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BoofCV is an open source Java library for real-time computer vision and robotics applications.
/*
* Copyright (c) 2011-2020, Peter Abeles. All Rights Reserved.
*
* This file is part of BoofCV (http://boofcv.org).
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package boofcv.alg.segmentation.slic;
import boofcv.alg.segmentation.ComputeRegionMeanColor;
import boofcv.alg.segmentation.ms.ClusterLabeledImage;
import boofcv.alg.segmentation.ms.MergeSmallRegions;
import boofcv.factory.segmentation.FactorySegmentationAlg;
import boofcv.struct.ConnectRule;
import boofcv.struct.feature.ColorQueue_F32;
import boofcv.struct.image.GrayS32;
import boofcv.struct.image.ImageBase;
import boofcv.struct.image.ImageType;
import org.ddogleg.struct.FastQueue;
import org.ddogleg.struct.GrowQueue_I32;
import org.ddogleg.struct.Stoppable;
import java.util.Arrays;
/**
*
* K-means based superpixel image segmentation, see [1]. The image is broken up into superpixels (clusters of
* connected pixels) in a grid like pattern. A connectivity rule of 4 or 8 is enforced across all the clusters.
* Clustering is done using k-means, where each point is composed on the 2D image coordinate and an intensity
* value in each color band, thus K = 2+numBands. Instead of computing the distance of each cluster's center
* from each point only points within a distance of S si considered. The difference in scale difference between
* pixels and image intensity is handled through a user configurable tuning parameter.
*
*
*
* Deviations from paper:
*
* - In the paper a LAB color space is always used. In this implementation a general purpose N-dimensional
* color space is used.
* - To correctly support LAB or other color spaces a specialized implementation might be needed to ensure
* the intensity of a pixel is computed correctly.
* - Small regions are merged into other regions based on how similar their color is. In the paper
* a small region is merged into the largest region it is connected to.
*
*
*
*
* [1] Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk,
* SLIC Superpixels, EPFL Technical Report no. 149300, June 2010.
*
*
* @author Peter Abeles
*/
public abstract class SegmentSlic>
implements Stoppable
{
// border which ensures there is a 3x3 neighborhood around the initial clusters and that there are pixels
// which can be sampled when computing the gradient
public static final int BORDER = 2;
// number of bands in the input image
private int numBands;
// the number of regions/superpixels. K in the paper
private int numberOfRegions;
// spacial weighting tuning parameter Is also m in the paper.
private float m;
// Number of iterations
private int totalIterations;
// Space between superpixel centers. S in the paper
protected int gridInterval;
// Adjustment to spacial distance. Computed from m and gridInterval
private float adjustSpacial;
// The image being processed
protected T input;
// Initial segmentation before connectivity is enforced
private GrayS32 initialSegments = new GrayS32(1,1);
// number of members in each region
private GrowQueue_I32 regionMemberCount = new GrowQueue_I32();
// color of each region
private FastQueue regionColor;
// merges smaller regions into larger ones
private MergeSmallRegions mergeSmall;
// ensures that all pixels in segment are connected
protected ClusterLabeledImage segment;
// storage for clusters and pixel information
protected FastQueue clusters;
protected FastQueue pixels = new FastQueue<>(Pixel::new);
// type of input image
protected ImageType imageType;
// connectivity rule
protected ConnectRule connectRule;
private volatile boolean stopRequested=false;
public SegmentSlic( int numberOfRegions , float m , int totalIterations ,
ConnectRule connectRule , ImageType imageType ) {
this.numberOfRegions = numberOfRegions;
this.m = m;
this.totalIterations = totalIterations;
this.numBands = imageType.getNumBands();
this.connectRule = connectRule;
this.imageType = imageType;
ComputeRegionMeanColor regionColor = FactorySegmentationAlg.regionMeanColor(imageType);
this.mergeSmall = new MergeSmallRegions<>(-1, connectRule, regionColor);
this.segment = new ClusterLabeledImage(connectRule);
this.regionColor = new ColorQueue_F32(numBands);
// custom declaration for pixel color
clusters = new FastQueue<>(Cluster.class, () -> {
Cluster c = new Cluster();
c.color = new float[ SegmentSlic.this.numBands ];
return c;
});
}
public void process( T input , GrayS32 output ) {
output.reshape(input.width,input.height);
stopRequested = false;
if( input.width < 2*BORDER || input.height < 2*BORDER)
throw new IllegalArgumentException(
"Image is too small to process. Must have a width and height of at least "+(2*BORDER));
// initialize all the data structures
initalize(input);
// Seed the clusters
initializeClusters();
// Perform the modified k-means iterations
for( int i = 0; i < totalIterations && !stopRequested; i++ ) {
computeClusterDistance();
updateClusters();
}
if( stopRequested )
return;
// Assign labels to each pixel based on how close it is to a cluster
computeClusterDistance();
assignLabelsToPixels(initialSegments,regionMemberCount,regionColor);
// Assign disconnected pixels to the largest cluster they touch
int N = input.width*input.height/numberOfRegions;
segment.process(initialSegments,output,regionMemberCount);
mergeSmall.setMinimumSize(N / 2);
mergeSmall.process(input,output,regionMemberCount,regionColor);
}
/**
* prepares all data structures
*/
protected void initalize(T input) {
this.input = input;
pixels.resize(input.width * input.height);
initialSegments.reshape(input.width, input.height);
// number of usable pixels that cluster centers can be placed in
int numberOfUsable = (input.width-2*BORDER)*(input.height-2*BORDER);
gridInterval = (int)Math.sqrt( numberOfUsable/(double)numberOfRegions);
if( gridInterval <= 0 )
throw new IllegalArgumentException("Too many regions for an image of this size");
// See equation (1)
adjustSpacial = m/gridInterval;
}
/**
* initialize all the clusters at regularly spaced intervals. Their locations are perturbed a bit to reduce
* the likelihood of a bad location. Initial color is set to the image color at the location
*/
protected void initializeClusters() {
int offsetX = Math.max(BORDER,((input.width-1) % gridInterval)/2);
int offsetY = Math.max(BORDER,((input.height-1) % gridInterval)/2);
int clusterId = 0;
clusters.reset();
for( int y = offsetY; y < input.height-BORDER; y += gridInterval ) {
for( int x = offsetX; x < input.width-BORDER; x += gridInterval ) {
Cluster c = clusters.grow();
c.id = clusterId++;
if( c.color == null)
c.color = new float[numBands];
// sets the location and color at the local minimal gradient point
perturbCenter( c , x , y );
}
}
}
/**
* Set the cluster's center to be the pixel in a 3x3 neighborhood with the smallest gradient
*/
protected void perturbCenter( Cluster c , int x , int y ) {
float best = Float.MAX_VALUE;
int bestX=0,bestY=0;
for( int dy = -1; dy <= 1; dy++ ) {
for( int dx = -1; dx <= 1; dx++ ) {
float d = gradient(x + dx, y + dy);
if( d < best ) {
best = d;
bestX = dx;
bestY = dy;
}
}
}
c.x = x+bestX;
c.y = y+bestY;
setColor(c.color,x+bestX,y+bestY);
}
/**
* Computes the gradient at the specified pixel
*/
protected float gradient(int x, int y) {
float dx = getIntensity(x+1,y) - getIntensity(x-1,y);
float dy = getIntensity(x,y+1) - getIntensity(x,y-1);
return dx*dx + dy*dy;
}
/**
* Sets the cluster's to the pixel color at that location
*/
public abstract void setColor( float[] color , int x , int y );
/**
* Performs a weighted add to the cluster's color at the specified pixel in the image
*/
public abstract void addColor( float[] color , int index , float weight );
/**
* Euclidean Squared distance away that the pixel is from the provided color
*/
public abstract float colorDistance( float[] color , int index );
/**
* Intensity of the pixel at the specified location
*/
public abstract float getIntensity(int x, int y);
/**
* Computes how far away each cluster is from each pixel. Expectation step.
*/
protected void computeClusterDistance() {
for( int i = 0; i < pixels.size; i++ ) {
pixels.data[i].reset();
}
for( int i = 0; i < clusters.size && !stopRequested; i++ ) {
Cluster c = clusters.data[i];
// compute search bounds
int centerX = (int)(c.x + 0.5f);
int centerY = (int)(c.y + 0.5f);
int x0 = centerX - gridInterval; int x1 = centerX + gridInterval + 1;
int y0 = centerY - gridInterval; int y1 = centerY + gridInterval + 1;
if( x0 < 0 ) x0 = 0;
if( y0 < 0 ) y0 = 0;
if( x1 > input.width ) x1 = input.width;
if( y1 > input.height ) y1 = input.height;
for( int y = y0; y < y1; y++ ) {
int indexPixel = y*input.width + x0;
int indexInput = input.startIndex + y*input.stride + x0;
int dy = y-centerY;
for( int x = x0; x < x1; x++ ) {
int dx = x-centerX;
float distanceColor = colorDistance(c.color,indexInput++);
float distanceSpacial = dx*dx + dy*dy;
pixels.data[indexPixel++].add(c,distanceColor + adjustSpacial*distanceSpacial);
}
}
}
}
/**
* Update the value of each cluster using Maximization step.
*/
protected void updateClusters() {
for( int i = 0; i < clusters.size; i++ ) {
clusters.data[i].reset();
}
int indexPixel = 0;
for( int y = 0; y < input.height&& !stopRequested; y++ ) {
int indexInput = input.startIndex + y*input.stride;
for( int x =0; x < input.width; x++ , indexPixel++ , indexInput++) {
Pixel p = pixels.get(indexPixel);
// convert the distance each cluster is from the pixel into weights
p.computeWeights();
for( int i = 0; i < p.clusters.size; i++ ) {
ClusterDistance d = p.clusters.data[i];
d.cluster.x += x*d.distance;
d.cluster.y += y*d.distance;
d.cluster.totalWeight += d.distance;
addColor(d.cluster.color,indexInput,d.distance);
}
}
}
// recompute the center of each cluster
for( int i = 0; i < clusters.size; i++ ) {
clusters.data[i].update();
}
}
/**
* Selects which region each pixel belongs to based on which cluster it is the closest to
*/
public void assignLabelsToPixels( GrayS32 pixelToRegions ,
GrowQueue_I32 regionMemberCount ,
FastQueue regionColor ) {
regionColor.reset();
for( int i = 0; i < clusters.size(); i++ ) {
float[] r = regionColor.grow();
float[] c = clusters.get(i).color;
for( int j = 0; j < numBands; j++ ) {
r[j] = c[j];
}
}
regionMemberCount.resize(clusters.size());
regionMemberCount.fill(0);
int indexPixel = 0;
for( int y = 0; y < pixelToRegions.height; y++ ) {
int indexOutput = pixelToRegions.startIndex + y*pixelToRegions.stride;
for( int x =0; x < pixelToRegions.width; x++ , indexPixel++ , indexOutput++) {
Pixel p = pixels.data[indexPixel];
// It is possible for a pixel to be unassigned if all the means move too far away from it
// Default to a non-existant cluster if that's the case
int best = -1;
float bestDistance = Float.MAX_VALUE;
// find the region/cluster which it is closest to
for( int j = 0; j < p.clusters.size; j++ ) {
ClusterDistance d = p.clusters.data[j];
if( d.distance < bestDistance ) {
bestDistance = d.distance;
best = d.cluster.id;
}
}
if( best == -1 ) {
regionColor.grow();
best = regionMemberCount.size();
regionMemberCount.add(0);
}
pixelToRegions.data[indexOutput] = best;
regionMemberCount.data[best]++;
}
}
}
public GrowQueue_I32 getRegionMemberCount() {
return regionMemberCount;
}
public FastQueue getClusters() {
return clusters;
}
/**
* K-means clustering information for each pixel. Stores distance from each cluster mean.
*/
public static class Pixel
{
// list of clusters it is interacting with
public FastQueue clusters = new FastQueue<>(12, ClusterDistance::new);
public void add( Cluster c , float distance ) {
// make sure it isn't already full. THis should almost never happen
ClusterDistance d = clusters.grow();
d.cluster = c;
d.distance = distance;
}
public void computeWeights() {
if( clusters.size == 1 ) {
clusters.data[0].distance = 1;
} else {
float sum = 0;
for( int i = 0; i < clusters.size; i++ ) {
sum += clusters.data[i].distance;
}
for( int i = 0; i < clusters.size; i++ ) {
clusters.data[i].distance = 1.0f - clusters.data[i].distance/sum;
}
}
}
public void reset() {
clusters.reset();
}
}
/**
* Stores how far a cluster is from the specified pixel
*/
public static class ClusterDistance
{
// the cluster
public Cluster cluster;
// how far the pixel is from the cluster
public float distance;
}
/**
* The mean in k-means. Point in image (x,y) and color space.
*/
public static class Cluster
{
// unique ID for the cluster
public int id;
// location of the cluster in the image and color space
public float x;
public float y;
public float color[];
// the total. Used when being updated
public float totalWeight;
public void reset() {
x = y = 0;
Arrays.fill(color,0);
totalWeight = 0;
}
public void update() {
x /= totalWeight;
y /= totalWeight;
for( int i = 0; i < color.length; i++ ) {
color[i] /= totalWeight;
}
}
}
public ImageType getImageType() {
return imageType;
}
public ConnectRule getConnectRule() {
return connectRule;
}
@Override
public void requestStop() {
stopRequested = true;
}
@Override
public boolean isStopRequested() {
return stopRequested;
}
}
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