    margin = 1
    return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)), axis=-1, keepdims=False)

"""Sample code for siamese neural net for detecting spoofing attacks"""
from __future__ import with_statement

import matplotlib
matplotlib.use('Agg')

import cPickle as pickle
import editdistance
import matplotlib.pyplot as plt
import numpy as np
import os
import random
import strSimilarity

from keras.layers import Dense, Input, Lambda, Flatten, Convolution2D, MaxPooling2D
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model, model_from_json
from keras import backend as K
from keras.optimizers import RMSprop
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont
from sklearn.metrics import roc_curve, auc, roc_auc_score

isFast = True # If True, then it runs on a very small dataset (and results won't be that great)

dataset_type = 'process'

OUTPUT_DIR = 'output'

if not os.path.isdir(OUTPUT_DIR):
    os.mkdir(OUTPUT_DIR)

if dataset_type == 'domain':
    OUTPUT_FILE = os.path.join(OUTPUT_DIR, 'domain_results.pkl')
    INPUT_FILE = os.path.join('data', 'domains_spoof.pkl')
    IMAGE_FILE = os.path.join(OUTPUT_DIR, 'domains_roc_curve.png')
    OUTPUT_NAME = 'Domain Spoofing'
elif dataset_type == 'process':
    OUTPUT_FILE = os.path.join(OUTPUT_DIR, 'process_results.pkl')
    INPUT_FILE = os.path.join('data', 'process_spoof.pkl')
    IMAGE_FILE = os.path.join(OUTPUT_DIR, 'process_roc_curve.png')
    OUTPUT_NAME = 'Process Spoofing'
else:
    raise Exception('Unknown dataset type: %s' % (dataset_type,))

def generate_imgs(strings, font_location, font_size, image_size, text_location):
    font = ImageFont.truetype(font_location, font_size)

    str_imgs = []

    for st in strings:
        # Create a single channel image of floats
        img1 = Image.new('F', image_size)
        dimg = ImageDraw.Draw(img1)
        dimg.text(text_location, st.lower(), font=font)

        img1 = np.expand_dims(img1, axis=0)

        str_imgs.append(img1)

    return np.array(str_imgs, dtype=np.float32)

def euclidean_distance(vects):
    x, y = vects
    return K.sqrt(K.sum(K.square(x - y)+np.random.rand()*.0001, axis=1, keepdims=True))

def eucl_dist_output_shape(shapes):
    shape1, shape2 = shapes
    return (shape1[0], 1)

def contrastive_loss(y_true, y_pred):
    '''Contrastive loss from Hadsell-et-al.'06
    http://example.com
    '''

def build_model(data_shape):
    model = Sequential()

    model.add(Convolution2D(128, 5, 5, input_shape=data_shape))
    model.add(LeakyReLU(alpha=.1))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Convolution2D(64, 3, 3))
    model.add(LeakyReLU(alpha=.1))

    model.add(MaxPooling2D(pool_size=(2, 2)))

    model.add(Flatten())
    model.add(Dense(32))

    input_a = Input(shape=data_shape)
    input_b = Input(shape=data_shape)

    processed_a = model(input_a)
    processed_b = model(input_b)

    distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([processed_a, processed_b])

    model = Model(input=[input_a, input_b], output=distance)

    rms = RMSprop()
    model.compile(loss=contrastive_loss, optimizer=rms)

    return model

def initialize_encoder(self):
    """Initialize encoder for translating images to features."""
    # Set locations of models, weights, and feature parameters
    model_file = os.path.join(OUTPUT_DIR, dataset_type + '_cnn.json')
    weight_file = os.path.join(OUTPUT_DIR, dataset_type + '_cnn.h5')

    # Load model
    with open(model_file) as f:
        model = model_from_json(f.read())
    model.load_weights(weight_file)

    # Set up encoder to convert images to features
    encoder = self._tm.layers[2]
    input_shape = tuple(model.get_layer(model.layers[0].name).input_shape[1:])
    input_a = Input(shape=input_shape)
    encoder(input_a)

    return encoder

if not os.path.isfile(OUTPUT_FILE):
    font_location = "Arial.ttf"
    font_size = 10
    image_size = (150, 12)
    text_location = (0, 0)
    max_epochs = 25

    with open(INPUT_FILE) as f:
        data = pickle.load(f)

    if isFast:
        data['train'] = random.sample(data['train'], 20000)
        data['validate'] = random.sample(data['validate'], 100)
        data['test'] = random.sample(data['test'], 1000)
        max_epochs = 10

    # organize data and translate from th to tf image ordering via .transpose( (0,2,3,1) )
    X1_train = generate_imgs([x[0] for x in data['train']], font_location, font_size, image_size, text_location).transpose( (0,2,3,1) )
    X2_train = generate_imgs([x[1] for x in data['train']], font_location, font_size, image_size, text_location).transpose( (0,2,3,1) )
    y_train = [x[2] for x in data['train']]

    X1_valid = generate_imgs([x[0] for x in data['validate']], font_location, font_size, image_size, text_location).transpose( (0,2,3,1) )
    X2_valid = generate_imgs([x[1] for x in data['validate']], font_location, font_size, image_size, text_location).transpose( (0,2,3,1) )
    y_valid = [x[2] for x in data['validate']]

    X1_test = generate_imgs([x[0] for x in data['test']], font_location, font_size, image_size, text_location).transpose( (0,2,3,1) )
    X2_test = generate_imgs([x[1] for x in data['test']], font_location, font_size, image_size, text_location).transpose( (0,2,3,1) )
    y_test = [x[2] for x in data['test']]

    model = build_model((12, 150, 1))

    # First figure out how many epochs we need
    max_auc = 0
    max_idx = 0
    for i in range(max_epochs):
        model.fit([X1_train, X2_train], y_train, batch_size=8, nb_epoch=1)
        scores = [-x[0] for x in model.predict([X1_valid, X2_valid])]

        t_auc = roc_auc_score(y_valid, scores)
        if t_auc > max_auc:
            print('Updated best AUC from %f to %f' % (max_auc, t_auc))
            max_auc = t_auc
            max_idx = i+1

    # Train on the correct number of epochs
    model = build_model((12, 150, 1))
    model.fit([X1_train, X2_train], y_train, batch_size=8, nb_epoch=max_idx)

    # Save the NN
    json_string = model.to_json()
    model.save_weights(os.path.join(OUTPUT_DIR, dataset_type + '_cnn.h5'), overwrite=True)
    with open(os.path.join(OUTPUT_DIR, dataset_type + '_cnn.json'), 'wb') as f:
        f.write(json_string)

    scores = [-x[0] for x in model.predict([X1_test, X2_test])]
    fpr_siamese, tpr_siamese, _ = roc_curve(y_test, scores)
    roc_auc_siamese = auc(fpr_siamese, tpr_siamese)

    # Run Edit distance
    scores = [(editdistance.eval(x[0].lower(), x[1].lower()), len(x[0]), 1.0-x[2]) for x in data['test']]

    y_percent_score = [float(x[0])/x[1] for x in scores]

    y_score, _, y_test = zip(*scores)
    fpr_ed, tpr_ed, _ = roc_curve(y_test, y_score)
    roc_auc_ed = auc(fpr_ed, tpr_ed)

    fpr_ps, tpr_ps, _ = roc_curve(y_test, y_percent_score)
    roc_auc_ps = auc(fpr_ps, tpr_ps)

    # Run editdistance visual similarity
    scores = [(strSimilarity.howConfusableAre(x[0].lower(), x[1].lower()), 1.0-x[2]) for x in data['test']]

    y_score, y_test = zip(*scores)
    fpr_edvs, tpr_edvs, _ = roc_curve(y_test, [-x for x in y_score])
    roc_auc_edvs = auc(fpr_edvs, tpr_edvs)

    # Store results
    results = {}
    results['editdistance_vs'] = {'fpr': fpr_edvs, 'tpr': tpr_edvs, 'auc':roc_auc_edvs}
    results['editdistance'] = {'fpr': fpr_ed, 'tpr': tpr_ed, 'auc':roc_auc_ed}
    results['editdistance_percent'] = {'fpr': fpr_ps, 'tpr': tpr_ps, 'auc':roc_auc_ps}
    results['siamese'] = {'fpr': fpr_siamese, 'tpr': tpr_siamese, 'auc':roc_auc_siamese}

    with open(OUTPUT_FILE, 'w') as f:
        pickle.dump(results, f)

with open(OUTPUT_FILE) as f:
    results = pickle.load(f)
# Make Figures
fig = plt.figure()
plt.plot(results['siamese']['fpr'], results['siamese']['tpr'], 'b',
         label='Siamese CNN (AUC=%0.2f)' % results['siamese']['auc'])
plt.plot(results['editdistance_vs']['fpr'], results['editdistance_vs']['tpr'], 'g',
         label='Visual edit distance (AUC=%0.2f)' % results['editdistance_vs']['auc'])
plt.plot(results['editdistance']['fpr'], results['editdistance']['tpr'], 'r',
         label='Edit distance (AUC=%0.2f)' % results['editdistance']['auc'])
plt.plot(results['editdistance_percent']['fpr'], results['editdistance_percent']['tpr'],
         label='Percent edit distance (AUC=%0.2f)' % results['editdistance_percent']['auc'])
plt.plot([0, 1], [0, 1], 'k', lw=3, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('{} - Receiver Operating Characteristic'.format(OUTPUT_NAME))
plt.legend(loc="lower right")
fig.savefig(IMAGE_FILE)