


'''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 (__lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Any								=      k_size // 2
		_UpperCAmelCase					,    _UpperCAmelCase		:       Dict								=      mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		_UpperCAmelCase		:       List[str]								=      1 / (2 * pi * sigma) * exp(-(square(__lowerCAmelCase   ) + square(__lowerCAmelCase   )) / (2 * square(__lowerCAmelCase   ))   )
		return g






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase					,    _UpperCAmelCase		:       Dict								=      image.shape[0], image.shape[1]
		# dst image height and width
		_UpperCAmelCase		:       Tuple								=      height - k_size + 1
		_UpperCAmelCase		:       Optional[int]								=      width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		_UpperCAmelCase		:       Union[str, Any]								=      zeros((dst_height * dst_width, k_size * k_size)   )
		_UpperCAmelCase		:       List[Any]								=      0
		for i, j in product(range(__lowerCAmelCase   )      , range(__lowerCAmelCase   )   ):
				_UpperCAmelCase		:       Union[str, Any]								=      ravel(image[i : i + k_size, j : j + k_size]   )
				_UpperCAmelCase		:       List[Any]								=      window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		_UpperCAmelCase		:       Tuple								=      gen_gaussian_kernel(__lowerCAmelCase      , __lowerCAmelCase   )
		_UpperCAmelCase		:       Optional[int]								=      ravel(__lowerCAmelCase   )

		# reshape and get the dst image
		_UpperCAmelCase		:       List[str]								=      dot(__lowerCAmelCase      , __lowerCAmelCase   ).reshape(__lowerCAmelCase      , __lowerCAmelCase   ).astype(__lowerCAmelCase   )

		return dst


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

						# get values with two different mask size
						lowerCamelCase__    	=     gaussian_filter(gray, 3, sigma=1)
						lowerCamelCase__    	=     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 __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return math.sqrt(sum(pow(a - b      , 2   ) for a, b in zip(__lowerCAmelCase      , __lowerCAmelCase   )   )   )






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):

		if dataset.ndim != value_array.ndim:
				_UpperCAmelCase		:       Optional[Any]								=      (
				    "Wrong input data's dimensions... "
				    F"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
				)
				raise ValueError(__lowerCAmelCase   )

		try:
				if dataset.shape[1] != value_array.shape[1]:
						_UpperCAmelCase		:       Optional[int]								=      (
						    "Wrong input data's shape... "
						    F"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
						)
						raise ValueError(__lowerCAmelCase   )
		except IndexError:
				if dataset.ndim != value_array.ndim:
						raise TypeError("Wrong shape"   )

		if dataset.dtype != value_array.dtype:
				_UpperCAmelCase		:       Union[str, Any]								=      (
				    "Input data have different datatype... "
				    F"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
				)
				raise TypeError(__lowerCAmelCase   )

		_UpperCAmelCase		:       Optional[int]								=      []

		for value in value_array:
				_UpperCAmelCase		:       List[str]								=      euclidean(__lowerCAmelCase      , dataset[0]   )
				_UpperCAmelCase		:       Dict								=      dataset[0].tolist()

				for dataset_value in dataset[1:]:
						_UpperCAmelCase		:       int								=      euclidean(__lowerCAmelCase      , __lowerCAmelCase   )

						if dist > temp_dist:
								_UpperCAmelCase		:       Tuple								=      temp_dist
								_UpperCAmelCase		:       Dict								=      dataset_value.tolist()

				answer.append([vector, dist]   )

		return answer






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		return np.dot(__lowerCAmelCase      , __lowerCAmelCase   ) / (norm(__lowerCAmelCase   ) * norm(__lowerCAmelCase   ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



