import os.path
from functools import cached_property
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Union
import albumentations
import numpy as np
import tifffile
from PIL import Image
from skimage.util import img_as_float32
from ..base import MaskDataset
[docs]class DigitalPathology(MaskDataset):
"""Deep learning for digital pathology image analysis: A comprehensive
tutorial with selected use cases [1]_
Parameters
----------
root_dir : str
Path to root directory
output : {'both', 'image', 'mask'}, default: 'both'
Change outputs. 'both' returns {'image': image, 'mask': mask}.
transforms : albumentations.Compose, optional
An instance of Compose (albumentations pkg) that defines augmentation in
sequence.
num_samples : int, optional
Useful when ``transforms`` is set. Define the total length of the
dataset. If it is set, it overwrites ``__len__``.
grayscale : bool, default: False
Convert images to grayscale
grayscale_mode : {'cv2', 'equal', Sequence[float]}, default: 'cv2'
How to convert to grayscale. If set to 'cv2', it follows opencv
implementation. Else if set to 'equal', it sums up values along channel
axis, then divides it by the number of expected channels.
Notes
-----
- Annotation is partial
- Boolean mask to UINT8 mask (0, 255)
References
----------
.. [1] A. Janowczyk and A. Madabhushi, “Deep learning for digital pathology
image analysis: A comprehensive tutorial with selected use cases,” J
Pathol Inform, vol. 7, Jul. 2016, doi: 10.4103/2153-3539.186902.
See Also
--------
MaskDataset : Super class
Dataset : Base class
DatasetInterface : Interface
"""
# Dataset's acronym
acronym = 'DigitPath'
def __init__(
self,
root_dir: str,
*,
output: str = 'both',
transforms: Optional[albumentations.Compose] = None,
num_samples: Optional[int] = None,
grayscale: bool = False,
grayscale_mode: Union[str, Sequence[float]] = 'cv2',
**kwargs
):
self._root_dir = os.path.join(root_dir, 'nuclei')
self._output = output
self._transforms = transforms
self._num_samples = num_samples
self._grayscale = grayscale
self._grayscale_mode = grayscale_mode
[docs] def get_image(self, p: Path) -> np.ndarray:
tif = tifffile.imread(p)
return img_as_float32(tif)
[docs] def get_mask(self, p: Path) -> np.ndarray:
mask = np.asarray(Image.open(p))
return 255 * mask.astype(np.uint8)
def __len__(self):
if self.num_samples:
return self.num_samples
return len(self.file_list)
@cached_property
def file_list(self) -> List[Path]:
root_dir = self.root_dir
suffix = 'original'
file_list = sorted(root_dir.glob(f'*{suffix}.tif'))
return file_list
@cached_property
def anno_dict(self) -> Dict[int, Path]:
root_dir = self.root_dir
suffix = 'mask'
anno_list = sorted(root_dir.glob(f'*{suffix}.png'))
return dict((k, v) for k, v in enumerate(anno_list))