Example Multi Baseline Stereo
From BoofCV
Jump to navigationJump to search
|
|
| Multiple disparity images are computed from a common view and combined into a single disparity image. | Video showing resulting point cloud |
The idea behind Multi Baseline Stereo is combine multiple stereo pairs that all share a common view into a single disparity image that is in theory more complete and less noisy. An example of how to do using an already provided algorithm is shown below. This is basically a first pass and doesn't yet have all the more advanced features included. As a warning, this is built on top of the scene reconstruction demo and the first time you run it it might take a couple of minutes as it solves a lot of other problems first.
Example Code:
Concepts:
- Structure from Motion
- Sparse Bundle Adjustment
- Uncalibrated Sparse Reconstruction
- Dense Multiview Reconstruction
Example Code
/**
* Multi Baseline Stereo (MBS) is a problem where you have one common/center image with multiple paired images that have
* different baselines from each other and the goal is to output a single combined stereo image. In BoofCV, this
* combined stereo image will be the original image's pixel as compared to the more standard rectified image. At
* the time of this writing, BoofCV only contains one MBS algorithm, which is just about the simplest one possible
* and works by combining independently computed disparity images together using a median filter to select the output.
*
* @author Peter Abeles
*/
public class ExampleMultiBaselineStereo {
public static void main( String[] args ) {
// Compute a sparse reconstruction. This will give us intrinsic and extrinsic for all views
var example = new ExampleMultiViewSparseReconstruction();
// Specifies the "center" frame to use
int centerViewIdx = 15;
example.compute("tree_snow_01.mp4", true);
// example.compute("ditch_02.mp4", true);
// example.compute("holiday_display_01.mp4", true);
// example.compute("log_building_02.mp4", true);
// example.compute("drone_park_01.mp4", false);
// example.compute("stone_sign.mp4", true);
// We need a way to load images based on their ID. In this particular case the ID encodes the array index.
var imageLookup = new LookUpImageFilesByIndex(example.imageFiles);
// Next we tell it which view to use as the "center", which acts as the common view for all disparity images.
// The process of selecting the best views to use as centers is a problem all it's own. To keep things
// we just pick a frame.
SceneWorkingGraph.View center = example.working.getAllViews().get(centerViewIdx);
// The final scene refined by bundle adjustment is created by the Working graph. However the 3D relationship
// between views is contained in the pairwise graph. A View in the working graph has a reference to the view
// in the pairwise graph. Using that we will find all connected views that have a 3D relationship
var pairedViewIdxs = new DogArray_I32();
var sbaIndexToImageID = new TIntObjectHashMap<String>();
// This relationship between pairwise and working graphs might seem (and is) a bit convoluted. The Pairwise
// graph is the initial crude sketch of what might be connected. The working graph is an intermediate
// data structure for computing the metric scene. SBA is a refinement of the working graph.
// Iterate through all connected views in the pairwise graph and mark their indexes in the working graph
center.pview.connections.forEach(( m ) -> {
// if there isn't a 3D relationship just skip it
if (!m.is3D)
return;
String connectedID = m.other(center.pview).id;
SceneWorkingGraph.View connected = example.working.views.get(connectedID);
// Make sure the pairwise view exists in the working graph too
if (connected == null)
return;
// Add this view to the index to name/ID lookup table
sbaIndexToImageID.put(connected.index, connectedID);
// Note that this view is one which acts as the second image in the stereo pair
pairedViewIdxs.add(connected.index);
});
// Add the center camera image to the ID look up table
sbaIndexToImageID.put(centerViewIdx, center.pview.id);
// Configure there stereo disparity algorithm which is used
var configDisparity = new ConfigDisparityBMBest5();
configDisparity.validateRtoL = 1;
configDisparity.texture = 0.5;
configDisparity.regionRadiusX = configDisparity.regionRadiusY = 4;
configDisparity.disparityRange = 120;
// This is the actual MBS algorithm mentioned previously. It selects the best disparity for each pixel
// in the original image using a median filter.
var multiBaseline = new MultiBaselineStereoIndependent<>(imageLookup, ImageType.SB_U8);
multiBaseline.setStereoDisparity(
FactoryStereoDisparity.blockMatchBest5(configDisparity, GrayU8.class, GrayF32.class));
// Print out verbose debugging and profile information
multiBaseline.setVerbose(System.out, null);
multiBaseline.setVerboseProfiling(System.out);
// Improve stereo by removing small regions, which tends to be noise. Consider adjusting the region size.
multiBaseline.setDisparitySmoother(FactoryStereoDisparity.removeSpeckle(null, GrayF32.class));
// Print out debugging information from the smoother
//Objects.requireNonNull(multiBaseline.getDisparitySmoother()).setVerbose(System.out,null);
// Creates a list where you can switch between different images/visualizations
var listDisplay = new ListDisplayPanel();
listDisplay.setPreferredSize(new Dimension(1000, 300));
ShowImages.showWindow(listDisplay, "Intermediate Results", true);
// We will display intermediate results as they come in
multiBaseline.setListener(( leftView, rightView, rectLeft, rectRight,
disparity, parameters, rect ) -> {
// Visualize the rectified stereo pair. You can interact with this window and verify
// that the y-axis is aligned
var rectified = new RectifiedPairPanel(true);
rectified.setImages(ConvertBufferedImage.convertTo(rectLeft, null),
ConvertBufferedImage.convertTo(rectRight, null));
// Display the colorized disparity
BufferedImage colorized = VisualizeImageData.disparity(disparity, null, parameters.disparityRange, 0);
SwingUtilities.invokeLater(() -> {
listDisplay.addItem(rectified, "Rectified " + leftView + " " + rightView);
listDisplay.addImage(colorized, leftView + " " + rightView);
});
});
// Process the images and compute a single combined disparity image
if (!multiBaseline.process(example.scene, center.index, pairedViewIdxs, sbaIndexToImageID::get)) {
throw new RuntimeException("Failed to fuse stereo views");
}
// Extract the point cloud from the fused disparity image
GrayF32 fusedDisparity = multiBaseline.getFusedInvDepth();
BufferedImage colorizedDisp = VisualizeImageData.inverseDepth(fusedDisparity, null, -1f, 0);
ShowImages.showWindow(colorizedDisp, "Fused InverseDepth");
// Now compute the point cloud it represents and the color of each pixel.
// For the fused image, instead of being in rectified image coordinates it's in the original image coordinates
// this makes extracting color much easier.
var cloud = new DogArray<>(Point3D_F64::new);
var cloudRgb = new DogArray_I32(cloud.size);
// Load the center image in color
var colorImage = new InterleavedU8(1, 1, 3);
imageLookup.loadImage(center.pview.id, colorImage);
// Since the fused image is in the original (i.e. distorted) pixel coordinates and is not rectified,
// that needs to be taken in account by undistorting the image to create the point cloud.
CameraPinholeBrown intrinsic = BundleAdjustmentOps.convert(example.scene.cameras.get(center.cameraIdx).model,
colorImage.width, colorImage.height, null);
Point2Transform2_F64 pixel_to_norm = new LensDistortionBrown(intrinsic).distort_F64(true, false);
MultiViewStereoOps.inverseToCloud(fusedDisparity, new PointToPixelTransform_F64(pixel_to_norm),
( pixX, pixY, x, y, z ) -> {
cloud.grow().setTo(x, y, z);
cloudRgb.add(colorImage.get24(pixX, pixY));
});
// Configure the point cloud viewer
PointCloudViewer pcv = VisualizeData.createPointCloudViewer();
pcv.setCameraHFov(UtilAngle.radian(70));
pcv.setTranslationStep(0.15);
pcv.addCloud(cloud.toList(), cloudRgb.data);
// pcv.setColorizer(new SingleAxisRgb.Z().fperiod(30.0));
JComponent viewer = pcv.getComponent();
viewer.setPreferredSize(new Dimension(600, 600));
ShowImages.showWindow(viewer, "Point Cloud", true);
System.out.println("Done");
}
}
