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Commit 552f07d7 authored by Paul McCarthy's avatar Paul McCarthy :mountain_bicyclist:
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TEST: Resample tests separated out into separate module. Add to 

transform.decompose test
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tests/test_image.py
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− 167
View file @ 552f07d7
......@@ -1065,173 +1065,6 @@ def _test_Image_save(imgtype):
img2 = None
def test_image_resample(seed):
with tempdir() as td:
fname = op.join(td, 'test.nii')
# Random base image shapes
for i in range(25):
shape = np.random.randint(5, 50, 3)
make_random_image(fname, shape)
img = fslimage.Image(fname, mmap=False)
# bad shape
with pytest.raises(ValueError):
img.resample((10, 10))
with pytest.raises(ValueError):
img.resample((10, 10, 10, 10))
# resampling to the same shape should be a no-op
samei, samex = img.resample(shape)
assert np.all(samei == img[:])
assert np.all(samex == img.voxToWorldMat)
# Random resampled image shapes
for j in range(10):
rshape = np.random.randint(5, 50, 3)
resampled, xf = img.resample(rshape, order=0)
img.save('base.nii.gz')
fslimage.Image(resampled, xform=xf,
mmap=False).save('res.nii.gz')
assert tuple(resampled.shape) == tuple(rshape)
# We used nearest neighbour interp, so the
# values in the resampled image should match
# corresponding values in the original. Let's
# check some whynot.
restestcoords = np.array([
np.random.randint(0, rshape[0], 100),
np.random.randint(0, rshape[1], 100),
np.random.randint(0, rshape[2], 100)]).T
resx, resy, resz = restestcoords.T
resvals = resampled[resx, resy, resz]
res2orig = transform.concat(img.worldToVoxMat, xf)
origtestcoords = transform.transform(restestcoords, res2orig)
# remove any coordinates which are out of
# bounds in the original image space, or
# are right on a voxel boundary (where the
# nn interp could have gone either way), or
# have value == 0 in the resampled space.
out = ((origtestcoords < 0) |
(origtestcoords >= shape - 0.5) |
(np.isclose(np.modf(origtestcoords)[0], 0.5)))
out = np.any(out, axis=1) | (resvals == 0)
origtestcoords = np.array(origtestcoords.round(), dtype=np.int)
origtestcoords = origtestcoords[~out, :]
restestcoords = restestcoords[ ~out, :]
resx, resy, resz = restestcoords.T
origx, origy, origz = origtestcoords.T
origvals = img[:][origx, origy, origz]
resvals = resampled[resx, resy, resz]
assert np.all(np.isclose(resvals, origvals))
del img
img = None
def test_image_resample_4d(seed):
fname = 'test.nii.gz'
with tempdir():
make_random_image(fname, (10, 10, 10, 10))
# resample one volume
img = fslimage.Image(fname)
slc = (slice(None), slice(None), slice(None), 3)
resampled = img.resample(img.shape[:3], slc)[0]
assert np.all(resampled == img[..., 3])
# resample up
resampled = img.resample((15, 15, 15), slc)[0]
assert tuple(resampled.shape) == (15, 15, 15)
# resample down
resampled = img.resample((5, 5, 5), slc)[0]
assert tuple(resampled.shape) == (5, 5, 5)
# resample the entire image
resampled = img.resample((15, 15, 15, 10), None)[0]
assert tuple(resampled.shape) == (15, 15, 15, 10)
resampled = img.resample((5, 5, 5, 10), None)[0]
assert tuple(resampled.shape) == (5, 5, 5, 10)
# resample along the fourth dim
resampled = img.resample((15, 15, 15, 15), None)[0]
assert tuple(resampled.shape) == (15, 15, 15, 15)
resampled = img.resample((5, 5, 5, 15), None)[0]
assert tuple(resampled.shape) == (5, 5, 5, 15)
del img
del resampled
img = None
resampled = None
def test_Image_resample_offset(seed):
with tempdir() as td:
fname = op.join(td, 'test.nii')
make_random_image(fname, (10, 10, 10))
img = fslimage.Image(fname)
# with origin='corner', image
# bounding boxes should match
for i in range(25):
shape = np.random.randint(5, 50, 3)
res = img.resample(shape, origin='corner')
res = fslimage.Image(res[0], xform=res[1])
imgb = transform.axisBounds(img.shape, img.voxToWorldMat)
resb = transform.axisBounds(res.shape, res.voxToWorldMat)
assert np.all(np.isclose(imgb, resb, rtol=1e-5, atol=1e-5))
# with origin='centre' image
# bounding boxes should be offset
# by (size_resampled - size_orig) / 2
for i in range(25):
shape = np.random.randint(5, 50, 3)
res = img.resample(shape, origin='centre')
res = fslimage.Image(res[0], xform=res[1])
off = (np.array(img.shape) / np.array(res.shape) - 1) / 2
imgb = np.array(transform.axisBounds(img.shape, img.voxToWorldMat))
resb = np.array(transform.axisBounds(res.shape, res.voxToWorldMat))
assert np.all(np.isclose(imgb, resb + off, rtol=1e-5, atol=1e-5))
# with origin='corner', using
# linear interp, when we down-
# sample an image to a shape
# that divides evenly into the
# original shape, a downsampled
# voxel should equal the average
# of the original voxels inside
# it.
res = img.resample((5, 5, 5), smooth=False, origin='corner')[0]
for x, y, z in it.product(range(5), range(5), range(5)):
block = img[x * 2: x * 2 + 2,
y * 2: y * 2 + 2,
z * 2: z * 2 + 2]
assert np.isclose(res[x, y, z], block.mean())
def test_Image_init_xform_nifti1(): _test_Image_init_xform(1)
def test_Image_init_xform_nifti2(): _test_Image_init_xform(2)
def _test_Image_init_xform(imgtype):
......
tests/test_image_resample.py 0 → 100644
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View file @ 552f07d7
#!/usr/bin/env python
import itertools as it
import os.path as op
import numpy as np
import pytest
import fsl.data.image as fslimage
import fsl.utils.transform as transform
import fsl.utils.image.resample as resample
from fsl.utils.tempdir import tempdir
from . import make_random_image
def test_resample(seed):
with tempdir() as td:
fname = op.join(td, 'test.nii')
# Random base image shapes
for i in range(25):
shape = np.random.randint(5, 50, 3)
make_random_image(fname, shape)
img = fslimage.Image(fname, mmap=False)
# bad shape
with pytest.raises(ValueError):
resample.resample(img, (10, 10))
with pytest.raises(ValueError):
resample.resample(img, (10, 10, 10, 10))
# resampling to the same shape should be a no-op
samei, samex = resample.resample(img, shape)
assert np.all(samei == img[:])
assert np.all(samex == img.voxToWorldMat)
# Random resampled image shapes
for j in range(10):
rshape = np.random.randint(5, 50, 3)
resampled, xf = resample.resample(img, rshape, order=0)
img.save('base.nii.gz')
fslimage.Image(resampled, xform=xf,
mmap=False).save('res.nii.gz')
assert tuple(resampled.shape) == tuple(rshape)
# We used nearest neighbour interp, so the
# values in the resampled image should match
# corresponding values in the original. Let's
# check some whynot.
restestcoords = np.array([
np.random.randint(0, rshape[0], 100),
np.random.randint(0, rshape[1], 100),
np.random.randint(0, rshape[2], 100)]).T
resx, resy, resz = restestcoords.T
resvals = resampled[resx, resy, resz]
res2orig = transform.concat(img.worldToVoxMat, xf)
origtestcoords = transform.transform(restestcoords, res2orig)
# remove any coordinates which are out of
# bounds in the original image space, or
# are right on a voxel boundary (where the
# nn interp could have gone either way), or
# have value == 0 in the resampled space.
out = ((origtestcoords < 0) |
(origtestcoords >= shape - 0.5) |
(np.isclose(np.modf(origtestcoords)[0], 0.5)))
out = np.any(out, axis=1) | (resvals == 0)
origtestcoords = np.array(origtestcoords.round(), dtype=np.int)
origtestcoords = origtestcoords[~out, :]
restestcoords = restestcoords[ ~out, :]
resx, resy, resz = restestcoords.T
origx, origy, origz = origtestcoords.T
origvals = img[:][origx, origy, origz]
resvals = resampled[resx, resy, resz]
assert np.all(np.isclose(resvals, origvals))
del img
img = None
def test_resample_4d(seed):
fname = 'test.nii.gz'
with tempdir():
make_random_image(fname, (10, 10, 10, 10))
# resample one volume
img = fslimage.Image(fname)
slc = (slice(None), slice(None), slice(None), 3)
resampled = resample.resample(img, img.shape[:3], slc)[0]
assert np.all(resampled == img[..., 3])
# resample up
resampled = resample.resample(img, (15, 15, 15), slc)[0]
assert tuple(resampled.shape) == (15, 15, 15)
# resample down
resampled = resample.resample(img, (5, 5, 5), slc)[0]
assert tuple(resampled.shape) == (5, 5, 5)
# resample the entire image
resampled = resample.resample(img, (15, 15, 15, 10), None)[0]
assert tuple(resampled.shape) == (15, 15, 15, 10)
resampled = resample.resample(img, (5, 5, 5, 10), None)[0]
assert tuple(resampled.shape) == (5, 5, 5, 10)
# resample along the fourth dim
resampled = resample.resample(img, (15, 15, 15, 15), None)[0]
assert tuple(resampled.shape) == (15, 15, 15, 15)
resampled = resample.resample(img, (5, 5, 5, 15), None)[0]
assert tuple(resampled.shape) == (5, 5, 5, 15)
del img
del resampled
img = None
resampled = None
def test_resample_origin(seed):
with tempdir() as td:
fname = op.join(td, 'test.nii')
make_random_image(fname, (10, 10, 10))
img = fslimage.Image(fname)
# with origin='corner', image
# bounding boxes should match
for i in range(25):
shape = np.random.randint(5, 50, 3)
res = resample.resample(img, shape, origin='corner')
res = fslimage.Image(res[0], xform=res[1])
imgb = transform.axisBounds(img.shape, img.voxToWorldMat)
resb = transform.axisBounds(res.shape, res.voxToWorldMat)
assert np.all(np.isclose(imgb, resb, rtol=1e-5, atol=1e-5))
# with origin='centre' image
# bounding boxes should be offset
# by (size_resampled - size_orig) / 2
for i in range(25):
shape = np.random.randint(5, 50, 3)
res = resample.resample(img, shape, origin='centre')
res = fslimage.Image(res[0], xform=res[1])
off = (np.array(img.shape) / np.array(res.shape) - 1) / 2
imgb = np.array(transform.axisBounds(img.shape, img.voxToWorldMat))
resb = np.array(transform.axisBounds(res.shape, res.voxToWorldMat))
assert np.all(np.isclose(imgb, resb + off, rtol=1e-5, atol=1e-5))
# with origin='corner', using
# linear interp, when we down-
# sample an image to a shape
# that divides evenly into the
# original shape, a downsampled
# voxel should equal the average
# of the original voxels inside
# it.
res = resample.resample(img, (5, 5, 5), smooth=False, origin='corner')[0]
for x, y, z in it.product(range(5), range(5), range(5)):
block = img[x * 2: x * 2 + 2,
y * 2: y * 2 + 2,
z * 2: z * 2 + 2]
assert np.isclose(res[x, y, z], block.mean())
def test_resampleToPixdims():
pass
def test_resampleToReference():
pass
tests/test_transform.py
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− 0
View file @ 552f07d7
......@@ -268,6 +268,17 @@ def test_compose_and_decompose():
assert np.all(np.isclose(rotat, rots))
assert np.all(np.isclose(rotaf, rmat))
# decompose should accept a 3x3
# affine, and return translations of 0
transform.decompose(xform[:3, :3])
sc, of, rot = transform.decompose(xform[:3, :3])
sc, of, rot = np.array(sc), np.array(of), np.array(rot)
assert np.all(np.isclose(sc, [1, 1, 1]))
assert np.all(np.isclose(of, [0, 0, 0]))
assert np.all(np.isclose(rot, rots))
def test_rotMatToAxisAngles(seed):
......
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