OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.

Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.

Requirements:

opencv for windows:

pip install opencv-python
pip install numpy

opencv for mac:

pip3 install opencv-python
pip3 install numpy

source code:

for scan a image:

import cv2
import numpy as np

detect = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

cam = cv2.VideoCapture('image.jpg')
check, img = cam.read()

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

faces = detect.detectMultiScale(gray, 1.2, 5)

for (x, y, w, h) in faces:
    cv2.rectangle(img, (x,y), (x+w, y+h), (255, 0, 0), 2)

cv2.imshow('Face Detection', img)

cv2.waitKey(None)

cam.release()
cv2.destroyAllWindows()

for video scanner:

import cv2
import numpy as np

detect = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

cam = cv2.VideoCapture(0)
check, img = cam.read()

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

faces = detect.detectMultiScale(gray, 1.2, 5)

for (x, y, w, h) in faces:
    cv2.rectangle(img, (x,y), (x+w, y+h), (255, 0, 0), 2)

cv2.imshow('Face Detection', img)

cv2.waitKey(None)

cam.release()
cv2.destroyAllWindows()