def test_infinite_distances():
x = np.array([1, 1, 0])
l = np.array([0, 1, 0])
assert_equals(evaluation.distance_point_to_line(x, l), np.finfo(float).max)
x = np.array([1, 1, -7])
l = np.array([0, 0, 2.3])
assert_equals(evaluation.distance_point_to_line(x, l), np.finfo(float).max)
def test_finite_distances():
x = np.array([1, 1, 1])
l = np.array([0, 1, 0])
assert_equals(evaluation.distance_point_to_line(x, l), 1)
x = np.array([-1, -1, -1])
l = np.array([0, 1, 0])
assert_equals(evaluation.distance_point_to_line(x, l), 1)
x = np.array([1, 1, 1])
l = np.array([3, 2, -1])
assert_equals(evaluation.distance_point_to_line(x, l), 4 / np.sqrt(13))
def filter_matches_epipolar_constraint(F, matches, thresh):
"""
Discards matches that are not consistent with the epipolar constraint.
Args:
F: fundamental matrix
matches: list of pairs of 2D points, stored as a Nx4 numpy array
thresh: maximum accepted distance between a point and its matched
epipolar line
Returns:
the list of matches that satisfy the constraint. It is a sub-list of
the input list.
"""
out = []
for match in matches:
x = np.array([match[0], match[1], 1])
xx = np.array([match[2], match[3], 1])
d1 = evaluation.distance_point_to_line(x, np.dot(F.T, xx))
d2 = evaluation.distance_point_to_line(xx, np.dot(F, x))
if max(d1, d2) < thresh:
out.append(match)
return np.array(out)