Face Detection Using OpenCV – Python Mini Project with program

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First of all The objective of the program given is to detect object of interest(Face). You can train your own image if to do so, Object of your choice.

Steps to Download Requirement & How to Create Face Detection Using OpenCV – Python Mini Project with program Pycharm IDE Follow these steps:-

  • Download Python 2.7 or higher version
  • Make sure that bumpy is running in python
  • Then install opencv.
  • Put the Harrashcade_Eye.XML & Harrashcade_Frontalface_deafult.XML file in same folder.

Code

#OpenCV program to detect face in real time
#import libraries of python OpenCV
#wgere its functionality resides
import cv2

# load the required trained XML classifiers 
# Trained XML classifiers describes some features of some 
# object we want to detect a cascade function is trained 
# from a lot of positive(faces) and negative(non-faces) 
# images. 
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') 
  
# Trained XML file for detecting eyes 
eye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')  
  
# capture frames from a camera 
cap = cv2.VideoCapture(0) 

# loop runs if capturing has been initialized. 
while 1:  
  
    # reads frames from a camera 
    ret, img = cap.read()  
  
    # convert to gray scale of each frames 
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
  
    # Detects faces of different sizes in the input image 
    faces = face_cascade."detectMultiScale"(gray, 1.3, 5) 
  
    for (x,y,w,h) in faces: 
        # To draw a rectangle in a face  
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)  
        roi_gray = gray[y:y+h, x:x+w] 
        roi_color = img[y:y+h, x:x+w] 
  
        # Detects eyes of different sizes in the input image 
        eyes = eye_cascade.detectMultiScale(roi_gray)  
  
        #To draw a rectangle in eyes 
        for (ex,ey,ew,eh) in eyes: 
            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(0,127,255),2) 
  
    # Display an image in a window 
    cv2.imshow('img',img) 
  
    # Wait for Esc key to stop 
    k = cv2.waitKey(30) & 0xff
    if k == 27: 
        break
  
# Close the window 
cap.release() 
  
# De-allocate any associated memory usage 
cv2.destroyAllWindows()

Output

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