上一篇實現了圖片CNN單標籤分類（貓狗圖片分類任務）
預告：下一篇用LSTM+CTC實現不定長文字的OCR，本質上是一種不固定標籤個數的多標籤分類問題
本文所用到的10w驗證碼資料集百度網盤下載地址（也可使用下文程式碼自行生成）：
利用本文程式碼訓練並生成的模型（對應專案中的model資料夾）：
專案簡介：
（需要預先安裝pip install captcha==0.1.1,pip install opencv-python,pip install flask, pip install tensorflow/pip install tensorflow-gpu） 本文采用CNN實現4位定長驗證碼圖片OCR（生成的驗證碼固定由隨機的4位大寫字母組成），本質上是一張圖片多個標籤的分類問題（資料如下圖所示）
整體訓練邏輯：
1，將影象傳入到CNN中提取特徵
2，將特徵圖拉伸輸入到FC layer中得出分類預測向量
3，通過sigmoid交叉熵函式對預測向量和標籤向量進行訓練，得出最終模型（注意：多標籤分類任務採用sigmoid，單標籤分類採用softmax）
整體預測邏輯：
1，將影象傳入到CNN（VGG16）中提取特徵
2，將特徵圖拉伸輸入到FC layer中得出分類預測向量
3，將預測向量做sigmoid操作，由於驗證碼固定是4位，所以將向量切分成4條，從每條中找到最大值，並對映到對應的字母上
製作成web服務：
利用flask框架將整個專案啟動成web服務，使得專案支援http方式呼叫 啟動服務後呼叫以下地址測試
http://127.0.0.1:5050/captchaOcr?img_path=./dataset/test/0_HZDZ.png
http://127.0.0.1:5050/captchaOcr?img_path=./dataset/test/1_CKAN.png
後續優化邏輯：
提取特徵部分的CNN可以用RNN取代
本方案只能OCR固定長度文字，後續採用LSTM+CTC的方式來OCR非定長文字
執行命令：
自行生成驗證碼訓練寄（本文生成了10w張，修改self.im_total_num變數）： python CnnOcr.py create_dataset
對資料集進行訓練：python CnnOcr.py train
對新的圖片進行測試：python CnnOcr.py test
啟動成http服務：python CnnOcr.py start
專案目錄結構：
訓練過程：
整體程式碼如下：
# coding:utf-8
from captcha.image import ImageCaptcha
import numpy as np
import cv2
import tensorflow as tf
import random, os, sys
from flask import request
from flask import Flask
import json
app = Flask(__name__)
class CnnOcr:
def __init__(self):
self.epoch_max = 6 # 最大迭代epoch次數
self.batch_size = 64 # 訓練時每個批次參與訓練的影象數目，視訊記憶體不足的可以調小
self.lr = 1e-3 # 初始學習率
self.save_epoch = 1 # 每相隔多少個epoch儲存一次模型
self.im_width = 128
self.im_height = 64
self.im_total_num = 100000 # 總共生成的驗證碼圖片數量
self.train_max_num = self.im_total_num # 訓練時讀取的最大圖片數目
self.val_num = 50 * self.batch_size # 不能大於self.train_max_num 做驗證集用
self.words_num = 4 # 每張驗證碼圖片上的數字個數
self.words = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
self.label_num = self.words_num * len(self.words)
self.keep_drop = tf.placeholder(tf.float32)
self.x = None
self.y = None
def captchaOcr(self, img_path):
"""
驗證碼識別
:param img_path:
:return:
"""
im = cv2.imread(img_path)
im = cv2.resize(im, (self.im_width, self.im_height))
im = [im]
im = np.array(im, dtype=np.float32)
im -= 147
output = self.sess.run(self.max_idx_p, feed_dict={self.x: im, self.keep_drop: 1.})
ret = ''
for i in output.tolist()[0]:
ret = ret + self.words[int(i)]
return ret
def test(self, img_path):
"""
測試介面
:param img_path:
:return:
"""
self.x = tf.placeholder(tf.float32, [None, self.im_height, self.im_width, 3]) # 輸入資料
self.pred = self.cnnNet()
self.output = tf.nn.sigmoid(self.pred)
self.predict = tf.reshape(self.pred, [-1, self.words_num, len(self.words)])
self.max_idx_p = tf.argmax(self.predict, 2)
saver = tf.train.Saver()
# tfconfig = tf.ConfigProto(allow_soft_placement=True)
# tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.3 # 佔用視訊記憶體的比例
# self.ses = tf.Session(config=tfconfig)
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer()) # 全域性tf變數初始化
# 載入w,b引數
saver.restore(self.sess, './model/CnnOcr-6')
im = cv2.imread(img_path)
im = cv2.resize(im, (self.im_width, self.im_height))
im = [im]
im = np.array(im, dtype=np.float32)
im -= 147
output = self.sess.run(self.max_idx_p, feed_dict={self.x: im, self.keep_drop: 1.})
ret = ''
for i in output.tolist()[0]:
ret = ret + self.words[int(i)]
print(ret)
def train(self):
x_train_list, y_train_list, x_val_list, y_val_list = self.getTrainDataset()
print('開始轉換tensor佇列')
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.float32)
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.float32)
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('開啟訓練')
self.learning_rate = tf.placeholder(dtype=tf.float32) # 動態學習率
self.x = tf.placeholder(tf.float32, [None, self.im_height, self.im_width, 3]) # 訓練資料
self.y = tf.placeholder(tf.float32, [None, self.label_num]) # 標籤
self.pred = self.cnnNet()
self.loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=self.pred, labels=self.y))
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(self.loss)
self.predict = tf.reshape(self.pred, [-1, self.words_num, len(self.words)])
self.max_idx_p = tf.argmax(self.predict, 2)
self.y_predict = tf.reshape(self.y, [-1, self.words_num, len(self.words)])
self.max_idx_l = tf.argmax(self.y_predict, 2)
self.correct_pred = tf.equal(self.max_idx_p, self.max_idx_l)
self.accuracy = tf.reduce_mean(tf.cast(self.correct_pred, tf.float32))
with tf.Session() as self.sess:
# 全域性tf變數初始化
self.sess.run(tf.global_variables_initializer())
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=self.sess, coord=coordinator)
# 模型儲存
saver = tf.train.Saver()
batch_max = len(x_train_list) // self.batch_size
total_step = 1
for epoch_num in range(self.epoch_max):
lr = self.lr * (1 - (epoch_num/self.epoch_max) ** 2) # 動態學習率
for batch_num in range(batch_max):
x_train_tmp, y_train_tmp = self.sess.run(train_batch)
# print(x_train_tmp.shape, y_train_tmp.shape)
# sys.exit()
self.sess.run(self.optimizer, feed_dict={self.x: x_train_tmp, self.y: y_train_tmp, self.learning_rate: lr, self.keep_drop: .5})
# 輸出評價標準
if total_step % 50 == 0 or total_step == 1:
print()
print('epoch:%d/%d batch:%d/%d step:%d lr:%.10f' % ((epoch_num + 1), self.epoch_max, (batch_num + 1), batch_max, total_step, lr))
# 輸出訓練集評價
train_loss, train_acc = self.sess.run([self.loss, self.accuracy], feed_dict={self.x: x_train_tmp, self.y: y_train_tmp, self.keep_drop: 1.})
print('train_loss:%.10f train_acc:%.10f' % (np.mean(train_loss), train_acc))
# 輸出驗證集評價
val_loss_list, val_acc_list = [], []
for i in range(int(self.val_num/self.batch_size)):
x_val_tmp, y_val_tmp = self.sess.run(val_batch)
val_loss, val_acc = self.sess.run([self.loss, self.accuracy], feed_dict={self.x: x_val_tmp, self.y: y_val_tmp, self.keep_drop: 1.})
val_loss_list.append(np.mean(val_loss))
val_acc_list.append(np.mean(val_acc))
print(' val_loss:%.10f val_acc:%.10f' % (np.mean(val_loss), np.mean(val_acc)))
total_step += 1
# 儲存模型
if (epoch_num + 1) % self.save_epoch == 0:
print('正在儲存模型：')
saver.save(self.sess, './model/CnnOcr', global_step=(epoch_num + 1))
coordinator.request_stop()
coordinator.join(threads)
def cnnNet(self):
"""
cnn網路
:return:
"""
weight = {
# 輸入 128*64*3
# 第一層
'wc1_1': tf.get_variable('wc1_1', [5, 5, 3, 32]), # 卷積 輸出：128*64*32
'wc1_2': tf.get_variable('wc1_2', [5, 5, 32, 32]), # 卷積 輸出：128*64*32
# 池化 輸出：64*32*32
# 第二層
'wc2_1': tf.get_variable('wc2_1', [5, 5, 32, 64]), # 卷積 輸出：64*32*64
'wc2_2': tf.get_variable('wc2_2', [5, 5, 64, 64]), # 卷積 輸出：64*32*64
# 池化 輸出：32*16*64
# 第三層
'wc3_1': tf.get_variable('wc3_1', [3, 3, 64, 64]), # 卷積 輸出：32*16*256
'wc3_2': tf.get_variable('wc3_2', [3, 3, 64, 64]), # 卷積 輸出：32*16*256
# 池化 輸出：16*8*256
# 第四層
'wc4_1': tf.get_variable('wc4_1', [3, 3, 64, 64]), # 卷積 輸出：16*8*64
'wc4_2': tf.get_variable('wc4_2', [3, 3, 64, 64]), # 卷積 輸出：16*8*64
# 池化 輸出：8*4*64
# 全連結第一層
'wfc_1': tf.get_variable('wfc_1', [8*4*64, 2048]),
# 全連結第二層
'wfc_2': tf.get_variable('wfc_2', [2048, 2048]),
# 全連結第三層
'wfc_3': tf.get_variable('wfc_3', [2048, self.label_num]),
}
biase = {
# 第一層
'bc1_1': tf.get_variable('bc1_1', [32]),
'bc1_2': tf.get_variable('bc1_2', [32]),
# 第二層
'bc2_1': tf.get_variable('bc2_1', [64]),
'bc2_2': tf.get_variable('bc2_2', [64]),
# 第三層
'bc3_1': tf.get_variable('bc3_1', [64]),
'bc3_2': tf.get_variable('bc3_2', [64]),
# 第四層
'bc4_1': tf.get_variable('bc4_1', [64]),
'bc4_2': tf.get_variable('bc4_2', [64]),
# 全連結第一層
'bfc_1': tf.get_variable('bfc_1', [2048]),
# 全連結第二層
'bfc_2': tf.get_variable('bfc_2', [2048]),
# 全連結第三層
'bfc_3': tf.get_variable('bfc_3', [self.label_num]),
}
# 第一層
net = tf.nn.conv2d(self.x, weight['wc1_1'], [1, 1, 1, 1], 'SAME') # 卷積
net = tf.nn.bias_add(net, biase['bc1_1'])
net = tf.nn.relu(net) # 加b 然後 啟用
print('conv1', net)
net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID') # 池化
print('pool1', net)
# 第二層
net = tf.nn.conv2d(net, weight['wc2_1'], [1, 1, 1, 1], padding='SAME') # 卷積
net = tf.nn.bias_add(net, biase['bc2_1'])
net = tf.nn.relu(net) # 加b 然後 啟用
print('conv2', net)
net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID') # 池化
print('pool2', net)
# 第三層
net = tf.nn.conv2d(net, weight['wc3_1'], [1, 1, 1, 1], padding='SAME') # 卷積
net = tf.nn.bias_add(net, biase['bc3_1'])
net = tf.nn.relu(net) # 加b 然後 啟用
print('conv3', net)
net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID') # 池化
print('pool3', net)
# 第四層
net = tf.nn.conv2d(net, weight['wc4_1'], [1, 1, 1, 1], padding='SAME') # 卷積
net = tf.nn.bias_add(net, biase['bc4_1'])
net = tf.nn.relu(net) # 加b 然後 啟用
print('conv4', net)
net = tf.nn.max_pool(net, [1, 2, 2, 1], [1, 2, 2, 1], padding='VALID') # 池化
print('pool4', net)
# 拉伸flatten，把多個圖片同時分別拉伸成一條向量
net = tf.reshape(net, shape=[-1, weight['wfc_1'].get_shape()[0]])
print('拉伸flatten', net)
# 全連結層
# fc第一層
net = tf.matmul(net, weight['wfc_1']) + biase['bfc_1']
net = tf.nn.dropout(net, self.keep_drop)
net = tf.nn.relu(net)
print('fc第一層', net)
# fc第二層
net = tf.matmul(net, weight['wfc_2']) + biase['bfc_2']
net = tf.nn.dropout(net, self.keep_drop)
net = tf.nn.relu(net)
print('fc第二層', net)
# fc第三層
net = tf.matmul(net, weight['wfc_3']) + biase['bfc_3']
print('fc第三層', net)
return net
def getTrainDataset(self):
"""
整理資料集，把影象resize為128*64*3，訓練集做成self.im_total_num*128*64*3，把label做成0,1向量形式
:return:
"""
train_data_list = os.listdir('./dataset/train/')
print('共有%d張訓練圖片， 讀取%d張：' % (len(train_data_list), self.train_max_num))
random.shuffle(train_data_list) # 打亂順序
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]
y_val_list.append([1 if _w == w else 0 for w in words_tmp for _w in self.words])
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]
y_train_list.append([1 if _w == w else 0 for w in words_tmp for _w in self.words])
return x_train_list, y_train_list, x_val_list, y_val_list
def createCaptchaDataset(self):
"""
生成訓練用圖片資料集
: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(self.words_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)
return True
def dataset_opt(self, x_train_queue, y_train_queue):
"""
處理圖片和標籤
:param queue:
:return:
"""
queue = x_train_queue[0]
contents = tf.read_file('./dataset/train/' + queue)
im = tf.image.decode_jpeg(contents)
im = tf.image.resize_images(images=im, size=[self.im_height, self.im_width])
im = tf.reshape(im, tf.stack([self.im_height, self.im_width, 3]))
im -= 147 # 去均值化
# im /= 255 # 將畫素處理在0~1之間，加速收斂
# im -= 0.5 # 將畫素處理在-0.5~0.5之間
return im, y_train_queue[0]
if __name__ == '__main__':
opt_type = sys.argv[1:][0]
instance = CnnOcr()
if opt_type == 'create_dataset':
instance.createCaptchaDataset()
elif opt_type == 'train':
instance.train()
elif opt_type == 'test':
instance.test('./dataset/test/0_HZDZ.png')
elif opt_type == 'start':
# 將session持久化到記憶體中
instance.test('./dataset/test/0_HZDZ.png')
# 啟動web服務
# http://127.0.0.1:5050/captchaOcr?img_path=./dataset/test/2_SYVD.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)
複製程式碼