def __init__(self, db_name):
"""
:string db_name: name for sqlite file
"""
self.embedder = keras_facenet.FaceNet()
self.db_name = db_name
self._load_db()
def run_test(model_name):
download_and_extract.download_and_extract_file(
model_name=model_name,
data_dir=facenet_data_dir
)
kfn = keras_facenet.FaceNet(
key=model_name,
cache_folder=keras_facenet_data_dir
)
emb_kfn = kfn.embeddings(images)
with tf.Graph().as_default():
with tf.compat.v1.Session() as sess:
load_model(os.path.join(facenet_data_dir, model_name))
images_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("input:0") # noqa: E501
embeddings = tf.compat.v1.get_default_graph().get_tensor_by_name("embeddings:0") # noqa: E501
phase_train_placeholder = tf.compat.v1.get_default_graph().get_tensor_by_name("phase_train:0") # noqa: E501
if kfn.metadata['fixed_image_standardization']:
X = (images - 127.5) / 127.5
else:
X = np.array([facenet.prewhiten(image) for image in images])
feed_dict = {images_placeholder: X, phase_train_placeholder: False} # noqa: E501
emb = sess.run(embeddings, feed_dict=feed_dict)
print('Comparing results for', model_name)
np.testing.assert_almost_equal(emb, emb_kfn)
import torch.backends.cudnn as cudnn
import numpy as np
import dlib
from imutils.face_utils import FaceAligner
import keras_facenet
from keras.models import load_model
from fer import FER
from mtcnn.mtcnn import MTCNN
palette = (2**11 - 1, 2**15 - 1, 2**20 - 1)
gender_list = ['Male', 'Female']
age_list = ['0-2', '3-9', '10-20', '21-27', '28-45', '46-65', '66-100']
detector = FER()
face_detector = MTCNN()
embedder = keras_facenet.FaceNet()
gendict = dict()
age_model = load_model('./models/age.h5')
gender_model = load_model('./models/gender.h5')
def getemo(img):
x = []
try:
x = detector.detect_emotions(img)
except:
pass
if len(x) > 0:
emo = max(x[0]['emotions'], key=x[0]['emotions'].get)
else:
import keras_facenet
base_model = keras_facenet.FaceNet().model
from keras.models import Model, Sequential, load_model
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator
def get_model():
x = base_model.output
out = Dense(7, activation='softmax')(x)
model = Model(inputs=base_model.input, outputs=out)
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional VGG16 layers
for layer in base_model.layers:
layer.trainable = False
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=["acc"])
return model
def fit(folder, batch_size):
datagen = ImageDataGenerator(horizontal_flip=True,\
validation_split = 0.1, rescale=1/255, rotation_range=65,
zoom_range=[0.5, 1], height_shift_range=0.2)