Erkennen, berühren/überlappenden Kreise/Ellipsen mit OpenCV und Python

Hier ist mein Versuch der Aufdeckung der Kreise. Zusammenfassend

• durchführen einer BGR->HSV-Umwandlung und-Nutzung der V-Kanal für die Verarbeitung

V Kanal:

• Schwellenwert, gelten morphologischen schließen, dann die Distanz-Transformation (nenne ich es dist)

dist Bild:

• eine Vorlage zu erstellen. Aus den Größen der Kreise in das Bild ein ~75 pixel radius Festplatte sieht zumutbar ist. Nehmen Sie seine Entfernung zu transformieren und verwenden es als Vorlage (ich nenne es temp)

temp Bild:

• durchführen template-matching: dist * temp

dist * temp Bild:

• finden Sie die lokalen maxima der resultierenden Bild. Lage der maxima entsprechen Kreis-Zentren und max-Werte entsprechen Ihre Radien

Schwellenwertbestimmung Vorlage abgestimmt Bild:

Erkennung von Kreisen, die als lokale maxima:

Habe ich diesen in C++, wie ich mich am wohlsten fühle mit ihm. Ich denke, Sie können leicht umwandeln diese zu python, wenn Sie diese nützlich finden. Beachten Sie, dass die oben genannten Bilder sind nicht maßstabsgetreu. Hoffe, das hilft.

EDIT: Hinzugefügt wurde die Python-version

C++:

    double min, max;
    Point maxLoc;

    Mat im = imread("04Bxy.jpg");
    Mat hsv;
    Mat channels[3];
    // bgr -> hsv
    cvtColor(im, hsv, CV_BGR2HSV);
    split(hsv, channels);
    // use v channel for processing
    Mat& ch = channels[2];
    // apply Otsu thresholding
    Mat bw;
    threshold(ch, bw, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU);
    // close small gaps
    Mat kernel = getStructuringElement(MORPH_ELLIPSE, Size(3, 3));
    Mat morph;
    morphologyEx(bw, morph, CV_MOP_CLOSE, kernel);
    // take distance transform
    Mat dist;
    distanceTransform(morph, dist, CV_DIST_L2, CV_DIST_MASK_PRECISE);
    // add a black border to distance transformed image. we are going to do
    // template matching. to get a good match for circles in the margin, we are adding a border
    int borderSize = 75;
    Mat distborder(dist.rows + 2*borderSize, dist.cols + 2*borderSize, dist.depth());
    copyMakeBorder(dist, distborder, 
            borderSize, borderSize, borderSize, borderSize, 
            BORDER_CONSTANT | BORDER_ISOLATED, Scalar(0, 0, 0));
    // create a template. from the sizes of the circles in the image, 
    // a ~75 radius disk looks reasonable, so the borderSize was selected as 75
    Mat distTempl;
    Mat kernel2 = getStructuringElement(MORPH_ELLIPSE, Size(2*borderSize+1, 2*borderSize+1));
    // erode the ~75 radius disk a bit
    erode(kernel2, kernel2, kernel, Point(-1, -1), 10);
    // take its distance transform. this is the template
    distanceTransform(kernel2, distTempl, CV_DIST_L2, CV_DIST_MASK_PRECISE);
    // match template
    Mat nxcor;
    matchTemplate(distborder, distTempl, nxcor, CV_TM_CCOEFF_NORMED);
    minMaxLoc(nxcor, &min, &max);
    // threshold the resulting image. we should be able to get peak regions.
    // we'll locate the peak of each of these peak regions
    Mat peaks, peaks8u;
    threshold(nxcor, peaks, max*.5, 255, CV_THRESH_BINARY);
    convertScaleAbs(peaks, peaks8u);
    //find connected components. we'll use each component as a mask for distance transformed image,
    // then extract the peak location and its strength. strength corresponds to the radius of the circle
    vector<vector<Point>> contours;
    vector<Vec4i> hierarchy;
    findContours(peaks8u, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
    for(int idx = 0; idx >= 0; idx = hierarchy[idx][0])
    {
        // prepare the mask
        peaks8u.setTo(Scalar(0, 0, 0));
        drawContours(peaks8u, contours, idx, Scalar(255, 255, 255), -1);
        // find the max value and its location in distance transformed image using mask
        minMaxLoc(dist, NULL, &max, NULL, &maxLoc, peaks8u);
        // draw the circles
        circle(im, maxLoc, (int)max, Scalar(0, 0, 255), 2);
    }

Python:

import cv2

im = cv2.imread('04Bxy.jpg')
hsv = cv2.cvtColor(im, cv2.COLOR_BGR2HSV)
th, bw = cv2.threshold(hsv[:, :, 2], 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3, 3))
morph = cv2.morphologyEx(bw, cv2.MORPH_CLOSE, kernel)
dist = cv2.distanceTransform(morph, cv2.cv.CV_DIST_L2, cv2.cv.CV_DIST_MASK_PRECISE)
borderSize = 75
distborder = cv2.copyMakeBorder(dist, borderSize, borderSize, borderSize, borderSize, 
                                cv2.BORDER_CONSTANT | cv2.BORDER_ISOLATED, 0)
gap = 10                                
kernel2 = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (2*(borderSize-gap)+1, 2*(borderSize-gap)+1))
kernel2 = cv2.copyMakeBorder(kernel2, gap, gap, gap, gap, 
                                cv2.BORDER_CONSTANT | cv2.BORDER_ISOLATED, 0)
distTempl = cv2.distanceTransform(kernel2, cv2.cv.CV_DIST_L2, cv2.cv.CV_DIST_MASK_PRECISE)
nxcor = cv2.matchTemplate(distborder, distTempl, cv2.TM_CCOEFF_NORMED)
mn, mx, _, _ = cv2.minMaxLoc(nxcor)
th, peaks = cv2.threshold(nxcor, mx*0.5, 255, cv2.THRESH_BINARY)
peaks8u = cv2.convertScaleAbs(peaks)
contours, hierarchy = cv2.findContours(peaks8u, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)
peaks8u = cv2.convertScaleAbs(peaks)    # to use as mask
for i in range(len(contours)):
    x, y, w, h = cv2.boundingRect(contours[i])
    _, mx, _, mxloc = cv2.minMaxLoc(dist[y:y+h, x:x+w], peaks8u[y:y+h, x:x+w])
    cv2.circle(im, (int(mxloc[0]+x), int(mxloc[1]+y)), int(mx), (255, 0, 0), 2)
    cv2.rectangle(im, (x, y), (x+w, y+h), (0, 255, 255), 2)
    cv2.drawContours(im, contours, i, (0, 0, 255), 2)

cv2.imshow('circles', im)