How to count the number of tooltips in an image in OpenCV?

Question

I have a hiragana character set and I would like to count the number of endpoints / hints that a character has.

Example: input image:

desired output image:

I tried using a convex hull

code: (based on the opencv tutorial here )

    findContours(threshold_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));

    vector<vector<Point> >hull(contours.size());

    for (int i = 0; i < contours.size(); i++)
    {
        convexHull(Mat(contours[i]), hull[i], false);
    }

    Mat drawing = Mat::zeros(threshold_output.size(), CV_8UC3);
    for (int i = 0; i< contours.size(); i++)
    {
        if (hierarchy[i][3] == 0) {
            Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
            drawContours(drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point());
        }
    }

then conrnerHarris (), but it returned too many unwanted angles

code: (based on the opencv tutorial here )

    int blockSize = 2;
    int apertureSize = 3;

    /// Detecting corners
    drawing = binarizeImage(drawing); // otsu's
    cornerHarris(drawing, dst, blockSize, apertureSize, 0.04, BORDER_DEFAULT);

    /// Normalizing
    normalize(dst, dst_norm, 0, 255, NORM_MINMAX, CV_32FC1, Mat());
    convertScaleAbs(dst_norm, dst_norm_scaled);

    int countCorner = 0;

    /// Drawing a circle around corners
    for (int j = 0; j < dst_norm.rows; j++)
    {
        for (int i = 0; i < dst_norm.cols; i++)
        {
            if ((int)dst_norm.at<float>(j, i) > 50)
            {
                circle(output, Point(i, j), 2, Scalar::all(255), -1, 8, 0);
                countCorner++;
            }
        }
    }

He discovered 11 corners.

I think this may be the same as fingertip detection, but I don’t know how to do it.

[I am using OpenCV 2.4.9.]

Answer 1

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# Thin input image down to a skeleton
convert char.jpg -threshold 50% -negate -morphology Thinning:-1 Skeleton skeleton.jpg

# Find line-ends, using Hit-or-Miss morphology, and make them green (lime). Save as "lineends.jpg"
convert skeleton.jpg -morphology HMT LineEnds -threshold 50% -fill lime -opaque white lineends.jpg

# Find line-junctions, using Hit-or-Miss morphology, and make them red. Save as "line junctions.jpg"
convert skeleton.jpg -morphology HMT LineJunctions -threshold 50% -fill red -opaque white linejunctions.jpg

# Superpose the line-ends and line junctions into a result
convert lineends.jpg linejunctions.jpg -compose lighten -composite result.jpg

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composite -blend 30% skeleton.jpg result.jpg z.jpg