four_point_transform.py

import numpy as np
import cv2
from scipy.spatial import distance as dist
def order_points(pts):
	# sort the points based on their x-coordinates
	xSorted = pts[np.argsort(pts[:, 0]), :]
	# grab the left-most and right-most points from the sorted
	# x-roodinate points
	leftMost = xSorted[:2, :]
	rightMost = xSorted[2:, :]
	# now, sort the left-most coordinates according to their
	# y-coordinates so we can grab the top-left and bottom-left
	# points, respectively
	leftMost = leftMost[np.argsort(leftMost[:, 1]), :]
	(tl, bl) = leftMost
	# now that we have the top-left coordinate, use it as an
	# anchor to calculate the Euclidean distance between the
	# top-left and right-most points; by the Pythagorean
	# theorem, the point with the largest distance will be
	# our bottom-right point
	D = dist.cdist(tl[np.newaxis], rightMost, "euclidean")[0]
	(br, tr) = rightMost[np.argsort(D)[::-1], :]
	# return the coordinates in top-left, top-right,
	# bottom-right, and bottom-left order
	return np.array([tl, tr, br, bl], dtype="float32")


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