Source code for

# Copyright (C) 2023 ISIS Rutherford Appleton Laboratory UKRI
# SPDX - License - Identifier: GPL-3.0-or-later
This module handles the loading of FIT, FITS, TIF, TIFF
from __future__ import annotations
from typing import Tuple, Optional, List, Callable, Union, TYPE_CHECKING

from import ImageStack
from mantidimaging.core.parallel import utility as pu
from mantidimaging.core.utility.progress_reporting import Progress

    import numpy as np
    import numpy.typing as npt
    from ...utility.data_containers import Indices

[docs]def execute(load_func: Callable[[str], np.ndarray], sample_path: List[str], img_format: str, dtype: 'npt.DTypeLike', indices: Union[List[int], Indices, None], progress: Optional[Progress] = None) -> ImageStack: """ Reads a stack of images into memory, assuming dark and flat images are in separate directories. Usual type in fits is 16-bit pixel depth, data type is denoted with: '>i2' - uint16 '>f2' - float16 '>f4' - float32 :returns: ImageStack object """ if not sample_path: raise RuntimeError("No filenames were provided.") # The following codes assume that all images have the same size and properties as the first. # This is always true in the case of raw data first_sample_img = load_func(sample_path[0]) # select the files loaded based on the indices, if any are provided chosen_input_filenames = sample_path[indices[0]:indices[1]:indices[2]] if indices else sample_path # get the shape of all images img_shape = first_sample_img.shape # forward all arguments to internal class for easy re-usage il = ImageLoader(load_func, img_format, img_shape, dtype, indices, progress) sample_data = il.load_sample_data(chosen_input_filenames) return ImageStack(sample_data, chosen_input_filenames, indices)
[docs]class ImageLoader(object): def __init__(self, load_func: Callable[[str], np.ndarray], img_format: str, img_shape: Tuple[int, ...], data_dtype: 'npt.DTypeLike', indices: Union[List[int], Indices, None], progress: Optional[Progress] = None): self.load_func = load_func self.img_format = img_format self.img_shape = img_shape self.data_dtype = data_dtype self.indices = indices self.progress = progress
[docs] def load_sample_data(self, input_file_names: List[str]) -> pu.SharedArray: # determine what the loaded data was if len(self.img_shape) == 2: # the loaded file was a single image return self.load_files(input_file_names) else: raise ValueError(f"Data loaded has invalid shape: {self.img_shape}")
def _do_files_load_seq(self, data: pu.SharedArray, files: List[str]) -> pu.SharedArray: progress = Progress.ensure_instance(self.progress, num_steps=len(files), task_name='Loading') with progress: for idx, in_file in enumerate(files): try: data.array[idx, :] = self.load_func(in_file) progress.update(msg='Image') except ValueError as exc: raise ValueError("An image has different width and/or height " "dimensions! All images must have the same " f"dimensions. Expected dimensions: {self.img_shape} Error " f"message: {exc}") from exc except IOError as exc: raise RuntimeError(f"Could not load file {in_file}. Error details: {exc}") from exc return data
[docs] def load_files(self, files: List[str]) -> pu.SharedArray: # Zeroing here to make sure that we can allocate the memory. # If it's not possible better crash here than later. num_images = len(files) shape = (num_images, self.img_shape[0], self.img_shape[1]) data = pu.create_array(shape, self.data_dtype) return self._do_files_load_seq(data, files)