mirror of
https://github.com/viliusle/miniPaint.git
synced 2026-02-06 15:31:53 +00:00
merge
This commit is contained in:
viliuslevickas
parent
a9848e6b2c
commit
82b7718a04
82
.gitignore
vendored
82
.gitignore
vendored
@ -1,41 +1,41 @@
|
||||
# Compiled source #
|
||||
###################
|
||||
*.com
|
||||
*.class
|
||||
*.dll
|
||||
*.exe
|
||||
*.o
|
||||
*.so
|
||||
|
||||
# Packages #
|
||||
############
|
||||
# it's better to unpack these files and commit the raw source
|
||||
# git has its own built in compression methods
|
||||
*.7z
|
||||
*.dmg
|
||||
*.gz
|
||||
*.iso
|
||||
*.jar
|
||||
*.rar
|
||||
*.tar
|
||||
*.zip
|
||||
|
||||
# Logs and databases #
|
||||
######################
|
||||
*.log
|
||||
*.sql
|
||||
*.sqlite
|
||||
|
||||
# OS generated files #
|
||||
######################
|
||||
.DS_Store
|
||||
.DS_Store?
|
||||
._*
|
||||
.Spotlight-V100
|
||||
.Trashes
|
||||
Icon?
|
||||
ehthumbs.db
|
||||
Thumbs.db
|
||||
nbproject/
|
||||
.git/
|
||||
/.project
|
||||
# Compiled source #
|
||||
###################
|
||||
*.com
|
||||
*.class
|
||||
*.dll
|
||||
*.exe
|
||||
*.o
|
||||
*.so
|
||||
|
||||
# Packages #
|
||||
############
|
||||
# it's better to unpack these files and commit the raw source
|
||||
# git has its own built in compression methods
|
||||
*.7z
|
||||
*.dmg
|
||||
*.gz
|
||||
*.iso
|
||||
*.jar
|
||||
*.rar
|
||||
*.tar
|
||||
*.zip
|
||||
|
||||
# Logs and databases #
|
||||
######################
|
||||
*.log
|
||||
*.sql
|
||||
*.sqlite
|
||||
|
||||
# OS generated files #
|
||||
######################
|
||||
.DS_Store
|
||||
.DS_Store?
|
||||
._*
|
||||
.Spotlight-V100
|
||||
.Trashes
|
||||
Icon?
|
||||
ehthumbs.db
|
||||
Thumbs.db
|
||||
nbproject/
|
||||
.git/
|
||||
/.project
|
||||
|
||||
278
libs/kdtree.js
278
libs/kdtree.js
@ -1,139 +1,139 @@
|
||||
/*
|
||||
author: Vladimir Seregin (Peko)
|
||||
|
||||
usage: var KDTREE = new datastructure.KDTree([{x: 22, y:55},...]);
|
||||
var nearest = KDTREE.getNearestNeighbours({x: x, y: y}, NUM_NEAREST);
|
||||
*/
|
||||
|
||||
var datastructure = (function() {
|
||||
var AXIS_X = 0;
|
||||
var AXIS_Y = 1;
|
||||
var K = 2; // I only require a 2d k-d tree
|
||||
function KDTreeNode(point) {
|
||||
this.point = point;
|
||||
this.leftChild = null;
|
||||
this.rightChild = null;
|
||||
}
|
||||
function KDTree(points) {
|
||||
var boundingRect = {
|
||||
maxX: -Math.pow(2, 31),
|
||||
minX: Math.pow(2, 31) - 1,
|
||||
maxY: -Math.pow(2, 31),
|
||||
minY: Math.pow(2, 31) - 1
|
||||
};
|
||||
for (var i = 0; i < points.length; ++i) {
|
||||
boundingRect.minX = Math.min(boundingRect.minX, points[i].x);
|
||||
boundingRect.minY = Math.min(boundingRect.minY, points[i].y);
|
||||
boundingRect.maxX = Math.max(boundingRect.maxX, points[i].x);
|
||||
boundingRect.maxY = Math.max(boundingRect.maxY, points[i].y);
|
||||
}
|
||||
this.rootNode = createKDTree_(points, 0, boundingRect);
|
||||
};
|
||||
function createKDTree_(points, depth, boundingRect) {
|
||||
if (points.length == 0) {
|
||||
return null;
|
||||
}
|
||||
var axis = depth % K;
|
||||
points.sort(function (a, b) {
|
||||
if (axis == AXIS_X) {
|
||||
return a.x - b.x;
|
||||
} else {
|
||||
return a.y - b.y;
|
||||
}
|
||||
});
|
||||
var medianIndex = Math.floor(points.length / 2);
|
||||
var node = new KDTreeNode(points[medianIndex]);
|
||||
node.boundingRect = boundingRect;
|
||||
var leftChildBoundingRect = {
|
||||
minX: boundingRect.minX,
|
||||
maxX: axis == AXIS_X ? node.point.x : boundingRect.maxX,
|
||||
minY: boundingRect.minY,
|
||||
maxY: axis == AXIS_Y ? node.point.y : boundingRect.maxY
|
||||
};
|
||||
var rightChildBoundingRect = {
|
||||
minX: axis == AXIS_X ? node.point.x : boundingRect.minX,
|
||||
maxX: boundingRect.maxX,
|
||||
minY: axis == AXIS_Y ? node.point.y : boundingRect.minY,
|
||||
maxY: boundingRect.maxY
|
||||
};
|
||||
node.leftChild = createKDTree_(points.slice(0, medianIndex), depth + 1, leftChildBoundingRect);
|
||||
node.rightChild = createKDTree_(points.slice(medianIndex + 1), depth + 1, rightChildBoundingRect);
|
||||
return node;
|
||||
}
|
||||
KDTree.prototype.getNearestNeighbour = function(searchCoord, opt_consideredPoints) {
|
||||
var results = [];
|
||||
this.getNearestNeighbours_(this.rootNode, searchCoord, 0, results, 1, opt_consideredPoints);
|
||||
return results.length == 0 ? null : results[0].node.point;
|
||||
};
|
||||
KDTree.prototype.getNearestNeighbours = function(searchCoord, maxResults, opt_consideredPoints) {
|
||||
var results = [];
|
||||
this.getNearestNeighbours_(this.rootNode, searchCoord, 0, results, maxResults, opt_consideredPoints);
|
||||
var points = [];
|
||||
for (var i = 0; i < results.length; ++i) {
|
||||
points.push(results[i].node.point);
|
||||
}
|
||||
return points;
|
||||
};
|
||||
KDTree.prototype.getNearestNeighbours_ = function(currNode, searchCoord, depth, results, maxResults, opt_consideredPoints) {
|
||||
if (opt_consideredPoints) {
|
||||
opt_consideredPoints.push(currNode.point);
|
||||
}
|
||||
var axis = depth % K;
|
||||
var currNodeDistanceToDesiredCoord = getSquaredEuclidianDistance(searchCoord.x, searchCoord.y, currNode.point.x, currNode.point.y);
|
||||
var bestSeen = {node:currNode, distance: currNodeDistanceToDesiredCoord};
|
||||
insertResult_(results, bestSeen, maxResults);
|
||||
var searchNodeSplittingCoord = axis == AXIS_X ? searchCoord.x : searchCoord.y;
|
||||
var currNodeSplittingCoord = axis == AXIS_X ? currNode.point.x : currNode.point.y;
|
||||
var searchLeft = searchNodeSplittingCoord < currNodeSplittingCoord;
|
||||
var targetChild = searchLeft ? currNode.leftChild : currNode.rightChild;
|
||||
var oppositeChild = searchLeft ? currNode.rightChild : currNode.leftChild;
|
||||
if (targetChild) {
|
||||
this.getNearestNeighbours_(targetChild, searchCoord, depth + 1, results, maxResults, opt_consideredPoints);
|
||||
}
|
||||
if (oppositeChild) {
|
||||
var toX, toY;
|
||||
if (axis == AXIS_X) {
|
||||
toX = currNode.point.x;
|
||||
toY = searchCoord.y;
|
||||
if (searchCoord.y < oppositeChild.boundingRect.minY) {
|
||||
toY = oppositeChild.boundingRect.minY;
|
||||
} else if (searchCoord.y > oppositeChild.boundingRect.maxY) {
|
||||
toY = oppositeChild.boundingRect.maxY;
|
||||
}
|
||||
} else {
|
||||
toY = currNode.point.y;
|
||||
toX = searchCoord.x;
|
||||
if (searchCoord.x < oppositeChild.boundingRect.minX) {
|
||||
toX = oppositeChild.boundingRect.minX;
|
||||
} else if (searchCoord.x > oppositeChild.boundingRect.maxX) {
|
||||
toX = oppositeChild.boundingRect.maxX;
|
||||
}
|
||||
}
|
||||
var squaredDist = getSquaredEuclidianDistance(searchCoord.x, searchCoord.y, toX, toY);
|
||||
if (squaredDist <= results[results.length - 1].distance) {
|
||||
this.getNearestNeighbours_(oppositeChild, searchCoord, depth + 1, results, maxResults, opt_consideredPoints);
|
||||
}
|
||||
}
|
||||
};
|
||||
function insertResult_(results, insertNode, maxResults) {
|
||||
var insertIndex;
|
||||
for (insertIndex = results.length - 1; insertIndex >= 0; --insertIndex) {
|
||||
var nearestNeighbourNode = results[insertIndex];
|
||||
if (insertNode.distance > nearestNeighbourNode.distance) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
results.splice(insertIndex + 1, 0, insertNode);
|
||||
if (results.length > maxResults) {
|
||||
results.pop();
|
||||
}
|
||||
}
|
||||
function getSquaredEuclidianDistance(x1, y1, x2, y2) {
|
||||
var dx = x1 - x2;
|
||||
var dy = y1 - y2;
|
||||
return dx * dx + dy * dy;
|
||||
}
|
||||
var module = {};
|
||||
module.KDTree = KDTree; // KDTree constructor.
|
||||
return module;
|
||||
})();
|
||||
/*
|
||||
author: Vladimir Seregin (Peko)
|
||||
|
||||
usage: var KDTREE = new datastructure.KDTree([{x: 22, y:55},...]);
|
||||
var nearest = KDTREE.getNearestNeighbours({x: x, y: y}, NUM_NEAREST);
|
||||
*/
|
||||
|
||||
var datastructure = (function() {
|
||||
var AXIS_X = 0;
|
||||
var AXIS_Y = 1;
|
||||
var K = 2; // I only require a 2d k-d tree
|
||||
function KDTreeNode(point) {
|
||||
this.point = point;
|
||||
this.leftChild = null;
|
||||
this.rightChild = null;
|
||||
}
|
||||
function KDTree(points) {
|
||||
var boundingRect = {
|
||||
maxX: -Math.pow(2, 31),
|
||||
minX: Math.pow(2, 31) - 1,
|
||||
maxY: -Math.pow(2, 31),
|
||||
minY: Math.pow(2, 31) - 1
|
||||
};
|
||||
for (var i = 0; i < points.length; ++i) {
|
||||
boundingRect.minX = Math.min(boundingRect.minX, points[i].x);
|
||||
boundingRect.minY = Math.min(boundingRect.minY, points[i].y);
|
||||
boundingRect.maxX = Math.max(boundingRect.maxX, points[i].x);
|
||||
boundingRect.maxY = Math.max(boundingRect.maxY, points[i].y);
|
||||
}
|
||||
this.rootNode = createKDTree_(points, 0, boundingRect);
|
||||
};
|
||||
function createKDTree_(points, depth, boundingRect) {
|
||||
if (points.length == 0) {
|
||||
return null;
|
||||
}
|
||||
var axis = depth % K;
|
||||
points.sort(function (a, b) {
|
||||
if (axis == AXIS_X) {
|
||||
return a.x - b.x;
|
||||
} else {
|
||||
return a.y - b.y;
|
||||
}
|
||||
});
|
||||
var medianIndex = Math.floor(points.length / 2);
|
||||
var node = new KDTreeNode(points[medianIndex]);
|
||||
node.boundingRect = boundingRect;
|
||||
var leftChildBoundingRect = {
|
||||
minX: boundingRect.minX,
|
||||
maxX: axis == AXIS_X ? node.point.x : boundingRect.maxX,
|
||||
minY: boundingRect.minY,
|
||||
maxY: axis == AXIS_Y ? node.point.y : boundingRect.maxY
|
||||
};
|
||||
var rightChildBoundingRect = {
|
||||
minX: axis == AXIS_X ? node.point.x : boundingRect.minX,
|
||||
maxX: boundingRect.maxX,
|
||||
minY: axis == AXIS_Y ? node.point.y : boundingRect.minY,
|
||||
maxY: boundingRect.maxY
|
||||
};
|
||||
node.leftChild = createKDTree_(points.slice(0, medianIndex), depth + 1, leftChildBoundingRect);
|
||||
node.rightChild = createKDTree_(points.slice(medianIndex + 1), depth + 1, rightChildBoundingRect);
|
||||
return node;
|
||||
}
|
||||
KDTree.prototype.getNearestNeighbour = function(searchCoord, opt_consideredPoints) {
|
||||
var results = [];
|
||||
this.getNearestNeighbours_(this.rootNode, searchCoord, 0, results, 1, opt_consideredPoints);
|
||||
return results.length == 0 ? null : results[0].node.point;
|
||||
};
|
||||
KDTree.prototype.getNearestNeighbours = function(searchCoord, maxResults, opt_consideredPoints) {
|
||||
var results = [];
|
||||
this.getNearestNeighbours_(this.rootNode, searchCoord, 0, results, maxResults, opt_consideredPoints);
|
||||
var points = [];
|
||||
for (var i = 0; i < results.length; ++i) {
|
||||
points.push(results[i].node.point);
|
||||
}
|
||||
return points;
|
||||
};
|
||||
KDTree.prototype.getNearestNeighbours_ = function(currNode, searchCoord, depth, results, maxResults, opt_consideredPoints) {
|
||||
if (opt_consideredPoints) {
|
||||
opt_consideredPoints.push(currNode.point);
|
||||
}
|
||||
var axis = depth % K;
|
||||
var currNodeDistanceToDesiredCoord = getSquaredEuclidianDistance(searchCoord.x, searchCoord.y, currNode.point.x, currNode.point.y);
|
||||
var bestSeen = {node:currNode, distance: currNodeDistanceToDesiredCoord};
|
||||
insertResult_(results, bestSeen, maxResults);
|
||||
var searchNodeSplittingCoord = axis == AXIS_X ? searchCoord.x : searchCoord.y;
|
||||
var currNodeSplittingCoord = axis == AXIS_X ? currNode.point.x : currNode.point.y;
|
||||
var searchLeft = searchNodeSplittingCoord < currNodeSplittingCoord;
|
||||
var targetChild = searchLeft ? currNode.leftChild : currNode.rightChild;
|
||||
var oppositeChild = searchLeft ? currNode.rightChild : currNode.leftChild;
|
||||
if (targetChild) {
|
||||
this.getNearestNeighbours_(targetChild, searchCoord, depth + 1, results, maxResults, opt_consideredPoints);
|
||||
}
|
||||
if (oppositeChild) {
|
||||
var toX, toY;
|
||||
if (axis == AXIS_X) {
|
||||
toX = currNode.point.x;
|
||||
toY = searchCoord.y;
|
||||
if (searchCoord.y < oppositeChild.boundingRect.minY) {
|
||||
toY = oppositeChild.boundingRect.minY;
|
||||
} else if (searchCoord.y > oppositeChild.boundingRect.maxY) {
|
||||
toY = oppositeChild.boundingRect.maxY;
|
||||
}
|
||||
} else {
|
||||
toY = currNode.point.y;
|
||||
toX = searchCoord.x;
|
||||
if (searchCoord.x < oppositeChild.boundingRect.minX) {
|
||||
toX = oppositeChild.boundingRect.minX;
|
||||
} else if (searchCoord.x > oppositeChild.boundingRect.maxX) {
|
||||
toX = oppositeChild.boundingRect.maxX;
|
||||
}
|
||||
}
|
||||
var squaredDist = getSquaredEuclidianDistance(searchCoord.x, searchCoord.y, toX, toY);
|
||||
if (squaredDist <= results[results.length - 1].distance) {
|
||||
this.getNearestNeighbours_(oppositeChild, searchCoord, depth + 1, results, maxResults, opt_consideredPoints);
|
||||
}
|
||||
}
|
||||
};
|
||||
function insertResult_(results, insertNode, maxResults) {
|
||||
var insertIndex;
|
||||
for (insertIndex = results.length - 1; insertIndex >= 0; --insertIndex) {
|
||||
var nearestNeighbourNode = results[insertIndex];
|
||||
if (insertNode.distance > nearestNeighbourNode.distance) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
results.splice(insertIndex + 1, 0, insertNode);
|
||||
if (results.length > maxResults) {
|
||||
results.pop();
|
||||
}
|
||||
}
|
||||
function getSquaredEuclidianDistance(x1, y1, x2, y2) {
|
||||
var dx = x1 - x2;
|
||||
var dy = y1 - y2;
|
||||
return dx * dx + dy * dy;
|
||||
}
|
||||
var module = {};
|
||||
module.KDTree = KDTree; // KDTree constructor.
|
||||
return module;
|
||||
})();
|
||||
|
||||
Loading…
Reference in New Issue
Block a user