--- /dev/null
+# This file is part of NIT (http://www.nitlanguage.org).
+#
+# Copyright 2014 Romain Chanoir <romain.chanoir@viacesi.fr>
+#
+# 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.
+
+# QuadTree API mostly used for 2 dimensional collision detection
+# The QuadTree data structure partition a 2D space by recursively
+# subdividing it into 4 regions when it's capacity is reached
+# This module introduce 2 principal implementation of the structure,
+# the static and the dynamic QuadTree
+module quadtree
+
+import boxes
+import pipeline
+
+# Abstract QuadTree implementing most of the basic functions
+# and introducing specific QuadTree data
+abstract class QuadTree[E: Boxed[Numeric]]
+ super BoxedCollection[E]
+
+ protected var center: nullable Point[Numeric]
+ var data: Array[E] = new Array[E]
+
+ # ________________
+ # | | |
+ # | 1 | 2 |
+ # |-------|-------|
+ # | 0 | 3 |
+ # |_______|_______|
+
+ protected var child0: nullable QuadTree[E]
+ protected var child1: nullable QuadTree[E]
+ protected var child2: nullable QuadTree[E]
+ protected var child3: nullable QuadTree[E]
+
+ # represent the threshold before subdividing the node
+ protected var item_limit = 4
+ protected var parent_node: nullable QuadTree[E]
+
+ # create the quadtree and set the item limit for each node
+ init(limit: Int)
+ do
+ self.item_limit = limit
+ end
+
+ # create a node, giving him self as a parent. Used to create children nodes
+ init with_parent(limit: Int, parent: QuadTree[E])
+ do
+ init(limit)
+ self.parent_node = parent
+ end
+
+ redef fun items_overlapping(region :Boxed[Numeric]): SimpleCollection[E] do
+ var res = new Array[E]
+ items_overlapping_in(region,res)
+ return res
+ end
+
+ # add the item to the tree, create children if the limit is reached
+ redef fun add(item: E) do if self.is_leaf then self.data.add(item) else add_to_children(item)
+
+ private fun add_to_children(item: Boxed[Numeric])
+ do
+ if self.child0 != null then
+ if self.center.x > item.right then
+ if self.center.y > item.top then
+ child0.add(item)
+ else if self.center.y < item.bottom then
+ child1.add(item)
+ else
+ self.data.add(item)
+ end
+ else if self.center.x < item.left then
+ if self.center.y > item.top then
+ child3.add(item)
+ else if self.center.y < item.bottom then
+ child2.add(item)
+ else
+ self.data.add(item)
+ end
+ else if self.center.y > item.top then
+ self.data.add(item)
+ else if self.center.y < item.bottom then
+ self.data.add(item)
+ else
+ self.data.add(item)
+ end
+ end
+ end
+
+ redef fun is_empty: Bool do return data.is_empty and (self.is_leaf or (child0.is_empty and child1.is_empty and child2.is_empty and child3.is_empty))
+
+ # Return whether or not the Node is a leaf of the tree
+ fun is_leaf: Bool do return child0 == null
+
+ # var dquadtree = new DQuadTree[Point[Int]](2)
+ # var p1 = new Point[Int](0,0)
+ # var p2 = new Point[Int](0,9)
+ # var p3 = new Point[Int](9,0)
+ # dquadtree.add(p1)
+ # dquadtree.add(p2)
+ # dquadtree.add(p3)
+ # var result = dquadtree.items_overlapping(p3)
+ # assert result.length == 1
+ # result.clear
+ # var p4 = new Point[Int](9,9)
+ # result = dquadtree.items_overlapping(p4)
+ # assert result.length == 0
+ # result = dquadtree.items_overlapping(p4.padded(10))
+ # assert result.length == 3
+ fun items_overlapping_in(region: Boxed[Numeric], mdata: SimpleCollection[E])
+ do
+ if self.is_leaf and data.length >= item_limit then
+ subdivide
+ var data_copy = data
+ data = new Array[E]
+ #add to the right Node
+ for d in data_copy do
+ add_to_children(d)
+ end
+ end
+ for i in data do if i.intersects(region) then mdata.add(i)
+ if self.child0 != null then
+ if self.center.x > region.right then
+ if self.center.y > region.top then
+ child0.items_overlapping_in(region, mdata)
+ else if self.center.y < region.bottom then
+ child1.items_overlapping_in(region, mdata)
+ else
+ child0.items_overlapping_in(region,mdata)
+ child1.items_overlapping_in(region, mdata)
+ end
+ else if self.center.x < region.left then
+ if self.center.y > region.top then
+ child3.items_overlapping_in(region, mdata)
+ else if self.center.y < region.bottom then
+ child2.items_overlapping_in(region, mdata)
+ else
+ child3.items_overlapping_in(region, mdata)
+ child2.items_overlapping_in(region, mdata)
+ end
+ else if self.center.y > region.top then
+ child0.items_overlapping_in(region, mdata)
+ child3.items_overlapping_in(region, mdata)
+ else if self.center.y < region.bottom then
+ child1.items_overlapping_in(region, mdata)
+ child2.items_overlapping_in(region, mdata)
+ else
+ child0.items_overlapping_in(region, mdata)
+ child1.items_overlapping_in(region, mdata)
+ child2.items_overlapping_in(region, mdata)
+ child3.items_overlapping_in(region, mdata)
+ end
+ end
+ end
+
+ # this function is responsible of the creation of the children,
+ # depending on your needs
+ protected fun subdivide is abstract
+
+ redef fun iterator do if self.is_leaf then return data.iterator else return data.iterator + child0.iterator + child1.iterator + child2.iterator + child3.iterator
+end
+
+# A dynamic implementation of the quadtree data structure
+# the center of the parent node is determined by the average
+# values of the data it contains when the item limit is reached
+class DQuadTree[E: Boxed[Numeric]]
+ super QuadTree[E]
+
+ redef fun subdivide
+ do
+ self.center = new Point[Numeric](average_x, average_y)
+ child0 = new DQuadTree[E].with_parent(self.item_limit, self)
+ child1 = new DQuadTree[E].with_parent(self.item_limit, self)
+ child2 = new DQuadTree[E].with_parent(self.item_limit, self)
+ child3 = new DQuadTree[E].with_parent(self.item_limit, self)
+ end
+
+ # average x of data in this node
+ fun average_x: Numeric
+ do
+ var x_total = data.first.left.zero
+ for data in self.data do
+ x_total += (data.left + data.right)/x_total.value_of(2)
+ end
+ return x_total/x_total.value_of(self.data.length)
+ end
+
+ # average y of data in this node
+ fun average_y: Numeric
+ do
+ var y_total = data.first.left.zero
+ for data in self.data do
+ y_total += (data.left + data.right)/y_total.value_of(2)
+ end
+ return y_total/y_total.value_of(self.data.length)
+ end
+end
+
+# Static implementation of the quadtree structure.
+# You need to specify a zone when creating the quadtree,
+# which will be the zone corresponding to the root node.
+# each subdivision cut the space in 4 equal regions from
+# the center of the parent node
+class SQuadTree[E: Boxed[Numeric]]
+ super QuadTree[E]
+
+ # the width of the current node of the QuadTree
+ var width: Numeric
+ # the height of the current node of the QuadTree
+ var height: Numeric
+
+ init(l: Int, c: Point[Numeric], w: Numeric, h: Numeric)
+ do
+ self.item_limit = l
+ self.center = c
+ self.width = w
+ self.height = h
+ end
+
+ init with_parent(l: Int, c: Point[Numeric], w: Numeric, h: Numeric, p: QuadTree[E])
+ do
+ init(l, c, w, h)
+ self.parent_node = p
+ end
+
+ redef fun subdivide
+ do
+ var two = self.center.x.value_of(2)
+ var tree = self.center.x.value_of(3)
+ child0 = new SQuadTree[E].with_parent(self.item_limit, new Point[Numeric](self.center.x/two, self.center.y/two), self.width/two, self.height/two, self)
+ child1 = new SQuadTree[E].with_parent(self.item_limit, new Point[Numeric](self.center.x/two, (self.center.y*tree)/two), self.width/two, self.height/two, self)
+ child2 = new SQuadTree[E].with_parent(self.item_limit, new Point[Numeric]((self.center.x*tree)/two, (self.center.y*tree)/two), self.width/two, self.height/two, self)
+ child3 = new SQuadTree[E].with_parent(self.item_limit, new Point[Numeric]((self.center.x*tree)/two, self.center.y/two), self.width/two, self.height/two, self)
+ end
+end
nitlanguage / nit.git / commitdiff