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,
method="top-to-bottom")[0]
correct = 0
# each question has 5 possible answers, to loop over the
# question in batches of 5
for (q, i) in enumerate(np.arange(0, len(questionCnts), 5)):
# sort the contours for the current question from
# left to right, then initialize the index of the
# bubbled answer
cnts = contours.sort_contours(questionCnts[i:i + 5])[0]
bubbled = None
# loop over the sorted contours
for (j, c) in enumerate(cnts):
# construct a mask that reveals only the current
# "bubble" for the question
mask = np.zeros(thresh.shape, dtype="uint8")
cv2.drawContours(mask, [c], -1, 255, -1)
# apply the mask to the thresholded image, then
# count the number of non-zero pixels in the
# bubble area
mask = cv2.bitwise_and(thresh, thresh, mask=mask)
total = cv2.countNonZero(mask)
# if the current total has a larger number of total
# non-zero pixels, then we are examining the currently
# bubbled-in answer
if bubbled is None or total > bubbled[0]:
bubbled = (total, j)
# initialize the contour color and the index of the
# *correct* answer
color = (0, 0, 255)
k = ANSWER_KEY[q]
# check to see if the bubbled answer is correct
if k == bubbled[1]:
color = (0, 255, 0)
correct += 1
# draw the outline of the correct answer on the test
cv2.drawContours(paper, [cnts[k]], -1, color, 3)
# grab the test taker
score = (correct / 5.0) * 100
print("[INFO] score: {:.2f}%".format(score))
cv2.putText(paper, "{:.2f}%".format(score), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
cv2.imshow("Original", image)
cv2.imshow("Exam", paper)
cv2.waitKey(0)
and code android
private void scanImage(String selectImagePath) {
Mat img = Imgcodecs.imread(selectImagePath);
if (img.empty()) {
Log.d(TAG, "Empty Image");
showMgs("empty");
}else{
Log.d(TAG, "onClick: ");
}
ArrayList<MatOfPoint> contours = findContours(img);
Quadrilateral quad = getQuadrilateral(contours, img.size());
if(quad !=null) {
showMgs("succes");
Mat doc;
doc = fourPointTransform(img, quad.points);
Mat thresh = new Mat();
Mat grayImage = new Mat();
Imgproc.cvtColor(doc, grayImage, Imgproc.COLOR_RGBA2GRAY, 4);
Imgproc.threshold(grayImage, thresh, 0, 255, Imgproc.THRESH_BINARY_INV | Imgproc.THRESH_OTSU);
contours = new ArrayList<MatOfPoint>();
ArrayList<Point> questionCnts = new ArrayList<Point>();
Mat hierarchy = new Mat();
Imgproc.findContours(thresh, contours, hierarchy, Imgproc.RETR_TREE, Imgproc.CHAIN_APPROX_SIMPLE, new Point(0, 0));
hierarchy.release();
for (int contourIdx = 0; contourIdx < contours.size(); contourIdx++) {
// Minimum size allowed for consideration
MatOfPoint2f approxCurve = new MatOfPoint2f();
MatOfPoint2f contour2f = new MatOfPoint2f(contours.get(contourIdx).toArray());
//Processing on mMOP2f1 which is in type MatOfPoint2f
double approxDistance = Imgproc.arcLength(contour2f, true) * 0.02;
Imgproc.approxPolyDP(contour2f, approxCurve, approxDistance, true);
//Convert back to MatOfPoint
MatOfPoint points = new MatOfPoint(approxCurve.toArray());
// Get bounding rect of contour
Rect rect = Imgproc.boundingRect(points);
float ar = rect.width /(float)rect.height;
if (rect.width >= 20 && rect.height >= 20 && ar >= 0.9 && ar <= 1.1) {
**// HERE I DON'T KNOWN HOW DO**
Imgproc.rectangle(doc, new Point(rect.x, rect.y), new Point(rect.x + rect.width, rect.y + rect.height), new Scalar(255, 0, 0, 255), 2);
}
}
showImage(doc);
}else{
showMgs("not succes");
}
}
THANKS FOR READING PLEASE HELP ME