Python/digital_image_processing/rotation/rotation.py

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from pathlib import Path
import cv2
import numpy as np
from matplotlib import pyplot as plt
def get_rotation(
img: np.ndarray, pt1: np.ndarray, pt2: np.ndarray, rows: int, cols: int
) -> np.ndarray:
"""
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(
str(Path(__file__).resolve().parent.parent / "image_data" / "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.array([[50, 50], [200, 50], [50, 200]], np.float32)
pts2 = np.array([[10, 100], [200, 50], [100, 250]], np.float32)
pts3 = np.array([[50, 50], [150, 50], [120, 200]], np.float32)
pts4 = np.array([[10, 100], [80, 50], [180, 250]], np.float32)
# 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()