A pipeline framework for developing video and image processing applications. Supports multiple GPUs and Machine Learning tooklits
Tested on Ubuntu 18.04 and Jetson Boards
- Clone with submodules
git clone --recursive https://github.com/kumaakh/ApraPipes.git
- CMake minimum version 3.14 - 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
- Install CUDA Toolkit 10.2
- Download Nvidia Video Codec SDK v10 and extract to
thirdparty
directory. Make surethirdparty/Video_Codec_SDK_10.0.26/Interface
andthirdparty/Video_Codec_SDK_10.0.26/Lib
exist
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.
- Setup the board with Jetpack 4.4
- sudo apt-get install libncurses5-dev
chmod +x build_jetson.sh
./build_jetson.sh
Build can take ~12 hours on Jetson Nano.
- 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.
git submodule update --init --recursive
- Open
docs/build/html/index.html
using Google Chrome