ApraPipes

A pipeline framework for developing video and image processing applications. Supports multiple GPUs and Machine Learning tooklits

Build and Run Tests

Tested on Ubuntu 18.04 and Jetson Boards

Setup

• Clone with submodules

git clone --recursive https://github.com/Apra-Labs/ApraPipes.git

Prerequisites

• Run sudo apt-get update && sudo apt-get install build-essential to get latest build tools
• CMake minimum version 3.17 - Follow this article to update cmake
• ffmpeg

sudo apt install yasm -y
cd thirdparty/ffmpeg
./configure --enable-pic
make -j"$(($(nproc) - 1))"

• zxing

cd thirdparty/zxing-cpp
chmod +x build.sh
./build.sh

Ubuntu 18.04 x64

Prerequisites

• Install CUDA Toolkit 10.2
• Download Nvidia Video Codec SDK v10 and extract to thirdparty directory. Make sure thirdparty/Video_Codec_SDK_10.0.26/Interface and thirdparty/Video_Codec_SDK_10.0.26/Lib exist

Build

• chmod +x build_linux_x64.sh
• ./build_linux_x64.sh

Build can take ~2 hours depending on the machine configuration. This project uses hunter package manager.

Jetson boards - Nano, TX2, NX, AGX

Prerequisites

• Setup the board with Jetpack 4.4
• sudo apt-get install libncurses5-dev

Build

• chmod +x build_jetson.sh
• ./build_jetson.sh

Build can take ~12 hours on Jetson Nano.

Run Tests

• list all tests _build/aprapipesut --list_content
• run all tests _build/aprapipesut
• run one test _build/aprapipesut --run_test=filenamestrategy_tests/boostdirectorystrategy
• run one test with arguments _build/aprapipesut --run_test=unit_tests/params_test -- -ip 10.102.10.121 -data ArgusCamera
  • Look at the unit_tests/params_test to check for sample usage of parameters in test code

This project uses boost tests for unit tests.

Update Submodules

git submodule update --init --recursive

Documentation

• Open docs/build/html/index.html using Google Chrome

To regenerate documentation

To build docs
apt-install get python-sphinx 
pip install sphinx-rtd-theme
cd docs
make html