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# First import the library
import pyrealsense2 as rs
import numpy as np
import cv2
import os
import shutil

import pyttsx3

from threading import Thread

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# gdrive mount

from google.colab import drive
drive.flush_and_unmount()
drive.mount('/content/drive', force_remount=True)

import os
os.chdir("/content/drive/My Drive/Computer Engineering/Karn's Research Assistance/Food Scanner/src/")
os.getcwd()

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from google.colab import drive
drive.mount('/content/drive', force_remount=True)

import os
os.chdir("/content/drive/My Drive/Computer Engineering/Karn's Research Assistance/CCC Grading/dev/segmentation")
os.getcwd()

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class MaxPoolingWithArgmax2D(Layer):

    def __init__(
            self,
            pool_size=(2, 2),
            strides=(2, 2),
            padding='same',
            **kwargs):
        super(MaxPoolingWithArgmax2D, self).__init__(**kwargs)
        self.padding = padding

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def create_model(
    pretrained_weights=None,
    input_size=(),
    num_classes=2,
    learning_rate=1e-4,
    batch_normalization=False,
    is_summary=True,
):
    # define params
    kernel = 3

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def create_model(
    pretrained_weights=None,
    input_size=(),
    num_classes=2,
    learning_rate=1e-4,
    batch_normalization=False,
    is_summary=True,
):
    # define params
    kernel = 3

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<?php
require_once 'vendor/autoload.php';

use PhpOffice\PhpSpreadsheet\Spreadsheet;
use PhpOffice\PhpSpreadsheet\Writer\Xlsx;

$spreadsheet = new Spreadsheet();
$sheet = $spreadsheet->getActiveSheet();
$sheet->setCellValue('A1', 'Hello World !');
$writer = new Xlsx($spreadsheet);

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<?php

require 'vendor/autoload.php';

use PhpOffice\PhpSpreadsheet\Spreadsheet;
use PhpOffice\PhpSpreadsheet\Writer\Xlsx;

$spreadsheet = new Spreadsheet();
$sheet = $spreadsheet->getActiveSheet();
$sheet->setCellValue('A1', 'Hello World !');

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ffmpeg -video_size 1366x768 -framerate 25 -f x11grab -i :0.0 -f pulse -ac 2 -i 0 output.mkv

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def nearest_upsampling(data, scale):
  """Nearest neighbor upsampling implementation.

  Args:
    data: A float32 tensor of size [batch, height_in, width_in, channels].
    scale: An integer multiple to scale resolution of input data.
  Returns:
    data_up: A float32 tensor of size
      [batch, height_in*scale, width_in*scale, channels].
  """