Skip to main content

Featured Post

OpenCV 4.5 simple optical flow GPU tutorial cuda::FarnebackOpticalFlow

This OpenCV tutorial is a very simple code example of GPU Cuda optical flow in OpenCV written in c++. The configuration of the project, code, and explanation are included for farneback Optical Flow method. Farneback algorithm is a dense method that is used to process all the pixels in the given image. The dense methods are slower but more accurate as all the pixels of the image are processed. In the following example, I am displaying just a few pixes based on a grid. I am not displaying all the pixes. In the opposite to dense method the sparse method like Lucas Kanade using just a selected subset of pixels. They are faster. Both methods have specific applications. Lucas-Kanade is widely used in tracking. The farneback can be used for the analysis of more complex movement in image scene and furder segmentation based on these changes. As dense methods are slightly slower, the GPU and Cuda implementation can lead to great performance improvements to calculate optical flow for all pixels o

Opencv tutorial RTMP video streaming to NGINX restream as HLS

Video streaming Tutorial of sending processed Opencv video to NGINX and distributing video from NGINX (broadcast) by HLS stream for a wider audience, like multiple web players, VLC, or any other video stream receiver.

Opencv RTMP HLS video streaming
Opencv application HLS streaming by GStreamer and NGINX

 We will use GStreamer to send video from the Opencv application by rtmp2sink to an RTMP module in NGINX. In our example, the server is a widely used NGINX server with an Nginx-RTMP-module. The NGINX will receive RTMP video from Opencv and restream as an HLS video stream considered for multiple end consumers. This is a follow-up to the previous article about Video streaming from Opencv to RtspSimpleServer by rtsp protocol. 

The goal is the same. Send video from Opencv to the server and restream the video for a wider audience. The difference is that RtspSimpleServer running on windows, NGINX is running in docker (WSL2). The one-to-one communication between Opencv and RtspSimpleServer was established by RTSP protocol (Gstreamer rtspclientsink). The one-to-one communication in today's tutorial between Opencv and Nginx is established by RTMP protocol rtmp2sink (Gstreamer rtmp2sink).

Streaming opencv video to NGINX by RTMP
Example architecture of streaming Opencv video to NGINX by RTMP and restream as HLS


The previous article used RTSP streaming to RtspSimpleServer by GStreamer rtspclientsin. This article uses RTMP to NGINX by GStreamer rtmp2sink. Let's discuss the difference between these two protocols.

  • RTMP — Real-time messaging protocol, TCP
  • RTSP — Real-time streaming protocol, UDP, multicast UDP, TCP and RTP

Should I use RTSP or RTMP? I do not know. It depends on your application. RTSP is widely used by almost all IP cameras. So, the IP camera RTSP stream is better to send to RtspSimpleServer. RTMP, on the other hand, is close to the end of support, but still used by such services as zoom, youtube live, twitch, Facebook, OBS studio. 

We are sending between the Opencv app to the server. If my server is RtspSimpleServer, I will use RTSP. If my server is NGINX with RTMP module. I will most probably use RTMP to establish communication between my app and NGINX. There are other aspects like underlining container and codec to deliver my video content which is slightly important. Another aspect is communication protocol suitable for your application and environment. Both protocols do not stream directly over HTTP. The last remark on this, We have other protocols as well especially more suitable protocols to serve video to a wider audience. 

Prerequisites — requirements for Opencv GStreamer application

It is really good to have some experience with building OpenCV with CMake. My environment is as follows:

  • Windows 11/10
  • CMake 3.22.0
  • Visual Studio 2022
  • OpenCV-4.5.4
  • Docker with WSL2 backend
  • Nginx with RTMP module (We will build docker container, from docker file later)
  • GStreamer MSVC 64-bit (VS 2019, Release CRT) both runtime and development installer from following links. (lead to the official website of GStreamer)

18.5 runtime installer

1.18.5 development installer

Steps to compile Opencv with Gstreamer

Since this is quite advanced, I will not go into so many details. You can still find them on my blog in posts related to GStreamer.

  • Install GStreamer runtime and development MSVC 64-bit installer. Links above.
  • Add GStreamer to system variable Path as on the picture below, The OpenCV will come later once lib is built. Path system variables:
Windows OpenCV GStreamer environmental variables
  • Now, restart your machine to get this, Path effected for linking your project.
  • Download, extract OpenCV source OpenCV-4.5.4
  • Get Visual Studio 2022 community (or 2019), Start CMake 3.22 tested
  • Set source code as a folder, where you extract OpenCV
  • Set “Where to build the binaries”, where your custom-built OpenCV lib will be assembled.
Build opencv by CMake
Opencv CMake configuration

CMake configuration

  • Hit Configure button, and select VS 2022, 64-bit compiler.
  • This part is a little bit advanced. You have to hit configure several times and select options, where Cmake hit some problem with your project. I usually uncheck any Java, Python staff, where I am missing prerequisites or I simply do not care.
  • Now, Check options WITH_FFMPEG, WITH_GSTREAMER and for GStreamer add GSTREAMER_app_LIBRARY and GSTREAMER_base_Library paths, where your GStreamer installation is located. Hit CMake Configure and all other GStreamer variables will be resolved automatically. Just follow the picture below.
CMake OpenCV Gstreamer configuration
  • Hit configure and resolve possible problems again.
  • If OK, Hit generate
  • If OK, Open project in Visual Studio 2022
  • Switch to solution configuration Release, x64.
Building Opencv in Visual Studio
  • First, build ALL_BUILD in CMakeTargets
  • Then build INSTALL in CMakeTarget

OpenCV lib with FFMPEG and GStreamer is ready.

Install NGINX with RTMP module in docker

In this part, You should have some experience with docker. I am using docker desktop under Windows 10/11 with WSL2 backend. My version is 4.4.4. What a great number. 

My Docker desktop version

In this step, I am going to create a docker container based on the definition of the following docker file. Place the Dockerfile into one folder.

FROM buildpack-deps:stretch
# Define variables.
# docker build -t nginx .
# -v c:/unp:/mnt
#PS C:\dockerNginx>docker run -it --name nginx -p 8080:80 -p #1935:1935 -v C:\streamServer:/mnt/c/streamServer -d nginx
# nginx -c /mnt/c/streamServer/nginx.conf
# Versions of Nginx and nginx-rtmp-module to use
ENV NGINX_VERSION nginx-1.19.8
# Install dependencies
RUN apt-get update && \
    apt-get install -y ca-certificates openssl libssl-dev && \
    rm -rf /var/lib/apt/lists/*
# Download and decompress Nginx
RUN mkdir -p /tmp/build/nginx && \
    cd /tmp/build/nginx && \
    wget -O ${NGINX_VERSION}.tar.gz${NGINX_VERSION}.tar.gz && \
    tar -zxf ${NGINX_VERSION}.tar.gz
# Download and decompress RTMP module
RUN mkdir -p /tmp/build/nginx-rtmp-module && \
    cd /tmp/build/nginx-rtmp-module && \
    wget -O nginx-rtmp-module-${NGINX_RTMP_MODULE_VERSION}.tar.gz${NGINX_RTMP_MODULE_VERSION}.tar.gz && \
    tar -zxf nginx-rtmp-module-${NGINX_RTMP_MODULE_VERSION}.tar.gz && \
    cd nginx-rtmp-module-${NGINX_RTMP_MODULE_VERSION}
# Build and install Nginx
# The default puts everything under /usr/local/nginx, so it's needed to change
# it explicitly. Not just for order but to have it in the PATH
RUN cd /tmp/build/nginx/${NGINX_VERSION} && \
    ./configure \
        --sbin-path=/usr/local/sbin/nginx \
        --conf-path=/etc/nginx/nginx.conf \
        --error-log-path=/var/log/nginx/error.log \
        --pid-path=/var/run/nginx/ \
        --lock-path=/var/lock/nginx/nginx.lock \
        --http-log-path=/var/log/nginx/access.log \
        --http-client-body-temp-path=/tmp/nginx-client-body \
        --with-http_ssl_module \
        --with-threads \
        --with-ipv6 \
        --add-module=/tmp/build/nginx-rtmp-module/nginx-rtmp-module-${NGINX_RTMP_MODULE_VERSION} && \
    make -j $(getconf _NPROCESSORS_ONLN) && \
    make install && \
    mkdir /var/lock/nginx && \
    rm -rf /tmp/build
# Forward logs to Docker
RUN ln -sf /dev/stdout /var/log/nginx/access.log && \
    ln -sf /dev/stderr /var/log/nginx/error.log

Start command line and issue the following command to build docker image from Dockerfile definition(cmd is opened at the same location as Dockerfile). 

docker build -t nginx .

If the Nginx image is successfully created, We can issue the following command to run image Nginx as container Nginx. There are three specifications. Port mapping for HTTP is 8080:80, RTMP is 1935:1935, -v option with following parameter C:\streamServer:/mnt/c/streamServer will make the content of my C:\streamServer folder in windows available inside a container under /mnt/c/streamServer mount point. I love this file sharing between Linux and windows. I can edit all nginx configurations in windows directly and run nginx server from the configuration placed in windows. Here is the command explained above:

PS C:\dockerNginx>docker run -it --name nginx -p 8080:80 -p 1935:1935 -v C:\streamServer:/mnt/c/streamServer -d nginx

Write Opencv GStreamer application 

This is our program. There is an RTSP pipeline used in the previous tutorial just commented out. There is a new RTMP Gstreamer pipeline used in this tutorial. In next section is described how to build this simple program in Visual Studio. 

#include <iostream>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/video.hpp>
#include <opencv2/videoio.hpp>
#include <opencv2/core.hpp>
#include <opencv2/highgui.hpp>
//#include "detector.h"
//#include "poseEstimation.h"
using namespace cv;
int main()
//VideoCapture cap(0, cv::CAP_MSMF);
VideoCapture cap("C:/www/town.avi", cv::CAP_MSMF);
VideoWriter writer;
OLD RTSP pipeline"appsrc ! videoconvert ! videoscale ! video/x-raw,width=640,height=480 ! x264enc speed-preset=veryfast tune=zerolatency bitrate=640 ! rtspclientsink location=rtsp://localhost:8554/mystream ",0,10,Size(640, 480),true);

//NEW RTMP2SINK pipeline"appsrc ! videoconvert ! videoscale ! video/x-raw,width=640,height=480
 ! x264enc ! flvmux ! rtmp2sink location=rtmp://localhost:1935/live/Mystream ",
 0,10,Size(640, 480),true);
poseEstimation pose = poseEstimation();
Detector dec = Detector();
Mat img;
for (;;) {
if (!cap.isOpened()) {
  std::cout << "Video Capture Fail" << std::endl;
  Mat proc = dec.getResult();
  cv::resize(img, img, Size(800, 600));
  cv::imshow("raw", img);
  cv::resize(img, img, Size(640, 480));
  writer << img;

Configure project and run the code in Visual Studio 2022

This is a standard Opencv configuration. Once OpenCV is built with GStreamer, Just ensure that the system variable path correctly points to GStreamer once again.

Windows Opencv Gstreamer environmental variables

Configure for release project and set Additional include directory to location of Opencv include directory.

Visual studio Gstreamer Opencv project configuration
Visual studio project configuration

In the Linker option, Additional Library Directories point to your OpenCV installation to vc17\lib location.

OpenCV Windows 10 Visual studio 2022 configuration

The last step is to set in Linker Input- Additional Dependencies with OpenCV libraries you want to use.

Libraries to build Opencv Gstreamer project
OpenCV Additional dependencies

NGINX configuration

There is a lot to explain. In simple, the Server listens on 1935 port and produces an HLS video stream. The server will convert the RTMP stream to multiple TS segments and Mystream.m3u8 that is available under live/path http://localhost:8080/live/Mystream.m3u8 . This file holds the reference to TS segments and other information about my HLS stream. This is very simple. You have to go into the documentation of the NGINX RTMP module for more details. The following is content of /mnt/c/streamServer/nginx.conf

worker_processes  auto;
 error_log  /mnt/c/streamServer/err.log;
events {
    worker_connections  4;
rtmp { 
    server { 
        listen 1935; 
        application live { 
            live on; 
            allow publish;
   allow publish all;
   allow play all;
            interleave on;
            hls on; 
            hls_path /mnt/c/streamServer/hls; 
            hls_fragment 15s; 
            dash on;
            dash_path /mnt/c/streamServer/dash;
            dash_fragment 15s;
http {
    default_type  application/octet-stream;
    sendfile        on;
    keepalive_timeout 65;
     access_log /mnt/c/streamServer/ng.log;
    error_log  /mnt/c/streamServer/nge.log;
    server {
        listen       80;
        server_name localhost;
        location / {
            root   /mnt/c/streamServer;
            index  index.html index.htm;
         location /live {
             types {
        application/ m3u8;
               video/mp2t ts;
                text/html html;
            alias   /mnt/c/streamServer/hls;
            add_header Cache-Control no-cache;
        location /hls {
            # Serve HLS fragments
            types {
                application/ m3u8;
                video/mp2t ts;
            root /tmp;
            add_header Cache-Control no-cache;
          location /tvds {
            root   /mnt/c/streamServer/dash;
            add_header Cache-Control no-cache;

Start NGINX from configuration located on mounted Windows location. 

NGINX streaming from WSL2 docker
Running NGINX in docker container
  •  Attach shell to Nginx container, < right click- select option attach shell
  • run following command that uses config on windows mounted location. (Check where Nginx executable is located.) 
/usr/local/sbin/nginx -c /mnt/c/streamServer/nginx.conf
  • use the TOP command to check running nginx
TOP command of running NGINX

Lets stream. You can receive the video by VLC, but let's write a simple web player. 

Web player

I am not a Web developer. My video player element uses the content distributed by http://localhost:8080/live/Mystream.m3u8 NGINX. 

<video id='hls-example' class="video-js vjs-default-skin" width="800" height="600" controls>
<source type="application/x-mpegURL" src="http://localhost:8080/live/Mystream.m3u8"></video>

The full source code of my simple player. To play HLS, I am using videojs-contrib-hls.js and video.js libs. 

<!DOCTYPE html>
<html lang="en">
<title>Bootstrap Example</title>
  <meta charset="utf-8">
     <meta name="viewport" content="width=device-width, initial-scale=1">
     <link href=""     rel="stylesheet">

  <body style="height:1500px"></body>
    <h3>Video camera</h3>
<video id='hls-example' class="video-js vjs-default-skin" width="800" height="600" controls>
<source type="application/x-mpegURL" src="http://localhost:8080/live/Mystream.m3u8"></video>
<script src=""></script>
<script src=""></script>
<script src=""></script>
var player = videojs('hls-example');;

Play video from NGINX

If NGINX is running. 

If Opencv Program is ready, compiled. 

Try to replicate my results. Stream processed Opencv output to multiple web players, where the middle man is the NGINX server with RTMP module. Let me know. Cheers.. 

Opencv video streaming
Opencv Streaming application
Opencv video stream for web
HLS stream from Opencv — Nginx path received in Web Player

Youtube tutorial introduction and demo for RTMP NGINX Opencv app




Opencv GStreamer (windows) video streaming tutorial + full source code for RTSP HLS streaming

Opencv C++ simple tutorial to use GStreamer to send video to Server that converts RTSP to HLS video stream. The source code and all needed configurations are included.  O pencv is a powerful computer vision library. You can use it in production and use it for image and video processing and modern machine learning. In some applications, You may want to stream your processed video results from your C++ OpenCV app outside and not just use a simple OpenCV graphical interface. The video streaming of your results is what you are looking for. Do you want to stream processed video from your IoT device? Yes, This is mainly for Linux. Do you want to stream processed video to the Web player, broadcast the video or just use VLC to play video processed by OpenCV? You may be interested in reading the next lines.  Opencv video stream to VLC or WEB There are basically two main options with OpenCV. The first one is to write a streaming application using FFMPEG. This is a little bit more advanced appro

Opencv Web camera and Video streams in Windows subsystem for Linux WSL, by FFmpeg and GStreamer

Opencv in Windows Subsystem for Linux (WSL) is a compatibility layer designed to running Linux binary executables (in ELF format) natively on Windows 10. I love it. There are some limitations to mention. The first biggest is the lack of support of CUDA, which could be a limitation for deep learning application and learning in WSL. The second trouble for Opencv development is the lack of Web camera support. This suspends WSL almost on a useless level for me until now.  VideoCapture cap;   is not working in WSL for now;  FFMPEG to WSL opencv program and back to WEB browser in windows This Video capture is right now not possible at in Ubuntu running under Windows (WSL). I will hit this limitation in this article. I will show you how to reach a video camera and learn something more about video streaming. Yes, the opencv processed frames will be stream to the web player on simple web site. Check the goal of this opencv tutorial on this video What you will learn

Compile Opencv with GStreamer for Visual Studio 2019 on windows 10 with and contribution modules

The goal of this tutorial is a simple step by step compilation of Opencv 4.2 with contribution extra modules with GStreamer as a bonus. The environment is Windows 10, Visual Studio 2019 C++ application. This took me almost one day of correcting of CMake setting. The goal of this tutorial is: compiled a set of OpenCV libraries with GStreamer and FFmpeg on Windows. I focus mainly on GStreamer. It is a little bit more tricky. You will reach the following information about your Opencv environment by compile and run this simple code. The Opencv GStreamer is turned as YES. GStreamer gives you a great opportunity to stream OpenCV output video outside of your program, for example, web application. I recently compiled with opencv 4.4. The update at the end of the post.  It is working!! wow, The working app and configuration in future tutorials. #include   <opencv2/opencv.hpp> #include   <iostream> using namespace cv; int   main () {      std ::cout <<  &q

Opencv HSL video stream to web

This tutorial will show you all components, configuration, and code needed to steam video output results from Opencv C++ to your Web player. The C++ program will take any video input and process this video. The processed video will be stream outside of OpenCV using the GStreamer pipeline (Windows part). The HLS video produces one Playlist file and several MPEG-TS segments of videos. This several HLS outputs are stored in the Windows file system. I am using WSL 2, windows subsystem for Linux to run Ubuntu distribution. Here the NGINX is installed with the RTMP module. NGINX is distributing a video stream from the windows file system to the web.  Let's have a look at this in more detail.  What is covered? Opencv C++ part + GStreamer pipeline NGINX configuration Architecture Web Player for your HLS stream What is not covered? Detailed instalation of Opencv + Gstreamer more here  GStreamer installation  ,  GStreamer Installation 2  on windows Detailed installation of NGINX + RTMP modul

OpenCV 4.5 simple optical flow GPU tutorial cuda::FarnebackOpticalFlow

This OpenCV tutorial is a very simple code example of GPU Cuda optical flow in OpenCV written in c++. The configuration of the project, code, and explanation are included for farneback Optical Flow method. Farneback algorithm is a dense method that is used to process all the pixels in the given image. The dense methods are slower but more accurate as all the pixels of the image are processed. In the following example, I am displaying just a few pixes based on a grid. I am not displaying all the pixes. In the opposite to dense method the sparse method like Lucas Kanade using just a selected subset of pixels. They are faster. Both methods have specific applications. Lucas-Kanade is widely used in tracking. The farneback can be used for the analysis of more complex movement in image scene and furder segmentation based on these changes. As dense methods are slightly slower, the GPU and Cuda implementation can lead to great performance improvements to calculate optical flow for all pixels o

Opencv C++ Tutorial Mat resize

Opencv Mat Resize   Resize the Mat or Image in the Opencv C++ tutorial. It is obviously simple task and important to learn. This tutorial is visualized step by step and well-described each of them. The main trick is in that simple code. Mat Input; Mat Resized; int ColumnOfNewImage = 60; int RowsOfNewImage = 60; resize(Input, Resized, Size( ColumnOfNewImage , RowsOfNewImage )); This code just takes an Input image and resized save to output Mat. How big is the resized image is based on the Size? Size just contains two parameters. Simple numbers of how the result should be big. The simple number of columns (width) and rows (height). That is basically it. Enjoy                                                 Boring same face again and again.  Load Image, resize and save Opencv C++ #include <Windows.h> #include "opencv2\highgui.hpp" #include "opencv2\imgproc.hpp" #include "opencv2\video\background_segm.hpp" #include &qu

Opencv C++ tutorial : Smoothing, blur, noise reduction / canceling

Smooth or blur, gaussian blur, and noise-canceling, This tutorial will learn OpenCV blur, GaussianBlur, median blur functions in C++. Additionally, the advanced technique for noise reduction  fastNlMeansDenoising family  will be introduced with a code example for each method.   You can use blurring of the image to hide identity or reduce the noise of the image.  Blur can be a very useful operation and it is a very common operation as well. For example, the anonymization of pedestrians, face or is one possible target for blue operation. The blur is the most common task to perform over the image to reduce noise. The noise reduction is more task for Gaussian blur than for simple blur operation. The various blur operations are very common for image processing on mobile devices.  The more important is the robustness issues of the data in pre-processing for machine learning. Sometimes, by blurring the images of the dataset can have a positive effect on the robustness of the achieved de

Opencv 4 C++ Tutorial simple Background Subtraction

This method is used to learn what belongs to the background of the image and what belongs to the foreground. The static cameras that monitor the area can very easily recognize, what is part of the image that is always here or there is something that is new and moving over the background.  Background subtraction Visual studio 2019 project setup If you have Opencv 4+ compiled or installed only steps you need to do is set the include directory with OpenCV header files. Set the Additional library Directories that point to \lib folder. Just note that Visual Studio 2019 should use VC16 \lib. Finally, As additional dependencies, specify the libs used to resolve the function implementation in the code. The list for Opencv 420 is here. The different version of opencv is using different numbering for example opencv 440 will use opencv_core440.lib.  opencv_bgsegm420.lib opencv_core420.lib opencv_videoio420.lib opencv_imgproc420.lib opencv_highgui420.lib opencv_video420.lib  Background sustract

Opencv VideoCapture File, Web Camera, RTSP stream

Opencv VideoCapture File, Camera and stream Opencv tutorial simple code in C++ to capture video from File, Ip camera stream and also the web camera plug into the computer. The key is to have installed the FFMPEG especially in case of reading the stream of IP cameras. In windows just use Opencv Installation by Nugets packages  Here . Simple easy under 2 minutes of installation. In Linux, you need to follow the instruction below. If you are on Debian Like package system. Under Fedora Red hat dist just use a different approach. Code is simple and installation is the key..  Windows use nugets packages Linux you have to install and build Opencv With FFMPEG. Also simple.  It is easy to capture video in OpenCV Video capture  in OpenCV is a really easy task, but for a little bit experienced user.  What is the problem? The problem is the installation of Opencv without recommended dependencies. Just install all basic libs that are recommended on the website. # Basic packa