
"""simple docstring"""







import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve
def       __UpperCAmelCase		(		a_      :		Union[str, Any]					)  ->							Dict:
   _UpperCAmelCase 						=       np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]					)
   _UpperCAmelCase 						=       np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]					)

   _UpperCAmelCase 						=       np.abs(img_convolve(a_     ,	a_					)					)
   _UpperCAmelCase 						=       np.abs(img_convolve(a_     ,	a_					)					)
   # modify the pix within [0, 255]
   _UpperCAmelCase 						=       dst_x * 2_55 / np.max(a_					)
   _UpperCAmelCase 						=       dst_y * 2_55 / np.max(a_					)

   _UpperCAmelCase 						=       np.sqrt((np.square(a_					)) + (np.square(a_					))					)
   _UpperCAmelCase 						=       dst_xy * 2_55 / np.max(a_					)
   _UpperCAmelCase 						=       dst_xy.astype(np.uinta					)

   _UpperCAmelCase 						=       np.arctana(a_     ,	a_					)
   return dst, theta


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

  SCREAMING_SNAKE_CASE_					,				SCREAMING_SNAKE_CASE_  					= sobel_filter(gray)

  # show result images
  imshow('sobel filter', sobel_grad)
  imshow('sobel theta', sobel_theta)
  waitKey(0)



"""simple docstring"""







from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION






class      __a      (       lowerCAmelCase_					,						lowerCAmelCase_      ):







       def __init__(       self		):



          '''simple docstring'''

          super().__init__()

          self.register_buffer("watermark_image"				,torch.zeros((62, 62, 4)		)		)
          _UpperCAmelCase 						=       None







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None		):



          '''simple docstring'''

          _UpperCAmelCase 						=       images[0].height
          _UpperCAmelCase 						=       images[0].width

          _UpperCAmelCase 						=       sample_size or h

          _UpperCAmelCase 						=       min(h / sample_size				,w / sample_size		)
          _UpperCAmelCase						,				_UpperCAmelCase 						=       (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       1024**2, img_w * img_h
          _UpperCAmelCase 						=       (Sa / Sa) ** 0.5
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

          if self.watermark_image_as_pil is None:
             _UpperCAmelCase 						=       self.watermark_image.to(torch.uinta		).cpu().numpy()
             _UpperCAmelCase 						=       Image.fromarray(lowerCamelCase_				,mode="RGBA"		)
             _UpperCAmelCase 						=       watermark_image

          _UpperCAmelCase 						=       self.watermark_image_as_pil.resize(
              (wm_size, wm_size)				,PIL_INTERPOLATION["bicubic"]				,reducing_gap=lowerCamelCase_		)

          for pil_img in images:
             pil_img.paste(lowerCamelCase_				,box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,mask=wm_img.split()[-1]		)

          return images

