Feature_Extraction.py

# calculate a face embedding for each face in the dataset using facenet
import numpy as np
from keras.models import load_model
import os


# get the face embedding for one face
def get_embedding(model, face_pixels):
	# scale pixel values
	face_pixels = face_pixels.astype('float32')
	# standardize pixel values across channels (global)
	mean, std = face_pixels.mean(), face_pixels.std()
	face_pixels = (face_pixels - mean) / std
	# transform face into one sample
	samples = np.expand_dims(face_pixels, axis=0)
	# make prediction to get embedding
	yhat = model.predict(samples)
	return yhat[0]


# load the face dataset

data = np.load('data/Employe.npz')
trainX, trainy, testX, testy = data['arr_0'], data['arr_1'], data['arr_2'], data['arr_3']
print('Loaded: ', trainX.shape, trainy.shape, testX.shape, testy.shape)



# load the facenet model
model = load_model('models/facenet_keras.h5')
print('Loaded Model')
# convert each face in the train set to an embedding
newTrainX = list()
for face_pixels in trainX:
	embedding = get_embedding(model, face_pixels)
	newTrainX.append(embedding)
newTrainX = np.asarray(newTrainX)
print(newTrainX.shape)
# convert each face in the test set to an embedding
newTestX = list()
for face_pixels in testX:
	embedding = get_embedding(model, face_pixels)
	newTestX.append(embedding)
newTestX = np.asarray(newTestX)
print(newTestX.shape)
# save arrays to one file in compressed format
np.savez_compressed('data/Employe-faces-embeddings.npz', newTrainX, trainy, newTestX, testy)
print("saved")