





from pathlib import Path

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





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   np.ndarray:







	'''simple docstring'''







	lowerCamelCase__              =	cva.getAffineTransform(__snake_case ,__snake_case							)
	return cva.warpAffine(__snake_case ,__snake_case ,(rows, cols)							)


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

			# set different points to rotate image
			_a						=   np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
			_a						=   np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
			_a						=   np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
			_a						=   np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

			# add all rotated images in a list
			_a						=   [
			    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						=   plt.figure(1)
			_a						=   ["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()










_a						=   "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_a						=   [{"type": "code", "content": INSTALL_CONTENT}]
_a						=   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



