--- title: What is ArGaze? --- # Enable modular gaze processing pipeline **Useful links**: [Installation](installation.md) | [Source Repository](https://git.recherche.enac.fr/projects/argaze/repository) | [Issue Tracker](https://git.recherche.enac.fr/projects/argaze/issues) | [Contact](mailto:achil-contact@recherche.enac.fr) **ArGaze** python toolkit provides a set of classes to build custom-made gaze processing pipelines that works with any kind of eye tracker devices. ![AGaze pipeline](img/argaze_pipeline.png) ## Gaze analysis pipeline Whether in real time or in post-processing, **ArGaze** provides extensible modules library allowing to select application specific algorithms at each pipeline step: * **Fixation/Saccade identification**: dispersion threshold identification, velocity threshold identification, ... * **Area Of Interest (AOI) matching**: focus point inside, deviation circle coverage, ... * **Scan path analysis**: transition matrix, entropy, exploit/explore ratio, ... Once incoming data formatted as required, all those gaze analysis features can be used with any screen-based eye tracker devices. [Learn how to build gaze analysis pipelines for various use cases by reading user guide dedicated section](./user_guide/gaze_analysis_pipeline/introduction.md). ## Augmented reality pipeline Things goes harder when gaze data comes from head-mounted eye tracker devices. That's why **ArGaze** enable 3D modeled **Augmented Reality (AR)** environment description including **Areas Of Interest (AOI)** mapped on OpenCV ArUco markers. ![AR environment axis](img/ar_environment_axis.png) This AR pipeline can be combined with any wearable eye tracking device python library like Tobii or Pupill glasses. !!! note *AR pipeline is greatly inspired by [Andrew T. Duchowski, Vsevolod Peysakhovich and Krzysztof Krejtz article](https://git.recherche.enac.fr/attachments/download/1990/Using_Pose_Estimation_to_Map_Gaze_to_Detected_Fidu.pdf) about using pose estimation to map gaze to detected fiducial markers.*