## Personal Development Environment ##

- VS Code for Windows 11
- Ubuntu 18.04 x86_64 / C++17  / GCC 7.5 / GTX 1080 GPU
- GStreamer 1.14.5 / OpenCV 4.6
---------

Install GStreamer (1.14.5 for Ubuntu 18.04 by default):
```
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
```

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:

```
step 1:
cd `the path of opencv 4.6.0`
mkdir build && cd build
```

```
step 2:
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D WITH_TBB=ON \
-D ENABLE_FAST_MATH=1 \
-D CUDA_FAST_MATH=1 \
-D WITH_CUBLAS=1 \
-D WITH_CUDA=ON \
-D BUILD_opencv_cudacodec=OFF \
-D WITH_CUDNN=ON \
-D OPENCV_DNN_CUDA=ON \
-D CUDA_ARCH_BIN=6.1 \
-D WITH_V4L=ON \
-D WITH_QT=OFF \
-D WITH_OPENGL=ON \
-D WITH_GSTREAMER=ON \
-D OPENCV_GENERATE_PKGCONFIG=ON \
-D OPENCV_PC_FILE_NAME=opencv.pc \
-D OPENCV_ENABLE_NONFREE=ON \
-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.6.0/modules \
-D INSTALL_PYTHON_EXAMPLES=OFF \
-D INSTALL_C_EXAMPLES=OFF \
-D BUILD_EXAMPLES=OFF ..
```

```
step 3:
make -j8
```

---------
`VcXsrv` for screen display from remote machine to local desktop in case of using SSH terminal.

- first install PC client from: https://sourceforge.net/p/vcxsrv/wiki/Home/
- then run `export DISPLAY=local_ip:0.0` (or add it to ~/.bashrc) on remote machine (linux server or embedded board)

---------
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.

- [how to install ZLMeidaKit](https://github.com/ZLMediaKit/ZLMediaKit/wiki/vcpkg%E6%96%B9%E5%BC%8F%E5%AE%89%E8%A3%85zlmediakit)
- [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.
- [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.

---------
Please prepare kafka server AND install `librdkafka` client sdk if you want to enable kafka-related codes.
- [how to install kafka server](https://kafka.apache.org/quickstart)
- how to install `librdkafka`? run `sudo apt-get install librdkafka-dev`


## tips ##
- Use shared_ptr/make_shared in whole project, do not use new/delete.
- The pipeline is driven by stream data, if your app is not responding, maybe no stream input.


## about Hardware Acceleration ##
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):
```cpp
appsrc ! videoconvert ! x264enc bitrate=%d ! mp4mux ! filesink location=%s
```
to
```
appsrc ! videoconvert ! nvv4l2h264enc bitrate=%d ! mp4mux ! filesink location=%s
```
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.

**soft/hard decode example**
```
gst-launch-1.0 filesrc location=./face.mp4 ! qtdemux ! h264parse ! avdec_h264 ! videoconvert ! autovideosink    // decode by avdec_h264 use CPUs
gst-launch-1.0 filesrc location=./face.mp4 ! qtdemux ! h264parse ! nvv4l2decoder ! videoconvert ! autovideosink // decode by nvv4l2decoder use NVIDIA GPUs
```

**soft/hard encode example**
```
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
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
```
[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.