four_point_transform.py

# https://www.pyimagesearch.com/2014/08/25/4-point-opencv-getperspective-transform-example/
# import the necessary packages
import numpy as np
import cv2


def order_points(points):
    # initialize a list of coordinates that will be ordered
    # such that the first entry in the list is the top-left,
    # the second entry is the top-right, the third is the
    # bottom-right, and the fourth is the bottom-left
    rectangle = np.zeros((4, 2), dtype="float32")

    # the top-left point will have the smallest sum, whereas
    # the bottom-right point will have the largest sum
    s = points.sum(axis=1)
    rectangle[0] = points[np.argmin(s)]
    rectangle[2] = points[np.argmax(s)]

    # now, compute the difference between the points, the
    # top-right point will have the smallest difference,
    # whereas the bottom-left will have the largest difference
    diff = np.diff(points, axis=1)
    rectangle[1] = points[np.argmin(diff)]
    rectangle[3] = points[np.argmax(diff)]

    # return the ordered coordinates
    return rectangle


def four_point_transform(image, points):
    # obtain a consistent order of the points and unpack them
    # individually
    rectangle = order_points(points)
    (tl, tr, br, bl) = rectangle

    # compute the width of the new image, which will be the
    # maximum distance between bottom-right and bottom-left
    # x-co-ordinates or the top-right and top-left x-coordinates
    width_a = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
    width_b = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
    max_width = max(int(width_a), int(width_b))

    # compute the height of the new image, which will be the
    # maximum distance between the top-right and bottom-right
    # y-coordinates or the top-left and bottom-left y-coordinates
    height_a = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
    height_b = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
    max_height = max(int(height_a), int(height_b))

    # now that we have the dimensions of the new image, construct
    # the set of destination points to obtain a "birds eye view",
    # (i.e. top-down view) of the image, again specifying points
    # in the top-left, top-right, bottom-right, and bottom-left
    # order
    dst = np.array([
        [0, 0],
        [max_width - 1, 0],
        [max_width - 1, max_height - 1],
        [0, max_height - 1]], dtype="float32")

    # compute the perspective transform matrix and then apply it
    perspective_transform_matrix = cv2.getPerspectiveTransform(rectangle, dst)
    warped = cv2.warpPerspective(image, perspective_transform_matrix, (max_width, max_height))

    # return the warped image
    return warped