






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

__magic_name__   :  str        =							imread(r'''digital_image_processing/image_data/lena_small.jpg''')
__magic_name__   :  int        =							cvtColor(img, COLOR_BGR2GRAY)
def 		lowercase__  (				) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  cn.convert_to_negative(_UpperCamelCase)
      # 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(_UpperCamelCase       ,							1_10)).startswith(
                '<PIL.Image.Image image mode=RGB size=100x100 at')
def 		lowercase__  (				) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  canny.gen_gaussian_kernel(9       ,							sigma=1.4)
      # Assert ambiguous array
      assert resp.all()
def 		lowercase__  (				) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  imread('digital_image_processing/image_data/lena_small.jpg'       ,							0)
      # assert ambiguous array for all == True
      assert canny_img.all()
      UpperCamelCase					       =  canny.canny(_UpperCamelCase)
      # assert canny array for at least one True
      assert canny_array.any()
def 		lowercase__  (				) ->			Optional[int]:
      """simple docstring"""

      assert gg.gaussian_filter(_UpperCamelCase       ,							5       ,							sigma=0.9).all()
def 		lowercase__  (				) ->			Union[str, Any]:
      """simple docstring"""

      UpperCamelCase					       =  array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]])
      UpperCamelCase					       =  conv.img_convolve(_UpperCamelCase       ,							_UpperCamelCase).astype(_UpperCamelCase)
      assert res.any()
def 		lowercase__  (				) ->			int:
      """simple docstring"""

      assert med.median_filter(_UpperCamelCase       ,							3).any()
def 		lowercase__  (				) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  sob.sobel_filter(_UpperCamelCase)
      assert grad.any() and theta.any()
def 		lowercase__  (				) ->			List[Any]:
      """simple docstring"""

      UpperCamelCase					       =  sp.make_sepia(_UpperCamelCase       ,							20)
      assert sepia.all()
def 		lowercase__  (				_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg") ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  bs.Burkes(imread(_UpperCamelCase       ,							1)       ,							1_20)
      burkes.process()
      assert burkes.output_img.any()
def 		lowercase__  (				_UpperCamelCase = "digital_image_processing/image_data/lena_small.jpg"       ,							) ->			Optional[int]:
      """simple docstring"""

      UpperCamelCase					       =  rs.NearestNeighbour(imread(_UpperCamelCase       ,							1)       ,							4_00       ,							2_00)
      nn.process()
      assert nn.output.any()
def 		lowercase__  (				) ->			str:
      """simple docstring"""

      UpperCamelCase					       =  "digital_image_processing/image_data/lena.jpg"

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

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

      UpperCamelCase					       =  lbp.get_neighbors_pixel(
          _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

      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
      UpperCamelCase					       =  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]):
                  UpperCamelCase					       =  lbp.local_binary_value(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

      assert lbp_image.any()






from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	# A local function to see if a dot lands in the circle.
	def is_in_circle(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      ) -> bool:
		UpperCAmelCase__:	Any									= sqrt((x**2) + (y**2)      )
		# Our circle has a radius of 1, so a distance
		# greater than 1 would land outside the circle.
		return distance_from_centre <= 1

	# The proportion of guesses that landed in the circle
	UpperCAmelCase__:	Optional[int]									= mean(
	    int(is_in_circle(uniform(-1.0	,1.0      )	,uniform(-1.0	,1.0      )      )      )
	    for _ in range(SCREAMING_SNAKE_CASE      )      )
	# The ratio of the area for circle to square is pi/4.
	UpperCAmelCase__:	Optional[int]									= proportion * 4
	print(f"The estimated value of pi is {pi_estimate}"      )
	print(f"The numpy value of pi is {pi}"      )
	print(f"The total error is {abs(pi - pi_estimate      )}"      )



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0	,):
	return mean(
	    function_to_integrate(uniform(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )      ) for _ in range(SCREAMING_SNAKE_CASE      )      ) * (max_value - min_value)



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE = 0.0	,SCREAMING_SNAKE_CASE = 1.0      ):

	def identity_function(SCREAMING_SNAKE_CASE      ) -> float:
		return x

	UpperCAmelCase__:	Tuple									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE      )
	UpperCAmelCase__:	Optional[Any]									= (max_value * max_value - min_value * min_value) / 2

	print("******************"      )
	print(f"Estimating area under y=x where x varies from {min_value} to {max_value}"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {expected_value}"      )
	print(f"Total error is {abs(estimated_value - expected_value      )}"      )
	print("******************"      )



def 			_A	(      SCREAMING_SNAKE_CASE      ):

	def function_to_integrate(SCREAMING_SNAKE_CASE      ) -> float:
		return sqrt(4.0 - x * x      )

	UpperCAmelCase__:	Union[str, Any]									= area_under_curve_estimator(
	    SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,0.0	,2.0      )

	print("******************"      )
	print("Estimating pi using area_under_curve_estimator"      )
	print(f"Estimated value is {estimated_value}"      )
	print(f"Expected value is {pi}"      )
	print(f"Total error is {abs(estimated_value - pi      )}"      )
	print("******************"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()