import numpy as np

import random

import copy

def data_resample4seg(train_datas, train_masks,index=-2, splitvalue=3, resample_rate=1, is_big=True):

    print('Before Resample datas:', len(train_datas))

    res_datas = copy.deepcopy(train_datas)

    res_masks = copy.deepcopy(train_masks)

    for train_data, train_mask in zip(train_datas,train_masks):

        if (is_big is True and int(train_data.split('_')[index]) >= splitvalue) or (is_big is False and int(train_data.split('_')[index]) < splitvalue):

            for i in range(resample_rate):

                res_datas.append(train_data)

                res_masks.append(train_mask)

    sorted(res_datas)

    sorted(res_masks)

    temp = list(zip(res_datas, res_masks))

    random.shuffle(temp)

    res_datas, res_masks = zip(*temp)

    print('After Resample datas:', len(res_datas))

    return res_datas, res_masks

def data_resample4cls(train_datas, index=-2, splitvalue=3, resample_rate=1, is_big=True):

    print('Before Resample datas:',len(train_datas))

    resample_datas = copy.deepcopy(train_datas)

    for train_data in train_datas:

        # print(train_data)

        value = int(train_data.split('_')[index])

        if (is_big is True and value >= splitvalue) or (is_big is False and value <= splitvalue):

            for i in range(resample_rate):

                resample_datas.append(train_data)

                # print(train_data)

    print('After Resample datas:', len(resample_datas))

    random.shuffle(resample_datas)

    return resample_datas