
import math
import sys

import cva
import numpy as np

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						np.ndarray:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   math.sqrt(lowercase_ )
  lowercase__	: Tuple     		=   1 / (sigma * math.sqrt(2 * math.pi ))
  return cons * np.exp(-((img / sigma) ** 2) * 0.5 )

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						np.ndarray:


  '''simple docstring'''






  lowercase__	: Tuple     		=   kernel_size // 2
  return img[x - half : x + half + 1, y - half : y + half + 1]

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						np.ndarray:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   np.zeros((kernel_size, kernel_size) )
  for i in range(0			,			lowercase_ ):
    for j in range(0			,			lowercase_ ):
      lowercase__	: int     		=   math.sqrt(
          abs(i - kernel_size // 2 ) ** 2 + abs(j - kernel_size // 2 ) ** 2 )
  return vec_gaussian(lowercase_			,			lowercase_ )

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			)		->						np.ndarray:


  '''simple docstring'''






  lowercase__	: Any     		=   np.zeros(img.shape )
  lowercase__	: Any     		=   get_gauss_kernel(lowercase_			,			lowercase_ )
  lowercase__		,     lowercase__	: Dict     		=   img.shape
  for i in range(kernel_size // 2			,			size_x - kernel_size // 2 ):
    for j in range(kernel_size // 2			,			size_y - kernel_size // 2 ):
      lowercase__	: Any     		=   get_slice(lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )
      lowercase__	: Optional[int]     		=   img_s - img_s[kernel_size // 2, kernel_size // 2]
      lowercase__	: Optional[Any]     		=   vec_gaussian(lowercase_			,			lowercase_ )
      lowercase__	: Any     		=   np.multiply(lowercase_			,			lowercase_ )
      lowercase__	: int     		=   np.multiply(lowercase_			,			lowercase_ )
      lowercase__	: List[Any]     		=   np.sum(lowercase_ ) / np.sum(lowercase_ )
      lowercase__	: List[Any]     		=   val
  return imga

def 		UpperCamelCase						(   lowercase_ )		->						tuple:


  '''simple docstring'''






  lowercase__	: Optional[int]     		=   args[1] if args[1:] else """../image_data/lena.jpg"""
  lowercase__	: str     		=   float(args[2] ) if args[2:] else 1.0
  lowercase__	: Tuple     		=   float(args[3] ) if args[3:] else 1.0
  if args[4:]:
    lowercase__	: int     		=   int(args[4] )
    lowercase__	: List[Any]     		=   kernel_size + abs(kernel_size % 2 - 1 )
  else:
    lowercase__	: Tuple     		=   5
  return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
     lowerCamelCase__   ,    lowerCamelCase__   ,    lowerCamelCase__   ,    lowerCamelCase__     :		Optional[Any]        = parse_args(sys.argv)
     lowerCamelCase__     :		List[Any]        = cva.imread(filename, 0)
     cva.imshow("""input image""", img)

     lowerCamelCase__     :		str        = img / 2_5_5
     lowerCamelCase__     :		Dict        = out.astype("""float32""")
     lowerCamelCase__     :		Optional[int]        = bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     lowerCamelCase__     :		Optional[Any]        = out * 2_5_5
     lowerCamelCase__     :		str        = np.uinta(out)
     cva.imshow("""output image""", out)
     cva.waitKey(0)
     cva.destroyAllWindows()








from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


lowerCamelCase__     :		Union[str, Any]        = logging.get_logger(__name__)



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['pixel_values']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 1 / 2_55       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 8       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   do_rescale
   lowercase__	: List[Any]     		=   rescale_factor
   lowercase__	: Tuple     		=   do_pad
   lowercase__	: Optional[Any]     		=   pad_size


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return rescale(SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_       ,	data_format=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__		,     lowercase__	: Optional[int]     		=   get_image_size(SCREAMING_SNAKE_CASE_)
   lowercase__	: Optional[Any]     		=   (old_height // size + 1) * size - old_height
   lowercase__	: str     		=   (old_width // size + 1) * size - old_width

   return pad(SCREAMING_SNAKE_CASE_       ,	((0, pad_height), (0, pad_width))       ,	mode="""symmetric"""       ,	data_format=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = ChannelDimension.FIRST       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   do_rescale if do_rescale is not None else self.do_rescale
   lowercase__	: int     		=   rescale_factor if rescale_factor is not None else self.rescale_factor
   lowercase__	: Union[str, Any]     		=   do_pad if do_pad is not None else self.do_pad
   lowercase__	: Optional[Any]     		=   pad_size if pad_size is not None else self.pad_size

   lowercase__	: str     		=   make_list_of_images(SCREAMING_SNAKE_CASE_)

   if not valid_images(SCREAMING_SNAKE_CASE_):
     raise ValueError(
         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
         """torch.Tensor, tf.Tensor or jax.ndarray.""")

   if do_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True.""")

   # All transformations expect numpy arrays.
   lowercase__	: List[Any]     		=   [to_numpy_array(SCREAMING_SNAKE_CASE_) for image in images]

   if do_rescale:
     lowercase__	: str     		=   [self.rescale(image=SCREAMING_SNAKE_CASE_       ,	scale=SCREAMING_SNAKE_CASE_) for image in images]

   if do_pad:
     lowercase__	: List[str]     		=   [self.pad(SCREAMING_SNAKE_CASE_       ,	size=SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Optional[Any]     		=   [to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) for image in images]

   lowercase__	: Dict     		=   {"""pixel_values""": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,	tensor_type=SCREAMING_SNAKE_CASE_)






