bioimageloader.transforms#

Custom transforms for bioimages based on albumentations

class bioimageloader.transforms.HWCToCHW(always_apply: bool = False, p: float = 1.0)[source]#

Transpose axes from (H, W, C) to (C, H, W)

By default, bioimageloader returns images in shape of (H, W, C=3) regardless of its color mode for easy handling. Some models expect (C, H, W) shape of images as input. It converts (H, W, C) to (C, H, W).

See also

albumentations.ImageOnlyTransform: super class

Examples

>>> import albumentations as A
>>> from bioimageloader import Config

>>> cfg = Config('config.yml')
>>> transforms = A.Compose([
        HWCToCHW(),
    ])
>>> datasets = cfg.load_datasets(transforms=transforms)
>>> dset = datasets[0]  # select only the first dataset
>>> data = dset[0]  # select only the first image
>>> print(data['image'].shape)
(3, H, W)

class bioimageloader.transforms.SqueezeGrayImageCHW(keep_dim=True, always_apply: bool = False, p: float = 1.0)[source]#

Squeeze grayscale image from (3, H, W) to (1, H, W)|(H, W)

By default, bioimageloader returns images in 3 channels regardless of their color mode for easy handling. If a model requires (C=1, H, W) shape of input, first use HWCToCHW and use this transform to convert from (3, H, W) to (1, H, W).

Parameters

keep_dimbool, default: True: Keep channel axis to 1
always_applybool, default: False
pfloat, default: 1.0: Value between [0.0, 1.0]

See also

albumentations.ImageOnlyTransform: super class
bioimageloader.transforms.HWCToCHW
bioimageloader.transforms.SqueezeGrayImageHWC

Examples

>>> import albumentations as A
>>> from bioimageloader import Config

>>> cfg = Config('config.yml')
>>> transforms = A.Compose([
        HWCToCHW(),
        SqueezeGrayImageCHW(),
    ])
>>> datasets = cfg.load_datasets(transforms=transforms)
>>> dset = datasets[0]  # select only the first dataset
>>> data = dset[0]  # select only the first image
>>> print(data['image'].shape)
(1, H, W)

You can set keep_dim False to entirely drop channel axis, as some models require. But use SqueezeGrayImageHWC instead for that.

>>> transforms = A.Compose([
        HWCToCHW(),
        SqueezeGrayImageCHW(keep_dim=False),  # drop channel axis
    ])
>>> datasets = cfg.load_datasets(transforms=transforms)
>>> dset = datasets[0]  # select only the first dataset
>>> data = dset[0]  # select only the first image
>>> print(data['image'].shape)
(H, W)

class bioimageloader.transforms.SqueezeGrayImageHWC(keep_dim=False, always_apply: bool = False, p: float = 1.0)[source]#

Squeeze grayscale image from (H, W, 3) to (H, W)|(H, W, 1)

By default, bioimageloader returns images in 3 channels regardless of their color mode for easy handling. It converts (H, W, 3) to (H, W).

If a model requires (H, W) shape of input, use this transform to convert from (H, W, 3) to (H, W).

Parameters

keep_dimbool, default: False
always_applybool, default: False
pfloat, default: 1.0: Value between [0.0, 1.0]

See also

albumentations.ImageOnlyTransform: super class
bioimageloader.transforms.SqueezeGrayImageHWC

Examples

>>> import albumentations as A
>>> from bioimageloader import Config

>>> cfg = Config('config.yml')
>>> transforms = A.Compose([
        SqueezeGrayImageHWC(),
    ])
>>> datasets = cfg.load_datasets(transforms=transforms)
>>> dset = datasets[0]  # select only the first dataset
>>> data = dset[0]  # select only the first image
>>> print(data['image'].shape)
(H, W)

class bioimageloader.transforms.ExpandToRGB(always_apply: bool = False, p: float = 1.0)[source]#

Make sure image/mask has 3 channels, either from HW or HW1 to HW3

Expand the channel axis of image array

Notes

When used with albumentations.pytoch.ToTensorV2, set transpose_mask=True to transpose masks.

Targets:: image, mask

class bioimageloader.transforms.RGBToGray(always_apply: bool = False, p: float = 1.0)[source]#

ToGray preserve all 3 channels from the input. This transform truncates channels dimension.

Warning

This will be deprecated. Grayscale conversion is done in eash Dataset

Notes

Targets:: image
Image types:: uint8, float32

class bioimageloader.transforms.ToGrayBySum(always_apply: bool = False, p: float = 1.0, num_channels: Optional[int] = None)[source]#

Convert image to gray scale by tacking mean of existing channels

For 2 channels, multi-modal images, ToGray does not make sense. Normally, rgb2gray conversions is a linear sum of RGB values (opencv [1], pillow [2]). Just summing with eqaul weights would be more correct for bioimages.

Warning

This will be deprecated. Grayscale conversion is done through grayscale and grayscale_mode arguments in eash Dataset

References

1: https://docs.opencv.org/3.4/de/d25/imgproc_color_conversions.html#color_convert_rgb_gray
2: https://pillow.readthedocs.io/en/stable/reference/Image.html#PIL.Image.Image.convert

class bioimageloader.transforms.ChannelReorder(order: Tuple[int, int, int], always_apply: bool = False, p: float = 1.0)[source]#

Reorder channel

Expect images with 3 channels. Reorder and make it continuous in ‘C’ order.

Parameters

ordertuple of three integers: Reorder by indexing
always_applybool, default: False
pfloat, default: 1.0: Value between [0.0, 1.0]

See also

albumentations.ImageOnlyTransform: super class
albumentations.augmentations.transforms.ChannelShuffle: random shuffling

Examples

>>> import numpy as np
>>> from bioimageloader.transforms import ChannelReorder

>>> arr = np.arange(12).reshape((2, 2, 3))
>>> print(arr)
[[[ 0  1  2]
  [ 3  4  5]]
 [[ 6  7  8]
  [ 9 10 11]]]

>>> reorder = ChannelReorder((2, 1, 0))
>>> arr_reordered = reorder.apply(arr)
>>> print(arr_reordered)
[[[ 2  1  0]
  [ 5  4  3]]
 [[ 8  7  6]
  [11 10  9]]]

class bioimageloader.transforms.NormalizePercentile(qmin: float = 0.0, qmax: float = 99.8, per_channel: bool = False, clip: bool = False, always_apply: bool = False, p: float = 1.0)[source]#

Normalize using percentile

Compute q-th percentile min- and max-values from given image array and normalize.

Use numpy.percentile() [1]

Expect images with 3 channels. Reorder and make it continuous in ‘C’ order.

Parameters

qminfloat, default: 0.0: Lower bound quantile in range of [0, 100)
qmaxfloat, default: 99.8: Upper bound quantile in range of (0, 100]
per_channelbool, default: False: Whether to calculate percentile per channel or not
clipbool, default: False: Whether to clip in [0, 1] or not. Read more in Returns section.
ordertuple of three integers: Reorder by indexing
always_applybool, default: False
pfloat, default: 1.0: Value between [0.0, 1.0]

Returns

img_normnumpy.ndarray: Normalized image in float32 in range of [0.0, 1.0] if clip set to True, else its value overflows lower beyond 0.0 and higher beyond 1.0.

See also

albumentations.ImageOnlyTransform: super class
albumentations.augmentations.transforms.ChannelShuffle: random shuffling

References

1: https://numpy.org/doc/stable/reference/generated/numpy.percentile.html

Examples

>>> import numpy as np
>>> from bioimageloader.transforms import ChannelReorder

>>> arr = np.arange(12).reshape((2, 2, 3))
>>> print(arr)
[[[ 0  1  2]
  [ 3  4  5]]
 [[ 6  7  8]
  [ 9 10 11]]]

>>> reorder = ChannelReorder((2, 1, 0))
>>> arr_reordered = reorder.apply(arr)
>>> print(arr_reordered)
[[[ 2  1  0]
  [ 5  4  3]]
 [[ 8  7  6]
  [11 10  9]]]

class bioimageloader.transforms.BinarizeMask(always_apply: bool = False, p: float = 1.0, dtype: Optional[str] = None, val: Optional[Union[float, int]] = None)[source]#

Transform instance masks into binary masks

Note that when composed with other transforms, BinarizeMask would rather come after them, because dtype will be boolean and albumentations does not like it. When you set val and dtype compatible with albumentations, you can place BinarizeMask in any order safely.

Parameters

always_applybool, default: False
pfloat, default: 1.0: Value between [0.0, 1.0]
dtypestr or dtype, optional: Determine dtype. Default dtype becomes float32, when val is set. Otherwise, it becomes boolean.
valfloat, optional: Change binarized mask value other than True if set. It also enforces dtype to be float32.

Returns

masknumpy.ndarray: Mask that has binary value either [False, True] or [0, val]

See also

albumentations.DualTransform: super class

Examples

>>> import numpy as np
>>> from bioimageloader.transforms import BinarizeMask

>>> # instance mask
>>> mask_inst = np.arange(12).reshape((3, 4))
>>> print(mask_inst)
[[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]

>>> binarizemask = BinarizeMask()
>>> mask_binary = binarizemask.apply_to_mask(mask_inst)
>>> print(mask_binary)
[[False  True  True  True]
 [ True  True  True  True]
 [ True  True  True  True]]