Compiling OpenEB on Linux from sources

This section describes how to install OpenEB on Linux Ubuntu 22.04 and 24.04 64-bit with packages.

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

  • Linux: Ubuntu 22.04 or 24.04 64-bit

  • Architecture: amd64 (a.k.a. x64)

  • Graphic card with support of OpenGL 3.0 minimum

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.

If you don’t want to go through the compilation steps, you can install pre-built Ubuntu packages of OpenEB by following our specific installation guide.

Note

OpenEB is the open source version of our code base for the modules Base, Core, Core ML, Stream, UI, and HAL (see Metavision modules organization). If you own the full source code of the SDK (see our SDK offers), OpenEB will also be the basis of your installation, but you should follow the installation guide of SDK from sources.

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 OpenEB 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).

Retrieving OpenEB source code

To retrieve OpenEB source code, you can just clone the GitHub repository:

git clone https://github.com/prophesee-ai/openeb.git --branch 5.0.0

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

Warning

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.

Installing Dependencies

Note

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 BASE_IMAGE_TAG=22.04 -f Dockerfile.OpenEB -t openeb:ubuntu-22.04 .
sudo docker run -ti --rm openeb:ubuntu-22.04 /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-22.04 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

To execute our Python Samples and use the Python API, you will need Python and some additional libraries.

We support Python 3.9 and 3.10 on Ubuntu 22.04 and Python 3.11 and 3.12 on Ubuntu 24.04.

We recommend using Python with virtualenv to avoid conflicts with other installed Python packages. So, first install it along with some Python development tools:

sudo apt -y install python3.x-venv python3.x-dev
# where "x" is 9, 10, 11 or 12 depending on your Python version

Next, create a virtual environment:

python3 -m venv /tmp/prophesee/py3venv --system-site-packages

When creating the virtual environment, it is necessary to use the --system-site-packages option to ensure that the OpenEB Python packages installed in the system directories are accessible. However, this option also makes your local user site-packages visible by default. To prevent this and maintain a cleaner virtual environment, you should set the environment variable PYTHONNOUSERSITE to true (which you may add to your ~/.bashrc to make it permanent):

export PYTHONNOUSERSITE=true

Finally, install the necessary dependencies:

/tmp/prophesee/py3venv/bin/python -m pip install pip --upgrade
/tmp/prophesee/py3venv/bin/python -m pip install -r OPENEB_SRC_DIR/utils/python/requirements_openeb.txt

Optionally, you can run the activate command (source /tmp/prophesee/py3venv/bin/activate) to modify your shell’s environment variables, setting the virtual environment’s Python interpreter and scripts as the default for your current session. This allows you to use simple commands like python without needing to specify the full path each time.

For simplicity, throughout the documentation of the Code Samples, we will assume the python command refers to the interpreter within the activated virtual environment.

Note

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

In this documentation, we chose to use pip as a package manager.

The Python bindings of the C++ API rely on the pybind11 library (version >= 2.11.0).

Note

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:

wget https://github.com/pybind/pybind11/archive/v2.11.0.zip
unzip v2.11.0.zip
cd pybind11-2.11.0
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.

Note

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

Compilation

To compile OpenEB, follow those steps:

  1. Create and open the build directory

mkdir build && cd build

  1. Generate the makefiles using CMake:

cmake .. -DCMAKE_BUILD_TYPE=Release -DBUILD_TESTING=OFF

Note

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/setup_env.sh

    • 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

      Note

      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 2 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/hdf5/lib/plugin  # On Ubuntu 22.04
export HDF5_PLUGIN_PATH=$HDF5_PLUGIN_PATH:/usr/local/lib/hdf5/plugin  # On Ubuntu 24.04

Note

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 and click on the Download All button to retrieve an archive of all the files necessary to run the tests. Please be aware that the archive you’ll receive is approximately 1.5 Gb in size.

  • 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
cmake .. -DBUILD_TESTING=ON

  • Compile again:

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

  • Make sure you launched the utils/scripts/setup_env.sh 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.