Compiling OpenEB on Linux

This section describes how to compile OpenEB on Linux.

Compilation and execution were tested on platforms that meet the following requirements:

  • Linux: Ubuntu 20.04 or 22.04 64-bit

  • Architecture: amd64 (a.k.a. x64)

  • Graphic card with support of OpenGL 3.0 minimum

  • CPU with support of AVX2

Compilation on other platforms (alternate Linux distributions, different versions of Ubuntu, ARM processor architecture etc.) was not tested. For those platforms some adjustments to this guide or to the code itself may be required.


OpenEB is the open source version of our code base for the modules Base, Core, Core ML, Driver, UI, and HAL (see Metavision modules organization). If you acquired the full source code of the SDK (through our SDK Pro offer), OpenEB will also be the basis of your installation, but you should follow the installation guide of SDK Pro.

If you don’t want to go through some compilation steps, you can download pre-built Ubuntu packages of Metavision modules. Do do so, follow our specific installation guide.

Supported Cameras

OpenEB contains the source code of the Prophesee camera plugin, enabling to stream data from our event-based cameras and to read recordings of event-based data. The supported cameras are:

If you want to use a Prophesee EVK that is not in the list above, it might have been discontinued. Refer to the Prophesee EVKs Support section to find out which previous version of the SDK is supporting it.

If you own a third-party vendor event-based camera, refer to the Camera Plugin Installation page to see how it can be deployed and configured.

Upgrading OpenEB

If you are upgrading OpenEB from a previous version, you should first read carefully the Release Notes as some changes may impact your usage of our SDK (e.g. API updates) and cameras (e.g. firmware update might be necessary).

Then, you need to clean your system from previously installed Prophesee software. If after a previous compilation, you chose to deploy the Metavision files in your system path, then go to the build folder in the source code directory and launch the following command to remove those files:

sudo make uninstall

In addition, remove Prophesee or Metavision folders and files in your system paths (/usr/include, /usr/lib, /usr/bin, /usr/share along with their /usr/local equivalents) and in your environment variables (PATH, PYTHONPATH and LD_LIBRARY_PATH).

Installing Dependencies


If you want to simplify the installation of the dependencies, you can use Docker with our Dockerfile.OpenEB file.

The main steps to create your image and launch a terminal in a container are the following:

sudo docker build --build-arg UBUNTU_VERSION=20.04 -f Dockerfile.OpenEB -t openeb:ubuntu-20.04 .
sudo docker run -ti --rm openeb:ubuntu-20.04 /bin/bash

Then, you can follow the compilation steps from this terminal in the container. Alternatively, you can include the compilation and deployment steps in the Dockerfile itself and then you can launch an application like Metavision Viewer on a live USB camera with commands similar to:

xhost '+local:*';
sudo docker run -ti --privileged -v /dev/bus/usb:/deb/bus/usb -e DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix/ --rm --net=host openeb:ubuntu-20.04 metavision_viewer

Install the following dependencies:

sudo apt update
sudo apt -y install apt-utils build-essential software-properties-common wget unzip curl git cmake
sudo apt -y install libopencv-dev libboost-all-dev libusb-1.0-0-dev libprotobuf-dev protobuf-compiler
sudo apt -y install libhdf5-dev hdf5-tools libglew-dev libglfw3-dev libcanberra-gtk-module ffmpeg

Optionally, if you want to run the tests, you need to install Google Gtest and Gmock packages. For more details, see Google Test User Guide :

sudo apt -y install libgtest-dev libgmock-dev

For the Python API, you will need Python and some additional libraries. If Python is not available on your system, install it. We support Python 3.8 and 3.9 on Ubuntu 20.04 and Python 3.9 and 3.10 on Ubuntu 22.04. If you want to use other versions of Python, some source code modifications will be necessary.

Then install some extra libraries:

sudo apt -y install python3-pip python3-distutils
sudo apt -y install python3.X-dev  # where X is 8, 9 or 10 depending on your Python version (3.8, 3.9 or 3.10)
python3 -m pip install pip --upgrade
python3 -m pip install "opencv-python==" "sk-video==1.1.10" "fire==0.4.0" "numpy==1.23.4" "h5py==3.7.0" pandas scipy
python3 -m pip install matplotlib "ipywidgets==7.6.5" pytest command_runner


You can use anaconda to install Python and conda to manage your Python packages, but be sure to use a Python version that we support. You will need to adapt the library installation steps accordingly and use conda whenever we use pip.

In addition, check that conda PYTHONPATH contains /usr/lib/python3/dist-packages/. If not, add it. For example with sys.path.append() method of python module sys:

import sys
# Add /usr/lib/python3/dist-packages/ to PYTHONPATH if the output of print(sys.path) does not mention it.

In this documentation, we chose to use pip as a package manager. We recommend using it with virtualenv to avoid conflicts with other installed Python packages.

The Python bindings of the C++ API rely on the pybind11 library (version >= 2.6.0).


pybind11 is required only if you want to use the Python bindings of the C++ API. You can skip compiling these bindings by passing the argument -DCOMPILE_PYTHON3_BINDINGS=OFF during compilation (see step 3 of the compilation below). This allows you to skip installing pybind11, but you won’t be able to use our Python interface to the C++ API.

Unfortunately, there is no pre-compiled version of pybind11 available, so you need to install it manually:

cd pybind11-2.6.0
mkdir build && cd build
cmake .. -DPYBIND11_TEST=OFF
cmake --build .
sudo cmake --build . --target install

To use Machine Learning features, you need to install some additional dependencies.

First, if you have some Nvidia hardware with GPUs, you can optionally install CUDA (11.6 or 11.7) and cuDNN to leverage them with PyTorch and LibTorch. Make sure that you install a version of CUDA that is compatible with your GPUs by checking Nvidia compatibility page.


At the moment, we don’t support OpenCL and AMD GPUs.

Then, you need to install PyTorch 1.13.1. Retrieve and execute the pip command of version 1.13.1 from the previous versions install guide section.

Then install some extra Python libraries:

python3 -m pip install "numba==0.56.3" "profilehooks==1.12.0" "pytorch_lightning==1.8.6" "tqdm==4.63.0" "kornia==0.6.8"


To compile OpenEB, follow those steps:

  1. Retrieve the source code of OpenEB and go in the created directory

git clone --branch 4.5.2
cd openeb

In the following sections, absolute path to this directory is called OPENEB_SRC_DIR


If you choose to download an archive of OpenEB from GitHub rather than cloning the repository, you need to ensure that you select a Full.Source.Code.* archive instead of using the automatically generated Source.Code.* archives. This is because the latter do not include a necessary submodule.

  1. Create and open the build directory

mkdir build && cd build
  1. Generate the makefiles using CMake:



If you want to specify to cmake which version of Python to consider, you should use the option -DPython3_EXECUTABLE=<path_to_python_to_use>. This is useful, for example, when you have a more recent version of Python than the ones we support installed on your system. In that case, cmake would select it and compilation might fail.

  1. Compile:

cmake --build . --config Release -- -j `nproc`

Once the compilation is done, you have two options: you can choose to work directly from the build folder or you can deploy the OpenEB files in the system path (/usr/local/lib, /usr/local/include…).

  • Option 1 - working from build folder

    • To use OpenEB directly from the build folder, you need to update some environment variables using this script (which you may add to your ~/.bashrc to make it permanent):

      source utils/scripts/
    • Prophesee camera plugin is included in OpenEB, but you still need to copy the udev rules files in the system path and reload them so that your camera is detected with this command:

    sudo cp <OPENEB_SRC_DIR>/hal_psee_plugins/resources/rules/*.rules /etc/udev/rules.d
    sudo udevadm control --reload-rules
    sudo udevadm trigger
  • Option 2 - deploying in the system path

    • To deploy OpenEB, launch the following command:

      sudo cmake --build . --target install


      You can also deploy the OpenEB files (applications, samples, libraries etc.) in a directory of your choice by using the CMAKE_INSTALL_PREFIX variable (-DCMAKE_INSTALL_PREFIX=<OPENEB_INSTALL_DIR>) when generating the makefiles in step 3 of the compilation. Similarly, you can configure the directory where the Python packages will be deployed using the PYTHON3_SITE_PACKAGES variable (-DPYTHON3_SITE_PACKAGES=<PYTHON3_PACKAGES_INSTALL_DIR>).

    • you also need to update LD_LIBRARY_PATH and HDF5_PLUGIN_PATH (which you may add to your ~/.bashrc to make it permanent):

      export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib
      export HDF5_PLUGIN_PATH=$HDF5_PLUGIN_PATH:/usr/local/lib/hdf5/plugin


If you are using a third-party camera, you need to install the plugin provided by the camera vendor and specify the location of the plugin using the MV_HAL_PLUGIN_PATH environment variable.

Running tests

Running the test suite is a sure-fire way to ensure you did everything well with your compilation and installation process.

  • Go to this page to download the files necessary to run the tests. Click the Download button on the top right of the page. The obtained archive weighs around 1.2 Gb.

  • Extract and put the contents of this archive into <OPENEB_SRC_DIR>/datasets. For instance, the correct path of sequence gen31_timer.raw should be <OPENEB_SRC_DIR>/datasets/openeb/gen31_timer.raw.

  • Make sure you installed gtest as listed in the “Installing Dependencies” section.

  • Regenerate the makefiles with the test options enabled:

cd <OPENEB_SRC_DIR>/build
  • Compile again:

cmake --build . --config Release -- -j `nproc`
  • Make sure you launched the utils/scripts/ mentioned before, and run the test suite:

ctest -C Release

Get started!

You are now ready to use OpenEB. The best way to start getting familiar with the event-based technology is to open an event-based camera with Metavision Viewer to begin data collection and visualization. You can choose to dive directly in the SDK by following a getting started guide or looking at a Code Sample.