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 reading IP camera, Video stream, Web camera, images and

Opencv reading video files, reading video stream, Images, IP and Web cameras. I would like to cover this all in one post. Yes, video writer is also important to store your results and achievements in video. There is couple of simple trick and if you follow them, you will never have a problem with the reading and writing video, stream, files in future.
opencv web camera

Basic opencv web camera reading

There is couple think you need to take care. My favorite installation on windows platform is trough NUGET package system. It is easy in few steps. I describe this many times for example VS 2017 here. Nuget set up your project without any linking settings, library path selection, global environmental variables and you can directly start coding in few seconds. Just select and install nuget and compile code below. Nothing else.  You need to take care if you have included several thinks. highgui.hpp core.hpp, imgproc.hpp, videoio, imgcodecs. All of them are not necessary to read the web camera but for example for video stream from IP camera is possible that you really need them all.

VideoCapture web camera code

VideoCapture cap(0); is mean open the default camera web camera. Most of the time this mean web camera on your laptop or plugged in any USB camera. The video is read in 'never ending for(;;) loop' which is break when the video from camera is not available by condition if (!cap.isOpened()). Finally the Mat img;   cap >> img;  copy image from default camera devices into your MAT container. The rest is just display. 
#include "opencv2\imgproc.hpp"
#include "opencv2\objdetect\objdetect.hpp"
#include "opencv2\videoio\videoio.hpp"
#include "opencv2\imgcodecs\imgcodecs.hpp"
#include "opencv2\core\core.hpp"
#include "opencv2\highgui.hpp"
#include <vector>
#include <stdio.h>
#include <windows.h>
#include <iostream>
#include <time.h>
using namespace cv;
using namespace std;
int main(int argcconst char** argv)
{
 VideoCapture cap(0);
 for (;;)
 {
  if (!cap.isOpened()) {
   cout << "Video Capture Fail" << endl;
   break;
  }
  else {
   Mat img;
   cap >> img;
   namedWindow("Video", WINDOW_AUTOSIZE);
   imshow("Video", img);
   int key2 = waitKey(20);
  }
 }
 return 0;

Opencv video file reading

Look at the example above for reading the camera. There is almost no difference. Just one, small and straightforward. As a parameter of cap put instead of default devices cap(0) the file name or path you want to open. There is almost always trouble with path.  In this example you just read the files that are located under your project. You can also read the file from different location or on one place by using the full path into some video folder as you can see in following examples. 

VideoCapture cap("movie.vmw");
VideoCapture cap("movie.mp4");
VideoCapture cap("movie.mov");
VideoCapture cap("movie.xxx");
VideoCapture cap("C:/cm/movie.mov");
VideoCapture cap("C:/cm/movie.mp4");

Opencv Image read from file and writing 

This is super easy task. Into the our Mat container  image load the image 6.jpg on this C:/adress/ path. There is something different what is great to have in case you are reading lots of images inside the folder. 

Mat image;
image = imread("C:/adress/6.jpg", CV_LOAD_IMAGE_COLOR);

CV_LOAD_IMAGE_COLOR is defined parameter to tell reader that i want MAT with 3 colors. Basically 3 MAT array container of image size. One for blue, red and green color channel. CV_LOAD_IMAGE_GRAYSCALE is defined to tall reader that i want gray scale. Basically only one Mat of the weight(cols) and high(rows) of the image.. 

To write results into file just use imwrite where the first string is just name of your result and image. Image is MAT containing what you want to save..


imwrite("image.jpg", image);

Opencv video stream verification

I am using good practice. instead of try stream directly in opencv. I prefer to verify my stream in VLC player. It is faster than modify code and compile again of passing the camera URL as parameter. Also the VLC ask for potential user name and password if its necessary. What is annoying is that all the cameras own stream URL format.  The best approach is to find your IP camera model on http://www.ispyconnect.com and apply to verify inside the VLC. After verification put this directly to VideoCapture cap("http://IP:PORT/mjpeg/video.mjpg?counter"); 

http://IP:PORT/mjpeg/video.mjpg?counter
rtsp://IP:PORT/various url
rtsp://IP:PORT/axis-cgi/mjpg/video.cgi
http://IP:PORT/mjpg/video.mjpg
Remember that VLC ask for password Opencv NOT.. Just add rtsp://username:password@IP:PORT
("rtsp://USER:PASS@xxx.xxx.xxx.xxx/axis-media/media.amp?camera=2")
Important FFMPEG is needed in Linux. In case of Nuget packages depends but the stream sometimes needs special installation. 

Opencv tutorial code IP camera pseudo code

There is 3 function.. 
First of all, the main function at the end, where are established 2 threads to read the camera stream..

In Main
  • Thread call the stream function for both camera with different IP camera URL                       thread cam1(stream, "http://xxxxxxxR");
  • To run the function stream inside the thread with url as parametr use.                       cam1.join();
void stream
  • Capture video from url strCamera VideoCapture cap(strCamera) 
  • Fill the frame from cap  cap >> frame;
  • Detect people in camera detect(frame, strCamera);
void detect

Opencv C++ IP camera code, video stream

#include <iostream>
#include <thread>
#include "opencv2/opencv.hpp"
#include <vector>
using namespace std;
using namespace cv;
void detect(Mat imgString strCamera) {
  string cascadeName1 = "haar_cascade_for_people_detection.xml";
  CascadeClassifier detectorBody;
  bool loaded1 = detectorBody.load(cascadeName1);
  Mat original;
  img.copyTo(original);
  vector human;
  cvtColor(img, img, CV_BGR2GRAY);
  equalizeHist(img, img);
  detectorBody.detectMultiScale(img, human, 1.120 | 1Size(4080), Size(400,480 ));
  if (human.size() > 0
    {
      for (int gg = 0; gg < human.size(); gg++) 
      {
      rectangle(original, human[gg].tl(), human[gg].br(), Scalar(00255), 280);
      }
    }
  imshow("Detect " + strCamera, original);
  int key6 = waitKey(40);
//End of the detect
}
void stream(String strCamera) {
VideoCapture cap(strCamera);
 if (cap.isOpened()) { 
      while (true) {
        Mat frame;
        cap >> frame; 
        resize(frame, frame, Size(640480));  
        detect(frame, strCamera);
     }
   }
}
int main() {
    thread cam1(stream, "http://xxxxxxxR");
    thread cam2(stream, "http://xxxxxxxR");
    cam1.join();
    cam2.join();
    return 0;
}

write video into file

On windows machine i usually works with simple wmv format. Works perfectly. Remember the golden rule of video writer in opencv. image Mat have to match same size as VideoWriter. The image is mat that i want to write as frame into the video.. Before I put them into the VideoWriter, I always resize to target size.. This causing the lots of trouble. You cannot see the video result only for that reason.  
Size SizeOfVideo = cv::Size(1024740);  VideoWriter video("Result.wmv"
CV_FOURCC('W''M''V''2'), CAP_PROP_FPS,SizeOfVideotrue);
resize(image, image, Size(800600)); 
 video << image; 
OR
 video.write(image);

Comments

  1. I am using Raspberry pi cam as IP camera and it shows "std::invoke no matching overloaded function found" error.

    ReplyDelete
  2. Your site is actually awe-inspiring. I've discovered numerous brand new points. The right path associated with setting up can also be intriguing. You've chosen really amazing subject. We valued this.
    USA computer vision apps

    ReplyDelete
  3. This comment has been removed by a blog administrator.

    ReplyDelete
  4. Now you have a video stream available and you need to capture still images from it. For that, use getsnapshot() command. Hikvision 8MP Cameras

    ReplyDelete
  5. Fantastic and useful we blog thanks for publishing this.it's useful and informative.keep up the great.
    ENER-J CCTV Security Camera

    ReplyDelete
  6. PeopleLink iCam WHD 720 USB cameras are designed for boardrooms.The sleek and compact design of these cameras help in easy portability and storage.They offer easy connectivity with laptops/PC through USB ports and deliver HD quality video and the Pan and Tilt feature allows a wider angle coverage capturing a larger audience

    ReplyDelete
  7. HighMark Security is a direct supplier of security cameras, video surveillance systems, and CCTV equipment. We supply analog CCTV cameras, HD security cameras, IP cameras, and complete video surveillance systems worldwide. We supply our equipment to homeowners, business owners, government agencies, and any other type of organization, any size. Most of our business comes from the Da Nang, however, we do ship our products everywhere in the world. No project is too small or too large for us to handle. We have trained sales engineers that can help design a system that will fit your requirements and budget. Mua may dinh vi, CCTV Camera, IP Camera Lap Camera Da Nang, Security Systems, Analog Camera, Smart Home Store, Omnipolis, DVR, NVR, Video Management Software, camera ip wifi da nang, read more: lap dat camera da nang. HighMark Security has earned a reputation with the best technical support and customer service in the Da Nang security camera industry, lap camera da nang, sua chua camera da nang dich vu camera da nang

    ReplyDelete

  8. Thanks for sharing this kind of useful information zicom offers best advance home security.
    For More Information. click here

    ReplyDelete
  9. Nice blog. Easy to understable,thanks for sharing

    PeopleLink Fisheye camera are designed for conference rooms. They offer easy connectivity with laptops/PC through USB ports and deliver HD quality video.

    ReplyDelete
  10. Fantastic Post! Lot of information is helpful in some or the other way. Keep updating.mini home security IP camera

    ReplyDelete

Post a Comment

Popular

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 cap.open(0);  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 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 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

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