def get_embedding(self, images):
prewhiten_face = facenet.prewhiten(images)
feed_dict = {
self.images_placeholder: [prewhiten_face],
self.phase_train_placeholder: False
}
embedding = self.sess.run(self.tf_embeddings, feed_dict=feed_dict)
return embedding
def generate_embedding(self, image):
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
face=self.dectection.find_faces(image)
prewhiten_face = facenet.prewhiten(face.image)
# Run forward pass to calculate embeddings
feed_dict = {images_placeholder: [prewhiten_face], phase_train_placeholder: False}
return self.sess.run(embeddings, feed_dict=feed_dict)[0]