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#!/usr/bin/env python
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2016 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
"""Test of the filtered backprojection module"""
from __future__ import division, print_function
__authors__ = ["Pierre paleo"]
__license__ = "MIT"
__copyright__ = "2013-2017 European Synchrotron Radiation Facility, Grenoble, France"
__date__ = "19/01/2018"
import time
import logging
import numpy as np
import unittest
from math import pi
try:
import mako
except ImportError:
mako = None
from ..common import ocl
if ocl:
from .. import backprojection
from ...image.tomography import compute_fourier_filter
from silx.test.utils import utilstest
logger = logging.getLogger(__name__)
def generate_coords(img_shp, center=None):
"""
Return two 2D arrays containing the indexes of an image.
The zero is at the center of the image.
"""
l_r, l_c = float(img_shp[0]), float(img_shp[1])
R, C = np.mgrid[:l_r, :l_c]
if center is None:
center0, center1 = l_r / 2., l_c / 2.
else:
center0, center1 = center
R = R + 0.5 - center0
C = C + 0.5 - center1
return R, C
def clip_circle(img, center=None, radius=None):
"""
Puts zeros outside the inscribed circle of the image support.
"""
R, C = generate_coords(img.shape, center)
M = R * R + C * C
res = np.zeros_like(img)
if radius is None:
radius = img.shape[0] / 2. - 1
mask = M < radius * radius
res[mask] = img[mask]
return res
@unittest.skipUnless(ocl and mako, "PyOpenCl is missing")
class TestFBP(unittest.TestCase):
def setUp(self):
if ocl is None:
return
self.getfiles()
self.fbp = backprojection.Backprojection(self.sino.shape, profile=True)
if self.fbp.compiletime_workgroup_size < 16 * 16:
self.skipTest("Current implementation of OpenCL backprojection is "
"not supported on this platform yet")
# Astra does not use the same backprojector implementation.
# Therefore, we cannot expect results to be the "same" (up to float32
# numerical error)
self.tol = 5e-2
if self.fbp.is_cpu:
# Precision is less when using CPU
self.tol *= 2
def tearDown(self):
self.sino = None
# self.fbp.log_profile()
self.fbp = None
def getfiles(self):
# load sinogram of 512x512 MRI phantom
self.sino = np.load(utilstest.getfile("sino500.npz"))["data"]
# load reconstruction made with ASTRA FBP (with filter designed in spatial domain)
self.reference_rec = np.load(utilstest.getfile("rec_astra_500.npz"))["data"]
def measure(self):
"Common measurement of timings"
t1 = time.time()
try:
result = self.fbp.filtered_backprojection(self.sino)
except RuntimeError as msg:
logger.error(msg)
return
t2 = time.time()
return t2 - t1, result
def compare(self, res):
"""
Compare a result with the reference reconstruction.
Only the valid reconstruction zone (inscribed circle) is taken into
account
"""
res_clipped = clip_circle(res)
ref_clipped = clip_circle(self.reference_rec)
delta = abs(res_clipped - ref_clipped)
bad = delta > 1
logger.debug("Absolute difference: %s with %s outlier pixels out of %s"
"", delta.max(), bad.sum(), np.prod(bad.shape))
return delta.max()
@unittest.skipUnless(ocl and mako, "pyopencl is missing")
def test_fbp(self):
"""
tests FBP
"""
# Test single reconstruction
# --------------------------
t, res = self.measure()
if t is None:
logger.info("test_fp: skipped")
else:
logger.info("test_backproj: time = %.3fs" % t)
err = self.compare(res)
msg = str("Max error = %e" % err)
logger.info(msg)
self.assertTrue(err < self.tol, "Max error is too high")
# Test multiple reconstructions
# -----------------------------
res0 = np.copy(res)
for i in range(10):
res = self.fbp.filtered_backprojection(self.sino)
errmax = np.max(np.abs(res - res0))
self.assertTrue(errmax < 1.e-6, "Max error is too high")
@unittest.skipUnless(ocl and mako, "pyopencl is missing")
def test_fbp_filters(self):
"""
Test the different available filters of silx FBP.
"""
avail_filters = [
"ramlak", "shepp-logan", "cosine", "hamming",
"hann"
]
# Create a Dirac delta function at a single angle view.
# As the filters are radially invarant:
# - backprojection yields an image where each line is a Dirac.
# - FBP yields an image where each line is the spatial filter
# One can simply filter "dirac" without backprojecting it, but this
# test will also ensure that backprojection behaves well.
dirac = np.zeros_like(self.sino)
na, dw = dirac.shape
dirac[0, dw//2] = na / pi * 2
for filter_name in avail_filters:
B = backprojection.Backprojection(dirac.shape, filter_name=filter_name)
r = B(dirac)
# Check that radial invariance is kept
std0 = np.max(np.abs(np.std(r, axis=0)))
self.assertTrue(
std0 < 5.e-6,
"Something wrong with FBP(filter=%s)" % filter_name
)
# Check that the filter is retrieved
r_f = np.fft.fft(np.fft.fftshift(r[0])).real / 2. # filter factor
ref_filter_f = compute_fourier_filter(dw, filter_name)
errmax = np.max(np.abs(r_f - ref_filter_f))
logger.info("FBP filter %s: max error=%e" % (filter_name, errmax))
self.assertTrue(
errmax < 1.e-3,
"Something wrong with FBP(filter=%s)" % filter_name
)
@unittest.skipUnless(ocl and mako, "pyopencl is missing")
def test_fbp_oddsize(self):
# Generate a 513-sinogram.
# The padded width will be nextpow(513*2).
# silx [0.10, 0.10.1] will give 1029, which makes R2C transform fail.
sino = np.pad(self.sino, ((0, 0), (1, 0)), mode='edge')
B = backprojection.Backprojection(sino.shape, axis_position=self.fbp.axis_pos+1)
res = B(sino)
# Compare with self.reference_rec. Tolerance is high as backprojector
# is not fully shift-invariant.
errmax = np.max(np.abs(clip_circle(res[1:, 1:] - self.reference_rec)))
self.assertLess(
errmax, 1.e-1,
"Something wrong with FBP on odd-sized sinogram"
)
def suite():
testSuite = unittest.TestSuite()
testSuite.addTest(TestFBP("test_fbp"))
testSuite.addTest(TestFBP("test_fbp_filters"))
testSuite.addTest(TestFBP("test_fbp_oddsize"))
return testSuite
if __name__ == '__main__':
unittest.main(defaultTest="suite")
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