def train_net(self):
#读取数据集splitcode，获取分割图片路径
all_split_path = self.db.splitcode.find({}, {"_id": 0})
if all_split_path:
list_code_path = []
list_split_path = []
for each_code in all_split_path:
list_code_path.append(each_code["codepath"])
for each_split_path in each_code["splitpath"]:
list_split_path.append(each_split_path)
#读取数据集downcode，获取验证码明码text
list_text_num = []
for each_code in list_code_path:
results = self.db.downcode.find_one({"codepath": each_code}, {"_id": 0, "text": 1})
if results:
for each in results["text"]:
each = each * 1.0 / 100
each_text = []
each_text.append(each)
list_text_num.append(each_text)
#生成输入数据
list_train_input_data = []
for each_code in list_split_path:
each_code_temp = "../%s" % each_code
image = Image.open(each_code_temp)
each_train_input_data = []
for x in xrange(image.size[1]):
for y in xrange(image.size[0]):
if image.getpixel((y, x)):
each_train_input_data.append(0)
else:
each_train_input_data.append(1)
list_train_input_data.append(each_train_input_data)
#生成目标数据
list_train_output_data = list_text_num
for each in list_train_input_data:
print len(each)
#开始训练
net_size = [10, 1]
neural = NeuralWork()
net = neural.train_net(list_train_input_data, list_train_output_data, net_size)
def reg_code(self, list_split_image):
#识别
all_plain_text = "0123456789abcdef"
reg_plain_text = []
neural = NeuralWork()
list_input_data = []
for each_split_image in list_split_image:
each_input_data = []
for x in xrange(each_split_image.size[1]):
for y in xrange(each_split_image.size[0]):
if each_split_image.getpixel((y, x)):
each_input_data.append(0)
else:
each_input_data.append(1)
list_input_data.append(each_input_data)
out = neural.reg_net(list_input_data)
for each in out:
plain_text = int(round(each[0] * 100))
if plain_text < 16:
reg_plain_text.append(all_plain_text[plain_text])
return reg_plain_text
