




import cva
import numpy as np





class 					__lowerCamelCase			:




    """simple docstring"""

    def __init__(			self	:	Any  ,      SCREAMING_SNAKE_CASE__	:	float  ,      SCREAMING_SNAKE_CASE__	:	int  ) ->			Optional[Any]:

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


    def __str__(			self	:	List[str]  ) ->			str:
         return str(self.k  )


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

                   lowerCAmelCase__            =					(wxx * wyy) - (wxy**2)
                   lowerCAmelCase__            =					wxx + wyy
                   lowerCAmelCase__            =					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__":
     UpperCamelCase								=	HarrisCorner(0.04, 3)
     UpperCamelCase		,    UpperCamelCase								=	edge_detect.detect('path_to_image')
     cva.imwrite('detect.png', color_img)







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase 					=      logging.get_logger(__name__)




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "encoder-decoder"
							lowerCamelCase_							     =       True


							def __init__(				self     :Optional[int]				,					**__A     :str		) -> int:



									"""simple docstring"""



									super().__init__(**__A		)
									assert (
									    "encoder" in kwargs and "decoder" in kwargs
									), "Config has to be initialized with encoder and decoder config"
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""encoder"""		)
									SCREAMING_SNAKE_CASE__								=   encoder_config.pop("""model_type"""		)
									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""decoder"""		)
									SCREAMING_SNAKE_CASE__								=   decoder_config.pop("""model_type"""		)

									from ..auto.configuration_auto import AutoConfig

									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   AutoConfig.for_model(__A				,					**__A		)
									SCREAMING_SNAKE_CASE__								=   True


							@classmethod
							def   _snake_case	(				cls     :str				,					__A     :PretrainedConfig				,					__A     :PretrainedConfig				,					**__A     :List[str]		) -> PretrainedConfig:



									"""simple docstring"""



									logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""		)
									SCREAMING_SNAKE_CASE__								=   True
									SCREAMING_SNAKE_CASE__								=   True

									return cls(encoder=encoder_config.to_dict()				,					decoder=decoder_config.to_dict()				,					**__A		)


							def   _snake_case	(				self     :str		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__								=   self.encoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.decoder.to_dict()
									SCREAMING_SNAKE_CASE__								=   self.__class__.model_type
									return output