
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

__lowerCAmelCase					   =       imread(R'digital_image_processing/image_data/lena_small.jpg')
__lowerCAmelCase					   =       cvtColor(img, COLOR_BGR2GRAY)


def    a      ( )     ->List[Any]:




			'''simple docstring'''


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


def    a      ( )     ->Tuple:




			'''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(_SCREAMING_SNAKE_CASE		,						110	)	).startswith(
						    '''<PIL.Image.Image image mode=RGB size=100x100 at'''	)


def    a      ( )     ->Any:




			'''simple docstring'''


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


def    a      ( )     ->Dict:




			'''simple docstring'''


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


def    a      ( )     ->Any:




			'''simple docstring'''


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


def    a      ( )     ->str:




			'''simple docstring'''


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


def    a      ( )     ->str:




			'''simple docstring'''


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


def    a      ( )     ->str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= sob.sobel_filter(_SCREAMING_SNAKE_CASE	)
			assert grad.any() and theta.any()


def    a      ( )     ->str:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= sp.make_sepia(_SCREAMING_SNAKE_CASE		,						20	)
			assert sepia.all()


def    a      ( a = "digital_image_processing/image_data/lena_small.jpg"	)     ->Dict:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= bs.Burkes(imread(_SCREAMING_SNAKE_CASE		,						1	)		,						120	)
			burkes.process()
			assert burkes.output_img.any()


def    a      ( a = "digital_image_processing/image_data/lena_small.jpg"		,						)     ->str:




			'''simple docstring'''


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





def    a      ( )     ->Any:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= """digital_image_processing/image_data/lena.jpg"""

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

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

			SCREAMING_SNAKE_CASE     						= lbp.get_neighbors_pixel(
			    _SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE	)

			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
			SCREAMING_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]	):
									SCREAMING_SNAKE_CASE     						= lbp.local_binary_value(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE	)

			assert lbp_image.any()
import numpy as np






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				np.array			):
						return (2 / (1 + np.exp(-2 * vector			))) - 1


if __name__ == "__main__":
	import doctest

	doctest.testmod()






