What is OpenCV?

OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture, and analysis including features like face detection and object detection.

Here we are focused on face detection(frontal face).

Before that, u may have some confusion between what are computer vision and image processing...Let me explain

Image processing deals with image-to-image transformation. The input and output of image processing are both images.

Computer vision is the construction of explicit, meaningful descriptions of physical objects from their image. The output of computer vision is a description or an interpretation of structures in a 3D scene.

To use OpenCV first you should install OpenCV on your device. If you have installed it already let's go...

We need a trained model so that we can use it to detect your faces. you can use the haar cascade data from

https://raw.githubusercontent.com/Adithya-jh/Python-OpenCV-CleverProgrammer-FaceDetection/main/haarcascade_frontalface_default.xml

copy the whole thing and paste it into an XML file in the editor you are using and name it 'haarcascade_frontalface_default.xml'

#importing required dependent cv2 from OpenCV.
import cv2
from random import randrange

#using CascadeClassifier from cv2 to get trained face data.
trained_face_data = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

# 0 for default webcam - It opens the webcam
webcam = cv2.VideoCapture(0) 

while True:
    #reading data
    successful_frame_read, frame = webcam.read()

    #converting the instant images into grayscale
    grayscaled_img = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    #finding the faces using the trained model
    face_coordinates = trained_face_data.detectMultiScale(grayscaled_img)

    #locating coordinates on the screen and drawing rectangle around our face
    for (x, y, w, h) in face_coordinates:
        cv2.rectangle(frame, (x, y), (x+w, y+h), (randrange(256), randrange(256), randrange(256)), 10)

    cv2.imshow('My face', frame)
    key = cv2.waitKey(1)
    if key == 81 or key== 113:
        break

webcam.release()