face.py

import cv2
import face_recognition
import numpy as np
import os

def face_detection():
    path = str(os.getcwd()) + '\Humane-Diary\ImagesResources'
    image = []
    classNames = []
    myList = os.listdir(path)
    for cls in myList:
        curImg = cv2.imread(f'{path}/{cls}')
        image.append(curImg)
        classNames.append(os.path.splitext(cls)[0])

    def encoding_finder(images):
        encoded_list = []
        for img in images:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            encoded_list.append(face_recognition.face_encodings(img)[0])
        return encoded_list
    
    encoded_known = encoding_finder(image)
    print('Encoding Completed')

    cap = cv2.VideoCapture(0)
    global presence
    presence = False
    global loop
    loop = False


    while True:
        success, img = cap.read()
        imgS  = cv2.resize(img, (0,0), None, 0.25, 0.25)
        imgS = cv2.cvtColor(imgS, cv2.COLOR_BGR2RGB)

        faces = face_recognition.face_locations(imgS)
        encodings = face_recognition.face_encodings(imgS, faces)

        for encode_faces, facesLoc in zip(encodings, faces):
            matches = face_recognition.compare_faces(encoded_known, encode_faces) 
            faceDis = face_recognition.face_distance(encoded_known, encode_faces)
            matchindex = np.argmin(faceDis) 

            if matches[matchindex]:
                name = classNames[matchindex].upper()
                presence=True
                y1,x2,y2,x1 = facesLoc
                y1,x2,y2,x1 = y1*4,x2*4,y2*4,x1*4
                cv2.rectangle(img, (x1, y2-35), (x2,y1), (0,255, 0), 5)
                cv2.putText(img, name, (x1+6,y2-6), cv2.FONT_HERSHEY_COMPLEX, 1, (0,200,255), 2)
                return presence, name
            
            else:
                
                return presence, "none"
                
                

        cv2.imshow('Webcam', img)
        cv2.waitKey(1)
    

if __name__ == "__main__":
    print(face_detection())