Detect variations of a subimage in image

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My attempt considers that painted nails have high brightness and fingers have orientation almost similar. My code can extract images from single nails but it get some noise too. Anyway result could be used as input for a classifier.

As threshold I used the absolute peak for Brightness. I removed some noise using a threshold on Saturation in the middle of 2 biggest peaks

For example starting from this image

A get histogram for HSV And get threshold for brightness and saturation (you can calculate it automatically if you have a peak detector)

This is the binary image after some cleaning

Some contour and rotated rect to get orientation and a ROI for the single detection

Finally sort the detections, rotate and extract single images. From quadrant 1

From quadrant 2

here the full code. (Get FillHolesfrom @theodore answer here: http://answers.opencv.org/question/74...

#define CL_BLU      cv::Scalar(255,  0,   0  )
#define CL_GREEN    cv::Scalar(0,    255, 0  )
#define CL_RED      cv::Scalar(0,    0,   255)
#define CL_BLACK    cv::Scalar(0,    0,   0  )
#define CL_CYAN     cv::Scalar(255,  255, 0  )
#define CL_MAGENTA  cv::Scalar(255,  0,   255)
#define CL_YELLOW   cv::Scalar(0,    255, 255)
#define CL_WHITE    cv::Scalar(255,  255, 255)

void Nails()
{
    cv::Scalar cl;
    cv::Mat src, dst, src_hsv, src_saturation, src_bright, bw, dist;
    src = cv::imread("../img/nails.jpg");
    src.copyTo(dst);


    vector<cv::Mat> hsv_planes;
    cv::cvtColor(src, src_hsv, cv::COLOR_BGR2HSV);
    cv::split(src_hsv, hsv_planes);
    src_saturation = hsv_planes[1];
    src_bright = hsv_planes[2];

    int thBright = 186, thSat=180;
    cv::inRange(src_hsv, cv::Scalar(0, thSat, thBright), cv::Scalar(180, 255, 255), bw);
    FillHoles(bw, bw);
    cv::bitwise_not(bw, bw);
    Morph(bw, bw, cv::MORPH_CLOSE, cv::MORPH_RECT, 3);
    Morph(bw, bw, cv::MORPH_OPEN, cv::MORPH_RECT, 1);
    cv::imshow("After TH1", bw);
    cv::imwrite("../img/bw.jpg", dst);

    // Get external contours ignoring holes
    std::vector<std::vector<cv::Point> > contours;
    cv::findContours(bw, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);
    cv::RotatedRect rr;
    map <int, vector<cv::RotatedRect>> quadrant;
    vector<cv::Scalar> quadrantColor({ CL_MAGENTA, CL_YELLOW, CL_CYAN, CL_GREEN });
    double area,minArea = 0.005*bw.cols*bw.rows;
    cv::Point2f rect_points[4];
    // draw contours, select them and group by orientation
    for (int i = 0; i < contours.size(); i++)
    {
        cv::drawContours(dst, contours, i, CL_WHITE, 1);
        //filter out small detections
        if (contours[i].size() < 10) continue;

        area = cv::contourArea(contours[i]);
        if (area < minArea) continue;

        // Get a rotated rect around the detection
        rr = cv::fitEllipse(contours[i]);

        // group detections by orientation in quadrant 
        // because there is a good chance that all fingers
        // have same orientation. 
        int q = floor(rr.angle / 90);
        quadrant[q].push_back(rr);
        // Draw rotated rect around the detection
        cl = quadrantColor[q];

        rr.points(rect_points);
        for (int j = 0; j < 4; j++)
            cv::line(dst, rect_points[j], rect_points[(j + 1) % 4], cl, 2, 8);

        cv::putText(dst, "Q" + to_string(q)+"C" + to_string(i), rr.center - cv::Point2f(3, 0), cv::FONT_HERSHEY_PLAIN, 1, CL_BLACK, 2,CV_AA);
    }

    // Get quadrant with more detections count
    // you could consider this as related to fingers
    int cnt = 0;
    int populatedQuadrant ...