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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,20 @@ | ||
| FROM polusai/bfio:2.3.3 | ||
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| # environment variables defined in polusai/bfio | ||
| ENV EXEC_DIR="/opt/executables" | ||
| ENV POLUS_IMG_EXT=".ome.tif" | ||
| ENV POLUS_TAB_EXT=".csv" | ||
| ENV POLUS_LOG="INFO" | ||
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| # Work directory defined in the base container | ||
| WORKDIR ${EXEC_DIR} | ||
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| COPY pyproject.toml ${EXEC_DIR} | ||
| COPY VERSION ${EXEC_DIR} | ||
| COPY README.md ${EXEC_DIR} | ||
| COPY src ${EXEC_DIR}/src | ||
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| RUN pip3 install ${EXEC_DIR} --no-cache-dir | ||
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| ENTRYPOINT ["python3", "-m", "polus.plugins.transforms.images.apply_flatfield"] | ||
| CMD ["--help"] |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,29 @@ | ||
| #!/bin/bash | ||
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| version=$(<VERSION) | ||
| datapath=$(readlink --canonicalize ./data) | ||
| echo ${datapath} | ||
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| # Inputs | ||
| imgDir="/data/images" | ||
| imgPattern="p{p:d+}_x{x:d+}_y{y:d+}_wx{r:d+}_wy{z:d+}_c{c:d+}.ome.tif" | ||
| ffDir="/data/estimation" | ||
| brightPattern="p{p:d+}_x\\(01-24\\)_y\\(01-16\\)_wx\\(1-3\\)_wy\\(1-3\\)_c{c:d+}_flatfield.ome.tif" | ||
| darkPattern="p{p:d+}_x\\(01-24\\)_y\(01-16\\)_wx\\(1-3\\)_wy\\(1-3\\)_c{c:d+}_darkfield.ome.tif" | ||
| # photoPattern="" | ||
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| # Output paths | ||
| outDir=/data/outputs | ||
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| FILE_EXT=".ome.zarr" | ||
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| docker run --mount type=bind,source=${datapath},target=/data/ \ | ||
| -e POLUS_EXT=${FILE_EXT} \ | ||
| --user $(id -u):$(id -g) \ | ||
| polusai/apply-flatfield-plugin:${version} \ | ||
| --imgDir ${imgDir} \ | ||
| --imgPattern ${imgPattern} \ | ||
| --ffDir ${ffDir} \ | ||
| --brightPattern ${brightPattern} \ | ||
| --darkPattern ${darkPattern} \ | ||
| --outDir ${outDir} |
158 changes: 1 addition & 157 deletions
158
...es/apply-flatfield-plugin/src/polus/plugins/transforms/images/apply_flatfield/__init__.py
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| """Provides the apply_flatfield module.""" | ||
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| import concurrent.futures | ||
| import logging | ||
| import operator | ||
| import pathlib | ||
| import sys | ||
| import typing | ||
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| import bfio | ||
| import numpy | ||
| import tqdm | ||
| from filepattern import FilePattern | ||
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| from . import utils | ||
| from .apply_flatfield import apply | ||
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| __version__ = "2.0.0-dev8" | ||
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| logger = logging.getLogger(__name__) | ||
| logger.setLevel(utils.POLUS_LOG) | ||
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| def apply( # noqa: PLR0913 | ||
| img_dir: pathlib.Path, | ||
| img_pattern: str, | ||
| ff_dir: pathlib.Path, | ||
| ff_pattern: str, | ||
| df_pattern: typing.Optional[str], | ||
| out_dir: pathlib.Path, | ||
| ) -> None: | ||
| """Run batch-wise flatfield correction on the image collection.""" | ||
| img_fp = FilePattern(str(img_dir), img_pattern) | ||
| img_variables = img_fp.get_variables() | ||
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| ff_fp = FilePattern(str(ff_dir), ff_pattern) | ||
| ff_variables = ff_fp.get_variables() | ||
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| # check that ff_variables are a subset of img_variables | ||
| if set(ff_variables) - set(img_variables): | ||
| msg = ( | ||
| f"Flatfield variables are not a subset of image variables: " | ||
| f"{ff_variables} - {img_variables}" | ||
| ) | ||
| raise ValueError(msg) | ||
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| if (df_pattern is None) or (not df_pattern): | ||
| df_fp = None | ||
| else: | ||
| df_fp = FilePattern(str(ff_dir), df_pattern) | ||
| df_variables = df_fp.get_variables() | ||
| if set(df_variables) != set(ff_variables): | ||
| msg = ( | ||
| f"Flatfield and darkfield variables do not match: " | ||
| f"{ff_variables} != {df_variables}" | ||
| ) | ||
| raise ValueError(msg) | ||
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| for group, files in img_fp(group_by=ff_variables): | ||
| img_paths = [p for _, [p] in files] | ||
| variables = dict(group) | ||
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| ff_path: pathlib.Path = ff_fp.get_matching(**variables)[0][1][0] | ||
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| df_path = None if df_fp is None else df_fp.get_matching(**variables)[0][1][0] | ||
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| _unshade_images(img_paths, out_dir, ff_path, df_path) | ||
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| def _unshade_images( | ||
| img_paths: list[pathlib.Path], | ||
| out_dir: pathlib.Path, | ||
| ff_path: pathlib.Path, | ||
| df_path: typing.Optional[pathlib.Path], | ||
| ) -> None: | ||
| """Remove the given flatfield components from all images and save outputs. | ||
| Args: | ||
| img_paths: list of paths to images to be processed | ||
| out_dir: directory to save the corrected images | ||
| ff_path: path to the flatfield image | ||
| df_path: path to the darkfield image | ||
| """ | ||
| with bfio.BioReader(ff_path, max_workers=2) as bf: | ||
| ff_image = bf[:, :, :, 0, 0].squeeze() | ||
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| if df_path is not None: | ||
| with bfio.BioReader(df_path, max_workers=2) as df: | ||
| df_image = df[:, :, :, 0, 0].squeeze() | ||
| else: | ||
| df_image = None | ||
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| batch_indices = list(range(0, len(img_paths), 16)) | ||
| if batch_indices[-1] != len(img_paths): | ||
| batch_indices.append(len(img_paths)) | ||
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| for i_start, i_end in tqdm.tqdm( | ||
| zip(batch_indices[:-1], batch_indices[1:]), | ||
| total=len(batch_indices) - 1, | ||
| ): | ||
| _unshade_batch( | ||
| img_paths[i_start:i_end], | ||
| out_dir, | ||
| ff_image, | ||
| df_image, | ||
| ) | ||
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| def _unshade_batch( | ||
| batch_paths: list[pathlib.Path], | ||
| out_dir: pathlib.Path, | ||
| ff_image: numpy.ndarray, | ||
| df_image: typing.Optional[numpy.ndarray] = None, | ||
| ) -> None: | ||
| """Apply flatfield correction to a batch of images. | ||
| Args: | ||
| batch_paths: list of paths to images to be processed | ||
| out_dir: directory to save the corrected images | ||
| ff_image: component to be used for flatfield correction | ||
| df_image: component to be used for flatfield correction | ||
| """ | ||
| # Load images | ||
| images = [] | ||
| with concurrent.futures.ProcessPoolExecutor( | ||
| max_workers=utils.MAX_WORKERS, | ||
| ) as load_executor: | ||
| load_futures = [] | ||
| for i, inp_path in enumerate(batch_paths): | ||
| load_futures.append(load_executor.submit(utils.load_img, inp_path, i)) | ||
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| for lf in tqdm.tqdm( | ||
| concurrent.futures.as_completed(load_futures), | ||
| total=len(load_futures), | ||
| desc="Loading batch", | ||
| ): | ||
| images.append(lf.result()) | ||
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| images = [img for _, img in sorted(images, key=operator.itemgetter(0))] | ||
| img_stack = numpy.stack(images, axis=0) | ||
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| # Apply flatfield correction | ||
| if df_image is not None: | ||
| img_stack -= df_image | ||
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| img_stack /= ff_image | ||
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| # Save outputs | ||
| with concurrent.futures.ProcessPoolExecutor( | ||
| max_workers=utils.MAX_WORKERS, | ||
| ) as save_executor: | ||
| save_futures = [] | ||
| for inp_path, img in zip(batch_paths, img_stack): | ||
| save_futures.append( | ||
| save_executor.submit(utils.save_img, inp_path, img, out_dir), | ||
| ) | ||
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| for sf in tqdm.tqdm( | ||
| concurrent.futures.as_completed(save_futures), | ||
| total=len(save_futures), | ||
| desc="Saving batch", | ||
| ): | ||
| sf.result() |
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