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 

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));

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));

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(640, 480), 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(640, 480));

                // 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, 1.1, 2, 0 | 1, Size(40, 70), Size(80, 300));

                detectorUpper.detectMultiScale(img, upperBody, 1.1, 2, 0 | 1, Size(40, 70), Size(80, 300));

                // 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(0, 0, 255), 2, 8, 0);

                    }

                }

                // 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(255, 0, 0), 2, 8, 0);

                    }

                }

                // 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(100, 20), 1, 2, Scalar(255, 255, 255), 2, 8, 0);

                putText(original, "ms", Point(150, 20), 1, 2, Scalar(255, 255, 255), 2, 8, 0);

                // draw results

                namedWindow("prew", WINDOW_AUTOSIZE);

                imshow("prew", original);

                // make video output

                outputVideo << original;

                int key1 = waitKey(20);

            }

        }

    }