本次文章主要是为了探讨学习，如有出现任何非正常渠道获利行为与本人无关。
import tensorflow as tf
from captcha.image import ImageCaptcha
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import random
from cracking.machine_learning_demo.keras_cnn import config
from cracking.machine_learning_demo.keras_cnn import helper
from imutils import paths
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u',
'v', 'w', 'x', 'y', 'z']
ALPHABET = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U',
'V', 'W', 'X', 'Y', 'Z']
CHAR_SET = number + alphabet + ALPHABET
image_height = 60
image_width = 160
max_captcha = 4
# print("验证码文本最长字符数", max_captcha)
char_set = CHAR_SET
char_set_len = len(char_set)
# print(CHAR_SET)
def random_captcha_text(char_set=CHAR_SET, captcha_size=4):
captcha_text = []
for i in range(captcha_size):
c = random.choice(char_set)
captcha_text.append(c)
return captcha_text
def gen_captcha_text_image():
image = ImageCaptcha()
captcha_text = random_captcha_text()
captcha_text = ''.join(captcha_text)
captcha = image.generate(captcha_text)
captcha_image = Image.open(captcha)
captcha_image = np.array(captcha_image)
return captcha_text, captcha_image
def convert2gray(img):
if len(img.shape) > 2:
r, g, b = img[:, :, 0], img[:, :, 1], img[:, :, 2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
return gray
else:
return img
def text2vec(text):
text_len = len(text)
if text_len > max_captcha:
# raise ValueError('验证码最长4个字符')
print('验证码最长4个字符', text)
vector = np.zeros(max_captcha * char_set_len)
def char2pos(c):
if c == '_':
k = 62
return k
k = ord(c) - 48
if k > 9:
k = ord(c) - 55
if k > 35:
k = ord(c) - 61
if k > 61:
raise ValueError('No Map')
return k
for i, c in enumerate(text):
idx = i * char_set_len + char2pos(c)
vector[idx] = 1
return vector
def vec2text(vec):
text = []
for i, c in enumerate(vec):
char_idx = c % char_set_len
if char_idx < 10:
char_code = char_idx + ord('0')
elif char_idx < 36:
char_code = char_idx - 10 + ord('A')
elif char_idx < 62:
char_code = char_idx - 36 + ord('a')
elif char_idx == 62:
char_code = ord('_')
else:
raise ValueError('error')
text.append(chr(char_code))
return "".join(text)
def read_local_image(method):
##读取特定文件下的验证码
if method == 0:
CAPTCHA_IMAGE_FOLDER = config.train_pic_path ###训练图片的存储路径
elif method == 1:
CAPTCHA_IMAGE_FOLDER = config.test_pic_path ###测试图片存储的路径
else:
print("请输出步骤", method)
captcha_image_files = list(paths.list_images(CAPTCHA_IMAGE_FOLDER))
# print(captcha_image_files)
image_file = random.sample(captcha_image_files, 1)[0]
text = image_file.split("\")[-1].split(".")[0]
# text = text.lower()
# print(text)
# print("验证码大小2：", image.shape) # (60,160,3)
image = Image.open(image_file)
image = helper.change_image_channels(image)
image = image.resize((160, 60), Image.ANTIALIAS)
image = np.array(image)
# max_captcha = len(text)
return text, image
def get_next_batch(batch_size, method):
batch_x = np.zeros([batch_size, image_height * image_width])
batch_y = np.zeros([batch_size, max_captcha * char_set_len])
def wrap_gen_captcha_text_and_image():
while True:
##随机生成的验证码
# text, image = gen_captcha_text_image()
##本地的验证码
text, image = read_local_image(method)
if image.shape == (60, 160, 3):
return text, image
for i in range(batch_size):
text, image = wrap_gen_captcha_text_and_image()
image = convert2gray(image)
batch_x[i, :] = image.flatten() / 255
if len(text) == 4:
batch_y[i, :] = text2vec(text)
else:
continue
return batch_x, batch_y
def cnn_structure(X, Y, keep_prob, b_alpha=0.1):
x = tf.reshape(X, shape=[-1, image_height, image_width, 1])
wc1 = tf.get_variable(name='wc1', shape=[3, 3, 1, 32], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
# wc1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
bc1 = tf.Variable(b_alpha * tf.random_normal([32]))
conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, wc1, strides=[1, 1, 1, 1], padding='SAME'), bc1))
conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv1 = tf.nn.dropout(conv1, keep_prob)
wc2 = tf.get_variable(name='wc2', shape=[3, 3, 32, 64], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
# wc2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
bc2 = tf.Variable(b_alpha * tf.random_normal([64]))
conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, wc2, strides=[1, 1, 1, 1], padding='SAME'), bc2))
conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv2 = tf.nn.dropout(conv2, keep_prob)
wc3 = tf.get_variable(name='wc3', shape=[3, 3, 64, 128], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
# wc3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 128]))
bc3 = tf.Variable(b_alpha * tf.random_normal([128]))
conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, wc3, strides=[1, 1, 1, 1], padding='SAME'), bc3))
conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
conv3 = tf.nn.dropout(conv3, keep_prob)
wd1 = tf.get_variable(name='wd1', shape=[8 * 20 * 128, 1024], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
# wd1 = tf.Variable(w_alpha * tf.random_normal([7*20*128,1024]))
bd1 = tf.Variable(b_alpha * tf.random_normal([1024]))
dense = tf.reshape(conv3, [-1, wd1.get_shape().as_list()[0]])
dense = tf.nn.relu(tf.add(tf.matmul(dense, wd1), bd1))
dense = tf.nn.dropout(dense, keep_prob)
wout = tf.get_variable('name', shape=[1024, max_captcha * char_set_len], dtype=tf.float32,
initializer=tf.contrib.layers.xavier_initializer())
# wout = tf.Variable(w_alpha * tf.random_normal([1024, max_captcha * char_set_len]))
bout = tf.Variable(b_alpha * tf.random_normal([max_captcha * char_set_len]))
out = tf.add(tf.matmul(dense, wout), bout)
return out
def train_cnn(X, Y, keep_prob, method):
output = cnn_structure(X, Y, keep_prob)
cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
predict = tf.reshape(output, [-1, max_captcha, char_set_len])
max_idx_p = tf.argmax(predict, 2)
max_idx_l = tf.argmax(tf.reshape(Y, [-1, max_captcha, char_set_len]), 2)
correct_pred = tf.equal(max_idx_p, max_idx_l)
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
saver = tf.train.Saver()
with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)
step = 0
while True:
batch_x, batch_y = get_next_batch(10, method)
# print('batch_x=', batch_x)
# print('batch_y=', batch_y)
_, cost_ = sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
print(step, cost_)
if step % 100 == 0:
batch_x_test, batch_y_test = get_next_batch(100, method)
acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
print(step, acc)
# 如果准确率大于90%,保存模型,完成训练
if acc > 0.99:
saver.save(sess, config.save_model_path, global_step=step)###模型存储的配置路径
break
# if acc > 0.95:
# saver.save(sess, config.save_model_path, global_step=step)
step += 1
def crack_captcha(captcha_image, X, Y, keep_prob):
output = cnn_structure(X, Y, keep_prob)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, config.download_model_path) ###模型生成存储的配置路径
predict = tf.argmax(tf.reshape(output, [-1, max_captcha, char_set_len]), 2)
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1.})
vec = text_list[0].tolist()
predict_text = vec2text(vec)
return predict_text
def operate_cnn(method, image_path):
if method == 0:
X = tf.placeholder(tf.float32, [None, image_height * image_width])
Y = tf.placeholder(tf.float32, [None, max_captcha * char_set_len])
keep_prob = tf.placeholder(tf.float32)
train_cnn(X, Y, keep_prob, method)
return "训练模型完成！"
if method == 1:
num = 10
true_num = 0
for i in range(num):
tf.reset_default_graph()
# text, image = gen_captcha_text_image()
text, image = read_local_image(method)
image = np.array(image)
image = convert2gray(image)
image = image.flatten() / 255
X = tf.placeholder(tf.float32, [None, image_height * image_width])
Y = tf.placeholder(tf.float32, [None, max_captcha * char_set_len])
keep_prob = tf.placeholder(tf.float32)
predict_text = crack_captcha(image, X, Y, keep_prob)
# print("正确: {} 预测: {}".format(text, predict_text))
predict_text_str = str(predict_text).replace("['", "").replace("', '", "").replace("']", "")
# print(predict_text_str.lower())
# predict_value = predict_text_str.lower()
# normal_value = text.lower()
if text == predict_text:
true_num += 1
else:
print("正确: {} 预测: {}".format(text, predict_text))
return "预测正确的个数==", true_num
if method == 2:
image = image_path
image = Image.open(image)
image = helper.change_image_channels(image)
image = image.resize((160, 60), Image.ANTIALIAS)
image = np.array(image)
image = convert2gray(image)
image = image.flatten() / 255
X = tf.placeholder(tf.float32, [None, image_height * image_width])
Y = tf.placeholder(tf.float32, [None, max_captcha * char_set_len])
keep_prob = tf.placeholder(tf.float32)
predict_text = crack_captcha(image, X, Y, keep_prob)
predict_text_str = str(predict_text).replace("['", "").replace("', '", "").replace("']", "")
predict_value = predict_text_str.lower()
return predict_value
if __name__ == '__main__':
'''
# step=0: 训练模型
# step=1: 批量测试模型
# step=2: 单张测试模型
'''
value = operate_cnn(method=1, image_path="2ceb.jpg")
print(value)
import te
