Example Image Stitching

From BoofCV
Jump to navigationJump to search

This example is designed to demonstrate several aspects of BoofCV by stitching images together. Image stitching refers to combining two or more overlapping images together into a single large image. When stitching images together the goal is to find a 2D geometric transform which minimize the error (difference in appearance) in overlapping regions. There are many ways to do this, in the example below point image features are found, associated, and then a 2D transform is found robustly using the associated features.

Example File: ExampleImageStitching.java


  • Interest point detection
  • Region descriptions
  • Feature association
  • Robust model fitting
  • Homography

Relevant Videos:

Related Examples:

Algorithm Introduction

Described at a high level this image stitching algorithm can be summarized as follows:

  1. Detect and describe point features
  2. Associate features together
  3. Robust fitting to find transform
  4. Render combined image

The core algorithm has been coded up using abstracted code which allows different models and algorithms to be changed easily. Output examples are shown at the top of this page.

Example Code

public class ExampleImageStitching {
	 * Using abstracted code, find a transform which minimizes the difference between corresponding features
	 * in both images. This code is completely model independent and is the core algorithms.
	public static <T extends ImageGray<T>, TD extends TupleDesc<TD>> Homography2D_F64
	computeTransform( T imageA, T imageB,
					  DetectDescribePoint<T, TD> detDesc,
					  AssociateDescription<TD> associate,
					  ModelMatcher<Homography2D_F64, AssociatedPair> modelMatcher ) {
		// get the length of the description
		List<Point2D_F64> pointsA = new ArrayList<>();
		DogArray<TD> descA = UtilFeature.createArray(detDesc, 100);
		List<Point2D_F64> pointsB = new ArrayList<>();
		DogArray<TD> descB = UtilFeature.createArray(detDesc, 100);

		// extract feature locations and descriptions from each image
		describeImage(imageA, detDesc, pointsA, descA);
		describeImage(imageB, detDesc, pointsB, descB);

		// Associate features between the two images

		// create a list of AssociatedPairs that tell the model matcher how a feature moved
		FastAccess<AssociatedIndex> matches = associate.getMatches();
		List<AssociatedPair> pairs = new ArrayList<>();

		for (int i = 0; i < matches.size(); i++) {
			AssociatedIndex match = matches.get(i);

			Point2D_F64 a = pointsA.get(match.src);
			Point2D_F64 b = pointsB.get(match.dst);

			pairs.add(new AssociatedPair(a, b, false));

		// find the best fit model to describe the change between these images
		if (!modelMatcher.process(pairs))
			throw new RuntimeException("Model Matcher failed!");

		// return the found image transform
		return modelMatcher.getModelParameters().copy();

	 * Detects features inside the two images and computes descriptions at those points.
	private static <T extends ImageGray<T>, TD extends TupleDesc<TD>>
	void describeImage( T image,
						DetectDescribePoint<T, TD> detDesc,
						List<Point2D_F64> points,
						DogArray<TD> listDescs ) {

		for (int i = 0; i < detDesc.getNumberOfFeatures(); i++) {

	 * Given two input images create and display an image where the two have been overlayed on top of each other.
	public static <T extends ImageGray<T>>
	void stitch( BufferedImage imageA, BufferedImage imageB, Class<T> imageType ) {
		T inputA = ConvertBufferedImage.convertFromSingle(imageA, null, imageType);
		T inputB = ConvertBufferedImage.convertFromSingle(imageB, null, imageType);

		// Detect using the standard SURF feature descriptor and describer
		DetectDescribePoint detDesc = FactoryDetectDescribe.surfStable(
				new ConfigFastHessian(1, 2, 200, 1, 9, 4, 4), null, null, imageType);
		ScoreAssociation<TupleDesc_F64> scorer = FactoryAssociation.scoreEuclidean(TupleDesc_F64.class, true);
		AssociateDescription<TupleDesc_F64> associate = FactoryAssociation.greedy(new ConfigAssociateGreedy(true, 2), scorer);

		// fit the images using a homography. This works well for rotations and distant objects.
		ModelMatcher<Homography2D_F64, AssociatedPair> modelMatcher =
				FactoryMultiViewRobust.homographyRansac(null, new ConfigRansac(60, 3));

		Homography2D_F64 H = computeTransform(inputA, inputB, detDesc, associate, modelMatcher);

		renderStitching(imageA, imageB, H);

	 * Renders and displays the stitched together images
	public static void renderStitching( BufferedImage imageA, BufferedImage imageB,
										Homography2D_F64 fromAtoB ) {
		// specify size of output image
		double scale = 0.5;

		// Convert into a BoofCV color format
		Planar<GrayF32> colorA =
				ConvertBufferedImage.convertFromPlanar(imageA, null, true, GrayF32.class);
		Planar<GrayF32> colorB =
				ConvertBufferedImage.convertFromPlanar(imageB, null, true, GrayF32.class);

		// Where the output images are rendered into
		Planar<GrayF32> work = colorA.createSameShape();

		// Adjust the transform so that the whole image can appear inside of it
		Homography2D_F64 fromAToWork = new Homography2D_F64(scale, 0, colorA.width/4, 0, scale, colorA.height/4, 0, 0, 1);
		Homography2D_F64 fromWorkToA = fromAToWork.invert(null);

		// Used to render the results onto an image
		PixelTransformHomography_F32 model = new PixelTransformHomography_F32();
		InterpolatePixelS<GrayF32> interp = FactoryInterpolation.bilinearPixelS(GrayF32.class, BorderType.ZERO);
		ImageDistort<Planar<GrayF32>, Planar<GrayF32>> distort =
				DistortSupport.createDistortPL(GrayF32.class, model, interp, false);

		// Render first image
		distort.apply(colorA, work);

		// Render second image
		Homography2D_F64 fromWorkToB = fromWorkToA.concat(fromAtoB, null);
		distort.apply(colorB, work);

		// Convert the rendered image into a BufferedImage
		BufferedImage output = new BufferedImage(work.width, work.height, imageA.getType());
		ConvertBufferedImage.convertTo(work, output, true);

		Graphics2D g2 = output.createGraphics();

		// draw lines around the distorted image to make it easier to see
		Homography2D_F64 fromBtoWork = fromWorkToB.invert(null);
		Point2D_I32 corners[] = new Point2D_I32[4];
		corners[0] = renderPoint(0, 0, fromBtoWork);
		corners[1] = renderPoint(colorB.width, 0, fromBtoWork);
		corners[2] = renderPoint(colorB.width, colorB.height, fromBtoWork);
		corners[3] = renderPoint(0, colorB.height, fromBtoWork);

		g2.setStroke(new BasicStroke(4));
		g2.setRenderingHint(RenderingHints.KEY_ANTIALIASING, RenderingHints.VALUE_ANTIALIAS_ON);
		g2.drawLine(corners[0].x, corners[0].y, corners[1].x, corners[1].y);
		g2.drawLine(corners[1].x, corners[1].y, corners[2].x, corners[2].y);
		g2.drawLine(corners[2].x, corners[2].y, corners[3].x, corners[3].y);
		g2.drawLine(corners[3].x, corners[3].y, corners[0].x, corners[0].y);

		ShowImages.showWindow(output, "Stitched Images", true);

	private static Point2D_I32 renderPoint( int x0, int y0, Homography2D_F64 fromBtoWork ) {
		Point2D_F64 result = new Point2D_F64();
		HomographyPointOps_F64.transform(fromBtoWork, new Point2D_F64(x0, y0), result);
		return new Point2D_I32((int)result.x, (int)result.y);

	public static void main( String[] args ) {
		BufferedImage imageA, imageB;
		imageA = UtilImageIO.loadImageNotNull(UtilIO.pathExample("stitch/mountain_rotate_01.jpg"));
		imageB = UtilImageIO.loadImageNotNull(UtilIO.pathExample("stitch/mountain_rotate_03.jpg"));
		stitch(imageA, imageB, GrayF32.class);
		imageA = UtilImageIO.loadImageNotNull(UtilIO.pathExample("stitch/kayak_01.jpg"));
		imageB = UtilImageIO.loadImageNotNull(UtilIO.pathExample("stitch/kayak_03.jpg"));
		stitch(imageA, imageB, GrayF32.class);
		imageA = UtilImageIO.loadImageNotNull(UtilIO.pathExample("scale/rainforest_01.jpg"));
		imageB = UtilImageIO.loadImageNotNull(UtilIO.pathExample("scale/rainforest_02.jpg"));
		stitch(imageA, imageB, GrayF32.class);