"""
Utility functions for manipulating image stacks.
"""
from typing import List, Tuple
import scyjava as sj
[docs]
def rai_slice(rai, imin: Tuple, imax: Tuple, istep: Tuple):
"""Slice ImgLib2 images.
Slice ImgLib2 images using Python's slice notation to define the
desired slice range. Returned interval includes both imin and imax
:param rai: An ImgLib2 RandomAccessibleInterval
:param imin: Tuple of minimum interval range values.
:param imax: Tuple of maximum interval range values.
:return: Sliced ImgLib2 RandomAccessibleInterval.
"""
Views = sj.jimport("net.imglib2.view.Views")
shape = rai.shape
imin_fix = sj.jarray("j", [len(shape)])
imax_fix = sj.jarray("j", [len(shape)])
dim_itr = range(len(shape))
for py_dim, j_dim in zip(dim_itr, dim_itr):
# Set minimum
if imin[py_dim] is None:
index = 0
else:
index = imin[py_dim]
if index < 0:
index += shape[j_dim]
imin_fix[j_dim] = index
# Set maximum
if imax[py_dim] is None:
index = shape[j_dim] - 1
else:
index = imax[py_dim]
if index < 0:
index += shape[j_dim]
imax_fix[j_dim] = index
istep_fix = sj.jarray("j", [istep])
if _index_within_range(imin_fix, shape) and _index_within_range(imax_fix, shape):
intervaled = Views.interval(rai, imin_fix, imax_fix)
stepped = Views.subsample(intervaled, istep_fix)
# TODO: better match NumPy squeeze behavior. See imagej/pyimagej#1231
dimension_reduced = Views.dropSingletonDimensions(stepped)
return dimension_reduced
def _index_within_range(query: List[int], source: List[int]) -> bool:
"""Check if query is within range of source index.
:param query: List of query int
:param source: List of soure int
"""
dim_num = len(query)
for i in range(dim_num):
if query[i] > source[i]:
raise IndexError(
f"index {query[i]} is out of bound for axis {i} with size {source[i]}"
)
return True