





from typing import TYPE_CHECKING

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


lowerCAmelCase_   =   {
    "configuration_mobilenet_v1": [
        "MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV1Config",
        "MobileNetV1OnnxConfig",
    ],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase_   =   ["MobileNetV1FeatureExtractor"]
   lowerCAmelCase_   =   ["MobileNetV1ImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase_   =   [
       "MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST",
       "MobileNetV1ForImageClassification",
       "MobileNetV1Model",
       "MobileNetV1PreTrainedModel",
       "load_tf_weights_in_mobilenet_v1",
   ]


if TYPE_CHECKING:
   from .configuration_mobilenet_va import (
       MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileNetVaConfig,
       MobileNetVaOnnxConfig,
   )

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
      from .image_processing_mobilenet_va import MobileNetVaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mobilenet_va import (
          MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
          MobileNetVaForImageClassification,
          MobileNetVaModel,
          MobileNetVaPreTrainedModel,
          load_tf_weights_in_mobilenet_va,
      )


else:
   import sys

   lowerCAmelCase_   =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













import cva
import numpy as np




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:

       try:
              return int(image[x_coordinate][y_coordinate] >= center  )
       except (IndexError, TypeError):
              return 0




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_  )			->						int:
       _snake_case      :							List[str]     				=						image[x_coordinate][y_coordinate]
       _snake_case      :							List[str]     				=						[1, 2, 4, 8, 16, 32, 64, 128]

       # skip get_neighbors_pixel if center is null
       if center is None:
              return 0

       # Starting from the top right, assigning value to pixels clockwise
       _snake_case      :							Any     				=						[
           get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate + 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate + 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		lowercase_      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate + 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		lowercase_      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate + 1      ,		y_coordinate - 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		lowercase_      ,		y_coordinate - 1      ,		lowercase_  ),
           get_neighbors_pixel(lowercase_      ,		x_coordinate - 1      ,		y_coordinate - 1      ,		lowercase_  ),
       ]

       # Converting the binary value to decimal.
       return sum(
           binary_value * power for binary_value, power in zip(lowercase_      ,		lowercase_  )  )


if __name__ == "__main__":
   # Reading the image and converting it to grayscale.
   lowerCAmelCase_   =   cva.imread(
       "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
   )

   # Create a numpy array as the same height and width of read image
   lowerCAmelCase_   =   np.zeros((image.shape[0], image.shape[1]))

   # Iterating through the image and calculating the
   # local binary pattern value for each pixel.
   for i in range(0, image.shape[0]):
      for j in range(0, image.shape[1]):
         lowerCAmelCase_   =   local_binary_value(image, i, j)

   cva.imshow("local binary pattern", lbp_image)
   cva.waitKey(0)
   cva.destroyAllWindows()






