






import cva
import numpy as np

class 		_snake_case  :






   def __init__(       self		,					a		,					a)      ->       Tuple:

      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)      ->       str:
      return str(self.k)







   def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       tuple[cva.Mat, list[list[int]]]:
      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__":
    a_		:						Dict             =    HarrisCorner(0.04, 3)
    a_		:						List[Any]             =    edge_detect.detect('path_to_image')
    cva.imwrite('detect.png', color_img)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_		:						List[Any]             =    {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Dict             =    [
				    'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BigBirdPegasusForCausalLM',
				    'BigBirdPegasusForConditionalGeneration',
				    'BigBirdPegasusForQuestionAnswering',
				    'BigBirdPegasusForSequenceClassification',
				    'BigBirdPegasusModel',
				    'BigBirdPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BigBirdPegasusConfig,
				    BigBirdPegasusOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bigbird_pegasus import (
								    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BigBirdPegasusForCausalLM,
								    BigBirdPegasusForConditionalGeneration,
								    BigBirdPegasusForQuestionAnswering,
								    BigBirdPegasusForSequenceClassification,
								    BigBirdPegasusModel,
								    BigBirdPegasusPreTrainedModel,
								)


else:
				import sys

				a_		:						Tuple             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




