上一篇实现了图片CNN多标签分类（4位定长验证码识别任务）
本文继续优化，实现不定长文本图片的识别任务
下一篇考虑玩一玩GAN网络
本文所用到的10w不定长验证码文本数据集百度网盘下载地址（也可使用下文代码自行生成）： pan.baidu.com/s/11BzIvuT4…
利用本文代码训练并生成的模型（对应项目中的my-model文件夹）： pan.baidu.com/s/1AoKtZVys…
项目简介： 需要预先安装pip install captcha==0.1.1,pip install opencv-python,pip install flask, pip install tensorflow/pip install tensorflow-gpu） 本文采用LSTM+CTC实现1-10位不定长验证码图片OCR（生成的验证码由随机的1-10位大写字母组成），本质上是一张图片多个标签的分类问题，且每个图片的标签数量不固定（数据如下图所示）
整体训练逻辑： 1，将图像传入到LSTM中获得sequence，和sequence的长度（大致的原理是：将图像的width看做LSTM中的time_step，将图像的height看做每个time_step输入tensor的size） 2，将真实的y_label转为稀疏矩阵张量（此处的sparseTensor是个重点，同学们可以把代码中的153行y_train_tmp打印出来观察一下） 3，损失函数采用tf.nn.ctc_loss，然后对以上两步获得的数据进行训练，最终使得损失函数尽可能的减小
关于ctc_loss的原理可以百度科普一下，它的主要作用可以大概理解为将上层网络预测出的AAABBBBCCDEE收敛成ABBCDE，这里面牵涉到AAA到底收敛为几个A，BBBB又收敛为几个B，这也是他的核心
整体预测逻辑： 1，将图像传入到LSTM中获得sequence，和sequence的长度 2，将sequence，sequence的长度输入到tf.nn.ctc_beam_search_decoder函数预测出稀疏矩阵张量 3，将第二步得到的稀疏矩阵张量反向转化为sequence，并最终解码成A~Z的大写字母并输出
后续优化逻辑： 1，可以在LSTM之前先采用CNN对图像特征进行一次提取 2，TF自带的ctc_loss可以换成百度开源的Warp_CTC 3，针对少量原始图片为AAA结果最终识别为AA，丢掉了一个A的情况，是否可以把原先的标签['A', 'A', 'A']扩充为['A-left', 'A-middle', 'A-right', 'A-left', 'A-middle', 'A-right', 'A-left', 'A-middle', 'A-right']将每个字由原先的1个标签扩充为三个标签，此处抛砖引玉，可以自行尝试优化
优缺点： 1，LSTM+CTC考虑了一行文本从左到右的序列关系，这一点上比CNN更强，同时可以轻松实现不定长的OCR 2，也正是由于RNN网络考虑了时序间的关系，所以运算量相对于CNN网络大幅增加，收敛比较慢，有条件的同学还是上一块好点的GPU吧，能提升很多效率
运行命令： 自行生成验证码训练寄（本文生成了10w张，修改self.im_total_num变量）：
python LstmCtcOcr.py create_dataset
对数据集进行训练： python LstmCtcOcr.py train
对新的图片进行测试： python LstmCtcOcr.py test
启动成http服务： python LstmCtcOcr.py start
利用flask框架将整个项目启动成web服务，使得项目支持http方式调用 启动服务后调用以下地址测试
http://127.0.0.1:5050/captchaOcr?img_path=./dataset/test/0_PIY.png
http://127.0.0.1:5050/captchaOcr?img_path=./dataset/test/1_BCAVDPXT.png
http://127.0.0.1:5050/captchaOcr?img_path=./dataset/test/2_N.png
项目目录结构：
训练200个epoch之后，可以看到model在val上的acc已经能达到84%了，后续大家可以自行修改学习率和增大epoch次数来提升精度（True表示预测正确，左边为预测值，右边为真实标签）：
整体代码如下（LstmCtcOcr.py文件）：
# coding:utf-8
from captcha.image import ImageCaptcha
import numpy as np
import cv2
import tensorflow as tf
import random, os, sys
import operator
from flask import request
from flask import Flask
import json
app = Flask(__name__)
class LstmCtcOcr:
def __init__(self):
self.epoch_max = 200 # 最大迭代epoch次数
self.batch_size = 16 # 训练时每个批次参与训练的图像数目，显存不足的可以调小
self.lr = 5e-5 # 初始学习率
self.save_epoch = 5 # 每相隔多少个epoch保存一次模型
self.n_hidden = 256 # 隐藏神经元个数
self.im_width = 256
self.im_height = 64
self.im_total_num = 100000 # 总共生成的验证码图片数量
self.train_max_num = self.im_total_num # 训练时读取的最大图片数目
self.val_num = 30 * self.batch_size # 不能大于self.train_max_num 做验证集用
self.words_max_num = 10 # 每张验证码图片上的最大字母个数
self.words = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
self.n_classes = len(self.words) + 1 # 26个字母 + blank
self.x = None
self.y = None
def captchaOcr(self, img_path):
"""
验证码识别
:param img_path:
:return:
"""
im = cv2.imread(img_path)
im = cv2.resize(im, (self.im_width, self.im_height))
im = np.array([im[:, :, 0]], dtype=np.float32)
im -= 147
pred = self.sess.run(self.pred, feed_dict={self.x: im})
sequence = self.sparseTensor2sequence(pred)
return ''.join(sequence[0])
def test(self, img_path):
"""
测试接口
:param img_path:
:return:
"""
self.batch_size = 1
self.learning_rate = tf.placeholder(dtype=tf.float32) # 动态学习率
self.weight = tf.Variable(tf.truncated_normal([self.n_hidden, self.n_classes], stddev=0.1))
self.bias = tf.Variable(tf.constant(0., shape=[self.n_classes]))
self.x = tf.placeholder(tf.float32, [None, self.im_height, self.im_width])
logits, seq_len = self.rnnNet(self.x, self.weight, self.bias)
decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits, seq_len, merge_repeated=False)
self.pred = tf.cast(decoded[0], tf.int32)
saver = tf.train.Saver()
# tfconfig = tf.ConfigProto(allow_soft_placement=True)
# tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.3 # 占用显存的比例
# self.ses = tf.Session(config=tfconfig)
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer()) # 全局tf变量初始化
# 加载w,b参数
saver.restore(self.sess, './my-model/LstmCtcOcr-200')
im = cv2.imread(img_path)
im = cv2.resize(im, (self.im_width, self.im_height))
im = np.array([im[:, :, 0]], dtype=np.float32)
im -= 147
pred = self.sess.run(self.pred, feed_dict={self.x: im})
sequence = self.sparseTensor2sequence(pred)
print(''.join(sequence[0]))
def train(self):
"""
训练
:return:
"""
x_train_list, y_train_list, x_val_list, y_val_list = self.getTrainDataset()
print('开始转换tensor队列')
x_train_list_tensor = tf.convert_to_tensor(x_train_list, dtype=tf.string)
y_train_list_tensor = tf.convert_to_tensor(y_train_list, dtype=tf.int32)
x_val_list_tensor = tf.convert_to_tensor(x_val_list, dtype=tf.string)
y_val_list_tensor = tf.convert_to_tensor(y_val_list, dtype=tf.int32)
x_train_queue = tf.train.slice_input_producer(tensor_list=[x_train_list_tensor], shuffle=False)
y_train_queue = tf.train.slice_input_producer(tensor_list=[y_train_list_tensor], shuffle=False)
x_val_queue = tf.train.slice_input_producer(tensor_list=[x_val_list_tensor], shuffle=False)
y_val_queue = tf.train.slice_input_producer(tensor_list=[y_val_list_tensor], shuffle=False)
train_im, train_label = self.dataset_opt(x_train_queue, y_train_queue)
train_batch = tf.train.batch(tensors=[train_im, train_label], batch_size=self.batch_size, num_threads=2)
val_im, val_label = self.dataset_opt(x_val_queue, y_val_queue)
val_batch = tf.train.batch(tensors=[val_im, val_label], batch_size=self.batch_size, num_threads=2)
print('准备训练')
self.learning_rate = tf.placeholder(dtype=tf.float32) # 动态学习率
self.weight = tf.Variable(tf.truncated_normal([self.n_hidden, self.n_classes], stddev=0.1))
self.bias = tf.Variable(tf.constant(0., shape=[self.n_classes]))
# self.global_step = tf.Variable(0, trainable=False) # 全局步骤计数
# im_width看成LSTM的time_step ，im_height看成是每个time_step输入tensor的size
self.x = tf.placeholder(tf.float32, [None, self.im_height, self.im_width])
# 定义ctc_loss需要的稀疏矩阵
self.y = tf.sparse_placeholder(tf.int32)
logits, seq_len = self.rnnNet(self.x, self.weight, self.bias)
# loss
self.loss = tf.nn.ctc_loss(self.y, logits, seq_len)
# cost
self.cost = tf.reduce_mean(self.loss)
# optimizer
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.cost)
# 前面说的划分块之后找每块的类属概率分布，ctc_beam_search_decoder方法,是每次找最大的K个概率分布
# 还有一种贪心策略是只找概率最大那个，也就是K=1的情况ctc_ greedy_decoder
decoded, log_prob = tf.nn.ctc_beam_search_decoder(logits, seq_len, merge_repeated=False)
self.pred = tf.cast(decoded[0], tf.int32)
self.distance = tf.reduce_mean(tf.edit_distance(self.pred, self.y))
print('开始训练')
saver = tf.train.Saver() # 保存tf模型
with tf.Session() as self.sess:
self.sess.run(tf.global_variables_initializer())
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=self.sess, coord=coordinator)
batch_max = len(x_train_list) // self.batch_size
print('batch:', batch_max)
total_step = 0
for epoch_num in range(self.epoch_max):
lr_tmp = self.lr * (1 - (epoch_num / self.epoch_max) ** 2) # 动态学习率
print('lr:', lr_tmp)
for batch_num in range(batch_max):
# print(epoch_num, batch_num)
x_train_tmp, y_train_tmp = self.sess.run(train_batch)
y_train_tmp = self.sequence2sparseTensor(y_train_tmp) # 将labels转为稀疏矩阵张量
self.sess.run(self.optimizer, feed_dict={self.x: x_train_tmp, self.y: y_train_tmp, self.learning_rate: lr_tmp})
if total_step % 100 == 0 or total_step == 0:
print('epoch:%d/%d batch:%d/%d total_step:%d lr:%.10f' % (epoch_num, self.epoch_max, batch_num, batch_max, total_step, lr_tmp))
# train部分
train_loss, train_distance = self.sess.run([self.cost, self.distance], feed_dict={self.x: x_train_tmp, self.y: y_train_tmp})
# val部分
val_loss_list, val_distance_list, val_acc_list = [], [], []
for i in range(int(self.val_num / self.batch_size)):
x_val_tmp, y_val_tmp_true = self.sess.run(val_batch)
y_val_tmp = self.sequence2sparseTensor(y_val_tmp_true) # 将labels转为稀疏矩阵张量
val_loss, val_distance, val_pred = self.sess.run([self.cost, self.distance, self.pred], feed_dict={self.x: x_val_tmp, self.y: y_val_tmp})
val_loss_list.append(val_loss)
val_distance_list.append(val_distance)
val_sequence = self.sparseTensor2sequence(val_pred)
ok = 0.
for idx, val_seq in enumerate(val_sequence):
val_pred_tmp = [self.words.find(x) if self.words.find(x) > -1 else 26 for x in val_seq]
val_y_true_tmp = [x for x in y_val_tmp_true[idx] if x != 26]
is_eq = operator.eq(val_pred_tmp, val_y_true_tmp)
if idx == 0:
print(is_eq, [self.words[n] for n in val_pred_tmp], '<<==>>', [self.words[n] for n in val_y_true_tmp])
if is_eq:
ok += 1
val_acc_list.append(ok / len(val_sequence))
val_acc_list = np.array(val_acc_list, dtype=np.float32)
print('train_loss:%.10f train_distance:%.10f' % (train_loss, train_distance))
print(' val_loss:%.10f val_distance:%.10f val_acc:%.10f' % (np.mean(val_loss_list), np.mean(val_distance_list), np.mean(val_acc_list)))
print()
print()
total_step += 1
# 保存模型
if (epoch_num + 1) % self.save_epoch == 0:
saver.save(self.sess, './my-model/LstmCtcOcr', global_step=(epoch_num + 1))
coordinator.request_stop()
coordinator.join(threads)
def rnnNet(self, inputs, weight, bias):
"""
获取LSTM网络结构
:param inputs:
:param weight:
:param bias:
:return:
"""
# 对于tf.nn.dynamic_rnn，默认time_major=false，此时inputs的shape=[batch_size, max_time_steps, features]
# (batch_size, im_height, im_width) ==> (batch_size, im_width, im_height)
inputs = tf.transpose(inputs, [0, 2, 1])
# 变长序列的最大值
# seq_len = np.ones(self.batch_size) * self.im_width
seq_len = np.ones(self.batch_size) * self.im_width
cell = tf.nn.rnn_cell.LSTMCell(self.n_hidden, forget_bias=0.8, state_is_tuple=True)
# 动态rnn实现输入变长
outputs1, _ = tf.nn.dynamic_rnn(cell, inputs, seq_len, dtype=tf.float32)
# (self.batch_size * self.im_width, self.hidden)
outputs = tf.reshape(outputs1, [-1, self.n_hidden])
logits = tf.matmul(outputs, weight) + bias # w * x + b
logits = tf.reshape(logits, [self.batch_size, -1, self.n_classes])
logits = tf.transpose(logits, (1, 0, 2)) # (im_width, batch_size, im_height)
return logits, seq_len
def sequence2sparseTensor(self, sequences, dtype=np.int32):
"""
序列 转化为 稀疏矩阵
:param sequences:
:param dtype:
:return:
"""
values, indices= [], []
for n, seq in enumerate(sequences):
indices.extend(zip([n] * len(seq), range(len(seq))))
values.extend(seq)
indices = np.asarray(indices, dtype=np.int64)
values = np.asarray(values, dtype=dtype)
shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)
return indices, values, shape
def sparseTensor2sequence(self, sparse_tensor):
"""
稀疏矩阵 转化为 序列
:param sparse_tensor:
:return:
"""
decoded_indexes = list()
current_i = 0
current_seq = []
for offset, i_and_index in enumerate(sparse_tensor[0]):
i = i_and_index[0]
if i != current_i:
decoded_indexes.append(current_seq)
current_i = i
current_seq = list()
current_seq.append(offset)
decoded_indexes.append(current_seq)
result = []
for index in decoded_indexes:
result.append(self.sequence2words(index, sparse_tensor))
return result
def sequence2words(self, indexes, spars_tensor):
"""
序列 转化为 文本
:param indexes:
:param spars_tensor:
:return:
"""
decoded = []
for m in indexes:
str_tmp = self.words[spars_tensor[1][m]]
decoded.append(str_tmp)
return decoded
def dataset_opt(self, x_train_queue, y_train_queue):
"""
处理图片和标签
:param queue:
:return:
"""
queue = x_train_queue[0]
contents = tf.read_file('./dataset/train/' + queue)
im = tf.image.decode_jpeg(contents)
tf.image.rgb_to_grayscale(im)
im = tf.image.resize_images(images=im, size=[self.im_height, self.im_width])
im = tf.reshape(im[:, :, 0], tf.stack([self.im_height, self.im_width]))
im -= 147 # 去均值化
return im, y_train_queue[0]
def getTrainDataset(self):
train_data_list = os.listdir('./dataset/train/')
print('共有%d张训练图片， 读取%d张：' % (len(train_data_list), self.train_max_num))
random.shuffle(train_data_list) # 打乱顺序
y_val_list, y_train_list = [], []
x_val_list = train_data_list[:self.val_num]
for x_val in x_val_list:
words_tmp = x_val.split('.')[0].split('_')[1]
words_tmp = words_tmp + '?' * (self.words_max_num - len(words_tmp))
y_val_list.append([self.words.find(x) if self.words.find(x) > -1 else 26 for x in words_tmp])
x_train_list = train_data_list[self.val_num:self.train_max_num]
for x_train in x_train_list:
words_tmp = x_train.split('.')[0].split('_')[1]
words_tmp = words_tmp + '?' * (self.words_max_num - len(words_tmp))
y_train_list.append([self.words.find(x) if self.words.find(x) > -1 else 26 for x in words_tmp])
return x_train_list, y_train_list, x_val_list, y_val_list
def createCaptchaDataset(self):
"""
生成训练用图片数据集
:return:
"""
image = ImageCaptcha(width=self.im_width, height=self.im_height, font_sizes=(56,))
for i in range(self.im_total_num):
words_tmp = ''
for j in range(random.randint(1, self.words_max_num)):
words_tmp = words_tmp + random.choice(self.words)
print(words_tmp, type(words_tmp))
im_path = './dataset/train/%d_%s.png' % (i, words_tmp)
print(im_path)
image.write(words_tmp, im_path)
if __name__ == '__main__':
opt_type = sys.argv[1:][0]
instance = LstmCtcOcr()
if opt_type == 'create_dataset':
instance.createCaptchaDataset()
elif opt_type == 'train':
instance.train()
elif opt_type == 'test':
instance.test('./dataset/test/0_PIY.png')
elif opt_type == 'start':
# 将session持久化到内存中
instance.test('./dataset/test/0_PIY.png')
# 启动web服务
# http://127.0.0.1:5050/captchaOcr?img_path=./dataset/test/1_BCAVDPXT.png
@app.route('/captchaOcr', methods=['GET'])
def captchaOcr():
img_path = request.args.to_dict().get('img_path')
print(img_path)
ret = instance.captchaOcr(img_path)
print(ret)
return json.dumps({'img_path': img_path, 'ocr_ret': ret})
app.run(host='0.0.0.0', port=5050, debug=False)
