Using Purine model to predict

PurineModel.py

#! /usr/bin/env python
#################################################################################
#     File Name           :     PlateModel.py
#     Created By          :     Hao Li
#     Creation Date       :     [2016-04-19 16:27]
#     Last Modified       :     [2016-04-23 15:17]
#     Description         :      
#################################################################################
from pymongo import MongoClient
import os
import sys
import numpy as np
import importlib
import ConfigParser
reload(sys)                         # 2
sys.setdefaultencoding('utf-8')     # 3

class PurineModel:

    def __init__(self):
        sys.path.append('./libs')
        self.net = importlib.import_module("purine.libautopurine")
        self.cf = ConfigParser.ConfigParser()
        self.cf.read('conf.ini')
        self.gray_reg_net_idx = self.net.init_model(self.cf.get('plate_detection', 'purine_gray_reg_model'), 0)

    def __del__(self):
        self.net.delete_model(self.gray_reg_net_idx)

    def getProb(self, imgpath):
        jpeg_contents = [open(imgpath).read()]
        prob = [[0 for i in range(73)]]
        self.net.predict(self.gray_reg_net_idx, jpeg_contents, prob, {'data_type':1, 'multi_view':0})
        return prob

    def predict(self, segments):
        probs = []
        for s in segments:
            probs.extend(self.getProb(s))
        probs = np.array(probs)
        return probs