'''simple docstring'''

import cva
import numpy as np







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    Optional[Any]:

  if k in (0.0_4, 0.0_6):
   __lowerCAmelCase											=						k
   __lowerCAmelCase											=						window_size
  else:
   raise ValueError("""invalid k value"""							)
 def __str__(      self   :	List[Any]							)  ->    str:
  return str(self.k							)





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	List[Any]							)  ->    tuple[cva.Mat, list[list[int]]]:
  __lowerCAmelCase											=						cva.imread(A_      ,       0							)
  __lowerCAmelCase							,       __lowerCAmelCase											=						img.shape
  __lowerCAmelCase											=						[]
  __lowerCAmelCase											=						img.copy()
  __lowerCAmelCase											=						cva.cvtColor(A_      ,       cva.COLOR_GRAY2RGB							)
  __lowerCAmelCase							,       __lowerCAmelCase											=						np.gradient(A_							)
  __lowerCAmelCase											=						dx**2
  __lowerCAmelCase											=						dy**2
  __lowerCAmelCase											=						dx * dy
  __lowerCAmelCase											=						0.0_4
  __lowerCAmelCase											=						self.window_size // 2
  for y in range(A_      ,       h - offset							):
   for x in range(A_      ,       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)      ,       2_55							)
  return color_img, corner_list


if __name__ == "__main__":
 _A			:      str					      =				HarrisCorner(0.0_4, 3)
 _A      ,     _A			:      int					      =				edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A		       =						{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['VivitImageProcessor']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'VivitModel',
       'VivitPreTrainedModel',
       'VivitForVideoClassification',
   ]


if TYPE_CHECKING:
   from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_vivit import VivitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vivit import (
          VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VivitForVideoClassification,
          VivitModel,
          VivitPreTrainedModel,
      )


else:
   import sys

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





