diff --git a/.flake8 b/.flake8 new file mode 100644 index 0000000..63ea673 --- /dev/null +++ b/.flake8 @@ -0,0 +1,3 @@ +[flake8] +max-line-length=88 +extend-ignore=E203 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/.gitlab-ci.yml b/.gitlab-ci.yml new file mode 100644 index 0000000..b132c47 --- /dev/null +++ b/.gitlab-ci.yml @@ -0,0 +1,6 @@ +include: + project: rationai/digital-pathology/templates/ci-templates + file: Python-Lint.gitlab-ci.yml + +stages: + - lint diff --git a/.mypy.ini b/.mypy.ini new file mode 100644 index 0000000..1c6f44e --- /dev/null +++ b/.mypy.ini @@ -0,0 +1,5 @@ +[mypy] +strict = True +ignore_missing_imports = True +disallow_untyped_calls = False +disable_error_code = no-any-return \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 0000000..8b99ad0 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +# See https://pre-commit.com for more information +# See https://pre-commit.com/hooks.html for more hooks +repos: + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v5.0.0 + hooks: + - id: check-yaml + args: [--unsafe] + + - repo: https://github.com/commitizen-tools/commitizen + rev: v3.30.1 + hooks: + - id: commitizen + + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: v0.7.3 + hooks: + # Run the linter. + - id: ruff + entry: pdm lint --force-exclude + # Run the formatter. + - id: ruff-format + entry: pdm format --force-exclude diff --git a/.ruff.toml b/.ruff.toml new file mode 100644 index 0000000..fb5e992 --- /dev/null +++ b/.ruff.toml @@ -0,0 +1,51 @@ +fix = true +line-length = 88 +target-version = "py311" + +[format] +# Enable reformatting of code snippets in docstrings. +docstring-code-format = true + +[lint] +unfixable = [ + "ERA", # do not autoremove commented out code +] +extend-select = [ + "B", # flake8-bugbear + "C4", # flake8-comprehensions + "ERA", # flake8-eradicate/eradicate + "I", # isort + "N", # pep8-naming + "PIE", # flake8-pie + "PGH", # pygrep + "RUF", # ruff checks + "SIM", # flake8-simplify + "TCH", # flake8-type-checking + "TID", # flake8-tidy-imports + "UP", # pyupgrade + "D", # pydocstyle +] +extend-ignore = [ + "ERA001", # commented out code + "D100", # missing docstring in public module + "D101", # missing docstring in public class + "D102", # missing docstring in public method + "D103", # missing docstring in public function + "D104", # missing docstring in public package + "D105", # missing docstring in magic method + "D106", # missing docstring in public nested class + "D107", # missing docstring in __init__ + "N812", # lowercase imported as non lowercase + "TCH002", # move third-party into a type-checking block + "F722", # jaxtyping +] + +[lint.flake8-tidy-imports] +ban-relative-imports = "all" + +[lint.isort] +lines-after-imports = 2 +known-first-party = ["tests"] + +[lint.pydocstyle] +convention = "google" diff --git a/README.md b/README.md index 075c594..a6d034d 100644 --- a/README.md +++ b/README.md @@ -1 +1,34 @@ -# stain-normalization \ No newline at end of file +# Stain Normalization + +Tento repozitár slúži ako doplnkový materiál k bakalárskej práci **"Normalizácia farbenia histopatologických snímkov pomocou neuronových sietí"**. + +Celý kód nie je možné spustiť samostatne, pretože vyžaduje prístup k citlivým dátam a k platforme na správu strojového učenia MLflow. Avšak je možné spustiť demo, ku ktorému je pripravených pár vzoriek na demonštráciu. + +## Demo + +Demo skript umožňuje načítať jeden obrázok alebo celý priečinok, normalizovať ich pomocou predtrénovaného modelu a uložiť výsledné obrázky do určeného priečinka. + +## Priložené dáta + +- **Originálne obrázky** (v priečinku `./demo_data/original`) – referenčné vzorky. +- **Upravené obrázky** (v priečinku `./demo_data/modified`) – referenčné obrázky s modifikovaným sfarbením, ktoré sa následne modelom znormalizujú späť k originálnemu vzhľadu. +- **Obrázky z iného datasetu** (v priečinku `./demo_data/to_predict`) – ukazujú, ako model dokáže prispôsobiť farby vstupov tak, aby zodpovedali farebnému štýlu trénovacích dát. +- **Model checkpoint** + +## Spustenie dema + +Projekt je spravovaný pomocou nástroja **pdm**, ktorý umožňuje jednoduchú správu závislostí a prostredia. Stačí nainštalovať závislosti pomocou: + +```bash +pdm install +``` +a potom spustiť demo skript: + +```bash +pdm run python demo.py --input ./demo_data/modified +``` + +## Dostupné arguemnty: +- **input**: cesta k obrázku alebo priečinku s obrázkami na normalizáciu (default ./demo_data/modified) +- **output**: priečinok, kam sa uložia normalizované obrázky (default ./demo_data) +- **use_cpu**: defaultne nadstavené na použitie GPU ak je dostupná, avšak ak by nastali problémy odporúčam použivať iba CPU \ No newline at end of file diff --git a/analyze_dataset.py b/analyze_dataset.py new file mode 100644 index 0000000..d10064f --- /dev/null +++ b/analyze_dataset.py @@ -0,0 +1,139 @@ +"""Compare a dataset against a reference image or between two datasets. + +Usage: + # Compare dataset against a reference image + python analyze_dataset.py --reference ref.png --uri "mlflow-artifacts:/79/..." + + # Compare two datasets (e.g. original vs normalized) + # Assume that both datasets have the same slides and tiles in the same order for paired comparison + python analyze_dataset.py --original "mlflow-artifacts:/79/...original" \ + --compared "mlflow-artifacts:/79/...normalized" + + # Subsample for faster run + python analyze_dataset.py --reference ref.png --uri "mlflow-artifacts:/79/..." --max-tiles 5000 +""" + +import argparse +from pathlib import Path + +import numpy as np +from PIL import Image +from rationai.mlkit.data.datasets import MetaTiledSlides, OpenSlideTilesDataset +from tqdm import tqdm + +from stain_normalization.analysis import StainAnalyzer +from stain_normalization.analysis.report import REPORT_METRICS + + +def load_image(path: str | Path) -> np.ndarray: + return np.array(Image.open(path).convert("RGB")) + + +def iterate_tiles(slides, tiles): + """Yield (slide_name, tile_uint8, image_id) for each tile.""" + for _, slide in slides.iterrows(): + slide_name = Path(slide.path).stem + slide_tiles = tiles[tiles["slide_id"] == slide["id"]] + + if slide_tiles.empty: + continue + + dataset = OpenSlideTilesDataset( + slide_path=slide.path, + level=slide.level, + tile_extent_x=slide.tile_extent_x, + tile_extent_y=slide.tile_extent_y, + tiles=slide_tiles, + ) + + for i in range(len(dataset)): + image_id = f"{slide_name}_{slide_tiles.iloc[i]['x']}_{slide_tiles.iloc[i]['y']}" + yield slide_name, dataset[i], image_id + + +def run_reference_mode(args): + """Compare all tiles in a dataset against a single reference image.""" + ref_img = load_image(args.reference) + slides, tiles = MetaTiledSlides.load_slides_and_tiles(paths=[], uris=args.uri) + print(f"Dataset: {len(slides)} slides, {len(tiles)} tiles") + print(f"Reference: {args.reference}") + + if args.max_tiles and len(tiles) > args.max_tiles: + tiles = tiles.sample(n=args.max_tiles, random_state=42) + print(f"Subsampled to {args.max_tiles} tiles") + + analyzer = StainAnalyzer(reference=ref_img) + for _, tile, image_id in tqdm(iterate_tiles(slides, tiles), total=len(tiles)): + analyzer.compare(tile, image_id=image_id) + + return analyzer, len(analyzer.results) + + +def run_paired_mode(args): + """Compare matching tiles between two datasets (original vs compared).""" + orig_slides, orig_tiles = MetaTiledSlides.load_slides_and_tiles(paths=[], uris=args.original) + comp_slides, comp_tiles = MetaTiledSlides.load_slides_and_tiles(paths=[], uris=args.compared) + print(f"Original: {len(orig_slides)} slides, {len(orig_tiles)} tiles") + print(f"Compared: {len(comp_slides)} slides, {len(comp_tiles)} tiles") + + if args.max_tiles and len(orig_tiles) > args.max_tiles: + orig_tiles = orig_tiles.sample(n=args.max_tiles, random_state=42) + comp_tiles = comp_tiles.loc[orig_tiles.index] + print(f"Subsampled to {args.max_tiles} tile pairs") + + analyzer = StainAnalyzer() + orig_iter = iterate_tiles(orig_slides, orig_tiles) + comp_iter = iterate_tiles(comp_slides, comp_tiles) + + for (_, orig_tile, image_id), (_, comp_tile, _) in tqdm( + zip(orig_iter, comp_iter), total=len(orig_tiles) + ): + analyzer.compare(comp_tile, image_id=image_id, reference=orig_tile) + + return analyzer, len(analyzer.results) + + +def main(): + parser = argparse.ArgumentParser( + description="Dataset stain analysis", + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument("--output", default="./analysis_output", + help="Output directory (default: ./analysis_output)") + parser.add_argument("--max-tiles", type=int, default=None, + help="Limit number of tiles to analyze") + + # Mode 1: reference image + parser.add_argument("--reference", help="Path to reference image") + parser.add_argument("--uri", nargs="+", help="MLflow dataset URI(s) to analyze") + + # Mode 2: two datasets + parser.add_argument("--original", nargs="+", help="MLflow URI(s) for original dataset") + parser.add_argument("--compared", nargs="+", help="MLflow URI(s) for compared dataset") + + args = parser.parse_args() + + if args.reference and args.uri: + analyzer, count = run_reference_mode(args) + elif args.original and args.compared: + analyzer, count = run_paired_mode(args) + else: + parser.error("Use either (--reference + --uri) or (--original + --compared)") + + if analyzer is None: + return + + print(f"\nAnalyzed {count} tiles") + + analyzer.save_csv(args.output) + print(f"Results saved to: {args.output}/") + + stats = analyzer.get_statistics() + print("\nStatistics:") + for m in REPORT_METRICS: + if m in stats.columns: + print(f" {m:25s}: mean={stats[m]['mean']:.4f} std={stats[m]['std']:.4f}") + + +if __name__ == "__main__": + main() 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..be01300 --- /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:/79/b987e3e7329342fe96906eb3b17bfcc4/artifacts/Stain Normalization - test (updated)"] diff --git a/configs/data/datasets/stain_normalization/train.yaml b/configs/data/datasets/stain_normalization/train.yaml new file mode 100644 index 0000000..6754a96 --- /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:/79/b987e3e7329342fe96906eb3b17bfcc4/artifacts/Stain Normalization - train (updated)"] diff --git a/configs/data/datasets/stain_normalization/val.yaml b/configs/data/datasets/stain_normalization/val.yaml new file mode 100644 index 0000000..4c4c13c --- /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:/79/b987e3e7329342fe96906eb3b17bfcc4/artifacts/Stain Normalization - val (updated)"] + diff --git a/configs/data/modify/test.yaml b/configs/data/modify/test.yaml new file mode 100644 index 0000000..9491339 --- /dev/null +++ b/configs/data/modify/test.yaml @@ -0,0 +1,15 @@ +_target_: albumentations.OneOf +p: 1.0 +transforms: + - _target_: stain_normalization.data.modification.HEDFactor + h_range: [0.8, 1.2] + e_range: [0.8, 1.2] + - _target_: stain_normalization.data.modification.ExposureAdjustment + brightness_range: [0.8, 1.2] + - _target_: stain_normalization.data.modification.HVSModification + hue_shift_range: [-0.4, 0.4] + saturation_range: [0.8, 1.5] + value_range: [0.8, 1.3] + - _target_: stain_normalization.data.modification.CombinedModifications + intensity_range: [0.65, 1.35] + brightness_range: [-0.4, 0.4] \ No newline at end of file diff --git a/configs/data/modify/train.yaml b/configs/data/modify/train.yaml new file mode 100644 index 0000000..9491339 --- /dev/null +++ b/configs/data/modify/train.yaml @@ -0,0 +1,15 @@ +_target_: albumentations.OneOf +p: 1.0 +transforms: + - _target_: stain_normalization.data.modification.HEDFactor + h_range: [0.8, 1.2] + e_range: [0.8, 1.2] + - _target_: stain_normalization.data.modification.ExposureAdjustment + brightness_range: [0.8, 1.2] + - _target_: stain_normalization.data.modification.HVSModification + hue_shift_range: [-0.4, 0.4] + saturation_range: [0.8, 1.5] + value_range: [0.8, 1.3] + - _target_: stain_normalization.data.modification.CombinedModifications + intensity_range: [0.65, 1.35] + brightness_range: [-0.4, 0.4] \ No newline at end of file diff --git a/configs/data/normalize/default.yaml b/configs/data/normalize/default.yaml new file mode 100644 index 0000000..08d72da --- /dev/null +++ b/configs/data/normalize/default.yaml @@ -0,0 +1,10 @@ +_target_: albumentations.Normalize +mean: + - 0.780361961 + - 0.614529804 + - 0.725567843 +std: + - 0.144428627 + - 0.183275686 + - 0.140768627 +max_pixel_value: 1 diff --git a/configs/default.yaml b/configs/default.yaml new file mode 100644 index 0000000..1ccce2a --- /dev/null +++ b/configs/default.yaml @@ -0,0 +1,64 @@ +defaults: + - hydra: default + - logger: mlflow + - /data/datasets@data.train: stain_normalization/train + - /data/datasets@data.val: stain_normalization/val + - /data/datasets@data.test: stain_normalization/test + - /data/datasets@data.predict: stain_normalization/predict + - _self_ + +seed: ${random_seed:} +mode: fit +checkpoint: mlflow-artifacts:/79/978f5d5e54844be3b42509cce76793d7/artifacts/checkpoints/epoch=7-step=152375/checkpoint.ckpt + + +callbacks: + model_checkpoint: + _target_: lightning.pytorch.callbacks.ModelCheckpoint + save_top_k: 2 + monitor: validation/loss + mode: min + + tiles_export: + _target_: stain_normalization.callbacks.TilesExport + output_dir: ${output_dir} + normalization_config: ${data.test.normalize} + + analysis_export: + _target_: stain_normalization.callbacks.AnalysisExport + output_dir: ${output_dir} + normalization_config: ${data.test.normalize} + + wsi_assembler: + _target_: stain_normalization.callbacks.WSIAssembler + output_dir: ${output_dir} + normalization_config: ${data.predict.normalize} + +trainer: + enable_checkpointing: True + max_epochs: 30 + log_every_n_steps: 5 + val_check_interval: 0.5 + + callbacks: + - ${callbacks.model_checkpoint} + - ${callbacks.analysis_export} + +data: + batch_size: 24 + num_workers: 10 + +metadata: + user: xlopatka + experiment_name: Stain-Normalization + run_name: fit bright + description: Stain Normalization + hyperparams: {} + + +output_dir: ./data/test-dataset + + + + + diff --git a/configs/hydra/default.yaml b/configs/hydra/default.yaml new file mode 100644 index 0000000..275e331 --- /dev/null +++ b/configs/hydra/default.yaml @@ -0,0 +1,7 @@ +defaults: + - _self_ + - override hydra_logging: disabled + - override job_logging: disabled +output_subdir: null +run: + dir: . diff --git a/configs/logger/mlflow.yaml b/configs/logger/mlflow.yaml new file mode 100644 index 0000000..10355c7 --- /dev/null +++ b/configs/logger/mlflow.yaml @@ -0,0 +1,6 @@ +_target_: rationai.mlkit.lightning.loggers.MLFlowLogger +experiment_name: ${metadata.experiment_name} +run_name: ${metadata.run_name} +tags: + mlflow.user: ${metadata.user} + mlflow.note.content: ${metadata.description} diff --git a/demo.py b/demo.py new file mode 100644 index 0000000..69ca771 --- /dev/null +++ b/demo.py @@ -0,0 +1,122 @@ +import argparse +from pathlib import Path + +import albumentations as A +import numpy as np +import torch +from albumentations.pytorch import ToTensorV2 +from PIL import Image + +from stain_normalization.stain_normalization_model import StainNormalizationModel + + +class StainNormalizerDemo: + CHECKPOINT_PATH = "./demo_data/checkpoint.ckpt" + MEAN = (0.780361961, 0.614529804, 0.725567843) + STD = (0.144428627, 0.183275686, 0.140768627) + + NORMALIZE_TRANSFORM = A.Normalize(mean=MEAN, std=STD, max_pixel_value=1) + TO_TENSOR = ToTensorV2() + + def __init__(self, use_cpu=True): + self.device = torch.device( + "cpu" if use_cpu else ("cuda" if torch.cuda.is_available() else "cpu") + ) + print(f"Using device: {self.device}") + + self.model = StainNormalizationModel() + checkpoint = torch.load(self.CHECKPOINT_PATH, map_location=self.device) + if "state_dict" in checkpoint: + self.model.load_state_dict(checkpoint["state_dict"]) + else: + self.model.load_state_dict(checkpoint) + self.model.to(self.device) + self.model.eval() + + def load_image(self, path: Path) -> torch.Tensor: + img = Image.open(path).convert("RGB") + img_np = np.array(img).astype(np.float32) / 255.0 + normalized = self.NORMALIZE_TRANSFORM(image=img_np)["image"] + tensor = self.TO_TENSOR(image=normalized)["image"].to(self.device).unsqueeze(0) + return tensor + + def denormalize(self, tensor: torch.Tensor) -> torch.Tensor: + mean = torch.tensor(self.MEAN).view(3, 1, 1).to(tensor.device) + std = torch.tensor(self.STD).view(3, 1, 1).to(tensor.device) + return tensor * std + mean + + def tensor_to_image(self, tensor: torch.Tensor) -> Image.Image: + tensor = self.denormalize(tensor) + tensor = tensor.clamp(0, 1) + tensor = (tensor * 255).byte() + return Image.fromarray(tensor.permute(1, 2, 0).cpu().numpy()) + + def save_image(self, tensor: torch.Tensor, path: Path): + img = self.tensor_to_image(tensor.squeeze(0)) + img.save(path) + + def predict_image(self, input_path: Path, output_path: Path): + with torch.no_grad(): + input_tensor = self.load_image(input_path) + output = self.model(input_tensor) + self.save_image(output, output_path) + print(f"Saved normalized image to {output_path}") + + +def main(): + parser = argparse.ArgumentParser(description="Stain Normalization Demo") + parser.add_argument( + "--input", + type=str, + default="./demo_data/modified", + help="Input image or folder path", + ) + parser.add_argument( + "--output", + type=str, + default="./demo_data", + help="Output folder path", + ) + parser.add_argument( + "--use_cpu", + action="store_true", + help="Force CPU even if GPU available", + ) + args = parser.parse_args() + + normalizer = StainNormalizerDemo(use_cpu=args.use_cpu) + + input_path = Path(args.input) + output_path = Path(args.output) + + if not output_path.exists(): + output_path.mkdir(parents=True) + + if input_path.is_file(): + out_filename = input_path.stem + "_normalized" + input_path.suffix + out_path = output_path / out_filename + normalizer.predict_image(input_path, out_path) + + elif input_path.is_dir(): + norm_folder = output_path / "normalized" + norm_folder.mkdir(exist_ok=True) + + input_files = sorted( + f + for f in input_path.iterdir() + if f.is_file() and f.suffix.lower() in {".png", ".jpg", ".jpeg"} + ) + if not input_files: + print(f"No image files found in {input_path}.") + return + for in_path in input_files: + out_filename = in_path.stem + "_normalized" + in_path.suffix + out_path = norm_folder / out_filename + normalizer.predict_image(in_path, out_path) + + else: + raise ValueError(f"Input path {input_path} does not exist.") + + +if __name__ == "__main__": + main() diff --git a/nohup.out b/nohup.out new file mode 100644 index 0000000..1485bf9 --- /dev/null +++ b/nohup.out @@ -0,0 +1,2772 @@ + + 0%| | 0/235 [00:00 + main() + File "/home/jovyan/machine-learning/preprocessing/mask_generator.py", line 63, in main + process_items(slides, process_item=process_slide) + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/rationai/masks/processing.py", line 56, in process_items + ready, pending = ray.wait(pending) + ^^^^^^^^^^^^^^^^^ + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/auto_init_hook.py", line 21, in auto_init_wrapper + return fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/client_mode_hook.py", line 103, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/worker.py", line 2976, in wait + ready_ids, remaining_ids = worker.core_worker.wait( + ^^^^^^^^^^^^^^^^^^^^^^^^ + File "python/ray/_raylet.pyx", line 3816, in ray._raylet.CoreWorker.wait + File "python/ray/includes/common.pxi", line 79, in ray._raylet.check_status +KeyboardInterrupt +Exception ignored in atexit callback: +Traceback (most recent call last): + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/client_mode_hook.py", line 103, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/worker.py", line 1905, in shutdown + _global_node.kill_all_processes(check_alive=False, allow_graceful=True) + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/node.py", line 1637, in kill_all_processes + self._kill_process_type( + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/node.py", line 1458, in _kill_process_type + self._kill_process_impl( + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/node.py", line 1514, in _kill_process_impl + process.wait(timeout_seconds) + File "/usr/lib/python3.11/subprocess.py", line 1277, in wait + self._wait(timeout=sigint_timeout) + File "/usr/lib/python3.11/subprocess.py", line 2025, in _wait + if self._waitpid_lock.acquire(False): + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +KeyboardInterrupt: + File "/home/jovyan/machine-learning/preprocessing/mask_generator.py", line 46 + folders = [ + ^ +SyntaxError: '[' was never closed + + 0%| | 0/87 [00:00 + main() + File "/home/jovyan/machine-learning/preprocessing/tiler.py", line 88, in main + train_slides_df, train_tiles_df = tiling( + ^^^^^^^ + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/rationai/tiling/tiling.py", line 107, in tiling + ready, pending = ray.wait(pending) + ^^^^^^^^^^^^^^^^^ + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/auto_init_hook.py", line 21, in auto_init_wrapper + return fn(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^ + File "/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/ray/_private/client_mode_hook.py", line 103, in wrapper + return func(*args, **kwargs) + ^^^^^^^^^^^^^^^^^^^^^ + File 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Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details. + warnings.warn( +/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/mlflow/types/utils.py:407: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details. + warnings.warn( + 0%| | 0/73 [00:00`_ for more details. + warnings.warn( +/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/mlflow/types/utils.py:407: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. See `Handling Integers With Missing Values `_ for more details. + warnings.warn( +/home/jovyan/machine-learning/.venv/lib/python3.11/site-packages/mlflow/types/utils.py:407: UserWarning: Hint: Inferred schema contains integer column(s). Integer columns in Python cannot represent missing values. If your input data contains missing values at inference time, it will be encoded as floats and will cause a schema enforcement error. The best way to avoid this problem is to infer the model schema based on a realistic data sample (training dataset) that includes missing values. Alternatively, you can declare integer columns as doubles (float64) whenever these columns may have missing values. 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wrapper for zip files" +groups = ["default"] +marker = "python_full_version == \"3.12.5\"" +files = [ + {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, + {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, +] diff --git a/preprocessing/mask_generator.py b/preprocessing/mask_generator.py new file mode 100644 index 0000000..234f846 --- /dev/null +++ b/preprocessing/mask_generator.py @@ -0,0 +1,44 @@ +from pathlib import Path + +import pyvips +import ray +from openslide import PROPERTY_NAME_MPP_X, PROPERTY_NAME_MPP_Y, OpenSlide +from rationai.masks import process_items, tissue_mask, write_big_tiff + + +# folders: +# archive tumor cases +# chive negative cases +# Prospective negative cases +# Prospective test cases +# Prospective tumor cases + +SLIDES_PATH = "/mnt/data/MOU/prostate/tile_level_annotations/" +MASK_DEST = "./mask/tissue_masks" +LEVEL = 3 + + +@ray.remote +def process_slide(slide_path: Path) -> None: + # pyvips.Image xres and yres variables don't respect level therefore we + # extract proper spatial resulution based on desired level using OpenSlide + with OpenSlide(slide_path) as slide: + downsample = slide.level_downsamples[LEVEL] + xres = 1000 / (float(slide.properties[PROPERTY_NAME_MPP_X]) * downsample) + yres = 1000 / (float(slide.properties[PROPERTY_NAME_MPP_Y]) * downsample) + + slide = pyvips.Image.new_from_file(slide_path, level=LEVEL) + mask = tissue_mask(slide, xres) + mask_path = Path(MASK_DEST, f"{Path(slide_path).stem}.tiff") + mask_path.parent.mkdir(exist_ok=True, parents=True) + write_big_tiff(mask, path=mask_path, mpp_x=xres, mpp_y=yres) + + + +def main() -> None: + slides = list(Path(SLIDES_PATH).rglob("*.mrxs")) + # process_slide(slides[0]) + process_items(slides, process_item=process_slide) + +if __name__ == "__main__": + main() diff --git a/preprocessing/tiler.py b/preprocessing/tiler.py new file mode 100644 index 0000000..f1b1561 --- /dev/null +++ b/preprocessing/tiler.py @@ -0,0 +1,115 @@ +from pathlib import Path + +import mlflow +import ray +from rationai.tiling import tiling +from rationai.tiling.modules.masks import PyvipsMask +from rationai.tiling.modules.tile_sources import OpenSlideTileSource +from rationai.tiling.typing import TiledSlideMetadata, TileMetadata +from rationai.tiling.writers import save_mlflow_dataset +from sklearn.model_selection import train_test_split + + +SLIDES_PATH = "/mnt/data/MOU/prostate/tile_level_annotations/" +TISSUE_MASKS_PATH = "./mask/tissue_masks" +TISSUE_MASKS_PATH = "/home/jovyan/staining/demo_data/masks" + + +# level avg_mpp_x avg_mpp_y +# 0 0.233876 0.234331 +# 1 0.467751 0.468661 +# 2 0.935503 0.937323 +# 3 1.871006 1.874646 +# 4 3.742012 3.749291 +# 5 7.484024 7.498583 +# 6 14.968047 14.997165 +# 7 29.936095 29.994331 +# 8 59.872189 59.988661 +# 9 119.744379 119.97732 +SlideMPP = 0.23 + +source = OpenSlideTileSource(mpp=SlideMPP, tile_extent=512, stride=256) + + +MIN_TISSUE_PERCENTAGE = 0.0 + + +class TissueMask(PyvipsMask[TileMetadata]): + def forward_tile( + self, tile_labels: TileMetadata, class_overlaps: dict[int, float] + ) -> TileMetadata | None: + tissue = 1.0 - class_overlaps.get(0, 0) + if tissue <= MIN_TISSUE_PERCENTAGE: + return None + return tile_labels + + +tissue_mask = TissueMask( + tile_extent=source.tile_extent, absolute_roi_extent=256, relative_roi_offset=0 +) + + +@ray.remote +def handler(slide_path: Path) -> TiledSlideMetadata: + slide, tiles = source(slide_path) + + tissue_mask_path = Path(TISSUE_MASKS_PATH, slide_path.name[:-5] + ".tiff") + tiles = tissue_mask(tissue_mask_path, slide.extent, tiles) + + return slide, tiles + + + + +def main() -> None: + slides = list(Path(SLIDES_PATH).rglob("*.mrxs")) + + + slides, test_slides = train_test_split(slides, test_size=0.2) + train_slides, val_slides = train_test_split(slides, test_size=0.1) + + train_slides_df, train_tiles_df = tiling(slides=train_slides, handler=handler) + val_slides_df, val_tiles_df = tiling(slides=list(val_slides), handler=handler) + test_slides_df, test_tiles_df = tiling(slides=list(test_slides), handler=handler) + + train_slides_df.to_csv("./data/datasets/train_slides.csv", index=False) + train_tiles_df.to_csv("./data/datasets/train_tiles.csv", index=False) + + val_slides_df.to_csv("./data/datasets/val_slides.csv", index=False) + val_tiles_df.to_csv("./data/datasets/val_tiles.csv", index=False) + + test_slides_df.to_csv("./data/datasets/test_slides.csv", index=False) + test_tiles_df.to_csv("./data/datasets/test_tiles.csv", index=False) + + mlflow.set_experiment(experiment_name="Stain-Normalization") + with mlflow.start_run(run_name="Stain Normalization Dataset") as _: + save_mlflow_dataset( + slides=train_slides_df, + tiles=train_tiles_df, + dataset_name="Stain Normalization - train", + ) + save_mlflow_dataset( + slides=val_slides_df, + tiles=val_tiles_df, + dataset_name="Stain Normalization - val", + ) + save_mlflow_dataset( + slides=test_slides_df, + tiles=test_tiles_df, + dataset_name="Stain Normalization - test", + ) + + +if __name__ == "__main__": + #main() + slides = [("/home/jovyan/staining/demo_data/P-2016_0077-08-1_hed_h0.6_e1.5.tiff")] + train_slides_df, train_tiles_df = tiling(slides=slides, handler=handler) + + mlflow.set_experiment(experiment_name="Stain-Normalization") + with mlflow.start_run(run_name="P-2016_0077-08-1_hed all tissue tiles") as _: + save_mlflow_dataset( + slides=train_slides_df, + tiles=train_tiles_df, + dataset_name="P-2016_0077-08-1_hed", + ) + diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..22faeae --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,45 @@ +[project] +name = "Stain Normalization" +version = "0.1.0" +authors = [{name = "Adam Lopatka"}] +requires-python = "3.12.5" +readme = "README.md" +license = { file = "LICENSE" } +dependencies = [ + "lightning>=2.0.0", + "albumentations>=1.4.14", + "torchmetrics>=1.4.14", + "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", + "openslide-bin>=4.0.0.6", + "rationai-staining @ git+https://git@gitlab.ics.muni.cz/rationai/digital-pathology/libraries/staining.git", +] + +[tool.pdm.dev-dependencies] +dev = ["mypy", "pre-commit", "ruff"] + +[tool.pdm.scripts] +mask_generate = "python preprocessing/mask_generator.py" +tiler = "python preprocessing/tiler.py" + +train = "python -m stain_normalization mode=fit" +validate = "python -m stain_normalization mode=validate" +test = "python -m stain_normalization mode=test" +predict = "python -m stain_normalization mode=predict" +l = { composite = ["lint", "format", "mypy"] } +lint = "ruff check --fix" +format = "ruff format" +mypy = "mypy ." +# post_install = { composite = [ +# "pre-commit autoupdate", +# "pre-commit install", +# "pre-commit install --hook-type commit-msg", +#] } + +[tool.pdm] +distribution = false diff --git a/stain_normalization/__init__.py b/stain_normalization/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/stain_normalization/__main__.py b/stain_normalization/__main__.py new file mode 100644 index 0000000..f9b86dd --- /dev/null +++ b/stain_normalization/__main__.py @@ -0,0 +1,34 @@ +from random import randint + +import hydra +from lightning import seed_everything +from lightning.pytorch.loggers import Logger +from omegaconf import DictConfig, OmegaConf +from rationai.mlkit import Trainer, autolog + +from stain_normalization.data import DataModule +from stain_normalization.stain_normalization_model import StainNormalizationModel + + +OmegaConf.register_new_resolver( + "random_seed", lambda: randint(0, 2**31), use_cache=True +) + + +@hydra.main(config_path="../configs", config_name="default", version_base=None) +@autolog +def main(config: DictConfig, logger: Logger | None) -> None: + seed_everything(config.seed, workers=True) + + data = hydra.utils.instantiate( + config.data, + _recursive_=False, # to avoid instantiating all the datasets + _target_=DataModule, + ) + model = StainNormalizationModel() + trainer = hydra.utils.instantiate(config.trainer, _target_=Trainer, logger=logger) + getattr(trainer, config.mode)(model, datamodule=data, ckpt_path=config.checkpoint) + + +if __name__ == "__main__": + main() # pylint: disable=no-value-for-parameter diff --git a/stain_normalization/analysis/__init__.py b/stain_normalization/analysis/__init__.py new file mode 100644 index 0000000..33f4d99 --- /dev/null +++ b/stain_normalization/analysis/__init__.py @@ -0,0 +1,20 @@ +from .analyzer import StainAnalyzer +from .report import ( + REPORT_METRICS, + aggregate_average, + aggregate_rms, + aggregate_max, + aggregate_penalized_mean, + aggregate_power_mean, +) + + +__all__ = [ + "StainAnalyzer", + "REPORT_METRICS", + "aggregate_average", + "aggregate_rms", + "aggregate_max", + "aggregate_penalized_mean", + "aggregate_power_mean", +] diff --git a/stain_normalization/analysis/analyzer.py b/stain_normalization/analysis/analyzer.py new file mode 100644 index 0000000..9b0b6c5 --- /dev/null +++ b/stain_normalization/analysis/analyzer.py @@ -0,0 +1,189 @@ +"""Stain normalization analysis tool. + +Compares images using selected metrics, accumulates results, +and provides statistics and saving. +""" +from pathlib import Path +from typing import Union, Optional + +import numpy as np +import pandas as pd +from rationai.staining import estimate_stain_vectors +from skimage.metrics import structural_similarity + +from ..metrics.vector_metrics import compare_vectors +from ..metrics.image_metrics import compute_nmi, compute_pcc, compute_lab_brightness_psnr + + +class StainAnalyzer: + """Compare images using selected metrics, accumulate results. + + Args: + reference: Optional fixed reference image. If given, stain vectors + and NMI are pre-computed once. + metrics: Which metrics to compute. None = all. + """ + + AVAILABLE_METRICS = ['vectors', 'ssim', 'pcc', 'nmi', 'lab_psnr'] + PAIRED_ONLY = {'ssim', 'pcc', 'lab_psnr'} + + def __init__( + self, + reference: Optional[np.ndarray] = None, + metrics: Optional[list[str]] = None, + ): + self.metrics = metrics or self.AVAILABLE_METRICS + self._results: list[dict] = [] + + for m in self.metrics: + if m not in self.AVAILABLE_METRICS: + raise ValueError(f"Unknown metric '{m}'. Available: {self.AVAILABLE_METRICS}") + + # if we have reference image, precompute stain vectors and NMI + self._ref_img = reference + self._ref_vectors = None + self._ref_nmi = None + + if reference is not None: + if 'vectors' in self.metrics: + self._ref_vectors = estimate_stain_vectors(reference) + if 'nmi' in self.metrics: + self._ref_nmi = compute_nmi(reference) + + @property + def results(self) -> pd.DataFrame: + """Accumulated comparison results as DataFrame.""" + return pd.DataFrame(self._results) + + def clear(self) -> None: + """Clear accumulated results.""" + self._results.clear() + + def compare( + self, + image: np.ndarray, + image_id: Optional[str] = None, + reference: Optional[np.ndarray] = None, + ) -> dict: + """Compare an image against the reference and store the result. + + Args: + image: Image to compare. + image_id: Optional identifier for this comparison. + reference: Override reference image for this call. + + Returns: + Dict with metric results. + """ + + ref_img = reference if reference is not None else self._ref_img + if ref_img is None: + raise ValueError("No reference image. Pass one to __init__ or to compare().") + + is_paired = reference is not None + + if reference is not None: + ref_vectors = ( + estimate_stain_vectors(ref_img) + if 'vectors' in self.metrics else None + ) + ref_nmi = compute_nmi(ref_img) if 'nmi' in self.metrics else None + else: + ref_vectors = self._ref_vectors + ref_nmi = self._ref_nmi + + result = {'id': image_id} if image_id is not None else {} + + if 'vectors' in self.metrics: + img_vectors = estimate_stain_vectors(image) + vec_result = compare_vectors(ref_vectors, img_vectors) + result.update(vec_result) + img_vectors_paired = ( + img_vectors[[1, 0]] if vec_result['was_swapped'] else img_vectors + ) + for j, val in enumerate(ref_vectors.flatten()): + result[f'ref_vec_{j}'] = float(val) + for j, val in enumerate(img_vectors_paired.flatten()): + result[f'img_vec_{j}'] = float(val) + + if 'ssim' in self.metrics and is_paired: + result['ssim'] = float(structural_similarity( + ref_img, image, channel_axis=-1, data_range=255, + )) + + if 'pcc' in self.metrics and is_paired: + result['pcc'] = compute_pcc(ref_img, image) + + if 'nmi' in self.metrics: + img_nmi = compute_nmi(image) + result['ref_nmi'] = ref_nmi + result['nmi'] = img_nmi + result['nmi_diff'] = img_nmi - ref_nmi + + if 'lab_psnr' in self.metrics and is_paired: + result['lab_brightness_psnr'] = compute_lab_brightness_psnr(ref_img, image) + + self._results.append(result) + return result + + # --- Statistics --- + + def get_statistics(self) -> pd.DataFrame: + """Summary statistics for accumulated results. + + Returns: + DataFrame with mean, std, min, max, percentiles. + """ + df = self.results + numeric_cols = df.select_dtypes(include=[np.number]).columns + return df[numeric_cols].describe(percentiles=[0.05, 0.25, 0.5, 0.75, 0.95]) + + def get_baseline_ranges( + self, + percentile_low: float = 5, + percentile_high: float = 95, + ) -> dict: + """Get acceptable value ranges for each metric. + + Args: + percentile_low: Lower percentile bound. Defaults to 5. + percentile_high: Upper percentile bound. Defaults to 95. + + Returns: + Dict mapping metric names to (low, high) tuples. + """ + df = self.results + numeric_cols = df.select_dtypes(include=[np.number]).columns + return { + col: ( + float(df[col].quantile(percentile_low / 100)), + float(df[col].quantile(percentile_high / 100)), + ) + for col in numeric_cols + } + + def save_csv(self, output_dir: Union[str, Path]) -> Path: + """Save accumulated results and statistics as CSV files. + + Args: + output_dir: Directory to save files into. + + Returns: + Path to the output directory. + """ + output_dir = Path(output_dir) + output_dir.mkdir(parents=True, exist_ok=True) + + self.results.to_csv(output_dir / "results.csv", index=True) + self.get_statistics().to_csv(output_dir / "statistics.csv") + + ranges = self.get_baseline_ranges() + with open(output_dir / "baseline_ranges.txt", 'w') as f: + f.write("Baseline Metric Ranges (5th-95th percentile)\n") + f.write("=" * 55 + "\n\n") + for metric, (low, high) in sorted(ranges.items()): + f.write(f"{metric:30s}: [{low:8.4f}, {high:8.4f}]\n") + + return output_dir + + diff --git a/stain_normalization/callbacks/__init__.py b/stain_normalization/callbacks/__init__.py new file mode 100644 index 0000000..4aeaf95 --- /dev/null +++ b/stain_normalization/callbacks/__init__.py @@ -0,0 +1,7 @@ +from stain_normalization.callbacks._base import NormalizationCallback +from stain_normalization.callbacks.analysis_export import AnalysisExport +from stain_normalization.callbacks.tiles_export import TilesExport +from stain_normalization.callbacks.wsi_assembler import WSIAssembler + + +__all__ = ["AnalysisExport", "NormalizationCallback", "TilesExport", "WSIAssembler"] diff --git a/stain_normalization/callbacks/_base.py b/stain_normalization/callbacks/_base.py new file mode 100644 index 0000000..4dba6cc --- /dev/null +++ b/stain_normalization/callbacks/_base.py @@ -0,0 +1,25 @@ +import numpy as np +import torch +from lightning import Callback +from omegaconf import DictConfig + + +class NormalizationCallback(Callback): + """Base callback providing denormalization helpers for model outputs.""" + + def __init__(self, normalization_config: DictConfig) -> None: + super().__init__() + self.mean = torch.tensor(normalization_config.mean).view(3, 1, 1) + self.std = torch.tensor(normalization_config.std).view(3, 1, 1) + + def denormalize(self, tensor: torch.Tensor) -> torch.Tensor: + """Reverse normalization: tensor → [0, 1] float.""" + device = tensor.device + return (tensor * self.std.to(device)) + self.mean.to(device) + + def tensor_to_image(self, tensor: torch.Tensor) -> np.ndarray: + """Convert model output tensor to uint8 HWC numpy array.""" + return ( + self.denormalize(tensor).clamp(0, 1).mul(255).byte() + .permute(1, 2, 0).cpu().numpy() + ) diff --git a/stain_normalization/callbacks/analysis_export.py b/stain_normalization/callbacks/analysis_export.py new file mode 100644 index 0000000..ad0f603 --- /dev/null +++ b/stain_normalization/callbacks/analysis_export.py @@ -0,0 +1,55 @@ +from pathlib import Path + +import mlflow +import torch +from lightning import LightningModule, Trainer +from omegaconf import DictConfig + +from ..analysis.analyzer import StainAnalyzer +from ._base import NormalizationCallback + + +class AnalysisExport(NormalizationCallback): + """Exports analysis metrics during testing.""" + + def __init__(self, output_dir: str | Path, normalization_config: DictConfig) -> None: + super().__init__(normalization_config) + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + self.mod_analyzer = StainAnalyzer() + self.pred_analyzer = StainAnalyzer() + + def on_test_batch_end( + self, + trainer: Trainer, + pl_module: LightningModule, + outputs: list[torch.Tensor], + batch: tuple[torch.Tensor, list[dict]], + batch_idx: int, + dataloader_idx: int = 0, + ) -> None: + """Computes metrics for each sample and accumulates results.""" + for b in range(len(outputs)): + original_img = batch[1][b]["original_image"].astype("uint8") + modified_img = (batch[1][b]["modified_image"] * 255).astype("uint8") + predicted_img = self.tensor_to_image(outputs[b]) + + self.mod_analyzer.compare(modified_img, reference=original_img) + self.pred_analyzer.compare(predicted_img, reference=original_img) + + def on_test_end( + self, + trainer: Trainer, + pl_module: LightningModule, + ) -> None: + """Saves collected metrics as CSV files and logs them as mlflow artifacts.""" + metrics_dir = self.output_dir / "analysis_metrics" + + mod_dir = self.mod_analyzer.save_csv(metrics_dir / "modified") + pred_dir = self.pred_analyzer.save_csv(metrics_dir / "predicted") + + for f in mod_dir.glob("*"): + mlflow.log_artifact(str(f), artifact_path="analysis_metrics/modified") + for f in pred_dir.glob("*"): + mlflow.log_artifact(str(f), artifact_path="analysis_metrics/predicted") diff --git a/stain_normalization/callbacks/tiles_export.py b/stain_normalization/callbacks/tiles_export.py new file mode 100644 index 0000000..bfd52f9 --- /dev/null +++ b/stain_normalization/callbacks/tiles_export.py @@ -0,0 +1,70 @@ +from pathlib import Path + +import torch +from lightning import LightningModule, Trainer +from omegaconf import DictConfig +from PIL import Image + +from ._base import NormalizationCallback + + +class TilesExport(NormalizationCallback): + def __init__( + self, output_dir: str | Path, normalization_config: DictConfig + ) -> None: + super().__init__(normalization_config) + self.output_dir = Path(output_dir) + self.output_dir.mkdir(parents=True, exist_ok=True) + + def tensor_to_image(self, tensor: torch.Tensor) -> Image.Image: + return Image.fromarray(super().tensor_to_image(tensor)) + + def on_test_batch_end( + self, + trainer: Trainer, + pl_module: LightningModule, + outputs: list[torch.Tensor], + batch: tuple[torch.Tensor, list], + batch_idx: int, + dataloader_idx: int = 0, + ) -> None: + _, data = batch + for b in range(len(outputs)): + slide_name = data[b]["slide_name"] + xy = data[b]["xy"] + + slide_dir = self.output_dir / slide_name + slide_dir.mkdir(parents=True, exist_ok=True) + + self.tensor_to_image(outputs[b]).save( + slide_dir / f"{xy}_predicted.png" + ) + + original_image = Image.fromarray( + data[b]["original_image"].astype("uint8") + ) + original_image.save(slide_dir / f"{xy}_original.png") + + modified_image = Image.fromarray( + (data[b]["modified_image"] * 255).astype("uint8") + ) + modified_image.save(slide_dir / f"{xy}_modified.png") + + def on_predict_batch_end( + self, + trainer: Trainer, + pl_module: LightningModule, + outputs: list[torch.Tensor], + batch: tuple[torch.Tensor, list], + batch_idx: int, + dataloader_idx: int = 0, + ) -> None: + _, data = batch + for b in range(len(outputs)): + slide_name = data[b]["slide_name"] + xy = data[b]["xy"] + + slide_dir = self.output_dir / slide_name + slide_dir.mkdir(parents=True, exist_ok=True) + + self.tensor_to_image(outputs[b]).save(slide_dir / f"{xy}.png") diff --git a/stain_normalization/callbacks/wsi_assembler.py b/stain_normalization/callbacks/wsi_assembler.py new file mode 100644 index 0000000..31a0745 --- /dev/null +++ b/stain_normalization/callbacks/wsi_assembler.py @@ -0,0 +1,232 @@ +"""Callback for assembling predicted tiles into whole-slide pyramid TIFFs.""" + +# DEV: Key design decisions: +# DEV: - One slide buffer at a time — slides are processed sequentially by ConcatDataset, +# DEV: so we open a buffer on the first tile of a slide and close it when the slide changes. +# DEV: This keeps peak temp disk at ~one slide (~5-10 GB at level 0 with sparse tissue), +# DEV: regardless of how many slides are in the predict set. +# DEV: - Buffers are zero-initialized (sparse mmap) — only pages where tiles actually land +# DEV: get written to disk. White fill for untouched background is applied by pyvips at save. +# DEV: - Running average (uint8 result + uint8 count) instead of float32 sum accumulator. +# DEV: Cuts temp disk ~4x vs the sum approach. Float math is done only per-tile (512x512). +# DEV: - Slide transition is detected per-tile inside the batch loop, not per-batch, +# DEV: so transition batches (last tiles of A + first tiles of B) are handled correctly. + +import tempfile +import traceback +from dataclasses import dataclass +from pathlib import Path + +import numpy as np +import torch +from lightning import LightningModule, Trainer +from omegaconf import DictConfig +from PIL.ImageCms import createProfile + +from ._base import NormalizationCallback + + +def _srgb_icc_bytes() -> bytes: + """Generate sRGB ICC profile bytes for embedding in TIFFs.""" + raw = createProfile("sRGB") + # Pillow < 10: .tobuffer(), Pillow >= 10: .tobytes() + try: + return raw.tobuffer() + except AttributeError: + from PIL.ImageCms import ImageCmsProfile + return ImageCmsProfile(raw).tobytes() + + +@dataclass +class _SlideMeta: + path: str + level: int + extent_x: int + extent_y: int + tile_extent_x: int + tile_extent_y: int + mpp_x: float + mpp_y: float + + +@dataclass +class _SlideBuffers: + meta: _SlideMeta + temp_dir: tempfile.TemporaryDirectory + result_buffer: np.memmap + count_buffer: np.memmap + + +class WSIAssembler(NormalizationCallback): + """Assembles predicted tiles back into whole-slide pyramid TIFFs.""" + + def __init__( + self, + output_dir: str | Path, + normalization_config: DictConfig, + temp_dir: str | Path | None = None, + ) -> None: + super().__init__(normalization_config) + self.output_dir = Path(output_dir) + self.temp_dir = str(temp_dir) if temp_dir else None + self._slide_meta: dict[str, _SlideMeta] = {} + self._active: _SlideBuffers | None = None + self._active_name: str | None = None + + def on_predict_start(self, trainer: Trainer, pl_module: LightningModule) -> None: + self.output_dir.mkdir(parents=True, exist_ok=True) + slides_df = trainer.datamodule.predict.slides + + # Cache metadata only — buffers are opened lazily per slide + for _, row in slides_df.iterrows(): + name = Path(row.path).stem + self._slide_meta[name] = _SlideMeta( + path=row.path, + level=int(row.level), + extent_x=int(row.extent_x), + extent_y=int(row.extent_y), + tile_extent_x=int(row.tile_extent_x), + tile_extent_y=int(row.tile_extent_y), + mpp_x=float(row.mpp_x), + mpp_y=float(row.mpp_y), + ) + + def _open_slide(self, slide_name: str) -> None: + """Allocate memmap buffers for one slide.""" + meta = self._slide_meta[slide_name] + h, w = meta.extent_y, meta.extent_x + + tmp = tempfile.TemporaryDirectory( + prefix=f"wsi_{slide_name}_", dir=self.temp_dir + ) + result_buf = np.memmap( + Path(tmp.name) / "result.raw", + dtype=np.uint8, + mode="w+", + shape=(h, w, 3), + ) + count_buf = np.memmap( + Path(tmp.name) / "count.raw", + dtype=np.uint8, + mode="w+", + shape=(h, w), + ) + + self._active = _SlideBuffers( + meta=meta, + temp_dir=tmp, + result_buffer=result_buf, + count_buffer=count_buf, + ) + self._active_name = slide_name + + def _close_slide(self) -> None: + """Save and free the currently active slide.""" + if self._active is None: + return + try: + self._save_slide(self._active_name, self._active) + except Exception: + traceback.print_exc() + finally: + del self._active.result_buffer + del self._active.count_buffer + self._active.temp_dir.cleanup() + self._active = None + self._active_name = None + + def on_predict_batch_end( + self, + trainer: Trainer, + pl_module: LightningModule, + outputs: list[torch.Tensor], + batch: tuple[torch.Tensor, list[dict]], + batch_idx: int, + dataloader_idx: int = 0, + ) -> None: + for b in range(len(outputs)): + metadata = batch[1][b] + slide_name = metadata["slide_name"] + + if slide_name not in self._slide_meta: + print(f"Unknown slide '{slide_name}', skipping tile.") + continue + + if slide_name != self._active_name: + if self._active_name is not None: + print(f"Slide transition: {self._active_name} → {slide_name}") + self._close_slide() + self._open_slide(slide_name) + + tile = self.tensor_to_image(outputs[b]) + x, y = (int(v) for v in metadata["xy"].split("_")) + self._place_tile(tile, x, y) + + def _place_tile(self, tile: np.ndarray, x: int, y: int) -> None: + """Place a predicted tile into the active slide buffer with overlap averaging.""" + sb = self._active + ex, ey = sb.meta.extent_x, sb.meta.extent_y + + h, w = min(tile.shape[0], ey - y), min(tile.shape[1], ex - x) + tile = tile[:h, :w] + + region = sb.result_buffer[y:y + h, x:x + w] + count = sb.count_buffer[y:y + h, x:x + w] + + # Running average: avg = (old * n + new) / (n + 1) + overlap = count > 0 + if overlap.any(): + n = count[:, :, np.newaxis].astype(np.float32) + blended = np.where( + overlap[:, :, np.newaxis], + (region.astype(np.float32) * n + tile) / (n + 1), + tile, + ) + sb.result_buffer[y:y + h, x:x + w] = np.clip(blended, 0, 255).astype(np.uint8) + else: + sb.result_buffer[y:y + h, x:x + w] = tile + + sb.count_buffer[y:y + h, x:x + w] = count + 1 + + def on_predict_end(self, trainer: Trainer, pl_module: LightningModule) -> None: + self._close_slide() + self._slide_meta.clear() + + def _save_slide(self, slide_name: str, sb: _SlideBuffers) -> None: + # Imported here — module-level import causes OpenSlide segfault (libtiff conflict). + import pyvips + + meta = sb.meta + sb.result_buffer.flush() + sb.count_buffer.flush() + + result_path = Path(sb.temp_dir.name) / "result.raw" + count_path = Path(sb.temp_dir.name) / "count.raw" + + result_img = pyvips.Image.rawload(str(result_path), meta.extent_x, meta.extent_y, 3) + result_img = result_img.copy(interpretation=pyvips.Interpretation.SRGB) + + count_img = pyvips.Image.rawload(str(count_path), meta.extent_x, meta.extent_y, 1) + mask = count_img > 0 + # add white background for untouched areas (count=0) + white = (pyvips.Image.black(meta.extent_x, meta.extent_y, bands=3) + 255).cast( + pyvips.BandFormat.UCHAR + ) + final_img = mask.ifthenelse(result_img, white) + + final_img.set_type( + pyvips.GValue.blob_type, "icc-profile-data", _srgb_icc_bytes() + ) + + output_path = self.output_dir / f"{slide_name}_norm.tiff" + final_img.tiffsave( + str(output_path), + bigtiff=True, + compression=pyvips.enums.ForeignTiffCompression.DEFLATE, + tile=True, + tile_width=512, + tile_height=512, + pyramid=True, + xres=1000.0 / meta.mpp_x, + yres=1000.0 / meta.mpp_y, + ) 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..bd462bf --- /dev/null +++ b/stain_normalization/data/data_module.py @@ -0,0 +1,65 @@ +from collections.abc import Iterable + +from hydra.utils import instantiate +from lightning import LightningDataModule +from omegaconf import DictConfig +from torch.utils.data import DataLoader + +from stain_normalization.data.utils import collate_fn +from stain_normalization.type_aliases import Batch, PredictBatch + + +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..9fc6650 --- /dev/null +++ b/stain_normalization/data/datasets/predict_dataset.py @@ -0,0 +1,70 @@ +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]): + 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, { + "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..ef094f9 --- /dev/null +++ b/stain_normalization/data/datasets/test_dataset.py @@ -0,0 +1,85 @@ +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]): + 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 + 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..1d46afe --- /dev/null +++ b/stain_normalization/data/datasets/train_dataset.py @@ -0,0 +1,72 @@ +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]): + 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 = self.slide_tiles[idx] + + # Create "wrong" image to use as input. Outputs image in float 0-1 + modified_image = self.modify(image=original_image)["image"] + original_image = original_image / 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 diff --git a/stain_normalization/data/modification/__init__.py b/stain_normalization/data/modification/__init__.py new file mode 100644 index 0000000..0e01429 --- /dev/null +++ b/stain_normalization/data/modification/__init__.py @@ -0,0 +1,16 @@ +from stain_normalization.data.modification.combiend_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.hvs_modification import HVSModification + + +__all__ = [ + "CombinedModifications", + "ExposureAdjustment", + "HEDFactor", + "HVSModification", +] diff --git a/stain_normalization/data/modification/combiend_modification.py b/stain_normalization/data/modification/combiend_modification.py new file mode 100644 index 0000000..6eebbc3 --- /dev/null +++ b/stain_normalization/data/modification/combiend_modification.py @@ -0,0 +1,53 @@ +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): + """Apply combined modifications to the H&E channels in HED color space. + + Attributes: + intensity_range: Range of multiplicative factors to scale stain channel intensities. + brightness_range: Range for gamma correction to simulate brightness shift. + always_apply: Whether the transformation should always be applied. + p: Probability of applying the transformation. + """ + + def __init__( + self, + intensity_range: tuple[float, float] = (0.4, 1.5), + brightness_range: tuple[float, float] = (-0.4, 0.4), + always_apply: bool = True, + p: float = 1.0, + ): + super().__init__(always_apply, p) + self.intensity_range = intensity_range + self.brightness_range = brightness_range + + def apply(self, img: NDArray, **params) -> NDArray: + """Apply intensity 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 modified_rgb + + def modify_channel(self, channel: NDArray[np.float32]) -> NDArray[np.float32]: + intensity_scale = np.random.uniform(*self.intensity_range) + channel = channel * intensity_scale + brightness_shift = np.random.uniform(*self.brightness_range) + channel = exposure.adjust_gamma(channel, gamma=1 + brightness_shift) + return np.clip(channel, 0, 1) diff --git a/stain_normalization/data/modification/exposure_adjustment.py b/stain_normalization/data/modification/exposure_adjustment.py new file mode 100644 index 0000000..e1ff4c2 --- /dev/null +++ b/stain_normalization/data/modification/exposure_adjustment.py @@ -0,0 +1,43 @@ +import numpy as np +from albumentations import ImageOnlyTransform +from numpy.typing import NDArray + + +class ExposureAdjustment(ImageOnlyTransform): + """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. + always_apply: Whether this transformation should always be applied. + p: Probability of applying the transformation. + """ + + def __init__( + self, + brightness_range: tuple[float, float] = (0.8, 1.2), + always_apply: bool = True, + p: float = 1, + ): + super().__init__(always_apply, p) + self.brightness_range = brightness_range + + def apply(self, img: NDArray, **params) -> NDArray: + """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 = img.astype(np.float32) + if img.max() > 1.0: + img = img / 255.0 + img = np.clip(img * brightness_factor, 0.0, 1.0) + + return img diff --git a/stain_normalization/data/modification/hed_factor.py b/stain_normalization/data/modification/hed_factor.py new file mode 100644 index 0000000..a5031c9 --- /dev/null +++ b/stain_normalization/data/modification/hed_factor.py @@ -0,0 +1,48 @@ +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): + """Adjust the intensity of Hematoxylin and Eosin stains in HED color space. + + Attributes: + h_intensity_range: Range for the random intensity adjustment factor for the Hematoxylin channel. + e_intensity_range: Range for the random intensity adjustment factor for the Eosin channel. + always_apply: Whether this transformation should always be applied. + 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), + always_apply: bool = True, + p: float = 1.0, + ): + super().__init__(always_apply, p) + self.h_range = h_range + self.e_range = e_range + + def apply(self, img: NDArray, **params) -> NDArray: + """Apply the modification to the image. + + Args: + img: Image to which the transformation will be applied. + params: Additional parameters. + + Returns: + RGB image with modified 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 = np.clip(hed_image[:, :, 0] * h_factor, 0, 1) + e = np.clip(hed_image[:, :, 1] * e_factor, 0, 1) + d = hed_image[:, :, 2] # DAB channel unchanged + modified_rgb = combine_stains(np.stack((h, e, d), axis=-1), rgb_from_hed) + + return modified_rgb diff --git a/stain_normalization/data/modification/hvs_modification.py b/stain_normalization/data/modification/hvs_modification.py new file mode 100644 index 0000000..53e4697 --- /dev/null +++ b/stain_normalization/data/modification/hvs_modification.py @@ -0,0 +1,55 @@ +import numpy as np +from albumentations import ImageOnlyTransform +from numpy.typing import NDArray +from skimage.color import hsv2rgb, rgb2hsv + + +class HVSModification(ImageOnlyTransform): + """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. + always_apply: Whether the transformation is always applied. + 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), + always_apply: bool = True, + p: float = 1.0, + ): + super().__init__(always_apply, p) + self.hue_shift_range = hue_shift_range + self.saturation_range = saturation_range + self.value_range = value_range + + def apply(self, img: NDArray, **params) -> NDArray: + """Apply the modifications to an image. + + Args: + img: Image to which the transformation will be applied. + params: Additional parameters. + + Returns: + RGB image with HVS modifiedications 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 diff --git a/stain_normalization/data/utils/__init__.py b/stain_normalization/data/utils/__init__.py new file mode 100644 index 0000000..dca47bc --- /dev/null +++ b/stain_normalization/data/utils/__init__.py @@ -0,0 +1,4 @@ +from stain_normalization.data.utils.collate_fn import collate_fn + + +__all__ = ["collate_fn"] diff --git a/stain_normalization/data/utils/collate_fn.py b/stain_normalization/data/utils/collate_fn.py new file mode 100644 index 0000000..3455e79 --- /dev/null +++ b/stain_normalization/data/utils/collate_fn.py @@ -0,0 +1,8 @@ +from typing import Any + +import torch +from torch import Tensor + + +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] diff --git a/stain_normalization/metrics/__init__.py b/stain_normalization/metrics/__init__.py new file mode 100644 index 0000000..0f4f8f3 --- /dev/null +++ b/stain_normalization/metrics/__init__.py @@ -0,0 +1,16 @@ +from .image_metrics import ( + compute_lab_brightness_psnr, + compute_nmi, + compute_pcc, +) +from .vector_metrics import ( + compare_vectors, +) + + +__all__ = [ + "compare_vectors", + "compute_lab_brightness_psnr", + "compute_nmi", + "compute_pcc", +] diff --git a/stain_normalization/metrics/image_metrics.py b/stain_normalization/metrics/image_metrics.py new file mode 100644 index 0000000..28f806d --- /dev/null +++ b/stain_normalization/metrics/image_metrics.py @@ -0,0 +1,56 @@ +import numpy as np +from skimage.color import rgb2lab +from skimage.metrics import peak_signal_noise_ratio + + +def compute_nmi(img: np.ndarray) -> float: + """Normalized Median Intensity — measures relative brightness of an image. + + Args: + img: RGB image. + + Returns: + Ratio of median to 95th percentile intensity. + """ + avg_rgb = img.mean(axis=2) + median_val = np.median(avg_rgb) + p95_val = np.percentile(avg_rgb, 95) + + if p95_val == 0: + return 0.0 + + return float(median_val / p95_val) + + +def compute_pcc(img1: np.ndarray, img2: np.ndarray) -> float: + """Pearson Correlation Coefficient between two images. + + Args: + img1: First image. + img2: Second image. + + Returns: + PCC value, or 0.0 if either image has zero variance. + """ + img1_flat = img1.flatten().astype(np.float64) + img2_flat = img2.flatten().astype(np.float64) + + if np.std(img1_flat) == 0 or np.std(img2_flat) == 0: + return 0.0 + + return float(np.corrcoef(img1_flat, img2_flat)[0, 1]) + + +def compute_lab_brightness_psnr(img1: np.ndarray, img2: np.ndarray) -> float: + """PSNR on the L* channel in Lab color space. + + Args: + img1: First RGB image. + img2: Second RGB image. + + Returns: + PSNR in dB on the lightness channel. + """ + lab1 = rgb2lab(img1.astype(np.float32) / 255.0) + lab2 = rgb2lab(img2.astype(np.float32) / 255.0) + return float(peak_signal_noise_ratio(lab1[:, :, 0], lab2[:, :, 0], data_range=100.0)) diff --git a/stain_normalization/metrics/vector_metrics.py b/stain_normalization/metrics/vector_metrics.py new file mode 100644 index 0000000..1348f72 --- /dev/null +++ b/stain_normalization/metrics/vector_metrics.py @@ -0,0 +1,75 @@ +import numpy as np +from skimage.color import rgb2lab + + +def _od_to_lab(od_vector: np.ndarray) -> np.ndarray: + """Convert optical density vector to Lab color. + + Args: + od_vector: Stain vector in optical density space. + + Returns: + Color in Lab space as [L, a, b]. + """ + # Calculate RGB from optical density by reversing the process in estimate_stain_vectors. + # default i0=240 (transmitted light intensity) + rgb = np.clip(240 * np.exp(-od_vector), 0, 255) / 255.0 + return rgb2lab(rgb.reshape(1, 1, 3)).flatten() + + +def delta_e76(lab1: np.ndarray, lab2: np.ndarray) -> float: + """CIE76 Delta E with dL=0 (chromaticity only). + + CIE76 Delta E is sqrt(dL^2 + da^2 + db^2). We set dL=0 because we compare + dyes not colors, so brightness is irrelevant. + + Args: + lab1: First color in Lab space. + lab2: Second color in Lab space. + + Returns: + sqrt(da^2 + db^2). + """ + da = lab1[1] - lab2[1] + db = lab1[2] - lab2[2] + return float(np.sqrt(da**2 + db**2)) + + +def compare_vectors( + vecs1: np.ndarray, + vecs2: np.ndarray, +) -> dict: + """Compare two sets of stain vectors in Lab chromaticity space. + + Args: + vecs1: Stain vectors from the first image in OD space. + vecs2: Stain vectors from the second image in OD space. + + Returns: + Dict with d_hematoxylin, d_eosin and was_swapped. + Returns NaN distances if either vector set contains NaN. + """ + if np.any(np.isnan(vecs1)) or np.any(np.isnan(vecs2)): + return { + 'd_hematoxylin': float('nan'), 'd_eosin': float('nan'), + 'was_swapped': False, + } + + + sim_straight = np.dot(vecs1[0], vecs2[0]) + np.dot(vecs1[1], vecs2[1]) + sim_swapped = np.dot(vecs1[0], vecs2[1]) + np.dot(vecs1[1], vecs2[0]) + was_swapped = sim_swapped > sim_straight + vecs2_paired = vecs2[[1, 0]] if was_swapped else vecs2 + + lab1_a = _od_to_lab(vecs1[0]) + lab1_b = _od_to_lab(vecs1[1]) + lab2_a = _od_to_lab(vecs2_paired[0]) + lab2_b = _od_to_lab(vecs2_paired[1]) + + return { + 'd_hematoxylin': delta_e76(lab1_a, lab2_a), + 'd_eosin': delta_e76(lab1_b, lab2_b), + 'was_swapped': was_swapped, + } + + diff --git a/stain_normalization/modeling/__init__.py b/stain_normalization/modeling/__init__.py new file mode 100644 index 0000000..7a8746c --- /dev/null +++ b/stain_normalization/modeling/__init__.py @@ -0,0 +1,5 @@ +from stain_normalization.modeling.l1ssim_loss import L1SSIMLoss +from stain_normalization.modeling.unet import UNet + + +__all__ = ["L1SSIMLoss", "UNet"] diff --git a/stain_normalization/modeling/l1ssim_loss.py b/stain_normalization/modeling/l1ssim_loss.py new file mode 100644 index 0000000..be13691 --- /dev/null +++ b/stain_normalization/modeling/l1ssim_loss.py @@ -0,0 +1,119 @@ +"""Original SSIM code based on pytorch-ssim by Evan Su (MIT License). + +https://github.com/Po-Hsun-Su/pytorch-ssim . +""" + +""" +The SSIM is based on implementation from gaussian-splatting and slightly simplified (pre-computed windows and removal of unused arguments) +https://github.com/graphdeco-inria/gaussian-splatting/blob/472689c0dc70417448fb451bf529ae532d32c095/utils/loss_utils.py +""" + + +import torch +import torch.nn as nn +import torch.nn.functional as F +from math import exp + +class L1SSIMLoss(nn.Module): + def __init__(self, lambda_dssim: float = 0.6, lambda_l1: float = 0.2, lambda_lum: float = 0.2, lambda_gdl: float = 0.1): + super().__init__() + self.lambda_dssim = lambda_dssim + self.lambda_l1 = lambda_l1 + self.lambda_lum = lambda_lum + self.lambda_gdl = lambda_gdl + + # precompute SSIM windows to avoid repetation + self.window_size = 11 + self.channel = 3 + self._1d_window = gaussian(self.window_size, 1.5).unsqueeze(1) + self._2d_window = self._1d_window.mm(self._1d_window.t()).float().unsqueeze(0).unsqueeze(0) + self.window = self._2d_window.expand(self.channel, 1, self.window_size, self.window_size).contiguous() + + def forward(self, image: torch.Tensor, target_image: torch.Tensor) -> torch.Tensor: + if self.window.device != image.device: + self.window = self.window.to(image.device) + # L1 color loss + l1_loss = F.l1_loss(image, target_image, reduction="mean") + + # SSIM structural loss + ssim_loss = 1.0 - self._ssim(image, target_image, self.window) + + # Gradient loss for edges + gdl_loss = gradient_loss(image, target_image) + + # Luminance / brightness loss + brig_loss = brightness_loss(image, target_image) + + # total weighted loss + total_loss = ( + self.lambda_l1 * l1_loss + + self.lambda_dssim * ssim_loss + + self.lambda_gdl * gdl_loss + + self.lambda_lum * brig_loss + ) + + return total_loss + + @torch.compile + def _ssim(self, img1, img2, window): + # Modified _ssim that uses pre-computed window + mu1 = F.conv2d(img1, window, padding=self.window_size // 2, groups=self.channel) + mu2 = F.conv2d(img2, window, padding=self.window_size // 2, groups=self.channel) + + mu1_sq = mu1.pow(2) + mu2_sq = mu2.pow(2) + mu1_mu2 = mu1 * mu2 + + sigma1_sq = F.conv2d(img1 * img1, window, padding=self.window_size // 2, groups=self.channel) - mu1_sq + sigma2_sq = F.conv2d(img2 * img2, window, padding=self.window_size // 2, groups=self.channel) - mu2_sq + sigma12 = F.conv2d(img1 * img2, window, padding=self.window_size // 2, groups=self.channel) - mu1_mu2 + + c1 = 0.01**2 + c2 = 0.03**2 + + ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ( + (mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2) + ) + + return ssim_map.mean() + + +def gaussian(window_size, sigma): + gauss = torch.Tensor( + [ + exp(-((x - window_size // 2) ** 2) / float(2 * sigma**2)) + for x in range(window_size) + ] + ) + return gauss / gauss.sum() + + +def brightness_loss(pred, target, he_weights=None): + device = pred.device + if he_weights is None: + he_weights = [0.33, 0.33, 0.33] + weights = torch.tensor(he_weights, device=device).view(1, 3, 1, 1) + + pred_mean = (pred * weights).mean(dim=[2, 3], keepdim=True) + target_mean = (target * weights).mean(dim=[2, 3], keepdim=True) + + return F.l1_loss(pred_mean, target_mean) + +def gradient_loss(image, target_image): + def gradient(x): + dx = torch.abs(x[:, :, :-1, :] - x[:, :, 1:, :]) # Horizontal gradient + dy = torch.abs(x[:, :, :, :-1] - x[:, :, :, 1:]) # Vertical gradient + return dx, dy + + image_dx, image_dy = gradient(image) + target_dx, target_dy = gradient(target_image) + + loss_x = F.l1_loss(image_dx, target_dx, reduction="mean") + loss_y = F.l1_loss(image_dy, target_dy, reduction="mean") + + return loss_x + loss_y + + + + + diff --git a/stain_normalization/modeling/unet.py b/stain_normalization/modeling/unet.py new file mode 100644 index 0000000..77f9b32 --- /dev/null +++ b/stain_normalization/modeling/unet.py @@ -0,0 +1,111 @@ +"""Adapted U-Net implementation based on the GitHub repository. + +https://github.com/milesial/Pytorch-UNet . +Original U-Net architecture proposed in the paper. +Ronneberger, O., Fischer, P., & Brox, T. (2015). +U-Net: Convolutional Networks for Biomedical Image Segmentation. +arXiv:1505.04597 [cs.CV]. +Retrieved from https://arxiv.org/abs/1505.04597 . +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class DoubleConv(nn.Module): + """(convolution => [BN] => ReLU) * 2.""" + + def __init__(self, in_channels, out_channels, mid_channels=None): + super().__init__() + if not mid_channels: + mid_channels = out_channels + self.double_conv = nn.Sequential( + nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(mid_channels), + nn.ReLU(inplace=True), + nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True), + ) + + def forward(self, x): + return self.double_conv(x) + + +class Down(nn.Module): + """Downscaling with maxpool then double conv.""" + + def __init__(self, in_channels, out_channels): + super().__init__() + self.maxpool_conv = nn.Sequential( + nn.MaxPool2d(2), DoubleConv(in_channels, out_channels) + ) + + def forward(self, x): + return self.maxpool_conv(x) + + +class Up(nn.Module): + """Upscaling then double conv.""" + + def __init__(self, in_channels, out_channels, bilinear=True): + super().__init__() + + # if bilinear, use the normal convolutions to reduce the number of channels + if bilinear: + self.up = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True) + self.conv = DoubleConv(in_channels, out_channels, in_channels // 2) + else: + self.up = nn.ConvTranspose2d( + in_channels, in_channels // 2, kernel_size=2, stride=2 + ) + self.conv = DoubleConv(in_channels, out_channels) + + def forward(self, x1, x2): + x1 = self.up(x1) + diffy = x2.size()[2] - x1.size()[2] + diffx = x2.size()[3] - x1.size()[3] + + x1 = F.pad(x1, [diffx // 2, diffx - diffx // 2, diffy // 2, diffy - diffy // 2]) + x = torch.cat([x2, x1], dim=1) + return self.conv(x) + + +class OutConv(nn.Module): + def __init__(self, in_channels, out_channels): + super().__init__() + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1) + + def forward(self, x): + return self.conv(x) + + +class UNet(nn.Module): + def __init__(self, in_channels=3, out_channels=3, bilinear=True): + super().__init__() + self.in_conv = DoubleConv(in_channels, 64) + self.down1 = Down(64, 128) + self.down2 = Down(128, 256) + self.down3 = Down(256, 512) + + factor = 2 if bilinear else 1 + self.down4 = Down(512, 1024 // factor) + self.up1 = Up(1024, 512 // factor, bilinear) + self.up2 = Up(512, 256 // factor, bilinear) + self.up3 = Up(256, 128 // factor, bilinear) + self.up4 = Up(128, 64, bilinear) + self.out_conv = OutConv(64, out_channels) + + def forward(self, x): + x1 = self.in_conv(x) + x2 = self.down1(x1) + x3 = self.down2(x2) + x4 = self.down3(x3) + x5 = self.down4(x4) + + x = self.up1(x5, x4) + x = self.up2(x, x3) + x = self.up3(x, x2) + x = self.up4(x, x1) + return self.out_conv(x) diff --git a/stain_normalization/stain_normalization_model.py b/stain_normalization/stain_normalization_model.py new file mode 100644 index 0000000..7530224 --- /dev/null +++ b/stain_normalization/stain_normalization_model.py @@ -0,0 +1,70 @@ +from lightning import LightningModule +from torch import Tensor, stack +from torch.optim import Adam +from torch.optim.optimizer import Optimizer +from torchmetrics import MetricCollection +from torchmetrics.image import StructuralSimilarityIndexMeasure +from torchmetrics.regression import MeanAbsoluteError + +from stain_normalization.modeling import L1SSIMLoss, UNet +from stain_normalization.type_aliases import Batch, Outputs, PredictBatch + + +class StainNormalizationModel(LightningModule): + def __init__(self) -> None: + super().__init__() + self.unet = UNet(in_channels=3, out_channels=3) + self.criterion = L1SSIMLoss() + + self.val_metrics = MetricCollection( + { + "ssim": StructuralSimilarityIndexMeasure(), + "l1": MeanAbsoluteError() + } + ) + self.test_metrics = self.val_metrics.clone(prefix="test/") + self.val_metrics.prefix = "validation/" + + def forward(self, x: Tensor) -> Outputs: + return self.unet(x) + + def training_step(self, batch: Batch) -> Tensor: + inputs, targets = batch + outputs = self(inputs) + + loss = self.criterion(outputs, targets) + self.log("train/loss", loss, on_step=True, prog_bar=True) + + return loss + + def validation_step(self, batch: Batch) -> None: + inputs, targets = batch + outputs = self(inputs) + + loss = self.criterion(outputs, targets) + self.log("validation/loss", loss, on_step=False, on_epoch=True, logger=True) + self.val_metrics.update(outputs, targets) + self.log_dict( + self.val_metrics, + batch_size=len(inputs), + on_epoch=True, + ) + + def test_step(self, batch: PredictBatch) -> Outputs: + inputs, data = batch + outputs = self(inputs) + targets = stack([item["original_image_tensor"] for item in data]) + self.test_metrics.update(outputs, targets) + self.log_dict( + self.test_metrics, + batch_size=len(inputs), + on_epoch=True, + ) + return outputs + + def predict_step(self, batch: PredictBatch, batch_idx: int) -> Outputs: + inputs = batch[0] + return self(inputs) + + def configure_optimizers(self) -> Optimizer: + return Adam(self.parameters(), lr=1e-4) diff --git a/stain_normalization/type_aliases.py b/stain_normalization/type_aliases.py new file mode 100644 index 0000000..535edda --- /dev/null +++ b/stain_normalization/type_aliases.py @@ -0,0 +1,13 @@ +from typing import Any, TypeAlias + +from torch import Tensor + + +Sample: TypeAlias = tuple[Tensor, Tensor] +PredictSample: TypeAlias = tuple[Tensor, dict[str, Any]] + +# Batches - after collate +Batch: TypeAlias = tuple[Tensor, Tensor] +PredictBatch: TypeAlias = tuple[Tensor, list[dict[str, Any]]] + +Outputs: TypeAlias = Tensor