"""Image series
The Series class is a subclassed Numpy.ndarray enhancing the array with relevant medical image methods and attributes.
Typical example usage:
si = Series('input')
"""
import copy
import numpy as np
# from numpy.compat import basestring
import logging
import pydicom.dataset
import pydicom.datadict
# import numpy.core._multiarray_umath
import imagedata.axis
import imagedata.formats
import imagedata.readdata as readdata
import imagedata.formats.dicomlib.uid
from imagedata.header import Header, add_template, add_geometry
logger = logging.getLogger(__name__)
# Verify that pathlib.Path module is available
# Set Path variable to reflect state
try:
from pathlib import Path
except ImportError:
Path = None
class DoNotSetSlicesError(Exception):
pass
def is_pathlib_path(obj):
"""
Check whether obj is a pathlib.Path object.
"""
return Path is not None and isinstance(obj, Path)
[docs]class Series(np.ndarray):
"""Series -- a multidimensional array of medical imaging pixels.
The Series class is a subclassed Numpy.ndarray enhancing the ndarray
with relevant medical image methods and attributes.
Series(data, input_order='none', opts=None, shape=(0,), dtype=float, order=None, template=None, geometry=None)
Examples:
Read the contents of an input directory
>>> image = Series('directory/')
Make a Series instance from a Numpy.ndarray
>>> a = np.eye(128)
>>> image = Series(a)
Args:
data: (array_like or URL)
Input data, either explicit as np.ndarray, np.uint16, np.float32, or by URL to input data.
input_order (str): How to sort the input data. Typical values are:
* 'none' : 3D volume or 2D slice (default).
* 'time' : Time-resolved data.
* 'b' : Diffusion data with variable b values.
* 'te' : Varying echo times.
* 'fa' : Varying flip angles.
opts: Dict of input options, mostly for format specific plugins (argparse.Namespace or dict)
shape: Tuple of ints, specifying shape of input data.
dtype: Numpy data type. Default: float
template: Input data to use as template for DICOM header (Series, array_like or URL)
geometry: Input data to use as template for geometry (Series, array_like or URL)
order: Row-major (C-style) or column-major (Fortran-style) order, {'C', 'F'}, optional
Returns:
Series instance
"""
name = "Series"
description = "Image series"
authors = "Erling Andersen"
version = "1.1.1"
url = "www.helse-bergen.no"
def __new__(cls, data, input_order='none',
opts=None, shape=(0,), dtype=float, buffer=None, offset=0,
strides=None, order=None,
template=None, geometry=None, axes=None):
if issubclass(type(template), Series):
template = template.header
if issubclass(type(geometry), Series):
geometry = geometry.header
if issubclass(type(data), np.ndarray):
logger.debug('Series.__new__: data ({}) is subclass of np.ndarray'.format(type(data)))
obj = np.asarray(data).view(cls)
# Initialize attributes to defaults
# cls.__init_attributes(cls, obj)
# obj.header = Header() # Already set in __array_finalize__
if issubclass(type(data), Series):
# Copy attributes from existing Series to newly created obj
# obj.__dict__ = data.__dict__.copy() # carry forward attributes
obj.header = copy.copy(data.header) # carry forward attributes
# set the new 'input_order' attribute to the value passed
obj.header.input_order = input_order
# obj.header.set_default_values() # Already done in __array_finalize__
if axes is not None:
obj.header.axes = copy.copy(axes)
add_template(obj.header, template)
add_geometry(obj.header, template, geometry)
return obj
logger.debug('Series.__new__: data is NOT subclass of Series, type {}'.format(type(data)))
if issubclass(type(data), np.uint16) or issubclass(type(data), np.float32):
obj = np.asarray(data).view(cls)
# cls.__init_attributes(cls, obj)
obj.header = Header()
obj.header.input_order = input_order
obj.header.set_default_values(obj.axes if axes is None else axes)
add_template(obj.header, template)
add_geometry(obj.header, template, geometry)
return obj
# Assuming data is url to input data
if isinstance(data, np.compat.basestring):
urls = data
elif isinstance(data, list):
urls = data
elif is_pathlib_path(data):
urls = data.resolve()
else:
# raise ValueError("Input data could not be resolved: ({}) {}".format(type(data),data))
obj = np.asarray(data).view(cls)
# cls.__init_attributes(cls, obj)
obj.header = Header()
obj.header.input_order = input_order
obj.header.set_default_values(obj.axes if axes is None else axes)
add_template(obj.header, template)
add_geometry(obj.header, template, geometry)
return obj
# Read input, hdr is dict of attributes
hdr, si = readdata.read(urls, input_order, opts)
obj = np.asarray(si).view(cls)
assert obj.header, "No Header found in obj.header"
# set the new 'input_order' attribute to the value passed
obj.header.input_order = input_order
# Copy attributes from hdr dict to newly created obj
logger.debug('Series.__new__: Copy attributes from hdr dict to newly created obj')
if axes is not None:
new_axes = axes
elif obj.axes is None and 'axes' in hdr:
new_axes = hdr['axes']
else:
new_axes = obj.axes
obj.header.set_default_values(new_axes)
for attr in hdr.keys():
setattr(obj.header, attr, hdr[attr])
# Store any template and geometry headers,
add_template(obj.header, template)
add_geometry(obj.header, template, geometry)
# Finally, we must return the newly created object
return obj
def __array_finalize__(self, obj) -> None:
# logger.debug("Series.__array_finalize__: entered obj: {}".format(type(obj)))
# ``self`` is a new object resulting from
# ndarray.__new__(Series, ...), therefore it only has
# attributes that the ndarray.__new__ constructor gave it -
# i.e. those of a standard ndarray.
#
# We could have got to the ndarray.__new__ call in 3 ways:
# From an explicit constructor - e.g. Series():
# obj is None
# (we're in the middle of the Series.__new__
# constructor, and self.__input_order will be set when we return to
# Series.__new__)
if obj is None:
return
# From view casting - e.g arr.view(Series):
# obj is arr
# (type(obj) can be Series)
# From new-from-template - e.g Series[:3]
# type(obj) is Series
#
# Note that it is here, rather than in the __new__ method,
# that we set the default value for 'input_order', because this
# method sees all creation of default objects - with the
# Series.__new__ constructor, but also with arr.view(Series).
# logger.debug("Series.__array_finalize__: obj: {}".format(type(obj)))
if issubclass(type(obj), Series):
# Copy attributes from obj to newly created self
# logger.debug('Series.__array_finalize__: Copy attributes from {}'.format(type(obj)))
# self.__dict__ = obj.__dict__.copy() # carry forward attributes
self.header = copy.copy(obj.header) # carry forward attributes
else:
self.header = Header()
self.header.set_default_values(self.axes)
# We do not need to return anything
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
# logger.debug('Series.__array_ufunc__ method: %s' % method)
args = []
in_no = []
# logger.debug('Series.__array_ufunc__ inputs: %s %d' % (
# type(inputs), len(inputs)))
for i, input_ in enumerate(inputs):
if isinstance(input_, Series):
in_no.append(i)
args.append(input_.view(np.ndarray))
# logger.debug(' input %d: Series' % i)
# logger.debug(' input %s: ' % type(input_))
# logger.debug(' input spacing {} '.format(
# input_.spacing))
else:
args.append(input_)
outputs = kwargs.pop('out', None)
out_no = []
# logger.debug('Series.__array_ufunc__ inputs: %d' % len(inputs))
if outputs:
out_args = []
for j, output in enumerate(outputs):
if isinstance(output, Series):
out_no.append(j)
out_args.append(output.view(np.ndarray))
else:
out_args.append(output)
kwargs['out'] = tuple(out_args)
else:
outputs = (None,) * ufunc.nout
info = {}
if in_no:
info['inputs'] = in_no
if out_no:
info['outputs'] = out_no
results = super(Series, self).__array_ufunc__(ufunc, method,
*args, **kwargs)
if results is NotImplemented:
return NotImplemented
if method == 'at':
if isinstance(inputs[0], Series):
inputs[0].header = info
return
if ufunc.nout == 1:
if np.isscalar(results):
return results # Do not pack scalar results as Series object
results = (results,)
results = tuple((np.asarray(result).view(Series)
if output is None else output)
for result, output in zip(results, outputs))
if results and isinstance(results[0], Series):
# logger.debug('Series.__array_ufunc__ add info to results:\n{}'.format(info))
results[0].header = self._unify_headers(inputs)
return results[0] if len(results) == 1 else results
@staticmethod
def _unify_headers(inputs: tuple) -> Header:
"""Unify the headers of the inputs.
Typical usage is in expressions like c = a + b where at least
one of the arguments is a Series instance.
This function will provide a header for the result of
the expression.
Inputs:
- inputs: a tuple of arguments (ndarray or Series)
Return:
- header: a Header() class
"""
header = None
for i, input_ in enumerate(inputs):
if isinstance(input_, Series):
if header is None:
header = input_.header
# logger.debug('Series._unify_headers: copy header')
# else:
# Here we could have compared the headers of
# the arguments and resolved discrepancies.
# The simplest resolution, however, is to take the
# header of the first argument.
return header
def __getitem__(self, item):
"""__getitem__(self, item)
Called to implement evaluation of self[item]. For sequence types, the
accepted items should be integers and slice objects. Note that the
special interpretation of negative indexes (if the class wishes to
emulate a sequence type) is up to the __getitem__() method. If item is
of an inappropriate type, TypeError may be raised; if of a value
outside the set of indexes for the sequence (after any special
interpretation of negative values), IndexError should be raised. For
mapping types, if item is missing (not in the container), KeyError
should be raised. Note: for loops expect that an IndexError will be
raised for illegal indexes to allow proper detection of the end of the
sequence.
"""
def _set_geometry(_ret, _todo):
for attr, value in _todo:
try:
setattr(_ret, attr, value)
except AttributeError:
pass
def _number_of_ellipses(_items):
n = 0 # type: int
for _item in _items:
if _item is Ellipsis:
n += 1
return n
def _calculate_spec(obj, _items):
_slicing = False
_spec = {}
if isinstance(obj, Series):
# Calculate slice range
try:
for _dim in range(obj.ndim): # Loop over actual array shape
_spec[_dim] = (0, obj.shape[_dim], 1, obj.axes[_dim]) # Initial start,stop,step,axis
except (AttributeError, NameError):
raise ValueError('No header in _calculate_spec')
# Determine how to loop over slice spec and items
if _number_of_ellipses(_items) > 1:
raise IndexError('Multiple ellipses are not allowed.')
# Assume Ellipsis anywhere in items
index_spec = []
index_item = []
for _item in range(len(_items)):
if _items[_item] is not Ellipsis:
index_spec.append(_item)
index_item.append(_item)
else: # Ellipsis
remaining_items = len(_items) - _item - 1
index_spec += range(len(_spec) - remaining_items, len(_spec))
index_item += range(len(_items) - remaining_items, len(_items))
break
if len(index_spec) != len(index_item):
raise IndexError('Index problem: spec length %d, items length %d' %
(len(index_spec), len(index_item)))
for _item in index_item:
# If any item is of unknown type, we will not slice the data
if not isinstance(_items[_item], slice) and not isinstance(_items[_item], int):
return _slicing, _spec
for _dim, _item in zip(index_spec, index_item):
if isinstance(_items[_item], slice):
_start = _items[_item].start or _spec[_dim][0]
_stop = _items[_item].stop or _spec[_dim][1]
_step = _items[_item].step or _spec[_dim][2]
if _start < 0:
_start = obj.shape[_dim] + _start
if _stop < 0:
_stop = obj.shape[_dim] + _stop
_spec[_dim] = (_start, _stop, _step, obj.axes[_dim])
_slicing = True
elif isinstance(_items[_item], int):
_start = _items[_item] or _spec[_dim][0]
if _start < 0:
_start = obj.shape[_dim] + _start
_stop = _start + 1
_step = 1
_spec[_dim] = (_start, _stop, _step, obj.axes[_dim])
_slicing = True
return _slicing, _spec
if getattr(self, 'header', None) is None:
return super(Series, self).__getitem__(item)
if isinstance(item, tuple):
items = item
else:
items = (item,)
slicing, spec = _calculate_spec(self, items)
todo = [] # Collect header changes, apply after ndarray slicing
reduce_dim = False # Whether the slicing reduces the dimension
if slicing:
# Here we slice the header information
new_axes = []
for i in range(self.ndim):
# Slice dimension i
start, stop, step, axis = spec[i]
new_axes.append(axis[start:stop:step])
if axis.name == 'slice':
# Select slice of sliceLocations
sloc = self.__get_sliceLocations(spec[i])
todo.append(('sliceLocations', sloc))
# Select slice of imagePositions
ipp = self.__get_imagePositions(spec[i])
todo.append(('imagePositions', None)) # Wipe existing positions
if ipp is not None:
todo.append(('imagePositions', ipp))
elif axis.name == imagedata.formats.input_order_to_dirname_str(self.input_order):
# Select slice of tags
tags = self.__get_tags(spec)
todo.append(('tags', tags))
if len(tags[0]) == 1:
reduce_dim = True
# Select slice of DicomHeaderDict
hdr = self.__get_DicomHeaderDict(spec)
todo.append(('DicomHeaderDict', hdr))
# Slicing the ndarray is done here
ret = super(Series, self).__getitem__(item)
if slicing and isinstance(ret, Series):
# noinspection PyUnboundLocalVariable
todo.append(('axes', new_axes[-ret.ndim:]))
if reduce_dim:
# Must copy the ret object before modifying. Otherwise, ret is a view to self.
ret.header = copy.copy(ret.header)
if ret.axes[-ret.ndim].name in ['slice', 'row', 'column', 'color']:
ret.input_order = imagedata.formats.INPUT_ORDER_NONE
else:
raise IndexError('Unexpected axis {} after slicing'.format(ret.axes[0].name))
_set_geometry(ret, todo)
return ret
def __get_sliceLocations(self, spec):
# logger.debug('__get_sliceLocations: enter')
try:
sl = self.sliceLocations
except ValueError:
return None
start, stop, step = 0, self.slices, 1
if spec[0] is not None:
start = spec[0]
if spec[1] is not None:
stop = spec[1]
if spec[2] is not None:
step = spec[2]
sl = np.array(sl[start:stop:step])
# logger.debug('__get_sliceLocations: exit')
return sl
def __get_imagePositions(self, spec):
# logger.debug('__get_imagePositions: enter')
try:
ipp = self.imagePositions
except ValueError:
return None
start, stop, step = 0, self.slices, 1
if spec[0] is not None:
start = spec[0]
if spec[1] is not None:
stop = spec[1]
if spec[2] is not None:
step = spec[2]
ippdict = {}
j = 0
# logger.debug('__get_imagePositions: start,stop={},{}'.format(spec[0], stop))
for i in range(start, stop, step):
if i < 0:
raise ValueError('i < 0')
ippdict[j] = ipp[i]
j += 1
# logger.debug('__get_imagePositions: exit')
return ippdict
def __get_DicomHeaderDict(self, specs):
try:
slices = len(self.DicomHeaderDict)
except ValueError:
return None
slice_spec = slice(0, slices, 1)
assert len(self.tags) > 0, "No tags defined"
tags = len(self.tags[0])
tag_spec = slice(0, tags, 1)
for d in specs:
start, stop, step, axis = specs[d]
if axis.name == 'slice':
slice_spec = slice(start, stop, step)
elif axis.name == imagedata.formats.input_order_to_dirname_str(self.input_order):
tag_spec = slice(start, stop, step)
# DicomHeaderDict[slice].tuple(tagvalue, filename, dicomheader)
hdr = {}
j = 0
for s in range(slice_spec.start, slice_spec.stop, slice_spec.step):
_slice = min(s, slices - 1)
hdr[j] = list()
for t in range(tag_spec.start, tag_spec.stop, tag_spec.step):
try:
tag = self.tags[_slice][t]
except IndexError:
raise IndexError("Could not get tag for slice {}, tag {}".format(_slice, t))
try:
hdr[j].append(
self.__find_tag_in_hdr(self.DicomHeaderDict[_slice], tag)
)
except TypeError:
return None
j += 1
return hdr
def __repr__(self):
return super().__repr__()
def __str__(self):
return """Patient: {} {}\nStudy Time: {} {}\nSeries Time: {} {}\nSeries #{}: {}\nShape: {}, dtype: {}, input order: {}""".format(
self.patientID, self.patientName,
self.getDicomAttribute('StudyDate'), self.getDicomAttribute('StudyTime'),
self.getDicomAttribute('SeriesDate'), self.getDicomAttribute('SeriesTime'),
self.seriesNumber, self.seriesDescription,
imagedata.formats.shape_to_str(self.shape), self.dtype,
imagedata.formats.input_order_to_dirname_str(self.input_order))
@staticmethod
def __find_tag_in_hdr(hdr_list, find_tag):
for tag, filename, hdr in hdr_list:
if tag == find_tag:
return tag, filename, hdr
return None
def __get_tags(self, specs):
try:
tmpl_tags = self.tags
last_slice = len(self.tags) - 1 # known_slices might be less than actual data shape
tags = len(self.tags[0])
except ValueError:
return None
slice_spec = slice(0, self.slices, 1)
tag_spec = slice(0, tags, 1)
for d in specs:
start, stop, step, axis = specs[d]
if axis.name == "slice":
slice_spec = slice(start, stop, step)
elif axis.name == imagedata.formats.input_order_to_dirname_str(self.input_order):
tag_spec = slice(start, stop, step)
# tags: dict[slice] is np.array(tags)
new_tags = {}
j = 0
for s in range(slice_spec.start, slice_spec.stop, slice_spec.step):
new_tags[j] = list()
for t in range(tag_spec.start, tag_spec.stop, tag_spec.step):
# Limit slices to the known slices. Duplicates last slice and/or tag if too few.
try:
new_tags[j].append(tmpl_tags[s][t])
except IndexError:
raise IndexError("Could not get tag for slice {}, tag {}".format(s, t))
j += 1
return new_tags
[docs] def write(self, url, opts=None, formats=None):
"""Write Series image
Args:
self: Series array
directory_name: directory name
filename_template: template including %d for image number
opts: Output options (argparse.Namespace or dict)
formats: list of output formats, overriding opts.output_format (list or str)
"""
logger.debug('Series.write: url : {}'.format(url))
logger.debug('Series.write: formats: {}'.format(formats))
logger.debug('Series.write: opts : {}'.format(opts))
readdata.write(self, url, formats=formats, opts=opts)
@property
def input_order(self):
"""str: Input order
How to sort input files:
* INPUT_ORDER_NONE ('none'): No sorting.
* INPUT_ORDER_TIME ('time'): Sort on image time (acquisition time or trigger time).
* INPUT_ORDER_B ('b'): Sort on b value.
* INPUT_ORDER_FA ('fa'): Sort on flip angle.
* INPUT_ORDER_TE ('te'): Sort on echo time.
* INPUT_ORDER_FAULTY ('faulty'): Correct erroneous attributes.
Returns:
Current input order.
Raises:
ValueError: when order is illegal.
"""
return self.header.input_order
@input_order.setter
def input_order(self, order):
if order in imagedata.formats.input_order_set:
self.header.input_order = order
else:
raise ValueError("Unknown input order: {}".format(order))
@property
def input_format(self):
"""str: Input format
Possible input formats depend on the available `formats` plugins,
and include `'dicom'`, `'itk'` and `'nifti'`."""
return self.header.input_format
@input_format.setter
def input_format(self, fmt):
self.header.input_format = fmt
@property
def input_sort(self):
"""int: Input order
How to sort output files:
* SORT_ON_SLICE: Run over slices first
* SORT_ON_TAG : Run over input order first, then slices
Returns:
The input order.
Raises:
ValueError: when input order is not defined.
"""
try:
if self.header.input_sort is not None:
return self.header.input_sort
except AttributeError:
pass
raise ValueError("Input sort order not set.")
@input_sort.setter
def input_sort(self, order):
if order is None or order in imagedata.formats.sort_on_set:
self.header.input_sort = order
else:
raise ValueError("Unknown sort order: {}".format(order))
@property
def sort_on(self):
"""int: Output order
How to sort output files:
* SORT_ON_SLICE: Run over slices first
* SORT_ON_TAG : Run over input order first, then slices
Returns:
Current output order.
Raises:
ValueError: when output order is not defined.
"""
try:
if self.header.sort_on is not None:
return self.header.sort_on
except AttributeError:
pass
raise ValueError("Output sort order not set.")
@sort_on.setter
def sort_on(self, order):
if order in imagedata.formats.sort_on_set:
self.header.sort_on = order
else:
raise ValueError("Unknown sort order: {}".format(order))
@property
def shape(self):
"""tuple: Matrix shape
Raises:
IndexError: always when set (do not set shape). Should set axes instead.
"""
return super(Series, self).shape
@shape.setter
def shape(self, s):
raise IndexError('Should set axes instead of shape.')
@property
def rows(self):
"""int: Number of rows
Raises:
ValueError: when number of rows is not defined.
"""
try:
row_axis = self.find_axis('row')
return len(row_axis)
except ValueError:
_color = 0
if self.color:
_color = 1
if self.ndim - _color < 2:
raise ValueError("{}D dataset has no rows".format(self.ndim))
return self.shape[-2 - _color]
@property
def columns(self):
"""int: Number of columns
Raises:
ValueError: when number of columns is not defined.
"""
try:
column_axis = self.find_axis('column')
return len(column_axis)
except ValueError:
_color = 0
if self.color:
_color = 1
if self.ndim - _color < 1:
raise ValueError("Dataset has no columns")
return self.shape[-1 - _color]
@property
def slices(self):
"""int: Number of slices
Raises:
ValueError: when number of slices is not defined.
DoNotSetSlicesError: Always (do not set slices)
"""
try:
slice_axis = self.find_axis('slice')
# logger.debug("Series.slices: {}D dataset slice_axis {}".format(self.ndim, slice_axis))
return len(slice_axis)
except ValueError:
_color = 0
if self.color:
_color = 1
if self.ndim - _color < 3:
logger.debug("Series.slices: {}D dataset has no slices".format(self.ndim))
# raise ValueError("{}D dataset has no slices".format(self.ndim))
return 1
logger.debug("Series.slices: {}D dataset slice from shape ({}) {}".format(
self.ndim, self.shape, self.shape[-3 - _color]))
return self.shape[-3 - _color]
@slices.setter
def slices(self, nslices):
raise DoNotSetSlicesError('Do not set slices=%d explicitly. Slices are inferred from the shape.' %
nslices)
@property
def sliceLocations(self):
"""numpy.array: Slice locations
Sorted numpy array of slice locations, in mm.
Raises:
ValueError: When no slice locations are defined.
"""
try:
if self.header.sliceLocations is not None:
return self.header.sliceLocations
# Some image formats do not provide slice locations.
# If orientation and imagePositions are set, slice locations can
# be calculated.
if self.header.orientation is not None and self.header.imagePositions is not None:
logger.debug(
'sliceLocations: calculate {} slice from orientation and imagePositions'.format(self.slices))
loc = np.empty(self.slices)
normal = self.transformationMatrix[0, :3]
for _slice in range(self.slices):
loc[_slice] = np.inner(normal, self.imagePositions[_slice].flatten())
self.header.sliceLocations = loc
return self.header.sliceLocations
except AttributeError:
pass
raise ValueError("Slice locations are not defined.")
@sliceLocations.setter
def sliceLocations(self, loc):
if loc is not None:
self.header.sliceLocations = np.sort(loc)
else:
self.header.sliceLocations = None
@property
def DicomHeaderDict(self):
"""dict: DICOM header dictionary
DicomHeaderDict[slice].tuple(tagvalue, filename, dicomheader)
Raises:
ValueError: when DICOM header is not set.
Examples:
Get values for slice=0:
>>> tagvalue, filename, dicomheader = image.DicomHeaderDict()[0]
"""
# logger.debug('Series.DicomHeaderDict: here')
try:
if self.header.DicomHeaderDict is not None:
# logger.debug('Series.DicomHeaderDict: return')
# logger.debug('Series.DicomHeaderDict: return {}'.format(type(self.header.DicomHeaderDict)))
# logger.debug('Series.DicomHeaderDict: return {}'.format(self.header.DicomHeaderDict.keys()))
return self.header.DicomHeaderDict
except AttributeError:
pass
raise ValueError("Dicom Header Dict is not set.")
@DicomHeaderDict.setter
def DicomHeaderDict(self, dct):
self.header.DicomHeaderDict = dct
@property
def tags(self):
"""dict[slice] of numpy.array(tags): Image tags for each slice
Image tags can be an array of:
- time points
- diffusion weightings (b values)
- flip angles
Setting the tags will adjust the tags in DicomHeaderDict too.
tags is a dict with (slice keys, tag array)
dict[slice] is np.array(tags)
Examples:
>>>self.tags[slice][tag]
Raises:
ValueError: when tags are not set.
"""
try:
if self.header.tags is not None:
return self.header.tags
except AttributeError:
pass
return None
@tags.setter
def tags(self, tags):
self.header.tags = {}
hdr = {}
for s in tags.keys():
self.header.tags[s] = np.array(tags[s])
hdr[s] = list()
max_t = len(self.header.DicomHeaderDict[s]) - 1
for t in range(len(tags[s])):
if t <= max_t:
wrongtag, filename, dcm = self.header.DicomHeaderDict[s][t]
hdr[s].append(
(tags[s][t], filename, dcm)
)
else:
# Copy last DicomHeaderDict
wrongtag, filename, dcm = self.header.DicomHeaderDict[s][max_t]
hdr[s].append(
(tags[s][t], None, dcm)
)
self.header.DicomHeaderDict = hdr
@property
def axes(self):
"""list of Axis: axes objects
Raises:
ValueError: when the axes are not set.
"""
try:
if self.header.axes is not None:
return self.header.axes
except AttributeError:
pass
# Calculate axes from image shape
self.header.axes = []
shape = super(Series, self).shape
if len(shape) < 1:
return None
if shape[-1] == 3 and self.dtype == np.uint8:
_mono_shape = shape[:-1]
else:
_mono_shape = shape
_max_known_shape = min(3, len(_mono_shape))
_labels = ['slice', 'row', 'column'][-_max_known_shape:]
while len(_labels) < self.ndim:
_labels.insert(0, 'unknown')
i = 0
for d in super(Series, self).shape:
self.header.axes.append(
imagedata.axis.UniformLengthAxis(
_labels[i], 0, d, 1
)
)
i += 1
return self.header.axes
@axes.setter
def axes(self, ax):
# Verify that axes shape match ndarray shape
# Verify that axis names are used once only
used_name = {}
for i, axis in enumerate(ax):
# if len(axis) != self.shape[i]:
# raise IndexError("Axis length {} must match array shape {}".format(
# [len(x) for x in ax], self.shape))
if axis.name in used_name:
raise ValueError("Axis name {} is used multiple times.".format(axis.name))
used_name[axis.name] = True
self.header.axes = ax
# Update spacing from new axes
try:
spacing = self.spacing
except ValueError:
spacing = np.array((1.0, 1.0, 1.0))
for i, direction in enumerate(['slice', 'row', 'column']):
try:
axis = self.find_axis(direction)
spacing[i] = axis.step
except ValueError:
pass
self.header.spacing = spacing
[docs] def find_axis(self, name):
"""Find axis with given name
Args:
name: Axis name to search for
Returns:
axis object with given name
Raises:
ValueError: when no axis object has given name
Usage:
>>> axis = si.find_axis('slice')
"""
for axis in self.axes:
if axis.name == name:
return axis
raise ValueError("No axis object with name %s exist" % name)
@property
def spacing(self):
"""numpy.array([ds,dr,dc]): spacing
Given as ds,dr,dc in mm.
2D image will return ds=1.
Raises:
ValueError: when spacing is not set.
ValueError: when spacing is not a tuple of 3 coordinates
Usage:
>>> ds, dr, dc = si.spacing
>>> si.spacing = ds, dr, dc
"""
try:
#if self.header.spacing is not None:
# return self.header.spacing
slice_axis = self.find_axis('slice')
ds = slice_axis.step
except ValueError as e:
if self.header.spacing is not None:
ds = self.header.spacing[0]
else:
raise ValueError("Spacing is unknown: {}".format(e))
try:
row_axis = self.find_axis('row')
column_axis = self.find_axis('column')
return np.array((ds, row_axis.step, column_axis.step))
except ValueError as e:
raise ValueError("Spacing is unknown: {}".format(e))
@spacing.setter
def spacing(self, *args):
if args[0] is None:
# self.header.spacing = None
return
logger.debug("spacing.setter {} {}".format(len(args), args))
for arg in args:
logger.debug("spacing.setter arg {} {}".format(len(arg), arg))
# Invalidate existing transformation matrix
self.header.transformationMatrix = None
# Handle both tuple and component spacings
if len(args) == 3:
spacing = np.array(args)
elif len(args) == 1:
arg = args[0]
if len(arg) == 3:
spacing = np.array(arg)
elif len(arg) == 1:
arg0 = arg[0]
if len(arg0) == 3:
spacing = np.array(arg0)
else:
raise ValueError("Length of spacing in setSpacing(): %d" % len(arg0))
else:
raise ValueError("Length of spacing in setSpacing(): %d" % len(arg))
else:
raise ValueError("Length of spacing in setSpacing(): %d" % len(args))
try:
slice_axis = self.find_axis('slice')
slice_axis.step = spacing[0]
except ValueError as e:
# Assume 2D image with no slice dimension
pass
try:
row_axis = self.find_axis('row')
column_axis = self.find_axis('column')
row_axis.step = spacing[1]
column_axis.step = spacing[2]
self.header.spacing = np.array(spacing)
except ValueError as e:
raise ValueError("Spacing cannot be set: {}".format(e))
@property
def imagePositions(self):
"""dict of numpy.array([z,y,x]): imagePositions
The [z,y,x] coordinates of the upper left hand corner (first pixel)
of each slice.
dict(imagePositions[slice]) of [z,y,x] in mm, as numpy array
When setting, the position list is added to existing imagePositions.
Overlapping dict keys will replace exisiting imagePosition for given slice.
Examples:
>>> z,y,x = si.imagePositions[slice]
Examples:
>>> for slice in range(slices):
>>> si.imagePositions = {
>>> slice: si.getPositionForVoxel(np.array([slice,0,0]))
>>> }
Raises:
ValueError: when imagePositions are not set.
AssertionError: when positions have wrong shape or datatype.
"""
# logger.debug('Series.imagePositions.get:')
try:
if self.header.imagePositions is not None:
if len(self.header.imagePositions) > self.slices:
# Truncate imagePositions to actual number of slices.
# Could be the result of a slicing operation.
ipp = {}
for z in range(self.slices):
ipp[z] = \
self.header.imagePositions[z]
self.header.imagePositions = ipp
elif len(self.header.imagePositions) < self.slices and \
len(self.header.imagePositions) == 1:
# Some image formats define one imagePosition only.
# Could calculate the missing imagePositions from origin and
# orientation.
# Set imagePositions for additional slices
logger.debug(
'Series.imagePositions.get: 1 positions only. Calculating the other {} positions'.format(
self.slices - 1))
m = self.transformationMatrix
for _slice in range(1, self.slices):
self.header.imagePositions[_slice] = \
self.getPositionForVoxel(np.array([_slice, 0, 0]),
transformation=m)
return self.header.imagePositions
except AttributeError:
pass
raise ValueError("No imagePositions set.")
@imagePositions.setter
def imagePositions(self, poslist):
if poslist is None:
self.header.imagePositions = None
return
assert isinstance(poslist, dict), "ImagePositions is not dict() (%s)" % type(poslist)
# Invalidate existing transformation matrix
# self.header.transformationMatrix = None
try:
if self.header.imagePositions is None:
self.header.imagePositions = dict()
except AttributeError:
self.header.imagePositions = dict()
# logger.debug("imagePositions set for keys {}".format(poslist.keys()))
for _slice in poslist.keys():
pos = poslist[_slice]
# logger.debug("imagePositions set _slice {} to {}".format(_slice,pos))
assert isinstance(pos, np.ndarray), "Wrong datatype of position (%s)" % type(pos)
assert len(pos) == 3, "Wrong size of pos (is %d, should be 3)" % len(pos)
self.header.imagePositions[_slice] = np.array(pos)
@property
def orientation(self):
"""numpy.array: Orientation
The direction cosines of the first row and the first column with respect
to the patient.
These attributes shall be provided as a pair.
Row value (column index) for the z,y,x axes respectively,
followed by the column value (row index) for the z,y,x axes respectively.
Raises:
ValueError: when orientation is not set.
AssertionError: when len(orient) != 6
"""
try:
if self.header.orientation is not None:
return self.header.orientation
except AttributeError:
pass
raise ValueError("No orientation set.")
@orientation.setter
def orientation(self, orient):
if orient is None:
self.header.transformationMatrix = None
self.header.orientation = None
return
assert len(orient) == 6, "Wrong size of orientation"
# Invalidate existing transformation matrix
# self.header.transformationMatrix = None
self.header.orientation = np.array(orient)
@property
def seriesNumber(self):
"""int: Series number
DICOM series number.
Raises:
ValueError: when series number is not set.
ValueError: when series number cannot be converted to int.
"""
try:
if self.header.seriesNumber is not None:
return self.header.seriesNumber
except AttributeError:
pass
raise ValueError("No series number set.")
@seriesNumber.setter
def seriesNumber(self, sernum):
if sernum is None:
self.header.seriesNumber = None
return
try:
self.header.seriesNumber = int(sernum)
except AttributeError:
raise ValueError("Cannot convert series number to integer")
@property
def seriesDescription(self):
"""str: Series description
DICOM series description.
Raises:
ValueError: When series description is not set.
AssertionError: when series description is not str
"""
try:
if self.header.seriesDescription is not None:
return self.header.seriesDescription
except AttributeError:
pass
raise ValueError("No series description set.")
@seriesDescription.setter
def seriesDescription(self, descr):
if descr is None:
self.header.seriesDescription = None
return
assert isinstance(descr, str), "Given series description is not str"
self.header.seriesDescription = descr
@property
def imageType(self):
"""list of str: Image type
DICOM image type
Raises:
ValueError: when image type is not set.
TypeError: When imagetype is not printable.
"""
try:
if self.header.imageType is not None:
return self.header.imageType
except AttributeError:
pass
raise ValueError("No image type set.")
@imageType.setter
def imageType(self, imagetype):
if imagetype is None:
self.header.imageType = None
return
self.header.imageType = list()
try:
for s in imagetype:
self.header.imageType.append(str(s))
except AttributeError:
raise TypeError("Given image type is not printable (is %s)" % type(imagetype))
@property
def studyInstanceUID(self):
"""str: Study instance UID
DICOM study instance UID
Raises:
ValueError: when study instance UID is not set.
TypeError: When uid is not printable.
"""
try:
if self.header.studyInstanceUID is not None:
return self.header.studyInstanceUID
except AttributeError:
pass
raise ValueError("No study instance UID set.")
@studyInstanceUID.setter
def studyInstanceUID(self, uid):
if uid is None:
self.header.studyInstanceUID = None
return
try:
self.header.studyInstanceUID = str(uid)
except AttributeError:
raise TypeError("Given study instance UID is not printable")
@property
def studyID(self):
"""str: Study ID
DICOM study ID
Raises:
ValueError: when study ID is not set.
TypeError: When id is not printable
"""
try:
if self.header.studyID is not None:
return self.header.studyID
except AttributeError:
pass
raise ValueError("No study ID set.")
@studyID.setter
def studyID(self, id):
if id is None:
self.header.studyID = None
return
try:
self.header.studyID = str(id)
except AttributeError:
raise TypeError("Given study ID is not printable")
@property
def seriesInstanceUID(self):
"""str: Series instance UID
DICOM series instance UID
Raises:
ValueError: when series instance UID is not set
TypeError: When uid is not printable
"""
try:
if self.header.seriesInstanceUID is not None:
return self.header.seriesInstanceUID
except AttributeError:
pass
raise ValueError("No series instance UID set.")
@seriesInstanceUID.setter
def seriesInstanceUID(self, uid):
if uid is None:
self.header.seriesInstanceUID = None
return
try:
self.header.seriesInstanceUID = str(uid)
except AttributeError:
raise TypeError("Given series instance UID is not printable")
@property
def frameOfReferenceUID(self):
"""str: Frame of reference UID
DICOM frame of reference UID
Raises:
ValueError: when frame of reference UID is not set
TypeError: When uid is not printable
"""
try:
if self.header.frameOfReferenceUID is not None:
return self.header.frameOfReferenceUID
except AttributeError:
pass
raise ValueError("No frame of reference UID set.")
@frameOfReferenceUID.setter
def frameOfReferenceUID(self, uid):
if uid is None:
self.header.frameOfReferenceUID = None
return
try:
self.header.frameOfReferenceUID = str(uid)
except AttributeError:
raise TypeError("Given frame of reference UID is not printable")
@property
def SOPClassUID(self):
"""DICOM SOP Class UID
Raises:
ValueError: when SOP Class UID is not set
"""
try:
if self.header.SOPClassUID is not None:
return self.header.SOPClassUID
except AttributeError:
pass
raise ValueError("No SOP Class UID set.")
@SOPClassUID.setter
def SOPClassUID(self, uid):
if uid is None:
self.header.SOPClassUID = None
return
try:
self.header.SOPClassUID = imagedata.formats.dicomlib.uid.get_uid_for_storage_class(uid)
except ValueError:
raise
@property
def accessionNumber(self):
"""str: Accession number
DICOM accession number
Raises:
ValueError: when accession number is not set
TypeError: When accno is not printable
"""
try:
if self.header.accessionNumber is not None:
return self.header.accessionNumber
except AttributeError:
pass
raise ValueError("No accession number set.")
@accessionNumber.setter
def accessionNumber(self, accno):
if accno is None:
self.header.accessionNumber = None
return
try:
self.header.accessionNumber = str(accno)
except AttributeError:
raise TypeError("Given accession number is not printable")
@property
def patientName(self):
"""str: Patient name
DICOM patient name
Raises:
ValueError: when patient name is not set
TypeError: When patnam is not printable
"""
try:
if self.header.patientName is not None:
return self.header.patientName
except AttributeError:
pass
raise ValueError("No patient name set.")
@patientName.setter
def patientName(self, patnam):
if patnam is None:
self.header.patientName = None
return
try:
self.header.patientName = str(patnam)
except AttributeError:
raise TypeError("Given patient name is not printable")
@property
def patientID(self):
"""str: Patient ID
DICOM patient ID
Raises:
ValueError: when patient ID is not set
TypeError: When patID is not printable
"""
try:
if self.header.patientID is not None:
return self.header.patientID
except AttributeError:
pass
raise ValueError("No patient ID set.")
@patientID.setter
def patientID(self, patid):
if patid is None:
self.header.patientID = None
return
try:
self.header.patientID = str(patid)
except AttributeError:
raise TypeError("Given patient ID is not printable")
@property
def patientBirthDate(self):
"""str: Patient birth date
DICOM patient birth date
Raises:
ValueError: when patient birth date is not set.
TypeError: When patient birth date is not printable.
"""
try:
if self.header.patientBirthDate is not None:
return self.header.patientBirthDate
except AttributeError:
pass
raise ValueError("No patient birthdate set.")
@patientBirthDate.setter
def patientBirthDate(self, patbirdat):
if patbirdat is None:
self.header.patientBirthDate = None
return
try:
self.header.patientBirthDate = str(patbirdat)
except AttributeError:
raise TypeError("Given patient birth date is not printable")
@property
def color(self):
"""bool: Color interpretation
Whether the array stores a color image, and the
last index represents the color components
Raises:
ValueError: when color interpretation is not set
TypeError: When color is not bool
"""
try:
if self.header.color is not None:
return self.header.color
except AttributeError:
pass
raise ValueError("No Color Interpretation is set.")
@color.setter
def color(self, color):
if color is None:
self.header.color = False
return
try:
self.header.color = bool(color)
except AttributeError:
raise TypeError("Given color is not a boolean.")
@property
def photometricInterpretation(self):
"""str: Photometric Interpretation
DICOM Photometric Interpretation
Raises:
ValueError: when photometric interpretation is not set
TypeError: When photometric interpretation is not printable
"""
try:
if self.header.photometricInterpretation is not None:
return self.header.photometricInterpretation
except AttributeError:
pass
raise ValueError("No Photometric Interpretation is set.")
@photometricInterpretation.setter
def photometricInterpretation(self, string):
if string is None:
self.header.photometricInterpretation = None
return
try:
self.header.photometricInterpretation = str(string)
except AttributeError:
raise TypeError("Given phometric interpretation is not printable")
# noinspection PyPep8Naming
@property
def transformationMatrix(self):
"""numpy.array: Transformation matrix
If the transformation matrix is not set, an attempt will be made to calculate it
from spacing, imagePositions and orientation.
When setting the transformation matrix, spacing and slices must be set in advance.
A new transformation matrix will also impact orientation and imagePositions.
Raises:
ValueError: Transformation matrix cannot be constructed.
"""
# def normalize(v):
# """Normalize a vector
#
# https://stackoverflow.com/questions/21030391/how-to-normalize-an-array-in-numpy
#
# :param v: 3D vector
# :return: normalized 3D vector
# """
# norm=np.linalg.norm(v, ord=1)
# if norm==0:
# norm=np.finfo(v.dtype).eps
# return v/norm
debug = None
# debug = True
try:
if self.header.transformationMatrix is not None:
return self.header.transformationMatrix
# Calculate transformation matrix
logger.debug('Series.transformationMatrix: Calculate transformation matrix')
ds, dr, dc = self.spacing
slices = len(self.header.imagePositions)
T0 = self.header.imagePositions[0].reshape(3, 1) # z,y,x
Tn = self.header.imagePositions[slices - 1].reshape(3, 1)
orient = self.orientation
colr = np.array(orient[3:]).reshape(3, 1)
colc = np.array(orient[:3]).reshape(3, 1)
if slices > 1:
logger.debug('Series.transformationMatrix: multiple slices case (slices={})'.format(slices))
# Calculating normal vector based on first and last slice should be the correct method.
k = (T0 - Tn) / (1 - slices)
# Will just calculate normal to row and column to match other software.
# k = np.cross(colr, colc, axis=0)
# ##k = np.cross(colc, colr, axis=0)
# ##k = k * ds
else:
logger.debug('Series.transformationMatrix: single slice case')
k = np.cross(colr, colc, axis=0)
# k = normalize(k) * ds
k = k * ds
logger.debug('Series.transformationMatrix: k={}'.format(k.T))
# logger.debug("q: k {} colc {} colr {} T0 {}".format(k.shape,
# colc.shape, colr.shape, T0.shape))
A = np.eye(4)
A[:3, :4] = np.hstack([
k,
colr * dr,
colc * dc,
T0])
if debug:
logger.debug("A:\n{}".format(A))
self.header.transformationMatrix = A
return self.header.transformationMatrix
except AttributeError:
pass
raise ValueError('Transformation matrix cannot be constructed.')
@transformationMatrix.setter
def transformationMatrix(self, m):
self.header.transformationMatrix = m
# if M is not None:
# ds,dr,dc = self.spacing
# # Set imagePositions for first slice
# z,y,x = M[0:3,3]
# self.imagePositions = ({0: np.array([z,y,x])})
# # Set slice orientation
# orient = []
# orient.append(M[2,2]/dr)
# orient.append(M[1,2]/dr)
# orient.append(M[0,2]/dr)
# orient.append(M[2,1]/dc)
# orient.append(M[1,1]/dc)
# orient.append(M[0,1]/dc)
# self.orientation = orient
# # Set imagePositions for additional slices
# for _slice in range(1,self.slices):
# self.imagePositions = {
# _slice: self.getPositionForVoxel(np.array([_slice,0,0]),
# transformation=M)
# }
@property
def timeline(self):
"""numpy.array: Timeline in seconds, as numpy array of floats
Delta time is given as seconds. First image is t=0.
Length of array is number of tags.
Raises:
ValueError: tags for dataset is not time tags
"""
if self.input_order == imagedata.formats.INPUT_ORDER_TIME:
timeline = [0.0]
for t in range(1, len(self.tags[0])):
timeline.append(self.tags[0][t] - self.tags[0][0])
return np.array(timeline)
else:
raise ValueError("No timeline tags are available. Input order: {}".format(self.input_order))
@property
def bvalues(self):
"""numpy.array: b-values in s/mm2, as numpy array of floats
Length of array is number of tags.
Raises:
ValueError: tags for dataset is not b tags
"""
if self.input_order == imagedata.formats.INPUT_ORDER_B:
return np.array(self.tags[0])
else:
raise ValueError("No b-value tags are available. Input order: {}".format(self.input_order))
[docs] def getDicomAttribute(self, keyword, slice=0, tag=0):
"""Get named DICOM attribute.
Args:
keyword (str): name or dicom tag
slice (int): optional slice to get attribute from (default: 0)
tag (int): optional tag to get attribute from (default: 0)
Returns:
DICOM attribute
"""
if self.DicomHeaderDict is None:
return None
if issubclass(type(keyword), str):
_tag = pydicom.datadict.tag_for_keyword(keyword)
else:
_tag = keyword
if _tag is None:
return None
tg, fname, im = self.DicomHeaderDict[slice][tag]
if _tag in im:
return im[_tag].value
else:
return None
[docs] def setDicomAttribute(self, keyword, value, slice=None, tag=None):
"""Set named DICOM attribute.
Args:
keyword (str): name or dicom tag
value: new value for DICOM attribute
slice (int): optional slice to set attribute for (default: all)
tag (int): optional tag to set attribute for (default: all)
Raises:
ValueError: When no DICOM tag is set.
"""
if self.DicomHeaderDict is None:
return None
if issubclass(type(keyword), str):
_tag = pydicom.datadict.tag_for_keyword(keyword)
else:
_tag = keyword
if _tag is None:
raise ValueError('No DICOM tag set')
slices = [i for i in range(self.slices)]
tags = [i for i in range(len(self.tags[0]))]
if slice is not None:
slices = [slice]
if tag is not None:
tags = [tag]
for s in slices:
for t in tags:
tg, fname, im = self.DicomHeaderDict[s][t]
if _tag in im:
im[_tag].value = value
else:
VR = pydicom.datadict.dictionary_VR(_tag)
im.add_new(_tag, VR, value)
[docs] def getPositionForVoxel(self, r, transformation=None):
"""Get patient position for center of given voxel r
Use Patient Position and Image Orientation to calculate world
coordinates for given voxel
Args:
r (numpy.array): (s,r,c) of voxel in voxel coordinates
transformation (numpy.array, optional): transformation matrix when different from self.transformationMatrix
Returns:
numpy.array((z,y,x)): position of voxel in world coordinates (mm)
"""
if transformation is None:
transformation = self.transformationMatrix
# q = self.getTransformationMatrix()
# V = np.array([[r[2]], [r[1]], [r[0]], [1]]) # V is [x,y,z,1]
# if len(r) == 3 or (len(r) == 1 and len(r[0] == 3)):
# p = np.vstack((r.reshape((3, 1)), [1]))
# elif len(r) == 4:
# p = r.reshape((4, 1))
try:
p = r.reshape((4, 1))
except ValueError:
p = np.vstack((r.reshape((3, 1)), [1]))
newposition = np.dot(transformation, p)
return newposition[:3] # z,y,x
[docs] def getVoxelForPosition(self, p, transformation=None):
""" Get voxel for given patient position p
Use Patient Position and Image Orientation to calculate world
coordinates for given voxel
Args:
p (numpy.array): (z,y,x) of voxel in world coordinates (mm)
transformation (numpy.array, optional): transformation matrix when different from self.transformationMatrix
Returns:
numpy.array((s,r,c)): of voxel in voxel coordinates
"""
if transformation is None:
transformation = self.transformationMatrix
# q = self.getTransformationMatrix()
# V = np.array([[p[2]], [p[1]], [p[0]], [1]]) # V is [x,y,z,1]
try:
pt = p.reshape((4, 1))
except ValueError:
pt = np.vstack((p.reshape((3, 1)), [1])) # type: np.ndarray
qinv = np.linalg.inv(transformation)
r = np.dot(qinv, pt)
# z,y,x
# return np.array([int(r[2,0]+0.5),int(r[1,0]+0.5),int(r[0,0]+0.5)], dtype=int)
# return int(r+0.5)[:3]
return (r + 0.5).astype(int)[:3]
[docs] def to_rgb(self):
"""Create an RGB color image of self.
Returns:
Series: RGB Series object
"""
if self.color:
return self
shape = self.shape + (3,)
largest_image_pixel_value = (self.max().item())
img = np.zeros(shape, dtype=np.uint8)
if largest_image_pixel_value > 255:
scaling = 256 / largest_image_pixel_value
img[...,0] = np.uint8(self[:] * scaling)
img[...,1] = np.uint8(self[:] * scaling)
img[...,2] = np.uint8(self[:] * scaling)
else:
img[...,0] = self[:]
img[...,1] = self[:]
img[...,2] = self[:]
# rgb = Series(img, template=self, geometry=self)
rgb = Series(img, input_order=self.input_order, geometry=self,
axes=self.axes + [imagedata.axis.VariableAxis('rgb',['r', 'g', 'b'])]
)
# rgb.axes = self.axes + [imagedata.axis.VariableAxis('rgb',['r', 'g', 'b'])]
rgb.header.photometricInterpretation = 'RGB'
rgb.header.color = True
add_template(rgb.header, self.header)
return rgb
[docs] def show(self, im2=None, fig=None, cmap='gray', window=None, level=None, link=False):
"""Show image
With ideas borrowed from Erlend Hodneland (2021).
Args:
im2 (Series or list of Series): Series or list of Series which will be displayed in addition to self.
fig (matplotlib.plt.Figure, optional): if already exist
cmap (str): color map for display. Default: 'gray'
window (number): window width of signal intensities. Default is DICOM Window Width.
level (number): window level of signal intensities. Default is DICOM Window Center.
link (bool): whether scrolling is linked between displayed images. Default: False
Raises:
ValueError: when image is not a subclass of Series, or too many viewports are requested.
IndexError: when there is a mismatch with images and viewports.
"""
from imagedata.viewer import Viewer, default_layout
import matplotlib.pyplot as plt
# im2 can be single image or list of images
images = list()
images.append(self)
if im2 is not None:
if issubclass(type(im2), list):
images += im2 # Join lists of Series
else:
images.append(im2) # Append single Series instance
# Create or connect to canvas
if fig is None:
fig = plt.figure()
axes = default_layout(fig, len(images))
try:
viewer = Viewer(images, fig=fig, ax=axes,
cmap=cmap, window=window, level=level, link=link)
except AssertionError:
raise
v = viewer.connect()
plt.tight_layout()
plt.show()
viewer.disconnect()
[docs] def get_roi(self, roi=None, color='r', follow=False, vertices=False, im2=None, fig=None, cmap='gray', window=None, level=None, link=False):
"""Let user draw ROI on image
Args:
roi: Predefined vertices (optional). Dict of slices, index as [tag,slice] or [slice], each is list of (x,y) pairs.
color (str): Color of polygon ROI. Default: 'r'.
follow: (bool) Copy ROI to next tag. Default: False.
vertices (bool): Return both grid mask and dictionary of vertices. Default: False.
im2 (Series or list of Series): Series or list of Series which will be displayed in addition to self.
fig (matplotlib.plt.Figure, optional) if already exist
cmap (str): colour map for display. Default: 'gray'
window (number): window width of signal intensities. Default is DICOM Window Width.
level (number): window level of signal intensities. Default is DICOM Window Center.
link (bool): whether scrolling is linked between displayed objects. Default: False.
Returns:
If vertices, tuple of grid mask and vertices_dict. Otherwise, grid mask only.
- grid mask: Series object with voxel=1 inside ROI.
Series object with shape (nz,ny,nx) from original image,
dtype ubyte. Voxel inside ROI is 1, 0 outside.
- vertices_dict: if vertices: Dictionary of vertices.
Raises:
ValueError: when image is not a subclass of Series, or too many viewports are requested.
IndexError: when there is a mismatch with images and viewports.
"""
from imagedata.viewer import Viewer, default_layout, grid_from_roi
import matplotlib.pyplot as plt
# im2 can be single image or list of images
images = list()
images.append(self)
if im2 is not None:
if issubclass(type(im2), list):
images += im2 # Join lists of Series
else:
images.append(im2) # Append single Series instance
# Create or connect to canvas
if fig is None:
fig = plt.figure()
axes = default_layout(fig, len(images))
try:
viewer = Viewer(images, fig=fig, ax=axes, follow=follow,
cmap=cmap, window=window, level=level, link=link)
except AssertionError:
raise
v = viewer.connect_draw(roi=roi, color=color)
plt.tight_layout()
plt.show()
# vertices = viewer.get_roi()
viewer.disconnect_draw()
if follow:
input_order = self.input_order
else:
input_order = 'none'
try:
# new_roi = Series(grid_from_roi(self, viewer.get_roi()), input_order=input_order, template=self, geometry=self)
new_grid = grid_from_roi(self, viewer.get_roi())
except IndexError:
if follow:
new_grid = np.zeros_like(self, dtype=np.ubyte)
else:
new_grid = np.zeros((self.slices, self.rows, self.columns), dtype=np.ubyte)
new_roi = Series(new_grid, input_order=input_order, template=self, geometry=self)
new_roi.seriesDescription = 'ROI'
new_roi.setDicomAttribute('WindowCenter',.5)
new_roi.setDicomAttribute('WindowWidth',1)
if vertices:
return new_roi, viewer.get_roi() # Return grid and vertices
else:
return new_roi # Return grid only