

import enum
import shutil
import sys


A_ ,    A_ :				Optional[Any]						 =    shutil.get_terminal_size()

A_ :				Optional[int]						 =    {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}



class 					_a		(enum.Enum       ):


					'''simple docstring'''






					UpperCAmelCase__:     str													= 0
					UpperCAmelCase__:     Optional[Any]													= 1



def 		UpperCamelCase      (lowercase_:				Optional[Any]					,   lowercase_:				Optional[int]=""      )  ->   int:
	sys.stdout.write(str(lowercase_      ) + end      )
	sys.stdout.flush()

def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				List[str]					,   lowercase_:				Optional[int]=""      )  ->   Optional[Any]:
	forceWrite(f"""\u001b[{color}m{content}\u001b[0m"""					,   lowercase_      )

def 		UpperCamelCase      ()  ->   Union[str, Any]:
	forceWrite("""\r"""      )

def 		UpperCamelCase      (lowercase_:				int					,   lowercase_:				str      )  ->   Union[str, Any]:
	forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""      )

def 		UpperCamelCase      ()  ->   int:
	forceWrite(""" """ * TERMINAL_WIDTH      )
	reset_cursor()


def 		UpperCamelCase      ()  ->   Tuple:
	reset_cursor()
	forceWrite("""-""" * TERMINAL_WIDTH      )






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt

def 		UpperCamelCase      (lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				np.ndarray					,   lowercase_:				int					,   lowercase_:				int      )  ->   np.ndarray:
	A__							:					Any       =	cva.getAffineTransform(lowercase_					,   lowercase_      )
	return cva.warpAffine(lowercase_					,   lowercase_					,   (rows, cols)      )


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

	# set different points to rotate image
	A_ :				str						 =    np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
	A_ :				Dict						 =    np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
	A_ :				Optional[int]						 =    np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

	# add all rotated images in a list
	A_ :				Dict						 =    [
	    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_ :				Union[str, Any]						 =    plt.figure(1)
	A_ :				Union[str, Any]						 =    ['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()



