PyPI 官网下载 | wkcuber-0.8.14.tar.gz

# webKnossos cuber (wkcuber) [](https://pypi.python.org/pypi/wkcuber) [](https://pypi.python.org/pypi/wkcuber) [](https://github.com/scalableminds/webknossos-cuber/actions?query=workflow%3A%22CI%22) [](https://github.com/psf/black) Python library for creating and working with [webKnossos](https://webknossos.org) [WKW](https://github.com/scalableminds/webknossos-wrap) datasets. WKW is a container format for efficiently storing large, scale 3D image data as found in (electron) microscopy. The tools are modular components to allow easy integration into existing pipelines and workflows. ## Features * `wkcuber`: Convert supported input files to fully ready WKW datasets (includes type detection, downsampling, compressing and metadata generation) * `wkcuber.convert_image_stack_to_wkw`: Convert image stacks to fully ready WKW datasets (includes downsampling, compressing and metadata generation) * `wkcuber.export_wkw_as_tiff`: Convert WKW datasets to a tiff stack (writing as tiles to a `z/y/x.tiff` folder structure is also supported) * `wkcuber.cubing`: Convert image stacks (e.g., `tiff`, `jpg`, `png`, `dm3`, `dm4`) to WKW cubes * `wkcuber.tile_cubing`: Convert tiled image stacks (e.g. in `z/y/x.ext` folder structure) to WKW cubes * `wkcuber.convert_knossos`: Convert KNOSSOS cubes to WKW cubes * `wkcuber.convert_nifti`: Convert NIFTI files to WKW files (Currently without applying transformations). * `wkcuber.downsampling`: Create downsampled magnifications (with `median`, `mode` and linear interpolation modes). Downsampling compresses the new magnifications by default (disable via `--no-compress`). * `wkcuber.compress`: Compress WKW cubes for efficient file storage (especially useful for segmentation data) * `wkcuber.metadata`: Create (or refresh) metadata (with guessing of most parameters) * `wkcuber.recubing`: Read existing WKW cubes in and write them again specifying the WKW file length. Useful when dataset was written e.g. with file length 1. * `wkcuber.check_equality`: Compare two WKW datasets to check whether they are equal (e.g., after compressing a dataset, this task can be useful to double-check that the compressed dataset contains the same data). * Most modules support multiprocessing ## Supported input formats * Standard image formats, e.g. `tiff`, `jpg`, `png`, `bmp` * Proprietary image formats, e.g. `dm3` * Tiled image stacks (used for Catmaid) * KNOSSOS cubes * NIFTI files ## Installation ### Python 3 with pip from PyPi - `wkcuber` requires at least Python 3.6+ ``` # Make sure to have lz4 installed: # Mac: brew install lz4 # Ubuntu/Debian: apt-get install liblz4-1 # CentOS/RHEL: yum install lz4 pip install wkcuber ``` ### Docker Use the CI-built image: [scalableminds/webknossos-cuber](https://hub.docker.com/r/scalableminds/webknossos-cuber/). Example usage `docker run -v <host path>:/data --rm scalableminds/webknossos-cuber wkcuber --layer_name color --scale 11.24,11.24,25 --name great_dataset /data/source/color /data/target`. ## Usage ``` # Convert arbitrary, supported input files into wkw datasets. This sets reasonable defaults, but see other commands for customization. python -m wkcuber \ --scale 11.24,11.24,25 \ data/source data/target # Convert image stacks into wkw datasets python -m wkcuber.convert_image_stack_to_wkw \ --layer_name color \ --scale 11.24,11.24,25 \ --name great_dataset \ data/source/color data/target # Convert image files to wkw cubes python -m wkcuber.cubing --layer_name color data/source/color data/target python -m wkcuber.cubing --layer_name segmentation data/source/segmentation data/target # Convert tiled image files to wkw cubes python -m wkcuber.tile_cubing --layer_name color data/source data/target # Convert Knossos cubes to wkw cubes python -m wkcuber.convert_knossos --layer_name color data/source/mag1 data/target # Convert NIFTI file to wkw file python -m wkcuber.convert_nifti --layer_name color --scale 10,10,30 data/source/nifti_file data/target # Convert folder with NIFTI files to wkw files python -m wkcuber.convert_nifti --color_file one_nifti_file --segmentation_file --scale 10,10,30 another_nifti data/source/ data/target # Create downsampled magnifications python -m wkcuber.downsampling --layer_name color data/target python -m wkcuber.downsampling --layer_name segmentation --interpolation_mode mode data/target # Compress data in-place (mostly useful for segmentation) python -m wkcuber.compress --layer_name segmentation data/target # Compress data copy (mostly useful for segmentation) python -m wkcuber.compress --layer_name segmentation data/target data/target_compress # Create metadata python -m wkcuber.metadata --name great_dataset --scale 11.24,11.24,25 data/target # Refresh metadata so that new layers and/or magnifications are picked up python -m wkcuber.metadata --refresh data/target # Recubing an existing dataset python -m wkcuber.recubing --layer_name color --dtype uint8 /data/source/wkw /data/target # Check two datasets for equality python -m wkcuber.check_equality /data/source /data/target ``` ### Parallelization Most tasks can be configured to be executed in a parallelized manner. Via `--distribution_strategy` you can pass `multiprocessing` or `slurm`. The first can be further configured with `--jobs` and the latter via `--job_resources='{"mem": "10M"}'`. Use `--help` to get more information. ## Development Make sure to install all the required dependencies using Poetry: ``` pip install poetry poetry install ``` Please, format, lint, and unit test your code changes before merging them. ``` poetry run black . poetry run pylint -j4 wkcuber poetry run pytest tests ``` Please, run the extended test suite: ``` tests/scripts/all_tests.sh ``` PyPi releases are automatically pushed when creating a new Git tag/Github release. ## API documentation Check out the [latest version of the API documentation](https://static.webknossos.org/lib-docs/master/wkcuber/api.html). ### Generate the API documentation Run `docs/api.sh` to open a server displaying the API docs. `docs/api.sh --persist` persists the html to `docs/api`. ## Test Data Credits Excerpts for testing purposes have been sampled from: - Dow Jacobo Hossain Siletti Hudspeth (2018). **Connectomics of the zebrafish's lateral-line neuromast reveals wiring and miswiring in a simple microcircuit.** eLife. [DOI:10.7554/eLife.33988](https://elifesciences.org/articles/33988) - Zheng Lauritzen Perlman Robinson Nichols Milkie Torrens Price Fisher Sharifi Calle-Schuler Kmecova Ali Karsh Trautman Bogovic Hanslovsky Jefferis Kazhdan Khairy Saalfeld Fetter Bock (2018). **A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster.** Cell. [DOI:10.1016/j.cell.2018.06.019](https://www.cell.com/cell/fulltext/S0092-8674(18)30787-6). License: [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) ## License AGPLv3 Copyright scalable minds