

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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  int )   ->       Optional[Any]:
				_UpperCAmelCase       :		str 		=    k_size // 2
				_UpperCAmelCase       :		int 		=    mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				_UpperCAmelCase       :		Any 		=    1 / (2 * pi * sigma) * exp(-(square(__lowerCAmelCase ) + square(__lowerCAmelCase )) / (2 * square(__lowerCAmelCase )) )
				return g

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Any  ,      lowerCAmelCase:  int )   ->       str:
				_UpperCAmelCase       :		Union[str, Any] 		=    image.shape[0], image.shape[1]
				# dst image height and width
				_UpperCAmelCase       :		str 		=    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       :		Any 		=    zeros((dst_height * dst_width, k_size * k_size) )
				_UpperCAmelCase       :		int 		=    0
				for i, j in product(range(__lowerCAmelCase )  ,      range(__lowerCAmelCase ) ):
								_UpperCAmelCase       :		List[Any] 		=    ravel(image[i : i + k_size, j : j + k_size] )
								_UpperCAmelCase       :		Dict 		=    window
								row += 1

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

				# reshape and get the dst image
				_UpperCAmelCase       :		Any 		=    dot(__lowerCAmelCase  ,      __lowerCAmelCase ).reshape(__lowerCAmelCase  ,      __lowerCAmelCase ).astype(__lowerCAmelCase )

				return dst


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

							# get values with two different mask size
							SCREAMING_SNAKE_CASE_        =      gaussian_filter(gray, 3, sigma=1)
							SCREAMING_SNAKE_CASE_        =      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()





from __future__ import annotations

from collections.abc import Callable

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Callable[[int | float], int | float]  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int = 100  ,      )   ->       float:
				_UpperCAmelCase       :		str 		=    x_start
				_UpperCAmelCase       :		Union[str, Any] 		=    fnc(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    0.0
				for _ in range(lowerCAmelCase ):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_UpperCAmelCase       :		Union[str, Any] 		=    (x_end - x_start) / steps + xa
								_UpperCAmelCase       :		Optional[Any] 		=    fnc(lowerCAmelCase )
								area += abs(fxa + fxa ) * (xa - xa) / 2
								# Increment step
								_UpperCAmelCase       :		Any 		=    xa
								_UpperCAmelCase       :		Optional[Any] 		=    fxa
				return area


if __name__ == "__main__":

					def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       List[Any]:
									return x**3 + x**2

					print('f(x) = x^3 + x^2')
					print('The area between the curve, x = -5, x = 5 and the x axis is:')
					SCREAMING_SNAKE_CASE_        =      10
					while i <= 100000:
										print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
										i *= 10


