/
main_code.py
executable file
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/
main_code.py
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import face_recognition
import cv2
import numpy as np
from livenessmodel import get_liveness_model
from common import get_users
font = cv2.FONT_HERSHEY_DUPLEX
# 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 = []
while True:
# 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, 'UNLOCK', (frame.shape[1]//2, frame.shape[0]//2), font, 1.0, (255, 255, 255), 1)
else:
cv2.putText(frame, 'LOCKED!', (frame.shape[1]//2, frame.shape[0]//2), font, 1.0, (255, 255, 255), 1)
else:
cv2.putText(frame, 'WARNING!', (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
# Release handle to the webcam
video_capture.release()
cv2.destroyAllWindows()