Detecting And Tracking Face in Python Using Haarcascade Framework

 Detecting And Tracking Face in Python Using Haarcascade Classifier

In this blog we are going to learn how to detect and track human face using haarcascade framework

 

Before we start make sure you save the Haarcascade File in Xml format . Make sure you save the file in the same location where your python file or jupyter notebook.

import cv2

import numpy as np

#first we are going to call our face detection classifier the same file we save in xml format it contains the code to detect face

face =cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')

#take input from our webcam

cap = cv2.VideoCapture(0)

sf = 0.5 # scaling factor lower the value lower the resolution of the video

while True:

    ret, frame = cap.read()

    frame = cv2.resize(frame, None, fx=sf, fy=sf,interpolation=cv2.INTER_AREA)

    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    face_rects = face.detectMultiScale(gray, 1.3, 5)

    for (x,y,w,h) in face_rects:

        cv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,0), 3)

    cv2.imshow('Face Detector', frame)

    c = cv2.waitKey(1)

    if c == 27:

        break

cap.release()

cv2.destroyAllWindows()