Similarity Transform between two images

Question

I wish to know apart from distance formula, is there any other way to calculate the third point of an equilateral triangle ?. I was going through the below snippet which does similarity transform(Average faces) between two points. I don't understand how they are calculating the third point.

As far as reading the code through multiple iterations, I understood, they are calculating the third point by making use of sin and cosine and multiplying their difference.

def similarityTransform(inPoints, outPoints):
  s60 = math.sin(60*math.pi/180)
  c60 = math.cos(60*math.pi/180)

  inPts = np.copy(inPoints).tolist()
  outPts = np.copy(outPoints).tolist()

  # The third point is calculated so that the three points make an equilateral triangle
  xin = c60*(inPts[0][0] - inPts[1][0]) - s60*(inPts[0][1] - inPts[1][1]) + inPts[1][0]
  yin = s60*(inPts[0][0] - inPts[1][0]) + c60*(inPts[0][1] - inPts[1][1]) + inPts[1][1]

  inPts.append([np.int(xin), np.int(yin)])

  xout = c60*(outPts[0][0] - outPts[1][0]) - s60*(outPts[0][1] - outPts[1][1]) + outPts[1][0]
  yout = s60*(outPts[0][0] - outPts[1][0]) + c60*(outPts[0][1] - outPts[1][1]) + outPts[1][1]

  outPts.append([np.int(xout), np.int(yout)])

  # Now we can use estimateRigidTransform for calculating the similarity transform.
  tform = cv2.estimateAffinePartial2D(np.array([inPts]), np.array([outPts]))
  return tform[0]

Now i wish to know if we can calculate the third point using sin and cosine and the difference between the points ?.