# importing the required libraries

import cv2

import face_recognition

# capture the video from default camera

webcam_video_stream = cv2.VideoCapture('modi.mp4') # MAKE VIDEO FOR THIS

# TODO : make video for this man! ^^^^

# load the sample images and get the 128 face embeddings from them

beyonce_image = face_recognition.load_image_file('/Users/dc/Desktop/portfoliocCodes/Face/lib/Images/Test/Beyonce.jpg')

beyonce_face_encodings = face_recognition.face_encodings(beyonce_image)[0]

jayz_image = face_recognition.load_image_file('/Users/dc/Desktop/portfoliocCodes/Face/lib/Images/Test/Jay-z.jpg')

jayz_face_encodings = face_recognition.face_encodings(jayz_image)[0]

daniel_image = face_recognition.load_image_file('/Users/dc/Desktop/portfoliocCodes/Face/lib/Images/Test/Daniel.jpeg')

Daniel_face_encodings = face_recognition.face_encodings(daniel_image)[0]

# save the encodings and the corresponding labels in seperate arrays in the same order

known_face_encodings = [beyonce_face_encodings, jayz_face_encodings, Daniel_face_encodings]

known_face_names = ["Beyoncé", "Jay-z", "Daniel"]

# initialize the array variable to hold all face locations, encodings and names

all_face_locations = []

all_face_encodings = []

all_face_names = []

# loop through every frame in the video

while True:

# get the current frame from the video stream as an image

ret, current_frame = webcam_video_stream.read()

# resize the current frame to 1/4 size to proces faster

current_frame_small = cv2.resize(current_frame, (0, 0), fx=0.25, fy=0.25)

# detect all faces in the image

# arguments are image,no_of_times_to_upsample, model

all_face_locations = face_recognition.face_locations(current_frame_small, number_of_times_to_upsample=1,

model='hog')

# detect face encodings for all the faces detected

all_face_encodings = face_recognition.face_encodings(current_frame_small, all_face_locations)

# looping through the face locations and the face embeddings

for current_face_location, current_face_encoding in zip(all_face_locations, all_face_encodings):

# splitting the tuple to get the four position values of current face

top_pos, right_pos, bottom_pos, left_pos = current_face_location

# change the position maginitude to fit the actual size video frame

top_pos = top_pos * 4

right_pos = right_pos * 4

bottom_pos = bottom_pos * 4

left_pos = left_pos * 4

# find all the matches and get the list of matches

all_matches = face_recognition.compare_faces(known_face_encodings, current_face_encoding)

# string to hold the label

name_of_person = 'Unknown face'

# check if the all_matches have at least one item

# if yes, get the index number of face that is located in the first index of all_matches

# get the name corresponding to the index number and save it in name_of_person

if True in all_matches:

first_match_index = all_matches.index(True)

name_of_person = known_face_names[first_match_index]

# draw rectangle around the face

cv2.rectangle(current_frame, (left_pos, top_pos), (right_pos, bottom_pos), (255, 0, 0), 2)

# display the name as text in the image

font = cv2.FONT_HERSHEY_DUPLEX

cv2.putText(current_frame, name_of_person, (left_pos, bottom_pos), font, 0.5, (255, 255, 255), 1)

# display the video

cv2.imshow("Webcam Video", current_frame)

if cv2.waitKey(1) & 0xFF == ord('q'):

break

# release the stream and cam

# close all opencv windows open

webcam_video_stream.release()

cv2.destroyAllWindows()