def test_point_line_distance(self):
"""
Testing point line distance calculation
"""
line = [3, 4, 6]
point = [2, 1, 1]
distance = 16.0/5
test_distance = point_line_distance(line, point)
self.assertEqual(distance, test_distance)
def find_inliers(x_0s, F, x_1s, threshold):
""" Find the inliers' indices for a given model.
There are multiple methods you could use for calculating the error
to determine your inliers vs outliers at each pass. CoHowever, we suggest
using the line to point distance function we wrote for the
optimization in part 2.
Args:
- x_0s: A numpy array of shape (N, 2) representing the coordinates
of possibly matching points from the left image
- F: The proposed fundamental matrix
- x_1s: A numpy array of shape (N, 2) representing the coordinates
of possibly matching points from the right image
- threshold: the maximum error for a point correspondence to be
considered an inlier
Each row in x_1s and x_0s is a proposed correspondence (e.g. row #42 of x_0s is a point that
corresponds to row #42 of x_1s)
Returns:
- inliers: 1D array of the indices of the inliers in x_0s and x_1s
"""
inliers = []
for i in range(x_0s.shape[0]):
x0 = x_0s[i]
x1 = x_1s[i]
line0 = np.dot(F, x1)
dist0 = fundamental_matrix.point_line_distance(line0, x0)
line1 = np.dot(np.transpose(F), x0)
dist1 = fundamental_matrix.point_line_distance(line1, x1)
if np.abs(dist0) <= threshold and np.abs(dist1) <= threshold:
inliers.append(i)
return np.array(inliers)
def find_inliers(x_0s, F, x_1s, threshold):
""" Find the inliers' indices for a given model.
There are multiple methods you could use for calculating the error
to determine your inliers vs outliers at each pass. CoHowever, we suggest
using the line to point distance function we wrote for the
optimization in part 2.
Args:
- x_0s: A numpy array of shape (N, 2) representing the coordinates
of possibly matching points from the left image
- F: The proposed fundamental matrix
- x_1s: A numpy array of shape (N, 2) representing the coordinates
of possibly matching points from the right image
- threshold: the maximum error for a point correspondence to be
considered an inlier
Each row in x_1s and x_0s is a proposed correspondence (e.g. row #42 of x_0s is a point that
corresponds to row #42 of x_1s)
Returns:
- inliers: 1D array of the indices of the inliers in x_0s and x_1s
"""
##############################
# TODO: Student code goes here
inliers = []
for idx in range(len(x_1s)):
line = np.matmul(F, x_1s[idx])
line_T = np.matmul(F.transpose(), x_0s[idx])
dist = fundamental_matrix.point_line_distance(line, x_0s[idx])
dist2 = fundamental_matrix.point_line_distance(line_T, x_1s[idx])
if (abs(dist) + abs(dist2) < 2 * threshold): inliers.append(idx)
##############################
return np.array(inliers)
def find_inliers(x_0s, F, x_1s, threshold):
""" Find the inliers' indices for a given model.
There are multiple methods you could use for calculating the error
to determine your inliers vs outliers at each pass. However, we suggest
using the line to point distance function we wrote for the
optimization in part 2.
Args:
- x_0s: A numpy array of shape (N, 2) representing the coordinates
of possibly matching points from the left image
- F: The proposed fundamental matrix
- x_1s: A numpy array of shape (N, 2) representing the coordinates
of possibly matching points from the right image
- threshold: the maximum error for a point correspondence to be
considered an inlier
Each row in x_1s and x_0s is a proposed correspondence (e.g. row #42 of x_0s is a point that
corresponds to row #42 of x_1s)
Returns:
- inliers: list of the indices of the inliers in x_0s and x_1s
"""
##############################
# TODO: Student code goes here
inliers = []
for x in range(len(x_0s)):
p1 = x_1s[x]
p2 = x_0s[x]
if len(p1) < 3:
p1 = np.append(p1, 1)
p2 = np.append(p2, 1)
Fx1 = np.dot(F, p1)
d = fundamental_matrix.point_line_distance(Fx1, p2)
d = abs(d)
if (d <= threshold):
inliers.append(x)
inliers = np.array(inliers)
##############################
return inliers
def test_point_line_distance_zero(self):
line = [3, 3, -6]
point = [1, 1, 1]
test_distance = point_line_distance(line, point)
self.assertEqual(test_distance, 0)