# Face-Rhythm
# Installation
### 0. Requirements <br>
- Operating system:
- Ubuntu >= 18.04 (other linux versions usually okay but not actively maintained)
- Windows >= 10
- Mac >= 12
- [Anaconda](https://www.anaconda.com/distribution/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html).
- 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](https://developer.nvidia.com/cuda-gpus) and [drivers](https://developer.nvidia.com/cuda-toolkit-archive). 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).
<br>
### 1. Clone this repo <br>
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. <br>
**`git clone https://github.com/RichieHakim/face-rhythm/`**<br>
**`cd face-rhythm`**<br>
### 2. Create a conda environment
This will also install the **face-rhythm** package and all of its dependencies into the environment. <br>
**`conda env create --file environment.yml`**<br>
Activate the environment: <br>
**`conda activate face_rhythm`** <br>
### Optional Direct installation <br>
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. <br>
##### Option 1: Install from PyPI <br>
**`pip install face-rhythm[all]`**<br>
##### Option 2: Install from source <br>
**`pip install -e .[all]`**<br>
<br>
<br>
# Usage
#### Notebooks
The easiest way to use **face-rhythm** is through the interactive notebooks. They are found in the following directory: `face-rhythm/notebooks/`. <br>
- The `interactive_pipeline_basic.ipynb` notebook contains the main pipeline and instructions on how to use it. <br>
- 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. <br>
#### 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. <br>
<br>
<br>
# 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
<br>
<br>
# 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.
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"description": "# Face-Rhythm\n\n# Installation\n\n### 0. Requirements <br>\n- Operating system:\n - Ubuntu >= 18.04 (other linux versions usually okay but not actively maintained)\n - Windows >= 10\n - Mac >= 12\n- [Anaconda](https://www.anaconda.com/distribution/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html).\n- 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`.\n- **Optional:** [CUDA compatible NVIDIA GPU](https://developer.nvidia.com/cuda-gpus) and [drivers](https://developer.nvidia.com/cuda-toolkit-archive). Using a GPU can increase the speeds for the TCA step, but is not necessary.\n- The below commands should be run in the terminal (Mac/Linux) or Anaconda Prompt (Windows).\n<br>\n\n### 1. Clone this repo <br>\nThis 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. <br>\n**`git clone https://github.com/RichieHakim/face-rhythm/`**<br>\n**`cd face-rhythm`**<br>\n\n### 2. Create a conda environment\nThis will also install the **face-rhythm** package and all of its dependencies into the environment. <br>\n**`conda env create --file environment.yml`**<br>\n\nActivate the environment: <br>\n**`conda activate face_rhythm`** <br>\n\n### Optional Direct installation <br>\nYou 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. <br>\n##### Option 1: Install from PyPI <br>\n**`pip install face-rhythm[all]`**<br>\n##### Option 2: Install from source <br>\n**`pip install -e .[all]`**<br>\n\n<br>\n<br>\n\n# Usage\n\n#### Notebooks\nThe easiest way to use **face-rhythm** is through the interactive notebooks. They are found in the following directory: `face-rhythm/notebooks/`. <br>\n- The `interactive_pipeline_basic.ipynb` notebook contains the main pipeline and instructions on how to use it. <br>\n- 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. <br>\n\n#### Command line\nThe 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. <br>\n\n\n\n<br>\n<br>\n\n# Repository Organization\n face-rhythm\n \u251c\u2500\u2500 notebooks <- Jupyter notebooks containing the main pipeline and some demos.\n | \u251c\u2500\u2500 basic_face_rhythm_notebook.ipynb <- Main pipeline notebook.\n | \u2514\u2500\u2500 interactive_set_ROIs_only.ipynb <- Notebook for setting ROIs only.\n |\n \u251c\u2500\u2500 face-rhythm <- Source code for use in this project.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 project.py <- Contains methods for project directory organization and preparation\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 data_importing.py <- Contains classes for importing data (like videos)\n | \u251c\u2500\u2500 rois.py <- Contains classes for defining regions of interest (ROIs) to analyze\n | \u251c\u2500\u2500 point_tracking.py <- Contains classes for tracking points in videos\n | \u251c\u2500\u2500 spectral_analysis.py <- Contains classes for spectral decomposition\n | \u251c\u2500\u2500 decomposition.py <- Contains classes for TCA decomposition\n | \u251c\u2500\u2500 utils.py <- Contains utility functions for face-rhythm\n | \u251c\u2500\u2500 visualization.py <- Contains classes for visualizing data\n | \u251c\u2500\u2500 helpers.py <- Contains general helper functions (non-face-rhythm specific)\n | \u251c\u2500\u2500 h5_handling.py <- Contains classes for handling h5 files\n \u2502\u00a0\u00a0 \u2514\u2500\u2500 __init__.py <- Makes src a Python module \n |\n \u251c\u2500\u2500 setup.py <- makes project pip installable (pip install -e .) so src can be imported\n \u251c\u2500\u2500 LICENSE <- License file\n \u251c\u2500\u2500 Makefile <- Makefile with commands like `make data` or `make train`\n \u251c\u2500\u2500 README.md <- The top-level README for developers using this project.\n \u251c\u2500\u2500 docs <- A default Sphinx project; see sphinx-doc.org for details\n \u2514\u2500\u2500 tox.ini <- tox file with settings for running tox; see tox.readthedocs.io\n\n<br>\n<br>\n\n# Project Directory Organization\n\n Project Directory\n \u251c\u2500\u2500 config.yaml <- Configuration parameters to run each module in the pipeline. Dictionary.\n \u251c\u2500\u2500 run_info.json <- Output information from each module. Dictionary.\n \u2502\n \u251c\u2500\u2500 run_data <- Output data from each module.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 Dataset_videos.h5 <- Output data from Dataset_videos class. Contains metadata about the videos.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 ROIs.h5 <- Output data from ROIs class. Contains ROI masks.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 PointTracker.h5 <- Output data from PointTracker class. Contains point tracking data.\n | \u251c\u2500\u2500 VQT_Analyzer.h5 <- Output data from VQT_Analyzer class. Contains spectral decomposition data.\n \u2502\u00a0\u00a0 \u251c\u2500\u2500 TCA.h5 <- Output data from TCA class. Contains TCA decomposition data.\n \u2502 \n \u2514\u2500\u2500 visualizations <- Output visualizations.\n \u00a0\u00a0 \u251c\u2500\u2500 factors_rearranged_[frequency].png <- Example of a rearranged factor plot.\n \u00a0\u00a0 \u2514\u2500\u2500 point_tracking_demo.avi <- Example video.\n\n \n",
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