def multi_test(height=CAPTCHA_HEIGHT, width=CAPTCHA_WIDTH):
x = tf.placeholder(tf.float32, [None, height * width])
keep_prob = tf.placeholder(tf.float32)
y_conv = cnn_graph(x, keep_prob, (height, width))
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
while 1:
text, image = get_random_captcha_text_and_image()
image = convert2gray(image)
image = image.flatten() / 255
image_list = [image]
predict = tf.argmax(
tf.reshape(
y_conv,
[-1, CAPTCHA_LEN, len(CAPTCHA_LIST)]), 2)
vector_list = sess.run(predict,
feed_dict={
x: image_list,
keep_prob: 1
})
vector_list = vector_list.tolist()
text_list = [vec2text(vector) for vector in vector_list]
pre_text = text_list[0]
flag = u'错误'
if text == pre_text:
flag = u'正确'
print u"实际值(actual):%s, 预测值(predict):%s, 预测结果:%s" % (
text,
pre_text,
flag,
)
def captcha_to_text(image_list, height=CAPTCHA_HEIGHT, width=CAPTCHA_WIDTH):
'''
验证码图片转化为文本
:param image_list:
:param height:
:param width:
:return:
'''
x = tf.placeholder(tf.float32, [None, height * width])
keep_prob = tf.placeholder(tf.float32)
y_conv = cnn_graph(x, keep_prob, (height, width))
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
predict = tf.argmax(
tf.reshape(y_conv,
[-1, CAPTCHA_LEN, len(CAPTCHA_LIST)]), 2)
vector_list = sess.run(predict,
feed_dict={
x: image_list,
keep_prob: 1
})
vector_list = vector_list.tolist()
text_list = [vec2text(vector) for vector in vector_list]
return text_list[0]
def captcha2text(image_list, height=CAPTCHA_HEIGHT, width=CAPTCHA_WIDTH):
if not isdir('./model'):
print('Model directory does not exists.')
return
x = placeholder(float32, [None, height * width])
keep_prob = placeholder(float32)
y_conv = cnn_graph(x, keep_prob, (height, width))
saver = Saver()
with Session() as sess:
saver.restore(sess, latest_checkpoint('./model/'))
predict = argmax(reshape(
y_conv, [-1, CAPTCHA_LEN, len(CAPTCHA_LIST)]), 2)
vector_list = sess.run(predict,
feed_dict={
x: image_list,
keep_prob: 1
})
vector_list = vector_list.tolist()
text_list = [vec2text(vector) for vector in vector_list]
return text_list
def captcha2text(image_list, height=CAPTCHA_HEIGHT, width=CAPTCHA_WIDTH):
x = tf.placeholder(tf.float32, [None, height * width])
keep_prob = tf.placeholder(tf.float32)
y_conv = cnn_graph(x, keep_prob, (height, width))
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
predict = tf.argmax(
tf.reshape(y_conv,
[-1, CAPTCHA_LEN, len(CAPTCHA_LIST)]), 2)
vector_list = sess.run(predict,
feed_dict={
x: image_list,
keep_prob: 1
})
vector_list = vector_list.tolist()
text_list = [vec2text(vector) for vector in vector_list]
builder = tf.saved_model.builder.SavedModelBuilder("./saved_model/")
builder.add_meta_graph_and_variables(sess, ['cnnCaptcha'])
builder.save()
return text_list
def captcha2text(image_list, height=CAPTCHA_HEIGHT, width=CAPTCHA_WIDTH):
# disable the tensor flow 2 eager execution function
tf.compat.v1.disable_eager_execution()
x = tf.placeholder(tf.float32, [None, height * width])
keep_prob = tf.placeholder(tf.float32)
y_conv = cnn_graph(x, keep_prob, (height, width))
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('.'))
print(tf.train.latest_checkpoint('.'))
predict = tf.argmax(
tf.reshape(y_conv,
[-1, CAPTCHA_LEN, len(CAPTCHA_LIST)]), 2)
vector_list = sess.run(predict,
feed_dict={
x: image_list,
keep_prob: 1
})
vector_list = vector_list.tolist()
text_list = [vec2text(vector) for vector in vector_list]
return text_list