

"""simple docstring"""





from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros




def     _A    (    UpperCamelCase_	:			List[Any],     UpperCamelCase_	:			Tuple)      ->		Dict:
   '''simple docstring'''


   __lowercase						,__lowercase								=		image.shape
   __lowercase								=		cols - block_size[1] + 1
   __lowercase								=		rows - block_size[0] + 1
   __lowercase								=		zeros((dst_height * dst_width, block_size[1] * block_size[0]))
   __lowercase								=		0
   for i in range(0,     UpperCamelCase_):
      for j in range(0,     UpperCamelCase_):
         __lowercase								=		ravel(image[i : i + block_size[0], j : j + block_size[1]])
         __lowercase								=		window
         row += 1

   return image_array




def     _A    (    UpperCamelCase_	:			str,     UpperCamelCase_	:			Dict)      ->		str:
   '''simple docstring'''


   __lowercase						,__lowercase								=		image.shape[0], image.shape[1]
   __lowercase								=		filter_kernel.shape[0]
   __lowercase								=		k_size // 2
   # Pads image with the edge values of array.
   __lowercase								=		pad(UpperCamelCase_,     UpperCamelCase_,     mode="edge")

   # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
   __lowercase								=		imacol(UpperCamelCase_,     (k_size, k_size))

   #  turn the kernel into shape(k*k, 1)
   __lowercase								=		ravel(UpperCamelCase_)
   # reshape and get the dst image
   __lowercase								=		dot(UpperCamelCase_,     UpperCamelCase_).reshape(UpperCamelCase_,     UpperCamelCase_)
   return dst


if __name__ == "__main__":
   # read original image
   _a					 =				imread(r'../image_data/lena.jpg')
   # turn image in gray scale value
   _a					 =				cvtColor(img, COLOR_BGR2GRAY)
   # Laplace operator
   _a					 =				array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
   _a					 =				img_convolve(gray, Laplace_kernel).astype(uinta)
   imshow('Laplacian', out)
   waitKey(0)







"""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     _A    (    UpperCamelCase_	:			int)      ->		str:
   '''simple docstring'''


   __lowercase								=		np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
   __lowercase								=		np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])

   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   __lowercase								=		np.abs(img_convolve(UpperCamelCase_,     UpperCamelCase_))
   # modify the pix within [0, 255]
   __lowercase								=		dst_x * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_y * 255 / np.max(UpperCamelCase_)

   __lowercase								=		np.sqrt((np.square(UpperCamelCase_)) + (np.square(UpperCamelCase_)))
   __lowercase								=		dst_xy * 255 / np.max(UpperCamelCase_)
   __lowercase								=		dst_xy.astype(np.uinta)

   __lowercase								=		np.arctana(UpperCamelCase_,     UpperCamelCase_)
   return dst, theta


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

   _a ,		_a					 =				sobel_filter(gray)

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




