import cv2
import numpy as np
import dlib
import glob
from scipy.spatial import distance
from imutils import face_utils
from keras.models import load_model
from fr_utils import *
from inception_blocks_v2 import *
detector = dlib.get_frontal_face_detector()
FRmodel = load_model('face-rec_Google.h5')
print("Total Params:", FRmodel.count_params())
predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
thresh = 0.25
def eye_aspect_ratio(eye):
A = distance.euclidean(eye[1], eye[5])
B = distance.euclidean(eye[2], eye[4])
C = distance.euclidean(eye[0], eye[3])
ear = (A + B) / (2.0 * C)
return ear
def recognize_face(face_descriptor, database):
encoding = img_to_encoding(face_descriptor, FRmodel)
min_dist = 100
identity = None
# Loop over the database dictionary's names and encodings.
for (name, db_enc) in database.items():
# Compute L2 distance between the target "encoding" and the current "emb" from the database.
dist = np.linalg.norm(db_enc - encoding)
print('distance for %s is %s' % (name, dist))
# If this distance is less than the min_dist, then set min_dist to dist, and identity to name
if dist < min_dist:
min_dist = dist
identity = name
if int(identity) <=4:
return str('Akshay'), min_dist
if int(identity) <=8:
return str('Apoorva'), min_dist
def extract_face_info(img, img_rgb, database,ear):
faces = detector(img_rgb)
x, y, w, h = 0, 0, 0, 0
if len(faces) > 0:
for face in faces:
(x, y, w, h) = face_utils.rect_to_bb(face)
cv2.rectangle(img, (x, y), (x + w, y + h), (255, 255, 0), 2)
image = img[y:y + h, x:x + w]
name, min_dist = recognize_face(image, database)
if ear > thresh:
if min_dist < 0.1:
cv2.putText(img, "Face : " + name, (x, y - 50), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
cv2.putText(img, "Dist : " + str(min_dist), (x, y - 20), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 255, 0), 2)
else:
cv2.putText(img, 'No matching faces', (x, y - 20), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)
else:
cv2.putText(img, 'Eyes Closed', (x, y - 20), cv2.FONT_HERSHEY_PLAIN, 1.5, (0, 0, 255), 2)
def initialize():
#load_weights_from_FaceNet(FRmodel)
#we are loading model from keras hence we won't use the above method
database = {}
# load all the images of individuals to recognize into the database
for file in glob.glob("images/*"):
identity = os.path.splitext(os.path.basename(file))[0]
database[identity] = fr_utils.img_path_to_encoding(file, FRmodel)
return database
def recognize():
database = initialize()
cap = cv2.VideoCapture(0)
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
while True:
ret, img = cap.read()
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
subjects = detector(gray, 0)
for subject in subjects:
shape = predictor(gray, subject)
shape = face_utils.shape_to_np(shape) # converting to NumPy Array
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
ear = (leftEAR + rightEAR) / 2.0
leftEyeHull = cv2.convexHull(leftEye)
rightEyeHull = cv2.convexHull(rightEye)
cv2.drawContours(img, [leftEyeHull], -1, (0, 255, 0), 1)
cv2.drawContours(img, [rightEyeHull], -1, (0, 255, 0), 1)
extract_face_info(img, img_rgb, database,ear)
cv2.imshow('Recognizing faces', img)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
recognize()
