import tensorflow as tf from captcha.image import ImageCaptcha import numpy as np import matplotlib.pyplot as plt from PIL import Image import random 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'] def random_captcha_text(char_set=number,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') 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 get_next_batch(batch_size=128): 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: text, image = gen_captcha_text_image() 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 batch_y[i, :] = text2vec(text) return batch_x, batch_y def cnn_structure(w_alpha=0.01, 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(): output=cnn_structure() 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(100) _, cost_= sess.run([optimizer, cost], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75}) print(step, cost_) if step % 10 == 0: batch_x_test, batch_y_test = get_next_batch(100) acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.}) print(step, acc) if acc > 0.99: saver.save(sess, "./model/crack_capcha.model", global_step=step) break step += 1 def crack_captcha(captcha_image): output = cnn_structure() saver = tf.train.Saver() with tf.Session() as sess: saver.restore(sess, "./model/crack_capcha.model-1200") 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() return text if __name__=='__main__': train=1 if train==0: text,image=gen_captcha_text_image() print("验证码大小：",image.shape)#(60,160,3) image_height=60 image_width=160 max_captcha=len(text) print("验证码文本最长字符数",max_captcha) char_set=number char_set_len=len(char_set) 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() if train == 1: image_height = 60 image_width = 160 char_set = number char_set_len = len(char_set) text, image = gen_captcha_text_image() f = plt.figure() ax = f.add_subplot(111) ax.text(0.1, 0.9, text, ha='center', va='center', transform=ax.transAxes) plt.imshow(image) # plt.show() max_captcha = len(text) 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) print("正确: {} 预测: {}".format(text, predict_text)) plt.show()