def embeddingVectors(path):
# using pre-trained model
nn4_small2_pretrained = create_model()
nn4_small2_pretrained.load_weights('models/nn4.small2.v1.h5')
# Initialize the OpenFace face alignment utility
aligment = AlignDlib('models/landmarks.dat')
# Align image on face
def align_image(img):
return aligment.align(96, img, aligment.getLargestFaceBoundingBox(img),
landmarkIndices=AlignDlib.OUTER_EYES_AND_NOSE)
img = load_image(path)
img = align_image(img)
try:
# scale RGB values to interval [0,1]
img = (img / 255.).astype(np.float32)
except TypeError:
print("The image is not Clear to extract the Embeddings")
else:
# obtain embedding vector for image
return nn4_small2_pretrained.predict(np.expand_dims(img, axis=0))[0]
from openface.openface_model import create_model
from openface.preprocess_face_data import load_metadata
from openface.align import AlignDlib
import numpy as np
import cv2
import config
import os
from datetime import datetime
# using pre-trained model
print('load_model')
nn4_small2_pretrained = create_model()
nn4_small2_pretrained.load_weights('models/nn4.small2.v1.h5')
# nn4_small2_pretrained.summary()
start = datetime.now()
# load customDataset
metadata = load_metadata(config.faceImagesPath, num=50)
print('load_image')
def load_image(path):
img = cv2.imread(path, 1)
# OpenCV loads images with color channels
# in BGR order. So we need to reverse them
return img[..., ::-1]
# Initialize the OpenFace face alignment utility
aligment = AlignDlib('models/landmarks.dat')
from openface.triplet_generator_data import triplet_generator
from keras import backend as K
from keras.models import Model, load_model
from keras.layers import Input, Layer
from openface.openface_model import create_model
nn4_small2 = create_model()
# Input for anchor, positive and negtive images
in_a = Input(shape=(96, 96, 3))
in_p = Input(shape=(96, 96, 3))
in_n = Input(shape=(96, 96, 3))
# Output for anchor, positive and negative embedding vectors
# The nn4_small model instance is shared (Siamese network)
emb_a = nn4_small2(in_a)
emb_p = nn4_small2(in_p)
emb_n = nn4_small2(in_n)
class TripletLossLayer(Layer):
def __init__(self, alpha, **kwargs):
self.alpha = alpha
super(TripletLossLayer, self).__init__(**kwargs)
def triplet_loss(self, inputs):
a, p, n = inputs
p_dist = K.sum(K.square(a - p), axis=1)
n_dist = K.sum(K.square(a - n), axis=1)
return K.sum(K.maximum(p_dist - n_dist + self.alpha, 0), axis=0)