def batch_hack_captcha():
# 定义预测计算图
global_step = tf.Variable(0, trainable=False)
output = crack_captcha_cnn()
predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto(device_count={'gpu':0})) as sess:
# saver = tf.train.import_meta_graph(save_model + ".meta")
saver.restore(sess, tf.train.latest_checkpoint(model_path))
stime = time.time()
task_cnt = 100
right_cnt = 0
for i in range(task_cnt):
text, image = wrap_gen_captcha_text_and_image()
image = image.flatten() / 255
predict_text = hack_function(sess, predict, image)
if text == predict_text:
print()
text=text.replace('_','')
print("----MATCH: {}".format(text))
print()
right_cnt += 1
else:
print("标记: {} 预测: {}".format(text, predict_text))
print('task:', task_cnt, ' cost time:', (time.time() - stime), 's')
print('right/total-----', right_cnt, '/', task_cnt)
def test_hack_captcha_training_data(sess, output):
"""
批量生成验证码，然后再批量进行识别
:return:
"""
# 定义预测计算图
predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
stime = time.time()
right_cnt = 0
task_cnt = 300
for i in range(task_cnt):
text, image = wrap_gen_captcha_text_and_image()
#image = convert2gray(image)
image = image.flatten() / 255
predict_text = hack_function(sess, predict, image)
#predict_text=predict_text.replace('_','')
if text == predict_text:
print("----标记: {} 预测: {}".format(text, predict_text))
right_cnt += 1
else:
print("标记: {} 预测: {}".format(text, predict_text))
#print('task:', task_cnt, ' cost time:', (time.time() - stime), 's')
#print('right/total-----', right_cnt, '/', task_cnt)
return right_cnt / task_cnt
def get_next_batch_from_web(batch_size=64):
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
text, image = wrap_gen_captcha_text_and_image(batch_size)
for i in range(batch_size):
batch_x[i, :] = image[i].flatten(
) / 255 # (image.flatten()-128)/128 mean为0
batch_y[i, :] = text2vec(text[i])
return batch_x, batch_y
def get_next_batch(batch_size=128):
"""
# 生成一个训练batch
:param batch_size:
:return:
"""
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
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为0
batch_y[i, :] = text2vec(text)
return batch_x, batch_y
def get_next_batch(batch_size=128):
"""
# 生成一个训练batch
:param batch_size:
:return:
"""
batch_x = np.zeros([batch_size, IMAGE_HEIGHT * IMAGE_WIDTH])
batch_y = np.zeros([batch_size, MAX_CAPTCHA * CHAR_SET_LEN])
for i in range(batch_size):
text, image = wrap_gen_captcha_text_and_image()
if False: #此处改为Ture,用以输出查看训练图片
image_ = Image.fromarray(image)
image_.save('./test_out/%s.jpg' % text)
#exit()
batch_x[i, :] = image.flatten() / 255
batch_y[i, :] = text2vec(text)
return batch_x, batch_y
