from functools import cached_property
from pathlib import Path
from typing import List, Optional, Sequence, Union
import albumentations
import cv2
import numpy as np
import tifffile
from skimage.util import img_as_float32
from ..base import Dataset
from ..types import BundledPath
from ..utils import bundle_list, stack_channels_to_rgb
[docs]class BBBC016(Dataset):
"""Human U2OS cells transfluor
This image set is of a Transfluor assay where an orphan GPCR is stably
integrated into the b-arrestin GFP expressing U2OS cell line. After one hour
incubation with a compound the cells were fixed with (formaldehyde).
The plate was read on Cellomics ArrayScan HCS Reader using the GPCR
Bioapplication. File format is 8-bit TIFF with one image for green channel
(GFP) and one image for blue channel (DNA). Image size is 512 x 512 pixels.
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 : {'GFP', 'DNA'}, default: ('GFP', 'DNA')
Which channel(s) to load as image. Make sure to give it as a Sequence
when choose a single channel.
Notes
-----
- 2 channels (G,B), nuclei are Blue
References
----------
.. [1] https://bbbc.broadinstitute.org/BBBC016
See Also
--------
Dataset : Base class
DatasetInterface : Interface
"""
# Dataset's acronym
acronym = 'BBBC016'
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] = ('GFP', 'DNA'),
**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 ('GFP', 'DNA') for ch in image_ch]):
raise ValueError("Set `image_ch` in ('GFP', 'DNA') in sequence")
[docs] def get_image(self, p: Union[Path, BundledPath]) -> np.ndarray:
if isinstance(p, Path):
img = tifffile.imread(p)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
else:
img = stack_channels_to_rgb(tifffile.imread, p, 2, 1, 0)
return img_as_float32(img)
@cached_property
def file_list(self) -> Union[List[Path], List[BundledPath]]:
root_dir = self.root_dir
parent = 'BBBC016_v1_images'
file_list = sorted(root_dir.glob(f'{parent}/*.TIF'))
if len(ch := self.image_ch) == 1:
if ch[0] == 'DNA':
return file_list[::2]
elif ch[0] == 'GFP':
return file_list[1::2]
return bundle_list(file_list, 2)