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import _thread
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import queue
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import time
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import multiprocessing as mp
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from multiprocessing import Process # abc
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from multiprocessing import Value, Manager
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import tensorflow as tf # abc
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import numpy as np # abc
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from captcha.image import ImageCaptcha # abc
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from PIL import Image
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import random
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import matplotlib.pyplot as plt
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import os
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import threading
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import datetime
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number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
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alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
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'u',
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'v', 'w', 'x', 'y', 'z']
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ALPHABET = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
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'U',
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'V', 'W', 'X', 'Y', 'Z']
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IMAGE_HEIGHT=60
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IMAGE_WIDTH =160
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MAX_CAPTCHA =4
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CHAR_SET_LEN=63
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import sys
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class Logger(object):
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def __init__(self, filename="log.txt"):
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self.terminal = sys.stdout
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self.log = open(filename, "a")
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def write(self, message):
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self.terminal.write(message)
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self.log.write(message)
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self.log.flush()
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def random_captcha_text(char_set=number + alphabet + ALPHABET, captcha_size=4):
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captcha_text = []
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for i in range(captcha_size):
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c = random.choice(char_set)
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captcha_text.append(c)
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return captcha_text
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def gen_captcha_text_and_image(i=0):
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# 创建图像实例对象
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image = ImageCaptcha()
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# 随机选择4个字符
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captcha_text = random_captcha_text()
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# array 转化为 string
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captcha_text = ''.join(captcha_text)
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# 生成验证码
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captcha = image.generate(captcha_text)
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if i % 100 == 0:
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image.write(captcha_text, "D:\\DL\\captcha pics" + captcha_text + '.jpg')
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captcha_image = Image.open(captcha)
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captcha_image = np.array(captcha_image)
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return captcha_text, captcha_image
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def convert2gray(img):
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if len(img.shape) > 2:
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gray = np.mean(img, -1)
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return gray
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else:
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return img
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# 文本转向量
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def text2vec(text):
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text_len = len(text)
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if text_len > MAX_CAPTCHA:
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raise ValueError('验证码最长4个字符')
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vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
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def char2pos(c):
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if c == '_':
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k = 62
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return k
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k = ord(c) - 48
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if k > 9:
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k = ord(c) - 55
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if k > 35:
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k = ord(c) - 61
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if k > 61:
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raise ValueError('No Map')
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return k
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for i, c in enumerate(text):
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idx = i * CHAR_SET_LEN + char2pos(c)
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vector[idx] = 1
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return vector
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# 向量转回文本
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def vec2text(vec):
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char_pos = vec[0]
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text = []
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for i, c in enumerate(char_pos):
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char_idx = c % CHAR_SET_LEN
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if char_idx < 10:
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char_code = char_idx + ord('0')
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elif char_idx < 36:
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char_code = char_idx - 10 + ord('A')
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elif char_idx < 62:
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char_code = char_idx - 36 + ord('a')
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elif char_idx == 62:
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char_code = ord('_')
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else:
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raise ValueError('error')
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text.append(chr(char_code))
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return "".join(text)
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# 生成一个训练batch
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def get_next_batchDetail(batch_size=64):
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re=[]
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for loop in range(20):
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global IMAGE_HEIGHT
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global IMAGE_WIDTH
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global MAX_CAPTCHA
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global CHAR_SET_LEN
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batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
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batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
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def wrap_gen_captcha_text_and_image(i):
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while True:
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text, image = gen_captcha_text_and_image(i)
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if image.shape == (60, 160, 3):
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return text, image
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else:
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print("fail")
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for i in range(batch_size):
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text, image = wrap_gen_captcha_text_and_image(i)
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image = convert2gray(image)
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batch_x[i, :] = image.flatten() / 255
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batch_y[i, :] = text2vec(text)
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re.append((batch_x, batch_y))
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return re
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def spawn_n_processes(size):
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global batchLake
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while True:
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while(len(batchLake[size])>1000):
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time.sleep(1)
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re=[]
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print("batch:" + str(size) + "池大小" + str(len(batchLake[size])))
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pool=mp.Pool(processes=12)
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for loop in range(12):
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re.append(pool.apply_async(get_next_batchDetail,[size,]))
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pool.close()
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pool.join()
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for i in range(len(re)):
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batchLake[size]=batchLake[size]+re[i].get()
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def get_next_batch(batch_size=64):
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startTime = datetime.datetime.now()
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global batchLake
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if(batch_size not in batchLake.keys()):
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batchLake[batch_size] =[]
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print(batchLake[batch_size])
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try:
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_thread.start_new_thread ( spawn_n_processes, (batch_size,) )
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except Exception:
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print(Exception)
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leng=len(batchLake[batch_size])
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while(leng<=0):
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leng = len(batchLake[batch_size])
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time.sleep(0.1)
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#print("part1耗时:" + str(datetime.datetime.now() - startTime))
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temp=batchLake[batch_size][0]
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del batchLake[batch_size][0]
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print("获取batch耗时:"+str(datetime.datetime.now() - startTime))
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print("batch池："+str(batch_size)+"余量："+str(leng-1))
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return temp[0],temp[1]
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# 定义CNN
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def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
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x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
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w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32]))
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b_c1 = tf.Variable(b_alpha * tf.random_normal([32]))
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conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
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conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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conv1 = tf.nn.dropout(conv1, keep_prob=keep_prob)
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w_c2 = tf.Variable(w_alpha * tf.random_normal([3, 3, 32, 64]))
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b_c2 = tf.Variable(b_alpha * tf.random_normal([64]))
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conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
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conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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conv2 = tf.nn.dropout(conv2, keep_prob=keep_prob)
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w_c3 = tf.Variable(w_alpha * tf.random_normal([3, 3, 64, 64]))
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b_c3 = tf.Variable(b_alpha * tf.random_normal([64]))
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conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
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conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
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conv3 = tf.nn.dropout(conv3, keep_prob=keep_prob)
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w_d = tf.Variable(w_alpha * tf.random_normal([8 * 20 * 64, 1024]))
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b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
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dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
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dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
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dense = tf.nn.dropout(dense, keep_prob=keep_prob)
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w_out = tf.Variable(w_alpha * tf.random_normal([1024, MAX_CAPTCHA * CHAR_SET_LEN]))
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b_out = tf.Variable(b_alpha * tf.random_normal([MAX_CAPTCHA * CHAR_SET_LEN]))
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out = tf.add(tf.matmul(dense, w_out), b_out)
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return out
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# 训练
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def train_crack_captcha_cnn():
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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# 具体的代码
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output = crack_captcha_cnn()
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loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y)) # 计算损失
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optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss) # 计算梯度
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predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]) # 目标预测
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max_idx_p = tf.argmax(predict, 2) # 目标预测最大值
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max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2) # 真实标签最大值
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correct_pred = tf.equal(max_idx_p, max_idx_l)
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accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) # 准确率
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saver = tf.train.Saver()
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with tf.Session(config=config) as sess:
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sess.run(tf.global_variables_initializer())
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step = 0
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while True:
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batch_x, batch_y = get_next_batch(64)
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_, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
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print(step, loss_)
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if step % 100 == 0 and step!=0 :
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batch_x_test, batch_y_test = get_next_batch(100)
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acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
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print(step, "准确率：",acc)
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if acc > 0.93:
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saver.save(sess, "D:\\DL\\models\\85", global_step=step)
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step += 1
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def crack_captcha(captcha_image, output):
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saver = tf.train.Saver()
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with tf.Session() as sess:
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sess.run(tf.initialize_all_variables())
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# 获取训练后的参数
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checkpoint = tf.train.get_checkpoint_state("D:\\DL\\models")
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if checkpoint and checkpoint.model_checkpoint_path:
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saver.restore(sess, checkpoint.model_checkpoint_path)
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print("Successfully loaded:", checkpoint.model_checkpoint_path)
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else:
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print("Could not find old network weights")
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predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
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text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
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text = vec2text(text_list)
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return text
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if __name__ == '__main__':
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#global IMAGE_HEIGHT
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#global IMAGE_WIDTH
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#global MAX_CAPTCHA
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#global CHAR_SET_LEN
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#global number
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#global alphabet
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#global ALPHABET
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#global batchLake
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train = 0 # 0: 训练 1: 预测
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path = os.path.abspath(os.path.dirname(__file__))
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type = sys.getfilesystemencoding()
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sys.stdout = Logger()
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batchLake={}
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if train == 0:
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number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
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alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
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'u',
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'v', 'w', 'x', 'y', 'z']
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ALPHABET = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
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'U',
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'V', 'W', 'X', 'Y', 'Z']
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text, image = gen_captcha_text_and_image()
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print("验证码图像channel:", image.shape)
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# 图像大小
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IMAGE_HEIGHT = 60
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IMAGE_WIDTH = 160
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MAX_CAPTCHA = len(text)
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print("验证码文本最长字符数", MAX_CAPTCHA)
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# 文本转向量
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char_set = number + alphabet + ALPHABET + ['_'] # 如果验证码长度小于4, '_'用来补齐
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CHAR_SET_LEN = len(char_set)
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# placeholder占位符
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X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
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Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
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keep_prob = tf.placeholder(tf.float32)
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train_crack_captcha_cnn()
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# 预测时需要将训练的变量初始化
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if train == 1:
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# 自然计数
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step = 0
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# 正确预测计数
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rightCnt = 0
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# 设置测试次数
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count = 10
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number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
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alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't',
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'u', 'v', 'w', 'x', 'y', 'z']
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ALPHABET = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',
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'U', 'V', 'W', 'X', 'Y', 'Z']
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IMAGE_HEIGHT = 60
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IMAGE_WIDTH = 160
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char_set = number + alphabet + ALPHABET + ['_']
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CHAR_SET_LEN = len(char_set)
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MAX_CAPTCHA = 4
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# placeholder占位符
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X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
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Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA * CHAR_SET_LEN])
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keep_prob = tf.placeholder(tf.float32)
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output = crack_captcha_cnn()
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config = tf.ConfigProto()
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config.gpu_options.allow_growth = True
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saver = tf.train.Saver()
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with tf.Session(config=config) as sess:
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sess.run(tf.global_variables_initializer())
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# 获取训练后参数路径
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checkpoint = tf.train.get_checkpoint_state("D:\\DL\\models")
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if checkpoint and checkpoint.model_checkpoint_path:
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saver.restore(sess, checkpoint.model_checkpoint_path)
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print("Successfully loaded:", checkpoint.model_checkpoint_path)
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else:
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print("Could not find old network weights.")
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while True:
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text, image = gen_captcha_text_and_image()
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f = plt.figure()
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ax = f.add_subplot(111)
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ax.text(0.1, 0.9,text, ha='center', va='center', transform=ax.transAxes)
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plt.imshow(image)
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plt.show()
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image = convert2gray(image)
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image = image.flatten() / 255
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predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
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text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
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predict_text = vec2text(text_list)
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predict_text = crack_captcha(image, output)
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print("step:{} 真实值: {} 预测: {} 预测结果: {}".format(str(step), text, predict_text,
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|
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"正确" if text.lower() == predict_text.lower() else "错误"))
|
|
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if text.lower() == predict_text.lower():
|
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rightCnt += 1
|
|
|
if step == count - 1:
|
|
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print("测试总数: {} 测试准确率: {}".format(str(count), str(rightCnt / count)))
|
|
|
break
|
|
|
step += 1
|
