這篇文章主要介紹如何使用Tensorflow構(gòu)建和訓(xùn)練自己的CNN來做驗證碼識別方式，文中介紹的非常詳細(xì)，具有一定的參考價值，感興趣的小伙伴們一定要看完！
1. 準(zhǔn)備訓(xùn)練樣本
使用Python的庫captcha來生成我們需要的訓(xùn)練樣本，代碼如下：
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import sys
import os
import shutil
import random
import time
#captcha是用于生成驗證碼圖片的庫，可以 pip install captcha 來安裝它
from captcha.image import ImageCaptcha
#用于生成驗證碼的字符集
CHAR_SET = ['0','1','2','3','4','5','6','7','8','9']
#字符集的長度
CHAR_SET_LEN = 10
#驗證碼的長度，每個驗證碼由4個數(shù)字組成
CAPTCHA_LEN = 4
#驗證碼圖片的存放路徑
CAPTCHA_IMAGE_PATH = 'E:/Tensorflow/captcha/images/'
#用于模型測試的驗證碼圖片的存放路徑，它里面的驗證碼圖片作為測試集
TEST_IMAGE_PATH = 'E:/Tensorflow/captcha/test/'
#用于模型測試的驗證碼圖片的個數(shù)，從生成的驗證碼圖片中取出來放入測試集中
TEST_IMAGE_NUMBER = 50
#生成驗證碼圖片，4位的十進(jìn)制數(shù)字可以有10000種驗證碼
def generate_captcha_image(charSet = CHAR_SET, charSetLen=CHAR_SET_LEN, captchaImgPath=CAPTCHA_IMAGE_PATH):
k = 0
total = 1
for i in range(CAPTCHA_LEN):
total *= charSetLen
for i in range(charSetLen):
for j in range(charSetLen):
for m in range(charSetLen):
for n in range(charSetLen):
captcha_text = charSet[i] + charSet[j] + charSet[m] + charSet[n]
image = ImageCaptcha()
image.write(captcha_text, captchaImgPath + captcha_text + '.jpg')
k += 1
sys.stdout.write("\rCreating %d/%d" % (k, total))
sys.stdout.flush()
#從驗證碼的圖片集中取出一部分作為測試集，這些圖片不參加訓(xùn)練，只用于模型的測試
def prepare_test_set():
fileNameList = []
for filePath in os.listdir(CAPTCHA_IMAGE_PATH):
captcha_name = filePath.split('/')[-1]
fileNameList.append(captcha_name)
random.seed(time.time())
random.shuffle(fileNameList)
for i in range(TEST_IMAGE_NUMBER):
name = fileNameList[i]
shutil.move(CAPTCHA_IMAGE_PATH + name, TEST_IMAGE_PATH + name)
if __name__ == '__main__':
generate_captcha_image(CHAR_SET, CHAR_SET_LEN, CAPTCHA_IMAGE_PATH)
prepare_test_set()
sys.stdout.write("\nFinished")
sys.stdout.flush()運行上面的代碼，可以生成驗證碼圖片，
生成的驗證碼圖片如下圖所示：
2. 構(gòu)建CNN，訓(xùn)練分類器
代碼如下：
import tensorflow as tf
import numpy as np
from PIL import Image
import os
import random
import time
#驗證碼圖片的存放路徑
CAPTCHA_IMAGE_PATH = 'E:/Tensorflow/captcha/images/'
#驗證碼圖片的寬度
CAPTCHA_IMAGE_WIDHT = 160
#驗證碼圖片的高度
CAPTCHA_IMAGE_HEIGHT = 60
CHAR_SET_LEN = 10
CAPTCHA_LEN = 4
#60%的驗證碼圖片放入訓(xùn)練集中
TRAIN_IMAGE_PERCENT = 0.6
#訓(xùn)練集，用于訓(xùn)練的驗證碼圖片的文件名
TRAINING_IMAGE_NAME = []
#驗證集，用于模型驗證的驗證碼圖片的文件名
VALIDATION_IMAGE_NAME = []
#存放訓(xùn)練好的模型的路徑
MODEL_SAVE_PATH = 'E:/Tensorflow/captcha/models/'
def get_image_file_name(imgPath=CAPTCHA_IMAGE_PATH):
fileName = []
total = 0
for filePath in os.listdir(imgPath):
captcha_name = filePath.split('/')[-1]
fileName.append(captcha_name)
total += 1
return fileName, total
#將驗證碼轉(zhuǎn)換為訓(xùn)練時用的標(biāo)簽向量，維數(shù)是 40
#例如，如果驗證碼是 ‘0296' ，則對應(yīng)的標(biāo)簽是
# [1 0 0 0 0 0 0 0 0 0
# 0 0 1 0 0 0 0 0 0 0
# 0 0 0 0 0 0 0 0 0 1
# 0 0 0 0 0 0 1 0 0 0]
def name2label(name):
label = np.zeros(CAPTCHA_LEN * CHAR_SET_LEN)
for i, c in enumerate(name):
idx = i*CHAR_SET_LEN + ord(c) - ord('0')
label[idx] = 1
return label
#取得驗證碼圖片的數(shù)據(jù)以及它的標(biāo)簽
def get_data_and_label(fileName, filePath=CAPTCHA_IMAGE_PATH):
pathName = os.path.join(filePath, fileName)
img = Image.open(pathName)
#轉(zhuǎn)為灰度圖
img = img.convert("L")
image_array = np.array(img)
image_data = image_array.flatten()/255
image_label = name2label(fileName[0:CAPTCHA_LEN])
return image_data, image_label
#生成一個訓(xùn)練batch
def get_next_batch(batchSize=32, trainOrTest='train', step=0):
batch_data = np.zeros([batchSize, CAPTCHA_IMAGE_WIDHT*CAPTCHA_IMAGE_HEIGHT])
batch_label = np.zeros([batchSize, CAPTCHA_LEN * CHAR_SET_LEN])
fileNameList = TRAINING_IMAGE_NAME
if trainOrTest == 'validate':
fileNameList = VALIDATION_IMAGE_NAME
totalNumber = len(fileNameList)
indexStart = step*batchSize
for i in range(batchSize):
index = (i + indexStart) % totalNumber
name = fileNameList[index]
img_data, img_label = get_data_and_label(name)
batch_data[i, : ] = img_data
batch_label[i, : ] = img_label
return batch_data, batch_label
#構(gòu)建卷積神經(jīng)網(wǎng)絡(luò)并訓(xùn)練
def train_data_with_CNN():
#初始化權(quán)值
def weight_variable(shape, name='weight'):
init = tf.truncated_normal(shape, stddev=0.1)
var = tf.Variable(initial_value=init, name=name)
return var
#初始化偏置
def bias_variable(shape, name='bias'):
init = tf.constant(0.1, shape=shape)
var = tf.Variable(init, name=name)
return var
#卷積
def conv2d(x, W, name='conv2d'):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME', name=name)
#池化
def max_pool_2X2(x, name='maxpool'):
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME', name=name)
#輸入層
#請注意 X 的 name，在測試model時會用到它
X = tf.placeholder(tf.float32, [None, CAPTCHA_IMAGE_WIDHT * CAPTCHA_IMAGE_HEIGHT], name='data-input')
Y = tf.placeholder(tf.float32, [None, CAPTCHA_LEN * CHAR_SET_LEN], name='label-input')
x_input = tf.reshape(X, [-1, CAPTCHA_IMAGE_HEIGHT, CAPTCHA_IMAGE_WIDHT, 1], name='x-input')
#dropout,防止過擬合
#請注意 keep_prob 的 name，在測試model時會用到它
keep_prob = tf.placeholder(tf.float32, name='keep-prob')
#第一層卷積
W_conv1 = weight_variable([5,5,1,32], 'W_conv1')
B_conv1 = bias_variable([32], 'B_conv1')
conv1 = tf.nn.relu(conv2d(x_input, W_conv1, 'conv1') + B_conv1)
conv1 = max_pool_2X2(conv1, 'conv1-pool')
conv1 = tf.nn.dropout(conv1, keep_prob)
#第二層卷積
W_conv2 = weight_variable([5,5,32,64], 'W_conv2')
B_conv2 = bias_variable([64], 'B_conv2')
conv2 = tf.nn.relu(conv2d(conv1, W_conv2,'conv2') + B_conv2)
conv2 = max_pool_2X2(conv2, 'conv2-pool')
conv2 = tf.nn.dropout(conv2, keep_prob)
#第三層卷積
W_conv3 = weight_variable([5,5,64,64], 'W_conv3')
B_conv3 = bias_variable([64], 'B_conv3')
conv3 = tf.nn.relu(conv2d(conv2, W_conv3, 'conv3') + B_conv3)
conv3 = max_pool_2X2(conv3, 'conv3-pool')
conv3 = tf.nn.dropout(conv3, keep_prob)
#全鏈接層
#每次池化后，圖片的寬度和高度均縮小為原來的一半，進(jìn)過上面的三次池化，寬度和高度均縮小8倍
W_fc1 = weight_variable([20*8*64, 1024], 'W_fc1')
B_fc1 = bias_variable([1024], 'B_fc1')
fc1 = tf.reshape(conv3, [-1, 20*8*64])
fc1 = tf.nn.relu(tf.add(tf.matmul(fc1, W_fc1), B_fc1))
fc1 = tf.nn.dropout(fc1, keep_prob)
#輸出層
W_fc2 = weight_variable([1024, CAPTCHA_LEN * CHAR_SET_LEN], 'W_fc2')
B_fc2 = bias_variable([CAPTCHA_LEN * CHAR_SET_LEN], 'B_fc2')
output = tf.add(tf.matmul(fc1, W_fc2), B_fc2, 'output')
loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=Y, logits=output))
optimizer = tf.train.AdamOptimizer(0.001).minimize(loss)
predict = tf.reshape(output, [-1, CAPTCHA_LEN, CHAR_SET_LEN], name='predict')
labels = tf.reshape(Y, [-1, CAPTCHA_LEN, CHAR_SET_LEN], name='labels')
#預(yù)測結(jié)果
#請注意 predict_max_idx 的 name，在測試model時會用到它
predict_max_idx = tf.argmax(predict, axis=2, name='predict_max_idx')
labels_max_idx = tf.argmax(labels, axis=2, name='labels_max_idx')
predict_correct_vec = tf.equal(predict_max_idx, labels_max_idx)
accuracy = tf.reduce_mean(tf.cast(predict_correct_vec, tf.float32))
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
steps = 0
for epoch in range(6000):
train_data, train_label = get_next_batch(64, 'train', steps)
sess.run(optimizer, feed_dict={X : train_data, Y : train_label, keep_prob:0.75})
if steps % 100 == 0:
test_data, test_label = get_next_batch(100, 'validate', steps)
acc = sess.run(accuracy, feed_dict={X : test_data, Y : test_label, keep_prob:1.0})
print("steps=%d, accuracy=%f" % (steps, acc))
if acc > 0.99:
saver.save(sess, MODEL_SAVE_PATH+"crack_captcha.model", global_step=steps)
break
steps += 1
if __name__ == '__main__':
image_filename_list, total = get_image_file_name(CAPTCHA_IMAGE_PATH)
random.seed(time.time())
#打亂順序
random.shuffle(image_filename_list)
trainImageNumber = int(total * TRAIN_IMAGE_PERCENT)
#分成測試集
TRAINING_IMAGE_NAME = image_filename_list[ : trainImageNumber]
#和驗證集
VALIDATION_IMAGE_NAME = image_filename_list[trainImageNumber : ]
train_data_with_CNN()
print('Training finished')運行上面的代碼，開始訓(xùn)練，訓(xùn)練要花些時間，如果沒有GPU的話，會慢些，
訓(xùn)練完后，輸出如下結(jié)果，經(jīng)過4100次的迭代，訓(xùn)練出來的分類器模型在驗證集上識別的準(zhǔn)確率為99.5%
生成的模型文件如下，在模型測試時將用到這些文件
3. 測試模型
編寫代碼，對訓(xùn)練出來的模型進(jìn)行測試
import tensorflow as tf
import numpy as np
from PIL import Image
import os
import matplotlib.pyplot as plt
CAPTCHA_LEN = 4
MODEL_SAVE_PATH = 'E:/Tensorflow/captcha/models/'
TEST_IMAGE_PATH = 'E:/Tensorflow/captcha/test/'
def get_image_data_and_name(fileName, filePath=TEST_IMAGE_PATH):
pathName = os.path.join(filePath, fileName)
img = Image.open(pathName)
#轉(zhuǎn)為灰度圖
img = img.convert("L")
image_array = np.array(img)
image_data = image_array.flatten()/255
image_name = fileName[0:CAPTCHA_LEN]
return image_data, image_name
def digitalStr2Array(digitalStr):
digitalList = []
for c in digitalStr:
digitalList.append(ord(c) - ord('0'))
return np.array(digitalList)
def model_test():
nameList = []
for pathName in os.listdir(TEST_IMAGE_PATH):
nameList.append(pathName.split('/')[-1])
totalNumber = len(nameList)
#加載graph
saver = tf.train.import_meta_graph(MODEL_SAVE_PATH+"crack_captcha.model-4100.meta")
graph = tf.get_default_graph()
#從graph取得 tensor，他們的name是在構(gòu)建graph時定義的(查看上面第2步里的代碼)
input_holder = graph.get_tensor_by_name("data-input:0")
keep_prob_holder = graph.get_tensor_by_name("keep-prob:0")
predict_max_idx = graph.get_tensor_by_name("predict_max_idx:0")
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(MODEL_SAVE_PATH))
count = 0
for fileName in nameList:
img_data, img_name = get_image_data_and_name(fileName, TEST_IMAGE_PATH)
predict = sess.run(predict_max_idx, feed_dict={input_holder:[img_data], keep_prob_holder : 1.0})
filePathName = TEST_IMAGE_PATH + fileName
print(filePathName)
img = Image.open(filePathName)
plt.imshow(img)
plt.axis('off')
plt.show()
predictValue = np.squeeze(predict)
rightValue = digitalStr2Array(img_name)
if np.array_equal(predictValue, rightValue):
result = '正確'
count += 1
else:
result = '錯誤'
print('實際值：{}， 預(yù)測值：{}，測試結(jié)果：{}'.format(rightValue, predictValue, result))
print('\n')
print('正確率：%.2f%%(%d/%d)' % (count*100/totalNumber, count, totalNumber))
if __name__ == '__main__':
model_test()對模型的測試結(jié)果如下，在測試集上識別的準(zhǔn)確率為 94%
下面是兩個識別錯誤的驗證碼
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