def test_shape(self, batch_size, num_points, device, dtype):
B, N = batch_size, num_points
points1 = torch.rand(B, N, 2, device=device, dtype=dtype)
points2 = torch.rand(B, N, 2, device=device, dtype=dtype)
weights = torch.ones(B, N, device=device, dtype=dtype)
F_mat = epi.find_fundamental(points1, points2, weights)
assert F_mat.shape == (B, 3, 3)
def test_opencv(self, device, dtype):
points1 = torch.tensor(
[[[0.8569, 0.5982], [0.0059, 0.9649], [0.1968, 0.8846],
[0.6084, 0.3467], [0.9633, 0.5274], [0.8941, 0.8939],
[0.0863, 0.5133], [0.2645, 0.8882], [0.2411, 0.3045],
[0.8199, 0.4107]]],
device=device,
dtype=dtype)
points2 = torch.tensor(
[[[0.0928, 0.3013], [0.0989, 0.9649], [0.0341, 0.4827],
[0.8294, 0.4469], [0.2230, 0.2998], [0.1722, 0.8182],
[0.5264, 0.8869], [0.8908, 0.1233], [0.2338, 0.7663],
[0.4466, 0.5696]]],
device=device,
dtype=dtype)
weights = torch.ones(1, 10, device=device, dtype=dtype)
# generated with OpenCV using above points
# import cv2
# Fm_expected, _ = cv2.findFundamentalMat(
# points1.detach().numpy().reshape(-1, 1, 2),
# points2.detach().numpy().reshape(-1, 1, 2), cv2.FM_8POINT)
Fm_expected = torch.tensor([[[-0.47408533, 0.22033807, -0.00346677],
[0.54935973, 1.31080955, -1.25028275],
[-0.36690215, -1.08143769, 1.]]],
device=device,
dtype=dtype)
F_mat = epi.find_fundamental(points1, points2, weights)
assert_allclose(F_mat, Fm_expected, rtol=1e-4, atol=1e-4)
def test_synthetic_sampson(self, device, dtype):
scene: Dict[str, torch.Tensor] = utils.generate_two_view_random_scene(device, dtype)
x1 = scene['x1']
x2 = scene['x2']
weights = torch.ones_like(x1)[..., 0]
F_est = epi.find_fundamental(x1, x2, weights)
error = epi.sampson_epipolar_distance(x1, x2, F_est)
assert_close(error, torch.tensor(0.0, device=device, dtype=dtype), atol=1e-4, rtol=1e-4)
def test_smoke(self, device, dtype):
points1 = torch.rand(1, 1, 2, device=device, dtype=dtype)
points2 = torch.rand(1, 1, 2, device=device, dtype=dtype)
weights = torch.ones(1, 1, device=device, dtype=dtype)
F_mat = epi.find_fundamental(points1, points2, weights)
assert F_mat.shape == (1, 3, 3)
