


import cva
import numpy as np






class     UpperCAmelCase__       :



    """simple docstring"""






    def __init__(			self					,   A_					,   A_    )  ->       Optional[Any]:

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






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






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

                   __UpperCamelCase    							=(wxx * wyy) - (wxy**2)
                   __UpperCamelCase    							=wxx + wyy
                   __UpperCamelCase    							=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		       =						HarrisCorner(0.04, 3)
   _A       ,     _A		       =						edge_detect.detect('path_to_image')
   cva.imwrite('detect.png', color_img)











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:		Optional[Any]     =   {
    "configuration_whisper": ["WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP", "WhisperConfig", "WhisperOnnxConfig"],
    "feature_extraction_whisper": ["WhisperFeatureExtractor"],
    "processing_whisper": ["WhisperProcessor"],
    "tokenization_whisper": ["WhisperTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		Tuple     =   ["WhisperTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "WhisperForConditionalGeneration",
     "WhisperModel",
     "WhisperPreTrainedModel",
     "WhisperForAudioClassification",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		List[str]     =   [
     "TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFWhisperForConditionalGeneration",
     "TFWhisperModel",
     "TFWhisperPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:		str     =   [
     "FlaxWhisperForConditionalGeneration",
     "FlaxWhisperModel",
     "FlaxWhisperPreTrainedModel",
     "FlaxWhisperForAudioClassification",
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:		Tuple     =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



