from functools import cached_property
from pathlib import Path
from typing import Dict, List, Optional, Sequence, Union
import albumentations
import cv2
import numpy as np
import tifffile
from skimage.util import img_as_float32
from ..base import MaskDataset
from ..types import BundledPath
from ..utils import bundle_list, stack_channels, stack_channels_to_rgb
[docs]class BBBC008(MaskDataset):
"""Human HT29 colon-cancer cells [1]_
F/B semantic segmentation
The image set consists of 12 images. The samples were stained with Hoechst
(channel 1), pH3 (channel 2), and phalloidin (channel 3). Hoechst labels
DNA, which is present in the nucleus. Phalloidin labels actin, which is
present in the cytoplasm. The last stain, pH3, indicates cells in mitosis;
whereas this was important for Moffat et al.'s screen, it is irrelevant for
segmentation and counting, so this channel is left out.
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 overrides ``__len__``.
grayscale : bool, default: False
Convert images to grayscale
grayscale_mode : {'cv2', 'equal', 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'}, default: ('DNA', 'actin')
Which channel(s) to load as image. Make sure to give it as a Sequence
when choose a single channel.
anno_ch : {'DNA', 'actin'}, default: ('DNA',)
Which channel(s) to load as annotation. Make sure to give it as a
Sequence when choose a single channel.
Notes
-----
- Annotation F/B: BG=1, FG=0; very annoying...
References
----------
.. [1] https://bbbc.broadinstitute.org/BBBC008
.. [2] Carpenter et al., Genome Biology, 2006
See Also
--------
MaskDataset : Super class
Dataset : Base class
DatasetInterface : Interface
"""
# Dataset's acronym
acronym = 'BBBC008'
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]] = 'equal',
# specific to this dataset
image_ch: Sequence[str] = ('DNA', 'actin',),
anno_ch: Sequence[str] = ('DNA',),
**kwargs
):
self._root_dir = root_dir
self._output = output
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
self.anno_ch = anno_ch
if not any([ch in ('DNA', 'actin') for ch in anno_ch]):
raise ValueError("Set `anno_ch` in ('DNA', 'actin') 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:
# ch1 to red, ch3 to blue
img = stack_channels_to_rgb(tifffile.imread, p, 0, 2, 1)
return img_as_float32(img)
[docs] def get_mask(self, p: Union[Path, BundledPath]) -> np.ndarray:
if isinstance(p, Path):
mask = tifffile.imread(p)
else:
mask = stack_channels(tifffile.imread, p)
# dtype=bool originally and bool is not well handled by albumentations
return 255 * (~mask).astype(np.uint8)
@cached_property
def file_list(self) -> Union[List[Path], List[BundledPath]]:
parent = 'human_ht29_colon_cancer_2_images'
file_list = sorted(self.root_dir.glob(f'{parent}/*.tif'))
if len(ch := self.image_ch) == 1:
if ch[0] == 'DNA':
file_list = file_list[::2]
elif ch[0] == 'actin':
file_list = file_list[1::2]
else:
raise ValueError("Set `anno_ch` in ('DNA', 'actin')")
return file_list
elif len(ch) == 2:
file_blist = bundle_list(file_list, 2)
else:
raise ValueError("Set `anno_ch` in ('DNA', 'actin') or all")
return file_blist
@cached_property
def anno_dict(self) -> Union[Dict[int, Path], Dict[int, BundledPath]]:
parent = 'human_ht29_colon_cancer_2_foreground'
anno_list = sorted(self.root_dir.glob(f'{parent}/*.tif'))
if len(ch := self.anno_ch) == 1:
if ch[0] == 'DNA':
anno_list = anno_list[::2]
elif ch[0] == 'actin':
anno_list = anno_list[1::2]
return dict((k, v) for k, v in enumerate(anno_list))
elif len(ch) == 2:
anno_blist = bundle_list(anno_list, 2)
else:
raise ValueError
return dict((k, v) for k, v in enumerate(anno_blist))