diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..f1e9425 --- /dev/null +++ b/.gitignore @@ -0,0 +1,183 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm-project.org/#use-with-ide +.pdm.toml +.pdm-python +.pdm-build/ + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# ruff +.ruff_cache/ + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# VSCode +.vscode/ + +# MacOS +.DS_Store + +# Mlflow +mlruns/ +mlflow_store/ +mlflow_artifacts/ + +# Pytorch Lightning +lightning_logs/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +.idea/ + +# Project +/data +/demo_* \ No newline at end of file diff --git a/configs/data/datasets/stain_normalization/predict.yaml b/configs/data/datasets/stain_normalization/predict.yaml new file mode 100644 index 0000000..0cf3d2e --- /dev/null +++ b/configs/data/datasets/stain_normalization/predict.yaml @@ -0,0 +1,5 @@ +defaults: + - /data/normalize@normalize: default + +_target_: stain_normalization.data.datasets.PredictDataset +uris: ["mlflow-artifacts:/79/ce91ce61ac8e4b4784b76a7193e59da9/artifacts/FNBrno_400/2"] diff --git a/configs/data/datasets/stain_normalization/test.yaml b/configs/data/datasets/stain_normalization/test.yaml new file mode 100644 index 0000000..8d5b7d5 --- /dev/null +++ b/configs/data/datasets/stain_normalization/test.yaml @@ -0,0 +1,6 @@ +defaults: + - /data/modify@modify: test + - /data/normalize@normalize: default + +_target_: stain_normalization.data.datasets.TestDataset +uris: ["mlflow-artifacts:/65/c47cd842fbf14bf399cd4aca43af3127/artifacts/MMCI TL Test 512"] diff --git a/configs/data/datasets/stain_normalization/train.yaml b/configs/data/datasets/stain_normalization/train.yaml new file mode 100644 index 0000000..6cf01a5 --- /dev/null +++ b/configs/data/datasets/stain_normalization/train.yaml @@ -0,0 +1,6 @@ +defaults: + - /data/modify@modify: train + - /data/normalize@normalize: default + +_target_: stain_normalization.data.datasets.TrainDataset +uris: ["mlflow-artifacts:/65/b587398845c54e1c84e8b60f11c42b15/artifacts/MMCI TL Train 512"] diff --git a/configs/data/datasets/stain_normalization/val.yaml b/configs/data/datasets/stain_normalization/val.yaml new file mode 100644 index 0000000..500db12 --- /dev/null +++ b/configs/data/datasets/stain_normalization/val.yaml @@ -0,0 +1,7 @@ +defaults: + - /data/modify@modify: train + - /data/normalize@normalize: default + +_target_: stain_normalization.data.datasets.TrainDataset +uris: ["mlflow-artifacts:/65/a43da470f89848abb3a32e59c504ee1a/artifacts/MMCI TL Val 512"] + diff --git a/configs/data/normalize/default.yaml b/configs/data/normalize/default.yaml new file mode 100644 index 0000000..12f60f3 --- /dev/null +++ b/configs/data/normalize/default.yaml @@ -0,0 +1,13 @@ +_target_: albumentations.Normalize +# From PC-A-42 MOU prostate dataset +# Original 255-scale values: +# mean=[228.5544, 178.8584, 219.8793], std=[27.8285, 51.4639, 26.4458] +mean: + - 0.896291765 + - 0.70140549 + - 0.862271765 +std: + - 0.109131373 + - 0.201819216 + - 0.10370902 +max_pixel_value: 1 diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..6b19117 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,29 @@ +[project] +name = "stain-normalization" +version = "0.1.0" +authors = [{name = "Adam Lopatka"}] +requires-python = ">=3.12.5,<3.14" +readme = "README.md" +license = { file = "LICENSE" } +dependencies = [ + "lightning>=2.0.0", + "albumentations>=1.4.14", + "torchmetrics>=1.4.3", + "torchvision>=0.17.2", + "torch>=2.2.2", + "tqdm>=4.66.5", + "rationai-mlkit @ git+https://gitlab.ics.muni.cz/rationai/digital-pathology/libraries/mlkit.git", + "rationai-masks @ git+https://gitlab.ics.muni.cz/rationai/digital-pathology/libraries/masks.git", + "rationai-tiling @ git+https://gitlab.ics.muni.cz/rationai/digital-pathology/libraries/tiling.git", + "scikit-image>=0.25.2", + "rationai-staining @ git+https://gitlab.ics.muni.cz/rationai/digital-pathology/libraries/staining.git", + "openslide-python>=1.4.3", +] + +[dependency-groups] +dev = ["mypy", "ruff"] + +[tool.uv] +environments = ["sys_platform == 'linux'"] + +override-dependencies = ["mlflow>=2.15.1"] diff --git a/stain_normalization/data/__init__.py b/stain_normalization/data/__init__.py new file mode 100644 index 0000000..48b179a --- /dev/null +++ b/stain_normalization/data/__init__.py @@ -0,0 +1,4 @@ +from stain_normalization.data.data_module import DataModule + + +__all__ = ["DataModule"] diff --git a/stain_normalization/data/data_module.py b/stain_normalization/data/data_module.py new file mode 100644 index 0000000..a3c42f1 --- /dev/null +++ b/stain_normalization/data/data_module.py @@ -0,0 +1,71 @@ +from collections.abc import Iterable +from typing import Any + +import torch +from hydra.utils import instantiate +from lightning import LightningDataModule +from omegaconf import DictConfig +from torch import Tensor +from torch.utils.data import DataLoader + +from stain_normalization.type_aliases import Batch, PredictBatch + + +def collate_fn(batch: list[tuple[Tensor, Any]]) -> tuple[Tensor, list[Any]]: + return torch.stack([x[0] for x in batch]), [x[1] for x in batch] + + +class DataModule(LightningDataModule): + def __init__( + self, batch_size: int, num_workers: int = 0, **datasets: DictConfig + ) -> None: + super().__init__() + self.batch_size = batch_size + self.num_workers = num_workers + self.datasets = datasets + + def setup(self, stage: str) -> None: + match stage: + case "fit": + self.train = instantiate(self.datasets["train"]) + self.val = instantiate(self.datasets["val"]) + case "validate": + self.val = instantiate(self.datasets["val"]) + case "test": + self.test = instantiate(self.datasets["test"]) + case "predict": + self.predict = instantiate(self.datasets["predict"]) + + def train_dataloader(self) -> Iterable[Batch]: + return DataLoader( + self.train, + batch_size=self.batch_size, + shuffle=True, + drop_last=True, + num_workers=self.num_workers, + persistent_workers=self.num_workers > 0, + ) + + def val_dataloader(self) -> Iterable[Batch]: + return DataLoader( + self.val, + batch_size=self.batch_size, + num_workers=self.num_workers, + persistent_workers=self.num_workers > 0, + ) + + def test_dataloader(self) -> Iterable[PredictBatch]: + return DataLoader( + self.test, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=collate_fn, + ) + + def predict_dataloader(self) -> Iterable[PredictBatch]: + return DataLoader( + self.predict, + batch_size=self.batch_size, + num_workers=self.num_workers, + collate_fn=collate_fn, + ) diff --git a/stain_normalization/data/datasets/__init__.py b/stain_normalization/data/datasets/__init__.py new file mode 100644 index 0000000..a767f70 --- /dev/null +++ b/stain_normalization/data/datasets/__init__.py @@ -0,0 +1,6 @@ +from stain_normalization.data.datasets.predict_dataset import PredictDataset +from stain_normalization.data.datasets.test_dataset import TestDataset +from stain_normalization.data.datasets.train_dataset import TrainDataset + + +__all__ = ["PredictDataset", "TestDataset", "TrainDataset"] diff --git a/stain_normalization/data/datasets/predict_dataset.py b/stain_normalization/data/datasets/predict_dataset.py new file mode 100644 index 0000000..aba3372 --- /dev/null +++ b/stain_normalization/data/datasets/predict_dataset.py @@ -0,0 +1,80 @@ +from collections.abc import Iterable + +import pandas as pd +from albumentations import Transform3D +from albumentations.pytorch import ToTensorV2 +from rationai.mlkit.data.datasets import MetaTiledSlides, OpenSlideTilesDataset +from torch.utils.data import Dataset + +from stain_normalization.type_aliases import PredictSample + + +class PredictDataset(MetaTiledSlides[PredictSample]): + """Dataset for inference on real (unmodified) slides. + + Takes slides as-is — no modify transform. Returns the input tensor and a + metadata dict with slide_name and xy coordinates for WSI reassembly. + + Differences from TrainDataset: no modify transform; returns metadata. + Differences from TestDataset: no modify transform; does not keep raw image + copies (not needed for reassembly). + """ + + def __init__( + self, + uris: Iterable[str], + normalize: Transform3D | None = None, + ) -> None: + self.normalize = normalize + super().__init__(uris=uris) + + def generate_datasets(self) -> Iterable[Dataset[PredictSample]]: + return ( + _PredictSlideTiles( + slide, + tiles=self.filter_tiles_by_slide(slide["id"]), + normalize=self.normalize, + ) + for _, slide in self.slides.iterrows() + ) + + +class _PredictSlideTiles(Dataset[PredictSample]): + def __init__( + self, + slide_metadata: pd.Series, + tiles: pd.DataFrame, + normalize: Transform3D | None = None, + ) -> None: + super().__init__() + self.slide_tiles = OpenSlideTilesDataset( + slide_path=slide_metadata.path, + level=slide_metadata.level, + tile_extent_x=slide_metadata.tile_extent_x, + tile_extent_y=slide_metadata.tile_extent_y, + tiles=tiles, + ) + + self.normalize = normalize + self.to_tensor = ToTensorV2() + + def __len__(self) -> int: + return len(self.slide_tiles) + + def __getitem__(self, idx: int) -> PredictSample: + input_image_255 = self.slide_tiles[idx] + slide_name = self.slide_tiles.slide_path.stem + x = self.slide_tiles.tiles.iloc[idx]["x"] + y = self.slide_tiles.tiles.iloc[idx]["y"] + + input_image = input_image_255 / 255.0 + + if self.normalize: + input_image = self.normalize(image=input_image)["image"] + + input_image = self.to_tensor(image=input_image)["image"] + + return input_image, { # type: ignore[return-value] # untyped import + "slide_name": slide_name, + "xy": f"{x}_{y}", + } diff --git a/stain_normalization/data/datasets/test_dataset.py b/stain_normalization/data/datasets/test_dataset.py new file mode 100644 index 0000000..5a536b0 --- /dev/null +++ b/stain_normalization/data/datasets/test_dataset.py @@ -0,0 +1,97 @@ +from collections.abc import Iterable + +import pandas as pd +from albumentations import Transform3D +from albumentations.pytorch import ToTensorV2 +from rationai.mlkit.data.datasets import MetaTiledSlides, OpenSlideTilesDataset +from torch.utils.data import Dataset + +from stain_normalization.type_aliases import PredictSample + + +class TestDataset(MetaTiledSlides[PredictSample]): + """Dataset for testing and analysis. + + Same as TrainDataset but also returns a metadata dict with slide_name, xy + coordinates, and raw uint8 copies of both original and modified images — + needed for callbacks that export tiles or run analysis. + + Differences from TrainDataset: returns metadata dict alongside tensors; + keeps raw image copies for export. + Differences from PredictDataset: applies modify transform to simulate + degraded input; real slides are not pre-modified. + """ + + def __init__( + self, + uris: Iterable[str], + modify: Transform3D, + normalize: Transform3D | None = None, + ) -> None: + self.modify = modify + self.normalize = normalize + super().__init__(uris=uris) + + def generate_datasets(self) -> Iterable[Dataset[PredictSample]]: + return ( + _TestSlideTiles( + slide, + tiles=self.filter_tiles_by_slide(slide["id"]), + modify=self.modify, + normalize=self.normalize, + ) + for _, slide in self.slides.iterrows() + ) + + +class _TestSlideTiles(Dataset[PredictSample]): + def __init__( + self, + slide_metadata: pd.Series, + tiles: pd.DataFrame, + modify: Transform3D, + normalize: Transform3D | None = None, + ) -> None: + super().__init__() + self.slide_tiles = OpenSlideTilesDataset( + slide_path=slide_metadata.path, + level=slide_metadata.level, + tile_extent_x=slide_metadata.tile_extent_x, + tile_extent_y=slide_metadata.tile_extent_y, + tiles=tiles, + ) + self.modify = modify + self.normalize = normalize + self.to_tensor = ToTensorV2() + + def __len__(self) -> int: + return len(self.slide_tiles) + + def __getitem__(self, idx: int) -> PredictSample: + original_image_255 = self.slide_tiles[idx] + slide_name = self.slide_tiles.slide_path.stem + x = self.slide_tiles.tiles.iloc[idx]["x"] + y = self.slide_tiles.tiles.iloc[idx]["y"] + + # Create "wrong" image to use as input. Outputs image in float 0-1 + modified_image_raw = self.modify(image=original_image_255)["image"] + modified_image = modified_image_raw.copy() + original_image = original_image_255 / 255.0 + + if self.normalize: + original_image = self.normalize(image=original_image)["image"] + modified_image = self.normalize(image=modified_image)["image"] + + original_image = self.to_tensor(image=original_image)["image"] + modified_image = self.to_tensor(image=modified_image)["image"] + + return ( + modified_image, + { + "original_image_tensor": original_image, + "original_image": original_image_255, + "modified_image": modified_image_raw, + "slide_name": slide_name, + "xy": f"{x}_{y}", + }, + ) diff --git a/stain_normalization/data/datasets/train_dataset.py b/stain_normalization/data/datasets/train_dataset.py new file mode 100644 index 0000000..27e6d43 --- /dev/null +++ b/stain_normalization/data/datasets/train_dataset.py @@ -0,0 +1,84 @@ +from collections.abc import Iterable + +import pandas as pd +from albumentations import Transform3D +from albumentations.pytorch import ToTensorV2 +from rationai.mlkit.data.datasets import MetaTiledSlides, OpenSlideTilesDataset +from torch.utils.data import Dataset + +from stain_normalization.type_aliases import Sample + + +class TrainDataset(MetaTiledSlides[Sample]): + """Dataset for training and validation. + + Kept as separate classes for distinct return types and clean Hydra config + targets. + Main difference is in __getitem__; + + Returns (modified_tensor, original_tensor) pairs — no metadata, no raw copies. + + Differences from TestDataset: no metadata returned, no raw image copies kept. + Differences from PredictDataset: applies modify transform; no metadata. + """ + + def __init__( + self, + uris: Iterable[str], + modify: Transform3D, + normalize: Transform3D | None = None, + ) -> None: + self.modify = modify + self.normalize = normalize + super().__init__(uris=uris) + + def generate_datasets(self) -> Iterable[Dataset[Sample]]: + return ( + _TrainSlideTiles( + slide, + tiles=self.filter_tiles_by_slide(slide["id"]), + modify=self.modify, + normalize=self.normalize, + ) + for _, slide in self.slides.iterrows() + ) + + +class _TrainSlideTiles(Dataset[Sample]): + def __init__( + self, + slide_metadata: pd.Series, + tiles: pd.DataFrame, + modify: Transform3D, + normalize: Transform3D | None = None, + ) -> None: + super().__init__() + self.slide_tiles = OpenSlideTilesDataset( + slide_path=slide_metadata.path, + level=slide_metadata.level, + tile_extent_x=slide_metadata.tile_extent_x, + tile_extent_y=slide_metadata.tile_extent_y, + tiles=tiles, + ) + self.modify = modify + self.normalize = normalize + self.to_tensor = ToTensorV2() + + def __len__(self) -> int: + return len(self.slide_tiles) + + def __getitem__(self, idx: int) -> Sample: + original_image_255 = self.slide_tiles[idx] + + # Create "wrong" image to use as input. Outputs image in float 0-1 + modified_image = self.modify(image=original_image_255)["image"] + original_image = original_image_255 / 255.0 + + if self.normalize: + original_image = self.normalize(image=original_image)["image"] + modified_image = self.normalize(image=modified_image)["image"] + + original_image = self.to_tensor(image=original_image)["image"] + modified_image = self.to_tensor(image=modified_image)["image"] + + return modified_image, original_image # type: ignore[return-value] # untyped import diff --git a/stain_normalization/data/modification/__init__.py b/stain_normalization/data/modification/__init__.py new file mode 100644 index 0000000..3e6609a --- /dev/null +++ b/stain_normalization/data/modification/__init__.py @@ -0,0 +1,16 @@ +from stain_normalization.data.modification.combined_modification import ( + CombinedModifications, +) +from stain_normalization.data.modification.exposure_adjustment import ( + ExposureAdjustment, +) +from stain_normalization.data.modification.hed_factor import HEDFactor +from stain_normalization.data.modification.hsv_modification import HSVModification + + +__all__ = [ + "CombinedModifications", + "ExposureAdjustment", + "HEDFactor", + "HSVModification", +] diff --git a/stain_normalization/data/modification/combined_modification.py b/stain_normalization/data/modification/combined_modification.py new file mode 100644 index 0000000..ef252bf --- /dev/null +++ b/stain_normalization/data/modification/combined_modification.py @@ -0,0 +1,52 @@ +from typing import Any + +import numpy as np +from albumentations import ImageOnlyTransform +from numpy.typing import NDArray +from skimage import exposure +from skimage.color import combine_stains, hed_from_rgb, rgb_from_hed, separate_stains + + +class CombinedModifications(ImageOnlyTransform): # type: ignore[misc] # untyped import + """Apply combined modifications to the H&E channels in HED color space. + + Attributes: + od_scale_range: Range of multiplicative factors to scale stain OD (optical density) values. + brightness_range: Range for gamma correction to simulate brightness shift. + p: Probability of applying the transformation. + """ + + def __init__( + self, + od_scale_range: tuple[float, float] = (0.4, 1.5), + brightness_range: tuple[float, float] = (-0.4, 0.4), + p: float = 1.0, + ): + super().__init__(p=p) + self.od_scale_range = od_scale_range + self.brightness_range = brightness_range + + def apply(self, img: NDArray[Any], **params: Any) -> NDArray[Any]: + """Apply OD scaling and brightness adjustments to H and E channels. + + Args: + img: Image to which the transformation will be applied. + params: Additional parameters (unused). + + Returns: + Modified RGB image as a float32 NumPy array with values in [0.0, 1.0]. + """ + hed_image = separate_stains(img, hed_from_rgb) + h = self.modify_channel(hed_image[:, :, 0]) + e = self.modify_channel(hed_image[:, :, 1]) + d = hed_image[:, :, 2] + + modified_rgb = combine_stains(np.stack((h, e, d), axis=-1), rgb_from_hed) + + return np.clip(modified_rgb, 0, 1).astype(np.float32) + + def modify_channel(self, channel: NDArray[np.float32]) -> NDArray[np.float32]: + od_scale = np.random.uniform(*self.od_scale_range) + channel = channel * od_scale + brightness_shift = np.random.uniform(*self.brightness_range) + return exposure.adjust_gamma(channel, gamma=1 + brightness_shift) # type: ignore[return-value] diff --git a/stain_normalization/data/modification/exposure_adjustment.py b/stain_normalization/data/modification/exposure_adjustment.py new file mode 100644 index 0000000..b40ac9f --- /dev/null +++ b/stain_normalization/data/modification/exposure_adjustment.py @@ -0,0 +1,41 @@ +from typing import Any + +import numpy as np +from albumentations import ImageOnlyTransform +from numpy.typing import NDArray +from skimage.util import img_as_float + + +class ExposureAdjustment(ImageOnlyTransform): # type: ignore[misc] # untyped import + """Adjust the exposure of an image by scaling its brightness. + + Attributes: + brightness_range: Range specifying the lower and upper bounds for the + random brightness scaling factor. Values less than 1.0 darken the image, + while values greater than 1.0 brighten it. + p: Probability of applying the transformation. + """ + + def __init__( + self, + brightness_range: tuple[float, float] = (0.8, 1.2), + p: float = 1.0, + ): + super().__init__(p=p) + self.brightness_range = brightness_range + + def apply(self, img: NDArray[Any], **params: Any) -> NDArray[Any]: + """Apply brightness scaling to the image. + + Args: + img: Input image whose brightness will be adjusted. + params: Additional parameters. + + Returns: + RGB image with adjusted brightness as a float32 + NumPy array with values in [0.0, 1.]. + """ + brightness_factor = np.random.uniform(*self.brightness_range) + img_float = img_as_float(img) + img = np.clip(img_float * brightness_factor, 0.0, 1.0) + return img.astype(np.float32) diff --git a/stain_normalization/data/modification/hed_factor.py b/stain_normalization/data/modification/hed_factor.py new file mode 100644 index 0000000..6b6d9b0 --- /dev/null +++ b/stain_normalization/data/modification/hed_factor.py @@ -0,0 +1,48 @@ +from typing import Any + +import numpy as np +from albumentations import ImageOnlyTransform +from numpy.typing import NDArray +from skimage.color import combine_stains, hed_from_rgb, rgb_from_hed, separate_stains + + +class HEDFactor(ImageOnlyTransform): # type: ignore[misc] # untyped import + """Adjust the optical density (OD) of Hematoxylin and Eosin stains in HED color space. + + Attributes: + h_range: Range for the random OD scaling factor for the Hematoxylin channel. + e_range: Range for the random OD scaling factor for the Eosin channel. + p: Probability of applying the transformation. + """ + + def __init__( + self, + h_range: tuple[float, float] = (0.8, 1.2), + e_range: tuple[float, float] = (0.8, 1.2), + p: float = 1.0, + ): + super().__init__(p=p) + self.h_range = h_range + self.e_range = e_range + + def apply(self, img: NDArray[Any], **params: Any) -> NDArray[Any]: + """Apply the modification to the image. + + Args: + img: Image to which the transformation will be applied. + params: Additional parameters. + + Returns: + RGB image with OD-scaled Hematoxylin and Eosin channels + as a float32 NumPy array with values in [0.0, 1.]. + """ + h_factor = np.random.uniform(*self.h_range) + e_factor = np.random.uniform(*self.e_range) + + hed_image = separate_stains(img, hed_from_rgb) + h = hed_image[:, :, 0] * h_factor + e = hed_image[:, :, 1] * e_factor + d = hed_image[:, :, 2] # DAB channel unchanged + modified_rgb = combine_stains(np.stack((h, e, d), axis=-1), rgb_from_hed) + + return np.clip(modified_rgb, 0, 1).astype(np.float32) diff --git a/stain_normalization/data/modification/hsv_modification.py b/stain_normalization/data/modification/hsv_modification.py new file mode 100644 index 0000000..53d4f30 --- /dev/null +++ b/stain_normalization/data/modification/hsv_modification.py @@ -0,0 +1,55 @@ +from typing import Any + +import numpy as np +from albumentations import ImageOnlyTransform +from numpy.typing import NDArray +from skimage.color import hsv2rgb, rgb2hsv + + +class HSVModification(ImageOnlyTransform): # type: ignore[misc] # untyped import + """Randomly modify hue, saturation, and value (brightness) of an image in HSV color space. + + Attributes: + hue_shift_range: Range of values to randomly shift the hue channel. + Values are wrapped around the [0, 1) interval (modulo 1.0). + saturation_range: Range for randomly scaling the saturation channel. + Values >1.0 increase saturation, <1.0 decrease it. + value_range: Range for randomly scaling the value (brightness) channel. + p: Probability of applying the transformation. + """ + + def __init__( + self, + hue_shift_range: tuple[float, float] = (-0.2, 0.2), + saturation_range: tuple[float, float] = (0.8, 1.5), + value_range: tuple[float, float] = (0.8, 1.3), + p: float = 1.0, + ): + super().__init__(p=p) + self.hue_shift_range = hue_shift_range + self.saturation_range = saturation_range + self.value_range = value_range + + def apply(self, img: NDArray[Any], **params: Any) -> NDArray[Any]: + """Apply the modifications to an image. + + Args: + img: Image to which the transformation will be applied. + params: Additional parameters. + + Returns: + RGB image with HSV modifications as a float32 + NumPy array with values in [0.0, 1.]. + """ + hue_shift = np.random.uniform(*self.hue_shift_range) + saturation_scale = np.random.uniform(*self.saturation_range) + value_scale = np.random.uniform(*self.value_range) + + hsv_image = rgb2hsv(img) + hsv_image[:, :, 0] = (hsv_image[:, :, 0] + hue_shift) % 1.0 + hsv_image[:, :, 1] = np.clip(hsv_image[:, :, 1] * saturation_scale, 0, 1) + hsv_image[:, :, 2] = np.clip(hsv_image[:, :, 2] * value_scale, 0, 1) + + modified_rgb = hsv2rgb(hsv_image) + + return modified_rgb.astype(np.float32) diff --git a/stain_normalization/type_aliases.py b/stain_normalization/type_aliases.py new file mode 100644 index 0000000..e950436 --- /dev/null +++ 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