diff --git "a/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/README.md" "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/README.md" index 2c657efd..e2973eeb 100644 --- "a/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/README.md" +++ "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/README.md" @@ -7,7 +7,11 @@ 直接运行 ： -python TensorFlow_cnn.py +python TensorFlow_cnn_train.py +则开始训练，当前成功率设定为50%则停止， +训练完毕确保训练后模型在同一文件夹下则运行 +python TensorFlow_cnn_test_model.py +实时生成新验证码并且进行预测 当前程序设定一个成功率0.5或者0.7 diff --git "a/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn.py" "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/create_image/tensorflow_cnn.py" similarity index 100% rename from "1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn.py" rename to "1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/create_image/tensorflow_cnn.py" diff --git "a/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn0.py" "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/create_image/tensorflow_cnn0.py" similarity index 100% rename from "1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn0.py" rename to "1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/create_image/tensorflow_cnn0.py" diff --git "a/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn_test_model.py" "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn_test_model.py" new file mode 100644 index 00000000..8225ae92 --- /dev/null +++ "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn_test_model.py" @@ -0,0 +1,224 @@ +#coding:utf-8 +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 + +text, image = gen_captcha_text_and_image() +print("验证码图像channel:", image.shape) # (60, 160, 3) +# 图像大小 +IMAGE_HEIGHT = 60 +IMAGE_WIDTH = 160 +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: + 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 * b + return 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) #生成一个默认为0的向量 + 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 + for i, c in enumerate(text): + idx = i * CHAR_SET_LEN + 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_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) + +""" +#向量（大小MAX_CAPTCHA*CHAR_SET_LEN）用0,1编码 每63个编码一个字符，这样顺利有，字符也有 +vec = text2vec("F5Sd") +text = vec2text(vec) +print(text) # F5Sd +vec = text2vec("SFd5") +text = vec2text(vec) +print(text) # SFd5 +""" + +# 生成一个训练batchv 一个批次为 默认128 张图片 转换为向量 +def get_next_batch(batch_size=128): + 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) 反复获取验证码直到该验证码符合标准格式。 + def wrap_gen_captcha_text_and_image(): + while True: + text, image = gen_captcha_text_and_image() + if image.shape == (60, 160, 3): + return text, image + + for i in range(batch_size): + #获取图片，并灰度转换 + text, image = wrap_gen_captcha_text_and_image() + image = convert2gray(image) + + # flatten 图片一维化 以及对应的文字内容也一维化，形成一个128行每行一个图片及对应文本 + batch_x[i,:] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0 + batch_y[i,:] = text2vec(text) + + return batch_x, batch_y + +#################################################################### + +# 申请三个占位符 +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CNN +def 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 layer # 3 个 转换层 + w_c1 = tf.Variable(w_alpha*tf.random_normal([3, 3, 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([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)) + 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([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)) + 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 # 最后连接层 + w_d = tf.Variable(w_alpha*tf.random_normal([8*20*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) + + # 输出层 + 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) + #out = tf.nn.softmax(out) + return out + +# 训练 +def train_crack_captcha_cnn(): + output = crack_captcha_cnn() + # loss 损失数值 + # loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y)) + loss = 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？ + # 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]) + 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: 0.75}) + 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 + +def crack_captcha(captcha_image): + output = crack_captcha_cnn() + + saver = tf.train.Saver() + with tf.Session() as sess: + saver.restore(sess, tf.train.latest_checkpoint('.')) + + predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2) + text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1}) + + text = text_list[0].tolist() + vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN) + i = 0 + for n in text: + vector[i*CHAR_SET_LEN + n] = 1 + i += 1 + return vec2text(vector) + +if __name__ == '__main__': + + text, image = gen_captcha_text_and_image() + image = convert2gray(image) #生成一张新图 + image = image.flatten() / 255 # 将图片一维化 + predict_text = crack_captcha(image) #导入模型识别 + print("正确: {} 预测: {}".format(text, predict_text)) + #train_crack_captcha_cnn() diff --git "a/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn_train.py" "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn_train.py" new file mode 100644 index 00000000..5f96a826 --- /dev/null +++ "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/tensorflow_cnn_train.py" @@ -0,0 +1,197 @@ +#coding:utf-8 +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 + +text, image = gen_captcha_text_and_image() #先生成验证码和文字测试模块是否完全 +print("验证码图像channel:", image.shape) # (60, 160, 3) +# 图像大小 +IMAGE_HEIGHT = 60 +IMAGE_WIDTH = 160 +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: + 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 * b + return 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 + for i, c in enumerate(text): + idx = i * CHAR_SET_LEN + 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_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) + +""" +#向量（大小MAX_CAPTCHA*CHAR_SET_LEN）用0,1编码 每63个编码一个字符，这样顺利有，字符也有 +vec = text2vec("F5Sd") +text = vec2text(vec) +print(text) # F5Sd +vec = text2vec("SFd5") +text = vec2text(vec) +print(text) # SFd5 +""" + +# 生成一个训练batch +def get_next_batch(batch_size=128): + 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(): + ''' 获取一张图，判断其是否符合（60，160，3）的规格''' + while True: + text, image = gen_captcha_text_and_image() + if image.shape == (60, 160, 3):#此部分应该与开头部分图片宽高吻合 + return text, image + + for i in range(batch_size): + text, image = wrap_gen_captcha_text_and_image() + image = convert2gray(image) + + # 将图片数组一维化 同时将文本也对应在两个二维组的同一行 + batch_x[i,:] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0 + batch_y[i,:] = text2vec(text) + # 返回该训练批次 + return batch_x, batch_y + +#################################################################### +# 申请占位符 按照图片 +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CNN +def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1): + # 将占位符 转换为 按照图片给的新样式 + 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 layer + w_c1 = tf.Variable(w_alpha*tf.random_normal([3, 3, 1, 32])) # 从正太分布输出随机值 + 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([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)) + 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([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)) + 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([8*20*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) + #out = tf.nn.softmax(out) + return out + +# 训练 +def train_crack_captcha_cnn(): + output = crack_captcha_cnn() + #loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y)) + loss = 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？ + # 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]) + 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: 0.75}) + 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 + +train_crack_captcha_cnn() diff --git "a/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/text_model.py" "b/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/text_model.py" deleted file mode 100644 index 34133001..00000000 --- "a/1.\351\252\214\350\257\201\347\240\201/tensorflow_cnn/text_model.py" +++ /dev/null @@ -1,23 +0,0 @@ -def crack_captcha(captcha_image): - output = crack_captcha_cnn() - - saver = tf.train.Saver() - with tf.Session() as sess: - saver.restore(sess, tf.train.latest_checkpoint('.')) - - predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2) - text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1}) - - text = text_list[0].tolist() - vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN) - i = 0 - for n in text: - vector[i*CHAR_SET_LEN + n] = 1 - i += 1 - return vec2text(vector) - -text, image = gen_captcha_text_and_image() -image = convert2gray(image) -image = image.flatten() / 255 -predict_text = crack_captcha(image) -print("正确: {} 预测: {}".format(text, predict_text))