環(huán)境：Ubuntu 14.04.1 LTS (GNU/Linux 3.13.0-105-generic x86_64)
1 安裝captcha庫(kù)
sudo pip install captcha
2 生成驗(yàn)證碼訓(xùn)練數(shù)據(jù)
2.1 驗(yàn)證碼生成器
采用 python 中生成器方式來(lái)生成我們的訓(xùn)練數(shù)據(jù)，這樣的好處是，不需要提前生成大量的數(shù)據(jù)，訓(xùn)練過(guò)程中生成數(shù)據(jù)，并且可以無(wú)限生成數(shù)據(jù)。
2.1.1 示例代碼
現(xiàn)在在?/home/ubuntu?目錄下創(chuàng)建源文件 generate_captcha.py，內(nèi)容可參考：
generate_captcha.py
#!/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,#驗(yàn)證碼圖片的寬
? ? ? ? height = 60,#驗(yàn)證碼圖片的高
? ? ? ? char_num = 4,#驗(yàn)證碼字符個(gè)數(shù)
characters = string.digits +string.ascii_uppercase + string.ascii_lowercase):
#驗(yàn)證碼組成，數(shù)字+大寫字母+小寫字母
? ? ? ? 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 inenumerate(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 xin 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')
2.1.2 然后執(zhí)行
cd /home/ubuntu;
python
import generate_captcha
g = generate_captcha.generateCaptcha()
g.gen_test_captcha()
2.1.3 執(zhí)行結(jié)果
在?/home/ubuntu?目錄下查看生成的驗(yàn)證碼，jpg 格式的圖片可以點(diǎn)擊查看。
如：
3 驗(yàn)證碼識(shí)別模型
將驗(yàn)證碼識(shí)別問(wèn)題轉(zhuǎn)化為分類問(wèn)題，總共62^4 種類型，采用 4 個(gè) one-hot 編碼分別表示 4 個(gè)字符取值。
3.1 cnn 驗(yàn)證碼識(shí)別模型
3 層隱藏層、2 層全連接層，對(duì)每層都進(jìn)行dropout。input——>conv——>pool——>dropout——>conv——>pool——>dropout——>conv——>pool——>dropout——>fullyconnected layer——>dropout——>fully connected layer——>output
3.1.1 示例代碼
現(xiàn)在在?/home/ubuntu?目錄下創(chuàng)建源文件?captcha_model.py，內(nèi)容可參考：
captcha_model.py
#!/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
3.2 訓(xùn)練cnn 驗(yàn)證碼識(shí)別模型
每批次采用 64 個(gè)訓(xùn)練樣本，每 100 次循環(huán)采用 100 個(gè)測(cè)試樣本檢查識(shí)別準(zhǔn)確度，當(dāng)準(zhǔn)確度大于 99% 時(shí)，訓(xùn)練結(jié)束，采用 GPU 需要 5-6個(gè)小時(shí)左右，CPU 大概需要 20 個(gè)小時(shí)左右。
注：作為實(shí)驗(yàn)，你可以通過(guò)調(diào)整train_captcha.py文件中if acc > 0.99:代碼行的準(zhǔn)確度節(jié)省訓(xùn)練時(shí)間(比如將0.99 為 0.01)；同時(shí)，我們已經(jīng)通過(guò)長(zhǎng)時(shí)間的訓(xùn)練得到了一個(gè)訓(xùn)練集，可以通過(guò)如下命令將訓(xùn)練集下載到本地。
wget?http://tensorflow-1253902462.cosgz.myqcloud.com/captcha/capcha_model.zip
unzip capcha_model.zip
3.2.1 示例代碼
現(xiàn)在在?/home/ubuntu?目錄下創(chuàng)建源文件?train_captcha.py，內(nèi)容可參考：
train_captcha.py
#!/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
3.2.2 然后執(zhí)行
cd /home/ubuntu;
python train_captcha.py
3.2.3執(zhí)行結(jié)果
step:75173,loss:0.010555
step:75174,loss:0.009410
step:75175,loss:0.009978
step:75176,loss:0.008089
step:75177,loss:0.009949
step:75178,loss:0.010126
step:75179,loss:0.009584
step:75180,loss:0.012272
step:75181,loss:0.010157
step:75182,loss:0.009529
step:75183,loss:0.007636
step:75184,loss:0.009058
step:75185,loss:0.010061
step:75186,loss:0.009941
step:75187,loss:0.009339
step:75188,loss:0.009685
step:75189,loss:0.009879
step:75190,loss:0.007799
step:75191,loss:0.010866
step:75192,loss:0.009838
step:75193,loss:0.010931
step:75194,loss:0.012859
step:75195,loss:0.008747
step:75196,loss:0.009147
step:75197,loss:0.009351
step:75198,loss:0.009746
step:75199,loss:0.010014
step:75200,loss:0.009024
###############################################step:75200,accuracy:0.992500
3.3 測(cè)試
cnn 驗(yàn)證碼識(shí)別模型
3.3.1示例代碼
現(xiàn)在在?/home/ubuntu?目錄下創(chuàng)建源文件?predict_captcha.py，內(nèi)容可參考：
predict_captcha.py
#!/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)
? ? withtf.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
3.3.2 然后執(zhí)行
cd /home/ubuntu;
python predict_captcha.py Gxdl.jpg
3.3.3 執(zhí)行結(jié)果：
Gxdl
注：因時(shí)間的限制，我們可能調(diào)整了準(zhǔn)確度導(dǎo)致執(zhí)行結(jié)果不符合預(yù)期，這屬于正常情況。
在訓(xùn)練時(shí)間足夠長(zhǎng)的情況下，可以采用驗(yàn)證碼生成器生成測(cè)試數(shù)據(jù)，cnn訓(xùn)練出來(lái)的驗(yàn)證碼識(shí)別模型還是很強(qiáng)大的，大小寫的 z 都可以區(qū)分，甚至有時(shí)候人都無(wú)法區(qū)分，該模型也可以正確的識(shí)別。
4 完成
以上。
