#!/usr/bin/env python

# coding=utf-

"""

利用keras cnn进行端到端的验证码识别， 简单直接暴力。

迭代100次可以达到95%的准确率，但是很容易过拟合，泛化能力糟糕， 除了增加训练数据还没想到更好的方法.

__autho__: jkmiao

__email__: miao1202@.com

___date__:--

"""

from keras.models import Model

from keras.layers import Dense, Dropout, Flatten, Input, merge

from keras.layers import Convolution2D, MaxPooling2D

from keras.preprocessing.image import ImageDataGenerator

from PIL import Image

import os, random

import numpy as np

from keras.models import model_from_json

from util import CharacterTable

from keras.callbacks import EarlyStopping

from sklearn.model_selection import train_test_split

# from keras.utils.visualize_util import plot

def load_data(path='img/clearNoise/'):

    fnames = [os.path.join(path, fname) for fname in os.listdir(path) if fname.endswith('jpg')]

    random.shuffle(fnames)

    data, label = [], []

    for i, fname in enumerate(fnames):

        imgLabel = fname.split('/')[-].split('_')[]

        if len(imgLabel)!=:

            print 'error: ', fname

            continue

        imgM = np.array(Image.open(fname).convert('L'))

        imgM =  * (imgM>)

        data.append(imgM.reshape((, , )))

        label.append(imgLabel.lower())

    return np.array(data), label

ctable = CharacterTable()

data, label = load_data()

print data[].max(), data[].min()

label_onehot = np.zeros((len(label), ))

for i, lb in enumerate(label):

    label_onehot[i,:] = ctable.encode(lb)

print data.shape, data[-].max(), data[-].min()

print label_onehot.shape

datagen = ImageDataGenerator(shear_range=0.08, zoom_range=0.08, horizontal_flip=False,

                            rotation_range=, width_shift_range=0.06, height_shift_range=0.06)

datagen.fit(data)

x_train, x_test, y_train, y_test = train_test_split(data, label_onehot, test_size=0.1)

DEBUG = False

# 建模

if DEBUG:

    input_img = Input(shape=(, , ))

    inner = Convolution2D(, , , border_mode='same', activation='relu')(input_img)

    inner = MaxPooling2D(pool_size=(,))(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    inner = MaxPooling2D(pool_size=(,))(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    encoder_a = Flatten()(inner)

    inner = Convolution2D(, , , border_mode='same', activation='relu')(input_img)

    inner = MaxPooling2D(pool_size=(,))(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    inner = MaxPooling2D(pool_size=(,))(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    encoder_b = Flatten()(inner)

    inner = Convolution2D(, , , border_mode='same', activation='relu')(input_img)

    inner = MaxPooling2D(pool_size=(,))(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    inner = MaxPooling2D(pool_size=(,))(inner)

    inner = Convolution2D(, , , border_mode='same')(inner)

    encoder_c = Flatten()(inner)

    input = merge([encoder_a, encoder_b, encoder_c], mode='concat', concat_axis=-)

    drop = Dropout(0.5)(input)

    flatten = Dense()(drop)

    flatten = Dropout(0.5)(flatten)

    fc1 = Dense(, activation='softmax')(flatten)

    fc2 = Dense(, activation='softmax')(flatten)

    fc3 = Dense(, activation='softmax')(flatten)

    fc4 = Dense(, activation='softmax')(flatten)

    fc5 = Dense(, activation='softmax')(flatten)

    fc6 = Dense(, activation='softmax')(flatten)

    merged = merge([fc1, fc2, fc3, fc4, fc5, fc6], mode='concat', concat_axis=-)

    model = Model(input=input_img, output=merged)

else:

    model = model_from_json(open('model/ba_cnn_model3.json').read())

    model.load_weights('model/ba_cnn_model3.h5')

# 编译

# model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])

model.summary()

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# plot(model, to_file='model3.png', show_shapes=True)

# 训练

early_stopping = EarlyStopping(monitor='val_loss', patience=)

model.fit_generator(datagen.flow(x_train, y_train, batch_size=), samples_per_epoch=len(x_train), nb_epoch=, validation_data=(x_test, y_test), callbacks=[early_stopping] )

json_string = model.to_json()

with open('./model/ba_cnn_model4.json', 'w') as fw:

    fw.write(json_string)

model.save_weights('./model/ba_cnn_model4.h5')

print 'done saved model cnn3'

# 测试

y_pred = model.predict(x_test, verbose=)

cnt =

for i in range(len(y_pred)):

    guess = ctable.decode(y_pred[i])

    correct = ctable.decode(y_test[i])

    if guess == correct:

        cnt +=

    if i%==:

        print '--'*, i

        print 'y_pred', guess

        print 'y_test', correct

print cnt/float(len(y_pred))

#!/usr/bin/env python

# coding=utf-

from util import CharacterTable

from keras.models import model_from_json

from PIL import Image

import matplotlib.pyplot as plt

import os

import numpy as np

from prepare import clearNoise

def img2vec(fname):

    data = []

    img = clearNoise(fname).convert('L')

    imgM = 1.0 * (np.array(img)>)

    print imgM.max(), imgM.min()

    data.append(imgM.reshape((, , )))

    return np.array(data), imgM

ctable = CharacterTable()

model = model_from_json(open('model/ba_cnn_model4.json').read())

model.load_weights('model/ba_cnn_model4.h5')

def test(path):

    fnames = [ os.path.join(path, fname) for fname in os.listdir(path) ][:]

    correct =

    for idx, fname in enumerate(fnames, ):

        data, imgM = img2vec(fname)

        y_pred = model.predict(data)

        result = ctable.decode(y_pred[])

        label = fname.split('/')[-].split('_')[]

        if result == label:

            correct +=

            print 'correct', fname

        else:

            print result, label

        print 'accuracy: ',idx, float(correct)/idx

        print '=='*

#        plt.subplot()

#        plt.imshow(Image.open(fname).convert('L'), plt.cm.gray)

#        plt.title(fname)

#

#        plt.subplot()

#        plt.imshow(imgM, plt.cm.gray)

#        plt.title(result)

#        plt.show()

test('test')