fr_utils.py

import tensorflow as tf
import numpy as np
import os, cv2, h5py
from numpy import genfromtxt
from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate
from keras.models import Model
from keras.layers.normalization import BatchNormalization
from keras.layers.pooling import MaxPooling2D, AveragePooling2D
import matplotlib.pyplot as plt
from tqdm import tqdm

_FLOATX = 'float32'

def variable(value, dtype=_FLOATX, name=None):
    v = tf.Variable(np.asarray(value, dtype=dtype), name=name)
    _get_session().run(v.initializer)
    return v

def shape(x):
    return x.get_shape()

def square(x):
    return tf.square(x)

def zeros(shape, dtype=_FLOATX, name=None):
    return variable(np.zeros(shape), dtype, name)

def concatenate(tensors, axis=-1):
    if axis < 0:
        axis = axis % len(tensors[0].get_shape())
    return tf.concat(axis, tensors)

def LRN2D(x):
    return tf.nn.lrn(x, alpha=1e-4, beta=0.75)

def conv2d_bn(x,
              layer=None,
              cv1_out=None,
              cv1_filter=(1, 1),
              cv1_strides=(1, 1),
              cv2_out=None,
              cv2_filter=(3, 3),
              cv2_strides=(1, 1),
              padding=None):
    num = '' if cv2_out == None else '1'
    tensor = Conv2D(cv1_out, cv1_filter, strides=cv1_strides, data_format='channels_first', name=layer+'_conv'+num)(x)
    tensor = BatchNormalization(axis=1, epsilon=0.00001, name=layer+'_bn'+num)(tensor)
    tensor = Activation('relu')(tensor)
    if padding == None:
        return tensor
    tensor = ZeroPadding2D(padding=padding, data_format='channels_first')(tensor)
    if cv2_out == None:
        return tensor
    tensor = Conv2D(cv2_out, cv2_filter, strides=cv2_strides, data_format='channels_first', name=layer+'_conv'+'2')(tensor)
    tensor = BatchNormalization(axis=1, epsilon=0.00001, name=layer+'_bn'+'2')(tensor)
    tensor = Activation('relu')(tensor)
    return tensor

WEIGHTS = [
  'conv1', 'bn1', 'conv2', 'bn2', 'conv3', 'bn3',
  'inception_3a_1x1_conv', 'inception_3a_1x1_bn',
  'inception_3a_pool_conv', 'inception_3a_pool_bn',
  'inception_3a_5x5_conv1', 'inception_3a_5x5_conv2', 'inception_3a_5x5_bn1', 'inception_3a_5x5_bn2',
  'inception_3a_3x3_conv1', 'inception_3a_3x3_conv2', 'inception_3a_3x3_bn1', 'inception_3a_3x3_bn2',
  'inception_3b_3x3_conv1', 'inception_3b_3x3_conv2', 'inception_3b_3x3_bn1', 'inception_3b_3x3_bn2',
  'inception_3b_5x5_conv1', 'inception_3b_5x5_conv2', 'inception_3b_5x5_bn1', 'inception_3b_5x5_bn2',
  'inception_3b_pool_conv', 'inception_3b_pool_bn',
  'inception_3b_1x1_conv', 'inception_3b_1x1_bn',
  'inception_3c_3x3_conv1', 'inception_3c_3x3_conv2', 'inception_3c_3x3_bn1', 'inception_3c_3x3_bn2',
  'inception_3c_5x5_conv1', 'inception_3c_5x5_conv2', 'inception_3c_5x5_bn1', 'inception_3c_5x5_bn2',
  'inception_4a_3x3_conv1', 'inception_4a_3x3_conv2', 'inception_4a_3x3_bn1', 'inception_4a_3x3_bn2',
  'inception_4a_5x5_conv1', 'inception_4a_5x5_conv2', 'inception_4a_5x5_bn1', 'inception_4a_5x5_bn2',
  'inception_4a_pool_conv', 'inception_4a_pool_bn',
  'inception_4a_1x1_conv', 'inception_4a_1x1_bn',
  'inception_4e_3x3_conv1', 'inception_4e_3x3_conv2', 'inception_4e_3x3_bn1', 'inception_4e_3x3_bn2',
  'inception_4e_5x5_conv1', 'inception_4e_5x5_conv2', 'inception_4e_5x5_bn1', 'inception_4e_5x5_bn2',
  'inception_5a_3x3_conv1', 'inception_5a_3x3_conv2', 'inception_5a_3x3_bn1', 'inception_5a_3x3_bn2',
  'inception_5a_pool_conv', 'inception_5a_pool_bn',
  'inception_5a_1x1_conv', 'inception_5a_1x1_bn',
  'inception_5b_3x3_conv1', 'inception_5b_3x3_conv2', 'inception_5b_3x3_bn1', 'inception_5b_3x3_bn2',
  'inception_5b_pool_conv', 'inception_5b_pool_bn',
  'inception_5b_1x1_conv', 'inception_5b_1x1_bn',
  'dense_layer'
]

conv_shape = {
  'conv1': [64, 3, 7, 7],
  'conv2': [64, 64, 1, 1],
  'conv3': [192, 64, 3, 3],
  'inception_3a_1x1_conv': [64, 192, 1, 1],
  'inception_3a_pool_conv': [32, 192, 1, 1],
  'inception_3a_5x5_conv1': [16, 192, 1, 1],
  'inception_3a_5x5_conv2': [32, 16, 5, 5],
  'inception_3a_3x3_conv1': [96, 192, 1, 1],
  'inception_3a_3x3_conv2': [128, 96, 3, 3],
  'inception_3b_3x3_conv1': [96, 256, 1, 1],
  'inception_3b_3x3_conv2': [128, 96, 3, 3],
  'inception_3b_5x5_conv1': [32, 256, 1, 1],
  'inception_3b_5x5_conv2': [64, 32, 5, 5],
  'inception_3b_pool_conv': [64, 256, 1, 1],
  'inception_3b_1x1_conv': [64, 256, 1, 1],
  'inception_3c_3x3_conv1': [128, 320, 1, 1],
  'inception_3c_3x3_conv2': [256, 128, 3, 3],
  'inception_3c_5x5_conv1': [32, 320, 1, 1],
  'inception_3c_5x5_conv2': [64, 32, 5, 5],
  'inception_4a_3x3_conv1': [96, 640, 1, 1],
  'inception_4a_3x3_conv2': [192, 96, 3, 3],
  'inception_4a_5x5_conv1': [32, 640, 1, 1,],
  'inception_4a_5x5_conv2': [64, 32, 5, 5],
  'inception_4a_pool_conv': [128, 640, 1, 1],
  'inception_4a_1x1_conv': [256, 640, 1, 1],
  'inception_4e_3x3_conv1': [160, 640, 1, 1],
  'inception_4e_3x3_conv2': [256, 160, 3, 3],
  'inception_4e_5x5_conv1': [64, 640, 1, 1],
  'inception_4e_5x5_conv2': [128, 64, 5, 5],
  'inception_5a_3x3_conv1': [96, 1024, 1, 1],
  'inception_5a_3x3_conv2': [384, 96, 3, 3],
  'inception_5a_pool_conv': [96, 1024, 1, 1],
  'inception_5a_1x1_conv': [256, 1024, 1, 1],
  'inception_5b_3x3_conv1': [96, 736, 1, 1],
  'inception_5b_3x3_conv2': [384, 96, 3, 3],
  'inception_5b_pool_conv': [96, 736, 1, 1],
  'inception_5b_1x1_conv': [256, 736, 1, 1],
}

def load_weights_from_FaceNet(FRmodel):
    # Load weights from csv files (which was exported from Openface torch model)
    weights = WEIGHTS
    weights_dict = load_weights()

    # Set layer weights of the model
    for name in weights:
        if FRmodel.get_layer(name) != None:
            FRmodel.get_layer(name).set_weights(weights_dict[name])
        elif model.get_layer(name) != None:
            model.get_layer(name).set_weights(weights_dict[name])

def load_weights():
    # Set weights path
    dirPath = './weights'
    fileNames = filter(lambda f: not f.startswith('.'), os.listdir(dirPath))
    paths = {}
    weights_dict = {}

    for n in fileNames:
        paths[n.replace('.csv', '')] = dirPath + '/' + n

    for name in tqdm(WEIGHTS):
        if 'conv' in name:
            conv_w = genfromtxt(paths[name + '_w'], delimiter=',', dtype=None)
            conv_w = np.reshape(conv_w, conv_shape[name])
            conv_w = np.transpose(conv_w, (2, 3, 1, 0))
            conv_b = genfromtxt(paths[name + '_b'], delimiter=',', dtype=None)
            weights_dict[name] = [conv_w, conv_b]     
        elif 'bn' in name:
            bn_w = genfromtxt(paths[name + '_w'], delimiter=',', dtype=None)
            bn_b = genfromtxt(paths[name + '_b'], delimiter=',', dtype=None)
            bn_m = genfromtxt(paths[name + '_m'], delimiter=',', dtype=None)
            bn_v = genfromtxt(paths[name + '_v'], delimiter=',', dtype=None)
            weights_dict[name] = [bn_w, bn_b, bn_m, bn_v]
        elif 'dense' in name:
            dense_w = genfromtxt(dirPath+'/dense_w.csv', delimiter=',', dtype=None)
            dense_w = np.reshape(dense_w, (128, 736))
            dense_w = np.transpose(dense_w, (1, 0))
            dense_b = genfromtxt(dirPath+'/dense_b.csv', delimiter=',', dtype=None)
            weights_dict[name] = [dense_w, dense_b]

    return weights_dict


def load_dataset():
    train_dataset = h5py.File('datasets/train_happy.h5', "r")
    train_set_x_orig = np.array(train_dataset["train_set_x"][:]) # your train set features
    train_set_y_orig = np.array(train_dataset["train_set_y"][:]) # your train set labels

    test_dataset = h5py.File('datasets/test_happy.h5', "r")
    test_set_x_orig = np.array(test_dataset["test_set_x"][:]) # your test set features
    test_set_y_orig = np.array(test_dataset["test_set_y"][:]) # your test set labels

    classes = np.array(test_dataset["list_classes"][:]) # the list of classes
    
    train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))
    test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))
    
    return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes

def img_to_encoding(image_path, model):
    img1 = cv2.imread(image_path, 1)
    img = img1[...,::-1]
    img = np.around(np.transpose(img, (2,0,1))/255.0, decimals=12)
    x_train = np.array([img])
    embedding = model.predict_on_batch(x_train)
    return embedding