def two_view_reconstruction_rotation_only(p1, p2, camera1, camera2, threshold):
b1 = multiview.pixel_bearings(p1, camera1)
b2 = multiview.pixel_bearings(p2, camera2)
t = np.zeros(3)
R = pyopengv.relative_pose_ransac_rotation_only(b1, b2, 1 - np.cos(threshold), 1000)
inliers = _two_view_reconstruction_rotation_only_inliers(b1, b2, R, threshold)
# R = pyopengv.relative_pose_rotation_only(b1[inliers], b2[inliers])
# inliers = _two_view_reconstruction_rotation_only_inliers(b1, b2, R, threshold)
return cv2.Rodrigues(R.T)[0].ravel(), inliers
def two_view_reconstruction_rotation_only(p1, p2, camera1, camera2, threshold):
b1 = multiview.pixel_bearings(p1, camera1)
b2 = multiview.pixel_bearings(p2, camera2)
t = np.zeros(3)
R = pyopengv.relative_pose_ransac_rotation_only(b1, b2,
1 - np.cos(threshold),
1000)
inliers = _two_view_reconstruction_rotation_only_inliers(
b1, b2, R, threshold)
# R = pyopengv.relative_pose_rotation_only(b1[inliers], b2[inliers])
# inliers = _two_view_reconstruction_rotation_only_inliers(b1, b2, R, threshold)
return cv2.Rodrigues(R.T)[0].ravel(), inliers
def robust_match_calibrated(p1, p2, camera1, camera2, matches, config):
'''Computes robust matches by estimating the Essential matrix via RANSAC.
'''
if len(matches) < 8:
return np.array([])
p1 = p1[matches[:, 0]][:, :2].copy()
p2 = p2[matches[:, 1]][:, :2].copy()
b1 = multiview.pixel_bearings(p1, camera1)
b2 = multiview.pixel_bearings(p2, camera2)
threshold = config['robust_matching_threshold']
T = pyopengv.relative_pose_ransac(b1, b2, "STEWENIUS", 1 - np.cos(threshold), 1000)
inliers = compute_inliers_bearings(b1, b2, T)
return matches[inliers]
def robust_match_calibrated(p1, p2, camera1, camera2, matches, config):
'''Computes robust matches by estimating the Essential matrix via RANSAC.
'''
if len(matches) < 8:
return np.array([])
p1 = p1[matches[:, 0]][:, :2].copy()
p2 = p2[matches[:, 1]][:, :2].copy()
b1 = multiview.pixel_bearings(p1, camera1)
b2 = multiview.pixel_bearings(p2, camera2)
threshold = config['robust_matching_threshold']
T = pyopengv.relative_pose_ransac(b1, b2, "STEWENIUS",
1 - np.cos(threshold), 1000)
inliers = compute_inliers_bearings(b1, b2, T)
return matches[inliers]
def two_view_reconstruction(p1, p2, camera1, camera2, threshold):
b1 = multiview.pixel_bearings(p1, camera1)
b2 = multiview.pixel_bearings(p2, camera2)
# Note on threshold:
# See opengv doc on thresholds here: http://laurentkneip.github.io/opengv/page_how_to_use.html
# Here we arbitrarily assume that the threshold is given for a camera of focal length 1
# Also arctan(threshold) \approx threshold since threshold is small
T = run_relative_pose_ransac(b1, b2, "STEWENIUS", 1 - np.cos(threshold), 1000)
R = T[:, :3]
t = T[:, 3]
inliers = _two_view_reconstruction_inliers(b1, b2, R, t, threshold)
T = run_relative_pose_optimize_nonlinear(b1[inliers], b2[inliers], t, R)
R = T[:, :3]
t = T[:, 3]
inliers = _two_view_reconstruction_inliers(b1, b2, R, t, threshold)
return cv2.Rodrigues(R.T)[0].ravel(), -R.T.dot(t), inliers
def two_view_reconstruction(p1, p2, camera1, camera2, threshold):
b1 = multiview.pixel_bearings(p1, camera1)
b2 = multiview.pixel_bearings(p2, camera2)
# Note on threshold:
# See opengv doc on thresholds here: http://laurentkneip.github.io/opengv/page_how_to_use.html
# Here we arbitrarily assume that the threshold is given for a camera of focal length 1
# Also arctan(threshold) \approx threshold since threshold is small
T = run_relative_pose_ransac(b1, b2, "STEWENIUS", 1 - np.cos(threshold),
1000)
R = T[:, :3]
t = T[:, 3]
inliers = _two_view_reconstruction_inliers(b1, b2, R, t, threshold)
T = run_relative_pose_optimize_nonlinear(b1[inliers], b2[inliers], t, R)
R = T[:, :3]
t = T[:, 3]
inliers = _two_view_reconstruction_inliers(b1, b2, R, t, threshold)
return cv2.Rodrigues(R.T)[0].ravel(), -R.T.dot(t), inliers
