



"""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  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			):
						"""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(lowerCamelCase_     ,		1_1_0 ) ).startswith(
												    "<PIL.Image.Image image mode=RGB size=100x100 at" )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Dict						    = imread("digital_image_processing/image_data/lena_small.jpg"     ,		0 )
						# assert ambiguous array for all == True
						assert canny_img.all()
						snake_case   :			Optional[int]						    = canny.canny(lowerCamelCase_ )
						# assert canny array for at least one True
						assert canny_array.any()

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Any						    = array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]] )
						snake_case   :			Dict						    = conv.img_convolve(lowerCamelCase_     ,		lowerCamelCase_ ).astype(lowerCamelCase_ )
						assert res.any()

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case    ,			snake_case   :			Optional[Any]						    = sob.sobel_filter(lowerCamelCase_ )
						assert grad.any() and theta.any()

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Any						    = sp.make_sepia(lowerCamelCase_     ,		2_0 )
						assert sepia.all()

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str = "digital_image_processing/image_data/lena_small.jpg" ):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = bs.Burkes(imread(lowerCamelCase_     ,		1 )     ,		1_2_0 )
						burkes.process()
						assert burkes.output_img.any()

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str = "digital_image_processing/image_data/lena_small.jpg"     ,		):
						"""simple docstring"""






						snake_case   :			Any						    = rs.NearestNeighbour(imread(lowerCamelCase_     ,		1 )     ,		4_0_0     ,		2_0_0 )
						nn.process()
						assert nn.output.any()

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						snake_case   :			Optional[int]						    = "digital_image_processing/image_data/lena.jpg"

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

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

						snake_case   :			Tuple						    = lbp.get_neighbors_pixel(
						    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )

						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   :			List[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   :			Dict						    = lbp.local_binary_value(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_ )

						assert lbp_image.any()








"""simple docstring"""







from random import randint, random

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							bool = False     ,		lowerCamelCase_:							bool = False     ,		lowerCamelCase_:							int = 5     ,		):
						"""simple docstring"""






						snake_case   :			str						    = [[-1] * number_of_cells]  # Create a highway without any car
						snake_case   :			str						    = 0
						snake_case   :			Any						    = max(lowerCamelCase_     ,		0 )
						while i < number_of_cells:
												snake_case   :			Optional[int]						    = (
												    randint(0     ,		lowerCamelCase_ ) if random_speed else initial_speed
												)  # Place the cars
												i += (
												    randint(1     ,		max_speed * 2 ) if random_frequency else frequency
												)  # Arbitrary number, may need tuning
						return highway

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Any						    = 0
						snake_case   :			int						    = highway_now[car_index + 1 :]
						for cell in range(len(lowerCamelCase_ ) ):  # May need a better name for this
												if cells[cell] != -1:  # If the cell is not empty then
																		return distance  # we have the distance we wanted
												distance += 1
						# Here if the car is near the end of the highway
						return distance + get_distance(lowerCamelCase_     ,		-1 )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			List[str]						    = len(lowerCamelCase_ )
						# Beforce calculations, the highway is empty
						snake_case   :			Dict						    = [-1] * number_of_cells

						for car_index in range(lowerCamelCase_ ):
												if highway_now[car_index] != -1:
																		# Add 1 to the current speed of the car and cap the speed
																		snake_case   :			Tuple						    = min(highway_now[car_index] + 1     ,		lowerCamelCase_ )
																		# Number of empty cell before the next car
																		snake_case   :			int						    = get_distance(lowerCamelCase_     ,		lowerCamelCase_ ) - 1
																		# We can't have the car causing an accident
																		snake_case   :			str						    = min(next_highway[car_index]     ,		lowerCamelCase_ )
																		if random() < probability:
																								# Randomly, a driver will slow down
																								snake_case   :			Optional[Any]						    = max(next_highway[car_index] - 1     ,		0 )
						return next_highway

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							list     ,		lowerCamelCase_:							int     ,		lowerCamelCase_:							float     ,		lowerCamelCase_:							int ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = len(highway[0] )

						for i in range(lowerCamelCase_ ):
												snake_case   :			Union[str, Any]						    = update(highway[i]     ,		lowerCamelCase_     ,		lowerCamelCase_ )
												snake_case   :			Optional[int]						    = [-1] * number_of_cells

												for car_index in range(lowerCamelCase_ ):
																		snake_case   :			List[Any]						    = next_speeds_calculated[car_index]
																		if speed != -1:
																								# Change the position based on the speed (with % to create the loop)
																								snake_case   :			Optional[Any]						    = (car_index + speed) % number_of_cells
																								# Commit the change of position
																								snake_case   :			Tuple						    = speed
												highway.append(lowerCamelCase_ )

						return highway


if __name__ == "__main__":
			import doctest

			doctest.testmod()



