Examples with ConnectLinesGrid boofcv.alg.feature.detect.line.ConnectLinesGrid used on opensource projects

Example 1 with ConnectLinesGrid

use of boofcv.alg.feature.detect.line.ConnectLinesGrid in project BoofCV by lessthanoptimal.

the class FactoryDetectLineAlgs method lineRansac.

/**
 * Detects line segments inside an image using the {@link DetectLineSegmentsGridRansac} algorithm.
 *
 * @see DetectLineSegmentsGridRansac
 *
 * @param regionSize Size of the region considered.  Try 40 and tune.
 * @param thresholdEdge Threshold for determining which pixels belong to an edge or not. Try 30 and tune.
 * @param thresholdAngle Tolerance in angle for allowing two edgels to be paired up, in radians.  Try 2.36
 * @param connectLines Should lines be connected and optimized.
 * @param imageType Type of single band input image.
 * @param derivType Image derivative type.
 * @return Line segment detector
 */
public static <I extends ImageGray<I>, D extends ImageGray<D>> DetectLineSegmentsGridRansac<I, D> lineRansac(int regionSize, double thresholdEdge, double thresholdAngle, boolean connectLines, Class<I> imageType, Class<D> derivType) {
    ImageGradient<I, D> gradient = FactoryDerivative.sobel(imageType, derivType);
    ModelManagerLinePolar2D_F32 manager = new ModelManagerLinePolar2D_F32();
    GridLineModelDistance distance = new GridLineModelDistance((float) thresholdAngle);
    GridLineModelFitter fitter = new GridLineModelFitter((float) thresholdAngle);
    ModelMatcher<LinePolar2D_F32, Edgel> matcher = new Ransac<>(123123, manager, fitter, distance, 25, 1);
    GridRansacLineDetector<D> alg;
    if (derivType == GrayF32.class) {
        alg = (GridRansacLineDetector) new ImplGridRansacLineDetector_F32(regionSize, 10, matcher);
    } else if (derivType == GrayS16.class) {
        alg = (GridRansacLineDetector) new ImplGridRansacLineDetector_S16(regionSize, 10, matcher);
    } else {
        throw new IllegalArgumentException("Unsupported derivative type");
    }
    ConnectLinesGrid connect = null;
    if (connectLines)
        connect = new ConnectLinesGrid(Math.PI * 0.01, 1, 8);
    return new DetectLineSegmentsGridRansac<>(alg, connect, gradient, thresholdEdge, imageType, derivType);
}

Example 2 with ConnectLinesGrid

use of boofcv.alg.feature.detect.line.ConnectLinesGrid in project BoofCV by lessthanoptimal.

the class VisualizeLineRansac method process.

public void process(BufferedImage image) {
    int regionSize = 40;
    I input = GeneralizedImageOps.createSingleBand(imageType, image.getWidth(), image.getHeight());
    D derivX = GeneralizedImageOps.createSingleBand(derivType, image.getWidth(), image.getHeight());
    D derivY = GeneralizedImageOps.createSingleBand(derivType, image.getWidth(), image.getHeight());
    GrayF32 edgeIntensity = new GrayF32(input.width, input.height);
    GrayF32 suppressed = new GrayF32(input.width, input.height);
    GrayF32 orientation = new GrayF32(input.width, input.height);
    GrayS8 direction = new GrayS8(input.width, input.height);
    GrayU8 detected = new GrayU8(input.width, input.height);
    ModelManager<LinePolar2D_F32> manager = new ModelManagerLinePolar2D_F32();
    GridLineModelDistance distance = new GridLineModelDistance((float) (Math.PI * 0.75));
    GridLineModelFitter fitter = new GridLineModelFitter((float) (Math.PI * 0.75));
    ModelMatcher<LinePolar2D_F32, Edgel> matcher = new Ransac<>(123123, manager, fitter, distance, 25, 1);
    ImageGradient<I, D> gradient = FactoryDerivative.sobel(imageType, derivType);
    System.out.println("Image width " + input.width + " height " + input.height);
    ConvertBufferedImage.convertFromSingle(image, input, imageType);
    gradient.process(input, derivX, derivY);
    GGradientToEdgeFeatures.intensityAbs(derivX, derivY, edgeIntensity);
    // non-max suppression on the lines
    // GGradientToEdgeFeatures.direction(derivX,derivY,orientation);
    // GradientToEdgeFeatures.discretizeDirection4(orientation,direction);
    // GradientToEdgeFeatures.nonMaxSuppression4(edgeIntensity,direction,suppressed);
    GThresholdImageOps.threshold(edgeIntensity, detected, 30, false);
    GridRansacLineDetector<GrayF32> alg = new ImplGridRansacLineDetector_F32(40, 10, matcher);
    alg.process((GrayF32) derivX, (GrayF32) derivY, detected);
    MatrixOfList<LineSegment2D_F32> gridLine = alg.getFoundLines();
    ConnectLinesGrid connect = new ConnectLinesGrid(Math.PI * 0.01, 1, 8);
    // connect.process(gridLine);
    // LineImageOps.pruneClutteredGrids(gridLine,3);
    List<LineSegment2D_F32> found = gridLine.createSingleList();
    System.out.println("size = " + found.size());
    LineImageOps.mergeSimilar(found, (float) (Math.PI * 0.03), 5f);
    // LineImageOps.pruneSmall(found,40);
    System.out.println("after size = " + found.size());
    ImageLinePanel gui = new ImageLinePanel();
    gui.setBackground(image);
    gui.setLineSegments(found);
    gui.setPreferredSize(new Dimension(image.getWidth(), image.getHeight()));
    BufferedImage renderedBinary = VisualizeBinaryData.renderBinary(detected, false, null);
    ShowImages.showWindow(renderedBinary, "Detected Edges");
    ShowImages.showWindow(gui, "Detected Lines");
}