def zeroPaddData(self,desiredLength,paddmode='zero',where='end'):
#zero padds the time domain data, it is possible to padd at the beginning,
#or at the end, and further gaussian or real zero padding is possible
#might not work for gaussian mode!
desiredLength=int(desiredLength)
#escape the function
if desiredLength<0:
return 0
#calculate the paddvectors
if paddmode=='gaussian':
paddvec=py.normal(0,py.std(self.getPreceedingNoise())*0.05,desiredLength)
else:
paddvec=py.ones((desiredLength,self.tdData.shape[1]-1))
paddvec*=py.mean(self.tdData[-20:,1:])
timevec=self.getTimes()
if where=='end':
#timeaxis:
newtimes=py.linspace(timevec[-1],timevec[-1]+desiredLength*self.dt,desiredLength)
paddvec=py.column_stack((newtimes,paddvec))
longvec=py.row_stack((self.tdData,paddvec))
else:
newtimes=py.linspace(timevec[0]-(desiredLength+1)*self.dt,timevec[0],desiredLength)
paddvec=py.column_stack((newtimes,paddvec))
longvec=py.row_stack((paddvec,self.tdData))
self.setTDData(longvec)
def homog2D(xPrime, x):
"""
Compute the 3x3 homography matrix mapping a set of N 2D homogeneous
points (3xN) to another set (3xN)
"""
numPoints = xPrime.shape[1]
assert numPoints >= 4
A = None
for i in range(0, numPoints):
xiPrime = xPrime[:, i]
xi = x[:, i]
Ai_row0 = pl.concatenate((pl.zeros(3), -xiPrime[2] * xi, xiPrime[1] * xi))
Ai_row1 = pl.concatenate((xiPrime[2] * xi, pl.zeros(3), -xiPrime[0] * xi))
Ai = pl.row_stack((Ai_row0, Ai_row1))
if A is None:
A = Ai
else:
A = pl.vstack((A, Ai))
U, S, V = pl.svd(A)
V = V.T
h = V[:, -1]
H = pl.reshape(h, (3, 3))
return H
def homog2D (xPrime, x):
"""
Compute the 3x3 homography matrix mapping a set of N 2D homogeneous
points (3xN) to another set (3xN)
"""
numPoints = xPrime.shape[1]
assert (numPoints >= 4)
A = None
for i in range (0, numPoints):
xiPrime = xPrime[:,i]
xi = x[:,i]
Ai_row0 = pl.concatenate ((pl.zeros (3,), -xiPrime[2]*xi, xiPrime[1]*xi))
Ai_row1 = pl.concatenate ((xiPrime[2]*xi, pl.zeros (3,), -xiPrime[0]*xi))
Ai = pl.row_stack ((Ai_row0, Ai_row1))
if A is None:
A = Ai
else:
A = pl.vstack ((A, Ai))
U, S, V = pl.svd (A)
V = V.T
h = V[:,-1]
H = pl.reshape (h, (3, 3))
return H
def homog3D(points2d, points3d):
"""
Compute a matrix relating homogeneous 3D points (4xN) to homogeneous
2D points (3xN)
Not sure why anyone would do this. Note that the returned transformation
*NOT* an isometry. But it's here... so deal with it.
"""
numPoints = points2d.shape[1]
assert numPoints >= 4
A = None
for i in range(0, numPoints):
xiPrime = points2d[:, i]
xi = points3d[:, i]
Ai_row0 = pl.concatenate((pl.zeros(4), -xiPrime[2] * xi, xiPrime[1] * xi))
Ai_row1 = pl.concatenate((xiPrime[2] * xi, pl.zeros(4), -xiPrime[0] * xi))
Ai = pl.row_stack((Ai_row0, Ai_row1))
if A is None:
A = Ai
else:
A = pl.vstack((A, Ai))
U, S, V = pl.svd(A)
V = V.T
h = V[:, -1]
P = pl.reshape(h, (3, 4))
return P
def homog3D (points2d, points3d):
"""
Compute a matrix relating homogeneous 3D points (4xN) to homogeneous
2D points (3xN)
Not sure why anyone would do this. Note that the returned transformation
*NOT* an isometry. But it's here... so deal with it.
"""
numPoints = points2d.shape[1]
assert (numPoints >= 4)
A = None
for i in range (0, numPoints):
xiPrime = points2d[:,i]
xi = points3d[:,i]
Ai_row0 = pl.concatenate ((pl.zeros (4,), -xiPrime[2]*xi, xiPrime[1]*xi))
Ai_row1 = pl.concatenate ((xiPrime[2]*xi, pl.zeros (4,), -xiPrime[0]*xi))
Ai = pl.row_stack ((Ai_row0, Ai_row1))
if A is None:
A = Ai
else:
A = pl.vstack ((A, Ai))
U, S, V = pl.svd (A)
V = V.T
h = V[:,-1]
P = pl.reshape (h, (3, 4))
return P
