🚧 This repository is still under construction. 🚧

Please feel free to explore and contribute, but note that there may be frequent changes.

Real-time inference is performed using Torch/Tensorflow, Dacapo, and bioimage models on local data or any cloud-hosted data.

Prerequisites

This package requires Rust and Cargo to compile native extensions. Please ensure you have Rust installed before proceeding.

1. Install Rust: Visit rustup.rs and follow the instructions to install Rust. This installation will also include Cargo, Rust’s package manager.

2. Verify Cargo Installation: Open your terminal and run:

   cargo --version

You should see the Cargo version output, which confirms that Cargo is installed and accessible.

Installation

To install CellMapFlow, you can use pip:

pip install cellmap-flow

Note that the basic installation does not include DaCapo and BioImage.io core dependencies. To install CellMapFlow with DaCapo support, use the following command:

pip install cellmap-flow[dacapo]

To install CellMapFlow with BioImage.io support, use the following command:

pip install cellmap-flow[bioimage]

To install CellMapFlow with both DaCapo and BioImage.io support, use the following command:

pip install cellmap-flow[dacapo,bioimage]

Usage

$ cellmap_flow

Usage: cellmap_flow [OPTIONS] COMMAND [ARGS]...

  Examples:     
    To use Dacapo run the following commands:  
    cellmap_flow dacapo -r my_run -i iteration -d data_path

    To use custom script
    cellmap_flow script -s script_path -d data_path

    To use bioimage-io model 
    cellmap_flow bioimage -m model_path -d data_path


Commands:
  bioimage  Run the CellMapFlow server with a bioimage-io model.
  dacapo    Run the CellMapFlow server with a DaCapo model.
  script    Run the CellMapFlow server with a custom script.

Currently available:

Using custom script:

This enables using any model by providing a script e.g. example/model_spec.py e.g.

cellmap_flow script -s /groups/cellmap/cellmap/zouinkhim/cellmap-flow/example/model_spec.py -d /nrs/cellmap/data/jrc_mus-cerebellum-1/jrc_mus-cerebellum-1.zarr/recon-1/em/fibsem-uint8/s0 

Script keywords:

Define these variables in your script (cellmap_flow script -s path/to/your_script.py):

• model: The PyTorch model to be used for inference.
• read_shape: The voxel shape of the data to be input to the PyTorch model.
• write_shape: The voxel shape of the data in output by the PyTorch model.
• block_shape: The shape of the block output by the PyTorch model (i.e. (*write_shape, output_channels)).
• input_voxel_size: The voxel size of the data input to the model.
• output_voxel_size: The voxel size of the data output by the model.
• output_channels: The number of channels in the output of the model.
• process_chunk (optional): (Optional) A function that takes an ImageDataInterface and an ROI and returns the data to be display. This can be used to run a TensorFlow model or do other custom data processing.

Using Dacapo model:

which enable inference using a Dacapo model by providing the run name and iteration number e.g.

cellmap_flow dacapo -r 20241204_finetune_mito_affs_task_datasplit_v3_u21_kidney_mito_default_cache_8_1 -i 700000 -d /nrs/cellmap/data/jrc_ut21-1413-003/jrc_ut21-1413-003.zarr/recon-1/em/fibsem-uint8/s0

Using bioimage-io model:

still in development

Using TensorFlow model:

To run TensorFlow models, we suggest installing TensorFlow via conda: conda install tensorflow-gpu==2.16.1

Run multiple model at once:

cellmap_flow_multiple --script -s /groups/cellmap/cellmap/zouinkhim/cellmap-flow/example/model_spec.py -n script_base --dacapo -r 20241204_finetune_mito_affs_task_datasplit_v3_u21_kidney_mito_default_cache_8_1 -i 700000 -n using_dacapo -d /nrs/cellmap/data/jrc_ut21-1413-003/jrc_ut21-1413-003.zarr/recon-1/em/fibsem-uint8/s0

Limitation:

Currently only supporting data locating in /nrs/cellmap or /groups/cellmap because there is a data server already implemented for them.