Bubble sheet multiple choice scan

edit

This is the code I rewritten under the py but to this I do not understand how to do. please help me. or tell me how to do it. I do not understand py variable questionCnts add to what to do. When switching to java, what function to replace the contours.sort_contours function, I looked for java but did not find any function equivalent to this function.

code python

enter code here from imutils.perspective import four_point_transform
from imutils import contours
import numpy as np
import argparse
 import imutils
 import cv2

# construct the argument parse and parse the arguments
 ap = argparse.ArgumentParser()
 ap.add_argument("-i", "--image", required=True,
help="path to the input image")
 args = vars(ap.parse_args())

 # define the answer key which maps the question number
 # to the correct answer
 ANSWER_KEY = {0: 1, 1: 4, 2: 0, 3: 3, 4: 1}

 # load the image, convert it to grayscale, blur it
# slightly, then find edges
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  blurred = cv2.GaussianBlur(gray, (5, 5), 0)
       edged = cv2.Canny(blurred, 75, 200)

 # find contours in the edge map, then initialize
        # the contour that corresponds to the document
      cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
       cnts = cnts[0] if imutils.is_cv2() else cnts[1]
        docCnt = None

        # ensure that at least one contour was found
         if len(cnts) > 0:
# sort the contours according to their size in
# descending order
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)

# loop over the sorted contours
for c in cnts:
    # approximate the contour
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)

    # if our approximated contour has four points,
    # then we can assume we have found the paper
    if len(approx) == 4:
        docCnt = approx
        break

      # apply a four point perspective transform to both the
            # original image and grayscale image to obtain a top-down
        # birds eye view of the paper
    paper = four_point_transform(image, docCnt.reshape(4, 2))
        warped = four_point_transform(gray, docCnt.reshape(4, 2))

       # apply Otsu's thresholding method to binarize the warped
         # piece of paper
          thresh = cv2.threshold(warped, 0, 255,
cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]

         # find contours in the thresholded image, then initialize
         # the list of contours that correspond to questions
        cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)
       cnts = cnts[0] if imutils.is_cv2() else cnts[1]
          questionCnts = []

               # loop over the contours
        for c in cnts:
# compute the bounding box of the contour, then use the
# bounding box to derive the aspect ratio
(x, y, w, h) = cv2.boundingRect(c)
ar = w / float(h)

# in order to label the contour as a question, region
# should be sufficiently wide, sufficiently tall, and
# have an aspect ratio approximately equal to 1
if w >= 20 and h >= 20 and ar >= 0.9 and ar <= 1.1:
    questionCnts.append(c)  // HERE I DON'T KNOWN HOW DO
                                                 // I DO NOT UNDERSTAND questionCnts.append(c) CONVERT TO JAVA 

            # sort the question contours top-to-bottom, then initialize
          # the total number of correct answers
           questionCnts = contours.sort_contours(questionCnts ...