DepthAI Library

DepthAI library for interfacing with Luxonis DepthAI hardware. It’s written in C++ and offers Python bindings out of the box.

Important — You’re viewing the v3.x.y release-candidate branch.

• For production projects that still rely on v2, check out the v2_stable branch.
• Need to migrate? Follow the step-by-step v2 → v3 Porting Guide.

Documentation

Documentation is available over at Luxonis DepthAI API

Disclaimer

DepthAI library doesn’t yet provide API stability guarantees. While we take care to properly deprecate old functions, some changes might still be breaking.

Examples

Examples for both C++ and Python are available in the examples folder. To see how to build and run them see README.md for more information. To build the examples in C++ configure with the following option added:

cmake -S. -Bbuild -D'DEPTHAI_BUILD_EXAMPLES=ON'
cmake --build build

Dependencies

• CMake >= 3.20
• C/C++17 compiler
• [Linux] libudev >= 1.0.0
• [optional] OpenCV 4 (required if building examples and for record and replay)
• [optional] PCL (required for point cloud example)

To install libudev on Debian based systems (Ubuntu, etc.): sudo apt install libudev-dev

To install OpenCV: MacOS: brew install opencv Linux: sudo apt install libopencv-dev Windows: choco install opencv

To install PCL: MacOS: brew install pcl Linux: sudo apt install libpcl-dev

Using Python bindings

Installing the latest pre-released version of the library can be done with:

python3 -m pip install --extra-index-url https://artifacts.luxonis.com/artifactory/luxonis-python-release-local/ --pre -U depthai

or by running:

python3 examples/python/install_requirements.py on the branch you want to install

For more specific information about Python bindings, see Python README.

Building

Make sure submodules are updated

git submodule update --init --recursive

Then configure and build

cmake -S . -B build
cmake --build build --parallel [num CPU cores]

On Windows it’s often required to specify the location of the OpenCV installation. In case you used chocolatey to install OpenCV, you can use the following command:

cmake -S . -B build -DOpenCV_DIR=C:/tools/opencv/build -DCMAKE_BUILD_TYPE=Release
cmake --build build --config Release --parallel [num CPU cores]

ℹ️ To speed up build times, use cmake --build build --parallel [num CPU cores].

Dynamic library

To build a dynamic version of the library configure with the following option added

cmake -S . -B build -D'BUILD_SHARED_LIBS=ON'
cmake --build build --parallel [num CPU cores]

Installation and Integration

Installation of the DepthAI library is currently only available as a dynamic library. To install the library, use the following command:

cmake -S . -B build -D'BUILD_SHARED_LIBS=ON' -D'CMAKE_INSTALL_PREFIX=[path/to/install/dir]'
cmake --build build --target install --parallel [num CPU cores]

Verifying installation

To verify the installation works as expected, you can test if the integration project compiles and runs. This is done by running the following command:

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Changelog for package depthai

3.0.0 (2025-07-31)

# DepthAI v3.0.0 release candidate is out :tada: We’re proud to announce a new major revision of the DepthAI library is now in release candidate stage and ready to be used.

## Simplified API We've simplified and unified the API to lower the barrier to entry and make development more intuitive. Core tasks like setting up camera streams, running neural networks, and building spatial pipelines are now streamlined with fewer lines of code.

We have removed many deprecated functions and simplified node constructors, creating a cleaner, more consistent, and easier-to-use library for newcomers as well as more experienced users.

## Refreshed nodes We've revisited some of the most fundamental nodes to make them more powerful and flexible.

• Camera
  • The [ColorCamera]{.title-ref} and [MonoCamera]{.title-ref} nodes have been replaced by a single, generic [Camera]{.title-ref} node that works across all platforms.
  • It supports a variable number of outputs, where each can have an independent:
    • Type (NV12, RGB interleaved or planar, ...)
    • Size
    • FPS
  • [Python](https://github.com/luxonis/depthai-core/tree/main/examples/python/Camera) and [C++](https://github.com/luxonis/depthai-core/tree/main/examples/cpp/Camera) examples.
• ImageManip
  • The [ImageManip]{.title-ref} node has been revised for more robust and predictable behavior (we now track the order of operations to make the output fully deterministic).
  • It now supports relative crops and can be used as a host-side node in addition to running on RVC2 and RVC4 devices.
  • [Python](https://github.com/luxonis/depthai-core/tree/main/examples/python/ImageManip/) and [C++](https://github.com/luxonis/depthai-core/tree/main/examples/cpp/ImageManip/) examples.

## New nodes DepthAI v3 introduces new high-level nodes to simplify working with high level concepts (RGBD pointclouds, SLAM)

• RGBD: This node makes it easy to work with synchronized and aligned color and depth data. It features an "autocreate" mode that automatically constructs the required input pipeline, delivering a ready-to-use RGBD message or a colored point cloud.

  • [Python](https://github.com/luxonis/depthai-core/tree/main/examples/python/RGBD) and [C++](https://github.com/luxonis/depthai-core/tree/main/examples/cpp/RGBD) examples.

• VSLAM: We've added SLAM and VIO as host nodes, enabling advanced spatial AI applications. These are currently available on Linux and macOS and are in early preview.

  • [Python](https://github.com/luxonis/depthai-core/tree/main/examples/python/RVC2/VSLAM) and [C++](https://github.com/luxonis/depthai-core/tree/main/examples/cpp/RVC2/VSLAM) examples.

## Support for host nodes DepthAI v3 now supports custom host nodes, allowing you to run parts of your pipeline on the host with custom logic while keeping the rest on-device. Whether you need to run complex post-processing in Python or integrate with external libraries, it’s all part of the same unified pipeline. See the [Python](https://github.com/luxonis/depthai-core/tree/main/examples/python/HostNodes) and [C++](https://github.com/luxonis/depthai-core/tree/main/examples/cpp/HostNodes) examples.

## Visualizer Debugging and development just got easier with our new integrated visualizer. You can now visualize camera streams, neural network outputs, and detection overlays in real-time. The visualizer is also able to display the pipeline graph, making it a great tool for understanding and debugging your application's data flow. See the [Python](https://github.com/luxonis/depthai-core/tree/main/examples/python/Visualizer) and [C++](https://github.com/luxonis/depthai-core/tree/main/examples/cpp/Visualizer) examples.

## A lot of nice to have features: * Auto reconnect: If a device has a flaky connection and crashes, the library will now automatically try to reconnect instead of exiting, making your applications more resilient. * Unified Coordinate System: We’ve standardized all coordinate systems. Camera, IMU, and spatial data now use the RDF (Right-Down-Forward) convention, eliminating inconsistencies when fusing data from multiple sensors. * Native Model Zoo Support: Take advantage of the new Luxonis Model Zoo, which integrates natively into v3. Loading and deploying models is now seamless, with no boilerplate required. * Remapping points between different frames: We've much improved the developer experience where detections from one image in the pipeline (on the input of the NN for example) need to be remapped to another image in the pipeline, that's still in full resolution by internally tracking all of the image operations that are made to streams. See

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