openface real time

python/openface-real-time.py

@@ -0,0 +1,391 @@
+#Face Recognition with OpenFace
+#Author Sefik Ilkin Serengil (sefiks.com)
+
+#You can find the documentation of this code from the following link: 
+#https://sefiks.com/
+
+#tested for TensorFlow 1.9.0, Keras 2.2.0 and Python 3.5.5
+
+#-----------------------
+import tensorflow as tf
+from keras.models import Model, Sequential
+from keras.layers import Input, Conv2D, ZeroPadding2D, MaxPooling2D, Flatten, Dense, Dropout, Activation, concatenate
+from keras.layers.pooling import MaxPooling2D, AveragePooling2D
+from keras.layers.core import Dense, Activation, Lambda, Flatten
+from keras.layers.normalization import BatchNormalization
+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
+from keras.models import model_from_json
+from keras.layers.merge import Concatenate
+from keras import backend as K
+
+from os import listdir
+import numpy as np
+import cv2
+from PIL import Image
+import matplotlib.pyplot as plt
+#-----------------------
+
+dump = False
+
+color = (67,67,67)
+
+face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+def preprocess_image(image_path):
+    img = load_img(image_path, target_size=(96, 96))
+    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
+
+#------------------------
+
+def builtModel():
+	myInput = Input(shape=(96, 96, 3))
+
+	x = ZeroPadding2D(padding=(3, 3), input_shape=(96, 96, 3))(myInput)
+	x = Conv2D(64, (7, 7), strides=(2, 2), name='conv1')(x)
+	x = BatchNormalization(axis=3, epsilon=0.00001, name='bn1')(x)
+	x = Activation('relu')(x)
+	x = ZeroPadding2D(padding=(1, 1))(x)
+	x = MaxPooling2D(pool_size=3, strides=2)(x)
+	x = Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_1')(x)
+	x = Conv2D(64, (1, 1), name='conv2')(x)
+	x = BatchNormalization(axis=3, epsilon=0.00001, name='bn2')(x)
+	x = Activation('relu')(x)
+	x = ZeroPadding2D(padding=(1, 1))(x)
+	x = Conv2D(192, (3, 3), name='conv3')(x)
+	x = BatchNormalization(axis=3, epsilon=0.00001, name='bn3')(x)
+	x = Activation('relu')(x)
+	Lambda(lambda x: tf.nn.lrn(x, alpha=1e-4, beta=0.75), name='lrn_2')(x)
+	x = ZeroPadding2D(padding=(1, 1))(x)
+	x = MaxPooling2D(pool_size=3, strides=2)(x)
+
+	# Inception3a
+	inception_3a_3x3 = Conv2D(96, (1, 1), name='inception_3a_3x3_conv1')(x)
+	inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn1')(inception_3a_3x3)
+	inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
+	inception_3a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3a_3x3)
+	inception_3a_3x3 = Conv2D(128, (3, 3), name='inception_3a_3x3_conv2')(inception_3a_3x3)
+	inception_3a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_3x3_bn2')(inception_3a_3x3)
+	inception_3a_3x3 = Activation('relu')(inception_3a_3x3)
+
+	inception_3a_5x5 = Conv2D(16, (1, 1), name='inception_3a_5x5_conv1')(x)
+	inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn1')(inception_3a_5x5)
+	inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
+	inception_3a_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3a_5x5)
+	inception_3a_5x5 = Conv2D(32, (5, 5), name='inception_3a_5x5_conv2')(inception_3a_5x5)
+	inception_3a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_5x5_bn2')(inception_3a_5x5)
+	inception_3a_5x5 = Activation('relu')(inception_3a_5x5)
+
+	inception_3a_pool = MaxPooling2D(pool_size=3, strides=2)(x)
+	inception_3a_pool = Conv2D(32, (1, 1), name='inception_3a_pool_conv')(inception_3a_pool)
+	inception_3a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_pool_bn')(inception_3a_pool)
+	inception_3a_pool = Activation('relu')(inception_3a_pool)
+	inception_3a_pool = ZeroPadding2D(padding=((3, 4), (3, 4)))(inception_3a_pool)
+
+	inception_3a_1x1 = Conv2D(64, (1, 1), name='inception_3a_1x1_conv')(x)
+	inception_3a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3a_1x1_bn')(inception_3a_1x1)
+	inception_3a_1x1 = Activation('relu')(inception_3a_1x1)
+
+	inception_3a = concatenate([inception_3a_3x3, inception_3a_5x5, inception_3a_pool, inception_3a_1x1], axis=3)
+
+	# Inception3b
+	inception_3b_3x3 = Conv2D(96, (1, 1), name='inception_3b_3x3_conv1')(inception_3a)
+	inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn1')(inception_3b_3x3)
+	inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
+	inception_3b_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3b_3x3)
+	inception_3b_3x3 = Conv2D(128, (3, 3), name='inception_3b_3x3_conv2')(inception_3b_3x3)
+	inception_3b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_3x3_bn2')(inception_3b_3x3)
+	inception_3b_3x3 = Activation('relu')(inception_3b_3x3)
+
+	inception_3b_5x5 = Conv2D(32, (1, 1), name='inception_3b_5x5_conv1')(inception_3a)
+	inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn1')(inception_3b_5x5)
+	inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
+	inception_3b_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3b_5x5)
+	inception_3b_5x5 = Conv2D(64, (5, 5), name='inception_3b_5x5_conv2')(inception_3b_5x5)
+	inception_3b_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_5x5_bn2')(inception_3b_5x5)
+	inception_3b_5x5 = Activation('relu')(inception_3b_5x5)
+
+	inception_3b_pool = Lambda(lambda x: x**2, name='power2_3b')(inception_3a)
+	inception_3b_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_3b_pool)
+	inception_3b_pool = Lambda(lambda x: x*9, name='mult9_3b')(inception_3b_pool)
+	inception_3b_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_3b')(inception_3b_pool)
+	inception_3b_pool = Conv2D(64, (1, 1), name='inception_3b_pool_conv')(inception_3b_pool)
+	inception_3b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_pool_bn')(inception_3b_pool)
+	inception_3b_pool = Activation('relu')(inception_3b_pool)
+	inception_3b_pool = ZeroPadding2D(padding=(4, 4))(inception_3b_pool)
+
+	inception_3b_1x1 = Conv2D(64, (1, 1), name='inception_3b_1x1_conv')(inception_3a)
+	inception_3b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3b_1x1_bn')(inception_3b_1x1)
+	inception_3b_1x1 = Activation('relu')(inception_3b_1x1)
+
+	inception_3b = concatenate([inception_3b_3x3, inception_3b_5x5, inception_3b_pool, inception_3b_1x1], axis=3)
+
+	# Inception3c
+	inception_3c_3x3 = Conv2D(128, (1, 1), strides=(1, 1), name='inception_3c_3x3_conv1')(inception_3b)
+	inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_3x3_bn1')(inception_3c_3x3)
+	inception_3c_3x3 = Activation('relu')(inception_3c_3x3)
+	inception_3c_3x3 = ZeroPadding2D(padding=(1, 1))(inception_3c_3x3)
+	inception_3c_3x3 = Conv2D(256, (3, 3), strides=(2, 2), name='inception_3c_3x3_conv'+'2')(inception_3c_3x3)
+	inception_3c_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_3x3_bn'+'2')(inception_3c_3x3)
+	inception_3c_3x3 = Activation('relu')(inception_3c_3x3)
+
+	inception_3c_5x5 = Conv2D(32, (1, 1), strides=(1, 1), name='inception_3c_5x5_conv1')(inception_3b)
+	inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_5x5_bn1')(inception_3c_5x5)
+	inception_3c_5x5 = Activation('relu')(inception_3c_5x5)
+	inception_3c_5x5 = ZeroPadding2D(padding=(2, 2))(inception_3c_5x5)
+	inception_3c_5x5 = Conv2D(64, (5, 5), strides=(2, 2), name='inception_3c_5x5_conv'+'2')(inception_3c_5x5)
+	inception_3c_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_3c_5x5_bn'+'2')(inception_3c_5x5)
+	inception_3c_5x5 = Activation('relu')(inception_3c_5x5)
+
+	inception_3c_pool = MaxPooling2D(pool_size=3, strides=2)(inception_3b)
+	inception_3c_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_3c_pool)
+
+	inception_3c = concatenate([inception_3c_3x3, inception_3c_5x5, inception_3c_pool], axis=3)
+
+	#inception 4a
+	inception_4a_3x3 = Conv2D(96, (1, 1), strides=(1, 1), name='inception_4a_3x3_conv'+'1')(inception_3c)
+	inception_4a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_3x3_bn'+'1')(inception_4a_3x3)
+	inception_4a_3x3 = Activation('relu')(inception_4a_3x3)
+	inception_4a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4a_3x3)
+	inception_4a_3x3 = Conv2D(192, (3, 3), strides=(1, 1), name='inception_4a_3x3_conv'+'2')(inception_4a_3x3)
+	inception_4a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_3x3_bn'+'2')(inception_4a_3x3)
+	inception_4a_3x3 = Activation('relu')(inception_4a_3x3)
+
+	inception_4a_5x5 = Conv2D(32, (1,1), strides=(1,1), name='inception_4a_5x5_conv1')(inception_3c)
+	inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_5x5_bn1')(inception_4a_5x5)
+	inception_4a_5x5 = Activation('relu')(inception_4a_5x5)
+	inception_4a_5x5 = ZeroPadding2D(padding=(2,2))(inception_4a_5x5)
+	inception_4a_5x5 = Conv2D(64, (5,5), strides=(1,1), name='inception_4a_5x5_conv'+'2')(inception_4a_5x5)
+	inception_4a_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_5x5_bn'+'2')(inception_4a_5x5)
+	inception_4a_5x5 = Activation('relu')(inception_4a_5x5)
+
+	inception_4a_pool = Lambda(lambda x: x**2, name='power2_4a')(inception_3c)
+	inception_4a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_4a_pool)
+	inception_4a_pool = Lambda(lambda x: x*9, name='mult9_4a')(inception_4a_pool)
+	inception_4a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_4a')(inception_4a_pool)
+
+	inception_4a_pool = Conv2D(128, (1,1), strides=(1,1), name='inception_4a_pool_conv'+'')(inception_4a_pool)
+	inception_4a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_pool_bn'+'')(inception_4a_pool)
+	inception_4a_pool = Activation('relu')(inception_4a_pool)
+	inception_4a_pool = ZeroPadding2D(padding=(2, 2))(inception_4a_pool)
+
+	inception_4a_1x1 = Conv2D(256, (1, 1), strides=(1, 1), name='inception_4a_1x1_conv'+'')(inception_3c)
+	inception_4a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4a_1x1_bn'+'')(inception_4a_1x1)
+	inception_4a_1x1 = Activation('relu')(inception_4a_1x1)
+
+	inception_4a = concatenate([inception_4a_3x3, inception_4a_5x5, inception_4a_pool, inception_4a_1x1], axis=3)
+
+	#inception4e
+	inception_4e_3x3 = Conv2D(160, (1,1), strides=(1,1), name='inception_4e_3x3_conv'+'1')(inception_4a)
+	inception_4e_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_3x3_bn'+'1')(inception_4e_3x3)
+	inception_4e_3x3 = Activation('relu')(inception_4e_3x3)
+	inception_4e_3x3 = ZeroPadding2D(padding=(1, 1))(inception_4e_3x3)
+	inception_4e_3x3 = Conv2D(256, (3,3), strides=(2,2), name='inception_4e_3x3_conv'+'2')(inception_4e_3x3)
+	inception_4e_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_3x3_bn'+'2')(inception_4e_3x3)
+	inception_4e_3x3 = Activation('relu')(inception_4e_3x3)
+
+	inception_4e_5x5 = Conv2D(64, (1,1), strides=(1,1), name='inception_4e_5x5_conv'+'1')(inception_4a)
+	inception_4e_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_5x5_bn'+'1')(inception_4e_5x5)
+	inception_4e_5x5 = Activation('relu')(inception_4e_5x5)
+	inception_4e_5x5 = ZeroPadding2D(padding=(2, 2))(inception_4e_5x5)
+	inception_4e_5x5 = Conv2D(128, (5,5), strides=(2,2), name='inception_4e_5x5_conv'+'2')(inception_4e_5x5)
+	inception_4e_5x5 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_4e_5x5_bn'+'2')(inception_4e_5x5)
+	inception_4e_5x5 = Activation('relu')(inception_4e_5x5)
+
+	inception_4e_pool = MaxPooling2D(pool_size=3, strides=2)(inception_4a)
+	inception_4e_pool = ZeroPadding2D(padding=((0, 1), (0, 1)))(inception_4e_pool)
+
+	inception_4e = concatenate([inception_4e_3x3, inception_4e_5x5, inception_4e_pool], axis=3)
+
+	#inception5a
+	inception_5a_3x3 = Conv2D(96, (1,1), strides=(1,1), name='inception_5a_3x3_conv'+'1')(inception_4e)
+	inception_5a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_3x3_bn'+'1')(inception_5a_3x3)
+	inception_5a_3x3 = Activation('relu')(inception_5a_3x3)
+	inception_5a_3x3 = ZeroPadding2D(padding=(1, 1))(inception_5a_3x3)
+	inception_5a_3x3 = Conv2D(384, (3,3), strides=(1,1), name='inception_5a_3x3_conv'+'2')(inception_5a_3x3)
+	inception_5a_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_3x3_bn'+'2')(inception_5a_3x3)
+	inception_5a_3x3 = Activation('relu')(inception_5a_3x3)
+
+	inception_5a_pool = Lambda(lambda x: x**2, name='power2_5a')(inception_4e)
+	inception_5a_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3))(inception_5a_pool)
+	inception_5a_pool = Lambda(lambda x: x*9, name='mult9_5a')(inception_5a_pool)
+	inception_5a_pool = Lambda(lambda x: K.sqrt(x), name='sqrt_5a')(inception_5a_pool)
+
+	inception_5a_pool = Conv2D(96, (1,1), strides=(1,1), name='inception_5a_pool_conv'+'')(inception_5a_pool)
+	inception_5a_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_pool_bn'+'')(inception_5a_pool)
+	inception_5a_pool = Activation('relu')(inception_5a_pool)
+	inception_5a_pool = ZeroPadding2D(padding=(1,1))(inception_5a_pool)
+
+	inception_5a_1x1 = Conv2D(256, (1,1), strides=(1,1), name='inception_5a_1x1_conv'+'')(inception_4e)
+	inception_5a_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5a_1x1_bn'+'')(inception_5a_1x1)
+	inception_5a_1x1 = Activation('relu')(inception_5a_1x1)
+
+	inception_5a = concatenate([inception_5a_3x3, inception_5a_pool, inception_5a_1x1], axis=3)
+
+	#inception_5b
+	inception_5b_3x3 = Conv2D(96, (1,1), strides=(1,1), name='inception_5b_3x3_conv'+'1')(inception_5a)
+	inception_5b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_3x3_bn'+'1')(inception_5b_3x3)
+	inception_5b_3x3 = Activation('relu')(inception_5b_3x3)
+	inception_5b_3x3 = ZeroPadding2D(padding=(1,1))(inception_5b_3x3)
+	inception_5b_3x3 = Conv2D(384, (3,3), strides=(1,1), name='inception_5b_3x3_conv'+'2')(inception_5b_3x3)
+	inception_5b_3x3 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_3x3_bn'+'2')(inception_5b_3x3)
+	inception_5b_3x3 = Activation('relu')(inception_5b_3x3)
+
+	inception_5b_pool = MaxPooling2D(pool_size=3, strides=2)(inception_5a)
+
+	inception_5b_pool = Conv2D(96, (1,1), strides=(1,1), name='inception_5b_pool_conv'+'')(inception_5b_pool)
+	inception_5b_pool = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_pool_bn'+'')(inception_5b_pool)
+	inception_5b_pool = Activation('relu')(inception_5b_pool)
+
+	inception_5b_pool = ZeroPadding2D(padding=(1, 1))(inception_5b_pool)
+
+	inception_5b_1x1 = Conv2D(256, (1,1), strides=(1,1), name='inception_5b_1x1_conv'+'')(inception_5a)
+	inception_5b_1x1 = BatchNormalization(axis=3, epsilon=0.00001, name='inception_5b_1x1_bn'+'')(inception_5b_1x1)
+	inception_5b_1x1 = Activation('relu')(inception_5b_1x1)
+
+	inception_5b = concatenate([inception_5b_3x3, inception_5b_pool, inception_5b_1x1], axis=3)
+
+	av_pool = AveragePooling2D(pool_size=(3, 3), strides=(1, 1))(inception_5b)
+	reshape_layer = Flatten()(av_pool)
+	dense_layer = Dense(128, name='dense_layer')(reshape_layer)
+	norm_layer = Lambda(lambda  x: K.l2_normalize(x, axis=1), name='norm_layer')(dense_layer)
+
+	# Final Model
+	model = Model(inputs=[myInput], outputs=norm_layer)
+	return model
+
+model = builtModel()
+print("model built")
+
+#------------------------
+
+#https://drive.google.com/file/d/1LSe1YCV1x-BfNnfb7DFZTNpv_Q9jITxn/view
+model.load_weights('weights/openface_weights.h5')
+print("weights loaded")
+
+#------------------------
+def findCosineDistance(source_representation, test_representation):
+    a = np.matmul(np.transpose(source_representation), test_representation)
+    b = np.sum(np.multiply(source_representation, source_representation))
+    c = np.sum(np.multiply(test_representation, test_representation))
+    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))
+
+def l2_normalize(x, axis=-1, epsilon=1e-10):
+    output = x / np.sqrt(np.maximum(np.sum(np.square(x), axis=axis, keepdims=True), epsilon))
+    return output	
+
+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)
+    #euclidean_distance = l2_normalize(euclidean_distance)
+    return euclidean_distance
+
+#------------------------
+
+metric = "cosine"  #cosine, euclidean
+
+if metric == "cosine":
+	threshold = 0.45
+else:
+	threshold = 0.95
+
+#------------------------	
+
+#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), color, 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, (96, 96)) #resize to 96x96
+
+			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]
+
+				if metric == "cosine":
+					distance = findCosineDistance(captured_representation, source_representation)
+				elif metric == "euclidean":
+					distance = findEuclideanDistance(captured_representation, source_representation)
+
+				if dump:
+					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)
+
+						if metric == "euclidean":
+							similarity = 100 + (90 - 100*distance)
+						elif metric == "cosine":
+							similarity = 100 + (40 - 100*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, (255, 255, 255), 2)
+
+			if dump:
+				print("----------------------")
+
+			#connect face and text
+			cv2.line(img,(x+w, y-64),(x+w-25, y-64),color,1)
+			cv2.line(img,(int(x+w/2),y),(x+w-25,y-64),color,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()