def resample(self,
inputs,
sample_coords,
interpolation='linear',
boundary='constant',
constant_values=0):
assert interpolation == 'linear'
return general_grid_sample_nd(inputs, sample_coords, boundary,
constant_values, self)
def general_sample_at(self, points, reduce):
local_points = self.box.global_to_local(points)
local_points = math.mul(local_points, math.to_float(
self.resolution)) - 0.5
result = general_grid_sample_nd(
self.data,
local_points,
boundary=_pad_mode(self.extrapolation),
constant_values=_pad_value(self.extrapolation_value),
math=math.choose_backend([self.data, points]),
reduce=reduce)
return result
def resample(self,
inputs,
sample_coords,
interpolation='linear',
boundary='constant',
constant_values=0):
assert interpolation == 'linear'
if use_cuda(inputs):
return resample_cuda(inputs, sample_coords, boundary)
else:
return general_grid_sample_nd(
inputs, sample_coords, boundary, constant_values, self
) # while this is a bit slower than niftynet, it give consisten results at the boundaries