簡單的Tensorflow驗證碼識別應用，供大家參考，具體內容如下

1.Tensorflow的安裝方式簡單,在此就不贅述了.

2.訓練集訓練集以及測試及如下(純手工打造,所以數量不多):

3.實現代碼部分(參考了網上的一些實現來完成的)

main.py(主要的神經網絡代碼)

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from gen_check_code import gen_captcha_text_and_image_new,gen_captcha_text_and_image from gen_check_code import number from test_check_code import get_test_captcha_text_and_image import numpy as np import tensorflow as tf text, image = gen_captcha_text_and_image_new() print("驗證碼圖像channel:", image.shape) # (60, 160, 3) # 圖像大小 IMAGE_HEIGHT = image.shape[0] IMAGE_WIDTH = image.shape[1] image_shape = image.shape MAX_CAPTCHA = len(text) print("驗證碼文本最長字符數", MAX_CAPTCHA) # 驗證碼最長4字符; 我全部固定為4,可以不固定. 如果驗證碼長度小于4，用'_'補齊 # 把彩色圖像轉為灰度圖像（色彩對識別驗證碼沒有什么用） # 度化是將三分量轉化成一樣數值的過程 def convert2gray(img): ?if len(img.shape) > 2: ??gray = np.mean(img, -1) ??# 上面的轉法較快，正規轉法如下 ??# r, g, b = img[:,:,0], img[:,:,1], img[:,:,2] ??# gray = 0.2989 * r + 0.5870 * g + 0.1140 * b ??# int gray = (int) (0.3 * r + 0.59 * g + 0.11 * b); ??return gray ?else: ??return img """ cnn在圖像大小是2的倍數時性能最高, 如果你用的圖像大小不是2的倍數，可以在圖像邊緣補無用像素。 np.pad(image,((2,3),(2,2)), 'constant', constant_values=(255,)) # 在圖像上補2行，下補3行，左補2行，右補2行 """ char_set = number # 如果驗證碼長度小于4, '_'用來補齊 CHAR_SET_LEN = len(char_set) # 文本轉向量 def text2vec(text): ?text_len = len(text) ?if text_len > MAX_CAPTCHA: ??raise ValueError('驗證碼最長4個字符') ?vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN) ?def char2pos(c): ??try: ???k = ord(c)-ord('0') ??except: ???raise ValueError('No Map') ??return k ?for i, c in enumerate(text): ??idx = i * CHAR_SET_LEN + char2pos(c) ??vector[idx] = 1 ?return vector # 向量轉回文本 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) # 生成一個訓練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]) ?# 有時生成圖像大小不是(60, 160, 3) ?def wrap_gen_captcha_text_and_image(): ??while True: ???text, image = gen_captcha_text_and_image_new() ???if image.shape == image_shape: ????return text, 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為0 ??batch_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 # 定義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) ?# 定義三層的卷積神經網絡 ?# 定義第一層的卷積神經網絡 ?# 定義第一層權重 ?w_c1 = tf.Variable(w_alpha * tf.random_normal([3, 3, 1, 32])) ?# 定義第一層的偏置 ?b_c1 = tf.Variable(b_alpha * tf.random_normal([32])) ?# 定義第一層的激勵函數 ?conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1)) ?# conv1 為輸入 ksize 表示使用2*2池化，即將2*2的色塊轉化成1*1的色塊 ?conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') ?# dropout防止過擬合。 ?conv1 = tf.nn.dropout(conv1, keep_prob) ?# 定義第二層的卷積神經網絡 ?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) ?# 定義第三層的卷積神經網絡 ?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 ?# 隨機生成權重 ?w_d = tf.Variable(w_alpha * tf.random_normal([1536, 1024])) ?# 隨機生成偏置 ?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 # 訓練 def train_crack_captcha_cnn(): ?# 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 ?output = crack_captcha_cnn() ?# loss ?# loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y)) ?loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(output, Y)) ?# 最后一層用來分類的softmax和sigmoid有什么不同？ ?# optimizer 為了加快訓練 learning_rate應該開始大，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計算一次準確率 ????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) ?????# 如果準確率大于50%,保存模型,完成訓練 ?????if acc > 0.99: ??????saver.save(sess, "./crack_capcha.model", global_step=step) ??????break ????step += 1 ## 訓練(如果要訓練則去掉下面一行的注釋) train_crack_captcha_cnn() def crack_captcha(): ?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) ??count = 0 ??# 因為測試集共40個...寫的很草率 ??for i in range(40): ???text, image = get_test_captcha_text_and_image(i) ???image = convert2gray(image) ???captcha_image = image.flatten() / 255 ???text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1}) ???predict_text = text_list[0].tolist() ???predict_text = str(predict_text) ???predict_text = predict_text.replace("[", "").replace("]", "").replace(",", "").replace(" ","") ???if text == predict_text: ????count += 1 ????check_result = "，預測結果正確" ???else: ????check_result = "，預測結果不正確" ????print("正確: {} 預測: {}".format(text, predict_text) + check_result) ??print("正確率:" + str(count) + "/40") # 測試(如果要測試則去掉下面一行的注釋) # crack_captcha()

gen_check_code.py(得到訓練集輸入,需要注意修改root_dir為訓練集的輸入文件夾,下同)

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from captcha.image import ImageCaptcha # pip install captcha import numpy as np from PIL import Image import random # import matplotlib.pyplot as plt import os from random import choice # 驗證碼中的字符, 就不用漢字了 number = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'] # 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'] # 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'] root_dir = "d:\\train" # 驗證碼一般都無視大小寫；驗證碼長度4個字符 def random_captcha_text(char_set=number, captcha_size=4): ?captcha_text = [] ?for i in range(captcha_size): ??c = random.choice(char_set) ??captcha_text.append(c) ?return captcha_text # 生成字符對應的驗證碼 def gen_captcha_text_and_image(): ?image = ImageCaptcha() ?captcha_text = random_captcha_text() ?captcha_text = ''.join(captcha_text) ?captcha = image.generate(captcha_text) ?# image.write(captcha_text, captcha_text + '.jpg') # 寫到文件 ?captcha_image = Image.open(captcha) ?captcha_image = np.array(captcha_image) ?return captcha_text, captcha_image def gen_list(): ?img_list = [] ?for parent, dirnames, filenames in os.walk(root_dir): # 三個參數：分別返回1.父目錄 2.所有文件夾名字（不含路徑） 3.所有文件名字 ??for filename in filenames: # 輸出文件信息 ???img_list.append(filename.replace(".gif","")) ???# print("parent is:" + parent) ???# print("filename is:" + filename) ???# print("the full name of the file is:" + os.path.join(parent, filename)) # 輸出文件路徑信息 ?return img_list img_list = gen_list() def gen_captcha_text_and_image_new(): ?img = choice(img_list) ?captcha_image = Image.open(root_dir + "\\" + img + ".gif") ?captcha_image = np.array(captcha_image) ?return img, captcha_image # if __name__ == '__main__': #? # 測試 #? # text, image = gen_captcha_text_and_image() #? # #? # f = plt.figure() #? # ax = f.add_subplot(111) #? # ax.text(0.1, 0.9, text, ha='center', va='center', transform=ax.transAxes) #? # plt.imshow(image) #? # plt.show() #? # # #? text, image = gen_captcha_text_and_image_new() # #? f = plt.figure() #? ax = f.add_subplot(111) #? ax.text(0.1, 0.9, text, ha='center', va='center', transform=ax.transAxes) #? plt.imshow(image) #? plt.show()

test_check_code.py(得到測試集輸入)

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from captcha.image import ImageCaptcha # pip install captcha import numpy as np from PIL import Image import random import matplotlib.pyplot as plt import os from random import choice root_dir = "d:\\test" img_list = [] def gen_list(): ?for parent, dirnames, filenames in os.walk(root_dir): # 三個參數：分別返回1.父目錄 2.所有文件夾名字（不含路徑） 3.所有文件名字 ??for filename in filenames: # 輸出文件信息 ???img_list.append(filename.replace(".gif","")) ???# print("parent is:" + parent) ???# print("filename is:" + filename) ???# print("the full name of the file is:" + os.path.join(parent, filename)) # 輸出文件路徑信息 ?return img_list img_list = gen_list() def get_test_captcha_text_and_image(i=None): ?img = img_list[i] ?captcha_image = Image.open(root_dir + "\\" + img + ".gif") ?captcha_image = np.array(captcha_image) ?return img, captcha_image

4.效果

在測試集上的識別率?

5.相關文件下載

訓練集以及測試集?下載

以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支持腳本之家。

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