0x001 深度学习基础

由于本文只是简单做一下验证码的介绍 并不会过多深入讲述深度学习。只是简单概括一下 深度学习需要做的事情。总体来讲，深度学习的4个步骤

• 采样，制作样本文件
• 根据样本文件类型创建识别模型
• 对样本文件分为训练样本和测试样本来训练识别模型
• 保存识别模型和验证
• 下面按照上面四个历程来尝试 编写自用的验证码模型。
• 本文大多数代码来自于 腾讯开发者实验室

0x002 采样，制作样本文件

样本文件的来源有2种。

• 有生产验证码的代码 ，可以自己生成，例如众多的开源软件
• 人工采集，自行打码（最少最少需要200-300张左右）

为了快速验证结果，先直接使用ImageCaptcha 来生成验证码图案来识别 。
需要安装 captcha 库
sudo pip install captcha

#!/usr/bin/python
        # -*- coding: utf-8 -*

        from captcha.image import ImageCaptcha
        from PIL import Image
        import numpy as np
        import random
        import string

        class generateCaptcha():
            def __init__(self,
                         width = 160,#验证码图片的宽
                         height = 60,#验证码图片的高
                         char_num = 4,#验证码字符个数
                         characters = string.digits + string.ascii_uppercase + string.ascii_lowercase):#验证码组成，数字+大写字母+小写字母
                self.width = width
                self.height = height
                self.char_num = char_num
                self.characters = characters
                self.classes = len(characters)

            def gen_captcha(self,batch_size = 50):
                X = np.zeros([batch_size,self.height,self.width,1])
                img = np.zeros((self.height,self.width),dtype=np.uint8)
                Y = np.zeros([batch_size,self.char_num,self.classes])
                image = ImageCaptcha(width = self.width,height = self.height)

                while True:
                    for i in range(batch_size):
                        captcha_str = ''.join(random.sample(self.characters,self.char_num))
                        img = image.generate_image(captcha_str).convert('L')
                        img = np.array(img.getdata())
                        X[i] = np.reshape(img,[self.height,self.width,1])/255.0
                        for j,ch in enumerate(captcha_str):
                            Y[i,j,self.characters.find(ch)] = 1
                    Y = np.reshape(Y,(batch_size,self.char_num*self.classes))
                    yield X,Y

            def decode_captcha(self,y):
                y = np.reshape(y,(len(y),self.char_num,self.classes))
                return ''.join(self.characters[x] for x in np.argmax(y,axis = 2)[0,:])

            def get_parameter(self):
                return self.width,self.height,self.char_num,self.characters,self.classes

            def gen_test_captcha(self):
                image = ImageCaptcha(width = self.width,height = self.height)
                captcha_str = ''.join(random.sample(self.characters,self.char_num))
                img = image.generate_image(captcha_str)
                img.save(captcha_str + '.jpg')
if __name__ == '__main__':
    g = generateCaptcha()
    g.gen_test_captcha()

保存为 generate_captcha.py
进到该目录 运行 python generate_captcha.py
你会看到该目录下会生成图片文件

自此 样本的工作完成了

0x003 创建识别模型

模型使用了卷积神经网络（CNN）。（CNN是深度学习一个特殊示例，它在计算机视觉有非常重要的影响。） 这里使用了 3 层隐藏层、2 层全连接层，对每层都进行 dropout。

• dropout是用来防止过拟合

• 过拟合 简单的理解就是 对于训练模型识别率过高，但是真正的识别率过低。打个比喻就是你考试前背题背的很熟悉，结果考试出的不是你背的，结果很差

• 层的计算公式
  
  但是一般我们都是边调整 边测试已达到效率最优

模型代码 ：

#!/usr/bin/python
# -*- coding: utf-8 -*

import tensorflow as tf
import math

class captchaModel():
    def __init__(self,
                 width = 160,
                 height = 60,
                 char_num = 4,
                 classes = 62):
        self.width = width
        self.height = height
        self.char_num = char_num
        self.classes = classes

    def conv2d(self,x, W):
        return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')

    def max_pool_2x2(self,x):
        return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1], padding='SAME')

    def weight_variable(self,shape):
        initial = tf.truncated_normal(shape, stddev=0.1)
        return tf.Variable(initial)

    def bias_variable(self,shape):
        initial = tf.constant(0.1, shape=shape)
        return tf.Variable(initial)

    def create_model(self,x_images,keep_prob):
        #first layer
        w_conv1 = self.weight_variable([5, 5, 1, 32])
        b_conv1 = self.bias_variable([32])
        h_conv1 = tf.nn.relu(tf.nn.bias_add(self.conv2d(x_images, w_conv1), b_conv1))
        h_pool1 = self.max_pool_2x2(h_conv1)
        h_dropout1 = tf.nn.dropout(h_pool1,keep_prob)
        conv_width = math.ceil(self.width/2)
        conv_height = math.ceil(self.height/2)

        #second layer
        w_conv2 = self.weight_variable([5, 5, 32, 64])
        b_conv2 = self.bias_variable([64])
        h_conv2 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout1, w_conv2), b_conv2))
        h_pool2 = self.max_pool_2x2(h_conv2)
        h_dropout2 = tf.nn.dropout(h_pool2,keep_prob)
        conv_width = math.ceil(conv_width/2)
        conv_height = math.ceil(conv_height/2)

        #third layer
        w_conv3 = self.weight_variable([5, 5, 64, 64])
        b_conv3 = self.bias_variable([64])
        h_conv3 = tf.nn.relu(tf.nn.bias_add(self.conv2d(h_dropout2, w_conv3), b_conv3))
        h_pool3 = self.max_pool_2x2(h_conv3)
        h_dropout3 = tf.nn.dropout(h_pool3,keep_prob)
        conv_width = math.ceil(conv_width/2)
        conv_height = math.ceil(conv_height/2)

        #first fully layer
        conv_width = int(conv_width)
        conv_height = int(conv_height)
        w_fc1 = self.weight_variable([64*conv_width*conv_height,1024])
        b_fc1 = self.bias_variable([1024])
        h_dropout3_flat = tf.reshape(h_dropout3,[-1,64*conv_width*conv_height])
        h_fc1 = tf.nn.relu(tf.nn.bias_add(tf.matmul(h_dropout3_flat, w_fc1), b_fc1))
        h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

        #second fully layer
        w_fc2 = self.weight_variable([1024,self.char_num*self.classes])
        b_fc2 = self.bias_variable([self.char_num*self.classes])
        y_conv = tf.add(tf.matmul(h_fc1_drop, w_fc2), b_fc2)

        return y_conv

保存为captcha_model.py

0x004 训练识别模型

有了样本和模型以后 我们开始训练模型

#!/usr/bin/python
import tensorflow as tf
import numpy as np
import string
import generate_captcha
import captcha_model

if __name__ == '__main__':
    captcha = generate_captcha.generateCaptcha()
    width,height,char_num,characters,classes = captcha.get_parameter()

    x = tf.placeholder(tf.float32, [None, height,width,1])
    y_ = tf.placeholder(tf.float32, [None, char_num*classes])
    keep_prob = tf.placeholder(tf.float32)

    model = captcha_model.captchaModel(width,height,char_num,classes)
    y_conv = model.create_model(x,keep_prob)
    cross_entropy = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=y_,logits=y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

    predict = tf.reshape(y_conv, [-1,char_num, classes])
    real = tf.reshape(y_,[-1,char_num, classes])
    correct_prediction = tf.equal(tf.argmax(predict,2), tf.argmax(real,2))
    correct_prediction = tf.cast(correct_prediction, tf.float32)
    accuracy = tf.reduce_mean(correct_prediction)

    saver = tf.train.Saver()
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        step = 0
        while True:
            batch_x,batch_y = next(captcha.gen_captcha(64))
            _,loss = sess.run([train_step,cross_entropy],feed_dict={x: batch_x, y_: batch_y, keep_prob: 0.75})
            print ('step:%d,loss:%f' % (step,loss))
            if step % 100 == 0:
                batch_x_test,batch_y_test = next(captcha.gen_captcha(100))
                acc = sess.run(accuracy, feed_dict={x: batch_x_test, y_: batch_y_test, keep_prob: 1.})
                print ('###############################################step:%d,accuracy:%f' % (step,acc))
                if acc > 0.99:
                    saver.save(sess,"capcha_model.ckpt")
                    break
            step += 1

保存为 train_captcha.py
执行 python train_captcha.py

• 其中 39行号 acc 代表准确率 此时需要准确率大于99%才保存
  各位执行的时候可以设置成 0.01 先实验一下效果
  等训练完成后 你会看得到 目录下保存了 这几个文件
  

0x004 验证

验证比较简单 只要加载刚才保存的模型
然后 生成一张图识别即可 。

!/usr/bin/python

from PIL import Image, ImageFilter
import tensorflow as tf
import numpy as np
import string
import sys
import generate_captcha
import captcha_model

if __name__ == '__main__':
    captcha = generate_captcha.generateCaptcha()
    width,height,char_num,characters,classes = captcha.get_parameter()

    gray_image = Image.open(sys.argv[1]).convert('L')
    img = np.array(gray_image.getdata())
    test_x = np.reshape(img,[height,width,1])/255.0
    x = tf.placeholder(tf.float32, [None, height,width,1])
    keep_prob = tf.placeholder(tf.float32)

    model = captcha_model.captchaModel(width,height,char_num,classes)
    y_conv = model.create_model(x,keep_prob)
    predict = tf.argmax(tf.reshape(y_conv, [-1,char_num, classes]),2)
    init_op = tf.global_variables_initializer()
    saver = tf.train.Saver()
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.95)
    with tf.Session(config=tf.ConfigProto(log_device_placement=False,gpu_options=gpu_options)) as sess:
        sess.run(init_op)
        saver.restore(sess, "capcha_model.ckpt")
        pre_list =  sess.run(predict,feed_dict={x: [test_x], keep_prob: 1})
        for i in pre_list:
            s = ''
            for j in i:
                s += characters[j]
            print s

保存为 predict_captcha.py
执行 python predict_captcha.py Mlzv.jpg
即可