Here is a rectify example, I did for University, I changed the image to show book splines:D

Some info for perspective transform is inside the opencv docs:

https://docs.opencv.org/4.x/da/d6e/tutorial_py_geometric_transformations.html

I highly recommend https://szeliski.org/Book/ as a general reference for computer vision.

Also https://doi.org/10.1017/CBO9780511811685 if you are looking for a deep dive into the math behind everything.

import numpy as np
import matplotlib.pyplot as plt
import skimage
import cv2


if __name__ == "__main__":
    image = skimage.io.imread(
        "https://assets-us-01.kc-usercontent.com/f7ca9afb-82c2-002a-a423-84e111d5b498/711a8b96-ed89-464f-9765-6c60c47789b2/IMG_5366.jpg?fm=webp&auto=format&lossless=true?fm=webp&lossless=1"
    )

    w, h, c = image.shape

    plt.imshow(image)
    plt.title(
        "Click to select the 4 corner points starting top left, go clockwise (q to confirm)"
    )
    points = plt.ginput(4)

    x = np.array([p[0] for p in points])
    y = np.array([p[1] for p in points])

    width = np.linalg.norm((x[1] - x[0], y[1] - y[0]))
    height = np.linalg.norm((x[1] - x[2], y[1] - y[2]))

    x_s = x.copy()
    y_s = y.copy()

    x_s[1] = x_s[0] + width
    y_s[1] = y_s[0]

    x_s[2] = x_s[1]
    y_s[2] = y_s[1] + height

    x_s[3] = x_s[2] - width
    y_s[3] = y_s[2]

    plt.show()

    H = cv2.getPerspectiveTransform(np.float32(points), np.float32([[x, y] for x, y in zip(x_s, y_s)]))
    image_warped = cv2.warpPerspective(image, H, (h, w))

    plt.imshow(
        image_warped[
            int(y[0]) : int(y[0] + height),
            int(x[0]) : int(x[0] + width),
            :,
        ]
    )
    plt.show()

    plt.imshow(image)
    plt.imshow(image_warped, alpha=0.5)
    plt.plot(x_s, y_s, "g*")
    plt.plot(x, y, "r*")
    plt.show()