def hack_function(sess, predict, captcha_image):
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
return vec2text(vector)
def predict_func(captcha_image, _sess, _predict, _x, _keep_prob):
text_list = _sess.run(_predict,
feed_dict={
_x: [captcha_image],
_keep_prob: 1
})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA_LEN * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
return vec2text(vector)
def hack_function(sess, predict, captcha_image):
"""
装载完成识别内容后，
:param sess:
:param predict:
:param captcha_image:
:return:
"""
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
text = text_list[0].tolist()
vector = np.zeros(MAX_CAPTCHA * CHAR_SET_LEN)
i = 0
for n in text:
vector[i * CHAR_SET_LEN + n] = 1
i += 1
#print(vector)
return vec2text(vector)
def predict_captcha():
"""
识别测试集路径的验证码
:return:
"""
output = cnn_architecture.crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# 获取训练后参数路径
checkpoint = tf.train.get_checkpoint_state("model")
if checkpoint and checkpoint.model_checkpoint_path:
saver.restore(sess, checkpoint.model_checkpoint_path)
print("Successfully loaded:", checkpoint.model_checkpoint_path)
else:
print("Could not find CNN network Model.")
n = 0
test_image_files = os.listdir(config.test_data_path)
for f in test_image_files:
image = Image.open(os.path.join(config.test_data_path, f))
image = np.array(image)
image = utils.convert2gray(image)
image = image.flatten()
time1 = time.time()
predict = tf.argmax(
tf.reshape(output,
[-1, config.MAX_CAPTCHA, config.CHAR_SET_LEN]), 2)
text_list = sess.run(predict,
feed_dict={
cnn_architecture.X: [image],
cnn_architecture.keep_prob: 1
})
predict_text = utils.vec2text(text_list)
time2 = time.time()
elapsed = time2 - time1
print("{} predict:{} elapsed time: {} ms".format(
f, predict_text, format(elapsed * 1000, '0.2f')))
index = f.rfind(".")
if predict_text == f[index - config.MAX_CAPTCHA:index]:
n += 1
print("ACC {}".format(
n / (len(test_image_files) if len(test_image_files) > 0 else 1)))
def get_captcha(image_path):
"""
识别测试集路径的验证码
:return:
"""
tf.reset_default_graph()
image = Image.open(os.path.join(config.test_data_path, image_path))
image = np.array(image)
image = utils.convert2gray(image)
image = image.flatten()
image = np.reshape(image, (-1, config.IMAGE_HEIGHT, config.IMAGE_WIDTH, 1))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# 获取训练后参数路径
checkpoint = tf.train.get_checkpoint_state("model")
if checkpoint and checkpoint.model_checkpoint_path:
saver = tf.train.import_meta_graph(
checkpoint.model_checkpoint_path + '.meta')
saver.restore(sess, checkpoint.model_checkpoint_path)
print("Successfully loaded:", checkpoint.model_checkpoint_path)
graph = tf.get_default_graph() # 加载默认图
input_x = graph.get_tensor_by_name("input_x:0")
out_y = graph.get_tensor_by_name("out_y:0")
keep_prob = graph.get_tensor_by_name("keep_prob:0")
predict = tf.argmax(
tf.reshape(out_y,
[-1, config.MAX_CAPTCHA, config.CHAR_SET_LEN]), 2)
text_list = sess.run(predict,
feed_dict={
input_x: image,
keep_prob: 1
})
predict_text = utils.vec2text(text_list)
return predict_text
else:
print("Could not find CNN network Model.")
return None