def get_next_batch(batch_size=128):
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, CAPTCHA_TEXT_LEN * CHAR_SET_LEN])
for i in range(batch_size):
text, image = gen_captcha()
# log(i, text)
image = convert2gray(image)
batch_x[i, :] = image.flatten() / 255 # (image.flatten()-128)/128 mean为0
batch_y[i, :] = text2vec(text)
return batch_x, batch_y
def test_crack_captcha(count):
output = crack_captcha_cnn()
saver = tf.train.Saver()
correct = 0
for i in range(count):
text, image = gen_captcha()
image = convert2gray(image)
image = image.flatten() / 255
predict_text = crack_captcha(image, output, saver)
if text.lower() == predict_text.lower():
correct = correct + 1
logger.info("正确: {} 预测: {}".format(text, predict_text))
logger.info('总数 ' + str(i) + " 正确 " + str(correct) + ' 准确率：' + str(correct / (i + 1)))
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
from logger import log
# 当前文件所在目录
root_path = os.path.split(os.path.realpath(__file__))[0]
# 训练数据数字基本图片目录
MODEL_PATH = root_path + '/models/'
CHAR_SET_LEN = len(CHAR_SET)
log("验证码文本集合长度 {0} 集合：{1}".format(CHAR_SET_LEN, CHAR_SET))
CAPTCHA_TEXT, IMAGE = gen_captcha()
log("验证码图像channel: {0}".format(IMAGE.shape)) # (60, 160, 3)
# 图像大小
IMAGE_HEIGHT, IMAGE_WIDTH, a = IMAGE.shape
CAPTCHA_TEXT_LEN = len(CAPTCHA_TEXT)
log("验证码文本字符数 ".format(CAPTCHA_TEXT_LEN)) # 验证码最长4字符
####################################################################
X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT * IMAGE_WIDTH])
Y = tf.placeholder(tf.float32, [None, CAPTCHA_TEXT_LEN * CHAR_SET_LEN])
keep_prob = tf.placeholder(tf.float32) # dropout
# 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: