通过支持 CUDA 计算的 LAN 或 Wan 与 RTSP 流共享视频、屏幕、摄像头和音频_Makefile_代码_下载

# Multimedia Sharing Tool Share video, screen, camera and audio with an RTSP stream through Lan or Wan supporting CUDA computations in a High Performance embedded environment (Nvidia Jetson Nano), applying real-time AI techiques of Intrusion Detection with bounding boxes localization and frames manipulations. A demonstration video can be found here: https://www.youtube.com/watch?v=gbF5GQyb1T8 NV Developer Jetson Projects forum post: https://devtalk.nvidia.com/default/topic/1065923/jetson-projects/multimedia-sharing-tool-a-jetson-nano-project/ ## External Dependencies: Before installing dependencies: ```sh $ sudo apt-get update $ sudo apt-get upgrade $ sudo apt-get install gcc $ sudo apt-get install cmake $ sudo apt-get install git $ sudo apt-get install libgtk2.0-dev ``` ### QT Creator On ARM architectures: `sudo apt-get install qt5-default qtcreator -y` Other architectures: [QT Home Page](https://www.qt.io/) ### CUDA (Jetson users) Add the CUDA directory to $PATH ```sh $ export PATH=${PATH}:/usr/local/cuda/bin $ export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda/lib64 ``` ### CUDA (Non Jetson users) 1. Update your Nvidia GPU driver with the latest Nvidia proprietary one 2. Install CUDA Toolkit following the [CUDA Installation Guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html) ### cURL development library `$ sudo apt-get install libcurl4-openssl-dev` ### NVIDIA TensorRT (if not already installed or preinstalled) [TensorRT Installation Guide](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html) ### Jetson-inference library 1. (Non Jetson users only) Copy `glibconfig.h` header to `/usr/include/glib-2.0/` ```sh $ sudo cp /usr/lib/x86_64-linux-gnu/glib-2.0/include/glibconfig.h /usr/include/glib-2.0/glibconfig.h ``` 2. Follow the Build instructions from the [Jetson-inference GitHub page](https://github.com/dusty-nv/jetson-inference). N.B. (Non Jetson users only) Once downloaded the Jetson-inference library and before building the project, check that inside CMakeLists.txt your GPU architecture is specified (e.g. `-gencode arch=compute_62,code=sm_62`). Otherwise add it. A list of Nvidia GPU architectures could be found [here](https://github.com/Werther158/multimedia_sharing_tool/wiki/Nvidia-GPU-architectures-compatibility) ### FFmpeg `$ sudo apt-get install ffmpeg` ### CUDA Enabled OpenCV with Contrib 1. Remove any previous installations of x264 ```sh $ sudo apt-get remove x264 libx264-dev ``` 2. (Jetson Nano only) Set a swap file to allow the system to use more than 4 GB of RAM A swap file installer could be found [here](https://github.com/JetsonHacksNano/installSwapfile) 3. Install OpenCV dependencies ```sh $ sudo apt-get install build-essential checkinstall cmake pkg-config yasm $ sudo apt-get install git gfortran $ sudo apt-get install libjpeg8-dev libjasper-dev libpng12-dev $ sudo apt-get install libtiff5-dev $ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libdc1394-22-dev $ sudo apt-get install libxine2-dev libv4l-dev $ sudo apt-get install libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev $ sudo apt-get install libqt4-dev libtbb-dev $ sudo apt-get install libatlas-base-dev $ sudo apt-get install libfaac-dev libmp3lame-dev libtheora-dev $ sudo apt-get install libvorbis-dev libxvidcore-dev $ sudo apt-get install libopencore-amrnb-dev libopencore-amrwb-dev $ sudo apt-get install x264 v4l-utils ``` 4. Clone `opencv` and `opencv_contrib` version 3.4.7 repository ```sh $ git clone -b 3.4.7 https://github.com/opencv/opencv $ git clone -b 3.4.7 https://github.com/opencv/opencv_contrib ``` 5. Make a directory i.e. `build` inside `opencv` directory, build and install the library ```sh $ mkdir build $ cd build $ cmake -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_INSTALL_PREFIX=/usr/local \ -D WITH_CUDA=ON \ -D ENABLE_FAST_MATH=1 \ -D CUDA_FAST_MATH=1 \ -D WITH_CUBLAS=1 \ -D INSTALL_PYTHON_EXAMPLES=ON \ -D INSTALL_C_EXAMPLES=OFF \ -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \ -D PYTHON_EXECUTABLE=~/.virtualenvs/cv/bin/python \ -D WITH_GTK=ON \ -D ENABLE_PRECOMPILED_HEADERS=OFF \ -D BUILD_opencv_cudacodec=OFF \ -D BUILD_EXAMPLES=ON .. $ nproc # use the number that nproc returns which is the number of cores of your processor. Let's say it returns 4. $ make -j4 $ sudo make install ``` ## Building the project: 1. Clone the project ``` $ git clone https://github.com/Werther158/multimedia_sharing_tool ``` 2. Open the `.pro` file with QT Creator 3. Set `multimedia_sharing_tool/build/debug` and `multimedia_sharing_tool/build/release` as debug and release directories 4. Build the project