import os.path as osp
import numpy as np
import numpy.random as npr
import PIL

import torch
import torchvision
import xml.etree.ElementTree as ET
import json
import copy
import math

def singleton(class_):
    instances = {}
    def getinstance(*args, **kwargs):
        if class_ not in instances:
            instances[class_] = class_(*args, **kwargs)
        return instances[class_]
    return getinstance

@singleton
class get_estimator(object):
    def __init__(self):
        self.estimator = {}

    def register(self, estimf):
        self.estimator[estimf.__name__] = estimf

    def __call__(self, cfg):
        if cfg is None:
            return None
        t = cfg.type
        return self.estimator[t](**cfg.args)

def register():
    def wrapper(class_):
        get_estimator().register(class_)
        return class_
    return wrapper