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import torch |
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from torch.autograd import gradcheck |
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import kornia.geometry.epipolar as epi |
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import kornia.testing as utils |
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from kornia.testing import assert_close |
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class TestSymmetricalEpipolarDistance: |
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def test_smoke(self, device, dtype): |
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pts1 = torch.rand(1, 4, 3, device=device, dtype=dtype) |
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pts2 = torch.rand(1, 4, 3, device=device, dtype=dtype) |
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Fm = utils.create_random_fundamental_matrix(1).type_as(pts1) |
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assert epi.symmetrical_epipolar_distance(pts1, pts2, Fm).shape == (1, 4) |
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def test_batch(self, device, dtype): |
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batch_size = 5 |
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pts1 = torch.rand(batch_size, 4, 3, device=device, dtype=dtype) |
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pts2 = torch.rand(batch_size, 4, 3, device=device, dtype=dtype) |
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Fm = utils.create_random_fundamental_matrix(1).type_as(pts1) |
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assert epi.symmetrical_epipolar_distance(pts1, pts2, Fm).shape == (5, 4) |
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def test_gradcheck(self, device): |
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batch_size, num_points, num_dims = 2, 3, 2 |
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points1 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64, requires_grad=True) |
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points2 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64) |
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Fm = utils.create_random_fundamental_matrix(batch_size).type_as(points2) |
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assert gradcheck(epi.symmetrical_epipolar_distance, (points1, points2, Fm), raise_exception=True) |
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def test_shift(self, device, dtype): |
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pts1 = torch.zeros(3, 2, device=device, dtype=dtype)[None] |
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pts2 = torch.tensor([[2, 0.0], [2, 1], [2, 2.0]], device=device, dtype=dtype)[None] |
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Fm = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 1.0, 0.0]], dtype=dtype, device=device)[None] |
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expected = torch.tensor([0.0, 2.0, 8.0], device=device, dtype=dtype)[None] |
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assert_close(epi.symmetrical_epipolar_distance(pts1, pts2, Fm), expected, atol=1e-4, rtol=1e-4) |
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class TestSampsonEpipolarDistance: |
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def test_smoke(self, device, dtype): |
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pts1 = torch.rand(1, 4, 3, device=device, dtype=dtype) |
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pts2 = torch.rand(1, 4, 3, device=device, dtype=dtype) |
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Fm = utils.create_random_fundamental_matrix(1).type_as(pts1) |
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assert epi.sampson_epipolar_distance(pts1, pts2, Fm).shape == (1, 4) |
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def test_batch(self, device, dtype): |
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batch_size = 5 |
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pts1 = torch.rand(batch_size, 4, 3, device=device, dtype=dtype) |
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pts2 = torch.rand(batch_size, 4, 3, device=device, dtype=dtype) |
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Fm = utils.create_random_fundamental_matrix(1).type_as(pts1) |
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assert epi.sampson_epipolar_distance(pts1, pts2, Fm).shape == (5, 4) |
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def test_shift(self, device, dtype): |
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pts1 = torch.zeros(3, 2, device=device, dtype=dtype)[None] |
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pts2 = torch.tensor([[2, 0.0], [2, 1], [2, 2.0]], device=device, dtype=dtype)[None] |
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Fm = torch.tensor([[0.0, 0.0, 0.0], [0.0, 0.0, -1.0], [0.0, 1.0, 0.0]], dtype=dtype, device=device)[None] |
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expected = torch.tensor([0.0, 0.5, 2.0], device=device, dtype=dtype)[None] |
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assert_close(epi.sampson_epipolar_distance(pts1, pts2, Fm), expected, atol=1e-4, rtol=1e-4) |
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def test_gradcheck(self, device): |
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batch_size, num_points, num_dims = 2, 3, 2 |
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points1 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64, requires_grad=True) |
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points2 = torch.rand(batch_size, num_points, num_dims, device=device, dtype=torch.float64) |
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Fm = utils.create_random_fundamental_matrix(batch_size).type_as(points2) |
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assert gradcheck(epi.sampson_epipolar_distance, (points1, points2, Fm), raise_exception=True) |
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