opencv
Каскадные классификаторы

Использование каскадных классификаторов для обнаружения лица

питон

Код

import numpy as np
import cv2

#loading haarcascade classifiers for face and eye
#You can find these cascade classifiers here
#https://github.com/opencv/opencv/tree/master/data/haarcascades
#or where you download opencv inside data/haarcascades

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')

#loading the image
img = cv2.imread('civil_war.jpg')

#converting the image to gray scale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

#detecting face in the grayscale image
faces = face_cascade.detectMultiScale(gray, 1.3, 5)

#iterate through each detected face
for (x,y,w,h) in faces:
    cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2) #draw rectangle to each detected face

    #take the roi of the face (region of interest) 
    roi_gray = gray[y:y+h, x:x+w]
    roi_color = img[y:y+h, x:x+w]

    #detect the eyes
    eyes = eye_cascade.detectMultiScale(roi_gray)
    for (ex,ey,ew,eh) in eyes:

        #draw rectangle for each eye
        cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

#show the image
cv2.imshow('img',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Результат

Классификаторы каскадов для обнаружения лица с помощью Java

Джава

Код

import org.opencv.core.Mat;
import org.opencv.core.MatOfRect;
import org.opencv.core.Point;
import org.opencv.core.Rect;
import org.opencv.core.Scalar;
import org.opencv.highgui.Highgui;
import org.opencv.highgui.VideoCapture;
import org.opencv.objdetect.CascadeClassifier;

public class FaceDetector{
 
    public static void main(String[] args) {
 
        System.loadLibrary(Core.NATIVE_LIBRARY_NAME); 
        //Create object
        CascadeClassifier faceDetector = new CascadeClassifier(FaceDetector.class.getResource("haarcascade_frontalface_default.xml").getPath());
       
        //Read image
        Mat image = Highgui.imread("sourceimage.jpg");
       
     /*        
        //Or read from webcam
       
         * Mat image=new Mat();
         *VideoCapture videoCapture=new VideoCapture(0);
         *videoCapture.read(image);
     */
        MatOfRect faceDetections = new MatOfRect();
        //Result list
        faceDetector.detectMultiScale(image, faceDetections);

        for (Rect rect : faceDetections.toArray()) {
            //Draw rectangle on result
     
            Core.rectangle(image, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height),
                    new Scalar(0, 255, 0));
        }
        
        //write result
        Highgui.imwrite("result.png", image);
        System.out.println("Succesfull");
    }
    
    
}

Результат

Обнаружение лица с использованием хаскального каскадного классификатора

C ++

#include "opencv2/objdetect/objdetect.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/imgproc/imgproc.hpp"

#include <iostream>
#include <stdio.h>

using namespace std;
using namespace cv;

// Function Headers
void detectAndDisplay(Mat frame);

// Global variables
string face_cascade_name = "./data/haarcascade_frontalface_alt2.xml";
CascadeClassifier face_cascade;

// Function main
int main(void)
{
    // Load the cascade
    if (!face_cascade.load(face_cascade_name)){
        printf("--(!)Error on cascade loading\n");
        return (-1);
    }

    // Read the image file
    Mat frame = imread("d:/obama_01.jpg");

    // Apply the classifier to the frame
    if (!frame.empty())
        detectAndDisplay(frame);
    waitKey(0);
    return 0;
}

// Function detectAndDisplay
void detectAndDisplay(Mat frame)
{
    std::vector<Rect> faces;
    Mat frame_gray;
    
    cvtColor(frame, frame_gray, COLOR_BGR2GRAY);
    equalizeHist(frame_gray, frame_gray);

    // Detect faces
    face_cascade.detectMultiScale(frame_gray, faces, 1.1, 2, 0 | CASCADE_SCALE_IMAGE, Size(30, 30));

    for (int ic = 0; ic < faces.size(); ic++) // Iterate through all current elements (detected faces)
    {
        Point pt1(faces[ic].x, faces[ic].y); // Display detected faces on main window - live stream from camera
        Point pt2((faces[ic].x + faces[ic].height), (faces[ic].y + faces[ic].width));
        rectangle(frame, pt1, pt2, Scalar(0, 255, 0), 2, 8, 0);
    }

    imshow("original", frame);
}