





import cva
import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__):



             """simple docstring"""





             if k in (0.04, 0.06):
                    _lowerCamelCase				:  Optional[Any]						=		k
                    _lowerCamelCase				:  int						=		window_size
             else:
                    raise ValueError('''invalid k value''')


      def __str__(		self):



             """simple docstring"""





             return str(self.k)



      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		cva.imread(a__   ,				0)
             _lowerCamelCase,	_lowerCamelCase				:  List[str]						=		img.shape
             _lowerCamelCase				:  list[list[int]]						=		[]
             _lowerCamelCase				:  str						=		img.copy()
             _lowerCamelCase				:  str						=		cva.cvtColor(a__   ,				cva.COLOR_GRAY2RGB)
             _lowerCamelCase,	_lowerCamelCase				:  Tuple						=		np.gradient(a__)
             _lowerCamelCase				:  Optional[int]						=		dx**2
             _lowerCamelCase				:  Any						=		dy**2
             _lowerCamelCase				:  Optional[Any]						=		dx * dy
             _lowerCamelCase				:  Any						=		0.04
             _lowerCamelCase				:  Any						=		self.window_size // 2
             for y in range(a__   ,				h - offset):
                    for x in range(a__   ,				w - offset):
                           _lowerCamelCase				:  List[str]						=		ixx[
                               y - offset : y + offset + 1, x - offset : x + offset + 1
                           ].sum()
                           _lowerCamelCase				:  str						=		iyy[
                               y - offset : y + offset + 1, x - offset : x + offset + 1
                           ].sum()
                           _lowerCamelCase				:  Optional[Any]						=		ixy[
                               y - offset : y + offset + 1, x - offset : x + offset + 1
                           ].sum()

                           _lowerCamelCase				:  List[str]						=		(wxx * wyy) - (wxy**2)
                           _lowerCamelCase				:  int						=		wxx + wyy
                           _lowerCamelCase				:  List[str]						=		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      							=   HarrisCorner(0.04, 3)
       _lowerCamelCase							,						_lowerCamelCase      							=   edge_detect.detect('path_to_image')
       cva.imwrite('detect.png', color_img)








import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DeiTImageProcessor instead.'''   ,				a__   ,				)
             super().__init__(*a__   ,				**a__)

