




"""simple docstring"""



import cva
import numpy as np




class 	UpperCAmelCase_  :




    def __init__(    self						:       Optional[int]	, snake_case_						:       float	, snake_case_						:       int      )      ->							Dict:




     '''simple docstring'''

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




    def __str__(    self						:       Dict      )      ->							str:




     '''simple docstring'''

     return str(self.k      )




    def 							__magic_name__							(    self						:       int	, snake_case_						:       str      )      ->							tuple[cva.Mat, list[list[int]]]:




     '''simple docstring'''

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

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













"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_snake_case          =   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _snake_case          =   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

 _snake_case          =   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

