def xfmavg(inputs, output):
# TODO: handl inversion flag correctly
all_linear = True
all_nonlinear = True
input_xfms = []
input_grids = []
for j in inputs:
try:
x = minc2_xfm(j)
except:
print("Error reading:{} in {}".format(j, repr(inputs)))
raise
if x.get_n_concat() == 1 and x.get_n_type(
0) == minc2_xfm.MINC2_XFM_LINEAR:
# this is a linear matrix
input_xfms.append(np.asmatrix(x.get_linear_transform()))
else:
raise Exception("Unexpected XFM type")
acc = np.asmatrix(np.zeros([4, 4], dtype=np.complex))
for i in input_xfms:
acc += scipy.linalg.logm(i)
acc /= len(input_xfms)
acc = np.asarray(scipy.linalg.expm(acc).real, 'float64', 'C')
x = minc2_xfm()
x.append_linear_transform(acc)
x.save(output)
def xfmavg(inputs, output, verbose=False):
# TODO: handle inversion flag correctly
all_linear = True
all_nonlinear = True
input_xfms = []
input_grids = []
for j in inputs:
x = minc2_xfm(j)
if x.get_n_concat() == 1 and x.get_n_type(
0) == minc2_xfm.MINC2_XFM_LINEAR:
# this is a linear matrix
input_xfms.append(np.asmatrix(x.get_linear_transform()))
else:
all_linear &= False
# strip identity matrixes
nl = []
_identity = np.asmatrix(np.identity(4))
_eps = 1e-6
if x.get_n_type(0) == minc2_xfm.MINC2_XFM_LINEAR and x.get_n_type(
1) == minc2_xfm.MINC2_XFM_GRID_TRANSFORM:
if scipy.linalg.norm(_identity -
np.asmatrix(x.get_linear_transform(0))
) > _eps: # this is non-identity matrix
all_nonlinear &= False
else:
input_grids.append(x.get_grid_transform(1)[0])
elif x.get_n_type(1) == minc2_xfm.MINC2_XFM_GRID_TRANSFORM:
input_grids.append(x.get_grid_transform(0)[0])
if all_linear:
acc = np.asmatrix(np.zeros([4, 4], dtype=np.complex))
for i in input_xfms:
print(i)
acc += scipy.linalg.logm(i)
acc /= len(input_xfms)
acc = np.asarray(scipy.linalg.expm(acc).real, 'float64', 'C')
print("result:", acc)
x = minc2_xfm()
x.append_linear_transform(acc)
x.save(output)
elif all_nonlinear:
output_grid = output.rsplit('.xfm', 1)[0] + '_grid_0.mnc'
cmds = ['mincaverage', '-clob']
cmds.extend(input_grids)
cmds.append(output_grid)
do_cmd(cmds, verbose=verbose)
x = minc2_xfm()
x.append_grid_transform(output_grid, False)
x.save(output)
else:
raise Exception("Mixed XFM files provided as input")
def xfm_dist(xfm,ref):
xfm1=minc2_xfm(xfm)
xfm2=minc2_xfm(ref)
#concatenate inverted xfm2
xfm1.invert()
xfm1.concat_xfm(xfm2)
param=xfm1.get_linear_transform_param()
if param.invalid:
param.rotations.fill(float('nan'))
param.dist=0.0
for x in range(2):
for y in range(2):
for z in range(2):
p_in=np.array( [edges[x][0], edges[y][1], edges[z][2]] )
p_out=xfm1.transform_point(p_in)
dst=np.linalg.norm(p_in-p_out,ord=2)
if dst>param.dist: param.dist=dst
return param
def xfmconcat(inputs, output):
#
all_linear = True
input_xfms = []
input_grids = []
o = minc2_xfm()
for j in inputs:
try:
x = minc2_xfm(j)
except:
print("Error reading:{} in {}".format(j, repr(inputs)))
raise
if x.get_n_concat() == 1 and x.get_n_type(
0) == minc2_xfm.MINC2_XFM_LINEAR:
# this is a linear matrix
o.append_linear_transform(x.get_linear_transform())
else:
raise Exception("Unexpected XFM type")
o.save(output)
from minc2_simple import minc2_file
from minc2_simple import minc2_xfm
from minc2_simple import minc2_transform_parameters
import sys
import numpy as np
if __name__ == "__main__":
# experiment with XFM files
x = minc2_xfm()
identity = np.eye(4)
x.append_linear_transform(identity)
x.save('identity.xfm')
par = minc2_transform_parameters()
par.translations = np.array([1, 2, 3])
par.scales = np.array([1.1, 1.0, 0.9])
y = minc2_xfm()
y.append_linear_transform(par)
y.save('test_par.xfm')
z = minc2_xfm('test_par.xfm')
par2 = z.get_linear_transform_param(0)
print(par)
print(par.translations)
print(par2)
print(par2.translations)