Example Stereo Disparity

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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 Applets:

Related Examples:

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 maxDisparity Maximum disparity that is considered
	 * @return Disparity image
	 */
	public static ImageUInt8 denseDisparity( ImageUInt8 rectLeft , ImageUInt8 rectRight ,
											 int regionSize,
											 int minDisparity , int maxDisparity )
	{
		// A slower but more accuracy algorithm is selected
		// All of these parameters should be turned
		StereoDisparity<ImageUInt8,ImageUInt8> disparityAlg =
				FactoryStereoDisparity.regionWta(DisparityAlgorithms.RECT_FIVE,
						minDisparity, maxDisparity, regionSize, regionSize, 25, 1, 0.2, ImageUInt8.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 ImageFloat32 denseDisparitySubpixel( ImageUInt8 rectLeft , ImageUInt8 rectRight ,
													   int regionSize ,
													   int minDisparity , int maxDisparity )
	{
		// A slower but more accuracy algorithm is selected
		// All of these parameters should be turned
		StereoDisparity<ImageUInt8,ImageFloat32> disparityAlg =
				FactoryStereoDisparity.regionSubpixelWta(DisparityAlgorithms.RECT_FIVE,
						minDisparity, maxDisparity, regionSize, regionSize, 25, 1, 0.2, ImageUInt8.class);
 
		// process and return the results
		disparityAlg.process(rectLeft,rectRight);
 
		return disparityAlg.getDisparity();
	}
 
	/**
	 * Rectified the input images using known calibration.
	 */
	public static RectifyCalibrated rectify( ImageUInt8 origLeft , ImageUInt8 origRight ,
											 StereoParameters param ,
											 ImageUInt8 rectLeft , ImageUInt8 rectRight )
	{
		// Compute rectification
		RectifyCalibrated rectifyAlg = RectifyImageOps.createCalibrated();
		Se3_F64 leftToRight = param.getRightToLeft().invert(null);
 
		// original camera calibration matrices
		DenseMatrix64F K1 = PerspectiveOps.calibrationMatrix(param.getLeft(), null);
		DenseMatrix64F K2 = PerspectiveOps.calibrationMatrix(param.getRight(), null);
 
		rectifyAlg.process(K1,new Se3_F64(),K2,leftToRight);
 
		// rectification matrix for each image
		DenseMatrix64F rect1 = rectifyAlg.getRect1();
		DenseMatrix64F rect2 = rectifyAlg.getRect2();
		// New calibration matrix,
		DenseMatrix64F rectK = rectifyAlg.getCalibrationMatrix();
 
		// Adjust the rectification to make the view area more useful
		RectifyImageOps.allInsideLeft(param.left, rect1, rect2, rectK);
 
		// undistorted and rectify images
		ImageDistort<ImageUInt8,ImageUInt8> imageDistortLeft =
				RectifyImageOps.rectifyImage(param.getLeft(), rect1, ImageUInt8.class);
		ImageDistort<ImageUInt8,ImageUInt8> imageDistortRight =
				RectifyImageOps.rectifyImage(param.getRight(), rect2, ImageUInt8.class);
 
		imageDistortLeft.apply(origLeft, rectLeft);
		imageDistortRight.apply(origRight, rectRight);
 
		return rectifyAlg;
	}
 
	public static void main( String args[] ) {
		String calibDir = "../data/applet/calibration/stereo/Bumblebee2_Chess/";
		String imageDir = "../data/applet/stereo/";
 
		StereoParameters param = UtilIO.loadXML(calibDir + "stereo.xml");
 
		// load and convert images into a BoofCV format
		BufferedImage origLeft = UtilImageIO.loadImage(imageDir + "chair01_left.jpg");
		BufferedImage origRight = UtilImageIO.loadImage(imageDir + "chair01_right.jpg");
 
		ImageUInt8 distLeft = ConvertBufferedImage.convertFrom(origLeft,(ImageUInt8)null);
		ImageUInt8 distRight = ConvertBufferedImage.convertFrom(origRight,(ImageUInt8)null);
 
		// rectify images
		ImageUInt8 rectLeft = new ImageUInt8(distLeft.width,distLeft.height);
		ImageUInt8 rectRight = new ImageUInt8(distRight.width,distRight.height);
 
		rectify(distLeft,distRight,param,rectLeft,rectRight);
 
		// compute disparity
		ImageUInt8 disparity = denseDisparity(rectLeft,rectRight,5,10,60);
//		ImageFloat32 disparity = denseDisparitySubpixel(rectLeft,rectRight,5,10,60);
 
		// show results
		BufferedImage visualized = VisualizeImageData.disparity(disparity, null,10,60,0);
 
		ShowImages.showWindow(rectLeft,"Rectified");
		ShowImages.showWindow(visualized,"Disparity");
	}
}