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 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 test_crack_captcha(test_step):
output = crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('./models'))
predict = tf.argmax(
tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
sum_correct = 0
# for _ in range(test_step):
for g_image in gen_image('./captcha_image'):
text_source, image = g_image
# print(text_source, image)
image = convert2gray(image)
captcha_image = image.flatten() / 255
text_list = sess.run(predict,
feed_dict={
X: [captcha_image],
keep_prob: 1
})
# text_list = sess.run(predict, feed_dict={X: np.reshape(captcha_image,(-1,4000)), 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
predict_text = vec2text(vector)
print("正确: {} 预测: {}".format(text_source, predict_text))
if text_source.lower() == predict_text.lower():
sum_correct += 1
print('sum_correct:%s' % sum_correct)
print('成功率：%s' % (sum_correct / test_step))
def crack_captcha():
output = crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint('models'))
predict = tf.argmax(
tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
count_out = 0
right_number = 0
while (True):
root_text, image = gen_captcha_text_and_image()
image = convert2gray(image)
captcha_image = image.flatten() / 255
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
predict_text = vec2text(vector)
print("正确: {} 预测: {}".format(root_text, predict_text))
if count_out == 1000:
break
count_out += 1
def test():
img_base64 = request.form['img_base64']
# return img_base64
img_base64 = re.sub('^data:image/.+;base64,', '', img_base64)
img_base64 = base64.b64decode(img_base64)
buffer = cStringIO.StringIO(img_base64)
img = Image.open(buffer)
img = np.array(img)
img = convert2gray(img)
img = img.flatten() / 255
text_list = sess.run(pre, feed_dict={X: [img], keep_prop: 1})
text = text_list[0].tolist()
vector = np.zeros(6 * 36)
i = 0
for n in text:
vector[i * 36 + n] = 1
i += 1
text = vec2text(vector)
res = {"code": 200, "data": text, "msg": None}
return json.dumps(res)
def predict_result(captcha_image):
output = crack_captcha_cnn()
saver = tf.train.Saver()
# tf.reset_default_graph()
with tf.Session() as sess:
saver.restore(sess, "F:/CNN_2/model_3/crack_capcha.model-2000")
predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})
# text_list = sess.run(predict, feed_dict={X: [captcha_image]})
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 crack_captcha(captcha_image):
output = crack_captcha_cnn()
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, tf.train.latest_checkpoint(model_path))
predict = tf.argmax(
tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
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)
