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import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
import random
# import
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as T
#from torch.utils.data import DataLoader, SubsetRandomSampler
#from torch.utils.tensorboard import SummaryWriter
import pandas as pd
import numpy as np
import cv2 as cv
from PIL import Image,ImageDraw,ImageFont
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.cluster import KMeans
#from sklearn.externals import joblib
import pickle
# other
import tr
#import easyocr
#class mytr:
# def __init__(self):
# self._reader = easyocr.Reader(['ch_sim', 'en'])
# pass
# def recognize(self, img):
# ret = self._reader.readtext(np.array(img), detail=0)
# print(ret)
# return ret
#tr = mytr() |