

'''simple docstring'''
import cva
import numpy as np







class 							A__   :





  def __init__(		self				:     Union[str, Any]							, _a				:     float							, _a				:     int			)     ->   Tuple:





       '''simple docstring'''






       if k in (0.04, 0.06):
            _SCREAMING_SNAKE_CASE							   =k
            _SCREAMING_SNAKE_CASE							   =window_size
       else:
            raise ValueError('invalid k value'			)





  def __str__(		self				:     Optional[Any]			)     ->   str:





       '''simple docstring'''






       return str(self.k			)





  def 						A  (		self				:     List[str]							, _a				:     str			)     ->   tuple[cva.Mat, list[list[int]]]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							   =cva.imread(_a							, 0			)
       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =img.shape
       _SCREAMING_SNAKE_CASE							   =[]
       _SCREAMING_SNAKE_CASE							   =img.copy()
       _SCREAMING_SNAKE_CASE							   =cva.cvtColor(_a							, cva.COLOR_GRAY2RGB			)
       _SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE							   =np.gradient(_a			)
       _SCREAMING_SNAKE_CASE							   =dx**2
       _SCREAMING_SNAKE_CASE							   =dy**2
       _SCREAMING_SNAKE_CASE							   =dx * dy
       _SCREAMING_SNAKE_CASE							   =0.04
       _SCREAMING_SNAKE_CASE							   =self.window_size // 2
       for y in range(_a							, h - offset			):
            for x in range(_a							, w - offset			):
                 _SCREAMING_SNAKE_CASE							   =ixx[
                     y - offset : y + offset + 1, x - offset : x + offset + 1
                 ].sum()
                 _SCREAMING_SNAKE_CASE							   =iyy[
                     y - offset : y + offset + 1, x - offset : x + offset + 1
                 ].sum()
                 _SCREAMING_SNAKE_CASE							   =ixy[
                     y - offset : y + offset + 1, x - offset : x + offset + 1
                 ].sum()

                 _SCREAMING_SNAKE_CASE							   =(wxx * wyy) - (wxy**2)
                 _SCREAMING_SNAKE_CASE							   =wxx + wyy
                 _SCREAMING_SNAKE_CASE							   =det - k * (trace**2)
                 # Can change the value
                 if r > 0.5:
                      corner_list.append([x, y, r]			)
                      color_img.itemset((y, x, 0)							, 0			)
                      color_img.itemset((y, x, 1)							, 0			)
                      color_img.itemset((y, x, 2)							, 255			)
       return color_img, corner_list


if __name__ == "__main__":
     lowerCamelCase			:				int 			=					HarrisCorner(0.0_4, 3)
     lowerCamelCase			,   lowerCamelCase			:				Optional[Any] 			=					edge_detect.detect("path_to_image")
     cva.imwrite("detect.png", color_img)



'''simple docstring'''
from math import factorial


def 	_lowerCAmelCase (						_UpperCamelCase						:					int							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					float    )				->      float:
     """simple docstring"""




     if successes > trials:
          raise ValueError('successes must be lower or equal to trials'    )
     if trials < 0 or successes < 0:
          raise ValueError('the function is defined for non-negative integers'    )
     if not isinstance(_UpperCamelCase							,			_UpperCamelCase    ) or not isinstance(_UpperCamelCase							,			_UpperCamelCase    ):
          raise ValueError('the function is defined for non-negative integers'    )
     if not 0 < prob < 1:
          raise ValueError('prob has to be in range of 1 - 0'    )
     _SCREAMING_SNAKE_CASE							   =(prob**successes) * ((1 - prob) ** (trials - successes))
     # Calculate the binomial coefficient: n! / k!(n-k)!
     _SCREAMING_SNAKE_CASE							   =float(factorial(_UpperCamelCase    )    )
     coefficient /= factorial(_UpperCamelCase    ) * factorial(trials - successes    )
     return probability * coefficient


if __name__ == "__main__":
     from doctest import testmod

     testmod()
     print("Probability of 2 successes out of 4 trails")
     print("with probability of 0.75 is:", end=" ")
     print(binomial_distribution(2, 4, 0.7_5))
