Package process

Class Matching

java.lang.Object

process.Matching

public class Matching
extends Object

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static class	Matching.PeakSort

Field Summary

Fields

Modifier and Type	Field	Description
static boolean	applyScaling
static float	factor
protected static PrintWriter	outAll

Constructor Summary

Constructors

Constructor	Description
Matching()

Method Summary

Modifier and Type	Method	Description
static void	addPointMatches(ArrayList<mpicbg.models.PointMatch> correspondences, mpicbg.models.Tile<?> tileA, mpicbg.models.Tile<?> tileB)
protected static ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>>	computeDoG(mpicbg.imglib.image.Image<mpicbg.imglib.type.numeric.real.FloatType> image, float sigma1, float sigma2, boolean lookForMaxima, boolean lookForMinima, float threshold, int localization, int iterations, double[] sigmaGuess, int[] region)
static float[]	computeMinMax(ij.ImagePlus imp, int channel)	Computes the global intensity min/max over all slices and frames of the given (zero-based) channel.
protected static String	computeRANSAC(ArrayList<mpicbg.models.PointMatch> candidates, ArrayList<mpicbg.models.PointMatch> inliers, mpicbg.models.Model<?> model, float maxEpsilon)
static void	concatenateAxialScaling(mpicbg.models.AbstractAffineModel3D<?> model, double zStretching)	Concatenates an axial (z) scaling to a 3D affine/rigid model, in-place.
static mpicbg.imglib.image.Image<mpicbg.imglib.type.numeric.real.FloatType>	convertToFloat(ij.ImagePlus imp, int channel, int timepoint, float[] minmax)	Normalize and make a copy of the ImagePlus into an Image of FloatType for faster access when copying the slices
static mpicbg.imglib.image.Image<mpicbg.imglib.type.numeric.real.FloatType>	convertToFloat(ij.ImagePlus imp, int channel, int timepoint, float[] minmax, float[] minmaxUsed)
protected static int	countMatches(List<ComparePair> pairs)
protected static ArrayList<net.preibisch.mvrecon.process.pointcloud.pointdescriptor.AbstractPointDescriptor>	createModelPointDescriptors(fiji.util.KDTree<Particle> tree, ArrayList<Particle> basisPoints, int numNeighbors, net.preibisch.mvrecon.process.pointcloud.pointdescriptor.matcher.Matcher matcher, net.preibisch.mvrecon.process.pointcloud.pointdescriptor.similarity.SimilarityMeasure similarityMeasure, int dimensionality)
protected static ArrayList<net.preibisch.mvrecon.process.pointcloud.pointdescriptor.AbstractPointDescriptor>	createSimplePointDescriptors(fiji.util.KDTree<Particle> tree, ArrayList<Particle> basisPoints, int numNeighbors, net.preibisch.mvrecon.process.pointcloud.pointdescriptor.matcher.Matcher matcher, net.preibisch.mvrecon.process.pointcloud.pointdescriptor.similarity.SimilarityMeasure similarityMeasure)
static int	descriptorBasedRegistration(ij.ImagePlus imp1, ij.ImagePlus imp2, DescriptorParameters params)
static ArrayList<mpicbg.models.InvertibleBoundable>	descriptorBasedStackRegistration(ij.ImagePlus imp, DescriptorParameters params)
static Vector<ComparePair>	descriptorMatching(ArrayList<ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>>> peaks, int numImages, DescriptorParameters params, float zStretching)
static ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>>	extractCandidates(ij.ImagePlus imp, int channel, int timepoint, DescriptorParameters params, float[] minmax)
protected static ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>>	filterForROI(ij.gui.Roi roi, ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks)
protected static ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>>	filterForROI(ij.gui.Roi roi, ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks, mpicbg.models.Model<?> model)
protected static final ArrayList<mpicbg.models.PointMatch>	findCorrespondingDescriptors(ArrayList<net.preibisch.mvrecon.process.pointcloud.pointdescriptor.AbstractPointDescriptor> descriptorsA, ArrayList<net.preibisch.mvrecon.process.pointcloud.pointdescriptor.AbstractPointDescriptor> descriptorsB, float nTimesBetter)
protected static Vector<ComparePair>	getComparePairs(DescriptorParameters params, int numImages)
protected static ArrayList<mpicbg.models.PointMatch>	getCorrespondenceCandidates(double nTimesBetter, net.preibisch.mvrecon.process.pointcloud.pointdescriptor.matcher.Matcher matcher, ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks1, ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks2, mpicbg.models.Model<?> model, int dimensionality, float zStretching1, float zStretching2, String explanation)
static ArrayList<mpicbg.models.InvertibleBoundable>	globalOptimization(Vector<ComparePair> pairs, int numImages, DescriptorParameters params)
static void	main(String[] args)
static float[]	normalizeImage(mpicbg.imglib.image.Image<mpicbg.imglib.type.numeric.real.FloatType> image, float[] minmax)	Normalizes an imglib1 image in-place to the range [0, 1].
protected static mpicbg.models.Model<?>	pairwiseMatching(ArrayList<mpicbg.models.PointMatch> finalInliers, ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks1, ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks2, float zStretching1, float zStretching2, DescriptorParameters params, String explanation)
protected static int	performIteration(ArrayList<mpicbg.models.InvertibleBoundable> lastModels, ArrayList<ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>>> peaksComplete, int numImages, DescriptorParameters params, float zStretching)	Computes one iteration and updates the lastModels ArrayList with the new models
protected static int[]	removeInvalidAndCollectStatistics(ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks)
protected static void	setPointRois(ij.ImagePlus imp1, ij.ImagePlus imp2, ArrayList<mpicbg.models.PointMatch> inliers)
protected static void	writePoints(ArrayList<mpicbg.models.PointMatch> finalInliers, DescriptorParameters params, mpicbg.models.Model<?> finalModel, PrintWriter out)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Details

applyScaling

public static boolean applyScaling

factor

public static float factor

outAll

protected static PrintWriter outAll

Constructor Details

Matching

public Matching()

Method Details

descriptorBasedRegistration

public static int descriptorBasedRegistration(ij.ImagePlus imp1,
 ij.ImagePlus imp2,
 DescriptorParameters params)

Parameters:

imp1 -

imp2 -

params -

Returns:

- the number of inliers

descriptorBasedStackRegistration

public static ArrayList<mpicbg.models.InvertibleBoundable> descriptorBasedStackRegistration(ij.ImagePlus imp,
 DescriptorParameters params)

performIteration

protected static int performIteration(ArrayList<mpicbg.models.InvertibleBoundable> lastModels,
 ArrayList<ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>>> peaksComplete,
 int numImages,
 DescriptorParameters params,
 float zStretching)

Computes one iteration and updates the lastModels ArrayList with the new models

Parameters:

lastModels - - models from last iteration (or init)

peaksComplete - - all peaks for all images

numImages - - how many images are there

params - - the parameters

zStretching - - the zStretching if applicable

Returns:

the number of matches found

countMatches

protected static int countMatches(List<ComparePair> pairs)

descriptorMatching

public static Vector<ComparePair> descriptorMatching(ArrayList<ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>>> peaks,
 int numImages,
 DescriptorParameters params,
 float zStretching)

globalOptimization

public static ArrayList<mpicbg.models.InvertibleBoundable> globalOptimization(Vector<ComparePair> pairs,
 int numImages,
 DescriptorParameters params)

addPointMatches

public static void addPointMatches(ArrayList<mpicbg.models.PointMatch> correspondences,
 mpicbg.models.Tile<?> tileA,
 mpicbg.models.Tile<?> tileB)

getComparePairs

protected static Vector<ComparePair> getComparePairs(DescriptorParameters params,
 int numImages)

pairwiseMatching

protected static mpicbg.models.Model<?> pairwiseMatching(ArrayList<mpicbg.models.PointMatch> finalInliers,
 ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks1,
 ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks2,
 float zStretching1,
 float zStretching2,
 DescriptorParameters params,
 String explanation)

writePoints

protected static void writePoints(ArrayList<mpicbg.models.PointMatch> finalInliers,
 DescriptorParameters params,
 mpicbg.models.Model<?> finalModel,
 PrintWriter out)

computeMinMax

public static float[] computeMinMax(ij.ImagePlus imp,
 int channel)

Computes the global intensity min/max over all slices and frames of the given (zero-based) channel. The work is distributed over all available threads, one (z,t) plane per task.

extractCandidates

public static ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> extractCandidates(ij.ImagePlus imp,
 int channel,
 int timepoint,
 DescriptorParameters params,
 float[] minmax)

convertToFloat

public static mpicbg.imglib.image.Image<mpicbg.imglib.type.numeric.real.FloatType> convertToFloat(ij.ImagePlus imp,
 int channel,
 int timepoint,
 float[] minmax)

Normalize and make a copy of the ImagePlus into an Image of FloatType for faster access when copying the slices

Parameters:

imp - - the ImagePlus input image

Returns:

- the normalized copy [0...1]

convertToFloat

public static mpicbg.imglib.image.Image<mpicbg.imglib.type.numeric.real.FloatType> convertToFloat(ij.ImagePlus imp,
 int channel,
 int timepoint,
 float[] minmax,
 float[] minmaxUsed)

Parameters:

minmaxUsed - - if non-null (length >= 2), is filled with the intensity min/max actually applied for normalization (the auto-computed per-image range when minmax == null, otherwise minmax).

filterForROI

protected static ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> filterForROI(ij.gui.Roi roi,
 ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks)

filterForROI

protected static ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> filterForROI(ij.gui.Roi roi,
 ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks,
 mpicbg.models.Model<?> model)

setPointRois

protected static void setPointRois(ij.ImagePlus imp1,
 ij.ImagePlus imp2,
 ArrayList<mpicbg.models.PointMatch> inliers)

computeRANSAC

protected static String computeRANSAC(ArrayList<mpicbg.models.PointMatch> candidates,
 ArrayList<mpicbg.models.PointMatch> inliers,
 mpicbg.models.Model<?> model,
 float maxEpsilon)

getCorrespondenceCandidates

protected static ArrayList<mpicbg.models.PointMatch> getCorrespondenceCandidates(double nTimesBetter,
 net.preibisch.mvrecon.process.pointcloud.pointdescriptor.matcher.Matcher matcher,
 ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks1,
 ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks2,
 mpicbg.models.Model<?> model,
 int dimensionality,
 float zStretching1,
 float zStretching2,
 String explanation)

findCorrespondingDescriptors

protected static final ArrayList<mpicbg.models.PointMatch> findCorrespondingDescriptors(ArrayList<net.preibisch.mvrecon.process.pointcloud.pointdescriptor.AbstractPointDescriptor> descriptorsA,
 ArrayList<net.preibisch.mvrecon.process.pointcloud.pointdescriptor.AbstractPointDescriptor> descriptorsB,
 float nTimesBetter)

createSimplePointDescriptors

protected static ArrayList<net.preibisch.mvrecon.process.pointcloud.pointdescriptor.AbstractPointDescriptor> createSimplePointDescriptors(fiji.util.KDTree<Particle> tree,
 ArrayList<Particle> basisPoints,
 int numNeighbors,
 net.preibisch.mvrecon.process.pointcloud.pointdescriptor.matcher.Matcher matcher,
 net.preibisch.mvrecon.process.pointcloud.pointdescriptor.similarity.SimilarityMeasure similarityMeasure)

createModelPointDescriptors

protected static ArrayList<net.preibisch.mvrecon.process.pointcloud.pointdescriptor.AbstractPointDescriptor> createModelPointDescriptors(fiji.util.KDTree<Particle> tree,
 ArrayList<Particle> basisPoints,
 int numNeighbors,
 net.preibisch.mvrecon.process.pointcloud.pointdescriptor.matcher.Matcher matcher,
 net.preibisch.mvrecon.process.pointcloud.pointdescriptor.similarity.SimilarityMeasure similarityMeasure,
 int dimensionality)

computeDoG

protected static ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> computeDoG(mpicbg.imglib.image.Image<mpicbg.imglib.type.numeric.real.FloatType> image,
 float sigma1,
 float sigma2,
 boolean lookForMaxima,
 boolean lookForMinima,
 float threshold,
 int localization,
 int iterations,
 double[] sigmaGuess,
 int[] region)

concatenateAxialScaling

public static void concatenateAxialScaling(mpicbg.models.AbstractAffineModel3D<?> model,
 double zStretching)

Concatenates an axial (z) scaling to a 3D affine/rigid model, in-place. This is a local reimplementation of the former mpicbg.spim.registration.bead.BeadRegistration.concatenateAxialScaling, which was removed when SPIM_Registration was superseded by multiview-reconstruction.

Parameters:

model - - the model to modify (must be an AffineModel3D or RigidModel3D)

zStretching - - the axial scaling factor (pixelDepth / pixelWidth)

normalizeImage

public static float[] normalizeImage(mpicbg.imglib.image.Image<mpicbg.imglib.type.numeric.real.FloatType> image,
 float[] minmax)

Normalizes an imglib1 image in-place to the range [0, 1]. This is a local reimplementation of the former mpicbg.spim.registration.ViewDataBeads.normalizeImage, which was removed when SPIM_Registration was superseded by multiview-reconstruction.

Parameters:

image - - the image to normalize (modified in-place)

minmax - - the min/max to map to [0, 1]; if null/too short the current display range is used

Returns:

the original { min, max } that were mapped to { 0, 1 }

removeInvalidAndCollectStatistics

protected static int[] removeInvalidAndCollectStatistics(ArrayList<mpicbg.imglib.algorithm.scalespace.DifferenceOfGaussianPeak<mpicbg.imglib.type.numeric.real.FloatType>> peaks)

main

public static void main(String[] args)
                 throws mpicbg.models.NotEnoughDataPointsException

Throws:

mpicbg.models.NotEnoughDataPointsException