ai_web_cam.py

import numpy as np
import pandas as pd
import face_recognition
import os
from datetime import datetime
import cv2


def gen_frames(csv_path, class_no):
    path = 'ImagesBasic'
    images = []
    personName = []
    myList = os.listdir(path)

    for curImg in myList:
        currentImage = cv2.imread(f'{path}/{curImg}')
        images.append(currentImage)
        personName.append(os.path.splitext(curImg)[0])
    print(personName)

    def faceEncodings(images):
        encodeList = []
        for img in images:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            encode = face_recognition.face_encodings(img)[0]
            encodeList.append(encode)
        return encodeList

    def markAttendance(name):
        with open(csv_path, 'r+') as f:
            myDataList = f.readlines()
            nameList = []
            for line in myDataList:
                entry = line.split(',')
                nameList.append(entry[0])
            if name not in nameList:
                now = datetime.now()
                dtString = now.strftime('%H:%M:%S')
                print(f'\n{name}, {dtString}')
                attendance = pd.read_csv(csv_path)
                idx = attendance["name"].tolist().index(name)
                attendance[f'{datetime.now().date()}({class_no})'][idx] = 'p'
                attendance.to_csv(path_or_buf=csv_path, index=False)

    encodeListKnown = faceEncodings(images)
    print("Encoding complete")

    cap = cv2.VideoCapture(0)

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

        facesCurrent = face_recognition.face_locations(imgS)
        encodeCurrent = face_recognition.face_encodings(imgS, facesCurrent)

        for encodeFace, faceLoc in zip(encodeCurrent, facesCurrent):
            matches = face_recognition.compare_faces(
                encodeListKnown, encodeFace)
            faceDistance = face_recognition.face_distance(
                encodeListKnown, encodeFace)
            # print(faceDistance)
            matchIndex = np.argmin(faceDistance)

            if matches[matchIndex]:
                name = personName[matchIndex].upper()
                # print(name)
                y1, x2, y2, x1 = faceLoc
                y1, x2, y2, x1 = y1*4, x2*4, y2*4, x1*4
                cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
                cv2.rectangle(img, (x1, y2-35), (x2, y2),
                              (0, 255, 0), cv2.FILLED)
                cv2.putText(img, name, (x1+6, y2-6),
                            cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
                markAttendance(name)

        ret, buffer = cv2.imencode('.jpg', img)
        img = buffer.tobytes()
        yield (b'--frame\r\n'b'Content-Type: image/jpeg\r\n\r\n' + img + b'\r\n')