Example Stereo Disparity 3D

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Example stereo disparity3d pointcloud.jpg
3D point cloud generated from disparity image. Stereo Update Video

An additional example for stereo disparity. This one shows you how to properly resize input images and convert the disparity image into a 3D point cloud.

Example Code:


  • Stereo disparity
  • Point clouds

Related Videos

Related Examples:

Example Code

 * Expanding upon ExampleStereoDisparity, this example demonstrates how to rescale an image for stereo processing and
 * then compute its 3D point cloud.  Images are often rescaled to improve speed and some times quality.  Creating
 * 3D point clouds from disparity images is easy and well documented in the literature, but there are some nuances
 * to it.
 * @author Peter Abeles
public class ExampleStereoDisparity3D {

	// Specifies the disparity values which will be considered
	private static final int disparityMin = 15;
	private static final int disparityRange = 60;

	 * Given already computed rectified images and known stereo parameters, create a 3D cloud and visualize it
	public static JComponent computeAndShowCloud( StereoParameters param,
												  GrayU8 rectLeft,
												  RectifyCalibrated rectAlg, GrayF32 disparity ) {
		// The point cloud will be in the left cameras reference frame
		DMatrixRMaj rectK = rectAlg.getCalibrationMatrix();
		DMatrixRMaj rectR = rectAlg.getRectifiedRotation();

		// Put all the disparity parameters into one data structure
		var disparityParameters = new DisparityParameters();
		disparityParameters.baseline = param.getBaseline();
		disparityParameters.disparityMin = disparityMin;
		disparityParameters.disparityRange = disparityRange;
		PerspectiveOps.matrixToPinhole(rectK, rectLeft.width, rectLeft.height, disparityParameters.pinhole);

		// Iterate through each pixel in disparity image and compute its 3D coordinate
		PointCloudViewer pcv = VisualizeData.createPointCloudViewer();

		// Next create the 3D point cloud. The function below will handle conversion from disparity into
		// XYZ, then transform from rectified into normal camera coordinate system. Feel free to glance at the
		// source code to understand exactly what it's doing
		MultiViewStereoOps.disparityToCloud(disparity, disparityParameters, null,
				( pixX, pixY, x, y, z ) -> {
					// look up the gray value. Then convert it into RGB
					int v = rectLeft.unsafe_get(pixX, pixY);
					pcv.addPoint(x, y, z, v << 16 | v << 8 | v);

		// Configure the display
//		pcv.setFog(true);
//		pcv.setClipDistance(baseline*45);
//		PeriodicColorizer colorizer = new TwoAxisRgbPlane.Z_XY(4.0);
//		colorizer.setPeriod(baseline*5);
//		pcv.setColorizer(colorizer); // sometimes pseudo color can be easier to view
//		pcv.setCameraToWorld(cameraToWorld);
		JComponent viewer = pcv.getComponent();
		viewer.setPreferredSize(new Dimension(600, 600*param.left.height/param.left.width));
		return viewer;

	public static void main( String[] args ) {
		// ------------- Compute Stereo Correspondence

		// Load camera images and stereo camera parameters
		String calibDir = UtilIO.pathExample("calibration/stereo/Bumblebee2_Chess/");
		String imageDir = UtilIO.pathExample("stereo/");

		StereoParameters param = CalibrationIO.load(new File(calibDir, "stereo.yaml"));

		// load and convert images into a BoofCV format
		BufferedImage origLeft = UtilImageIO.loadImage(imageDir, "chair01_left.jpg");
		BufferedImage origRight = UtilImageIO.loadImage(imageDir, "chair01_right.jpg");

		GrayU8 distLeft = ConvertBufferedImage.convertFrom(origLeft, (GrayU8)null);
		GrayU8 distRight = ConvertBufferedImage.convertFrom(origRight, (GrayU8)null);

		// rectify images and compute disparity
		GrayU8 rectLeft = distLeft.createSameShape();
		GrayU8 rectRight = distRight.createSameShape();

		RectifyCalibrated rectAlg = ExampleStereoDisparity.rectify(distLeft, distRight, param, rectLeft, rectRight);

//		GrayU8 disparity = ExampleStereoDisparity.denseDisparity(rectLeft, rectRight, 3,disparityMin, disparityRange);
		GrayF32 disparity = ExampleStereoDisparity.denseDisparitySubpixel(
				rectLeft, rectRight, 5, disparityMin, disparityRange);

		// ------------- Convert disparity image into a 3D point cloud

		JComponent viewer = computeAndShowCloud(param, rectLeft, rectAlg, disparity);

		// display the results.  Click and drag to change point cloud camera
		BufferedImage visualized = VisualizeImageData.disparity(disparity, null, disparityRange, 0);
		ShowImages.showWindow(visualized, "Disparity", true);
		ShowImages.showWindow(viewer, "Point Cloud", true);