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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

Fast Opencv people pedestrian detection Tutorial by CascadeClassifier

Simple Opencv C++ tutorial and example of people detection in video samples and pictures. There is nothing you cannot achieve in few simple steps. People detection and performace tasks in opencv could be solved if you have little bit knowledge of programing. Or just simple follow the steps.. 

Opencv tutorial instalation of opencv

You can simple prepare the project inside the Visual Studio 2015 by Nuget Packages. This approach is easy for beginers and better than standard installation with all the environmental variables problems. Just follow the installation steps inside here 

Cascade Classifier detect Multi Scale


Opencv is great and complex tools. There is lot of image processing and also machine learning features. You can simply learn your own detector. I would like to prepare some tutorial how to learn your own detector. It is long time run. 

All, you need to do, is some experience and basic opencv tools. 
under opencv/build/x64/vc14/bin

opencv_createsamples.exe
opencv_traincascade.exe

Prepare your dataset and files with labels are better under linux. You can use simple shall scripts. 
This is really for long tutorial, but you can do image annotation in windows as well and maybe use the new tool opencv_annotation,exe, but i dont have any experience with this. My own scripts and programs crop, saves and annotating data. I have own image cropper to prepare datasets. You can build your own dataset in few days. It is tricky to.

First step
Use basic opencv cascades to crop some positive samples from video or better crop precisely the positive samples from your images. Negative samples is not problem. Generate them randomly from your images.  Clean and prepare data manually.. This is a worse part. Crop also images selected correctly by default opencv cascade to achieve better results.

Second step
Build your initial detector, which is used for another positive data collecting.  Again Clean and prepare data manually. Stupid boring and it takes a long time.


Third
!!Use this on your own risk. !!
Run cloud machine on aws and install opencv 3.1. 
Better is memory than number of cores.. Something like 

r3.4xlarge   cores 16  memory 52                              $1.33 per Hour
 !!!!On your own risk!!!!
8000 positive and 15000 negatives dataset with 10- 20 stages could take minutes - hours instead of days of learning on your own computer. And it's cheaper than the electricity bill.Time to learn depends on a number of data, parameters, selected machine and many things.I can not guarantee that it goes so fast with your data and parameters.. I do it this way saves time. I am pretty sure that your first detector will be terrible. In the cloud, you can learn 10 detectors day. Which is great

Performance detectMultiScale and parameters

  • Source image is 640x480 image. My PC is notebook I7, 4 cores, 8 threads and huge amount of RAM :). Opencv version is 3.1 default for windows machines. 
  • I have 2 people detector detectMultiScale in the main loop . 
  • The main performance issue is in scaleFactor parameter and minSize of the object. In faster case, I have 1.1 and in slower case is 1.02 scaleFactor.  
  • Choose appropriately as needed the minSize parameter. If you have to small window, your program need to check far more options. Size(40,70)

CascadeClassifier at approximately 13 FPS

detectorBody.detectMultiScale(img, human, 1.1, 1, 0 | 1, Size(40,70), Size(80, 300));
detectorUpper.detectMultiScale(img, upperBody, 1.1, 1, 0 | 1, Size(40, 70), Size(80, 300));


Cascade Classifier detect Multi Scale


CascadeClassifier at approximately 2 FPS

detectorBody.detectMultiScale(img, human, 1.02, 2, 0 | 1, Size(40,70), Size(80, 300));
detectorUpper.detectMultiScale(img, upperBody, 1.02, 2, 0 | 1, Size(40, 70), Size(80, 300));

Cascade Classifier detect Multi Scale


Opencv  HAAR LBP cascade download

I also learn some mine cascade on my own datasets. There are here. To be sure that you can reach them here are the links.. 

Cascade for car detector download

This is just a basic 5 stage haar cascade car detector, post where to find my cascade to detect cars

Cascade for head and people download 

This cascade is also able to use by the tutorial code below. For head and for people detection. 
Learned by me on my own data set. cascade for head and people detection

Opencv CascadeClassifier tutorial code

!!Simple as opencv Sample.. Let me add some useful remarks. Some windows OpenCV version of detect multiScale return errors.. 2.9.4 -3.0 maybe 3.1. I don't know why. The classifier returns to many output rectangles out of bound. This does not make sense and kill your program..
If this is an issue of your error on windows.. Switch from debug to release. It helps. !! Really


#include "opencv2\highgui.hpp"
#include "opencv2\imgproc.hpp"
#include "opencv2\objdetect\objdetect.hpp"
#include "opencv2/video/tracking.hpp"
#include <vector>
#include <stdio.h>
#include <Windows.h>
#include <iostream>
#include <time.h>
#include <ctime>

    using namespace cv;
    using namespace std;

    int main(int argc, const char **argv)
    {
        // prepare video input
        VideoCapture cap("input.mov");
        // prepare video output
        VideoWriter outputVideo;
        outputVideo.open("video4.wmv"CV_FOURCC('W''M''V''2'), cap.get(CV_CAP_PROP_FPS), Size(640480), true);
        // prepare cascadeClassifier
        CascadeClassifier detectorBody;
        CascadeClassifier detectorUpper;
        // !! Put your cascade or opencv cascede into project folder !!
        string cascadeName1 = "cascadeName.xml";
        string cascadeName2 = "cascadeName.xml";
        // Load cascade into CascadeClassifier
        bool loaded1 = detectorBody.load(cascadeName1);
        bool loaded3 = detectorUpper.load(cascadeName2);
        // Basic video input loop
        for (;;)
        {
            bool Is = cap.grab();
            if (Is == false)
            {
                cout << "Video Capture Fail" << endl;
                break;
            }
            else
            {
                // Just for measure time
                const clock_t begin_time = clock();
                // Store results in these 2 vectors
                vector<Rect> human;
                vector<Rect> upperBody;
                // prepare 2 Mat container
                Mat img;
                Mat original;
                // capture frame from video file
                cap.retrieve(img, CV_CAP_OPENNI_BGR_IMAGE);
                // Resize image if you want with same size as your VideoWriter
                resize(img, img, Size(640480));
                // Store original colored image
                img.copyTo(original);
                // color to gray image
                cvtColor(img, img, CV_BGR2GRAY);
                // detect people, more remarks in performace section
                detectorBody.detectMultiScale(img, human, 1.120 | 1Size(4070), Size(80300));
                detectorUpper.detectMultiScale(img, upperBody, 1.120 | 1Size(4070), Size(80300));
                // Draw results from detectorBody into original colored image
                if (human.size() > 0)
                {
                    for (int gg = 0; gg < human.size(); gg++)
                    {
                        rectangle(original, human[gg].tl(), human[gg].br(), Scalar(00255), 280);
                    }
                }
                // Draw results from detectorUpper into original colored image
                if (upperBody.size() > 0)
                {
                    for (int gg = 0; gg < upperBody.size(); gg++)
                    {
                        rectangle(original, upperBody[gg].tl(), upperBody[gg].br(), Scalar(25500), 280);
                    }
                }
                // measure time as current - begin_time
                clock_t diff = clock() - begin_time;
                // convert time into string
                char buffer[126];
                sprintf(buffer, "%d", diff);
                // display TIME ms on original image
                putText(original, buffer, Point(10020), 12Scalar(255255255), 280);
                putText(original, "ms"Point(15020), 12Scalar(255255255), 280);
                // draw results
                namedWindow("prew", WINDOW_AUTOSIZE);
                imshow("prew", original);
                // make video output

                outputVideo << original;
                int key1 = waitKey(20);
            }
        }
    }


Comments

  1. I will try this code on my own. Hope it will help me.

    Splendid Skyline Review

    ReplyDelete
  2. Awesome! Thanks for sharing code!!

    ReplyDelete
    Replies
    1. where is the complete code? I haven't seen .hpp files

      Delete
  3. Hi. I am referening this tutorial. Could you give me this code? I want to reference it. Thanks a lot. My email: tiennt@mta.edu.vn

    ReplyDelete
  4. This comment has been removed by the author.

    ReplyDelete
  5. Hi! What an interesting blog!!
    I'm working on a ROS module for person detection and I was wondering if you could give me the CascadeClassifier(body detector) you used because I couldn't find the link in this tutorial.
    Thanks for your help!
    My email: marisofia.iglesias@gmail.com

    ReplyDelete
  6. Hi,
    Very good example and useful for some use cases. It is possible to send the code? My email: madeirajp@gmail.com
    Thanks and good work

    ReplyDelete
  7. Thanks for providing good information,Thanks for your sharing.

    thai porn

    ReplyDelete
  8. This comment has been removed by the author.

    ReplyDelete
  9. Hi ad! In step 1, you mean using default .xml file of openCV to crop and get location for positive images, right ? And this solution is work for detecting human in different position like 'sitting'? thank you very much!!

    ReplyDelete
  10. This comment has been removed by the author.

    ReplyDelete
    Replies
    1. Thanks for your help.
      could you give me this code? i'm student, i want to reference it.
      My email: "gpham378@gmail.com"

      Delete
  11. Thanks for your help.
    could you give me this code? i'm student, i want to reference it.
    My email: "gpham378@gmail.com"

    ReplyDelete
  12. This comment has been removed by the author.

    ReplyDelete
  13. Thanks a lot for such a nice tutorial....can i get this code of people tracking...i'm a student..my id aratimcs@gmail.com....Thanks

    ReplyDelete
  14. can you send me complete code?
    rimsha479@gmail.com

    ReplyDelete

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