napari-n2v

Logo

A self-supervised denoising algorithm.

View the Project on GitHub juglab/napari-n2v

Installation

Windows/Linux

If you do not have conda, we recommend installing miniconda or Anaconda.

  1. Then, in your command line tool create a conda environment
        conda create -n napari-n2v python=3.9
        conda activate napari-n2v
    
  2. Follow the TensorFlow installation step-by-step for your operating system.
  3. Install napari and napari-n2v:
       pip install "napari[all]" napari-n2v
    

Note: napari-n2v was tested with TensorFlow 2.10 (cuda 11.2 and cudnn 8.1) and TensorFlow 2.13 (cuda 11.8 and cudnn 8.6) on a Linux machine (NVIDIA A40-16Q GPU).

Important: In order to access the GPU with Tensorflow, it is necessary to export the CUDA library path in your conda environment. Installation instructions on the TensorFlow website do just that.

For TF 2.10, we recommand running the following in your environment:

mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

If you encounter the following problem with TF 2.13: “DNN library is not found”, you can try to run in your environment:

CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/

macOS

Note: These instructions are for GPU support. Apple’s tensorflow-metal is only officially supported for macOS 12 and higher. For CPU, you can try the Follow the TensorFlow instructions

  1. Set up env with napari and pyqt5
       conda create -n napari-n2v -c conda-forge python=3.9 pyqt imagecodecs napari
    
  2. Install tensorflow following Apple’s instructions
  3. Install napari-n2v
       pip install napari-n2v
    

Start napari-N2V

  1. Using the terminal with the napari-n2v environment active, start napari:

     napari
    
  2. Load one of the napari-N2V plugin.