Difference between revisions of "Example Stereo Disparity 3D"

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| [[file:Example_stereo_disparity3d_pointcloud.jpg|400px]] || {{#ev:youtube|https://www.youtube.com/watch?v=8pn9Ebw90uk&t=672s|400|center}}
| [[file:Example_stereo_disparity3d_pointcloud.jpg|400px]] || {{#ev:youtube|https://www.youtube.com/watch?v=8pn9Ebw90uk|400|center|||start=672}}
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| 3D point cloud generated from disparity image. || Stereo Update Video
| 3D point cloud generated from disparity image. || Stereo Update Video

Revision as of 20:38, 21 June 2020

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:

Concepts:

  • 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 what disparity values are considered
	public static final int minDisparity = 10;
	public static final int rangeDisparity = 60;

	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,minDisparity, rangeDisparity);
		GrayF32 disparity = ExampleStereoDisparity.denseDisparitySubpixel(
				rectLeft, rectRight, 5, minDisparity, rangeDisparity);

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

		// The point cloud will be in the left cameras reference frame
		DMatrixRMaj rectK = rectAlg.getCalibrationMatrix();
		DMatrixRMaj rectR = rectAlg.getRectifiedRotation();

		// extract intrinsic parameters from rectified camera
		double baseline = param.getBaseline()*0.1;
		double fx = rectK.get(0,0);
		double fy = rectK.get(1,1);
		double cx = rectK.get(0,2);
		double cy = rectK.get(1,2);

		double maxZ = baseline*100;

		// Iterate through each pixel in disparity image and compute its 3D coordinate
		PointCloudViewer pcv = VisualizeData.createPointCloudViewer();
		pcv.setTranslationStep(1.5);

		Point3D_F64 pointRect = new Point3D_F64();
		Point3D_F64 pointLeft = new Point3D_F64();
		for( int y = 0; y < disparity.height; y++ ) {
			for( int x = 0; x < disparity.width; x++ ) {
				double d = disparity.unsafe_get(x,y) + minDisparity;

				// skip over pixels were no correspondence was found
				if( d >= rangeDisparity || d <= 0 )
					continue;

				// Coordinate in rectified camera frame
				pointRect.z = baseline*fx/d;
				pointRect.x = pointRect.z*(x - cx)/fx;
				pointRect.y = pointRect.z*(y - cy)/fy;

				// prune points which are likely to be noise
				if( pointRect.z >= maxZ )
					continue;

				// rotate into the original left camera frame
				GeometryMath_F64.multTran(rectR, pointRect, pointLeft);

				// add pixel to the view for display purposes and sets its gray scale value
				int v = rectLeft.unsafe_get(x, y);
				pcv.addPoint(pointLeft.x,pointLeft.y,pointLeft.z,v << 16 | v << 8 | v);
//				temp.add( pointLeft.copy() );
			}
		}

		// move it back a bit to make the 3D structure more apparent
		Se3_F64 cameraToWorld = new Se3_F64();
		cameraToWorld.T.z = -baseline*5;
		cameraToWorld.T.x = baseline*12;
		ConvertRotation3D_F64.eulerToMatrix(EulerType.XYZ,0.1,-0.4,0,cameraToWorld.R);

		// 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.setDotSize(1);
		pcv.setCameraHFov(PerspectiveOps.computeHFov(param.left));
		pcv.setCameraToWorld(cameraToWorld);
		JComponent viewer = pcv.getComponent();
		viewer.setPreferredSize(new Dimension(600,600*param.left.height/param.left.width));

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