



from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		k_size // 2
      lowerCamelCase			,							lowerCamelCase								=		mgrid[0 - center : k_size - center, 0 - center : k_size - center]
      lowerCamelCase								=		1 / (2 * pi * sigma) * exp(-(square(UpperCAmelCase__ ) + square(UpperCAmelCase__ )) / (2 * square(UpperCAmelCase__ )) )
      return g

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase								=		image.shape[0], image.shape[1]
      # dst image height and width
      lowerCamelCase								=		height - k_size + 1
      lowerCamelCase								=		width - k_size + 1

      # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
      lowerCamelCase								=		zeros((dst_height * dst_width, k_size * k_size) )
      lowerCamelCase								=		0
      for i, j in product(range(UpperCAmelCase__ )   ,   range(UpperCAmelCase__ ) ):
            lowerCamelCase								=		ravel(image[i : i + k_size, j : j + k_size] )
            lowerCamelCase								=		window
            row += 1

      #  turn the kernel into shape(k*k, 1)
      lowerCamelCase								=		gen_gaussian_kernel(UpperCAmelCase__   ,   UpperCAmelCase__ )
      lowerCamelCase								=		ravel(UpperCAmelCase__ )

      # reshape and get the dst image
      lowerCamelCase								=		dot(UpperCAmelCase__   ,   UpperCAmelCase__ ).reshape(UpperCAmelCase__   ,   UpperCAmelCase__ ).astype(UpperCAmelCase__ )

      return dst


if __name__ == "__main__":
     # read original image
     a_						:     int	     =		imread(R'../image_data/lena.jpg')
     # turn image in gray scale value
     a_						:     Dict	     =		cvtColor(img, COLOR_BGR2GRAY)

     # get values with two different mask size
     a_						:     str	     =		gaussian_filter(gray, 3, sigma=1)
     a_						:     str	     =		gaussian_filter(gray, 5, sigma=0.8)

     # show result images
     imshow('gaussian filter with 3x3 mask', gaussianaxa)
     imshow('gaussian filter with 5x5 mask', gaussianaxa)
     waitKey()




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_						:     List[str]	     =		{
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Any	     =		[
         'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
         'CLIPSegModel',
         'CLIPSegPreTrainedModel',
         'CLIPSegTextModel',
         'CLIPSegVisionModel',
         'CLIPSegForImageSegmentation',
     ]

if TYPE_CHECKING:
     from .configuration_clipseg import (
         CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
         CLIPSegConfig,
         CLIPSegTextConfig,
         CLIPSegVisionConfig,
     )
     from .processing_clipseg import CLIPSegProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_clipseg import (
              CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
              CLIPSegForImageSegmentation,
              CLIPSegModel,
              CLIPSegPreTrainedModel,
              CLIPSegTextModel,
              CLIPSegVisionModel,
          )

else:
     import sys

     a_						:     Dict	     =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)