assertAlmostEqual(results["num_matches"][0], 4)
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self.assertAlmostEqual(results["num_matches"][1], 2)
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