def test_outliers_essential_ransac(pairs_and_their_E):
for f1, f2, E, _ in pairs_and_their_E:
points = np.concatenate((f1, f2), axis=1)
scale = 1e-3
points += np.random.rand(*points.shape) * scale
ratio_outliers = 0.3
add_outliers(ratio_outliers, points, 0.1, 0.4)
f1, f2 = points[:, 0:3], points[:, 3:6]
f1 /= np.linalg.norm(f1, axis=1)[:, None]
f2 /= np.linalg.norm(f2, axis=1)[:, None]
scale_eps_ratio = 1e-1
params = pyrobust.RobustEstimatorParams()
result = pyrobust.ransac_essential(f1, f2,
scale * (1.0 + scale_eps_ratio),
params, pyrobust.RansacType.RANSAC)
tolerance = 0.12 # some outliers might have been moved along the epipolar
inliers_count = (1 - ratio_outliers) * len(points)
assert np.isclose(len(result.inliers_indices),
inliers_count,
rtol=tolerance)
def test_outliers_essential_ransac(one_pair_and_its_E):
f1, f2, E, _ = one_pair_and_its_E
points = np.concatenate((f1, f2), axis=1)
scale = 1e-3
points += np.random.rand(*points.shape) * scale
ratio_outliers = 0.4
add_outliers(ratio_outliers, points, 0.1, 0.4)
f1, f2 = points[:, 0:3], points[:, 3:6]
f1 /= np.linalg.norm(f1, axis=1)[:, None]
f2 /= np.linalg.norm(f2, axis=1)[:, None]
params = pyrobust.RobustEstimatorParams()
result = pyrobust.ransac_essential(f1, f2, scale, params,
pyrobust.RansacType.RANSAC)
tolerance = 0.04 # some outliers might have been moved along the epipolar
inliers_count = (1 - ratio_outliers) * len(points)
assert np.isclose(len(result.inliers_indices),
inliers_count,
rtol=tolerance)
# sometimes, the negative of E is the good one
correct_found = 0
for sign in [-1, 1]:
correct_found += np.linalg.norm(E - sign * result.lo_model,
ord='fro') < 5e-2
assert correct_found == 1
def test_uniform_essential_ransac(one_pair_and_its_E):
f1, f2, E, _ = one_pair_and_its_E
points = np.concatenate((f1, f2), axis=1)
scale = 1e-2
points += np.random.rand(*points.shape) * scale
f1, f2 = points[:, 0:3], points[:, 3:6]
f1 /= np.linalg.norm(f1, axis=1)[:, None]
f2 /= np.linalg.norm(f2, axis=1)[:, None]
params = pyrobust.RobustEstimatorParams()
result = pyrobust.ransac_essential(f1, f2, scale, params,
pyrobust.RansacType.RANSAC)
assert len(result.inliers_indices) == len(f1) == len(f2)
def test_uniform_essential_ransac(pairs_and_their_E):
for f1, f2, E, _ in pairs_and_their_E:
points = np.concatenate((f1, f2), axis=1)
scale = 1e-2
points += np.random.rand(*points.shape) * scale
f1, f2 = points[:, 0:3], points[:, 3:6]
f1 /= np.linalg.norm(f1, axis=1)[:, None]
f2 /= np.linalg.norm(f2, axis=1)[:, None]
scale_eps_ratio = 5e-1
params = pyrobust.RobustEstimatorParams()
params.use_iteration_reduction = False
result = pyrobust.ransac_essential(f1, f2,
scale * (1.0 + scale_eps_ratio),
params, pyrobust.RansacType.RANSAC)
assert len(result.inliers_indices) == len(f1) == len(f2)
