Recognize.py

import datetime
import os
import time

import cv2
import pandas as pd
from csv import writer 

#-------------------------
def recognize_attendence():
    recognizer = cv2.face.LBPHFaceRecognizer_create()  # cv2.createLBPHFaceRecognizer()
    recognizer.read("TrainingImageLabel"+os.sep+"Trainner.yml")
    harcascadePath = "haarcascade_frontalface_default.xml"
    faceCascade = cv2.CascadeClassifier(harcascadePath)
    df = pd.read_csv("EmployeeDetails"+os.sep+"EmployeeDetails.csv")
    font = cv2.FONT_HERSHEY_SIMPLEX
    col_names = ['Id', 'Name', 'Time']
    attendance = pd.DataFrame(columns=col_names)

    # Initialize and start realtime video capture
    cam = cv2.VideoCapture(0, cv2.CAP_DSHOW)
    cam.set(3, 640)  # set video width
    cam.set(4, 480)  # set video height
    # Define min window size to be recognized as a face
    minW = 0.1 * cam.get(3)
    minH = 0.1 * cam.get(4)

    minThreshold = 40 # Accurate minThresold = 67
    while True:

        ret, im = cam.read()
        gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
        faces = faceCascade.detectMultiScale(gray, 1.2, 5,minSize = (int(minW), int(minH)),flags = cv2.CASCADE_SCALE_IMAGE)
        for(x, y, w, h) in faces:
            cv2.rectangle(im, (x, y), (x+w, y+h), (10, 159, 255), 2)
            Id, conf = recognizer.predict(gray[y:y+h, x:x+w])

            if conf < 100:

                aa = df.loc[df['Id'] == Id]['Name'].values
                confstr = "  {0}%".format(round(100 - conf))
                tt = str(Id)+"-"+aa

            else:
                Id = '  Unknown  '
                tt = str(Id)
                confstr = "  {0}%".format(round(100 - conf))

            # if (100-conf) > 67:
            if (100-conf) > minThreshold:
                ts = time.time()
                timeStamp = datetime.datetime.fromtimestamp(ts).strftime('%H:%M:%S')
                aa = str(aa)[2:-2]

                attendance.loc[len(attendance)] = [Id, aa, timeStamp]
            tt = str(tt)[2:-2]
            if(100-conf) > minThreshold:
                tt = tt + " [Pass]"
                cv2.putText(im, str(tt), (x+5,y-5), font, 1, (255, 255, 255), 2)
            else:
                cv2.putText(im, str(tt), (x + 5, y - 5), font, 1, (255, 255, 255), 2)

            if (100-conf) > minThreshold:
                cv2.putText(im, str(confstr), (x + 5, y + h - 5), font,1, (0, 255, 0),1 )
            elif (100-conf) > 50:
                cv2.putText(im, str(confstr), (x + 5, y + h - 5), font, 1, (0, 255, 255), 1)
            else:
                cv2.putText(im, str(confstr), (x + 5, y + h - 5), font, 1, (0, 0, 255), 1)



        attendance = attendance.drop_duplicates(subset=['Id'], keep='first')
        cv2.imshow('Attendance', im)
        if (cv2.waitKey(1) == ord('q')):
            break
    ts = time.time()
    date = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
    timeStamp = datetime.datetime.fromtimestamp(ts).strftime('%H:%M:%S')
    Hour, Minute, Second = timeStamp.split(":")
    # fileName = "Attendance"+os.sep+"Attendance_"+date+"_"+Hour+"-"+Minute+"-"+Second+".csv"
    fileName ="Attendance"+os.sep+"Attendance_"+date+".csv"
    temp = os.getcwd()+os.sep+fileName
    if os.path.exists(temp):
        old_file = pd.read_csv(temp)
        new_file = pd.concat([old_file,attendance])
        new_file.drop_duplicates(subset=['Id'], keep='first',inplace=True)
        new_file.to_csv(fileName, index=False)
    else:
        attendance.to_csv(fileName, index=False)
    print("Attendance Successful")
    cam.release()
    cv2.destroyAllWindows()