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Project Objective

The goal is to create an efficient way to reconstruct and process data in real-time, in specific formats like NDarray, Numpy, or Arrow, without unnecessary copies.

  • Independent Library: We are creating an independent library for our formats, which everyone could use even without DORA. The current name is fastformat: https://github.com/dora-rs/fastformat

  • Agnosticism: We want our solution to be agnostic to Arrow, meaning it can work with Numpy and other data types easily by implementing to_[format] functions.

  • Rust and Python Code: The main implementation will be in Rust for performance and portability reasons, with a Python interface using PyO3. This will allow for fast vector manipulation and potentially GPU support in the future.

  • Portability and Dependencies: By using Rust, we aim to minimize dependencies and maximize portability. This will allow us to achieve optimal performance across different platforms.

  • Simplicity: The goal is not to create yet another library for creating formats to store/process data. The aim here is to create a simple interface to wrap data from one type and convert it to another.

DataTypes

  • Image: (Arrow representation is a UnionArray),

    • Field "data": UintXArray (e.g [0, 255, 0, 255, 0, 255, ...])
    • Field "width": Uint32Array (e.g [1280])
    • Field "height": Uint32Array (e.g [720])
    • Field "encoding": StringArray (e.g ["RGB8"])
    • Field "name" (Optional): StringArray (e.g,["image.front_camera"] or [None])

  • BBox: (Arrow representation is a UnionArray),

    • Field "data": Float32Array (e.g [0.0f32, 1.0f32, ...])
    • Field "confidence": Float32Array (e.g [0.98f32, 0.76f32, ...])
    • Field "label": StringArray (e.g ["cat", "car", ..."])
    • Field "encoding": StringArray (e.g ["XYXY"] or ["XYWH"])