OpenEB is the open source project associated with Metavision Intelligence

It enables anyone to get a better understanding of event-based vision, directly interact with events and build their own applications or plugins. As a camera manufacturer, ensure your customers benefit from the most advanced event-based software suite available by building your own plugin. As a creator, scientist, academic, join and contribute to the fast-growing event-based vision community.

OpenEB is composed of the Open modules of Metavision Intelligence:

• HAL: Hardware Abstraction Layer to operate any event-based vision device.
• Base: Foundations and common definitions of event-based applications.
• Core: Generic algorithms for visualization, event stream manipulation, applicative pipeline generation.
• Core ML: Generic functions for Machine Learning, event_to_video and video_to_event pipelines.
• Driver: High-level abstraction built on the top of HAL to easily interact with event-based cameras.
• UI: Viewer and display controllers for event-based data.

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

• EVK1 - Gen3/Gen3.1 VGA
• EVK2 - Gen4.1 HD
• EVK3 - Gen 3.1 VGA / Gen4.1 HD
• EVK4 - HD

This document describes how to compile and install the OpenEB codebase. For further information, refer to our online documentation where you will find some tutorials to get you started in C++ or Python, some samples to discover how to use our API and a more detailed description of our modules and packaging.

Currently, we support Ubuntu 18.04 and 20.04. Compilation on other versions of Ubuntu or other Linux distributions was not tested. For those platforms some adjustments to this guide or to the code itself may be required (specially for non-Debian Linux).

If you are upgrading OpenEB from a previous version, you should first read carefully the Release Notes <https://docs.prophesee.ai/stable/release_notes.html>_ as some changes may impact your usage of our SDK (e.g. API updates) and cameras (e.g. firmware update <https://support.prophesee.ai/portal/en/kb/articles/evk-firmware-versions>_ 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, make a global check in your system paths (/usr/lib, /usr/local/lib, /usr/include, /usr/local/include) and in your environment variables (PATH, PYTHONPATH and LD_LIBRARY_PATH) to remove occurrences of Prophesee or Metavision files.

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 googletest libgtest-dev libboost-all-dev libusb-1.0-0-dev
sudo apt -y install libglew-dev libglfw3-dev libcanberra-gtk-module ffmpeg

Optionally, if you want to run the tests, you need to compile the GoogleTest <https://google.github.io/googletest/>_ package:

cd /usr/src/googletest
sudo cmake .
sudo make
sudo make install

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 3.6 and 3.7 on Ubuntu 18.04 and Python 3.7 and 3.8 on Ubuntu 20.04).

Then install pip and some Python libraries:

sudo apt -y install python3-pip python3-distutils
sudo apt -y install python3.X-dev  # where X is 6, 7 or 8 depending on your Python version (3.6, 3.7 or 3.8)
python3 -m pip install pip --upgrade
python3 -m pip install "opencv-python>=4.5.5.64" "sk-video==1.1.10" "fire==0.4.0" "numpy<=1.21" pandas scipy h5py 
python3 -m pip install jupyter jupyterlab matplotlib "ipywidgets==7.6.5" pytest command_runner

The Python bindings of the C++ API rely on the pybind11 library, specifically version 2.6.0.

Note that pybind11 is required only if you want to use the Python bindings of the C++ API . You can opt out of creating these bindings by passing the argument -DCOMPILE_PYTHON3_BINDINGS=OFF at step 3 during compilation (see below). In that case, you will not need to install 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/v2.6.0.zip
unzip v2.6.0.zip
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 (10.2 or 11.1) <https://developer.nvidia.com/cuda-downloads>_ and cuDNN <https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html>_ 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 <https://docs.nvidia.com/deeplearning/cudnn/support-matrix/index.html>_.

Note that, at the moment, we don't support OpenCL <https://www.khronos.org/opencl/>_ and AMD GPUs.

Then, install PyTorch 1.8.2 LTS. This version was deprecated by PyTorch team but can still be downloaded in the Previous Versions page of pytorch.org <https://pytorch.org/get-started/previous-versions/#v182-with-lts-support>_ (in future releases of Metavision ML, more recent version of PyTorch will be leveraged). Retrieve and execute the pip command for the installation. Here is an example of a command that can be retrieved for pytorch using CUDA 11.1:

python3 -m pip install torch==1.8.2 torchvision==0.9.2 torchaudio==0.8.2 --extra-index-url https://download.pytorch.org/whl/lts/1.8/cu111

Then install some extra Python libraries:

python3 -m pip install numba profilehooks "pytorch_lightning==1.5.10" "tqdm==4.63.0" "kornia==0.6.1"

1. Retrieve the code git clone https://github.com/prophesee-ai/openeb.git
2. Create and open the build directory in the openeb folder (absolute path to this directory is called OPENEB_SRC_DIR in next sections): cd openeb; mkdir build && cd build
3. Generate the makefiles using CMake: cmake .. -DBUILD_TESTING=OFF
4. Compile: cmake --build . --config Release -- -j 4

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

source <OPENEB_SRC_DIR>/build/utils/scripts/setup_env.sh

Optionally, you can deploy the OpenEB files in the system paths to use them as 3rd party dependency in some other code with the following command: sudo cmake --build . --target install.

In that case, you will also need to update:

• LD_LIBRARY_PATH with export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib

If you want those settings to be permanent, you should add the previous commands in your ~/.bashrc.

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. 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>).

Since OpenEB 3.0.0, Prophesee camera plugins are included in OpenEB. If you did not perform the optional deployment step (sudo cmake --build . --target install) and instead used “setup_env.sh”, then you need to copy the udev rules files used by Prophesee cameras 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

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.

To get started with OpenEB, you can download some sample recordings and visualize them with metavision_viewer or you can stream data from your Prophesee-compatible event-based camera.

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

• Download the files necessary to run the tests. Click Download on the top right folder. Beware of the size of the obtained archive which weighs around 500 Mb.

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

• Regenerate the makefiles with the test options on.

cd <OPENEB_SRC_DIR>/build
cmake .. -DBUILD_TESTING=ON

• Compile again. cmake --build . --config Release -- -j 4

• Finally, run the test suite: ctest --verbose

Currently, we support only Windows 10. Compilation on other versions of Windows was not tested. For those platforms some adjustments to this guide or to the code itself may be required.

If you are upgrading OpenEB from a previous version, you should first read carefully the Release Notes <https://docs.prophesee.ai/stable/release_notes.html>_ as some changes may impact your usage of our SDK (e.g. :API updates) and cameras (e.g. firmware update <https://support.prophesee.ai/portal/en/kb/articles/evk-firmware-versions>_ might be necessary).

Then, if you have previously installed any Prophesee's software, you will need to uninstall it first. Remove the folders where you installed Metavision artifacts (check both the build folder of the source code and C:\Program Files\Prophesee which is the default install path of the deployment step).

Some steps of this procedure don't work on FAT32 and exFAT file system. Hence, make sure that you are using a NTFS file system before going further.

You must enable the support for long paths:

• Hit the Windows key, type gpedit.msc and press Enter
• Navigate to Local Computer Policy > Computer Configuration > Administrative Templates > System > Filesystem
• Double-click the "Enable Win32 long paths" option, select the "Enabled" option and click "OK"

To compile OpenEB, you will need to install some extra tools:

• install git
• install CMake 3.20
• install Microsoft C++ compiler (64-bit). You can choose one of the following solutions:
  • For building only, you can install MS Build Tools (free, part of Windows 10 SDK package)
    • Download and run "Build tools for Visual Studio 2019" installer
    • Select "C++ build tools", make sure Windows 10 SDK is checked, and add English Language Pack
  • For development, you can also download and run Visual Studio Installer
• install vcpkg that will be used for installing dependencies:
  • download and extract vcpkg version 2022.03.10
  • cd <VCPKG_SRC_DIR>
  • bootstrap-vcpkg.bat
• install the libraries by running vcpkg.exe install --triplet x64-windows libusb eigen3 boost opencv glfw3 glew gtest dirent
  • Note that to avoid using --triplet x64-windows, which informs vcpkg to install packages for a x64-windows target, you can run setx VCPKG_DEFAULT_TRIPLET x64-windows (you need to close the command line and re-open it to ensure that this variable is set)
• Finally, download and install ffmpeg and add the bin directory to your PATH.

Note that if you are using vcpkg for various projects or multiple versions of OpenEB, you might want to optimize the number of vcpkg install you manage. To do so, you will need the versions of the libraries we require. Those can be found in the vcpkg repository but we list them here for convenience:

• libusb: 1.0.24
• eigen3: 3.4.0
• boost: 1.78.0
• opencv: 4.5.5
• glfw3: 3.3.6
• glew: 2.2.0
• gtest: 1.11.0
• dirent: 1.23.2

• Download "Windows x86-64 executable installer" for one of these Python versions:
  • Python 3.7
  • Python 3.8
• Add Python install and script directories in your PATH and make sure they are listed before the WindowsApps folder which contains a Python alias launching the Microsoft Store. So, if you installed Python 3.8 in the default path, your user PATH should contain those three lines in that order:

%USERPROFILE%\AppData\Local\Programs\Python\Python38
%USERPROFILE%\AppData\Local\Programs\Python\Python38\Scripts
%USERPROFILE%\AppData\Local\Microsoft\WindowsApps

Then install pip and some Python libraries:

python -m pip install pip --upgrade
python -m pip install "opencv-python>=4.5.5.64" "sk-video==1.1.10" "fire==0.4.0" "numpy<=1.21" pandas scipy h5py
python -m pip install jupyter jupyterlab matplotlib "ipywidgets==7.6.5" pytest command_runner

The Python bindings of the C++ API rely on the pybind11 library. You should install pybind using vcpkg in order to get the appropriate version: vcpkg.exe install --triplet x64-windows pybind11

Note that pybind11 is required only if you plan to use the Python bindings of the C++ API. You can opt out of creating these bindings by passing the argument -DCOMPILE_PYTHON3_BINDINGS=OFF at step 2 during compilation (see section "Compilation using CMake"). In that case, you will not need to install pybind11, but you won't be able to use our Python interface to the C++ API.

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 (10.2 or 11.1) <https://developer.nvidia.com/cuda-downloads>_ and cuDNN <https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html>_ to leverage them with pytorch and libtorch.

Then, install pytorch. Go to pytorch.org <https://pytorch.org>_ to retrieve the pip command that you will launch in a console to install PyTorch 1.8.2 LTS. Here is an example of a command that can be retrieved for pytorch using CUDA 11.1:

python -m pip install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio==0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html

Then install some extra Python libraries:

python -m pip install numba profilehooks "pytorch_lightning==1.5.10" "tqdm==4.63.0" "kornia==0.6.1"

First, retrieve the codebase:

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

Open a command prompt inside the openeb folder (absolute path to this directory is called OPENEB_SRC_DIR in next sections) and do as follows:

1. Create and open the build directory, where temporary files will be created: mkdir build && cd build
2. Generate the makefiles using CMake: cmake -A x64 -DCMAKE_TOOLCHAIN_FILE=<OPENEB_SRC_DIR>\cmake\toolchains\vcpkg.cmake -DVCPKG_DIRECTORY=<VCPKG_SRC_DIR> ... Note that the value passed to the parameter -DCMAKE_TOOLCHAIN_FILE must be an absolute path, not a relative one.
3. Compile: cmake --build . --config Release --parallel 4

To use OpenEB directly from the build folder, update your environment variables using this script:

<OPENEB_SRC_DIR>\build\utils\scripts\setup_env.bat

Optionally, you can deploy the OpenEB files (applications, samples, libraries etc.) in a directory of your choice. To do so, configure the target folder (OPENEB_INSTALL_DIR) with CMAKE_INSTALL_PREFIX variable (default value is C:\Program Files\Prophesee) when generating the makefiles in step 2:

cmake .. -A x64 -DCMAKE_TOOLCHAIN_FILE=<OPENEB_SRC_DIR>\cmake\toolchains\vcpkg.cmake -DVCPKG_DIRECTORY=<VCPKG_SRC_DIR> -DCMAKE_INSTALL_PREFIX=<OPENEB_INSTALL_DIR> -DBUILD_TESTING=OFF

You can also configure the directory where the Python packages will be deployed using the PYTHON3_SITE_PACKAGES variable (note that in that case, you will also need to edit your environment variable PYTHONPATH and append the <PYTHON3_PACKAGES_INSTALL_DIR> path):

cmake .. -A x64 -DCMAKE_TOOLCHAIN_FILE=<OPENEB_SRC_DIR>\cmake\toolchains\vcpkg.cmake -DVCPKG_DIRECTORY=<VCPKG_SRC_DIR> -DCMAKE_INSTALL_PREFIX=<OPENEB_INSTALL_DIR> -DPYTHON3_SITE_PACKAGES=<PYTHON3_PACKAGES_INSTALL_DIR> -DBUILD_TESTING=OFF

Once you performed this configuration, you can launch the actual installation of the OpenEB files:

cmake --build . --config Release --target install

Open a command prompt inside the openeb folder (absolute path to this directory is called OPENEB_SRC_DIR in next sections) and do as follows:

1. Create and open the build directory, where temporary files will be created: mkdir build && cd build
2. Generate the Visual Studio files using CMake: cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_TOOLCHAIN_FILE=<OPENEB_SRC_DIR>\cmake\toolchains\vcpkg.cmake -DVCPKG_DIRECTORY=<VCPKG_SRC_DIR> .. (adapt to your Visual Studio version). Note that the value passed to the parameter -DCMAKE_TOOLCHAIN_FILE must be an absolute path, not a relative one.
3. Open the solution file metavision.sln, select the Release configuration and build the ALL_BUILD project.

Since OpenEB 3.0.0, Prophesee camera plugins are included in OpenEB, but you need to install the drivers for the cameras to be available on Windows. To do so, follow this procedure:

1. download wdi-simple.exe from our file server
2. execute the following commands in a Command Prompt launched as an administrator:

wdi-simple.exe -n "EVK" -m "Prophesee" -v 0x04b4 -p 0x00f4
wdi-simple.exe -n "EVK" -m "Prophesee" -v 0x03fd -p 0x5832 -i 00
wdi-simple.exe -n "EVK" -m "Prophesee" -v 0x04b4 -p 0x00f5
wdi-simple.exe -n "EVK" -m "Prophesee" -v 0x04b4 -p 0x00f3

If you are using a third-party camera, you need to follow the instructions provided by the camera vendor to install the driver and the camera plugin. Make sure that you reference the location of the plugin in the MV_HAL_PLUGIN_PATH environment variable.

To get started with OpenEB, you can download some sample recordings and visualize them with metavision_viewer or you can stream data from your Prophesee-compatible event-based camera.

Note that since OpenEB 3.0.0, Prophesee camera plugins are included in the OpenEB repository, so you don't need to perform any extra step to install them. 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 the test suite is a sure-fire way to ensure you did everything well with your compilation and installation process.

• Download the files necessary to run the tests. Click Download on the top right folder. Beware of the size of the obtained archive which weighs around 500 Mb.

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

• To run the test suite you need to reconfigure your build environment using CMake and to recompile

  • Compilation using CMake

  1. Regenerate the build using CMake (note that -DCMAKE_TOOLCHAIN_FILE must be absolute path, not a relative one)::

     cd <OPENEB_SRC_DIR>/build
     cmake -A x64 -DCMAKE_TOOLCHAIN_FILE=<OPENEB_SRC_DIR>\cmake\toolchains\vcpkg.cmake -DVCPKG_DIRECTORY=<VCPKG_SRC_DIR> -DBUILD_TESTING=ON ..
     

  2. Compile: cmake --build . --config Release --parallel 4

  • Compilation using MS Visual Studio

  1. Generate the Visual Studio files using CMake (adapt the command to your Visual Studio version):

     cmake -G "Visual Studio 16 2019" -A x64 -DCMAKE_TOOLCHAIN_FILE=<OPENEB_SRC_DIR>\cmake\toolchains\vcpkg.cmake -DVCPKG_DIRECTORY=<VCPKG_SRC_DIR> -DBUILD_TESTING=ON ..

     Note that the value passed to the parameter -DCMAKE_TOOLCHAIN_FILE must be an absolute path, not a relative one.

  2. Open the solution file metavision.sln, select the Release configuration and build the ALL_BUILD project.

• Running the test suite is then simply ctest -C Release