
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 						lowercase_      (       )							->							str:
				"""simple docstring"""

				snake_case								= cn.convert_to_negative(A__		)
				# assert negative_img array for at least one True
				assert negative_img.any()



def 						lowercase_      (       )							->							Union[str, Any]:
				"""simple docstring"""

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



def 						lowercase_      (       )							->							Union[str, Any]:
				"""simple docstring"""

				snake_case								= canny.gen_gaussian_kernel(9			,		sigma=1.4		)
				# Assert ambiguous array
				assert resp.all()



def 						lowercase_      (       )							->							Union[str, Any]:
				"""simple docstring"""

				snake_case								= imread("digital_image_processing/image_data/lena_small.jpg"			,		0		)
				# assert ambiguous array for all == True
				assert canny_img.all()
				snake_case								= canny.canny(A__		)
				# assert canny array for at least one True
				assert canny_array.any()



def 						lowercase_      (       )							->							Union[str, Any]:
				"""simple docstring"""

				assert gg.gaussian_filter(A__			,		5			,		sigma=0.9		).all()



def 						lowercase_      (       )							->							Union[str, Any]:
				"""simple docstring"""

				snake_case								= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]		)
				snake_case								= conv.img_convolve(A__			,		A__		).astype(A__		)
				assert res.any()



def 						lowercase_      (       )							->							str:
				"""simple docstring"""

				assert med.median_filter(A__			,		3		).any()



def 						lowercase_      (       )							->							str:
				"""simple docstring"""

				snake_case					,							snake_case								= sob.sobel_filter(A__		)
				assert grad.any() and theta.any()



def 						lowercase_      (       )							->							Tuple:
				"""simple docstring"""

				snake_case								= sp.make_sepia(A__			,		20		)
				assert sepia.all()



def 						lowercase_      (       A__ = "digital_image_processing/image_data/lena_small.jpg"		)							->							Tuple:
				"""simple docstring"""

				snake_case								= bs.Burkes(imread(A__			,		1		)			,		120		)
				burkes.process()
				assert burkes.output_img.any()



def 						lowercase_      (       A__ = "digital_image_processing/image_data/lena_small.jpg"			,		)							->							Union[str, Any]:
				"""simple docstring"""

				snake_case								= rs.NearestNeighbour(imread(A__			,		1		)			,		400			,		200		)
				nn.process()
				assert nn.output.any()





def 						lowercase_      (       )							->							List[Any]:
				"""simple docstring"""

				snake_case								= "digital_image_processing/image_data/lena.jpg"

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

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

				snake_case								= 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								= 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								= lbp.local_binary_value(A__			,		A__			,		A__		)

				assert lbp_image.any()



def 						lowercase_      (       A__ = 1000		)							->							int:
    """simple docstring"""

    return sum(2 * a * ((a - 1) // 2) for a in range(3			,		n + 1		)		)


if __name__ == "__main__":
   print(solution())

