Example Stereo Disparity

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  Left: is a color histogram of the disparity between two stereo images. Hot colors are closer objects. Right: is the rectified left stereo image.

Shows how to compute dense disparity between two rectified stereo images. BoofCV provides two different rectangular region based algorithms and noise reduction techniques targeted at real-time processing. Stereo vision can be difficult to get right, so please read all JavaDoc and cited papers. Dense stereo disparity is a computationally expensive and is likely to require a reduction in image size to achieve truly real-time performance.

For visualization purposes the disparity is encoded using a color histogram. Hotter colors indicate closer objects while cooler objects indicate objects that are farther away. Cameras must be accurate calibrated or else an error of a few pixels will drastically degrade performance. A common preprocessing step is to run a Laplacian of Gaussian edge detector across the image to provide invariance to lighting conditions. This was not done below because the cameras have their gain synchronized.

Example File: ExampleStereoDisparity.java

Concepts:

• Stereo Vision
• Disparity
• Rectification

Related Examples:

• Rectifying Calibrated Cameras
• Stereo from Single Camera Example

Related Videos

• Stereo Update 2020

Example Code

/**
 * The disparity between two stereo images is used to estimate the range of objects inside
 * the camera's view.  Disparity is the difference in position between the viewed location
 * of a point in the left and right stereo images.  Because input images are rectified,
 * corresponding points can be found by only searching along image rows.
 *
 * Values in the disparity image specify how different the two images are.  A value of X indicates
 * that the corresponding point in the right image from the left is at "x' = x - X - minDisparity",
 * where x' and x are the locations in the right and left images respectively.  An invalid value
 * with no correspondence is set to a value more than (max - min) disparity.
 *
 * @author Peter Abeles
 */
public class ExampleStereoDisparity {

	/**
	 * Computes the dense disparity between between two stereo images.  The input images
	 * must be rectified with lens distortion removed to work!  Floating point images
	 * are also supported.
	 *
	 * @param rectLeft Rectified left camera image
	 * @param rectRight Rectified right camera image
	 * @param regionSize Radius of region being matched
	 * @param minDisparity Minimum disparity that is considered
	 * @param rangeDisparity Number of disparity values considered.
	 * @return Disparity image
	 */
	public static GrayU8 denseDisparity(GrayU8 rectLeft , GrayU8 rectRight ,
										int regionSize,
										int minDisparity , int rangeDisparity )
	{
		// A slower but more accuracy algorithm is selected
		// All of these parameters should be turned
		ConfigDisparityBMBest5 config = new ConfigDisparityBMBest5();
		config.errorType = DisparityError.CENSUS;
		config.disparityMin = minDisparity;
		config.disparityRange = rangeDisparity;
		config.subpixel = false;
		config.regionRadiusX = config.regionRadiusY = regionSize;
		config.maxPerPixelError = 35;
		config.validateRtoL = 1;
		config.texture = 0.2;
		StereoDisparity<GrayU8, GrayU8> disparityAlg =
				FactoryStereoDisparity.blockMatchBest5(config, GrayU8.class, GrayU8.class);

		// process and return the results
		disparityAlg.process(rectLeft,rectRight);

		return disparityAlg.getDisparity();
	}

	/**
	 * Same as above, but compute disparity to within sub-pixel accuracy. The difference between the
	 * two is more apparent when a 3D point cloud is computed.
	 */
	public static GrayF32 denseDisparitySubpixel(GrayU8 rectLeft , GrayU8 rectRight ,
												 int regionSize ,
												 int minDisparity , int rangeDisparity )
	{
		// A slower but more accuracy algorithm is selected
		// All of these parameters should be turned
		ConfigDisparityBMBest5 config = new ConfigDisparityBMBest5();
		config.errorType = DisparityError.CENSUS;
		config.disparityMin = minDisparity;
		config.disparityRange = rangeDisparity;
		config.subpixel = true;
		config.regionRadiusX = config.regionRadiusY = regionSize;
		config.maxPerPixelError = 35;
		config.validateRtoL = 1;
		config.texture = 0.2;
		StereoDisparity<GrayU8, GrayF32> disparityAlg =
				FactoryStereoDisparity.blockMatchBest5(config, GrayU8.class, GrayF32.class);

		// process and return the results
		disparityAlg.process(rectLeft,rectRight);

		return disparityAlg.getDisparity();
	}

	/**
	 * Rectified the input images using known calibration.
	 */
	public static RectifyCalibrated rectify(GrayU8 origLeft , GrayU8 origRight ,
											StereoParameters param ,
											GrayU8 rectLeft , GrayU8 rectRight )
	{
		// Compute rectification
		RectifyCalibrated rectifyAlg = RectifyImageOps.createCalibrated();
		Se3_F64 leftToRight = param.getRightToLeft().invert(null);

		// original camera calibration matrices
		DMatrixRMaj K1 = PerspectiveOps.pinholeToMatrix(param.getLeft(), (DMatrixRMaj)null);
		DMatrixRMaj K2 = PerspectiveOps.pinholeToMatrix(param.getRight(), (DMatrixRMaj)null);

		rectifyAlg.process(K1,new Se3_F64(),K2,leftToRight);

		// rectification matrix for each image
		DMatrixRMaj rect1 = rectifyAlg.getRect1();
		DMatrixRMaj rect2 = rectifyAlg.getRect2();
		// New calibration matrix,
		DMatrixRMaj rectK = rectifyAlg.getCalibrationMatrix();

		// Adjust the rectification to make the view area more useful
		RectifyImageOps.allInsideLeft(param.left, rect1, rect2, rectK, null);

		// undistorted and rectify images
		FMatrixRMaj rect1_F32 = new FMatrixRMaj(3,3);
		FMatrixRMaj rect2_F32 = new FMatrixRMaj(3,3);
		ConvertMatrixData.convert(rect1, rect1_F32);
		ConvertMatrixData.convert(rect2, rect2_F32);

		ImageDistort<GrayU8,GrayU8> imageDistortLeft =
				RectifyImageOps.rectifyImage(param.getLeft(), rect1_F32, BorderType.SKIP, origLeft.getImageType());
		ImageDistort<GrayU8,GrayU8> imageDistortRight =
				RectifyImageOps.rectifyImage(param.getRight(), rect2_F32, BorderType.SKIP, origRight.getImageType());

		imageDistortLeft.apply(origLeft, rectLeft);
		imageDistortRight.apply(origRight, rectRight);

		return rectifyAlg;
	}

	public static void main( String args[] ) {
		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
		GrayU8 rectLeft = distLeft.createSameShape();
		GrayU8 rectRight = distRight.createSameShape();

		rectify(distLeft,distRight,param,rectLeft,rectRight);

		// compute disparity
		GrayU8 disparity = denseDisparity(rectLeft,rectRight,5,10,60);
//		GrayF32 disparity = denseDisparitySubpixel(rectLeft,rectRight,5,10,60);

		// show results
		BufferedImage visualized = VisualizeImageData.disparity(disparity, null,60,0);

		ListDisplayPanel gui = new ListDisplayPanel();
		gui.addImage(rectLeft, "Rectified");
		gui.addImage(visualized, "Disparity");

		ShowImages.showWindow(gui,"Stereo Disparity", true);
	}
}