def captcha2text(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('model/'))
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
def captcha2text(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, RESTORE_PATH)
success = 0
error = 0
for num in range(0, len(os.listdir(IMAGE_TEST_PATH))):
text, image = read_dir_image_for_position(num, IMAGE_TEST_PATH)
image = convert2gray(image)
image = image.flatten() / 255
image = [image]
predict = tf.argmax(
tf.reshape(
y_conv,
[-1, CAPTCHA_LEN, len(CAPTCHA_LIST)]), 2)
vector_list = sess.run(predict, feed_dict={x: image, keep_prob: 1})
vector_list = vector_list.tolist()
pre_text = [vec2text(vector) for vector in vector_list]
if (text == ''.join(pre_text)):
success += 1
else:
error += 1
print('Label:', text, ' Predict:', pre_text)
print('total:', success + error, ',success:', success, ',error:',
error)
def image2text(image, height=CAPTCHA_HEIGHT, width=CAPTCHA_WIDTH):
pre_text = '1111'
tf.reset_default_graph()
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, RESTORE_PATH)
image = convert2gray(image)
image = image.flatten() / 255
image = [image]
predict = tf.argmax(
tf.reshape(y_conv,
[-1, CAPTCHA_LEN, len(CAPTCHA_LIST)]), 2)
vector_list = sess.run(predict, feed_dict={x: image, keep_prob: 1})
vector_list = vector_list.tolist()
pre_text = [vec2text(vector) for vector in vector_list]
return pre_text
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
# -*- coding:utf-8 -*-
import numpy as np
from PIL import Image
from util import vec2text, text2vec
import tensorflow as tf
img = Image.open('./q7wy.jpg')
img_data = np.array(img)
# if len(img_data) > 2:
# img_data = np.mean(img_data, -1)
#
# img = Image.fromarray(img_data)
# img.show()
# print(img_data.shape)
code = '1234'
vec2 = text2vec(code)
print(vec2)
y = vec2text(vec2)
# t = tf.reshape(code, [2, 2])
print(y)