





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

__UpperCAmelCase					:     Any	      =	imread(r'digital_image_processing/image_data/lena_small.jpg')
__UpperCAmelCase					:     Dict	      =	cvtColor(img, COLOR_BGR2GRAY)




def 						lowerCamelCase_	(		):
      _a							:  Union[str, Any]				= cn.convert_to_negative(__UpperCamelCase       )
      # assert negative_img array for at least one True
      assert negative_img.any()




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




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




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




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




def 						lowerCamelCase_	(		):
      _a							:  Optional[Any]				= array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]]       )
      _a							:  int				= conv.img_convolve(__UpperCamelCase			,					__UpperCamelCase       ).astype(__UpperCamelCase       )
      assert res.any()




def 						lowerCamelCase_	(		):
      assert med.median_filter(__UpperCamelCase			,					3       ).any()




def 						lowerCamelCase_	(		):
      _a	, _a							:  List[str]				= sob.sobel_filter(__UpperCamelCase       )
      assert grad.any() and theta.any()




def 						lowerCamelCase_	(		):
      _a							:  Union[str, Any]				= sp.make_sepia(__UpperCamelCase			,					20       )
      assert sepia.all()




def 						lowerCamelCase_	(		UpperCamelCase_ = "digital_image_processing/image_data/lena_small.jpg"       ):
      _a							:  Optional[int]				= bs.Burkes(imread(__UpperCamelCase			,					1       )			,					120       )
      burkes.process()
      assert burkes.output_img.any()




def 						lowerCamelCase_	(		UpperCamelCase_ = "digital_image_processing/image_data/lena_small.jpg"			,					):
      _a							:  Tuple				= rs.NearestNeighbour(imread(__UpperCamelCase			,					1       )			,					400			,					200       )
      nn.process()
      assert nn.output.any()





def 						lowerCamelCase_	(		):
      _a							:  int				= '''digital_image_processing/image_data/lena.jpg'''

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

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

      _a							:  Union[str, Any]				= 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
      _a							:  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]       ):
                  _a							:  str				= lbp.local_binary_value(__UpperCamelCase			,					__UpperCamelCase			,					__UpperCamelCase       )

      assert lbp_image.any()









'''simple docstring'''
from statistics import mean, stdev

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int = 3		)					->	list:







     """simple docstring"""
     _A				=							min(__UpperCamelCase		)
     _A				=							max(__UpperCamelCase		)
     # normalize data
     return [round((x - x_min) / (x_max - x_min)							,    __UpperCamelCase		) for x in data]

def 					lowerCamelCase_ ( __UpperCamelCase					:       list							,    __UpperCamelCase					:       int = 3		)					->	list:







     """simple docstring"""
     _A				=							mean(__UpperCamelCase		)
     _A				=							stdev(__UpperCamelCase		)
     # standardize data
     return [round((x - mu) / (sigma)							,    __UpperCamelCase		) for x in data]





