Python/CNN/AutoGetImg.py · WindRunnerMax/SWVerifyCode

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SWVerifyCode

Python

CNN

AutoGetImg.py

AutoGetImg.py 5.22 KB

import numpy as np
import tensorflow as tf
import cv2
import os
import random
import time
import struct
import requests
from Convert import Convert
import re
import socket
 
# number
number = ['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']
 
# 图像大小
IMAGE_HEIGHT = 22 # 80
IMAGE_WIDTH = 62 # 160
MAX_CAPTCHA = 4
 
char_set = number
CHAR_SET_LEN = len(char_set) #10
model_path = "model/"
URL_PATH = "http://XXXXXXXXXXXXXXXXXXXXX/jsxsd/"
 
 
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) # dropout

# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
 x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
 
 # 3 conv layer
 w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
 b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
 conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
 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)
 
 w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
 b_c2 = tf.Variable(b_alpha * tf.random_normal([64]))
 conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
 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)
 
 w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64]))
 b_c3 = tf.Variable(b_alpha * tf.random_normal([64]))
 conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
 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)
 
 # Fully connected layer
 w_d = tf.Variable(w_alpha * tf.random_normal([3 * 8 * 64, 1024]))
 b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
 dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
 dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
 dense = tf.nn.dropout(dense, keep_prob)
 
 w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA * CHAR_SET_LEN]))
 b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA * CHAR_SET_LEN]))
 out = tf.add(tf.matmul(dense, w_out), b_out)
 # out = tf.nn.softmax(out)
 return out
 
 
# 向量转回文本
def vec2text(vec):
 char_pos = vec.nonzero()[0]
 text = []
 for i, c in enumerate(char_pos):
 text.append(char_set[c % 10])
 return "".join(text)
 
 
def predict_captcha(captcha_image):
 output = crack_captcha_cnn()
 
 saver = tf.train.Saver()
 with tf.Session() as sess:
 saver.restore(sess, tf.train.latest_checkpoint(model_path))
 
 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})
 
 text = text_list[0].tolist()
 vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
 i = 0
 for n in text:
 vector[i * CHAR_SET_LEN + n] = 1
 i += 1
 return vec2text(vector)

if __name__ == '__main__':
 
 output = crack_captcha_cnn()
 
 saver = tf.train.Saver()
 with tf.Session() as sess:
 saver.restore(sess, tf.train.latest_checkpoint(model_path))
 
 predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
 cvt = Convert()
 session = requests.Session()
 count = 1
 acceptCount = 1
 headers = {}

while True:
 try:
 IP = socket.inet_ntoa(struct.pack('>I', random.randint(1, 0xffffffff)))
 headers['X-FORWARDED-FOR'] = IP
 headers['CLIENT-IP'] = IP
 req = session.get(URL_PATH,headers = headers)
 req = session.get(URL_PATH + "verifycode.servlet",headers = headers)
 img = cvt.run(req.content)
 cv2.imwrite("vvvv.jpg",img)

image = np.float32(img)
 image = image.flatten() / 255
 text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
 text = text_list[0].tolist()
 vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
 i = 0
 for n in text:
 vector[i * CHAR_SET_LEN + n] = 1
 i += 1
 predict_text= vec2text(vector)
 # predict_text = input()
 print(predict_text)
 
 params={
 "encoded": "MjAyMDE2MTIyMzU=%%%MjAyMDE2MTIyMzU=",
 "RANDOMCODE": predict_text
 }
 req = session.post(URL_PATH + "xk/LoginToXk",data=params,headers = headers)
 if not re.search("验证码错误", req.text) :
 print("Load",acceptCount,count,acceptCount/count)
 acceptCount += 1
 cv2.imwrite("TrainImg/%s.jpg" % (predict_text),img)
 count += 1
 time.sleep(0.3) #稍微延时一下
 except Exception as e:
 print(e)
 pass

一键复制编辑原始数据按行查看历史

WindRunnerMax 提交于 2020年02月18日 10:31 +08:00 . Python Tensorflow CNN

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import numpy as np
import tensorflow as tf
import cv2
import os
import random
import time
import struct
import requests
from Convert import Convert
import re
import socket
 
# number
number = ['1', '2', '3', 'b', 'c', 'm', 'n', 'v', 'x', 'z']
 
# 图像大小
IMAGE_HEIGHT = 22 # 80
IMAGE_WIDTH = 62 # 160
MAX_CAPTCHA = 4
 
char_set = number
CHAR_SET_LEN = len(char_set) #10
model_path = "model/"
URL_PATH = "http://XXXXXXXXXXXXXXXXXXXXX/jsxsd/"
 
 
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) # dropout

 
# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
 x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
 
 # 3 conv layer
 w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
 b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
 conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
 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)
 
 w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
 b_c2 = tf.Variable(b_alpha * tf.random_normal([64]))
 conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
 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)
 
 w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64]))
 b_c3 = tf.Variable(b_alpha * tf.random_normal([64]))
 conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
 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)
 
 # Fully connected layer
 w_d = tf.Variable(w_alpha * tf.random_normal([3 * 8 * 64, 1024]))
 b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
 dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
 dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
 dense = tf.nn.dropout(dense, keep_prob)
 
 w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA * CHAR_SET_LEN]))
 b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA * CHAR_SET_LEN]))
 out = tf.add(tf.matmul(dense, w_out), b_out)
 # out = tf.nn.softmax(out)
 return out
 
 
# 向量转回文本
def vec2text(vec):
 char_pos = vec.nonzero()[0]
 text = []
 for i, c in enumerate(char_pos):
 text.append(char_set[c % 10])
 return "".join(text)
 
 
def predict_captcha(captcha_image):
 output = crack_captcha_cnn()
 
 saver = tf.train.Saver()
 with tf.Session() as sess:
 saver.restore(sess, tf.train.latest_checkpoint(model_path))
 
 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})
 
 text = text_list[0].tolist()
 vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
 i = 0
 for n in text:
 vector[i * CHAR_SET_LEN + n] = 1
 i += 1
 return vec2text(vector)
 

if __name__ == '__main__':
 
 output = crack_captcha_cnn()
 
 saver = tf.train.Saver()
 with tf.Session() as sess:
 saver.restore(sess, tf.train.latest_checkpoint(model_path))
 
 predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
 cvt = Convert()
 session = requests.Session()
 count = 1
 acceptCount = 1
 headers = {}

 while True:
 try:
 IP = socket.inet_ntoa(struct.pack('>I', random.randint(1, 0xffffffff)))
 headers['X-FORWARDED-FOR'] = IP
 headers['CLIENT-IP'] = IP
 req = session.get(URL_PATH,headers = headers)
 req = session.get(URL_PATH + "verifycode.servlet",headers = headers)
 img = cvt.run(req.content)
 cv2.imwrite("vvvv.jpg",img)

 image = np.float32(img)
 image = image.flatten() / 255
 text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
 text = text_list[0].tolist()
 vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
 i = 0
 for n in text:
 vector[i * CHAR_SET_LEN + n] = 1
 i += 1
 predict_text= vec2text(vector)
 # predict_text = input()
 print(predict_text)
 
 params={
 "encoded": "MjAyMDE2MTIyMzU=%%%MjAyMDE2MTIyMzU=",
 "RANDOMCODE": predict_text
 }
 req = session.post(URL_PATH + "xk/LoginToXk",data=params,headers = headers)
 if not re.search("验证码错误", req.text) :
 print("Load",acceptCount,count,acceptCount/count)
 acceptCount += 1
 cv2.imwrite("TrainImg/%s.jpg" % (predict_text),img)
 count += 1
 time.sleep(0.3) #稍微延时一下
 except Exception as e:
 print(e)
 pass