main_code.py

import face_recognition
import cv2
import numpy as np 
from livenessmodel import get_liveness_model
from common import get_users
import time
import datetime
import pandas as pd
df= pd.read_excel("attendance.xlsx")
font = cv2.FONT_HERSHEY_DUPLEX

def convert(date):
    year= date[:4]
    month= date[5:7]
    day= date[8:]
    date= day+"-"+month+"-"+year
    return date


# Get the liveness network
model = get_liveness_model()

# load weights into new model
model.load_weights("model/model.h5")
print("Loaded model from disk")

# Read the users data and create face encodings 
known_names, known_encods = get_users()


video_capture = cv2.VideoCapture(0)
video_capture.set(3, 640)
video_capture.set(4, 480)

# Initialize some variables
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True
input_vid = []
cond= True
flag_win= True
while (1):
    # Grab a single frame of video
    if len(input_vid) < 24:

        ret, frame = video_capture.read()

        liveimg = cv2.resize(frame, (100,100))
        liveimg = cv2.cvtColor(liveimg, cv2.COLOR_BGR2GRAY)
        input_vid.append(liveimg)
    else:
        ret, frame = video_capture.read()

        liveimg = cv2.resize(frame, (100,100))
        liveimg = cv2.cvtColor(liveimg, cv2.COLOR_BGR2GRAY)
        input_vid.append(liveimg)
        inp = np.array([input_vid[-24:]])
        inp = inp/255
        inp = inp.reshape(1,24,100,100,1)
        pred = model.predict(inp)
        input_vid = input_vid[-25:]
        if pred[0][0]> .95:

            # Resize frame of video to 1/4 size for faster face recognition processing
            small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)

            # Only process every other frame of video to save time
            if process_this_frame:
                # Find all the faces and face encodings in the current frame of video
                face_locations = face_recognition.face_locations(small_frame)
                face_encodings = face_recognition.face_encodings(small_frame, face_locations)
                name = "Unknown"
                face_names = []
                for face_encoding in face_encodings:
                    for ii in range(len(known_encods)):
                        # See if the face is a match for the known face(s)
                        match = face_recognition.compare_faces([known_encods[ii]], face_encoding)

                        if match[0]:
                            name = known_names[ii]

                    face_names.append(name)

            process_this_frame = not process_this_frame

            unlock = False
            for n in face_names:

                if n != 'Unknown':
                    unlock=True

            # Display the results
            for (top, right, bottom, left), name in zip(face_locations, face_names):
                # Scale back up face locations since the frame we detected in was scaled to 1/4 size
                top *= 4
                right *= 4
                bottom *= 4
                left *= 4

                # Draw a box around the face
                cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)

                # Draw a label with a name below the face
                cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
                
                cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
                if unlock:
                    cv2.putText(frame, 'Marked present', (frame.shape[1]//2, frame.shape[0]//2), font, 1.0, (255, 255, 255), 1)
                    if flag_win:
                        cv2.imshow('Video', frame)
                    date= str(datetime.datetime.now())[:(str(datetime.datetime.now()).index(' '))+1]
                    time_now= str(datetime.datetime.now())[(str(datetime.datetime.now()).index(' '))+1:]
                    time_now= time_now[:8]
                    date= convert(date)
                    if cond:
                        new_row= {'name':name, 'date':date, 'time': time_now}
                        df= df.append(new_row, ignore_index=True)
                        df.to_excel("attendance.xlsx", index= False)
                        cond= False

                else:
                    cv2.putText(frame, 'Can not recognise', (frame.shape[1]//2, frame.shape[0]//2), font, 1.0, (255, 255, 255), 1)
        else:
            cv2.putText(frame, 'INVALID', (frame.shape[1]//2, frame.shape[0]//2), font, 1.0, (255, 255, 255), 1)
        # Display the liveness score in top left corner     
        cv2.putText(frame, str(pred[0][0]), (20, 20), font, 1.0, (255, 255, 0), 1)
        # Display the resulting image
        cv2.imshow('Video', frame)

        # Hit 'q' on the keyboard to quit!
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
        #Or hit space on the keyboard to quit!
        if cv2.waitKey(1) & 0xFF == ord(' '):
            break

# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()