Tensorflow simple verification code recognition application, tensorflow Verification Code

Tensorflow simple verification code recognition application, tensorflow Verification Code

Simple Tensorflow verification code recognition application for your reference. The specific content is as follows:

1. Tensorflow Installation MethodI will not go into details here.

2. Training setAs well as testing and the following (manually created, so the number is small ):

3. Implementation Code(Based on some implementations on the Internet)

Main. py (main neural network code)

From gen_check_code import protocol, gen_captcha_text_and_imagefrom gen_check_code import numberfrom test_check_code import using numpy as npimport tensorflow as tftext, image = Signature () print ("Verification Code image channel:", image. shape) # (60,160, 3) # image size IMAGE_HEIGHT = image. shape [0] IMAGE_WIDTH = image. shape [1] image_shape = image. sha PeMAX_CAPTCHA = len (text) print ("Maximum number of characters in the verification code text", MAX_CAPTCHA) # The maximum length of the verification code is 4 characters. I have fixed it to 4 characters and it can be unfixed. if the verification code length is less than 4, fill it with '_' # convert the color image into a grayscale image (color is useless for the verification code) # degrees: def convert2gray (img): if len (img. shape)> 2: gray = np. mean (img,-1) # The preceding method is faster. The regular method is as follows # 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: r Eturn img "cnn has the highest performance when the image size is a multiple of 2. If the image size you use is not a multiple of 2, you can add useless pixels at the edge of the image. Np. pad (image, (2, 3), (2, 2), 'constant', constant_values = (255,) # Add 2 rows on the image and 3 rows below, add 2 rows to the left and 2 rows to the right. "" char_set = number # If the verification code length is less than 4, '_' is used to complete CHAR_SET_LEN = len (char_set) # text conversion quantity def text2vec (text): text_len = len (text) if text_len> MAX_CAPTCHA: raise ValueError ('verification Code cannot exceed 4 characters ') vector = np. zeros (MAX_CAPTCHA * CHAR_SET_LEN) def char2pos (c): try: k = ord (c)-ord ('0') handle T: raise ValueError ('no map') return k I, c in enumerate (text): idx = I * CHAR_SET_LEN + char2pos (c) vector [idx] = 1 return vector # vector back text 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) # generate a training batchdef 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]) # Sometimes the size of the generated image is not (60,160, 3) def wrap_gen_captcha_text_and_image (): while True: text, image = gen_captcha_t Ext_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 is 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 # define CNNdef 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) # define a layer-3 convolutional Neural Network # define the level-1 convolutional Neural Network # define the layer-1 weight w_c1 = tf. variable (w_alpha * tf. random_normal ([3, 3, 1, 32]) # defines the first layer offset B _c1 = tf. variable (B _alpha * tf. random_normal ([32]) # define the first-layer incentive function conv1 = tf. nn. relu (tf. nn. bias_add (tf. nn. conv2d (x, w_c1, strides = [1, 1, 1], padding = 'same'), B _c1) # conv1 indicates 2*2 pools for input ksize, convert the color block of 2*2 to the color block conv1 = of 1*1. Tf. nn. max_pool (conv1, ksize = [1, 2, 2, 1], strides = [1, 2, 2, 1], padding = 'same') # dropout prevents overfitting. Conv1 = tf. nn. dropout (conv1, keep_prob) # defines the second layer of the convolutional Neural Network 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], 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_p Rob) # define the third-layer convolutional Neural Network 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], 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 # randomly generated Weight w_d = tf. variable (w_alpha * tf. random_normal ([1536,102 4]) # randomly generated offset 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 # Train 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 = tf. r Educe_mean (tf. nn. softmax_cross_entropy_with_logits (output, Y) loss = tf. performance_mean (tf. nn. sigmoid_cross_entropy_with_logits (output, Y) # What is the difference between softmax and sigmoid used for classification at the last layer? # Optimizer should start to increase learning_rate in order to speed up training, and then slowly decline 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. performance_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 _) # accuracy calculated every 100 steps 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) # if the accuracy is greater than 50%, save the model and complete training if acc> 0.99: saver. save (sess ,". /crack_capcha.model ", global_step = step) break step + = 1 # training (if you want to train, remove the following line of comment) Explain () 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 # A total of 40 test sets... the write is sloppy 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 = ", the prediction result is correct" else: check_result = ", incorrect prediction result "print (" correct: {} prediction :{}". format (text, predict_text) + check_result) print ("correct rate:" + str (count) + "/40") # test (if you want to test, remove the comments of the following line) # crack_captcha ()

Gen_check_code.py (to get the input of the training set, you must change root_dir to the input folder of the training set, the same below)

From captcha. image import ImageCaptcha # pip install captchaimport numpy as npfrom PIL import Imageimport random # import matplotlib. pyplot as pltimport osfrom random import choice # characters in the verification code, no Chinese characters are needed. 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', '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" # The verification code is case insensitive. The verification code consists of four characters: 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 # generate the verification code def encode (): image = ImageCaptcha () captcha_text = random_captcha_text () captcha_text = ''. join (captcha_text) captcha = image. generate (captcha_text) # image. write (captcha_text, captcha_text + '.jpg ') # write to the file captcha_image = Image. open (captcha) captcha_image = np. array (captcha_image) return captcha_text, captcha_imagedef gen_list (): img_list = [] for parent, dirnames, filenames in OS. walk (root_dir): # three parameters: return 1. parent directory 2. all folder names (excluding paths) 3. all file names for filename in filenames: # output file information 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) # return img_listimg_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 _': ## test ## 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 (obtain the test set input)

From captcha. image import ImageCaptcha # pip install captchaimport numpy as npfrom PIL import Imageimport randomimport matplotlib. pyplot as pltimport osfrom random import choiceroot_dir = "d: \ test" img_list = [] def gen_list (): for parent, dirnames, filenames in OS. walk (root_dir): # three parameters: return 1. parent directory 2. all folder names (excluding paths) 3. all file names for filename in filenames: # output file information 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) # return img_listimg_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. Results

Recognition Rate on the Test Set

5. Download Related Files

Download training set and Test Set

The above is all the content of this article. I hope it will be helpful for your learning and support for helping customers.