import re
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 BBBC021(Dataset):
"""Human MCF7 cells – compound-profiling experiment [1]_
The images are of MCF-7 breast cancer cells treated for 24 h with a
collection of 113 small molecules at eight concentrations. The cells were
fixed, labeled for DNA, F-actin, and Β-tubulin, and imaged by fluorescent
microscopy as described [Caie et al. Molecular Cancer Therapeutics, 2010].
There are 39,600 image files (13,200 fields of view imaged in three
channels) in TIFF format. We provide the images in 55 ZIP archives, one for
each microtiter plate. The archives are ~750 MB each.
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 : {'DNA', 'actin', 'tublin'}, default: ('DNA', 'actin', 'tublin')
Which channel(s) to load as image. Make sure to give it as a Sequence
when choose a single channel.
Notes
-----
- HUGE dataset
- 3 channels
- w1 (DNA) -> Blue
- w2 (actin?) -> Green
- w4 (tublin??)-> Red
- UINT16
References
----------
.. [1] https://bbbc.broadinstitute.org/BBBC021
See Also
--------
Dataset : Base class
DatasetInterface : Interface
"""
# Dataset's acronym
acronym = 'BBBC021'
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] = ('DNA', 'actin', 'tublin'),
**kwargs
):
self._root_dir = root_dir
self._transforms = transforms
self._num_samples = num_samples
self._grayscale = grayscale
self._grayscale_mode = grayscale_mode
self.image_ch = image_ch
if not any([ch in ('DNA', 'actin', 'tublin') for ch in image_ch]):
raise ValueError("Set `image_ch` in ('DNA', 'actin', 'tublin') in sequence")
[docs] def get_image(self, p: Union[Path, List[Path]]) -> np.ndarray:
# 3 channels; DAPI(w1), Tubulin(w2), Actin(w4)
if isinstance(p, Path):
img = tifffile.imread(p)
img = img_as_float32(img)
return cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
if len(self.image_ch) == 2:
def _map_ch_to_ind(p: Path):
if 'w1' in p.stem:
# w1 'DNA' blue
return 2
elif 'w2' in p.stem:
# w2 'actin' green
return 1
# w4 'tublin' red
return 0
order = map(_map_ch_to_ind, p)
img = stack_channels_to_rgb(tifffile.imread, p, *order)
return img_as_float32(img)
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
_file_list = sorted(root_dir.glob('Week*/*.tif'))
if len(ch := self.image_ch) == 1:
if ch[0] == 'DNA':
return _file_list[::3]
elif ch[0] == 'actin':
return _file_list[1::3]
elif ch[0] == 'tublin':
return _file_list[2::3]
else:
raise ValueError
elif len(ch) == 2:
map_to_pat = {
'DNA': 'w1', # blue
'actin': 'w2', # green
'tublin': 'w4', # red
}
# regex pattern
pat = '(' + '|'.join(map_to_pat[c] for c in ch) + ')'
return bundle_list(
list(filter(lambda p: re.search(pat, p.stem), _file_list)), 2
)
return bundle_list(_file_list, 3)