paper edge detection and perspective transform

edit

before image

https://imgur.com/f190UFk

processed image

https://imgur.com/JkEhWkS

you can see the "processed image" has highlight, so the transform works bad.

any possible to make an rectangle that ignore that highlight area?

import os
import cv2
import numpy as np
from nanoid import generate

def processImage(imagepath, ext):
  img = cv2.imread(imagepath)

  hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
  h, s, v = cv2.split(hsv)

  # _, threshed = cv2.threshold(s, 50, 255, cv2.THRESH_BINARY_INV)
  _, threshed = cv2.threshold(s, 50, 255, cv2.THRESH_BINARY_INV)

  # cnts = cv2.findContours(threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
  cnts = cv2.findContours(threshed, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
  canvas = img.copy()
  #cv2.drawContours(canvas, cnts, -1, (0, 255, 0), 1)

  cnts = sorted(cnts, key = cv2.contourArea)
  cnt = cnts[-1]
  print(cnt)

  arclen = cv2.arcLength(cnt, True)
  approx = cv2.approxPolyDP(cnt, 0.005 * arclen, True)
  cv2.drawContours(canvas, [cnt], -1, (255, 0, 0), 5, cv2.LINE_AA)
  cv2.drawContours(canvas, [approx], -1, (0, 0, 255), 1, cv2.LINE_AA)
  print(approx)
  approx = rectify(approx)
  pts2 = np.float32([[0, 0], [2480, 0], [2480, 3508], [0, 3508]])
  M = cv2.getPerspectiveTransform(approx, pts2)
  dst = cv2.warpPerspective(canvas, M, (2480, 3508))

  filename_output = generate() + ext
  cv2.imwrite('./static/' + filename_output, dst)

  topLeft, topRight, bottomRight, bottomLeft = approx
  topLeft = topLeft.tolist()
  topRight = topRight.tolist()
  bottomRight = bottomRight.tolist()
  bottomLeft = bottomLeft.tolist()

  return {
    'filename': './static/' + filename_output,
    'shape': img.shape,
    'approx': {
      'topLeft': topLeft,
      'topRight': topLeft,
      'bottomRight': bottomRight,
      'bottomLeft': bottomLeft,
    },
  }

def rectify(h):
  h = h.reshape((13, 2))
  hnew = np.zeros((4, 2), dtype = np.float32)
  add = h.sum(1)
  hnew[0] = h[np.argmin(add)]
  hnew[2] = h[np.argmax(add)]
  diff = np.diff(h, axis = 1)
  hnew[1] = h[np.argmin(diff)]
  hnew[3] = h[np.argmax(diff)]
  return hnew

add similar condition image

https://imgur.com/wDXtLsd

https://imgur.com/KAvOtdG