From b3a82c7bf61b5580a5c073be0037644f13a04fb4 Mon Sep 17 00:00:00 2001
From: Paul McCarthy <pauldmccarthy@gmail.com>
Date: Tue, 23 Jul 2019 15:08:25 +0100
Subject: [PATCH] ENH: New image.roi module for extracting image ROI/expanding
FOV
---
fsl/utils/image/roi.py | 106 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 106 insertions(+)
create mode 100644 fsl/utils/image/roi.py
diff --git a/fsl/utils/image/roi.py b/fsl/utils/image/roi.py
new file mode 100644
index 000000000..84aee2434
--- /dev/null
+++ b/fsl/utils/image/roi.py
@@ -0,0 +1,106 @@
+#!/usr/bin/env python
+#
+# roi.py - Extract an ROI of an image.
+#
+# Author: Paul McCarthy <pauldmccarthy@gmail.com>
+#
+"""This module provides the :func:`roi` function, which can be used to extract
+a region-of-interest from, or expand the field-of-view of, an :class:`.Image`.
+"""
+
+
+import numpy as np
+
+import fsl.data.image as fslimage
+import fsl.utils.transform as transform
+
+
+def _normaliseBounds(shape, bounds):
+ """Adjust the given ``bounds`` so that it is compatible with the given
+ ``shape``.
+
+ Bounds must be specified for at least three dimensions - if the shape
+ has more than three dimensions, additional bounds are added.
+
+ A :exc:`ValueError` is raised if the provided bounds are invalid.
+
+ :arg bounds: Sequence of ``(lo, hi)`` bounds - see :func:`roi`.
+ :returns: An adjusted sequence of bounds.
+ """
+
+ bounds = list(bounds)
+
+ if len(bounds) < 3:
+ raise ValueError('')
+
+ if len(bounds) > len(shape):
+ raise ValueError('')
+
+ for b in bounds:
+ if len(b) != 2 or b[0] >= b[1]:
+ raise ValueError('')
+
+ if len(bounds) < len(shape):
+ for s in shape[len(bounds):]:
+ bounds.append((0, s))
+
+ return bounds
+
+
+def roi(image, bounds):
+ """Extract an ROI from the given ``image`` according to the given
+ ``bounds``.
+
+ This function can also be used to pad, or expand the field-of-view, of an
+ image, by passing in negative low bound values, or high bound values which
+ are larger than the image shape. The padded region will contain zeroes.
+
+ :arg image: :class:`.Image` object
+
+ :arg bounds: Must be a sequence of tuples, containing the low/high bounds
+ for each voxel dimension, where the low bound is *inclusive*,
+ and the high bound is *exclusive*. For 4D images, the bounds
+ for the fourth dimension are optional.
+
+ :returns: A new :class:`.Image` object containing the region specified
+ by the ``bounds``.
+ """
+
+ bounds = _normaliseBounds(image.shape, bounds)
+
+ newshape = [hi - lo for lo, hi in bounds]
+ oldslc = []
+ newslc = []
+
+ # Figure out how to slice the input image
+ # data array, and the corresponding slice
+ # in the output data array.
+ for (lo, hi), oldlen, newlen in zip(bounds, image.shape, newshape):
+
+ oldlo = max(lo, 0)
+ oldhi = min(hi, oldlen)
+ newlo = max(0, -lo)
+ newhi = newlo + (oldhi - oldlo)
+
+ oldslc.append(slice(oldlo, oldhi))
+ newslc.append(slice(newlo, newhi))
+
+ oldslc = tuple(oldslc)
+ newslc = tuple(newslc)
+
+ # Copy the ROI into the new data array
+ newdata = np.zeros(newshape, dtype=image.dtype)
+ newdata[newslc] = image.data[oldslc]
+
+ # Create a new affine for the ROI,
+ # with an appropriate offset along
+ # each spatial dimension
+ oldaff = image.voxToWorldMat
+ offset = [lo for lo, hi in bounds[:3]]
+ offset = transform.scaleOffsetXform([1, 1, 1], offset)
+ newaff = transform.concat(oldaff, offset)
+
+ return fslimage.Image(newdata,
+ xform=newaff,
+ header=image.header,
+ name=image.name + '_roi')
--
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