Lightning pose app 2.0

Status: In development

• Multiview prediction viewer is in a usable state.
• Labeler under development
• Model management not yet started.

Features

The new app will have three modules:

1. Viewer: support for scrubbing through synced multiview video and their error traces
2. Labeling: support for multiview with camera calibrations, no longer using LabelStudio
3. Model management

The modules will also be developed in roughly that order.

Installation

You can install the app either locally or to a remote server.

Using environment managers like conda

We recommend having a single lightning-pose environment (say, lp) for all your lightning-pose related package installations. This keeps lightning-pose dependencies separate from any other data analysis stacks you may be using, while allowing the various lightning-pose packages to share dependencies.

A conda example:

# Create the environment
conda create -n lp python=3.12

# Activate the environment
conda activate lp

Installation option 1: From the PyPi distribution

This is the simplest option appropriate for most users.

pip install lightning-pose lightning-pose-app

Installation option 2: From source

# (If you haven't already) Install lightning-pose core
git clone https://github.com/paninski-lab/lightning-pose.git
cd lightning-pose
pip install -e ".[dev]"
cd ..

# Install the app
git clone https://github.com/paninski-lab/lightning-pose-app.git
cd lightning-pose-app/app_server
pip install -e .

Usage

1. First create a config file at ~/.lightning-pose/project.toml

data_dir = "/Path to a directory containing all the data (videos, labels, etc)."
model_dir = "/Path to a directory containing all the models."
views = [
    "topLeft",
    "(Your video filenames must contain a view name, ie session123_topLeft.mp4)",
]

(This step will be part of the UI in the future, but for now you have to do it manually.) 2. Run the app: litpose app 3. The webserver is now listening at http://localhost:8080!