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facenet-real-time.py
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facenet-real-time.py
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#Face Recognition with Google's Facenet Model
#Author Sefik Ilkin Serengil (sefiks.com)
#You can find the documentation of this code from the following link:
#https://sefiks.com/2018/09/03/face-recognition-with-facenet-in-keras/
#Tested for TensorFlow 1.9.0, Keras 2.2.0 and Python 3.5.5
#-----------------------
import numpy as np
import cv2
from keras.models import Model, Sequential
from keras.layers import Input, Convolution2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation
from PIL import Image
from keras.preprocessing.image import load_img, save_img, img_to_array
from keras.applications.imagenet_utils import preprocess_input
from keras.preprocessing import image
import matplotlib.pyplot as plt
from keras.models import model_from_json
from os import listdir
#-----------------------
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
def preprocess_image(image_path):
img = load_img(image_path, target_size=(160, 160))
img = img_to_array(img)
img = np.expand_dims(img, axis=0)
#preprocess_input normalizes input in scale of [-1, +1]. You must apply same normalization in prediction.
#Ref: https://github.com/keras-team/keras-applications/blob/master/keras_applications/imagenet_utils.py (Line 45)
img = preprocess_input(img)
return img
#------------------------
#https://github.com/serengil/tensorflow-101/blob/master/model/facenet_model.json
model = model_from_json(open("facenet_model.json", "r").read())
print("model built")
#https://drive.google.com/file/d/1971Xk5RwedbudGgTIrGAL4F7Aifu7id1/view?usp=sharing
model.load_weights('weights/facenet_weights.h5')
print("weights loaded")
#------------------------
def findEuclideanDistance(source_representation, test_representation):
euclidean_distance = source_representation - test_representation
euclidean_distance = np.sum(np.multiply(euclidean_distance, euclidean_distance))
euclidean_distance = np.sqrt(euclidean_distance)
return euclidean_distance
#------------------------
threshold = 21 #tuned threshold for l2 disabled euclidean distance
#------------------------
#put your employee pictures in this path as name_of_employee.jpg
employee_pictures = "database/"
employees = dict()
for file in listdir(employee_pictures):
employee, extension = file.split(".")
img = preprocess_image('database/%s.jpg' % (employee))
representation = model.predict(img)[0,:]
employees[employee] = representation
print("employee representations retrieved successfully")
#------------------------
cap = cv2.VideoCapture(0) #webcam
while(True):
ret, img = cap.read()
faces = face_cascade.detectMultiScale(img, 1.3, 5)
for (x,y,w,h) in faces:
if w > 130: #discard small detected faces
cv2.rectangle(img, (x,y), (x+w,y+h), (67, 67, 67), 1) #draw rectangle to main image
detected_face = img[int(y):int(y+h), int(x):int(x+w)] #crop detected face
detected_face = cv2.resize(detected_face, (160, 160)) #resize to 224x224
img_pixels = image.img_to_array(detected_face)
img_pixels = np.expand_dims(img_pixels, axis = 0)
#employee dictionary is using preprocess_image and it normalizes in scale of [-1, +1]
img_pixels /= 127.5
img_pixels -= 1
captured_representation = model.predict(img_pixels)[0,:]
distances = []
for i in employees:
employee_name = i
source_representation = employees[i]
distance = findEuclideanDistance(captured_representation, source_representation)
#print(employee_name,": ",distance)
distances.append(distance)
label_name = 'unknown'
index = 0
for i in employees:
employee_name = i
if index == np.argmin(distances):
if distances[index] <= threshold:
#print("detected: ",employee_name)
#label_name = "%s (distance: %s)" % (employee_name, str(round(distance,2)))
similarity = 100 + (20 - distance)
if similarity > 99.99: similarity = 99.99
label_name = "%s (%s%s)" % (employee_name, str(round(similarity,2)), '%')
break
index = index + 1
cv2.putText(img, label_name, (int(x+w+15), int(y-64)), cv2.FONT_HERSHEY_SIMPLEX, 1, (67,67,67), 2)
#connect face and text
cv2.line(img,(x+w, y-64),(x+w-25, y-64),(67,67,67),1)
cv2.line(img,(int(x+w/2),y),(x+w-25,y-64),(67,67,67),1)
cv2.imshow('img',img)
if cv2.waitKey(1) & 0xFF == ord('q'): #press q to quit
break
#kill open cv things
cap.release()
cv2.destroyAllWindows()