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doc/env.md
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doc/env.md
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## Personal Development Environment ##
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- VS Code for Windows 11
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- Ubuntu 18.04 x86_64 / C++17 / GCC 7.5 / GTX 1080 GPU
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- GStreamer 1.14.5 / OpenCV 4.6
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---------
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Install GStreamer (1.14.5 for Ubuntu 18.04 by default):
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```
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apt-get install libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 gstreamer1.0-pulseaudio libgstrtspserver-1.0-dev gstreamer1.0-rtsp
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```
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Install OpenCV from source with `gstreamer` ON (CUDA optional). download source code of OpenCV 4.6.0 (with extra contrib modules) from github first, put them at the same directory then run `cmake` and `make` command:
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```
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step 1:
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cd `the path of opencv 4.6.0`
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mkdir build && cd build
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```
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```
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step 2:
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cmake -D CMAKE_BUILD_TYPE=RELEASE \
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-D CMAKE_INSTALL_PREFIX=/usr/local \
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-D WITH_TBB=ON \
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-D ENABLE_FAST_MATH=1 \
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-D CUDA_FAST_MATH=1 \
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-D WITH_CUBLAS=1 \
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-D WITH_CUDA=ON \
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-D BUILD_opencv_cudacodec=OFF \
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-D WITH_CUDNN=ON \
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-D OPENCV_DNN_CUDA=ON \
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-D CUDA_ARCH_BIN=6.1 \
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-D WITH_V4L=ON \
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-D WITH_QT=OFF \
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-D WITH_OPENGL=ON \
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-D WITH_GSTREAMER=ON \
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-D OPENCV_GENERATE_PKGCONFIG=ON \
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-D OPENCV_PC_FILE_NAME=opencv.pc \
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-D OPENCV_ENABLE_NONFREE=ON \
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-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.6.0/modules \
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-D INSTALL_PYTHON_EXAMPLES=OFF \
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-D INSTALL_C_EXAMPLES=OFF \
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-D BUILD_EXAMPLES=OFF ..
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```
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```
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step 3:
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make -j8
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```
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---------
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`VcXsrv` for screen display from remote machine to local desktop in case of using SSH terminal.
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- first install PC client from: https://sourceforge.net/p/vcxsrv/wiki/Home/
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- then run `export DISPLAY=local_ip:0.0` (or add it to ~/.bashrc) on remote machine (linux server or embedded board)
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---------
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Maybe you need install nginx with `http-rtmp-module` as rtmp server for debug purpose (other tools such as `ZLMediaKit` works fine **which was highly recommended**). Also, maybe you need a rtsp server from which we can receive rtsp stream for debug purpose.
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- [how to install ZLMeidaKit](https://github.com/ZLMediaKit/ZLMediaKit/wiki/vcpkg%E6%96%B9%E5%BC%8F%E5%AE%89%E8%A3%85zlmediakit)
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- [how to push stream to ZLMediaKit](https://github.com/ZLMediaKit/ZLMediaKit/wiki/ZLMediaKit%E6%8E%A8%E6%B5%81%E6%B5%8B%E8%AF%95), a few samples which used `vp_rtmp_des_node` in VidepPipe would push rtmp stream to server.
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- [how to pull/play stream from ZLMediaKit](https://github.com/ZLMediaKit/ZLMediaKit/wiki/%E6%92%AD%E6%94%BEurl%E8%A7%84%E5%88%99), VLC player or ffplay was recommended.
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---------
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Please prepare kafka server AND install `librdkafka` client sdk if you want to enable kafka-related codes.
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- [how to install kafka server](https://kafka.apache.org/quickstart)
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- how to install `librdkafka`? run `sudo apt-get install librdkafka-dev`
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## tips ##
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- Use shared_ptr/make_shared in whole project, do not use new/delete.
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- The pipeline is driven by stream data, if your app is not responding, maybe no stream input.
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## about Hardware Acceleration ##
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Since decode & encode in VideoPipe depend on gstreamer (encapsulated inside opencv), if you want to use your GPUs/NPUs to accelerate decoding and encoding performace, you need get/install HARD decode or HARD encode `gstreamer plugins` correctly first and modify gst launch string (take `vp_file_des_node` for example):
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```cpp
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appsrc ! videoconvert ! x264enc bitrate=%d ! mp4mux ! filesink location=%s
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```
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to
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```
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appsrc ! videoconvert ! nvv4l2h264enc bitrate=%d ! mp4mux ! filesink location=%s
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```
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the plugin `x264enc` use CPUs to encode video stream, but `nvv4l2h264enc`(comes from DeepStream SDK) use GPUs instread. if you use other platforms other than NVIDIA, you need Corresponding Hardware Acceleration plugins.
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**soft/hard decode example**
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```
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gst-launch-1.0 filesrc location=./face.mp4 ! qtdemux ! h264parse ! avdec_h264 ! videoconvert ! autovideosink // decode by avdec_h264 use CPUs
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gst-launch-1.0 filesrc location=./face.mp4 ! qtdemux ! h264parse ! nvv4l2decoder ! videoconvert ! autovideosink // decode by nvv4l2decoder use NVIDIA GPUs
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```
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**soft/hard encode example**
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```
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gst-launch-1.0 filesrc location=./face.mp4 ! qtdemux ! h264parse ! avdec_h264 ! x264enc ! h264parse ! flvmux ! filesink location=./new_face.flv // encode by x264enc use CPUs
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gst-launch-1.0 filesrc location=./face.mp4 ! qtdemux ! h264parse ! avdec_h264 ! nvv4l2h264enc ! h264parse ! flvmux ! filesink location=./new_face.flv // encode by nvv4l2h264enc use NVIDIA GPUs
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```
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[source code of hard decode/encode gstreamer plugins for NVIDIA](https://gitlab.freedesktop.org/gstreamer/gstreamer/-/tree/main/subprojects/gst-plugins-bad/sys/nvcodec).(developed by community, open source), we could also use decode/encode plugins from [DeepStream SDK](https://docs.nvidia.com/metropolis/deepstream/6.0/dev-guide/text/DS_Quickstart.html) which maintained by NVIDIA but closed source.
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