pip3 install opencv-python

import cv2

# loading the test image

image = cv2.imread("kids.jpg")

# converting to grayscale

image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# initialize the face recognizer (default face haar cascade)

face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml")

# detect all the faces in the image

faces = face_cascade.detectMultiScale(image_gray)

# print the number of faces detected

print(f"{len(faces)} faces detected in the image.")

# for every face, draw a blue rectangle

for x, y, width, height in faces:

    cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2)

# save the image with rectangles

cv2.imwrite("kids_detected.jpg", image)

import cv2




# create a new cam object

cap = cv2.VideoCapture(0)

# initialize the face recognizer (default face haar cascade)

face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml")




while True:

    # read the image from the cam

    _, image = cap.read()

    # converting to grayscale

    image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # detect all the faces in the image

    faces = face_cascade.detectMultiScale(image_gray, 1.3, 5)

    # for every face, draw a blue rectangle

    for x, y, width, height in faces:

        cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2)

    cv2.imshow("image", image)

    if cv2.waitKey(1) == ord("q"):

        break




cap.release()

cv2.destroyAllWindows()

Code for How to Detect Human Faces in Python using OpenCV

import cv2


# loading the test image

image = cv2.imread("kids.jpg")

# converting to grayscale

image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# initialize the face recognizer (default face haar cascade)

face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml")

# detect all the faces in the image

faces = face_cascade.detectMultiScale(image_gray, 1.3, 5)

# print the number of faces detected

print(f"{len(faces)} faces detected in the image.")


# for every face, draw a blue rectangle

for x, y, width, height in faces:

    cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2)

# save the image with rectangles

cv2.imwrite("kids_detected.jpg", image)

import cv2


# create a new cam object

cap = cv2.VideoCapture(0)

# initialize the face recognizer (default face haar cascade)

face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml")

while True:

    # read the image from the cam

    _, image = cap.read()

    # converting to grayscale

    image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # detect all the faces in the image

    faces = face_cascade.detectMultiScale(image_gray, 1.3, 5)

    # for every face, draw a blue rectangle

    for x, y, width, height in faces:

        cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2)

    cv2.imshow("image", image)

    if cv2.waitKey(1) == ord("q"):

        break

cap.release()

cv2.destroyAllWindows()