tensorflow（三）：cnn验证码识别，开启tensorflow

源代码链接：http://blog.topspeedsnail.com/archives/10858

验证码识别：维度推导

5000张验证码和csv的地址：http://pan.baidu.com/s/1eSH4ACa

#! /usr/bin/env python# -*- coding=utf-8 -*-import random from PIL import Imageimport numpy as npimport tensorflow as tfimport loggingFORMAT = '[%(asctime)s, %(levelname)-7s]: %(message)s'#日志打印到文件中logging.basicConfig(format=FORMAT,filename="train_128.log",filemode='w')logger = logging.getLogger('Train_128')logger.setLevel(logging.INFO)# 图像大小#MAX_CAPTCHA：验证码的位数IMAGE_HEIGHT = 64IMAGE_WIDTH = 128MAX_CAPTCHA = 4print("验证码文本最长字符数", MAX_CAPTCHA)   # 验证码最长4字符; 我全部固定为4,可以不固定. 如果验证码长度小于4，用'_'补齐 # 把彩色图像转为灰度图像（色彩对识别验证码没有什么用）def convert2gray(img):if len(img.shape) > 2:gray = np.mean(img, -1)# 上面的转法较快，正规转法如下# r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]# gray = 0.2989 * r + 0.5870 * g + 0.1140 * breturn grayelse:return img """cnn在图像大小是2的倍数时性能最高, 如果你用的图像大小不是2的倍数，可以在图像边缘补无用像素。np.pad(image,((2,3),(2,2)), 'constant', constant_values=(255,))  # 在图像上补2行，下补3行，左补2行，右补2行"""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']# 文本转向量char_set = alphabet  # 如果验证码长度小于4, '_'用来补齐，MAX_CAPTCHA=26CHAR_SET_LEN = len(char_set)#每一个返回的vector都是一个shape为4*26维度的向量；def text2vec(text):text = text.strip()text_len = len(text) #print text#print text_len#print MAX_CAPTCHAif 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):k = ord(c)-97return kfor i, c in enumerate(text):idx = i * CHAR_SET_LEN + char2pos(c)vector[idx] = 1return vector# 向量转回文本def vec2text(vec):char_pos = vec.nonzero()[0]text=[]for i, c in enumerate(char_pos):char_at_pos = i #c/63char_idx = c % CHAR_SET_LENchar_code = char_idx + 97text.append(chr(char_code))return "".join(text) """#向量（大小MAX_CAPTCHA*CHAR_SET_LEN）用0,1编码每26个编码一个字符，这样位置也有，字符也有"""#数据类型，两列，第一列是图片的name，第二列是图片的label（一个长度为4的字符串）fff = open("test.csv")x_test=[]y_test=[]while 1:   line = fff.readline()   if not line:       break   value = line.split(",")   img = value[0]   label = value[1]   x_test.append(img)   y_test.append(label) test_data = zip(x_test,y_test)y_train = []x_train = []ff = open("train.csv")while 1:   line = ff.readline()   if not line:       break   value = line.split(",")   img = value[0]   label = value[1]   x_train.append(img)   y_train.append(label)ff.close()train_data = zip(x_train,y_train)max_train = len(train_data)print max_trainffle = "all_data_128_64" # 生成一个训练batchdef get_next_batch(T_d,start , batch_size=64):logger.info("start to creat batch: from:%d to %d"%(start,batch_size))logger.info("the image of the shape:%d  %d"%(IMAGE_HEIGHT,IMAGE_WIDTH))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 = T_d[start+i][0],T_d[start+i][1].strip()img = Image.open('%s/%s' % (ffle, image))arr = np.asarray(img, dtype="float32")/255.0batch_x[i,:] = np.mean(arr,-1).flatten() # (image.flatten()-128)/128  mean为0batch_y[i,:] = text2vec(text) 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 # 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]) #w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) ##w_c2_alpha = np.sqrt(2.0/(3*3*32)) #w_c3_alpha = np.sqrt(2.0/(3*3*64)) #w_d1_alpha = np.sqrt(2.0/(8*32*64))#out_alpha = np.sqrt(2.0/1024) # 3 conv layerprint "xxxx"print X.get_shape()   # 图片大小 64*128 = 8192print x.get_shape()   #64 * 64 * 128 * 1#layer_1w_c1 = tf.Variable(w_alpha*tf.random_normal([3, 3, 1, 32]))b_c1 = tf.Variable(b_alpha*tf.random_normal([32]))#tf.nn.conv2d(input, filter, strides, padding, use_cudnn_on_gpu=None, name=None)，参数属性#x[64,64,128,1]  w_c1[3,3,1,32]# 卷积后：32个feature，每个shape还是（？，64，128，32）conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))print conv1.get_shape() #max_pool之后，就变成了，(?, 32, 64, 32)#max_pool不同于卷积，它能保证位置不变性，降低计算参数conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')print conv1.get_shape() conv1 = tf.nn.dropout(conv1, keep_prob)print conv1.get_shape() #layer_2w_c2 = tf.Variable(w_alpha*tf.random_normal([3, 3, 32, 64]))b_c2 = tf.Variable(b_alpha*tf.random_normal([64]))conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))#做完conv2d,(?, 32, 64, 64),conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')#做完pool之后（？，16, 32, 64)conv2 = tf.nn.dropout(conv2, keep_prob)    #layer_3w_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.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))#conv2d 后的数据，(?, 16, 32, 64）conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')#pool后的维度 (?, 8, 16, 64)conv3 = tf.nn.dropout(conv3, keep_prob)print b_c3.get_shape() # Fully connected layer 64*128，全连接层w_d = tf.Variable(w_alpha*tf.random_normal([8192, 1024]))b_d = tf.Variable(b_alpha*tf.random_normal([1024]))#reshap（-1,8192），8*16*64 = 8192dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])#dense的shape(?, 8192)，w_b（8192,1024），输出为1024，print dense.get_shape()dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))#dense = （？，8192）*（8192,0124）+（1024）#dense = （？，1024）dense = tf.nn.dropout(dense, keep_prob)#全连接层的输出是1024维度的    #全连接层后接着一层输出层：1024*（4*26）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]))#dense = (?, 1024)#w_out = （1024,104）#b_out = （104）out = tf.add(tf.matmul(dense, w_out), b_out)#out的shape为（？，104）#out = tf.nn.softmax(out)return outdef eval_once():    sess = tf.Session()    ckpt = tf.train.get_checkpoint_state("checkpoint")    if ckpt and ckpt.model_checkpoint_path:        saver.restore(sess, ckpt.model_checkpoint_path)        global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]    else:        print('No checkpoint file found')        sess.close()        return    coord = tf.train.Coordinator()    try:        print "aaaa"    finally:        sess.close() # 训练#####def train_crack_captcha_cnn():output = crack_captcha_cnn()# loss#之前tensorflow没升级的时候，用的是targets，当升级到1.3时，用的是labelsloss = 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？# 优化器，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=0.001).minimize(loss) predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])    #函数会返回tensor中参数指定的维度中的最大值的索引    #最后输出的是一个，4*26维度的向量,所以最大的索引值为4个，和正确的结果进行对比max_idx_p = tf.argmax(predict, 2)max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)    #通过tf.equal方法可以比较预测结果与实际结果是否相等：correct_pred = tf.equal(max_idx_p, max_idx_l)    #这行代码返回一个布尔列表。为得到哪些预测是正确的，我们可用如下代码将布尔值转换成浮点数：accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))global train_data#Tensorflow针对这一需求提供了Saver类，保存模型，恢复模型变量。saver = tf.train.Saver()sess = tf.Session()sess.run(tf.global_variables_initializer())step = 0#sess.close()#return for kk in range(42):  #epochs = 50chee = Truenum = 0random.shuffle(train_data)while True:if num + 64 > max_train:breakbatch_x, batch_y = get_next_batch(train_data,num,64)logger.info("start to get loss:epochs:%d"%(kk))_, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})logger.info("train of the step:%d loss  %f"%(step,loss_))num = num + 64# 每100 step计算一次准确率if chee:chee = Falserandom.shuffle(test_data)batch_x_test, batch_y_test = get_next_batch(test_data,10,64)                #feed_dict参数来计算准确率acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})logger.info("the true of the test:%d loss  %f"%(step,acc))# 如果准确率大于50%,保存模型,完成训练if acc > 0.6 or kk == 41:print "out-out-out"saver.save(sess, "crack_capcha.model", global_step=step)return step += 1 train_crack_captcha_cnn()‘’‘使用Saver.save()方法保存模型：sess：表示当前会话，当前会话记录了当前的变量值checkpoint_dir + 'model.ckpt'：表示存储的文件名global_step：表示当前是第几步’‘’