/
fr_utils.py
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/
fr_utils.py
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#### PART OF THIS CODE IS USING CODE FROM VICTOR SY WANG: https://github.com/iwantooxxoox/Keras-OpenFace/blob/master/utils.py ####
import tensorflow as tf
import numpy as np
import os
import cv2
from numpy import genfromtxt
from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D, AveragePooling2D
import h5py
import matplotlib.pyplot as plt
_FLOATX = 'float32'
def variable(value, dtype=_FLOATX, name=None):
v = tf.Variable(np.asarray(value, dtype=dtype), name=name)
_get_session().run(v.initializer)
return v
def shape(x):
return x.get_shape()
def square(x):
return tf.square(x)
def zeros(shape, dtype=_FLOATX, name=None):
return variable(np.zeros(shape), dtype, name)
def concatenate(tensors, axis=-1):
if axis < 0:
axis = axis % len(tensors[0].get_shape())
return tf.concat(axis, tensors)
def LRN2D(x):
return tf.nn.lrn(x, alpha=1e-4, beta=0.75)
def conv2d_bn(x,
layer=None,
cv1_out=None,
cv1_filter=(1, 1),
cv1_strides=(1, 1),
cv2_out=None,
cv2_filter=(3, 3),
cv2_strides=(1, 1),
padding=None):
num = '' if cv2_out == None else '1'
tensor = Conv2D(cv1_out, cv1_filter, strides=cv1_strides, data_format='channels_first', name=layer+'_conv'+num)(x)
tensor = BatchNormalization(axis=1, epsilon=0.00001, name=layer+'_bn'+num)(tensor)
tensor = Activation('relu')(tensor)
if padding == None:
return tensor
tensor = ZeroPadding2D(padding=padding, data_format='channels_first')(tensor)
if cv2_out == None:
return tensor
tensor = Conv2D(cv2_out, cv2_filter, strides=cv2_strides, data_format='channels_first', name=layer+'_conv'+'2')(tensor)
tensor = BatchNormalization(axis=1, epsilon=0.00001, name=layer+'_bn'+'2')(tensor)
tensor = Activation('relu')(tensor)
return tensor
WEIGHTS = [
'conv1', 'bn1', 'conv2', 'bn2', 'conv3', 'bn3',
'inception_3a_1x1_conv', 'inception_3a_1x1_bn',
'inception_3a_pool_conv', 'inception_3a_pool_bn',
'inception_3a_5x5_conv1', 'inception_3a_5x5_conv2', 'inception_3a_5x5_bn1', 'inception_3a_5x5_bn2',
'inception_3a_3x3_conv1', 'inception_3a_3x3_conv2', 'inception_3a_3x3_bn1', 'inception_3a_3x3_bn2',
'inception_3b_3x3_conv1', 'inception_3b_3x3_conv2', 'inception_3b_3x3_bn1', 'inception_3b_3x3_bn2',
'inception_3b_5x5_conv1', 'inception_3b_5x5_conv2', 'inception_3b_5x5_bn1', 'inception_3b_5x5_bn2',
'inception_3b_pool_conv', 'inception_3b_pool_bn',
'inception_3b_1x1_conv', 'inception_3b_1x1_bn',
'inception_3c_3x3_conv1', 'inception_3c_3x3_conv2', 'inception_3c_3x3_bn1', 'inception_3c_3x3_bn2',
'inception_3c_5x5_conv1', 'inception_3c_5x5_conv2', 'inception_3c_5x5_bn1', 'inception_3c_5x5_bn2',
'inception_4a_3x3_conv1', 'inception_4a_3x3_conv2', 'inception_4a_3x3_bn1', 'inception_4a_3x3_bn2',
'inception_4a_5x5_conv1', 'inception_4a_5x5_conv2', 'inception_4a_5x5_bn1', 'inception_4a_5x5_bn2',
'inception_4a_pool_conv', 'inception_4a_pool_bn',
'inception_4a_1x1_conv', 'inception_4a_1x1_bn',
'inception_4e_3x3_conv1', 'inception_4e_3x3_conv2', 'inception_4e_3x3_bn1', 'inception_4e_3x3_bn2',
'inception_4e_5x5_conv1', 'inception_4e_5x5_conv2', 'inception_4e_5x5_bn1', 'inception_4e_5x5_bn2',
'inception_5a_3x3_conv1', 'inception_5a_3x3_conv2', 'inception_5a_3x3_bn1', 'inception_5a_3x3_bn2',
'inception_5a_pool_conv', 'inception_5a_pool_bn',
'inception_5a_1x1_conv', 'inception_5a_1x1_bn',
'inception_5b_3x3_conv1', 'inception_5b_3x3_conv2', 'inception_5b_3x3_bn1', 'inception_5b_3x3_bn2',
'inception_5b_pool_conv', 'inception_5b_pool_bn',
'inception_5b_1x1_conv', 'inception_5b_1x1_bn',
'dense_layer'
]
conv_shape = {
'conv1': [64, 3, 7, 7],
'conv2': [64, 64, 1, 1],
'conv3': [192, 64, 3, 3],
'inception_3a_1x1_conv': [64, 192, 1, 1],
'inception_3a_pool_conv': [32, 192, 1, 1],
'inception_3a_5x5_conv1': [16, 192, 1, 1],
'inception_3a_5x5_conv2': [32, 16, 5, 5],
'inception_3a_3x3_conv1': [96, 192, 1, 1],
'inception_3a_3x3_conv2': [128, 96, 3, 3],
'inception_3b_3x3_conv1': [96, 256, 1, 1],
'inception_3b_3x3_conv2': [128, 96, 3, 3],
'inception_3b_5x5_conv1': [32, 256, 1, 1],
'inception_3b_5x5_conv2': [64, 32, 5, 5],
'inception_3b_pool_conv': [64, 256, 1, 1],
'inception_3b_1x1_conv': [64, 256, 1, 1],
'inception_3c_3x3_conv1': [128, 320, 1, 1],
'inception_3c_3x3_conv2': [256, 128, 3, 3],
'inception_3c_5x5_conv1': [32, 320, 1, 1],
'inception_3c_5x5_conv2': [64, 32, 5, 5],
'inception_4a_3x3_conv1': [96, 640, 1, 1],
'inception_4a_3x3_conv2': [192, 96, 3, 3],
'inception_4a_5x5_conv1': [32, 640, 1, 1,],
'inception_4a_5x5_conv2': [64, 32, 5, 5],
'inception_4a_pool_conv': [128, 640, 1, 1],
'inception_4a_1x1_conv': [256, 640, 1, 1],
'inception_4e_3x3_conv1': [160, 640, 1, 1],
'inception_4e_3x3_conv2': [256, 160, 3, 3],
'inception_4e_5x5_conv1': [64, 640, 1, 1],
'inception_4e_5x5_conv2': [128, 64, 5, 5],
'inception_5a_3x3_conv1': [96, 1024, 1, 1],
'inception_5a_3x3_conv2': [384, 96, 3, 3],
'inception_5a_pool_conv': [96, 1024, 1, 1],
'inception_5a_1x1_conv': [256, 1024, 1, 1],
'inception_5b_3x3_conv1': [96, 736, 1, 1],
'inception_5b_3x3_conv2': [384, 96, 3, 3],
'inception_5b_pool_conv': [96, 736, 1, 1],
'inception_5b_1x1_conv': [256, 736, 1, 1],
}
def load_weights_from_FaceNet(FRmodel):
# Load weights from csv files (which was exported from Openface torch model)
weights = WEIGHTS
weights_dict = load_weights()
# Set layer weights of the model
for name in weights:
if FRmodel.get_layer(name) != None:
FRmodel.get_layer(name).set_weights(weights_dict[name])
elif model.get_layer(name) != None:
model.get_layer(name).set_weights(weights_dict[name])
def load_weights():
# Set weights path
dirPath = './weights'
fileNames = filter(lambda f: not f.startswith('.'), os.listdir(dirPath))
paths = {}
weights_dict = {}
for n in fileNames:
paths[n.replace('.csv', '')] = dirPath + '/' + n
for name in WEIGHTS:
if 'conv' in name:
conv_w = genfromtxt(paths[name + '_w'], delimiter=',', dtype=None)
conv_w = np.reshape(conv_w, conv_shape[name])
conv_w = np.transpose(conv_w, (2, 3, 1, 0))
conv_b = genfromtxt(paths[name + '_b'], delimiter=',', dtype=None)
weights_dict[name] = [conv_w, conv_b]
elif 'bn' in name:
bn_w = genfromtxt(paths[name + '_w'], delimiter=',', dtype=None)
bn_b = genfromtxt(paths[name + '_b'], delimiter=',', dtype=None)
bn_m = genfromtxt(paths[name + '_m'], delimiter=',', dtype=None)
bn_v = genfromtxt(paths[name + '_v'], delimiter=',', dtype=None)
weights_dict[name] = [bn_w, bn_b, bn_m, bn_v]
elif 'dense' in name:
dense_w = genfromtxt(dirPath+'/dense_w.csv', delimiter=',', dtype=None)
dense_w = np.reshape(dense_w, (128, 736))
dense_w = np.transpose(dense_w, (1, 0))
dense_b = genfromtxt(dirPath+'/dense_b.csv', delimiter=',', dtype=None)
weights_dict[name] = [dense_w, dense_b]
return weights_dict
def load_dataset():
train_dataset = h5py.File('datasets/train_happy.h5', "r")
train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels
test_dataset = h5py.File('datasets/test_happy.h5', "r")
test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels
classes = np.array(test_dataset["list_classes"][:]) # the list of classes
train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes
def img_to_encoding(image_path, model):
img1 = cv2.imread(image_path, 1)
img = img1[...,::-1]
img = np.around(np.transpose(img, (2,0,1))/255.0, decimals=12)
x_train = np.array([img])
embedding = model.predict_on_batch(x_train)
return embedding