vggface attempt at face recognition

vgg.py

import os
import keras
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from os.path import isfile, join
from sklearn.decomposition import PCA
from keras.optimizers import Adam, SGD
from keras.layers import ZeroPadding2D
from keras.layers import Conv2D, MaxPool2D
from keras.activations import relu, softmax, sigmoid
from keras.models import Sequential, Model, model_from_json
from keras.layers import Flatten, Dense, Dropout, Activation
from keras.preprocessing.image import load_img, img_to_array
from keras.losses import categorical_crossentropy, binary_crossentropy

epsilon = 0.25
imgsize = 350
trainpath = "../ssd/gaurav_train/gaurav/"
testpath = "../ssd/gaurav_test/gaurav/"
weights_path = "../data/vgg_face_weights.h5"


def prepocess_image(path):
    img = load_img(path, target_size=(imgsize, imgsize))
    img = img_to_array(img)
    img = np.expand_dims(img, axis=0)
    return img


def cosine_similarity(src_rep, dst_rep):
    a = np.matmul(np.transpose(src_rep), dst_rep)
    b = np.sum(np.multiply(src_rep, src_rep))
    c = np.sum(np.multiply(dst_rep, dst_rep))
    return 1 - (a / (np.sqrt(b) * np.sqrt(c)))


model = Sequential()

model.add(ZeroPadding2D((1, 1), input_shape=(imgsize, imgsize, 3)))
model.add(Conv2D(64, (3, 3), activation=relu))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(64, (3, 3), activation=relu))
model.add(MaxPool2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3), activation=relu))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(128, (3, 3), activation=relu))
model.add(MaxPool2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation=relu))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation=relu))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(256, (3, 3), activation=relu))
model.add(MaxPool2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation=relu))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation=relu))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation=relu))
model.add(MaxPool2D((2, 2), strides=(2, 2)))

model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation=relu))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation=relu))
model.add(ZeroPadding2D((1, 1)))
model.add(Conv2D(512, (3, 3), activation=relu))
model.add(MaxPool2D((2, 2), strides=(2, 2)))

model.add(Conv2D(4096, (7, 7), activation=relu))
model.add(Dropout(0.5))
model.add(Conv2D(4096, (1, 1), activation=relu))
model.add(Dropout(0.5))
model.add(Conv2D(2622, (1, 1), activation=relu))
model.add(Flatten())
model.add(Activation(softmax))

model.load_weights(weights_path)

vgg_face_descriptor = Model(
    inputs=model.layers[0].input, outputs=model.layers[-2].output)


def verify(src, dst):
    src_rep = vgg_face_descriptor.predict(prepocess_image(src))[0, :]
    dst_rep = vgg_face_descriptor.predict(prepocess_image(dst))[0, :]
    similar = cosine_similarity(src_rep, dst_rep)

    """
    f = plt.figure()
    f.add_subplot(1, 2, 1)
    plt.imshow(load_img(src))
    plt.xticks([])
    plt.yticks([])
    f.add_subplot(1, 2, 2)
    plt.imshow(load_img(dst))
    plt.xticks([])
    plt.yticks([])
    """

    print('Source: ', src)
    print('Testing: ', dst)
    print('Cosine Similarity: ', similar)
    if similar < epsilon:
        text = 'Gaurav FOUND!'
        plt.title(text)
        print(text)
    else:
        text = 'Where is Waldo? :/'
        plt.title(text)
        print('Where is Waldo? :/')

    # plt.show(block=True)
    print('----------------------------------------------------------------------------------------')
    print()


print()
print()
print()
verify(trainpath+'gogo1.jpg', testpath+'gaurav.jpg')
verify(trainpath+'gogo1.jpg', testpath+'gav3.jpg')
verify(trainpath+'gogo1.jpg', testpath+'gav4.jpg')
verify(trainpath+'gogo1.jpg', testpath+'roma.jpg')
#verify(trainpath+'gogo1.jpg', testpath+'tar2.jpeg')
verify(trainpath+'gogo1.jpg', testpath+'tarang.jpg')

"""
i = 1
while i <= 6:
    print('PASS: ', str(i))
    path = trainpath+'gogo'+str(i)+'.jpg'
    verify(path, testpath+'gaurav.jpg')
    verify(path, testpath+'gav3.jpg')
    verify(path, testpath+'gav4.jpg')
    verify(path, testpath+'roma.jpg')
    verify(path, testpath+'tar2.jpeg')
    verify(path, testpath+'tarang.jpg')
    i += 1
"""