face_regonition_attendance.py

import cv2
from PIL import Image
import numpy as np
import face_recognition
import dlib
import os
class my_dictionary(dict): 
    def __init__(self): 
        self = dict() 

    def add(self, key, value): 
        self[key] = value 

def thug():
        maskPath = "hack.png"
        faceCascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
        mask = Image.open(maskPath)
        def thug_mask(image):
        	gray=cv2.cvtColor(image,cv2.COLOR_BGR2RGBA)
        	faces = faceCascade.detectMultiScale(gray, 2)
        	background = Image.fromarray(image)

        	for (x,y,w,h) in faces:
        		# resize mask
        		r = mask.resize((w,h), Image.ANTIALIAS)
        		o = (x,y)
        		background.paste(r, o, mask=r)

        	return np.asarray(background)

        cap = cv2.VideoCapture(cv2.CAP_ANY)

        while True:
        	ret, frame = cap.read()
        
        	if ret == True:
        		cv2.imshow('THUG LIFE', thug_mask(frame))
                
        		if cv2.waitKey(1) ==ord('q'):
        			break

        cap.release()
        cv2.destroyAllWindows()
video_capture = cv2.VideoCapture(0)
known_face_names=[]
# Load a sample picture and learn how to recognize it.
known_face_encodings=[]
list=os.listdir('data/')
attendence=my_dictionary()

for file in list:
    attendence.add(file,'absent')
    known_face_names.append(file)
    known_face_encodings.append(face_recognition.face_encodings(face_recognition.load_image_file('./data/'+file))[0])
face_locations = []
face_encodings = []
face_names = []
process_this_frame = True

while True:
    ret, frame = video_capture.read()
    small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
    rgb_small_frame = small_frame[:, :, ::-1]

    if process_this_frame:
        # Find all the faces and face encodings in the current frame of video
        face_locations = face_recognition.face_locations(rgb_small_frame)
        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
        face_names = []

        for face_encoding in face_encodings:
            # See if the face is a match for the known face(s)
            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
            name = "Unknown"
            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
            best_match_index = np.argmin(face_distances)
            if matches[best_match_index]:
                name = known_face_names[best_match_index]
                attendence.add(name,"present")
            face_names.append(name)

    print(attendence)
    process_this_frame = not process_this_frame

    for (top, right, bottom, left), name in zip(face_locations, face_names):
        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)
        font = cv2.FONT_HERSHEY_DUPLEX
        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)

    # Display the resulting image
    cv2.imshow('Video', frame)

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

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