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adler-j / example.py
Created April 19, 2016 08:41
odl projection CPU/CUDA example
import odl
import numpy as np
# Discrete reconstruction space: discretized functions on the rectangle
# [-20, 20]^2 with 300 samples per dimension.
reco_space = odl.uniform_discr(
min_corner=[-1, -1], max_corner=[1, 1], nsamples=[256, 256],
dtype='float32')
# Make a parallel beam geometry with flat detector
import odl
import numpy as np
# Discrete reconstruction space: discretized functions on the rectangle
# [-20, 20]^2 with 300 samples per dimension.
reco_space = odl.uniform_discr(
min_corner=[-1, -1], max_corner=[1, 1], nsamples=[256, 256],
dtype='float32')
# Make a parallel beam geometry with flat detector
277 # Blank image
278 1 71 71.0 0.0 p = np.zeros(space.shape)
279
280 # Create the pixel grid
281 1 318 318.0 0.0 grid_in = space.grid.meshgr
id()
282 1 26 26.0 0.0 minp = space.grid.min()
283 1 18 18.0 0.0 maxp = space.grid.max()
284
285 # move points to [-1, 1]
277 # Blank image
278 1 71 71.0 0.0 p = np.zeros(space.shape)
279
280 # Create the pixel grid
281 1 308 308.0 0.0 grid_in = space.grid.meshgr
id()
282 1 25 25.0 0.0 minp = space.grid.min()
283 1 18 18.0 0.0 maxp = space.grid.max()
284
285 # move points to [-1, 1]
C:\Users\Jonas\Desktop> python -m line_profiler gadgetzan.py.lprof
Timer unit: 3.00191e-07 s
Total time: 8.63965 s
File: d:\github\odl\odl\util\phantom.py
Function: _phantom_3d at line 231
Line # Hits Time Per Hit % Time Line Contents
==============================================================
231 @profile