code: def four_point_transform(image, pts):
      # obtain a consistent order of the points and unpack them
      # individually
      rect = order_points(pts)
      (tl, tr, br, bl) = rect
  
      # compute the width of the new image, which will be the
      # maximum distance between bottom-right and bottom-left
      # x-coordiates or the top-right and top-left x-coordinates
      widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
      widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
      maxWidth = max(int(widthA), int(widthB))
  
      # 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
      heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
      heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
      maxHeight = max(int(heightA), int(heightB))
  
      # 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],
              [maxWidth - 1, 0],
              [maxWidth - 1, maxHeight - 1],
              [0, maxHeight - 1],
          ],
          dtype="float32",
      )
  
      # compute the perspective transform matrix and then apply it
      M = cv2.getPerspectiveTransform(rect, dst)
      warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))
  
      # return the warped image
      return warped
  
imports: from scipy.spatial import distance
  import numpy
  import cv2
label_desc: Use image node_id 2 for calling a global function with example usage: four_point_transform(image, pts) and returns: warped