using imageNet for training .convergence sllowgit add .

gen_captcha.py

@@ -24,15 +24,12 @@ def random_captcha_text(char_set=number+alphabet+ALPHABET, captcha_size=4):
     return captcha_text
 
 # 生成字符对应的验证码
-def gen_captcha_text_and_image(w,h):
-    image = ImageCaptcha(width=w,height=h)
-
-    captcha_text = random_captcha_text()
+def gen_captcha_text_and_image(w,h,length):
+    image = ImageCaptcha(width=w,height=h,font_sizes=[60])
+    captcha_text = random_captcha_text(captcha_size=length)
     captcha_text = ''.join(captcha_text)
-
     captcha = image.generate(captcha_text)
     #image.write(captcha_text, captcha_text + '.jpg')  # 写到文件
-
     captcha_image = Image.open(captcha)
     captcha_image = np.array(captcha_image)
     return captcha_text, captcha_image
@@ -41,9 +38,10 @@ def gen_captcha_text_and_image(w,h):
     # 测试
     # 图像大小
     IMAGE_HEIGHT = 60
-    IMAGE_WIDTH = 160
-    text, image = gen_captcha_text_and_image(IMAGE_WIDTH,IMAGE_HEIGHT)
+    IMAGE_WIDTH = 60
+    text, image = gen_captcha_text_and_image(IMAGE_WIDTH,IMAGE_HEIGHT,1)
     print(image.shape)
+    print(len(number+alphabet+ALPHABET))
     f = plt.figure()
     ax = f.add_subplot(111)
     ax.text(0.1, 0.9,text, ha='center', va='center', transform=ax.transAxes)

test.py

@@ -15,9 +15,9 @@
 
 from PIL import Image
 # 图像大小
-IMAGE_HEIGHT = 60
-IMAGE_WIDTH = 160
-text, image = gen_captcha_text_and_image(IMAGE_WIDTH,IMAGE_HEIGHT)
+IMAGE_HEIGHT = 30
+IMAGE_WIDTH = 30
+text, image = gen_captcha_text_and_image(IMAGE_WIDTH,IMAGE_HEIGHT,1)
 print("验证码图像channel:", image.shape)  # (60, 160, 3)
 MAX_CAPTCHA = len(text)
 print("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
@@ -206,16 +206,15 @@ def train_crack_captcha_cnn():
 
             step += 1
 
-#import matplotlib.pylab as plt
-#f = plt.figure()
-#ax = f.add_subplot(111)
-#ax.text(0.1, 0.9,text, ha='center', va='center', transform=ax.transAxes)
-##image = convert2gray(image)
-#plt.imshow(image)
-#plt.show()
-#from picPreHandle import Handle_Image1
-#image = Handle_Image1(Image.fromarray(image),IMAGE_WIDTH,IMAGE_HEIGHT)
-#
-#image = np.array(image)
-#plt.show()
-train_crack_captcha_cnn()
+import matplotlib.pylab as plt
+f = plt.figure()
+ax = f.add_subplot(111)
+ax.text(0.1, 0.9,text, ha='center', va='center', transform=ax.transAxes)
+#image = convert2gray(image)
+plt.imshow(image)
+plt.show()
+from picPreHandle import Handle_Image1
+image = Handle_Image1(Image.fromarray(image),IMAGE_WIDTH,IMAGE_HEIGHT)
+plt.imshow(image)
+plt.show()
+#train_crack_captcha_cnn()

test1.py

@@ -0,0 +1,256 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Dec 25 10:27:29 2016
+
+@author: L
+"""
+
+from gen_captcha import gen_captcha_text_and_image
+from gen_captcha import number
+from gen_captcha import alphabet
+from gen_captcha import ALPHABET
+
+import numpy as np
+import tensorflow as tf
+
+from PIL import Image
+# 图像大小
+IMAGE_HEIGHT = 60
+IMAGE_WIDTH = 60
+text, image = gen_captcha_text_and_image(IMAGE_WIDTH,IMAGE_HEIGHT,1)
+print("验证码图像channel:", image.shape)  # (60, 160, 3)
+MAX_CAPTCHA = len(text)
+print("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:
+        from picPreHandle import Handle_Image1
+        gray = Handle_Image1(Image.fromarray(img),IMAGE_WIDTH,IMAGE_HEIGHT)
+        # 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 * b
+        return np.array(gray)
+    else:
+        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
+"""
+
+# 文本转向量
+char_set = number + alphabet + ALPHABET + ['_']  # 如果验证码长度小于4, '_'用来补齐
+CHAR_SET_LEN = len(char_set)
+def text2vec(text):
+    text_len = len(text)
+    if 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):
+        if c =='_':
+            k = 62
+            return k
+        k = ord(c)-48
+        if k > 9:
+            k = ord(c) - 55
+            if k > 35:
+                k = ord(c) - 61
+                if k > 61:
+                    raise ValueError('No Map') 
+        return k
+#    print('word to vec',text)
+    for i, c in enumerate(text): # enumerate(text)将字符串分离成位置和字符
+        idx = i * CHAR_SET_LEN + char2pos(c)
+        vector[idx] = 1
+#    print(vector.shape)
+    return vector
+# 向量转回文本
+def vec2text(vec):
+#    print('vec to word' ,vec)
+    char_pos = vec.nonzero()[0] # 显示vec中非0值的位置
+    text=[]
+    for i, c in enumerate(char_pos): # enumerate(char_pos)将字符串分离成位置和字符
+        char_at_pos = i #c/63
+        char_idx = c % CHAR_SET_LEN
+        if char_idx < 10:
+            char_code = char_idx + ord('0')
+        elif char_idx <36:
+            char_code = char_idx - 10 + ord('A')
+        elif char_idx < 62:
+            char_code = char_idx-  36 + ord('a')
+        elif char_idx == 62:
+            char_code = ord('_')
+        else:
+            raise ValueError('error')
+        text.append(chr(char_code))
+    return "".join(text)
+# 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])
+
+    # 有时生成图像大小不是(60, 160, 3)
+    def wrap_gen_captcha_text_and_image():
+        while True:
+            text, image = gen_captcha_text_and_image(IMAGE_WIDTH,IMAGE_HEIGHT,1)
+            if image.shape == (IMAGE_WIDTH,IMAGE_HEIGHT, 3):
+                return text, image
+#
+#    for i in range(batch_size):
+#        text, image = wrap_gen_captcha_text_and_image()
+    for i in range(batch_size):
+        text, image = wrap_gen_captcha_text_and_image()
+        image = convert2gray(image)
+
+        batch_x[i,:] = image.flatten() / 255 # (image.flatten()-128)/128  mean为0
+        batch_y[i,:] = text2vec(text)
+#    print(len(batch_x),batch_y.shape)
+    return batch_x, batch_y
+# 定义网络超参数
+learning_rate = 0.001
+training_iters = 200000
+batch_size = 64
+display_step = 20
+
+# 定义网络参数
+n_input = IMAGE_HEIGHT*IMAGE_WIDTH # 输入的维度
+n_classes = MAX_CAPTCHA*CHAR_SET_LEN # 标签的维度
+dropout = 0.8 # Dropout 的概率
+
+# 占位符输入
+x = tf.placeholder(tf.float32, [None, n_input])
+y = tf.placeholder(tf.float32, [None, n_classes])
+keep_prob = tf.placeholder(tf.float32)
+
+# 卷积操作
+
+def conv2d(name, l_input, w, b):
+    """Build the AlexNet model. 
+     
+    Args: 
+    name: 这个卷积器的名称
+    l_input: Images Tensor 
+    w : 权值
+    b ：偏置
+    
+     
+    Returns: 
+    a Tensors of this op or op itself
+    """  
+    myconv2d = tf.nn.conv2d(l_input, w, strides=[1, 1, 1, 1], padding='SAME')
+    bias = tf.nn.bias_add(myconv2d,b)
+    return tf.nn.relu(bias, name=name)
+
+# 最大下采样操作
+def max_pool(name, l_input, k):
+    return tf.nn.max_pool(l_input, ksize=[1, k, k, 1], strides=[1, k, k, 1], padding='SAME', name=name)
+
+# 归一化操作
+def norm(name, l_input, lsize=4):
+    return tf.nn.lrn(l_input, lsize, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name=name)
+
+# 定义整个网络 
+def alex_net(_X, _weights, _biases, _dropout):
+    # 向量转为矩阵
+    _X = tf.reshape(_X, shape=[-1, IMAGE_HEIGHT,IMAGE_WIDTH, 1])###
+#    print('x' , _X.get_shape())
+    # 卷积层
+    conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1'])
+    # 下采样层
+    pool1 = max_pool('pool1', conv1, k=2)
+    # 归一化层
+    norm1 = norm('norm1', pool1, lsize=4)
+    # Dropout
+    norm1 = tf.nn.dropout(norm1, _dropout)
+
+#    print('x' , norm1.get_shape())
+    # 卷积
+    conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2'])
+    # 下采样
+    pool2 = max_pool('pool2', conv2, k=2)
+    # 归一化
+    norm2 = norm('norm2', pool2, lsize=4)
+    # Dropout
+    norm2 = tf.nn.dropout(norm2, _dropout)
+#    print('x' , norm2.get_shape())
+
+    # 卷积
+    conv3 = conv2d('conv3', norm2, _weights['wc3'], _biases['bc3'])
+    # 下采样
+    pool3 = max_pool('pool3', conv3, k=2)
+    # 归一化
+    norm3 = norm('norm3', pool3, lsize=4)
+    # Dropout
+    norm3 = tf.nn.dropout(norm3, _dropout)
+#    print('norm3' , norm3.get_shape())
+
+    # 全连接层，先把特征图转为向量
+    dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) 
+    dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1') 
+    # 全连接层
+    dense2 = tf.reshape(dense1, [-1, _weights['wd2'].get_shape().as_list()[0]])  
+    dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Relu activation
+
+#    print(dense2.get_shape())
+#    print(_weights['out'].get_shape())
+#    print(_biases['out'].get_shape())
+    # 网络输出层
+    out = tf.matmul(dense2, _weights['out']) + _biases['out']
+    return out
+
+# 存储所有的网络参数
+weights = {
+    'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])),
+    'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])),
+    'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])),
+    'wd1': tf.Variable(tf.random_normal([8*8*256, 1024])),    #######最后这个全连接 层的参数非常重要 
+    'wd2': tf.Variable(tf.random_normal([1024, 1024])),
+    'out': tf.Variable(tf.random_normal([1024, n_classes]))
+}
+biases = {
+    'bc1': tf.Variable(tf.random_normal([64])),
+    'bc2': tf.Variable(tf.random_normal([128])),
+    'bc3': tf.Variable(tf.random_normal([256])),
+    'bd1': tf.Variable(tf.random_normal([1024])),
+    'bd2': tf.Variable(tf.random_normal([1024])),
+    'out': tf.Variable(tf.random_normal([n_classes]))
+}
+
+# 构建模型
+pred = alex_net(x, weights, biases, keep_prob)
+
+# 定义损失函数和学习步骤
+cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))
+optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
+
+# 测试网络
+correct_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))
+accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
+
+# 初始化所有的共享变量
+init = tf.global_variables_initializer()
+saver = tf.train.Saver()
+# 开启一个训练
+with tf.Session() as sess:
+    sess.run(init)
+    step = 1
+    # Keep training until reach max iterations
+    batch_xs, batch_ys = get_next_batch(64)
+    while step * batch_size < training_iters:
+        # 获取批数据
+        sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout})
+        if step % display_step == 0:
+            # 计算精度
+            acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
+            # 计算损失值
+            loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.})
+            print("Iter " + str(step*batch_size) + ", Minibatch Loss= " + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc))
+            # 如果准确率大于50%,保存模型,完成训练
+            if acc > 0.5:
+                saver.save(sess, "crack_capcha.model", global_step=step)
+                break
+        step += 1
+    print("Optimization Finished!")
+    # 计算测试精度
+#    print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.}))