





'''simple docstring'''






import math
import sys

import cva
import numpy as np
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							math.sqrt(lowerCAmelCase  )
      _lowerCAmelCase												=							1 / (sigma * math.sqrt(2 * math.pi  ))
      return cons * np.exp(-((img / sigma) ** 2) * 0.5  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							kernel_size // 2
      return img[x - half : x + half + 1, y - half : y + half + 1]
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							np.zeros((kernel_size, kernel_size)  )
      for i in range(0							,     lowerCAmelCase  ):
            for j in range(0							,     lowerCAmelCase  ):
                  _lowerCAmelCase												=							math.sqrt(
                      abs(i - kernel_size // 2  ) ** 2 + abs(j - kernel_size // 2  ) ** 2  )
      return vec_gaussian(lowerCAmelCase							,     lowerCAmelCase  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     ):





      """simple docstring"""


      _lowerCAmelCase												=							np.zeros(img.shape  )
      _lowerCAmelCase												=							get_gauss_kernel(lowerCAmelCase							,     lowerCAmelCase  )
      _lowerCAmelCase					,							_lowerCAmelCase												=							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  ):
                  _lowerCAmelCase												=							get_slice(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  )
                  _lowerCAmelCase												=							img_s - img_s[kernel_size // 2, kernel_size // 2]
                  _lowerCAmelCase												=							vec_gaussian(lowerCAmelCase							,     lowerCAmelCase  )
                  _lowerCAmelCase												=							np.multiply(lowerCAmelCase							,     lowerCAmelCase  )
                  _lowerCAmelCase												=							np.multiply(lowerCAmelCase							,     lowerCAmelCase  )
                  _lowerCAmelCase												=							np.sum(lowerCAmelCase  ) / np.sum(lowerCAmelCase  )
                  _lowerCAmelCase												=							val
      return imga
def        UpperCamelCase__				(		lowerCAmelCase  ):





      """simple docstring"""


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


if __name__ == "__main__":
   A__    :       Tuple              =parse_args(sys.argv)
   A__    :       Any              =cva.imread(filename, 0)
   cva.imshow('''input image''', img)

   A__    :       Optional[Any]              =img / 2_55
   A__    :       List[str]              =out.astype('''float32''')
   A__    :       Optional[int]              =bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
   A__    :       str              =out * 2_55
   A__    :       str              =np.uinta(out)
   cva.imshow('''output image''', out)
   cva.waitKey(0)
   cva.destroyAllWindows()













'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							cva.getAffineTransform(lowerCAmelCase							,     lowerCAmelCase  )
      return cva.warpAffine(lowerCAmelCase							,     lowerCAmelCase							,     (rows, cols)  )


if __name__ == "__main__":
      # read original image
      A__    :       Any              =cva.imread(
          str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
      )
      # turn image in gray scale value
      A__    :       Any              =cva.cvtColor(image, cva.COLOR_BGR2GRAY)
      # get image shape
      A__       ,						A__    :       Tuple              =gray_img.shape

      # set different points to rotate image
      A__    :       Optional[int]              =np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
      A__    :       Tuple              =np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
      A__    :       List[str]              =np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
      A__    :       Dict              =np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

      # add all rotated images in a list
      A__    :       List[Any]              =[
          gray_img,
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      ]

      # plot different image rotations
      A__    :       Tuple              =plt.figure(1)
      A__    :       int              =['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
      for i, image in enumerate(images):
            plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
            plt.title(titles[i])
            plt.axis('''off''')
            plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
      plt.show()






