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# coding: UTF-8
__auhtor__="alfredplpl"

#See also:
#https://westus.dev.cognitive.microsoft.com/docs/services/5639d931ca73072154c1ce89/operations/56f23eb019845524ec61c4d7
#http://labs.eecs.tottori-u.ac.jp/sd/Member/oyamada/OpenCV/html/py_tutorials/py_gui/py_video_display/py_video_display.html

import httplib,cv2,json

## constants

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# -*- coding: utf-8 -*-
# This code is distributed under the 3-Clause BSD license (New BSD license).
# 基本的に作者の名前を書いていただければ、商用利用も可能です。なお、保証はいたしかねます。

__author__ = 'alfredplpl'

# This fucntion can use on Unix systems (Mac or  Linux) .
TIMEOUT_VAL=-1
def waitKey(miliseconds):

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using UnityEngine;
using UnityEngine.UI;

using UnityEngine.Windows.Speech;
using System.Collections.Generic;
using System.Linq;

public class STT : MonoBehaviour
{
	public UnityEngine.UI.Text resultDisplay;

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# License: MIT License
__author__="Alfred Increment"

import glob
import os
import datetime

dt_now = datetime.datetime.now()
dateString=dt_now.strftime('%Y-%m-%d')

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cv::Mat image=cv::imread("./image.png",-1);

cv::Rect rect(startColumn,startRow,image.cols,image.rows);
cv::Mat subdst=dst(rect);
image.copyTo(subdst);

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import cv2  
import numpy as np

class BagOfFeatures:
    """This is a class of Bag-of-Features by K-means for OpenCV"""
    codebookSize=0
    classifier=None
    def __init__(self, codebookSize):
        self.codebookSize=codebookSize
        self.classifier=cv2.KNearest()

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import torch

from torch.utils.data import Dataset
from torchvision import datasets, transforms
import torch.optim as optim

import cv2
from tqdm import tqdm

from denoising_diffusion_pytorch import Unet, GaussianDiffusion

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import copy

import torch
from denoising_diffusion_pytorch import Unet, GaussianDiffusion, Trainer

from torchvision import datasets, transforms
from torch.optim import Adam
from torch.utils.data import Dataset
from torch.utils import data
from torch.cuda.amp import GradScaler