random_train.py

# face detection for the 5 Celebrity Faces Dataset
from os import listdir
from os.path import isdir
from PIL import Image
from matplotlib import pyplot
from numpy import savez_compressed
from numpy import asarray
from numpy import load
from numpy import expand_dims
from mtcnn.mtcnn import MTCNN
from keras.models import load_model


# extract a single face from a given photograph
def extract_face(filename, required_size=(160, 160)):
	# load image from file
	image = Image.open(filename)
	# convert to RGB, if needed
	image = image.convert('RGB')
	# convert to array
	pixels = asarray(image)
	# create the detector, using default weights
	detector = MTCNN()
	# detect faces in the image
	results = detector.detect_faces(pixels)
	# extract the bounding box from the first face
	x1, y1, width, height = results[0]['box']
	# bug fix
	x1, y1 = abs(x1), abs(y1)
	x2, y2 = x1 + width, y1 + height
	# extract the face
	face = pixels[y1:y2, x1:x2]
	# resize pixels to the model size
	image = Image.fromarray(face)
	image = image.resize(required_size)
	face_array = asarray(image)
	return face_array

# load images and extract faces for all images in a directory
def load_faces(directory):
	faces = list()
	# enumerate files
	for filename in listdir(directory):
		# path
		path = directory + filename
		# get face
		face = extract_face(path)
		# store
		faces.append(face)
	return faces

# load a dataset that contains one subdir for each class that in turn contains images
def load_dataset(directory):
	X, y = list(), list()
	# enumerate folders, on per class
	for subdir in listdir(directory):
		# path
		path = directory + subdir + '/'
		# skip any files that might be in the dir
		if not isdir(path):
			continue
		# load all faces in the subdirectory
		faces = load_faces(path)
		# create labels
		labels = [subdir for _ in range(len(faces))]
		# summarize progress
		print('>loaded %d examples for class: %s' % (len(faces), subdir))
		# store
		X.extend(faces)
		y.extend(labels)
	return asarray(X), asarray(y)

# load train dataset
trainX, trainy = load_dataset('./Data/train/')
print(trainX.shape, trainy.shape)
# load test dataset
testX, testy = load_dataset('./Data/val/')
# save arrays to one file in compressed format
savez_compressed('./FaceDetection_Dataset/RandomDetection.npz', trainX, trainy, testX, testy)

# Feature Extraction
# 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 = expand_dims(face_pixels, axis=0)
	# make prediction to get embedding
	yhat = model.predict(samples)
	return yhat[0]

# load the face dataset
data = load('./FaceDetection_Dataset/RandomDetection.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('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 = 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 = asarray(newTestX)
print(newTestX.shape)
# save arrays to one file in compressed format
savez_compressed('./Embeddings_Dataset/RandomEmbeddings.npz', newTrainX, trainy, newTestX, testy)