
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve






def     lowerCamelCase__				(   a ) ->  List[str]:
   _A:    Tuple						     =			np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]] )
   _A:    Union[str, Any]						     =			np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]] )

   _A:    Tuple						     =			np.abs(img_convolve(a						,						a ) )
   _A:    str						     =			np.abs(img_convolve(a						,						a ) )
   # modify the pix within [0, 255]
   _A:    Union[str, Any]						     =			dst_x * 2_55 / np.max(a )
   _A:    Any						     =			dst_y * 2_55 / np.max(a )

   _A:    List[str]						     =			np.sqrt((np.square(a )) + (np.square(a )) )
   _A:    str						     =			dst_xy * 2_55 / np.max(a )
   _A:    Optional[int]						     =			dst_xy.astype(np.uinta )

   _A:    List[str]						     =			np.arctana(a						,						a )
   return dst, theta


if __name__ == "__main__":
   # read original image
   UpperCAmelCase__						:	List[str]	     = imread('../image_data/lena.jpg')
   # turn image in gray scale value
   UpperCAmelCase__						:	Optional[Any]	     = cvtColor(img, COLOR_BGR2GRAY)

   UpperCAmelCase__     ,UpperCAmelCase__						:	Optional[int]	     = sobel_filter(gray)

   # show result images
   imshow('sobel filter', sobel_grad)
   imshow('sobel theta', sobel_theta)
   waitKey(0)





import math as m






def     lowerCamelCase__				(   a						,						a						,						a=0.001 ) ->  Optional[int]:
   return (f(a + h ) - f(a - h )) / (2 * h)






def     lowerCamelCase__				(   a						,						a=0						,						a=1_00						,						a=0.0001						,						a=1E-6						,						a=False ) ->  List[Any]:
   _A:    Optional[Any]						     =			xa  # set the initial guess
   _A:    Dict						     =			[a]
   _A:    Tuple						     =			abs(f(a ) )
   _A:    Tuple						     =			lambda a : calc_derivative(a						,						a						,						h=a )  # noqa: E731  Derivative of f(x)
   for _ in range(a ):
      if fa(a ) == 0:
         raise ValueError('''No converging solution found''' )
      _A:    Any						     =			a - f(a ) / fa(a )  # Calculate the next estimate
      if logsteps:
         steps.append(a )
      if error < maxerror:
         break
   else:
      raise ValueError('''Iteration limit reached, no converging solution found''' )
   if logsteps:
      # If logstep is true, then log intermediate steps
      return a, error, steps
   return a, error


if __name__ == "__main__":
   from matplotlib import pyplot as plt

   UpperCAmelCase__						:	Optional[Any]	     = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__						:	str	     = newton_raphson(
       f, xa=10, maxiter=1000, step=1E-6, logsteps=True
   )
   plt.plot([abs(f(x)) for x in steps])
   plt.xlabel('step')
   plt.ylabel('error')
   plt.show()
   print(F"""solution = {{{solution:f}}}, error = {{{error:f}}}""")



