


"""simple docstring"""



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   __lowerCAmelCase						(						lowercase  :  int						,							lowercase  :  str			)     ->						List[Any]:


    """simple docstring"""





    snake_case   :				List[Any]				 =   k_size // 2
    snake_case ,snake_case   :				Tuple				 =   mgrid[0 - center : k_size - center, 0 - center : k_size - center]
    snake_case   :				Optional[Any]				 =   1 / (2 * pi * sigma) * exp(-(square(lowercase			) + square(lowercase			)) / (2 * square(lowercase			))			)
    return g







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Dict						,							lowercase  :  Tuple			)     ->						Tuple:


    """simple docstring"""





    snake_case ,snake_case   :				Optional[int]				 =   image.shape[0], image.shape[1]
    # dst image height and width
    snake_case   :				Union[str, Any]				 =   height - k_size + 1
    snake_case   :				List[str]				 =   width - k_size + 1

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

    #  turn the kernel into shape(k*k, 1)
    snake_case   :				Any				 =   gen_gaussian_kernel(lowercase						,							lowercase			)
    snake_case   :				List[str]				 =   ravel(lowercase			)

    # reshape and get the dst image
    snake_case   :				List[Any]				 =   dot(lowercase						,							lowercase			).reshape(lowercase						,							lowercase			).astype(lowercase			)

    return dst


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

  # get values with two different mask size
  __snake_case             =   gaussian_filter(gray, 3, sigma=1)
  __snake_case             =   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()









"""simple docstring"""



from typing import TYPE_CHECKING

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


__snake_case             =   {
    """configuration_conditional_detr""": [
        """CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ConditionalDetrConfig""",
        """ConditionalDetrOnnxConfig""",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   ["""ConditionalDetrFeatureExtractor"""]
  __snake_case             =   ["""ConditionalDetrImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __snake_case             =   [
      """CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """ConditionalDetrForObjectDetection""",
      """ConditionalDetrForSegmentation""",
      """ConditionalDetrModel""",
      """ConditionalDetrPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ConditionalDetrConfig,
      ConditionalDetrOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
    from .image_processing_conditional_detr import ConditionalDetrImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_conditional_detr import (
        CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
        ConditionalDetrForObjectDetection,
        ConditionalDetrForSegmentation,
        ConditionalDetrModel,
        ConditionalDetrPreTrainedModel,
    )

else:
  import sys

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





