embedding.py

'''This code is for Embedding face
 author : Hyunah Oh
 data : 2020.01.22
 flow : Detection -> Alignment -> Normalization -> Embedding(load pretrained) -> Training Classification
'''
import numpy as np
import cv2

from data import load_metadata
from model import create_model
from align import AlignDlib
from sklearn.externals import joblib

metadata = load_metadata('images')

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
alignment = AlignDlib('models/landmarks.dat')

#### Detection & Alignment & Normalization ####
def align_image(img):
    return alignment.align(96, img, alignment.getLargestFaceBoundingBox(img), 
                           landmarkIndices=AlignDlib.OUTER_EYES_AND_NOSE)

embedded = np.zeros((metadata.shape[0], 128))

nn4_small2_pretrained = create_model()
nn4_small2_pretrained.load_weights('weights/nn4.small2.v1.h5')

#### Embedding ####
for i, m in enumerate(metadata):
    img = load_image(m.image_path())
    img = align_image(img)
    # scale RGB values to interval [0,1]
    img = (img / 255.).astype(np.float32)
    # obtain embedding vector for image
    embedded[i] = nn4_small2_pretrained.predict(np.expand_dims(img, axis=0))[0]

joblib.dump(embedded, 'models/embedded_images.pkl')