From e0d897b54dc04abfdaf36b30188e83fd89becd16 Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauld.mccarthy@gmail.com>
Date: Sun, 12 Mar 2017 18:31:55 +0000
Subject: [PATCH] ImageWrapper "adjustment" (sort of but not really a bugfix).
When an image is written to, the old coverages are not discarded - rather,
the new data range is calculated on the union of the old coverage and the
write slice, across all volumes.
---
fsl/data/image.py | 10 ++++-----
fsl/data/imagewrapper.py | 48 +++++++++++++++++++++++++++-------------
2 files changed, 38 insertions(+), 20 deletions(-)
diff --git a/fsl/data/image.py b/fsl/data/image.py
index c1cf28cc9..ea537f80c 100644
--- a/fsl/data/image.py
+++ b/fsl/data/image.py
@@ -486,11 +486,11 @@ class Nifti(notifier.Notifier):
@memoize.Instanceify(memoize.memoize)
def isNeurological(self):
- """Returns ``True`` if it looks like this ``Nifti`` object is in
- neurological orientation, ``False`` otherwise. This test is purely
- based on the determinant of the voxel-to-mm transformation matrix -
- if it has a positive determinant, the image is assumed to be in
- neurological orientation, otherwise it is assumed to be in
+ """Returns ``True`` if it looks like this ``Nifti`` object has a
+ neurological voxel orientation, ``False`` otherwise. This test is
+ purely based on the determinant of the voxel-to-mm transformation
+ matrix - if it has a positive determinant, the image is assumed to
+ be in neurological orientation, otherwise it is assumed to be in
radiological orientation.
See http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT/FAQ#What_is_the\
diff --git a/fsl/data/imagewrapper.py b/fsl/data/imagewrapper.py
index 921dd6e36..458865eaf 100644
--- a/fsl/data/imagewrapper.py
+++ b/fsl/data/imagewrapper.py
@@ -421,13 +421,15 @@ class ImageWrapper(notifier.Notifier):
log.debug('Updating image {} data range [slice: {}] '
'(current range: [{}, {}]; '
'number of expansions: {}; '
- 'current coverage: {})'.format(
+ 'current coverage: {}; '
+ 'volume ranges: {})'.format(
self.__name,
slices,
self.__range[0],
self.__range[1],
len(expansions),
- self.__coverage))
+ self.__coverage,
+ self.__volRanges))
# As we access the data for each expansions,
# we want it to have the same dimensionality
@@ -530,30 +532,46 @@ class ImageWrapper(notifier.Notifier):
# area and the current coverage, then it's
# easy - we just expand the coverage to
# include the newly written area.
+ #
+ # But if there is overlap between the written
+ # area and the current coverage, things are
+ # more complicated, because the portion of
+ # the image that has been written over may
+ # have contained the currently known data
+ # minimum/maximum. We have no way of knowing
+ # this, so we have to reset the coverage (on
+ # the affected volumes), and recalculate the
+ # data range.
if overlap in (OVERLAP_SOME, OVERLAP_ALL):
- # If there is overlap between the written
- # area and the current coverage, things are
- # more complicated, because the portion of
- # the image that has been written over may
- # have contained the currently known data
- # minimum/maximum. We have no way of knowing
- # this, so we have to reset the coverage (on
- # the affected volumes), and recalculate the
- # data range.
-
# TODO Could you store the location of the
# data minimum/maximum (in each volume),
# so you know whether resetting the
# coverage is necessary?
lowVol, highVol = slices[self.__numRealDims - 1]
+ # We create a single slice which
+ # encompasses the given slice, and
+ # all existing coverages for each
+ # volume in the given slice. The
+ # data range for this slice will
+ # be recalculated.
+ slices = adjustCoverage(self.__coverage[:, :, lowVol], slices)
+ for vol in range(lowVol + 1, highVol):
+ slices = adjustCoverage(slices, self.__coverage[:, :, vol].T)
+
+ slices = np.array(slices.T, dtype=np.uint32)
+ slices = tuple(it.chain(map(tuple, slices), [(lowVol, highVol)]))
+
log.debug('Image {} data written - clearing known data '
- 'range on volumes {} - {} (write slice: {})'.format(
+ 'range on volumes {} - {} (write slice: {}; '
+ 'coverage: {}; volRanges: {})'.format(
self.__name,
lowVol,
highVol,
- slices))
+ slices,
+ self.__coverage[:, :, lowVol:highVol],
+ self.__volRanges[lowVol:highVol, :]))
for vol in range(lowVol, highVol):
self.__coverage[:, :, vol] = np.nan
@@ -909,7 +927,7 @@ def adjustCoverage(oldCoverage, slices):
:return: A ``numpy`` array containing the adjusted/expanded coverage.
"""
- newCoverage = np.zeros(oldCoverage.shape, dtype=np.uint32)
+ newCoverage = np.zeros(oldCoverage.shape, dtype=oldCoverage.dtype)
for dim in range(oldCoverage.shape[1]):
--
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