Face-Rhythm

Installation

0. Requirements

• Operating system:
  • Ubuntu >= 18.04 (other linux versions usually okay but not actively maintained)
  • Windows >= 10
  • Mac >= 12
• Anaconda or Miniconda.
• If using linux/unix: GCC >= 5.4.0, ideally == 9.2.0. Google how to do this on your operating system. Check with: gcc --version.
• Optional: CUDA compatible NVIDIA GPU and drivers. Using a GPU can increase the speeds for the TCA step, but is not necessary.
• The below commands should be run in the terminal (Mac/Linux) or Anaconda Prompt (Windows).

1. Clone this repo

This will create a folder called face-rhythm in your current directory. This repository folder contains the source code AND the interactive notebooks needed to run the pipeline.
git clone https://github.com/RichieHakim/face-rhythm/
cd face-rhythm

2. Create a conda environment

This will also install the face-rhythm package and all of its dependencies into the environment.
conda env create --file environment.yml

Activate the environment:
conda activate face_rhythm

Optional Direct installation

You can also directly install the face-rhythm package from PyPI into the environment of your choice. Note that you will still need to download/clone the repository for the notebooks.

Option 1: Install from PyPI

pip install face-rhythm[all]

Option 2: Install from source

pip install -e .[all]

Usage

Notebooks

The easiest way to use face-rhythm is through the interactive notebooks. They are found in the following directory: face-rhythm/notebooks/.

• The interactive_pipeline_basic.ipynb notebook contains the main pipeline and instructions on how to use it.
  
• The interactive_set_ROIs_only.ipynb notebook is useful for when you want to run a batch job of many videos/sessions and need to set the ROIs for each video/session ahead of time.
  

Command line

The basic pipeline in the interactive notebook is also provided as a function within the face_rhythm/pipelines.py module. In the scripts folder, you'll find a script called run_pipeline_basic.py that can be used to run the pipeline from the command line. An example params.json file is also in that folder to use as a template for your runs.

Repository Organization

face-rhythm
├── notebooks  <- Jupyter notebooks containing the main pipeline and some demos.
|   ├── basic_face_rhythm_notebook.ipynb  <- Main pipeline notebook.
|   └── interactive_set_ROIs_only.ipynb   <- Notebook for setting ROIs only.
|
├── face-rhythm  <- Source code for use in this project.
│   ├── project.py           <- Contains methods for project directory organization and preparation
│   ├── data_importing.py    <- Contains classes for importing data (like videos)
|   ├── rois.py              <- Contains classes for defining regions of interest (ROIs) to analyze
|   ├── point_tracking.py    <- Contains classes for tracking points in videos
|   ├── spectral_analysis.py <- Contains classes for spectral decomposition
|   ├── decomposition.py     <- Contains classes for TCA decomposition
|   ├── utils.py             <- Contains utility functions for face-rhythm
|   ├── visualization.py     <- Contains classes for visualizing data
|   ├── helpers.py           <- Contains general helper functions (non-face-rhythm specific)
|   ├── h5_handling.py       <- Contains classes for handling h5 files
│   └── __init__.py          <- Makes src a Python module    
|
├── setup.py   <- makes project pip installable (pip install -e .) so src can be imported
├── LICENSE    <- License file
├── Makefile   <- Makefile with commands like `make data` or `make train`
├── README.md  <- The top-level README for developers using this project.
├── docs       <- A default Sphinx project; see sphinx-doc.org for details
└── tox.ini    <- tox file with settings for running tox; see tox.readthedocs.io

Project Directory Organization

Project Directory
├── config.yaml           <- Configuration parameters to run each module in the pipeline. Dictionary.
├── run_info.json         <- Output information from each module. Dictionary.
│
├── run_data              <- Output data from each module.
│   ├── Dataset_videos.h5 <- Output data from Dataset_videos class. Contains metadata about the videos.
│   ├── ROIs.h5           <- Output data from ROIs class. Contains ROI masks.
│   ├── PointTracker.h5   <- Output data from PointTracker class. Contains point tracking data.
|   ├── VQT_Analyzer.h5   <- Output data from VQT_Analyzer class. Contains spectral decomposition data.
│   ├── TCA.h5            <- Output data from TCA class. Contains TCA decomposition data.
│   
└── visualizations        <- Output visualizations.
    ├── factors_rearranged_[frequency].png  <- Example of a rearranged factor plot.
    └── point_tracking_demo.avi             <- Example video.