# napari-debcr
<!--
[](https://github.com/DeBCR/napari-debcr/raw/main/LICENSE)
[](https://pypi.org/project/napari-debcr)
[](https://python.org)
[](https://github.com/DeBCR/napari-debcr/actions)
[](https://napari-hub.org/plugins/napari-debcr)
-->
**DeBCR** is a Python-based framework for light microscopy data enhancement, including denoising and deconvolution.
[`napari-debcr`](https://github.com/DeBCR/napari-debcr/) is add-on plugin, created to provide a graphical interface for [DeBCR](https://github.com/DeBCR/DeBCR) in [Napari viewer](https://github.com/napari/napari).
This plugin was initialized with [copier](https://copier.readthedocs.io/en/stable/) using the [napari-plugin-template](https://github.com/napari/napari-plugin-template).
### License
This is an open-source project and is licensed under [MIT license](https://github.com/DeBCR/napari-debcr/blob/main/LICENSE).
### Contact
For any questions or bur-reports related to:
- the [`napari-debcr`](https://github.com/DeBCR/napari-debcr/) plugin - use the [napari-debcr GitHub Issue Tracker](https://github.com/DeBCR/napari-debcr/issues);
- the core [`debcr`](https://github.com/DeBCR/DeBCR) package - use the [DeBCR GitHub Issue Tracker](https://github.com/DeBCR/DeBCR/issues).
## Installation
As for the core package `debcr`, there are two hardware-based installation options for `napari-debcr`:
- `napari-debcr[tf-gpu]` - for a GPU-based trainig and prediction (**recommended**);
- `napari-debcr[tf-cpu]` - for a CPU-only execution (note: training on CPUs might be quite slow!).
### GPU prerequisites
For a GPU version you need:
- a GPU device with at least 12Gb of VRAM;
- a compatible CUDA Toolkit (recommemded: [CUDA-11.7](https://developer.nvidia.com/cuda-11-7-0-download-archive));
- a compatible cuDNN library (recommemded: v8.4.0 for CUDA-11.x from [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive)).
For more info on GPU dependencies please check our [GPU-advice page on DeBCR GitHub](https://github.com/DeBCR/DeBCR/blob/main/docs/GPU-advice.md).
### Create a package environment (optional)
For a clean isolated installation, we advice using one of Python package environment managers, for example:
- `micromamba`/`mamba` (see [mamba.readthedocs.io](https://mamba.readthedocs.io/))
- `conda-forge` (see [conda-forge.org](https://conda-forge.org/))
Create an environment for `napari-debcr` using
```bash
micromamba env create -n napari-debcr python=3.9 -y
```
and activate it for further installation or usage by
```bash
micromamba activate napari-debcr
```
### Install `napari`
Make sure you have [napari](https://github.com/napari/napari) installed. To install it via [pip](https://pypi.org/project/pip/) use:
```bash
pip install napari[all]
```
### Install napari-debcr
Install one of the `napari-debcr` versions:
- GPU (**recommended**; backend: TensorFlow-GPU-v2.11):
```bash
pip install 'napari-debcr[tf-gpu]'
```
- CPU (*limited*; backend: TensorFlow-CPU-v2.11)
```bash
pip install 'napari-debcr[tf-cpu]'
```
### Test GPU visibility
For a GPU version installation, it is recommended to check if your GPU device is recognised by **TensorFlow** using
```bash
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
```
which for a single GPU device should produce a similar output as below:
```
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
```
If your GPU device list is empty, please check our [GPU-advice page on DeBCR GitHub](https://github.com/DeBCR/DeBCR/blob/main/docs/GPU-advice.md).
## Usage
To start using `napari-debcr`,
1. activate `napari-debcr` environment, if was inactive, by
```bash
micromamba activate napari-debcr
```
2. start Napari by typing
```bash
napari
```
3. in Napari window, open `napari-debcr` plugin by clicking in the main menu
`Plugins` → `DeBCR (DeBCR)`
Raw data
{
"_id": null,
"home_page": "https://github.com/DeBCR/napari-debcr/",
"name": "napari-debcr",
"maintainer": "Artsemi Yushkevich",
"docs_url": null,
"requires_python": "<3.12,>=3.9",
"maintainer_email": null,
"keywords": "image processing, image restoration, image enhancement, deep learning, BCR, BCR-Net, m-rBCR, denoising, deblurring, deconvolution, confocal microscopy, widefield microscopy, fluorescence microscopy, light microscopy",
"author": "Artsemi Yushkevich",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/3b/12/3d42e8d6ac6f242fb36f588b7d2b44bdfc3bba0795360b6b580afb5f3427/napari_debcr-0.1.0.tar.gz",
"platform": null,
"description": "# napari-debcr\n\n<!--\n[](https://github.com/DeBCR/napari-debcr/raw/main/LICENSE)\n[](https://pypi.org/project/napari-debcr)\n[](https://python.org)\n[](https://github.com/DeBCR/napari-debcr/actions)\n[](https://napari-hub.org/plugins/napari-debcr)\n-->\n\n**DeBCR** is a Python-based framework for light microscopy data enhancement, including denoising and deconvolution.\n\n[`napari-debcr`](https://github.com/DeBCR/napari-debcr/) is add-on plugin, created to provide a graphical interface for [DeBCR](https://github.com/DeBCR/DeBCR) in [Napari viewer](https://github.com/napari/napari).\n\nThis plugin was initialized with [copier](https://copier.readthedocs.io/en/stable/) using the [napari-plugin-template](https://github.com/napari/napari-plugin-template).\n\n### License\nThis is an open-source project and is licensed under [MIT license](https://github.com/DeBCR/napari-debcr/blob/main/LICENSE).\n\n### Contact\nFor any questions or bur-reports related to:\n- the [`napari-debcr`](https://github.com/DeBCR/napari-debcr/) plugin - use the [napari-debcr GitHub Issue Tracker](https://github.com/DeBCR/napari-debcr/issues);\n- the core [`debcr`](https://github.com/DeBCR/DeBCR) package - use the [DeBCR GitHub Issue Tracker](https://github.com/DeBCR/DeBCR/issues).\n\n## Installation\n\nAs for the core package `debcr`, there are two hardware-based installation options for `napari-debcr`:\n- `napari-debcr[tf-gpu]` - for a GPU-based trainig and prediction (**recommended**);\n- `napari-debcr[tf-cpu]` - for a CPU-only execution (note: training on CPUs might be quite slow!).\n\n### GPU prerequisites\n\nFor a GPU version you need:\n- a GPU device with at least 12Gb of VRAM;\n- a compatible CUDA Toolkit (recommemded: [CUDA-11.7](https://developer.nvidia.com/cuda-11-7-0-download-archive));\n- a compatible cuDNN library (recommemded: v8.4.0 for CUDA-11.x from [cuDNN archive](https://developer.nvidia.com/rdp/cudnn-archive)).\n\nFor more info on GPU dependencies please check our [GPU-advice page on DeBCR GitHub](https://github.com/DeBCR/DeBCR/blob/main/docs/GPU-advice.md). \n\n### Create a package environment (optional)\n\nFor a clean isolated installation, we advice using one of Python package environment managers, for example:\n- `micromamba`/`mamba` (see [mamba.readthedocs.io](https://mamba.readthedocs.io/))\n- `conda-forge` (see [conda-forge.org](https://conda-forge.org/))\n\nCreate an environment for `napari-debcr` using\n```bash\nmicromamba env create -n napari-debcr python=3.9 -y\n```\nand activate it for further installation or usage by\n```bash\nmicromamba activate napari-debcr\n```\n\n### Install `napari`\n\nMake sure you have [napari](https://github.com/napari/napari) installed. To install it via [pip](https://pypi.org/project/pip/) use:\n\n```bash\npip install napari[all]\n```\n\n### Install napari-debcr\n\nInstall one of the `napari-debcr` versions:\n- GPU (**recommended**; backend: TensorFlow-GPU-v2.11):\n ```bash\n pip install 'napari-debcr[tf-gpu]'\n ```\n- CPU (*limited*; backend: TensorFlow-CPU-v2.11)\n ```bash\n pip install 'napari-debcr[tf-cpu]'\n ```\n\n### Test GPU visibility\n\nFor a GPU version installation, it is recommended to check if your GPU device is recognised by **TensorFlow** using\n```bash\npython -c \"import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))\"\n```\n\nwhich for a single GPU device should produce a similar output as below:\n```\n[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n```\n\nIf your GPU device list is empty, please check our [GPU-advice page on DeBCR GitHub](https://github.com/DeBCR/DeBCR/blob/main/docs/GPU-advice.md). \n\n## Usage\n\nTo start using `napari-debcr`,\n1. activate `napari-debcr` environment, if was inactive, by\n```bash\nmicromamba activate napari-debcr\n```\n2. start Napari by typing\n```bash\nnapari\n```\n3. in Napari window, open `napari-debcr` plugin by clicking in the main menu\n\n`Plugins` → `DeBCR (DeBCR)`\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "A Napari plugin to use DeBCR framework for light microscopy data enhancement via deep learning",
"version": "0.1.0",
"project_urls": {
"Homepage": "https://github.com/DeBCR/napari-debcr/"
},
"split_keywords": [
"image processing",
" image restoration",
" image enhancement",
" deep learning",
" bcr",
" bcr-net",
" m-rbcr",
" denoising",
" deblurring",
" deconvolution",
" confocal microscopy",
" widefield microscopy",
" fluorescence microscopy",
" light microscopy"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "a779d776961fc5c00269b2266b769ef98141c8a1dbb2323ab8aa060c362569dc",
"md5": "e61e725486d90a633df89107706a3afb",
"sha256": "1c8e5db82a503f8ea4f7b474584bb469315dff9d252cb8b3d0ab4c1bd3cf60ea"
},
"downloads": -1,
"filename": "napari_debcr-0.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "e61e725486d90a633df89107706a3afb",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": "<3.12,>=3.9",
"size": 20873,
"upload_time": "2025-07-08T14:38:22",
"upload_time_iso_8601": "2025-07-08T14:38:22.321883Z",
"url": "https://files.pythonhosted.org/packages/a7/79/d776961fc5c00269b2266b769ef98141c8a1dbb2323ab8aa060c362569dc/napari_debcr-0.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "3b123d42e8d6ac6f242fb36f588b7d2b44bdfc3bba0795360b6b580afb5f3427",
"md5": "d6a8a47f8d4e0036c00445aa80f8d895",
"sha256": "14bcaf130a27228370251fb825935d90254f8d00bb1a4906454e6d40a726ae3f"
},
"downloads": -1,
"filename": "napari_debcr-0.1.0.tar.gz",
"has_sig": false,
"md5_digest": "d6a8a47f8d4e0036c00445aa80f8d895",
"packagetype": "sdist",
"python_version": "source",
"requires_python": "<3.12,>=3.9",
"size": 15549,
"upload_time": "2025-07-08T14:38:23",
"upload_time_iso_8601": "2025-07-08T14:38:23.462373Z",
"url": "https://files.pythonhosted.org/packages/3b/12/3d42e8d6ac6f242fb36f588b7d2b44bdfc3bba0795360b6b580afb5f3427/napari_debcr-0.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-07-08 14:38:23",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "DeBCR",
"github_project": "napari-debcr",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"tox": true,
"lcname": "napari-debcr"
}
JSON
