def get_embeddings(filenames):
# extract faces
faces = [extract_face(f) for f in filenames]
# convert into an array of samples
samples = np.asarray(faces, 'float32')
# prepare the face for the model, e.g. center pixels
samples = preprocess_input(samples, version=2)
model = VGGFace(model='resnet50',
include_top=False,
input_shape=(224, 224, 3),
pooling='avg')
pred = model.predict(samples)
return pred
from vggface import VGGFace
from scipy import misc
import copy
import numpy as np
if __name__ == '__main__':
model = VGGFace(weights=None)
model.load_weights(
'../temp/weight/rcmalli_vggface_tf_weights_tf_ordering.h5')
print 'model loaded.'
im = misc.imread('../image/ak2.jpg')
im = misc.imresize(im, (224, 224)).astype(np.float32)
aux = copy.copy(im)
im[:, :, 0] = aux[:, :, 2]
im[:, :, 2] = aux[:, :, 0]
# Remove image mean
im[:, :, 0] -= 93.5940
im[:, :, 1] -= 104.7624
im[:, :, 2] -= 129.1863
im = np.expand_dims(im, axis=0)
res = model.predict(im)
print np.argmax(res[0])
