fourPointTransformUtility.py

import numpy as np
import cv2

def order_points(pts):
	#initialize a life 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

	rect= 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 = pts.sum(axis = 1)
	rect[0] = pts[np.argmin(s)]
	rect[2] = pts[np.argmax(s)]

	#now, compute the difference between the point, the
	#top-right point will have smallest difference,
	#whereas the bottom-left will have the largest difference

	diff = np.diff(pts, axis = 1)
	rect[1] = pts[np.argmin(diff)]
	rect[3] = pts[np.argmax(diff)]

	#return the ordered coordinates
	return rect



def four_point_tranform(image, pts):
	#obtain the consistent oder of the points an 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-coordinates or the top-right and top-left x-cpprdinates

	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))

	#computer th 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 mew image, construct the set of destination points to obtain
	# a "bird 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 tranform matrix and then apply it
	M = cv2.getPerspectiveTransform(rect, dst)
	warped = cv2.warpPerspective(image,M, (maxWidth, maxHeight))

	#return the warped image
	return warped