





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 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Optional[int]     =		cn.convert_to_negative(__a				)
	# assert negative_img array for at least one True
	assert negative_img.any()


def 							SCREAMING_SNAKE_CASE__  (      ):
	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 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     int     =		canny.gen_gaussian_kernel(9				,	sigma=1.4				)
	# Assert ambiguous array
	assert resp.all()


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Optional[Any]     =		imread('digital_image_processing/image_data/lena_small.jpg'				,	0				)
	# assert ambiguous array for all == True
	assert canny_img.all()
	snake_case_						:     str     =		canny.canny(__a				)
	# assert canny array for at least one True
	assert canny_array.any()


def 							SCREAMING_SNAKE_CASE__  (      ):
	assert gg.gaussian_filter(__a				,	5				,	sigma=0.9				).all()


def 							SCREAMING_SNAKE_CASE__  (      ):
	# laplace diagonals
	snake_case_						:     Union[str, Any]     =		array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]				)
	snake_case_						:     List[Any]     =		conv.img_convolve(__a				,	__a				).astype(__a				)
	assert res.any()


def 							SCREAMING_SNAKE_CASE__  (      ):
	assert med.median_filter(__a				,	3				).any()


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_  ,snake_case_						:     Union[str, Any]     =		sob.sobel_filter(__a				)
	assert grad.any() and theta.any()


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Union[str, Any]     =		sp.make_sepia(__a				,	20				)
	assert sepia.all()


def 							SCREAMING_SNAKE_CASE__  (      __a = "digital_image_processing/image_data/lena_small.jpg"				):
	snake_case_						:     Optional[int]     =		bs.Burkes(imread(__a				,	1				)				,	1_20				)
	burkes.process()
	assert burkes.output_img.any()


def 							SCREAMING_SNAKE_CASE__  (      __a = "digital_image_processing/image_data/lena_small.jpg"				,	):
	snake_case_						:     Union[str, Any]     =		rs.NearestNeighbour(imread(__a				,	1				)				,	4_00				,	2_00				)
	nn.process()
	assert nn.output.any()


def 							SCREAMING_SNAKE_CASE__  (      ):
	snake_case_						:     Union[str, Any]     =		'digital_image_processing/image_data/lena.jpg'

	# Reading the image and converting it to grayscale.
	snake_case_						:     str     =		imread(__a				,	0				)

	# Test for get_neighbors_pixel function() return not None
	snake_case_						:     List[Any]     =		0
	snake_case_						:     Optional[int]     =		0
	snake_case_						:     List[Any]     =		image[x_coordinate][y_coordinate]

	snake_case_						:     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
	snake_case_						:     Any     =		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]				):
			snake_case_						:     List[str]     =		lbp.local_binary_value(__a				,	__a				,	__a				)

	assert lbp_image.any()








from decimal import Decimal, getcontext
from math import ceil, factorial


def 							SCREAMING_SNAKE_CASE__  (      __a				):

	if not isinstance(__a				,	__a				):
		raise TypeError('Undefined for non-integers'				)
	elif precision < 1:
		raise ValueError('Undefined for non-natural numbers'				)

	snake_case_						:     Dict     =		precision
	snake_case_						:     str     =		ceil(precision / 14				)
	snake_case_						:     str     =		42_68_80 * Decimal(1_00_05				).sqrt()
	snake_case_						:     Tuple     =		1
	snake_case_						:     int     =		13_59_14_09
	snake_case_						:     Tuple     =		Decimal(__a				)
	for k in range(1				,	__a				):
		snake_case_						:     List[Any]     =		factorial(6 * k				) // (factorial(3 * k				) * factorial(__a				) ** 3)
		linear_term += 5_45_14_01_34
		exponential_term *= -26_25_37_41_26_40_76_80_00
		partial_sum += Decimal(multinomial_term * linear_term				) / exponential_term
	return str(constant_term / partial_sum				)[:-1]


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						50
						print(F'''The first {n} digits of pi is: {pi(n)}''')

