def random_captcha_text(char_set=captcha_params.get_char_set(),
captcha_size=captcha_params.get_captcha_size()):
captcha_text = []
for i in range(captcha_size):
c = random.choice(char_set)
captcha_text.append(c)
return captcha_text
def random_text(characters=captcha_params.get_char_set(),
captcha_size=captcha_params.get_captcha_size()):
captcha_text = []
for i in range(captcha_size):
char = random.choice(characters)
captcha_text.append(str(char))
captchaTextNoSpace = ''.join(captcha_text)
count_char(captchaTextNoSpace)
print('no space')
print(captchaTextNoSpace)
return captchaTextNoSpace
def load_data(tol_num,train_num):
# input,tol_num: the numbers of all samples(train and test)
# input,train_num: the numbers of training samples
# output,(X_train,y_train):trainging data
# ouput,(X_test,y_test):test data
MAX_CAPTCHA = captcha_params.get_captcha_size()
CHAR_SET_LEN = captcha_params.get_char_set_len()
# data = np.empty((tol_num, 1, height, width),dtype="float32")
data = np.empty((tol_num, height, int(width / captcha_params.get_captcha_nb_letters())),dtype="float32")
label = np.empty(tol_num,dtype="uint8")
# data dir
imgs = os.listdir(captcha_params.DIR_NAME)
nbImgs = len(imgs)
print("loading images : "+str(tol_num)+ " / "+ str(nbImgs))
for i in range(tol_num):
imgIdx = int(random.random() * nbImgs)
# load the images and convert them into gray images
img = get_image_from_file(captcha_params.DIR_NAME+"/"+imgs[imgIdx])
arr = np.asarray(img,dtype="float32")
part = 1 # on ignore la 1ere partie = bordure
widthOfPart = int(width / captcha_params.get_captcha_nb_letters())
startOfPart = part * widthOfPart
endOfPart = (part +1) * widthOfPart
data[i,:,:] = arr[:,startOfPart:endOfPart]
captcha_text = imgs[imgIdx].split('.')[0]
label[i]= get_index(captcha_text[0])# char2pos(capcha_text)# text2vec(captcha_text)
# print(" 2 "+captcha_text+" -> " +str(label[i]))
print("load data shuffle")
# the data, shuffled and split between train and test sets
rr = [i for i in range(tol_num)]
random.shuffle(rr)
x_train = data[rr][:train_num]
y_train = label[rr][:train_num]
x_test = data[rr][train_num:]
y_test = label[rr][train_num:]
# print("labels " + str(label[:50]))
# print("y_train " + str(y_train[:50]))
return (x_train, y_train), (x_test, y_test)
from keras.layers import Convolution2D, MaxPooling2D from keras.utils import np_utils from keras import backend as K from load_data import * import h5py from keras.models import model_from_json from keras.utils import plot_model from keras.preprocessing.image import ImageDataGenerator import matplotlib from matplotlib import pyplot as plt batch_size = 512 nb_epoch = 100 step_epoch = 7 MAX_CAPTCHA = captcha_params.get_captcha_size() CHAR_SET_LEN = captcha_params.get_char_set_len() # input image dimensions img_rows, img_cols = captcha_params.get_height(), captcha_params.get_width() # number of convolutional filters to use nb_filters1 = 32 nb_filters2 = 64 nb_filters3 = 64 # size of pooling area for max pooling pool_size = (2, 2) # convolution kernel size kernel_size = (3, 3)
