


import cva
import numpy as np







class  snake_case_     :
					def __init__(   self     :Tuple    ,__snake_case     :float    ,__snake_case     :int   )      ->					List[str]:

												if k in (0.04, 0.06):
																			a__    =  k
																			a__    =  window_size
												else:
																			raise ValueError('invalid k value'   )
					def __str__(   self     :Optional[int]   )      ->					str:
												return str(self.k   )



					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :str   )      ->					tuple[cva.Mat, list[list[int]]]:
												a__    =  cva.imread(__snake_case    ,0   )
												a__   ,      a__    =  img.shape
												a__    =  []
												a__    =  img.copy()
												a__    =  cva.cvtColor(__snake_case    ,cva.COLOR_GRAY2RGB   )
												a__   ,      a__    =  np.gradient(__snake_case   )
												a__    =  dx**2
												a__    =  dy**2
												a__    =  dx * dy
												a__    =  0.04
												a__    =  self.window_size // 2
												for y in range(__snake_case    ,h - offset   ):
																			for x in range(__snake_case    ,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)    ,2_55   )
												return color_img, corner_list


if __name__ == "__main__":
						snake_case							: int      =   HarrisCorner(0.04, 3)
						snake_case    ,  snake_case							: Union[str, Any]      =   edge_detect.detect('''path_to_image''')
						cva.imwrite('''detect.png''', color_img)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


snake_case							: Any      =   {
    '''configuration_mobilevit''': ['''MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MobileViTConfig''', '''MobileViTOnnxConfig'''],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   ['''MobileViTFeatureExtractor''']
						snake_case							: int      =   ['''MobileViTImageProcessor''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Dict      =   [
						    '''MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileViTForImageClassification''',
						    '''MobileViTForSemanticSegmentation''',
						    '''MobileViTModel''',
						    '''MobileViTPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Tuple      =   [
						    '''TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFMobileViTForImageClassification''',
						    '''TFMobileViTForSemanticSegmentation''',
						    '''TFMobileViTModel''',
						    '''TFMobileViTPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilevit import MobileViTFeatureExtractor
												from .image_processing_mobilevit import MobileViTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilevit import (
												    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileViTForImageClassification,
												    MobileViTForSemanticSegmentation,
												    MobileViTModel,
												    MobileViTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mobilevit import (
												    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMobileViTForImageClassification,
												    TFMobileViTForSemanticSegmentation,
												    TFMobileViTModel,
												    TFMobileViTPreTrainedModel,
												)


else:
						import sys

						snake_case							: Optional[int]      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




