1+ #### PART OF THIS CODE IS USING CODE FROM VICTOR SY WANG: https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/utils.py ####
2+
3+ import tensorflow as tf
4+ import numpy as np
5+ import os
6+ import cv2
7+ from numpy import genfromtxt
8+ from keras .layers import Conv2D , ZeroPadding2D , Activation , Input , concatenate
9+ from keras .models import Model
10+ from keras .layers .normalization import BatchNormalization
11+ from keras .layers .pooling import MaxPooling2D , AveragePooling2D
12+ import h5py
13+ import matplotlib .pyplot as plt
14+
15+
16+ _FLOATX = 'float32'
17+
18+ def variable (value , dtype = _FLOATX , name = None ):
19+ v = tf .Variable (np .asarray (value , dtype = dtype ), name = name )
20+ _get_session ().run (v .initializer )
21+ return v
22+
23+ def shape (x ):
24+ return x .get_shape ()
25+
26+ def square (x ):
27+ return tf .square (x )
28+
29+ def zeros (shape , dtype = _FLOATX , name = None ):
30+ return variable (np .zeros (shape ), dtype , name )
31+
32+ def concatenate (tensors , axis = - 1 ):
33+ if axis < 0 :
34+ axis = axis % len (tensors [0 ].get_shape ())
35+ return tf .concat (axis , tensors )
36+
37+ def LRN2D (x ):
38+ return tf .nn .lrn (x , alpha = 1e-4 , beta = 0.75 )
39+
40+ def conv2d_bn (x ,
41+ layer = None ,
42+ cv1_out = None ,
43+ cv1_filter = (1 , 1 ),
44+ cv1_strides = (1 , 1 ),
45+ cv2_out = None ,
46+ cv2_filter = (3 , 3 ),
47+ cv2_strides = (1 , 1 ),
48+ padding = None ):
49+ num = '' if cv2_out == None else '1'
50+ tensor = Conv2D (cv1_out , cv1_filter , strides = cv1_strides , data_format = 'channels_first' , name = layer + '_conv' + num )(x )
51+ tensor = BatchNormalization (axis = 1 , epsilon = 0.00001 , name = layer + '_bn' + num )(tensor )
52+ tensor = Activation ('relu' )(tensor )
53+ if padding == None :
54+ return tensor
55+ tensor = ZeroPadding2D (padding = padding , data_format = 'channels_first' )(tensor )
56+ if cv2_out == None :
57+ return tensor
58+ tensor = Conv2D (cv2_out , cv2_filter , strides = cv2_strides , data_format = 'channels_first' , name = layer + '_conv' + '2' )(tensor )
59+ tensor = BatchNormalization (axis = 1 , epsilon = 0.00001 , name = layer + '_bn' + '2' )(tensor )
60+ tensor = Activation ('relu' )(tensor )
61+ return tensor
62+
63+ WEIGHTS = [
64+ 'conv1' , 'bn1' , 'conv2' , 'bn2' , 'conv3' , 'bn3' ,
65+ 'inception_3a_1x1_conv' , 'inception_3a_1x1_bn' ,
66+ 'inception_3a_pool_conv' , 'inception_3a_pool_bn' ,
67+ 'inception_3a_5x5_conv1' , 'inception_3a_5x5_conv2' , 'inception_3a_5x5_bn1' , 'inception_3a_5x5_bn2' ,
68+ 'inception_3a_3x3_conv1' , 'inception_3a_3x3_conv2' , 'inception_3a_3x3_bn1' , 'inception_3a_3x3_bn2' ,
69+ 'inception_3b_3x3_conv1' , 'inception_3b_3x3_conv2' , 'inception_3b_3x3_bn1' , 'inception_3b_3x3_bn2' ,
70+ 'inception_3b_5x5_conv1' , 'inception_3b_5x5_conv2' , 'inception_3b_5x5_bn1' , 'inception_3b_5x5_bn2' ,
71+ 'inception_3b_pool_conv' , 'inception_3b_pool_bn' ,
72+ 'inception_3b_1x1_conv' , 'inception_3b_1x1_bn' ,
73+ 'inception_3c_3x3_conv1' , 'inception_3c_3x3_conv2' , 'inception_3c_3x3_bn1' , 'inception_3c_3x3_bn2' ,
74+ 'inception_3c_5x5_conv1' , 'inception_3c_5x5_conv2' , 'inception_3c_5x5_bn1' , 'inception_3c_5x5_bn2' ,
75+ 'inception_4a_3x3_conv1' , 'inception_4a_3x3_conv2' , 'inception_4a_3x3_bn1' , 'inception_4a_3x3_bn2' ,
76+ 'inception_4a_5x5_conv1' , 'inception_4a_5x5_conv2' , 'inception_4a_5x5_bn1' , 'inception_4a_5x5_bn2' ,
77+ 'inception_4a_pool_conv' , 'inception_4a_pool_bn' ,
78+ 'inception_4a_1x1_conv' , 'inception_4a_1x1_bn' ,
79+ 'inception_4e_3x3_conv1' , 'inception_4e_3x3_conv2' , 'inception_4e_3x3_bn1' , 'inception_4e_3x3_bn2' ,
80+ 'inception_4e_5x5_conv1' , 'inception_4e_5x5_conv2' , 'inception_4e_5x5_bn1' , 'inception_4e_5x5_bn2' ,
81+ 'inception_5a_3x3_conv1' , 'inception_5a_3x3_conv2' , 'inception_5a_3x3_bn1' , 'inception_5a_3x3_bn2' ,
82+ 'inception_5a_pool_conv' , 'inception_5a_pool_bn' ,
83+ 'inception_5a_1x1_conv' , 'inception_5a_1x1_bn' ,
84+ 'inception_5b_3x3_conv1' , 'inception_5b_3x3_conv2' , 'inception_5b_3x3_bn1' , 'inception_5b_3x3_bn2' ,
85+ 'inception_5b_pool_conv' , 'inception_5b_pool_bn' ,
86+ 'inception_5b_1x1_conv' , 'inception_5b_1x1_bn' ,
87+ 'dense_layer'
88+ ]
89+
90+ conv_shape = {
91+ 'conv1' : [64 , 3 , 7 , 7 ],
92+ 'conv2' : [64 , 64 , 1 , 1 ],
93+ 'conv3' : [192 , 64 , 3 , 3 ],
94+ 'inception_3a_1x1_conv' : [64 , 192 , 1 , 1 ],
95+ 'inception_3a_pool_conv' : [32 , 192 , 1 , 1 ],
96+ 'inception_3a_5x5_conv1' : [16 , 192 , 1 , 1 ],
97+ 'inception_3a_5x5_conv2' : [32 , 16 , 5 , 5 ],
98+ 'inception_3a_3x3_conv1' : [96 , 192 , 1 , 1 ],
99+ 'inception_3a_3x3_conv2' : [128 , 96 , 3 , 3 ],
100+ 'inception_3b_3x3_conv1' : [96 , 256 , 1 , 1 ],
101+ 'inception_3b_3x3_conv2' : [128 , 96 , 3 , 3 ],
102+ 'inception_3b_5x5_conv1' : [32 , 256 , 1 , 1 ],
103+ 'inception_3b_5x5_conv2' : [64 , 32 , 5 , 5 ],
104+ 'inception_3b_pool_conv' : [64 , 256 , 1 , 1 ],
105+ 'inception_3b_1x1_conv' : [64 , 256 , 1 , 1 ],
106+ 'inception_3c_3x3_conv1' : [128 , 320 , 1 , 1 ],
107+ 'inception_3c_3x3_conv2' : [256 , 128 , 3 , 3 ],
108+ 'inception_3c_5x5_conv1' : [32 , 320 , 1 , 1 ],
109+ 'inception_3c_5x5_conv2' : [64 , 32 , 5 , 5 ],
110+ 'inception_4a_3x3_conv1' : [96 , 640 , 1 , 1 ],
111+ 'inception_4a_3x3_conv2' : [192 , 96 , 3 , 3 ],
112+ 'inception_4a_5x5_conv1' : [32 , 640 , 1 , 1 ,],
113+ 'inception_4a_5x5_conv2' : [64 , 32 , 5 , 5 ],
114+ 'inception_4a_pool_conv' : [128 , 640 , 1 , 1 ],
115+ 'inception_4a_1x1_conv' : [256 , 640 , 1 , 1 ],
116+ 'inception_4e_3x3_conv1' : [160 , 640 , 1 , 1 ],
117+ 'inception_4e_3x3_conv2' : [256 , 160 , 3 , 3 ],
118+ 'inception_4e_5x5_conv1' : [64 , 640 , 1 , 1 ],
119+ 'inception_4e_5x5_conv2' : [128 , 64 , 5 , 5 ],
120+ 'inception_5a_3x3_conv1' : [96 , 1024 , 1 , 1 ],
121+ 'inception_5a_3x3_conv2' : [384 , 96 , 3 , 3 ],
122+ 'inception_5a_pool_conv' : [96 , 1024 , 1 , 1 ],
123+ 'inception_5a_1x1_conv' : [256 , 1024 , 1 , 1 ],
124+ 'inception_5b_3x3_conv1' : [96 , 736 , 1 , 1 ],
125+ 'inception_5b_3x3_conv2' : [384 , 96 , 3 , 3 ],
126+ 'inception_5b_pool_conv' : [96 , 736 , 1 , 1 ],
127+ 'inception_5b_1x1_conv' : [256 , 736 , 1 , 1 ],
128+ }
129+
130+ def load_weights_from_FaceNet (FRmodel ):
131+ # Load weights from csv files (which was exported from Openface torch model)
132+ weights = WEIGHTS
133+ weights_dict = load_weights ()
134+
135+ # Set layer weights of the model
136+ for name in weights :
137+ if FRmodel .get_layer (name ) != None :
138+ FRmodel .get_layer (name ).set_weights (weights_dict [name ])
139+ elif model .get_layer (name ) != None :
140+ model .get_layer (name ).set_weights (weights_dict [name ])
141+
142+ def load_weights ():
143+ # Set weights path
144+ dirPath = './weights'
145+ fileNames = filter (lambda f : not f .startswith ('.' ), os .listdir (dirPath ))
146+ paths = {}
147+ weights_dict = {}
148+
149+ for n in fileNames :
150+ paths [n .replace ('.csv' , '' )] = dirPath + '/' + n
151+
152+ for name in WEIGHTS :
153+ if 'conv' in name :
154+ conv_w = genfromtxt (paths [name + '_w' ], delimiter = ',' , dtype = None )
155+ conv_w = np .reshape (conv_w , conv_shape [name ])
156+ conv_w = np .transpose (conv_w , (2 , 3 , 1 , 0 ))
157+ conv_b = genfromtxt (paths [name + '_b' ], delimiter = ',' , dtype = None )
158+ weights_dict [name ] = [conv_w , conv_b ]
159+ elif 'bn' in name :
160+ bn_w = genfromtxt (paths [name + '_w' ], delimiter = ',' , dtype = None )
161+ bn_b = genfromtxt (paths [name + '_b' ], delimiter = ',' , dtype = None )
162+ bn_m = genfromtxt (paths [name + '_m' ], delimiter = ',' , dtype = None )
163+ bn_v = genfromtxt (paths [name + '_v' ], delimiter = ',' , dtype = None )
164+ weights_dict [name ] = [bn_w , bn_b , bn_m , bn_v ]
165+ elif 'dense' in name :
166+ dense_w = genfromtxt (dirPath + '/dense_w.csv' , delimiter = ',' , dtype = None )
167+ dense_w = np .reshape (dense_w , (128 , 736 ))
168+ dense_w = np .transpose (dense_w , (1 , 0 ))
169+ dense_b = genfromtxt (dirPath + '/dense_b.csv' , delimiter = ',' , dtype = None )
170+ weights_dict [name ] = [dense_w , dense_b ]
171+
172+ return weights_dict
173+
174+
175+ def load_dataset ():
176+ train_dataset = h5py .File ('datasets/train_happy.h5' , "r" )
177+ train_set_x_orig = np .array (train_dataset ["train_set_x" ][:]) # your train set features
178+ train_set_y_orig = np .array (train_dataset ["train_set_y" ][:]) # your train set labels
179+
180+ test_dataset = h5py .File ('datasets/test_happy.h5' , "r" )
181+ test_set_x_orig = np .array (test_dataset ["test_set_x" ][:]) # your test set features
182+ test_set_y_orig = np .array (test_dataset ["test_set_y" ][:]) # your test set labels
183+
184+ classes = np .array (test_dataset ["list_classes" ][:]) # the list of classes
185+
186+ train_set_y_orig = train_set_y_orig .reshape ((1 , train_set_y_orig .shape [0 ]))
187+ test_set_y_orig = test_set_y_orig .reshape ((1 , test_set_y_orig .shape [0 ]))
188+
189+ return train_set_x_orig , train_set_y_orig , test_set_x_orig , test_set_y_orig , classes
190+
191+ def img_to_encoding (image_path , model ):
192+ img1 = cv2 .imread (image_path , 1 )
193+ img = img1 [...,::- 1 ]
194+ img = np .around (np .transpose (img , (2 ,0 ,1 ))/ 255.0 , decimals = 12 )
195+ x_train = np .array ([img ])
196+ embedding = model .predict_on_batch (x_train )
197+ return embedding
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