



import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

SCREAMING_SNAKE_CASE__       =      imread(r'''digital_image_processing/image_data/lena_small.jpg''')
SCREAMING_SNAKE_CASE__       =      cvtColor(img, COLOR_BGR2GRAY)


def A						(       )						->      List[str]:
			A__       =    cn.convert_to_negative(__UpperCamelCase					)
			# assert negative_img array for at least one True
			assert negative_img.any()


def A						(       )						->      Tuple:
			with Image.open('digital_image_processing/image_data/lena_small.jpg'					) as img:
						# Work around assertion for response
						assert str(cc.change_contrast(__UpperCamelCase ,	110					)					).startswith(
						    '<PIL.Image.Image image mode=RGB size=100x100 at'					)


def A						(       )						->      Optional[int]:
			A__       =    canny.gen_gaussian_kernel(9 ,	sigma=1.4					)
			# Assert ambiguous array
			assert resp.all()


def A						(       )						->      Any:
			A__       =    imread('digital_image_processing/image_data/lena_small.jpg' ,	0					)
			# assert ambiguous array for all == True
			assert canny_img.all()
			A__       =    canny.canny(__UpperCamelCase					)
			# assert canny array for at least one True
			assert canny_array.any()


def A						(       )						->      List[str]:
			assert gg.gaussian_filter(__UpperCamelCase ,	5 ,	sigma=0.9					).all()


def A						(       )						->      Optional[int]:
			# laplace diagonals
			A__       =    array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]					)
			A__       =    conv.img_convolve(__UpperCamelCase ,	__UpperCamelCase					).astype(__UpperCamelCase					)
			assert res.any()


def A						(       )						->      Any:
			assert med.median_filter(__UpperCamelCase ,	3					).any()


def A						(       )						->      Optional[Any]:
			A__     , A__       =    sob.sobel_filter(__UpperCamelCase					)
			assert grad.any() and theta.any()


def A						(       )						->      Dict:
			A__       =    sp.make_sepia(__UpperCamelCase ,	20					)
			assert sepia.all()


def A						(       __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"					)						->      Optional[int]:
			A__       =    bs.Burkes(imread(__UpperCamelCase ,	1					) ,	120					)
			burkes.process()
			assert burkes.output_img.any()


def A						(       __UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg" ,	)						->      Optional[int]:
			A__       =    rs.NearestNeighbour(imread(__UpperCamelCase ,	1					) ,	400 ,	200					)
			nn.process()
			assert nn.output.any()






def A						(       )						->      List[str]:
			A__       =    'digital_image_processing/image_data/lena.jpg'

			# Reading the image and converting it to grayscale.
			A__       =    imread(__UpperCamelCase ,	0					)

			# Test for get_neighbors_pixel function() return not None
			A__       =    0
			A__       =    0
			A__       =    image[x_coordinate][y_coordinate]

			A__       =    lbp.get_neighbors_pixel(
			    __UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			A__       =    np.zeros((image.shape[0], image.shape[1])					)

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0 ,	image.shape[0]					):
						for j in range(0 ,	image.shape[1]					):
									A__       =    lbp.local_binary_value(__UpperCamelCase ,	__UpperCamelCase ,	__UpperCamelCase					)

			assert lbp_image.any()











import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       =      '''%20'''.join(argv[1:]) if len(argv) > 1 else quote(str(input('''Search: ''')))

							print('''Googling.....''')

							SCREAMING_SNAKE_CASE__       =      f'https://www.google.com/search?q={query}&num=100'

							SCREAMING_SNAKE_CASE__       =      requests.get(
							    url,
							    headers={'''User-Agent''': str(UserAgent().random)},
							)

							try:
														SCREAMING_SNAKE_CASE__       =      (
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''yuRUbf'''})
														    .find('''a''')
														    .get('''href''')
														)

							except AttributeError:
														SCREAMING_SNAKE_CASE__       =      parse_qs(
														    BeautifulSoup(res.text, '''html.parser''')
														    .find('''div''', attrs={'''class''': '''kCrYT'''})
														    .find('''a''')
														    .get('''href''')
														)['''url'''][0]

							webbrowser.open(link)




