OpenCV uses Haar feature-based cascade classifiers for the object detection. It is a machine learning based algorithm, where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. The algorithm uses the concept of Cascade of Classifiers.

Pretrained classifiers for face, eye etc. can be downloaded from
https://github.com

For the following example, download and copy haarcascade_frontalface_default.xml and haarcascade_eye.xml from this URL. Then, load our input image to be used for face detection in grayscale mode.

The DetectMultiScale() method of CascadeClassifier class detects objects in the input image. It returns the positions of detected faces as in the form of Rectangle and its dimensions (x,y,w,h). Once we get these locations, we can use it for eye detection since eyes are always on the face!

Example

The complete code for face detection is as follows −

import numpy as np
import cv2

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

img = cv2.imread(''Dhoni-and-virat.jpg'')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x,y,w,h) in faces:
   img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
   roi_gray = gray[y:y+h, x:x+w]
   roi_color = img[y:y+h, x:x+w]
   eyes = eye_cascade.detectMultiScale(roi_gray)
   for (ex,ey,ew,eh) in eyes:
      cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,255,0),2)

cv2.imshow(''img'',img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output

You will get rectangles drawn around faces in the input image as shown below −