def get_aligned_copy(self, other):
"""return copy of self that is scaled, translated, and rotated to best match other"""
assert isinstance( other, MultiCameraSystem)
orig_names = self.get_names()
new_names = other.get_names()
names = set(orig_names).intersection( new_names )
if len(names) < 3:
raise ValueError('need 3 or more cameras in common to align.')
orig_points = np.array([ self._cameras[name].get_camcenter() for name in names ]).T
new_points = np.array([ other._cameras[name].get_camcenter() for name in names ]).T
s,R,t = estsimt(orig_points,new_points)
assert is_rotation_matrix(R)
new_cams = []
for name in self.get_names():
orig_cam = self._cameras[name]
new_cam = orig_cam.get_aligned_camera(s,R,t)
new_cams.append( new_cam )
result = MultiCameraSystem(new_cams)
return result
def get_aligned_copy(self, other):
"""return copy of self that is scaled, translated, and rotated to best match other"""
assert isinstance(other, MultiCameraSystem)
orig_names = self.get_names()
new_names = other.get_names()
names = set(orig_names).intersection(new_names)
if len(names) < 3:
raise ValueError('need 3 or more cameras in common to align.')
orig_points = np.array(
[self._cameras[name].get_camcenter() for name in names]).T
new_points = np.array(
[other._cameras[name].get_camcenter() for name in names]).T
s, R, t = estsimt(orig_points, new_points)
assert is_rotation_matrix(R)
new_cams = []
for name in self.get_names():
orig_cam = self._cameras[name]
new_cam = orig_cam.get_aligned_camera(s, R, t)
new_cams.append(new_cam)
result = MultiCameraSystem(new_cams)
return result
def from_mcsc(cls, dirname ):
'''create MultiCameraSystem from output directory of MultiCamSelfCal'''
# FIXME: This is a bit convoluted because it's been converted
# from multiple layers of internal code. It should really be
# simplified and cleaned up.
do_normalize_pmat=True
all_Pmat = {}
all_Res = {}
all_K = {}
all_distortion = {}
opj = os.path.join
with open(opj(dirname,'camera_order.txt'),mode='r') as fd:
cam_ids = fd.read().strip().split('\n')
with open(os.path.join(dirname,'Res.dat'),'r') as res_fd:
for i, cam_id in enumerate(cam_ids):
fname = 'camera%d.Pmat.cal'%(i+1)
pmat = np.loadtxt(opj(dirname,fname)) # 3 rows x 4 columns
if do_normalize_pmat:
pmat_orig = pmat
pmat = normalize_M(pmat)
all_Pmat[cam_id] = pmat
all_Res[cam_id] = map(int,res_fd.readline().split())
# load non linear parameters
rad_files = [ f for f in os.listdir(dirname) if f.endswith('.rad') ]
for cam_id_enum, cam_id in enumerate(cam_ids):
filename = os.path.join(dirname,
'basename%d.rad'%(cam_id_enum+1,))
if os.path.exists(filename):
K, distortion = parse_radfile(filename)
all_K[cam_id] = K
all_distortion[cam_id] = distortion
else:
if len(rad_files):
raise RuntimeError(
'.rad files present but none named "%s"'%filename)
warnings.warn('no non-linear data (e.g. radial distortion) '
'in calibration for %s'%cam_id)
all_K[cam_id] = None
all_distortion[cam_id] = None
cameras = []
for cam_id in cam_ids:
w,h = all_Res[cam_id]
Pmat = all_Pmat[cam_id]
M = Pmat[:,:3]
K,R = my_rq(M)
if not is_rotation_matrix(R):
# RQ may return left-handed rotation matrix. Make right-handed.
R2 = -R
K2 = -K
assert np.allclose(np.dot(K2,R2), np.dot(K,R))
K,R = K2,R2
P = np.zeros((3,4))
P[:3,:3] = K
KK = all_K[cam_id] # from rad file or None
distortion = all_distortion[cam_id]
# (ab)use PyMVG's rectification to do coordinate transform
# for MCSC's undistortion.
# The intrinsic parameters used for 3D -> 2D.
ex = P[0,0]
bx = P[0,2]
Sx = P[0,3]
ey = P[1,1]
by = P[1,2]
Sy = P[1,3]
if KK is None:
rect = np.eye(3)
KK = P[:,:3]
else:
# Parameters used to define undistortion coordinates.
fx = KK[0,0]
fy = KK[1,1]
cx = KK[0,2]
cy = KK[1,2]
rect = np.array([[ ex/fx, 0, (bx+Sx-cx)/fx ],
[ 0, ey/fy, (by+Sy-cy)/fy ],
[ 0, 0, 1 ]]).T
if distortion is None:
distortion = np.zeros((5,))
C = center(Pmat)
rot = R
t = -np.dot(rot, C)[:,0]
d = {'width':w,
'height':h,
'P':P,
'K':KK,
'R':rect,
'translation':t,
'Q':rot,
'D':distortion,
'name':cam_id,
}
cam = CameraModel.from_dict(d)
cameras.append( cam )
return MultiCameraSystem( cameras=cameras )
def from_mcsc(cls, dirname):
'''create MultiCameraSystem from output directory of MultiCamSelfCal'''
# FIXME: This is a bit convoluted because it's been converted
# from multiple layers of internal code. It should really be
# simplified and cleaned up.
do_normalize_pmat = True
all_Pmat = {}
all_Res = {}
all_K = {}
all_distortion = {}
opj = os.path.join
with open(opj(dirname, 'camera_order.txt'), mode='r') as fd:
cam_ids = fd.read().strip().split('\n')
with open(os.path.join(dirname, 'Res.dat'), 'r') as res_fd:
for i, cam_id in enumerate(cam_ids):
fname = 'camera%d.Pmat.cal' % (i + 1)
pmat = np.loadtxt(opj(dirname, fname)) # 3 rows x 4 columns
if do_normalize_pmat:
pmat_orig = pmat
pmat = normalize_M(pmat)
all_Pmat[cam_id] = pmat
all_Res[cam_id] = map(int, res_fd.readline().split())
# load non linear parameters
rad_files = [f for f in os.listdir(dirname) if f.endswith('.rad')]
for cam_id_enum, cam_id in enumerate(cam_ids):
filename = os.path.join(dirname,
'basename%d.rad' % (cam_id_enum + 1, ))
if os.path.exists(filename):
K, distortion = parse_radfile(filename)
all_K[cam_id] = K
all_distortion[cam_id] = distortion
else:
if len(rad_files):
raise RuntimeError(
'.rad files present but none named "%s"' % filename)
warnings.warn('no non-linear data (e.g. radial distortion) '
'in calibration for %s' % cam_id)
all_K[cam_id] = None
all_distortion[cam_id] = None
cameras = []
for cam_id in cam_ids:
w, h = all_Res[cam_id]
Pmat = all_Pmat[cam_id]
M = Pmat[:, :3]
K, R = my_rq(M)
if not is_rotation_matrix(R):
# RQ may return left-handed rotation matrix. Make right-handed.
R2 = -R
K2 = -K
assert np.allclose(np.dot(K2, R2), np.dot(K, R))
K, R = K2, R2
P = np.zeros((3, 4))
P[:3, :3] = K
KK = all_K[cam_id] # from rad file or None
distortion = all_distortion[cam_id]
# (ab)use PyMVG's rectification to do coordinate transform
# for MCSC's undistortion.
# The intrinsic parameters used for 3D -> 2D.
ex = P[0, 0]
bx = P[0, 2]
Sx = P[0, 3]
ey = P[1, 1]
by = P[1, 2]
Sy = P[1, 3]
if KK is None:
rect = np.eye(3)
KK = P[:, :3]
else:
# Parameters used to define undistortion coordinates.
fx = KK[0, 0]
fy = KK[1, 1]
cx = KK[0, 2]
cy = KK[1, 2]
rect = np.array([[ex / fx, 0, (bx + Sx - cx) / fx],
[0, ey / fy, (by + Sy - cy) / fy], [0, 0,
1]]).T
if distortion is None:
distortion = np.zeros((5, ))
C = center(Pmat)
rot = R
t = -np.dot(rot, C)[:, 0]
d = {
'width': w,
'height': h,
'P': P,
'K': KK,
'R': rect,
'translation': t,
'Q': rot,
'D': distortion,
'name': cam_id,
}
cam = CameraModel.from_dict(d)
cameras.append(cam)
return MultiCameraSystem(cameras=cameras)