import cv2

​

video_capture = cv2.VideoCapture(0)

​

# Loading the required haar-cascade xml classifier file, classifies face from non face

haar_cascade = cv2.CascadeClassifier('Haarcascade_frontalface_default.xml')

print(haar_cascade)

while True:

    # Capture frame-by-frame

    ret, img = video_capture.read()

​

    # Converting image to grayscale, detection model sees it as grayscale

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

    # Applying the face detection method on the grayscale image

    faces_rect = haar_cascade.detectMultiScale(gray_img, 1.1, 9)

    # Iterating through rectangles of detected faces, displaying the rectangle

    for (x, y, w, h) in faces_rect:

        cv2.rectangle(img, (x, y), (x+w, y+h), (70, 0, 255), 2)

​

    # Display the resulting frame

    cv2.imshow('Video', img)

​

    if cv2.waitKey(1) & 0xFF == ord('q'):

        break

​

# When everything is done, release the capture

video_capture.release()

cv2.destroyAllWindows()

# simple python code: face recognition

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 Detect', img)

cv2.waitKey(600000)

​

cam.release()

cv2.destroyAllWindows()

​

import face_recognition

import cv2

​

def recognize_faces(people):

    # Create arrays to store known face encodings and corresponding names

    known_face_encodings = []

    known_face_names = []

​

    # Load face encodings and names for each person

    for person in people:

        # Load the image of the person

        known_image = face_recognition.load_image_file(person["image_path"])

​

        # Compute the face encoding for the person

        face_encoding = face_recognition.face_encodings(known_image)[0]

​

        # Append the face encoding and name to the lists

        known_face_encodings.append(face_encoding)

        known_face_names.append(person["name"])

​

    # Open the webcam

    video_capture = cv2.VideoCapture(0)

​

    while True:

        # Capture each frame from the webcam

        ret, frame = video_capture.read()

​

        # Find all face locations and face encodings in the current frame

        face_locations = face_recognition.face_locations(frame)

        face_encodings = face_recognition.face_encodings(frame, face_locations)

​

        # Loop through each face found in the frame

        for (top, right, bottom, left), face_encoding in zip(face_locations, face_encodings):

            # Check if the face matches any known faces

            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)

​

            name = "Unknown"

​

            # If a match is found, use the name of the known person

            if True in matches:

                first_match_index = matches.index(True)

                name = known_face_names[first_match_index]

​

            # Draw a rectangle around the face and display the name

            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

            font = cv2.FONT_HERSHEY_DUPLEX

            cv2.putText(frame, name, (left + 6, bottom - 6), font, 0.5, (255, 255, 255), 1)

​

        # Display the resulting frame

        cv2.imshow('Video', frame)

​

        # Break the loop when 'q' is pressed

        if cv2.waitKey(1) & 0xFF == ord('q'):

            break

​

    # Release the webcam and close all windows

    video_capture.release()

    cv2.destroyAllWindows()

​

# List of people and their image paths

people_list = [

    {"name": "Person 1", "image_path": "path/to/known_person_1.jpg"},

    {"name": "Person 2", "image_path": "path/to/known_person_2.jpg"},

    # Add more people as needed...

]

​

# Call the function to start face recognition

recognize_faces(people_list)

img = cv2.imread('baby.jpg')