import cv2 as cvimport numpy as npimport osdef create_digit_image(dir_path):    image = np.ones(shape=[24, 72], dtype=np.uint8)    image = image * 127    a = np.random.randint(0, 10)    b = np.random.randint(0, 10)    c = np.random.randint(0, 10)    d = np.random.randint(0, 10)    text = str(a)+str(b)+str(c)+str(d)    print(text)    cv.putText(image, text, (6, 20), cv.FONT_HERSHEY_PLAIN, 1.5, (255), 2)    for i in range(100):        row = np.random.randint(0, 24)        col = np.random.randint(0, 72)        image[row, col] = 0    full_path = dir_path + text + ".png"    cv.imwrite(full_path, image)os.mkdir(os.getcwd()+'\\train\\')os.mkdir(os.getcwd()+'\\test\\')for i in range(1000):    create_digit_image(os.getcwd()+'\\train\\')for i in range(100):    create_digit_image(os.getcwd()+'\\test\\')　　

def text2vec(text):    text_len = len(text)    if text_len > 4:        print("text code : ", text)        raise ValueError('验证码最长4个字符')    vector = np.zeros(4 * 10)    def char2pos(c):        k = ord(c)        if 48 <= k <= 57:            return k - 48    for i, c in enumerate(text):        idx = i * 10 + char2pos(c)        vector[idx] = 1    return vector# 向量转回文本def vec2text(vec):    char_pos = vec.nonzero()[0]    text = []    for i, c in enumerate(char_pos):        char_idx = c % 10        if char_idx < 10:            char_code = char_idx + ord('0')        else:            raise ValueError('error')        text.append(chr(char_code))    return "".join(text)s=text2vec('1030')print(s)s = vec2text(s)print(s)

[0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]1030

[  0. 1. 0. 0. 0. 0. 0. 0. 0. 0.   1. 0. 0. 0. 0. 0. 0. 0. 0. 0.  0. 0. 0. 1. 0. 0. 0. 0. 0. 0.  1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]第一行代表1第二行代表0第三行代表3第四行代表0

import osimport tensorflow as tffrom random import choicefrom tfdemo3.data_engine import get_one_image, get_image_filesw = 72h = 24label_vector_size = 40train_dir = os.getcwd()+'\\train\\'test_dir = os.getcwd()+'\\test\\'train_files = get_image_files(train_dir)test_files = get_image_files(test_dir)# 占位符x_image = tf.placeholder(shape=[None, h, w, 1], dtype=tf.float32)y = tf.placeholder(shape=[None, label_vector_size], dtype=tf.float32)keep_prob = tf.placeholder(dtype=tf.float32)# convolution layer 1conv1_w = tf.Variable(tf.random_normal(shape=[3, 3, 1, 32], stddev=0.1, dtype=tf.float32))conv1_bias = tf.Variable(tf.random_normal(shape=[32], stddev=0.1))conv1_out = tf.nn.conv2d(input=x_image, filter=conv1_w, strides=[1, 1, 1, 1], padding='SAME')conv1_relu = tf.nn.relu(tf.add(conv1_out, conv1_bias))# max pooling 1maxpooling_1 = tf.nn.max_pool(conv1_relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')# convolution layer 2conv2_w = tf.Variable(tf.random_normal(shape=[3, 3, 32, 64], stddev=0.1, dtype=tf.float32))conv2_bias = tf.Variable(tf.random_normal(shape=[64], stddev=0.1))conv2_out = tf.nn.conv2d(input=maxpooling_1, filter=conv2_w, strides=[1, 1, 1, 1], padding='SAME')conv2_relu = tf.nn.relu(tf.add(conv2_out, conv2_bias))# max pooling 2maxpooling_2 = tf.nn.max_pool(conv2_relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')# convolution layer 3conv3_w = tf.Variable(tf.random_normal(shape=[3, 3, 64, 64], stddev=0.1, dtype=tf.float32))conv3_bias = tf.Variable(tf.random_normal(shape=[64], stddev=0.1))conv3_out = tf.nn.conv2d(input=maxpooling_2, filter=conv3_w, strides=[1, 1, 1, 1], padding='SAME')conv3_relu = tf.nn.relu(tf.add(conv3_out, conv3_bias))# max pooling 2maxpooling_3 = tf.nn.max_pool(conv3_relu, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')# fc-1w_fc1 = tf.Variable(tf.random_normal(shape=[3*9*64, 1024], stddev=0.1, dtype=tf.float32))b_fc1 = tf.Variable(tf.constant(0.1, shape=[1024]))h_pool2 = tf.reshape(maxpooling_3, [-1, 3*9*64])output_fc1 = tf.nn.relu(tf.add(tf.matmul(h_pool2, w_fc1), b_fc1))# dropouth2 = tf.nn.dropout(output_fc1, keep_prob=keep_prob)# fc-2w_fc2 = tf.Variable(tf.random_normal(shape=[1024, 40], stddev=0.1, dtype=tf.float32))b_fc2 = tf.Variable(tf.constant(0.1, shape=[40]))y_conv = tf.add(tf.matmul(output_fc1, w_fc2), b_fc2)# losscross_loss = tf.nn.sigmoid_cross_entropy_with_logits(logits=y_conv, labels=y)loss = tf.reduce_mean(cross_loss)step = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)# accuracysaver = tf.train.Saver()predict = tf.reshape(y_conv, [-1, 4, 10])max_idx_p = tf.argmax(predict, 2)max_idx_l = tf.argmax(tf.reshape(y, [-1, 4, 10]), 2)correct_pred = tf.equal(max_idx_p, max_idx_l)accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))def get_train_batch(files, batch_size=128):    images = []    labels = []    for f in range(batch_size):        image, label = get_one_image(train_dir, choice(files))        images.append(image)        labels.append(label)    return images, labelsdef get_batch(root_dir, files):    images = []    labels = []    for f in files:        image, label = get_one_image(root_dir, f)        images.append(image)        labels.append(label)    return images, labelstest_images, test_labels = get_batch(test_dir, test_files)with tf.Session() as sess:    sess.run(tf.global_variables_initializer())    for i in range(500):        batch_xs, batch_ys = get_train_batch(train_files, 100)        curr_loss, curr_ = sess.run([loss, step], feed_dict={x_image: batch_xs, y: batch_ys, keep_prob: 0.5})        if (i + 1) % 100 == 0:            print("run step (%d) ..., loss : (%f)" % (i+1, curr_loss))            curr_acc = sess.run(accuracy, feed_dict={x_image: test_images, y: test_labels, keep_prob: 1.0})            print("current test Accuracy : %f" % (curr_acc))    saver.save(sess, "./ckp/code_break.ckpt", global_step=500)

import numpy as npimport cv2 as cvimport osdef text2vec(text):    text_len = len(text)    if text_len > 4:        print("text code : ", text)        raise ValueError('验证码最长4个字符')    vector = np.zeros(4 * 10)    def char2pos(c):        k = ord(c)        if 48 <= k <= 57:            return k - 48    for i, c in enumerate(text):        idx = i * 10 + char2pos(c)        vector[idx] = 1    return vector# 向量转回文本def vec2text(vec):    char_pos = vec.nonzero()[0]    text = []    for i, c in enumerate(char_pos):        char_idx = c % 10        if char_idx < 10:            char_code = char_idx + ord('0')        else:            raise ValueError('error')        text.append(chr(char_code))    return "".join(text)def get_one_image(root_dir, f):    gray = cv.imread(os.path.join(root_dir, f), cv.IMREAD_GRAYSCALE)    resize = cv.resize(gray, (72, 24))    result = np.zeros(resize.shape, dtype=np.float32)    cv.normalize(resize, result, 0, 1, cv.NORM_MINMAX, dtype=cv.CV_32F)    image = np.expand_dims(result, axis=2)    label = text2vec(f[0:4])    return image, labeldef get_image_files(root_dir):    img_list = []    files = os.listdir(root_dir)    for f in files:        if os.path.isfile(os.path.join(root_dir, f)):            img_list.append(f)    return img_list

run step (100) ..., loss : (0.023609)current test Accuracy : 0.992500run step (200) ..., loss : (0.000665)current test Accuracy : 1.000000run step (300) ..., loss : (0.000046)current test Accuracy : 1.000000run step (400) ..., loss : (0.000010)current test Accuracy : 1.000000run step (500) ..., loss : (0.000005)current test Accuracy : 1.000000