Java VisualizeBinaryData.renderContours方法代码示例

import boofcv.gui.binary.VisualizeBinaryData; //导入方法依赖的package包/类
private void run() throws IOException {
    BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("C:\\development\\readySET\\deck\\1221.png"));

    GrayU8 gray = ConvertBufferedImage.convertFrom(image,(GrayU8)null);
    GrayU8 edgeImage = gray.createSameShape();

    // Create a canny edge detector which will dynamically compute the threshold based on maximum edge intensity
    // It has also been configured to save the trace as a graph.  This is the graph created while performing
    // hysteresis thresholding.
    CannyEdge<GrayU8,GrayS16> canny = FactoryEdgeDetectors.canny(2,true, true, GrayU8.class, GrayS16.class);

    // The edge image is actually an optional parameter.  If you don't need it just pass in null
    canny.process(gray,0.1f,0.3f,edgeImage);

    // First get the contour created by canny
    List<EdgeContour> edgeContours = canny.getContours();
    // The 'edgeContours' is a tree graph that can be difficult to process.  An alternative is to extract
    // the contours from the binary image, which will produce a single loop for each connected cluster of pixels.
    // Note that you are only interested in verticesnal contours.
    List<Contour> contours = BinaryImageOps.contour(edgeImage, ConnectRule.EIGHT, null);

    // display the results
    BufferedImage visualBinary = VisualizeBinaryData.renderBinary(edgeImage, false, null);
    BufferedImage visualCannyContour = VisualizeBinaryData.renderContours(edgeContours,null,
            gray.width,gray.height,null);
    BufferedImage visualEdgeContour = new BufferedImage(gray.width, gray.height,BufferedImage.TYPE_INT_RGB);
    VisualizeBinaryData.render(contours, (int[]) null, visualEdgeContour);

    ListDisplayPanel panel = new ListDisplayPanel();
    panel.addImage(visualBinary,"Binary Edges from Canny");
    panel.addImage(visualCannyContour, "Canny Trace Graph");
    panel.addImage(visualEdgeContour,"Contour from Canny Binary");
    ShowImages.showWindow(panel,"Canny Edge", true);
}
 

import boofcv.gui.binary.VisualizeBinaryData; //导入方法依赖的package包/类
public static void main( String args[] ) {
	BufferedImage image = UtilImageIO.loadImage("../data/applet/simple_objects.jpg");

	ImageUInt8 gray = ConvertBufferedImage.convertFrom(image,(ImageUInt8)null);
	ImageUInt8 edgeImage = new ImageUInt8(gray.width,gray.height);

	// Create a canny edge detector which will dynamically compute the threshold based on maximum edge intensity
	// It has also been configured to save the trace as a graph.  This is the graph created while performing
	// hysteresis thresholding.
	CannyEdge<ImageUInt8,ImageSInt16> canny = FactoryEdgeDetectors.canny(2,true, true, ImageUInt8.class, ImageSInt16.class);

	// The edge image is actually an optional parameter.  If you don't need it just pass in null
	canny.process(gray,0.1f,0.3f,edgeImage);

	// First get the contour created by canny
	List<EdgeContour> edgeContours = canny.getContours();
	// The 'edgeContours' is a tree graph that can be difficult to process.  An alternative is to extract
	// the contours from the binary image, which will produce a single loop for each connected cluster of pixels.
	// Note that you are only interested in external contours.
	List<Contour> contours = BinaryImageOps.contour(edgeImage, 8, null);

	// display the results
	BufferedImage visualBinary = VisualizeBinaryData.renderBinary(edgeImage, null);
	BufferedImage visualCannyContour = VisualizeBinaryData.renderContours(edgeContours,null,
			gray.width,gray.height,null);
	BufferedImage visualEdgeContour = VisualizeBinaryData.renderExternal(contours, null,
			gray.width, gray.height, null);

	ShowImages.showWindow(visualBinary,"Binary Edges from Canny");
	ShowImages.showWindow(visualCannyContour,"Canny Trace Graph");
	ShowImages.showWindow(visualEdgeContour,"Contour from Canny Binary");
}
 

import boofcv.gui.binary.VisualizeBinaryData; //导入方法依赖的package包/类
public static void main( String args[] ) {
	// load and convert the image into a usable format
	BufferedImage image = UtilImageIO.loadImage("../data/applet/particles01.jpg");

	// convert into a usable format
	ImageFloat32 input = ConvertBufferedImage.convertFromSingle(image, null, ImageFloat32.class);
	ImageUInt8 binary = new ImageUInt8(input.width,input.height);
	ImageSInt32 label = new ImageSInt32(input.width,input.height);

	// the mean pixel value is often a reasonable threshold when creating a binary image
	double mean = ImageStatistics.mean(input);

	// create a binary image by thresholding
	ThresholdImageOps.threshold(input,binary,(float)mean,true);

	// remove small blobs through erosion and dilation
	// The null in the input indicates that it should internally declare the work image it needs
	// this is less efficient, but easier to code.
	ImageUInt8 filtered = BinaryImageOps.erode8(binary,null);
	filtered = BinaryImageOps.dilate8(filtered, null);

	// Detect blobs inside the image using an 8-connect rule
	List<Contour> contours = BinaryImageOps.contour(filtered, 8, label);

	// colors of contours
	int colorExternal = 0xFFFFFF;
	int colorInternal = 0xFF2020;

	// display the results
	BufferedImage visualBinary = VisualizeBinaryData.renderBinary(binary, null);
	BufferedImage visualFiltered = VisualizeBinaryData.renderBinary(filtered, null);
	BufferedImage visualLabel = VisualizeBinaryData.renderLabeled(label, contours.size(), null);
	BufferedImage visualContour = VisualizeBinaryData.renderContours(contours,colorExternal,colorInternal,
			input.width,input.height,null);

	ShowImages.showWindow(visualBinary,"Binary Original");
	ShowImages.showWindow(visualFiltered,"Binary Filtered");
	ShowImages.showWindow(visualLabel,"Labeled Blobs");
	ShowImages.showWindow(visualContour,"Contours");
}
 

import boofcv.gui.binary.VisualizeBinaryData; //导入方法依赖的package包/类
private void doProcess() {
	if( input == null )
		return;

	final BufferedImage temp;

	if( activeAlg == 0 ) {
		if( previousBlur != barCanny.getBlurRadius() ) {
			previousBlur = barCanny.getBlurRadius();
			canny =  FactoryEdgeDetectors.canny(previousBlur,true, true, imageType, derivType);
		}

		double thresh = barCanny.getThreshold()/100.0;
		canny.process(workImage,(float)thresh*0.1f,(float)thresh,null);
		List<EdgeContour> contours = canny.getContours();

		temp = VisualizeBinaryData.renderContours(contours,null,workImage.width,workImage.height,null);
	} else {
		// create a binary image by thresholding
		GThresholdImageOps.threshold(workImage, binary, barBinary.getThreshold(), barBinary.isDown());

		contour.process(binary,labeled);
		temp = VisualizeBinaryData.renderContours(contour.getContours().toList(),null,0xFF1010,
				workImage.width,workImage.height,null);
	}

	SwingUtilities.invokeLater(new Runnable() {
		public void run() {
			panel.setBufferedImage(temp);
			panel.repaint();
		}});
}
 

import boofcv.gui.binary.VisualizeBinaryData; //导入方法依赖的package包/类
/**
 * The main method.
 *
 * @param args the arguments
 */
public static void main( String args[] ) {
	// load and convert the image into a usable format
	BufferedImage image = UtilImageIO.loadImage(UtilIO.pathExample("/home/pete/development/gitrepo/iote2e/iote2e-tests/images/iote2e-test.png"));

	// convert into a usable format
	GrayF32 input = ConvertBufferedImage.convertFromSingle(image, null, GrayF32.class);
	GrayU8 binary = new GrayU8(input.width,input.height);
	GrayS32 label = new GrayS32(input.width,input.height);

	// Select a global threshold using Otsu's method.
	double threshold = GThresholdImageOps.computeOtsu(input, 0, 255);

	// Apply the threshold to create a binary image
	ThresholdImageOps.threshold(input,binary,(float)threshold,true);

	// remove small blobs through erosion and dilation
	// The null in the input indicates that it should internally declare the work image it needs
	// this is less efficient, but easier to code.
	GrayU8 filtered = BinaryImageOps.erode8(binary, 1, null);
	filtered = BinaryImageOps.dilate8(filtered, 1, null);

	// Detect blobs inside the image using an 8-connect rule
	List<Contour> contours = BinaryImageOps.contour(filtered, ConnectRule.EIGHT, label);

	// colors of contours
	int colorExternal = 0xFFFFFF;
	int colorInternal = 0xFF2020;

	// display the results
	BufferedImage visualBinary = VisualizeBinaryData.renderBinary(binary, false, null);
	BufferedImage visualFiltered = VisualizeBinaryData.renderBinary(filtered, false, null);
	BufferedImage visualLabel = VisualizeBinaryData.renderLabeledBG(label, contours.size(), null);
	BufferedImage visualContour = VisualizeBinaryData.renderContours(contours, colorExternal, colorInternal,
			input.width, input.height, null);

	ListDisplayPanel panel = new ListDisplayPanel();
	panel.addImage(visualBinary, "Binary Original");
	panel.addImage(visualFiltered, "Binary Filtered");
	panel.addImage(visualLabel, "Labeled Blobs");
	panel.addImage(visualContour, "Contours");
	ShowImages.showWindow(panel,"Binary Operations",true);
}