





from pathlib import Path

import numpy as np
from PIL import Image


def _A       (   __magic_name__	):
   lowercase__			       =  rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
   return 0.2_989 * r + 0.5_870 * g + 0.1_140 * b


def _A       (   __magic_name__	):
   return (gray > 127) & (gray <= 255)


def _A       (   __magic_name__  , __magic_name__	):
   lowercase__			       =  np.zeros_like(__lowerCAmelCase	)
   lowercase__			       =  np.zeros(
       (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

   # Copy image to padded image
   lowercase__			       =  image

   # Iterate over image & apply kernel
   for x in range(image.shape[1]	):
      for y in range(image.shape[0]	):
         lowercase__			       =  (
             kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
         ).sum()
         lowercase__			       =  int(summation > 0	)
   return output


if __name__ == "__main__":
      # read original image
      _snake_case           =			Path(__file__).resolve().parent / """image_data""" / """lena.jpg"""
      _snake_case           =			np.array(Image.open(lena_path))
      # kernel to be applied
      _snake_case           =			np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
      _snake_case           =			dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
      # Save the output image
      _snake_case           =			Image.fromarray(output).convert("""RGB""")
      pil_img.save("""result_dilation.png""")













from collections import namedtuple

import requests
from lxml import html  # type: ignore

_snake_case           =			namedtuple("""covid_data""", """cases deaths recovered""")


def _A       (   __magic_name__ = "https://www.worldometers.info/coronavirus/"	):
   lowercase__			       =  "//div[@class = \"maincounter-number\"]/span/text()"
   return covid_data(*html.fromstring(requests.get(__magic_name__	).content	).xpath(__magic_name__	)	)


_snake_case           =			"""Total COVID-19 cases in the world: {}
Total deaths due to COVID-19 in the world: {}
Total COVID-19 patients recovered in the world: {}"""







print(fmt.format(*covid_stats()))






