SkRTree.h revision 8f8c25eabb97da8eda488895f04f2d12cb5ea4cf
1 2/* 3 * Copyright 2012 Google Inc. 4 * 5 * Use of this source code is governed by a BSD-style license that can be 6 * found in the LICENSE file. 7 */ 8 9#ifndef SkRTree_DEFINED 10#define SkRTree_DEFINED 11 12#include "SkRect.h" 13#include "SkTDArray.h" 14#include "SkChunkAlloc.h" 15#include "SkBBoxHierarchy.h" 16 17/** 18 * An R-Tree implementation. In short, it is a balanced n-ary tree containing a hierarchy of 19 * bounding rectangles. 20 * 21 * Much like a B-Tree it maintains balance by enforcing minimum and maximum child counts, and 22 * splitting nodes when they become overfull. Unlike B-trees, however, we're using spatial data; so 23 * there isn't a canonical ordering to use when choosing insertion locations and splitting 24 * distributions. A variety of heuristics have been proposed for these problems; here, we're using 25 * something resembling an R*-tree, which attempts to minimize area and overlap during insertion, 26 * and aims to minimize a combination of margin, overlap, and area when splitting. 27 * 28 * One detail that is thus far unimplemented that may improve tree quality is attempting to remove 29 * and reinsert nodes when they become full, instead of immediately splitting (nodes that may have 30 * been placed well early on may hurt the tree later when more nodes have been added; removing 31 * and reinserting nodes generally helps reduce overlap and make a better tree). Deletion of nodes 32 * is also unimplemented. 33 * 34 * For more details see: 35 * 36 * Beckmann, N.; Kriegel, H. P.; Schneider, R.; Seeger, B. (1990). "The R*-tree: 37 * an efficient and robust access method for points and rectangles" 38 * 39 * It also supports bulk-loading from a batch of bounds and values; if you don't require the tree 40 * to be usable in its intermediate states while it is being constructed, this is significantly 41 * quicker than individual insertions and produces more consistent trees. 42 */ 43class SkRTree : public SkBBoxHierarchy { 44public: 45 SK_DECLARE_INST_COUNT(SkRTree) 46 47 /** 48 * Create a new R-Tree with specified min/max child counts. 49 * The child counts are valid iff: 50 * - (max + 1) / 2 >= min (splitting an overfull node must be enough to populate 2 nodes) 51 * - min < max 52 * - min > 0 53 * - max < SK_MaxU16 54 * If you have some prior information about the distribution of bounds you're expecting, you 55 * can provide an optional aspect ratio parameter. This allows the bulk-load algorithm to create 56 * better proportioned tiles of rectangles. 57 */ 58 static SkRTree* Create(int minChildren, int maxChildren, SkScalar aspectRatio = 1, 59 bool orderWhenBulkLoading = true); 60 virtual ~SkRTree(); 61 62 /** 63 * Insert a node, consisting of bounds and a data value into the tree, if we don't immediately 64 * need to use the tree; we may allow the insert to be deferred (this can allow us to bulk-load 65 * a large batch of nodes at once, which tends to be faster and produce a better tree). 66 * @param opIndex The data value 67 * @param bounds The corresponding bounding box 68 * @param defer Can this insert be deferred? (this may be ignored) 69 */ 70 virtual void insert(unsigned opIndex, const SkRect& bounds, bool defer = false) SK_OVERRIDE; 71 72 /** 73 * If any inserts have been deferred, this will add them into the tree 74 */ 75 virtual void flushDeferredInserts() SK_OVERRIDE; 76 77 /** 78 * Given a query rectangle, populates the passed-in array with the elements it intersects 79 */ 80 virtual void search(const SkRect& query, SkTDArray<unsigned>* results) const SK_OVERRIDE; 81 82 void clear(); 83 // Return the depth of the tree structure. 84 int getDepth() const { return this->isEmpty() ? 0 : fRoot.fChild.subtree->fLevel + 1; } 85 // Insertion count (not overall node count, which may be greater). 86 int getCount() const { return fCount; } 87 88private: 89 bool isEmpty() const { return 0 == this->getCount(); } 90 91 struct Node; 92 93 /** 94 * A branch of the tree, this may contain a pointer to another interior node, or a data value 95 */ 96 struct Branch { 97 union { 98 Node* subtree; 99 unsigned opIndex; 100 } fChild; 101 SkIRect fBounds; 102 }; 103 104 /** 105 * A node in the tree, has between fMinChildren and fMaxChildren (the root is a special case) 106 */ 107 struct Node { 108 uint16_t fNumChildren; 109 uint16_t fLevel; 110 bool isLeaf() { return 0 == fLevel; } 111 // Since we want to be able to pick min/max child counts at runtime, we assume the creator 112 // has allocated sufficient space directly after us in memory, and index into that space 113 Branch* child(size_t index) { 114 return reinterpret_cast<Branch*>(this + 1) + index; 115 } 116 }; 117 118 typedef int32_t SkIRect::*SortSide; 119 120 // Helper for sorting our children arrays by sides of their rects 121 struct RectLessThan { 122 RectLessThan(SkRTree::SortSide side) : fSide(side) { } 123 bool operator()(const SkRTree::Branch lhs, const SkRTree::Branch rhs) const { 124 return lhs.fBounds.*fSide < rhs.fBounds.*fSide; 125 } 126 private: 127 const SkRTree::SortSide fSide; 128 }; 129 130 struct RectLessX { 131 bool operator()(const SkRTree::Branch lhs, const SkRTree::Branch rhs) { 132 return ((lhs.fBounds.fRight - lhs.fBounds.fLeft) >> 1) < 133 ((rhs.fBounds.fRight - lhs.fBounds.fLeft) >> 1); 134 } 135 }; 136 137 struct RectLessY { 138 bool operator()(const SkRTree::Branch lhs, const SkRTree::Branch rhs) { 139 return ((lhs.fBounds.fBottom - lhs.fBounds.fTop) >> 1) < 140 ((rhs.fBounds.fBottom - lhs.fBounds.fTop) >> 1); 141 } 142 }; 143 144 SkRTree(int minChildren, int maxChildren, SkScalar aspectRatio, bool orderWhenBulkLoading); 145 146 /** 147 * Recursively descend the tree to find an insertion position for 'branch', updates 148 * bounding boxes on the way up. 149 */ 150 Branch* insert(Node* root, Branch* branch, uint16_t level = 0); 151 152 int chooseSubtree(Node* root, Branch* branch); 153 SkIRect computeBounds(Node* n); 154 int distributeChildren(Branch* children); 155 void search(Node* root, const SkIRect query, SkTDArray<unsigned>* results) const; 156 157 /** 158 * This performs a bottom-up bulk load using the STR (sort-tile-recursive) algorithm, this 159 * seems to generally produce better, more consistent trees at significantly lower cost than 160 * repeated insertions. 161 * 162 * This consumes the input array. 163 * 164 * TODO: Experiment with other bulk-load algorithms (in particular the Hilbert pack variant, 165 * which groups rects by position on the Hilbert curve, is probably worth a look). There also 166 * exist top-down bulk load variants (VAMSplit, TopDownGreedy, etc). 167 */ 168 Branch bulkLoad(SkTDArray<Branch>* branches, int level = 0); 169 170 void validate() const; 171 int validateSubtree(Node* root, SkIRect bounds, bool isRoot = false) const; 172 173 const int fMinChildren; 174 const int fMaxChildren; 175 const size_t fNodeSize; 176 177 // This is the count of data elements (rather than total nodes in the tree) 178 int fCount; 179 180 Branch fRoot; 181 SkChunkAlloc fNodes; 182 SkTDArray<Branch> fDeferredInserts; 183 SkScalar fAspectRatio; 184 bool fSortWhenBulkLoading; 185 186 Node* allocateNode(uint16_t level); 187 188 typedef SkBBoxHierarchy INHERITED; 189}; 190 191#endif 192