OpenCV comes with several pretrained cascade detectors for the most common detection problems. They are located under the OPENCV_SOURCEdata directory. The following is a list of some of them and their corresponding subdirectories:

The following pedestrianDetection example serves to illustrate how to use a cascade detector and localize pedestrians in a video file with OpenCV:

#include "opencv2/core/core.hpp"
#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <iostream>

using namespace std;
using namespace cv;

int main(int argc, char *argv[]){
    CascadeClassifier cascade(argv[1]);
    if (cascade.empty())
        return -1;

    VideoCapture vid(argv[2]);
    if (!vid.isOpened()){
        cout<<"Error. The video cannot be opened."<<endl;
        return -1;
    }

    namedWindow("Pedestrian Detection");
    Mat frame;
    while(1) {
        if (!vid.read(frame))
            break;

        Mat frame_gray;
        if(frame.channels()>1){
            cvtColor( frame, frame_gray, CV_BGR2GRAY );
            equalizeHist( frame_gray, frame_gray );
        }else{
            frame_gray = frame;
        }

        vector<Rect> pedestrians;
        cascade.detectMultiScale( frame_gray, pedestrians, 1.1, 2, 0, Size(30, 30), Size(150, 150) );

        for( size_t i = 0; i < pedestrians.size(); i++ ) {
            Point center( pedestrians[i].x + 
                          pedestrians[i].width*0.5, 
                          pedestrians[i].y + 
                          pedestrians[i].height*0.5 );
            ellipse( frame, center, Size( pedestrians[i].width*0.5,
                     pedestrians[i].height*0.5), 0, 0, 360, 
                     Scalar( 255, 0, 255 ), 4, 8, 0 );
        }

        imshow("Pedestrian Detection", frame);
        if(waitKey(100) >= 0)
            break;
    }
    return 0;
}

The code explanation is as follows:

The following screenshot shows the result of applying the hogcascade_pedestrians.xml pretrained HOG-based pedestrian detector over the frames of the 768x576.avi video, which is stored in the OPENCV_SCR/samples folder.

Pedestrian detection using the OpenCV-trained HOG cascade detector

There are several projects and contributions to the OpenCV community that solve other detection-related problems that involve not only detecting the object but also distinguishing its state. One example of this type of detectors is the smile detector included in OpenCV since Version 2.4.4. The code can be found in the file OPENCV_SCR/samples/c/smiledetect.cpp, and the XML that stores the cascade detector, haarcascade_smile.xml, can be found in OPENCV_SCR/data/haarcascades. This code first detects the frontal face using the pretrained cascade stored in haarcascade_frontalface_alt.xml and then detects the smiling mouth pattern at the bottom part of the image. Finally, the intensity of the smile is calculated based on the number of neighbors detected.

Although OpenCV provides pretrained cascades, in some cases it is necessary to train a cascade detector to look for a specific object. For these cases, OpenCV comes with tools to help train a cascade, generating all the data needed during the training process and the final files with the detector information. These are usually stored in the OPENCV_BUILDinstallx64mingwin directory. Some of the applications are listed as follows:

Since opencv_haartraining is the older version of the program and it comes with fewer features than opencv_traincascade, only the latter will be described here.

Here, the cascade training process is explained using the MIT CBCL face database. This database contains face and background images of 19 x 19 pixels arranged as shown in the following screenshot:

Image file organization

The training process involves the following steps:

Finally, the XML files of the cascade are stored in the output directory. These files are cascade.xml, params.xml, and a set of stageX.xml files where X is the stage number.