





"""simple docstring"""






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

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

def      __A   (      )		->		Optional[int]:
				__a			:    List[str]      							=	cn.convert_to_negative(a_)
				# assert negative_img array for at least one True
				assert negative_img.any()

def      __A   (      )		->		List[Any]:
				with Image.open('''digital_image_processing/image_data/lena_small.jpg''') as img:
								# Work around assertion for response
								assert str(cc.change_contrast(a_ ,							1_10)).startswith(
								    '''<PIL.Image.Image image mode=RGB size=100x100 at''')

def      __A   (      )		->		List[Any]:
				__a			:    int      							=	canny.gen_gaussian_kernel(9 ,							sigma=1.4)
				# Assert ambiguous array
				assert resp.all()

def      __A   (      )		->		Dict:
				__a			:    Dict      							=	imread('''digital_image_processing/image_data/lena_small.jpg''' ,							0)
				# assert ambiguous array for all == True
				assert canny_img.all()
				__a			:    List[str]      							=	canny.canny(a_)
				# assert canny array for at least one True
				assert canny_array.any()

def      __A   (      )		->		Dict:
				assert gg.gaussian_filter(a_ ,							5 ,							sigma=0.9).all()

def      __A   (      )		->		str:
				# laplace diagonals
				__a			:    Dict      							=	array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]])
				__a			:    Optional[int]      							=	conv.img_convolve(a_ ,							a_).astype(a_)
				assert res.any()

def      __A   (      )		->		Dict:
				assert med.median_filter(a_ ,							3).any()

def      __A   (      )		->		Union[str, Any]:
				__a							,    __a			:    List[Any]      							=	sob.sobel_filter(a_)
				assert grad.any() and theta.any()

def      __A   (      )		->		Tuple:
				__a			:    Dict      							=	sp.make_sepia(a_ ,							20)
				assert sepia.all()

def      __A   (      a_    :str = "digital_image_processing/image_data/lena_small.jpg")		->		Tuple:
				__a			:    Any      							=	bs.Burkes(imread(a_ ,							1) ,							1_20)
				burkes.process()
				assert burkes.output_img.any()

def      __A   (      a_    :str = "digital_image_processing/image_data/lena_small.jpg" ,							)		->		str:
				__a			:    List[Any]      							=	rs.NearestNeighbour(imread(a_ ,							1) ,							4_00 ,							2_00)
				nn.process()
				assert nn.output.any()






def      __A   (      )		->		List[Any]:
				__a			:    List[Any]      							=	'''digital_image_processing/image_data/lena.jpg'''

				# Reading the image and converting it to grayscale.
				__a			:    Union[str, Any]      							=	imread(a_ ,							0)

				# Test for get_neighbors_pixel function() return not None
				__a			:    Union[str, Any]      							=	0
				__a			:    Union[str, Any]      							=	0
				__a			:    List[Any]      							=	image[x_coordinate][y_coordinate]

				__a			:    Optional[int]      							=	lbp.get_neighbors_pixel(
				    a_ ,							a_ ,							a_ ,							a_)

				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			:    str      							=	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			:    Dict      							=	lbp.local_binary_value(a_ ,							a_ ,							a_)

				assert lbp_image.any()


from __future__ import annotations







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None   ):
      '''simple docstring'''







      if start is None:
            A_							       =		0

      if end is None:
            A_							       =		len(SCREAMING_SNAKE_CASE   ) - 1

      if start >= end:
            return

      A_							       =		(start + end) // 2

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
      slowsort(SCREAMING_SNAKE_CASE		,				mid + 1		,				SCREAMING_SNAKE_CASE   )

      if sequence[end] < sequence[mid]:
            A_       ,A_							       =		sequence[mid], sequence[end]

      slowsort(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				end - 1   )


if __name__ == "__main__":
      from doctest import testmod

      testmod()





