"""HDF5 adapter class
"""
import h5py
import warnings
import numpy as np
from . import ImageSeriesAdapter,RegionType
from ..imageseriesiter import ImageSeriesIterator
[docs]class HDF5ImageSeriesAdapter(ImageSeriesAdapter):
"""Collection of Images in HDF5 Format
Parameters
----------
fname : str or h5py.File object
filename of the HDF5 file, or an open h5py file. Note that this
class will close the h5py.File when finished.
path : str, required
The path to the HDF dataset containing the image data
dataname : str, optional
The name of the HDF dataset containing the 2-d or 3d image data.
The default values is 'images'.
close_when_finished: bool, optional
Whether to close the h5py file handle when this imageseries is
deleted. The default is `True`.
"""
format = 'hdf5'
def __init__(self, fname, **kwargs):
if isinstance(fname, h5py.File):
self.__h5name = fname.filename
self.__h5file = fname
else:
self.__h5name = fname
self.__h5file = h5py.File(self.__h5name, 'r')
self._close_when_finished = kwargs.get('close_when_finished', True)
self.__path = kwargs['path']
self.__dataname = kwargs.pop('dataname', 'images')
self.__images = '/'.join([self.__path, self.__dataname])
self._load_data()
self._meta = self._getmeta()
[docs] def close(self):
self.__image_dataset = None
self.__data_group = None
if self._close_when_finished and self.__h5file is not None:
self.__h5file.close()
self.__h5file = None
def __del__(self):
# !!! Note this is not ideal, as the use of __del__ is problematic.
# However, it is highly unlikely that the usage of a ImageSeries
# would pose a problem. A warning will (hopefully) be emitted if
# an issue arises at some point
try:
self.close()
except(Exception):
warnings.warn("HDF5ImageSeries could not close h5 file")
def __getitem__(self, key):
if not isinstance(key, int):
# FIXME: we do not yet support fancy indexing here.
# Fully expand the array then apply the fancy indexing.
return self[key[0]][tuple(key[1:])]
if self._ndim == 2:
if key != 0:
raise IndexError(
f'key {key} is out of range for imageseris with length 1'
)
# !!! necessary when not returning a slice
return np.asarray(self.__image_dataset)
else:
return self.__image_dataset[key]
[docs] def get_region(self, frame_idx: int, region: RegionType) -> np.ndarray:
r = region
return self.__image_dataset[frame_idx][r[0][0]:r[0][1], r[1][0]:r[1][1]]
def __iter__(self):
return ImageSeriesIterator(self)
def __len__(self):
if self._ndim == 2:
return 1
else:
# !!! must be 3-d; exception handled in load_data()
return len(self.__image_dataset)
def __getstate__(self):
# Remove any non-pickleable attributes
to_remove = [
'__h5file',
'__image_dataset',
'__data_group',
]
# Prefix them with the private prefix
prefix = f'_{self.__class__.__name__}'
to_remove = [f'{prefix}{x}' for x in to_remove]
# Make a copy of the dict to modify
state = self.__dict__.copy()
# Remove them
for attr in to_remove:
state.pop(attr)
return state
def __setstate__(self, state):
self.__dict__.update(state)
self.__h5file = h5py.File(self.__h5name, 'r')
self._load_data()
def _load_data(self):
self.__image_dataset = self.__h5file[self.__images]
self._ndim = self.__image_dataset.ndim
if self._ndim not in [2, 3]:
raise RuntimeError(
f'Image data must be a 2-d or 3-d array; yours is {self._ndim}'
)
self.__data_group = self.__h5file[self.__path]
def _getmeta(self):
mdict = {}
for k, v in list(self.__data_group.attrs.items()):
mdict[k] = v
return mdict
@property
def metadata(self):
"""(read-only) Image sequence metadata
note: metadata loaded on open and allowed to be modified
"""
return self._meta
@property
def dtype(self):
return self.__image_dataset.dtype
@property
def shape(self):
if self._ndim == 2:
return self.__image_dataset.shape
else:
return self.__image_dataset.shape[1:]