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()

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: face-recognition face

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Contributed on Apr 22 2022

Dylan1080

0 Answers Avg Quality 2/10

python face recognition

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# 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()

Popularity 10/10 Helpfulness 3/10 Language python

Source: Grepper

Tags: face-recognition face

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Contributed on May 12 2022

Becon

0 Answers Avg Quality 2/10

python Face Recognition

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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)

Popularity 9/10 Helpfulness 2/10 Language python

Source: chat.openai.com

Tags: face-recognition face

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Contributed on Feb 25 2024

Sudhanshu Matkar

0 Answers Avg Quality 2/10

face recognition project in python with source code

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