from functools import cached_property
from pathlib import Path
from typing import List, Optional, Sequence, Union
import albumentations
import cv2
import numpy as np
from PIL import Image
from skimage.util import img_as_float32
from ..base import Dataset
from ..types import BundledPath
from ..utils import bundle_list, imread_asarray, stack_channels_to_rgb
[docs]class BBBC014(Dataset):
"""Human U2OS cells cytoplasm–nucleus translocation
This 96-well plate has images of cytoplasm to nucleus translocation of the
transcription factor NFκB in MCF7 (human breast adenocarcinoma cell line)
and A549 (human alveolar basal epithelial) cells in response to TNFα
concentration.
Images are at 10x objective magnification. The plate was acquired at Vitra
Bioscience on the CellCard reader. For each well there is one field with two
images: a nuclear counterstain (DAPI) image and a signal stain (FITC) image.
Image size is 1360 x 1024 pixels. Images are in 8-bit BMP format.
Parameters
----------
root_dir : str
Path to root directory
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 : {'equal', 'cv2', Sequence[float]}, default: 'equal'
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.
image_ch : {'DAPI', 'FITC'}, default: ('DAPI', 'FITC')
Which channel(s) to load as image. Make sure to give it as a Sequence
when choose a single channel.
Notes
-----
- Second channel is usually very clear with a few artifacts
- Biological annotation
- CellProfiler's LoadText module format annotation also available (not
implemented)
- Zoom in?
References
----------
.. [1] https://bbbc.broadinstitute.org/BBBC014
See Also
--------
Dataset : Base class
DatasetInterface : Interface
"""
# Dataset's acronym
acronym = 'BBBC014'
def __init__(
self,
root_dir: str,
*,
transforms: Optional[albumentations.Compose] = None,
num_samples: Optional[int] = None,
grayscale: bool = False,
grayscale_mode: Union[str, Sequence[float]] = 'equal',
# specific to this dataset
image_ch: Sequence[str] = ('DAPI', 'FITC'),
**kwargs
):
self._root_dir = root_dir
self._transforms = transforms
self._num_samples = num_samples
self._grayscale = grayscale
self._grayscale_mode = grayscale_mode
# specific to this dataset
self.image_ch = image_ch
if not any([ch in ('DAPI', 'FITC') for ch in image_ch]):
raise ValueError("Set `image_ch` in ('DAPI', 'FITC') in sequence")
[docs] def get_image(self, p: Union[Path, BundledPath]) -> np.ndarray:
if isinstance(p, Path):
img = imread_asarray(p)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
else:
img = stack_channels_to_rgb(Image.open, p, 1, 2, 0)
return img_as_float32(img)
@cached_property
def file_list(self) -> Union[List[Path], List[BundledPath]]:
root_dir = self.root_dir
parent = 'BBBC014_v1_images'
file_list = sorted(root_dir.glob(f'{parent}/*.Bmp'), key=self._sort_key)
if len(ch := self.image_ch) == 1:
if ch[0] == 'DAPI':
return file_list[::2]
elif ch[0] == 'FITC':
return file_list[1::2]
return bundle_list(file_list, 2)
@staticmethod
def _sort_key(p: Path):
channel, ind, t, subind, _ = p.stem.split('-')
return '-'.join([ind, t, subind, channel])