tf21: 身份证识别——识别身份证号

上一篇： 身份证识别——生成身份证号和汉字

代码直接参考，验证码识别（tf20: CNN—识别字符验证码）

#!/usr/bin/env python2# -*- coding: utf-8 -*-"""tf 训练识别身份证数字(18个字符)图片@author: 刘鹏"""from genIDCard  import *import numpy as npimport tensorflow as tf#obj = gen_id_card()#image,text,vec = obj.gen_image()#图像大小IMAGE_HEIGHT = 32IMAGE_WIDTH = 256MAX_CAPTCHA = obj.max_sizeCHAR_SET_LEN = obj.len# 生成一个训练batchdef get_next_batch(batch_size=128):    obj = gen_id_card()    batch_x = np.zeros([batch_size, IMAGE_HEIGHT*IMAGE_WIDTH])    batch_y = np.zeros([batch_size, MAX_CAPTCHA*CHAR_SET_LEN])      for i in range(batch_size):image, text, vec = obj.gen_image()batch_x[i,:] = image.reshape((IMAGE_HEIGHT*IMAGE_WIDTH))batch_y[i,:] = vec    return batch_x, batch_y ####################################################################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# 是否在训练阶段train_phase = tf.placeholder(tf.bool)#Batch Normalization? 有空再理解,tflearn or slim都有封装## http://stackoverflow.com/a/34634291/2267819def batch_norm(x, beta, gamma, phase_train, scope='bn', decay=0.9, eps=1e-5):with tf.variable_scope(scope):#beta = tf.get_variable(name='beta', shape=[n_out], initializer=tf.constant_initializer(0.0), trainable=True)#gamma = tf.get_variable(name='gamma', shape=[n_out], initializer=tf.random_normal_initializer(1.0, stddev), trainable=True)batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')ema = tf.train.ExponentialMovingAverage(decay=decay)def mean_var_with_update():ema_apply_op = ema.apply([batch_mean, batch_var])with tf.control_dependencies([ema_apply_op]):return tf.identity(batch_mean), tf.identity(batch_var)mean, var = tf.cond(phase_train, mean_var_with_update, lambda: (ema.average(batch_mean), ema.average(batch_var)))normed = tf.nn.batch_normalization(x, mean, var, beta, gamma, eps)return normed  # 定义CNNdef crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1]) # 4 conv layerw_c1 = tf.Variable(w_alpha*tf.random_normal([5, 5, 1, 32]))b_c1 = tf.Variable(b_alpha*tf.random_normal([32]))conv1 = tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1)conv1 = batch_norm(conv1, tf.constant(0.0, shape=[32]), tf.random_normal(shape=[32], mean=1.0, stddev=0.02), train_phase, scope='bn_1')conv1 = tf.nn.relu(conv1)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([5, 5, 32, 64]))b_c2 = tf.Variable(b_alpha*tf.random_normal([64]))conv2 = tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2)conv2 = batch_norm(conv2, tf.constant(0.0, shape=[64]), tf.random_normal(shape=[64], mean=1.0, stddev=0.02), train_phase, scope='bn_2')conv2 = tf.nn.relu(conv2)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.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3)conv3 = batch_norm(conv3, tf.constant(0.0, shape=[64]), tf.random_normal(shape=[64], mean=1.0, stddev=0.02), train_phase, scope='bn_3')conv3 = tf.nn.relu(conv3)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)w_c4 = tf.Variable(w_alpha*tf.random_normal([3, 3, 64, 64]))b_c4 = tf.Variable(b_alpha*tf.random_normal([64]))conv4 = tf.nn.bias_add(tf.nn.conv2d(conv3, w_c4, strides=[1, 1, 1, 1], padding='SAME'), b_c4)conv4 = batch_norm(conv4, tf.constant(0.0, shape=[64]), tf.random_normal(shape=[64], mean=1.0, stddev=0.02), train_phase, scope='bn_4')conv4 = tf.nn.relu(conv4)conv4 = tf.nn.max_pool(conv4, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')conv4 = tf.nn.dropout(conv4, keep_prob)     # Fully connected layerw_d = tf.Variable(w_alpha*tf.random_normal([2*16*64, 1024]))b_d = tf.Variable(b_alpha*tf.random_normal([1024]))dense = tf.reshape(conv4, [-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 # 训练def train_crack_captcha_cnn():output = crack_captcha_cnn()# loss#loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=Y))loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))      # 最后一层用来分类的softmax和sigmoid有什么不同？# optimizer 为了加快训练 learning_rate应该开始大，然后慢慢衰optimizer = tf.train.AdamOptimizer(learning_rate=0.002).minimize(loss) 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:sess.run(tf.global_variables_initializer()) step = 0while True:batch_x, batch_y = get_next_batch(64)_, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75, train_phase:True})print(step, loss_)# 每100 step计算一次准确率if step % 100 == 0 and step != 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., train_phase:False})print  "第%s步，训练准确率为：%s" % (step, acc)# 如果准确率大80%,保存模型,完成训练if acc > 0.98:saver.save(sess, "crack_capcha.model", global_step=step)breakstep += 1train_crack_captcha_cnn()