tf21: 身份证识别——识别身份证号

#!/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 np
import tensorflow as tf

#obj = gen_id_card()
#image,text,vec = obj.gen_image()

#图像大小
IMAGE_HEIGHT = 32
IMAGE_WIDTH = 256
MAX_CAPTCHA = obj.max_size
CHAR_SET_LEN = obj.len

# 生成一个训练batch
def 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/34634291/2267819
def 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CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
	x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])
 
	# 4 conv layer
	w_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 layer
	w_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 = 0
		while 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)
					break
			step += 1

train_crack_captcha_cnn()