rotation Algorithm
The rotation algorithm is a widely used technique in computer science and mathematics that deals with the process of changing the position of elements within a data structure, such as an array or a list, by shifting them to new positions. This technique is particularly useful in various applications, such as image processing, data manipulation, and cryptography. The primary goal of a rotation algorithm is to ensure that the elements within the data structure are rearranged effectively while maintaining their original order relative to each other.
There are different types of rotation algorithms, such as left rotation, right rotation, and multi-dimensional rotations. In a left rotation, elements are shifted to the left by a specified number of positions, and the elements that fall out of the array's boundary are placed back at the end of the array. Similarly, in a right rotation, elements are shifted to the right, and the elements that fall out of the boundary are placed back at the beginning of the array. Multi-dimensional rotations involve performing rotations on more complex data structures, such as matrices or multi-dimensional arrays. Rotation algorithms are commonly implemented using various techniques, such as temporary storage of elements, circular buffers, or in-place rotations, depending on the specific use case and performance requirements.
from matplotlib import pyplot as plt
import numpy as np
import cv2
def get_rotation(
img: np.array, pt1: np.float32, pt2: np.float32, rows: int, cols: int
) -> np.array:
"""
Get image rotation
:param img: np.array
:param pt1: 3x2 list
:param pt2: 3x2 list
:param rows: columns image shape
:param cols: rows image shape
:return: np.array
"""
matrix = cv2.getAffineTransform(pt1, pt2)
return cv2.warpAffine(img, matrix, (rows, cols))
if __name__ == "__main__":
# read original image
image = cv2.imread("lena.jpg")
# turn image in gray scale value
gray_img = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# get image shape
img_rows, img_cols = gray_img.shape
# set different points to rotate image
pts1 = np.float32([[50, 50], [200, 50], [50, 200]])
pts2 = np.float32([[10, 100], [200, 50], [100, 250]])
pts3 = np.float32([[50, 50], [150, 50], [120, 200]])
pts4 = np.float32([[10, 100], [80, 50], [180, 250]])
# add all rotated images in a list
images = [
gray_img,
get_rotation(gray_img, pts1, pts2, img_rows, img_cols),
get_rotation(gray_img, pts2, pts3, img_rows, img_cols),
get_rotation(gray_img, pts2, pts4, img_rows, img_cols),
]
# plot different image rotations
fig = plt.figure(1)
titles = ["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()
