# ===========================================================================
# ===========================================================================
# Copyright (c) 2021 Nghia T. Vo. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===========================================================================
# Author: Nghia T. Vo
# E-mail:
# Description: Python module for converting data format.
# Contributors:
# ===========================================================================
"""
Module for converting data type:
- Convert a list of tif files to a hdf/nxs file.
- Extract tif images from a hdf/nxs file.
- Emulate an HDF5-like interface for TIF files in a folder.
"""
from pathlib import Path
import numpy as np
from joblib import Parallel, delayed
import algotom.io.loadersaver as losa
[docs]def convert_tif_to_hdf(input_path, output_path, key_path="entry/data",
crop=(0, 0, 0, 0), pattern=None, **options):
"""
Convert a folder of tif files to a hdf/nxs file.
Parameters
----------
input_path : str
Folder path to the tif files.
output_path : str
Path to the hdf/nxs file.
key_path : str, optional
Key path to the dataset.
crop : tuple of int, optional
Crop the images from the edges, i.e.
crop = (crop_top, crop_bottom, crop_left, crop_right).
pattern : str, optional
Used to find tif files with names matching the pattern.
options : dict, optional
Add metadata. E.g options={"entry/angles": angles, "entry/energy": 53}.
Returns
-------
str
Path to the hdf/nxs file.
"""
if pattern is None:
list_file = losa.find_file(input_path + "/*.tif*")
else:
list_file = losa.find_file(input_path + "/*" + pattern + "*.tif*")
depth = len(list_file)
(height, width) = np.shape(losa.load_image(list_file[0]))
output_path = Path(output_path)
output_path = output_path.resolve()
if output_path.suffix.lower() not in {'.hdf', '.h5', '.nxs', '.hdf5'}:
output_path = output_path.with_suffix('.hdf')
cr_top, cr_bottom, cr_left, cr_right = crop
cr_height = height - cr_top - cr_bottom
cr_width = width - cr_left - cr_right
if cr_height < 1 or cr_width < 1:
raise ValueError("Can't crop images with the given parameters !!!")
data_out = losa.open_hdf_stream(output_path, (depth, cr_height, cr_width),
key_path=key_path, overwrite=True,
**options)
for i, file_path in enumerate(list_file):
data_out[i] = losa.load_image(file_path)[cr_top:cr_height + cr_top,
cr_left:cr_width + cr_left]
return output_path
[docs]class HdfEmulatorFromTif:
"""
Emulate an HDF5-like interface for TIF files in a folder, allowing
indexed and sliced data access.
Parameters
----------
folder_path : str
Path to the folder containing TIFF files.
ncore : int, optional
Number of cores to use for parallel processing. The default is 1
(sequential processing).
Examples
--------
>>> hdf_emulator = HdfEmulatorFromTif('C:/path/to/tif/files', ncore=4)
>>> print(hdf_emulator.shape)
>>> last_image = hdf_emulator[-1]
>>> image_stack = hdf_emulator[:, 0:4, :]
"""
def __init__(self, folder_path, ncore=1):
self.files = losa.find_file(folder_path + "/*tif*")
if len(self.files) == 0:
files = losa.find_file(folder_path + "/*TIF*")
if len(self.files) == 0:
raise ValueError(f"!!! No tif files found in: {folder_path}")
self.n_jobs = ncore
self._shape, self._dtype = self._get_shape_and_dtype()
def _get_shape_and_dtype(self):
img = losa.load_image(self.files[0])
shape = (len(self.files), *img.shape)
dtype = img.dtype
return shape, dtype
def __getitem__(self, index):
if isinstance(index, int):
return losa.load_image(self.files[index])
elif isinstance(index, slice):
indices = range(*index.indices(len(self.files)))
return np.stack(Parallel(n_jobs=self.n_jobs)(
delayed(losa.load_image)(self.files[i]) for i in indices))
elif isinstance(index, tuple):
z, y, x = index
if isinstance(z, slice):
images = Parallel(n_jobs=self.n_jobs)(
delayed(losa.load_image)(self.files[i]) for i in
range(*z.indices(self.shape[0])))
return np.stack([img[y, x] for img in images])
else:
return losa.load_image(self.files[z])[y, x]
else:
raise TypeError("Invalid index type")
@property
def shape(self):
return self._shape
@property
def dtype(self):
return self._dtype
def __len__(self):
return len(self.files)