





import cva
import numpy as np

class  __A:

     def __init__(      self,       A,       A      ):





            """simple docstring"""

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

     def __str__(      self      ):





            """simple docstring"""

            return str(self.k      )



     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

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

                          _UpperCamelCase              =						(wxx * wyy) - (wxy**2)
                          _UpperCamelCase              =						wxx + wyy
                          _UpperCamelCase              =						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__":
       lowercase    : Dict    	=  HarrisCorner(0.04, 3)
       lowercase	,lowercase    : str    	=  edge_detect.detect("""path_to_image""")
       cva.imwrite("""detect.png""", color_img)













from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return math.sqrt(sum(pow(a - b    ,       2					) for a, b in zip(lowerCAmelCase    ,       lowerCAmelCase					)					)					)


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):

       if dataset.ndim != value_array.ndim:
              _UpperCamelCase              =						(
                  '''Wrong input data\'s dimensions... '''
                  f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
              )
              raise ValueError(lowerCAmelCase					)

       try:
              if dataset.shape[1] != value_array.shape[1]:
                     _UpperCamelCase              =						(
                         '''Wrong input data\'s shape... '''
                         f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
                     )
                     raise ValueError(lowerCAmelCase					)
       except IndexError:
              if dataset.ndim != value_array.ndim:
                     raise TypeError('''Wrong shape'''					)

       if dataset.dtype != value_array.dtype:
              _UpperCamelCase              =						(
                  '''Input data have different datatype... '''
                  f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
              )
              raise TypeError(lowerCAmelCase					)

       _UpperCamelCase              =						[]

       for value in value_array:
              _UpperCamelCase              =						euclidean(lowerCAmelCase    ,       dataset[0]					)
              _UpperCamelCase              =						dataset[0].tolist()

              for dataset_value in dataset[1:]:
                     _UpperCamelCase              =						euclidean(lowerCAmelCase    ,       lowerCAmelCase					)

                     if dist > temp_dist:
                            _UpperCamelCase              =						temp_dist
                            _UpperCamelCase              =						dataset_value.tolist()

              answer.append([vector, dist]					)

       return answer


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase					):
       return np.dot(lowerCAmelCase    ,       lowerCAmelCase					) / (norm(lowerCAmelCase					) * norm(lowerCAmelCase					))


if __name__ == "__main__":
       import doctest

       doctest.testmod()






