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k-nearest neighbors search in R-tree (#725)

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Izumi Kawashima 2019-05-17 03:41:54 +09:00 committed by Emux
parent efab21f4f2
commit ffeaf1b81c
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4 changed files with 247 additions and 9 deletions
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153
vtm-tests/test/org/oscim/utils/RTreeTest.java
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@ -1,5 +1,7 @@
/*
* Copyright 2014 Hannes Janetzek
* Copyright 2016 devemux86
* Copyright 2019 Izumi Kawashima
*
* This file is part of the OpenScienceMap project (http://www.opensciencemap.org).
*
@ -19,9 +21,11 @@ package org.oscim.utils;
import org.junit.Assert;
import org.junit.Test;
import org.oscim.core.Box;
import org.oscim.core.Point;
import org.oscim.utils.SpatialIndex.SearchCb;
import java.util.ArrayList;
import java.util.List;
import java.util.Random;
import static org.junit.Assert.assertEquals;
@ -39,6 +43,12 @@ public class RTreeTest {
this.max = max.clone();
}
Item(int xmin, int ymin, int xmax, int ymax, int val) {
this.val = val;
this.min = new double[]{xmin, ymin};
this.max = new double[]{xmax, ymax};
}
@Override
public String toString() {
// return val + "/"
@ -314,8 +324,7 @@ public class RTreeTest {
int cnt = 0;
for (@SuppressWarnings("unused")
Item it : t) {
for (@SuppressWarnings("unused") Item it : t) {
//System.out.println(it.val);
cnt++;
}
@ -326,6 +335,146 @@ public class RTreeTest {
}
/**
* Use values from https://github.com/mourner/rbush-knn/blob/master/test.js
*/
private List<Item> generateKnnTestFixture() {
List<Item> items = new ArrayList<Item>();
items.add(new Item(87, 55, 87, 56, items.size()));
items.add(new Item(38, 13, 39, 16, items.size()));
items.add(new Item(7, 47, 8, 47, items.size()));
items.add(new Item(89, 9, 91, 12, items.size()));
items.add(new Item(4, 58, 5, 60, items.size()));
items.add(new Item(0, 11, 1, 12, items.size()));
items.add(new Item(0, 5, 0, 6, items.size()));
items.add(new Item(69, 78, 73, 78, items.size()));
items.add(new Item(56, 77, 57, 81, items.size()));
items.add(new Item(23, 7, 24, 9, items.size()));
items.add(new Item(68, 24, 70, 26, items.size()));
items.add(new Item(31, 47, 33, 50, items.size()));
items.add(new Item(11, 13, 14, 15, items.size()));
items.add(new Item(1, 80, 1, 80, items.size()));
items.add(new Item(72, 90, 72, 91, items.size()));
items.add(new Item(59, 79, 61, 83, items.size()));
items.add(new Item(98, 77, 101, 77, items.size()));
items.add(new Item(11, 55, 14, 56, items.size()));
items.add(new Item(98, 4, 100, 6, items.size()));
items.add(new Item(21, 54, 23, 58, items.size()));
items.add(new Item(44, 74, 48, 74, items.size()));
items.add(new Item(70, 57, 70, 61, items.size()));
items.add(new Item(32, 9, 33, 12, items.size()));
items.add(new Item(43, 87, 44, 91, items.size()));
items.add(new Item(38, 60, 38, 60, items.size()));
items.add(new Item(62, 48, 66, 50, items.size()));
items.add(new Item(16, 87, 19, 91, items.size()));
items.add(new Item(5, 98, 9, 99, items.size()));
items.add(new Item(9, 89, 10, 90, items.size()));
items.add(new Item(89, 2, 92, 6, items.size()));
items.add(new Item(41, 95, 45, 98, items.size()));
items.add(new Item(57, 36, 61, 40, items.size()));
items.add(new Item(50, 1, 52, 1, items.size()));
items.add(new Item(93, 87, 96, 88, items.size()));
items.add(new Item(29, 42, 33, 42, items.size()));
items.add(new Item(34, 43, 36, 44, items.size()));
items.add(new Item(41, 64, 42, 65, items.size()));
items.add(new Item(87, 3, 88, 4, items.size()));
items.add(new Item(56, 50, 56, 52, items.size()));
items.add(new Item(32, 13, 35, 15, items.size()));
items.add(new Item(3, 8, 5, 11, items.size()));
items.add(new Item(16, 33, 18, 33, items.size()));
items.add(new Item(35, 39, 38, 40, items.size()));
items.add(new Item(74, 54, 78, 56, items.size()));
items.add(new Item(92, 87, 95, 90, items.size()));
items.add(new Item(12, 97, 16, 98, items.size()));
items.add(new Item(76, 39, 78, 40, items.size()));
items.add(new Item(16, 93, 18, 95, items.size()));
items.add(new Item(62, 40, 64, 42, items.size()));
items.add(new Item(71, 87, 71, 88, items.size()));
items.add(new Item(60, 85, 63, 86, items.size()));
items.add(new Item(39, 52, 39, 56, items.size()));
items.add(new Item(15, 18, 19, 18, items.size()));
items.add(new Item(91, 62, 94, 63, items.size()));
items.add(new Item(10, 16, 10, 18, items.size()));
items.add(new Item(5, 86, 8, 87, items.size()));
items.add(new Item(85, 85, 88, 86, items.size()));
items.add(new Item(44, 84, 44, 88, items.size()));
items.add(new Item(3, 94, 3, 97, items.size()));
items.add(new Item(79, 74, 81, 78, items.size()));
items.add(new Item(21, 63, 24, 66, items.size()));
items.add(new Item(16, 22, 16, 22, items.size()));
items.add(new Item(68, 97, 72, 97, items.size()));
items.add(new Item(39, 65, 42, 65, items.size()));
items.add(new Item(51, 68, 52, 69, items.size()));
items.add(new Item(61, 38, 61, 42, items.size()));
items.add(new Item(31, 65, 31, 65, items.size()));
items.add(new Item(16, 6, 19, 6, items.size()));
items.add(new Item(66, 39, 66, 41, items.size()));
items.add(new Item(57, 32, 59, 35, items.size()));
items.add(new Item(54, 80, 58, 84, items.size()));
items.add(new Item(5, 67, 7, 71, items.size()));
items.add(new Item(49, 96, 51, 98, items.size()));
items.add(new Item(29, 45, 31, 47, items.size()));
items.add(new Item(31, 72, 33, 74, items.size()));
items.add(new Item(94, 25, 95, 26, items.size()));
items.add(new Item(14, 7, 18, 8, items.size()));
items.add(new Item(29, 0, 31, 1, items.size()));
items.add(new Item(48, 38, 48, 40, items.size()));
items.add(new Item(34, 29, 34, 32, items.size()));
items.add(new Item(99, 21, 100, 25, items.size()));
items.add(new Item(79, 3, 79, 4, items.size()));
items.add(new Item(87, 1, 87, 5, items.size()));
items.add(new Item(9, 77, 9, 81, items.size()));
items.add(new Item(23, 25, 25, 29, items.size()));
items.add(new Item(83, 48, 86, 51, items.size()));
items.add(new Item(79, 94, 79, 95, items.size()));
items.add(new Item(33, 95, 33, 99, items.size()));
items.add(new Item(1, 14, 1, 14, items.size()));
items.add(new Item(33, 77, 34, 77, items.size()));
items.add(new Item(94, 56, 98, 59, items.size()));
items.add(new Item(75, 25, 78, 26, items.size()));
items.add(new Item(17, 73, 20, 74, items.size()));
items.add(new Item(11, 3, 12, 4, items.size()));
items.add(new Item(45, 12, 47, 12, items.size()));
items.add(new Item(38, 39, 39, 39, items.size()));
items.add(new Item(99, 3, 103, 5, items.size()));
items.add(new Item(41, 92, 44, 96, items.size()));
items.add(new Item(79, 40, 79, 41, items.size()));
items.add(new Item(29, 2, 29, 4, items.size()));
return items;
}
@Test
public void shouldWorkKnn() {
List<Item> items = generateKnnTestFixture();
RTree<Item> t = new RTree<Item>();
for (Item item : items)
t.insert(item.min, item.max, item);
List<Item> result = t.searchKNearestNeighbors(new Point(40, 40), 10, Double.POSITIVE_INFINITY, null);
Assert.assertEquals(10, result.size());
result = t.searchKNearestNeighbors(new Point(40, 40), 10, 17, null);
Assert.assertEquals(10, result.size());
result = t.searchKNearestNeighbors(new Point(40, 60), 90, Double.POSITIVE_INFINITY, result);
Assert.assertEquals(90, result.size());
}
public static void main(String[] args) {
RTreeTest t = new RTreeTest();
t.shouldWork2();

12
vtm/src/org/oscim/utils/QuadTree.java
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@ -1,6 +1,7 @@
package org.oscim.utils;
import org.oscim.core.Box;
import org.oscim.core.Point;
import org.oscim.utils.pool.Pool;
import org.oscim.utils.quadtree.BoxTree;
import org.oscim.utils.quadtree.BoxTree.BoxItem;
@ -78,4 +79,15 @@ public class QuadTree<T> extends BoxTree<BoxItem<T>, T> implements SpatialIndex<
boxPool.release(box);
return finished;
}
@Override
public List<T> searchKNearestNeighbors(Point center, int k, double maxDistance, List<T> results) {
// TODO
return results;
}
@Override
public void searchKNearestNeighbors(Point center, int k, double maxDistance, SearchCb<T> cb, Object context) {
// TODO
}
}

80
vtm/src/org/oscim/utils/RTree.java
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@ -1,5 +1,6 @@
/*
* Copyright 2014 Hannes Janetzek
* Copyright 2019 Izumi Kawashima
*
* This file is part of the OpenScienceMap project (http://www.opensciencemap.org).
*
@ -17,6 +18,7 @@
package org.oscim.utils;
import org.oscim.core.Box;
import org.oscim.core.Point;
import org.oscim.utils.RTree.Branch;
import org.oscim.utils.RTree.Node;
import org.oscim.utils.RTree.Rect;
@ -28,14 +30,13 @@ import org.slf4j.LoggerFactory;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.PriorityQueue;
/**
* Implementation of RTree, a multidimensional bounding rectangle tree.
*
* @author 1983 Original algorithm and test code by Antonin Guttman and Michael
* Stonebraker, UC Berkely
* @author 1994 ANCI C ported from original test code by Melinda Green -
* melinda@superliminal.com
* @author 1983 Original algorithm and test code by Antonin Guttman and Michael Stonebraker, UC Berkely
* @author 1994 ANCI C ported from original test code by Melinda Green - melinda@superliminal.com
* @author 1995 Sphere volume fix for degeneracy problem submitted by Paul Brook
* @author 2004 Templated C++ port by Greg Douglas
* @author 2008 Portability issues fixed by Maxence Laurent
@ -69,6 +70,17 @@ public class RTree<T> implements SpatialIndex<T>, Iterable<T> {
}
}
class KnnItem implements Comparable<KnnItem> {
Branch<?> branch;
boolean isLeaf;
double squareDistance;
@Override
public int compareTo(KnnItem o) {
return Double.compare(squareDistance, o.squareDistance);
}
}
/**
* Node for each branch level
*/
@ -173,6 +185,10 @@ public class RTree<T> implements SpatialIndex<T>, Iterable<T> {
ymax = max[1];
}
double axisDistance(double k, double min, double max) {
return k < min ? min - k : k <= max ? 0 : k - max;
}
/**
* Calculate the n-dimensional volume of a rectangle
*/
@ -247,6 +263,12 @@ public class RTree<T> implements SpatialIndex<T>, Iterable<T> {
add(node.branch[idx]);
}
}
double squareDistance(Point xy) {
double dx = axisDistance(xy.x, xmin, xmax);
double dy = axisDistance(xy.y, ymin, ymax);
return dx * dx + dy * dy;
}
}
/**
@ -370,6 +392,56 @@ public class RTree<T> implements SpatialIndex<T>, Iterable<T> {
return results;
}
/**
* See https://github.com/mourner/rbush-knn/blob/master/index.js
*/
@Override
public List<T> searchKNearestNeighbors(Point center, int k, double maxDistance, List<T> results) {
if (results == null)
results = new ArrayList<>(16);
PriorityQueue<KnnItem> queue = new PriorityQueue<>();
double maxSquareDistance = maxDistance * maxDistance;
Node node = mRoot;
while (node != null) {
for (int idx = 0; idx < node.count; idx++) {
Branch[] branch = node.branch;
double squareDistance = branch[idx].squareDistance(center);
if (squareDistance <= maxSquareDistance) {
KnnItem knnItem = new KnnItem();
knnItem.branch = branch[idx];
knnItem.isLeaf = node.level == 0;
knnItem.squareDistance = squareDistance;
queue.add(knnItem);
}
}
while (!queue.isEmpty() && queue.peek().isLeaf) {
KnnItem knnItem = queue.poll();
T obj = (T) (knnItem.branch);
results.add(obj);
if (results.size() >= k)
return results;
}
KnnItem knnItem = queue.poll();
if (knnItem != null)
node = (Node) knnItem.branch.node;
else
node = null;
}
return results;
}
@Override
public void searchKNearestNeighbors(Point center, int k, double maxDistance, SearchCb<T> cb, Object context) {
List<T> results = searchKNearestNeighbors(center, k, maxDistance, null);
for (T result : results)
cb.call(result, context);
}
/**
* Count the data elements in this container. This is slow as no internal
* counter is maintained.

5
vtm/src/org/oscim/utils/SpatialIndex.java
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@ -1,6 +1,7 @@
package org.oscim.utils;
import org.oscim.core.Box;
import org.oscim.core.Point;
import java.util.List;
@ -24,6 +25,10 @@ public interface SpatialIndex<T> {
public boolean search(Box bbox, SearchCb<T> cb, Object context);
public List<T> searchKNearestNeighbors(Point center, int k, double maxDistance, List<T> results);
public void searchKNearestNeighbors(Point center, int k, double maxDistance, SearchCb<T> cb, Object context);
public int size();
public void clear();