Python Tensorflow CNN 识别验证码

Python+Tensorflow的CNN技术快速识别验证码

文章来源于： https://www.jianshu.com/p/26ff7b9075a1

验证码处理的流程是：验证码分析和处理—— tensorflow安装 —— 模型训练 —— 模型预测

需要的准备。

　　1. 安装TensorFlow

　　2.  PIL

　　3. numpy

　　4. 用于训练的图片

 0.文件目录：

　　红色部分有用，其他不用

　　

 

1. 训练模型的图片：链接：https://pan.baidu.com/s/1kpgt7Pc-ni4WnN6qj8U-pw 密码：nzea

 

2.  训练模型代码：

　　训练好的模型：链接：https://pan.baidu.com/s/1dNpEtguITKBgbsUU6tCluQ 密码：j07f

from PIL import Image
import numpy as np
import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import random


IMAGE_HEIGHT = 114
IMAGE_WIDTH = 450
MAX_CAPTCHA = 6
CHAR_SET_LEN = 26


def get_name_and_image():
    all_image = os.listdir('C:\\Users\\xuchunlin\\PycharmProjects\\ML\\20180402\\captcha4\\')
    random_file = random.randint(0, 3429)
    base = os.path.basename('C:\\Users\\xuchunlin\\PycharmProjects\\ML\\20180402\\captcha4\\' + all_image[random_file])
    name = os.path.splitext(base)[0]
    image = Image.open('C:\\Users\\xuchunlin\\PycharmProjects\\ML\\20180402\\captcha4\\' + all_image[random_file])
    image = np.array(image)
    return name, image


def name2vec(name):
    vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
    for i, c in enumerate(name):
        idx = i * 26 + ord(c) - 97
        vector[idx] = 1
    return vector


def vec2name(vec):
    name = []
    for i in vec:
        a = chr(i + 97)
        name.append(a)
    return "".join(name)


# 生成一个训练batch
def get_next_batch(batch_size=64):
    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):
        name, image = get_name_and_image()
        batch_x[i, :] = 1*(image.flatten())
        batch_y[i, :] = name2vec(name)
    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)


# 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])
    # 3 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.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
    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.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
    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([5, 5, 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))
    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)

    # Fully connected layer
    w_d = tf.Variable(w_alpha * tf.random_normal([15 * 57 * 64, 1024]))
    b_d = tf.Variable(b_alpha * tf.random_normal([1024]))
    dense = tf.reshape(conv3, [-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)
    return out


# 训练
def train_crack_captcha_cnn():
    output = crack_captcha_cnn()
    loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001).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: 0.5})
            print(step, loss_)

            # 每100 step计算一次准确率
            if step % 100 == 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.})
                print(step, acc)
                # 如果准确率大于60%,保存模型,完成训练
                if acc > 0.6:
                    saver.save(sess, "./crack_capcha.model", global_step=step)
                    break

            step += 1

train_crack_captcha_cnn()

3.  模型测试代码：

　　　　

def crack_captcha():
    output = crack_captcha_cnn()

    saver = tf.train.Saver()
    with tf.Session() as sess:
        saver.restore(sess, tf.train.latest_checkpoint('.'))
        n = 1
        while n <= 10:
            text, image = get_name_and_image()
            image = 1 * (image.flatten())
            predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
            text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})
            vec = text_list[0].tolist()
            predict_text = vec2name(vec)
            print("正确: {}  预测: {}".format(text, predict_text))
            n += 1

crack_captcha()

训练代码和测试代码文件： 链接：https://pan.baidu.com/s/1VY9rYZizCEjHzim3-XaGyw 密码：epv2

 

结果展示：

 

 你会发现识别率并不高，那是因为上面训练模型中有这几行代码

　　　　　　　　 # 如果准确率大于60%,保存模型,完成训练
                if acc > 0.6:
                    saver.save(sess, "./crack_capcha.model", global_step=step)
                    break

设定的准确率只有百分之六十，如果时间充足的话，可以设置0.99或者0.98.会得到一个不错的模型。

详细讲解请去原网址看，地址：https://www.jianshu.com/p/26ff7b9075a1

所有学习资料：链接：https://pan.baidu.com/s/19BoO5sUhLrzpL0a9_rNTRQ 密码：q4ri