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MDDN342 Assignment 3: Face Mappings
Raw
.block
license: mit
Raw
README.md

Assignment 3 MDDN 342 2019

This is a placeholder README. Replace this with a description of your current work.

Raw
face.js
/*
* FaceMap class - holds all informaiton about one mapped
* face and is able to draw itself.
*/
// other variables can be in here too
// these control the colors used
const bg_color = [225, 206, 187];
const fg_color = [151, 102, 52];
const stroke_color = [95, 52, 8];
function Face() {
// these are state variables for a face
// (your variables may be different)
this.eye_value = 2; // can be either 2 (eyes) or 3 (no eyes)
this.mouth_value = 1; // range is 0.5 to 8
this.tilt_value = 0; // range is -30 to 30
/*
* Draw a face with position lists that include:
* chin, right_eye, left_eye, right_eyebrow, left_eyebrow
* bottom_lip, top_lip, nose_tip, nose_bridge,
*/
this.draw = function(positions) {
rotate(this.tilt_value);
// head
stroke(stroke_color);
fill(fg_color);
ellipse(0, 0, 3, 4);
noStroke();
// eyes
if (this.eye_value === 2) {
fill(bg_color);
ellipse(-0.45, -0.73, 0.45, 0.27);
ellipse( 0.45, -0.73, 0.45, 0.27);
fill(fg_color);
ellipse(-0.55, -0.73, 0.18, 0.18);
ellipse( 0.36, -0.73, 0.18, 0.18);
}
// mouth
fill(bg_color);
ellipse(0, 0.64, 1.36, 0.25 * this.mouth_value);
}
/* set internal properties based on list numbers 0-100 */
this.setProperties = function(settings) {
this.eye_value = int(map(settings[0], 0, 100, 2, 3));
this.mouth_value = map(settings[1], 0, 100, 0.5, 8);
this.tilt_value = map(settings[2], 0, 100, -30, 30);
}
/* get internal properties as list of numbers 0-100 */
this.getProperties = function() {
let settings = new Array(3);
settings[0] = map(this.eye_value, 2, 3, 0, 100);
settings[1] = map(this.mouth_value, 0.5, 8, 0, 100);
settings[2] = map(this.tilt_value, -30, 30, 0, 100);
return settings;
}
}
Raw
index.html
<head>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.1/p5.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/p5.js/0.6.1/addons/p5.dom.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/seedrandom/2.4.3/seedrandom.min.js"></script>
<script src="https://d3js.org/d3-random.v1.min.js"></script>
<script src="z_clmtrackr.js"></script>
<script src="z_model_pca_20_svm.js"></script>
<script language="javascript" type="text/javascript" src="z_purview_helper.js"></script>
<script language="javascript" type="text/javascript" src="z_focused_random.js"></script>
<script language="javascript" type="text/javascript" src="kdTree.js"></script>
<script language="javascript" type="text/javascript" src="face.js"></script>
<script language="javascript" type="text/javascript" src="system_runner.js"></script>
<style>
body { padding: 0; margin: 0; }
.inner { position: absolute; }
#controls {
font: 300 12px "Helvetica Neue";
padding: 5;
margin: 5;
background: #f0f0f0;
opacity: 0.0;
-webkit-transition: opacity 0.2s ease;
-moz-transition: opacity 0.2s ease;
-o-transition: opacity 0.2s ease;
-ms-transition: opacity 0.2s ease;
}
#controls:hover { opacity: 0.9; }
</style>
</head>
<body style="background-color:white">
<div class="outer">
<div class="inner" id="controls" height="500">
<table>
<tr>
<td>setting 1</td>
<td id="slider1Container"></td>
</tr>
<tr>
<td>setting 2</td>
<td id="slider2Container"></td>
</tr>
<tr>
<td>setting 3</td>
<td id="slider3Container"></td>
</tr>
<tr>
<td>setting 4</td>
<td id="slider4Container"></td>
</tr>
<tr>
<td>setting 5</td>
<td id="slider5Container"></td>
</tr>
<tr>
<td>setting 6</td>
<td id="slider6Container"></td>
</tr>
<tr>
<td>setting 7</td>
<td id="slider7Container"></td>
</tr>
<tr>
<td>setting 8</td>
<td id="slider8Container"></td>
</tr>
<tr>
<td>setting 9</td>
<td id="slider9Container"></td>
</tr>
<tr>
<td>setting 10</td>
<td id="slider10Container"></td>
</tr>
<tr>
<td>setting 11</td>
<td id="slider11Container"></td>
</tr>
<tr>
<td>setting 12</td>
<td id="slider12Container"></td>
</tr>
<tr>
</tr>
<tr>
<td>show target</td>
<td id="sliderTintContainer"></td>
</tr>
<tr>
<td>Draw function</td>
<td id="selector1Container"></td>
</tr>
<tr>
<td>Face Draw</td>
<td id="checkbox1Container"></td>
</tr>
<tr>
<td>Face Targets</td>
<td id="checkbox2Container"></td>
</tr>
<tr>
<td>Face Points</td>
<td id="checkbox3Container"></td>
</tr>
<tr>
<td></td>
<td id="button1Container"></td>
</tr>
<tr>
<td></td>
<td id="button2Container"></td>
</tr>
<tr>
<td></td>
<td id="button3Container"></td>
</tr>
<tr>
<td></td>
<td id="button4Container"></td>
</tr>
</table>
</div>
<div>
<div id="canvasContainer"></div>
<a href="face.js">face code</a><br>
<a href="sketch.html">sketches</a>
</div>
</div>
<pre>
<p id="output">
</p>
</pre>
</body>
Raw
kdTree.js
/**
* k-d Tree JavaScript - V 1.01
*
* https://github.com/ubilabs/kd-tree-javascript
*
* @author Mircea Pricop <pricop@ubilabs.net>, 2012
* @author Martin Kleppe <kleppe@ubilabs.net>, 2012
* @author Ubilabs http://ubilabs.net, 2012
* @license MIT License <http://www.opensource.org/licenses/mit-license.php>
*/
(function (root, factory) {
if (typeof define === 'function' && define.amd) {
define(['exports'], factory);
} else if (typeof exports === 'object') {
factory(exports);
} else {
factory((root.commonJsStrict = {}));
}
}(this, function (exports) {
function Node(obj, dimension, parent) {
this.obj = obj;
this.left = null;
this.right = null;
this.parent = parent;
this.dimension = dimension;
}
function kdTree(points, metric, dimensions) {
var self = this;
function buildTree(points, depth, parent) {
var dim = depth % dimensions.length,
median,
node;
if (points.length === 0) {
return null;
}
if (points.length === 1) {
return new Node(points[0], dim, parent);
}
points.sort(function (a, b) {
return a[dimensions[dim]] - b[dimensions[dim]];
});
median = Math.floor(points.length / 2);
node = new Node(points[median], dim, parent);
node.left = buildTree(points.slice(0, median), depth + 1, node);
node.right = buildTree(points.slice(median + 1), depth + 1, node);
return node;
}
// Reloads a serialied tree
function loadTree (data) {
// Just need to restore the `parent` parameter
self.root = data;
function restoreParent (root) {
if (root.left) {
root.left.parent = root;
restoreParent(root.left);
}
if (root.right) {
root.right.parent = root;
restoreParent(root.right);
}
}
restoreParent(self.root);
}
// If points is not an array, assume we're loading a pre-built tree
if (!Array.isArray(points)) loadTree(points, metric, dimensions);
else this.root = buildTree(points, 0, null);
// Convert to a JSON serializable structure; this just requires removing
// the `parent` property
this.toJSON = function (src) {
if (!src) src = this.root;
var dest = new Node(src.obj, src.dimension, null);
if (src.left) dest.left = self.toJSON(src.left);
if (src.right) dest.right = self.toJSON(src.right);
return dest;
};
this.insert = function (point) {
function innerSearch(node, parent) {
if (node === null) {
return parent;
}
var dimension = dimensions[node.dimension];
if (point[dimension] < node.obj[dimension]) {
return innerSearch(node.left, node);
} else {
return innerSearch(node.right, node);
}
}
var insertPosition = innerSearch(this.root, null),
newNode,
dimension;
if (insertPosition === null) {
this.root = new Node(point, 0, null);
return;
}
newNode = new Node(point, (insertPosition.dimension + 1) % dimensions.length, insertPosition);
dimension = dimensions[insertPosition.dimension];
if (point[dimension] < insertPosition.obj[dimension]) {
insertPosition.left = newNode;
} else {
insertPosition.right = newNode;
}
};
this.remove = function (point) {
var node;
function nodeSearch(node) {
if (node === null) {
return null;
}
if (node.obj === point) {
return node;
}
var dimension = dimensions[node.dimension];
if (point[dimension] < node.obj[dimension]) {
return nodeSearch(node.left, node);
} else {
return nodeSearch(node.right, node);
}
}
function removeNode(node) {
var nextNode,
nextObj,
pDimension;
function findMin(node, dim) {
var dimension,
own,
left,
right,
min;
if (node === null) {
return null;
}
dimension = dimensions[dim];
if (node.dimension === dim) {
if (node.left !== null) {
return findMin(node.left, dim);
}
return node;
}
own = node.obj[dimension];
left = findMin(node.left, dim);
right = findMin(node.right, dim);
min = node;
if (left !== null && left.obj[dimension] < own) {
min = left;
}
if (right !== null && right.obj[dimension] < min.obj[dimension]) {
min = right;
}
return min;
}
if (node.left === null && node.right === null) {
if (node.parent === null) {
self.root = null;
return;
}
pDimension = dimensions[node.parent.dimension];
if (node.obj[pDimension] < node.parent.obj[pDimension]) {
node.parent.left = null;
} else {
node.parent.right = null;
}
return;
}
// If the right subtree is not empty, swap with the minimum element on the
// node's dimension. If it is empty, we swap the left and right subtrees and
// do the same.
if (node.right !== null) {
nextNode = findMin(node.right, node.dimension);
nextObj = nextNode.obj;
removeNode(nextNode);
node.obj = nextObj;
} else {
nextNode = findMin(node.left, node.dimension);
nextObj = nextNode.obj;
removeNode(nextNode);
node.right = node.left;
node.left = null;
node.obj = nextObj;
}
}
node = nodeSearch(self.root);
if (node === null) { return; }
removeNode(node);
};
this.nearest = function (point, maxNodes, maxDistance) {
var i,
result,
bestNodes;
bestNodes = new BinaryHeap(
function (e) { return -e[1]; }
);
function nearestSearch(node) {
var bestChild,
dimension = dimensions[node.dimension],
ownDistance = metric(point, node.obj),
linearPoint = {},
linearDistance,
otherChild,
i;
function saveNode(node, distance) {
bestNodes.push([node, distance]);
if (bestNodes.size() > maxNodes) {
bestNodes.pop();
}
}
for (i = 0; i < dimensions.length; i += 1) {
if (i === node.dimension) {
linearPoint[dimensions[i]] = point[dimensions[i]];
} else {
linearPoint[dimensions[i]] = node.obj[dimensions[i]];
}
}
linearDistance = metric(linearPoint, node.obj);
if (node.right === null && node.left === null) {
if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {
saveNode(node, ownDistance);
}
return;
}
if (node.right === null) {
bestChild = node.left;
} else if (node.left === null) {
bestChild = node.right;
} else {
if (point[dimension] < node.obj[dimension]) {
bestChild = node.left;
} else {
bestChild = node.right;
}
}
nearestSearch(bestChild);
if (bestNodes.size() < maxNodes || ownDistance < bestNodes.peek()[1]) {
saveNode(node, ownDistance);
}
if (bestNodes.size() < maxNodes || Math.abs(linearDistance) < bestNodes.peek()[1]) {
if (bestChild === node.left) {
otherChild = node.right;
} else {
otherChild = node.left;
}
if (otherChild !== null) {
nearestSearch(otherChild);
}
}
}
if (maxDistance) {
for (i = 0; i < maxNodes; i += 1) {
bestNodes.push([null, maxDistance]);
}
}
if(self.root)
nearestSearch(self.root);
result = [];
for (i = 0; i < Math.min(maxNodes, bestNodes.content.length); i += 1) {
if (bestNodes.content[i][0]) {
result.push([bestNodes.content[i][0].obj, bestNodes.content[i][1]]);
}
}
return result;
};
this.balanceFactor = function () {
function height(node) {
if (node === null) {
return 0;
}
return Math.max(height(node.left), height(node.right)) + 1;
}
function count(node) {
if (node === null) {
return 0;
}
return count(node.left) + count(node.right) + 1;
}
return height(self.root) / (Math.log(count(self.root)) / Math.log(2));
};
}
// Binary heap implementation from:
// http://eloquentjavascript.net/appendix2.html
function BinaryHeap(scoreFunction){
this.content = [];
this.scoreFunction = scoreFunction;
}
BinaryHeap.prototype = {
push: function(element) {
// Add the new element to the end of the array.
this.content.push(element);
// Allow it to bubble up.
this.bubbleUp(this.content.length - 1);
},
pop: function() {
// Store the first element so we can return it later.
var result = this.content[0];
// Get the element at the end of the array.
var end = this.content.pop();
// If there are any elements left, put the end element at the
// start, and let it sink down.
if (this.content.length > 0) {
this.content[0] = end;
this.sinkDown(0);
}
return result;
},
peek: function() {
return this.content[0];
},
remove: function(node) {
var len = this.content.length;
// To remove a value, we must search through the array to find
// it.
for (var i = 0; i < len; i++) {
if (this.content[i] == node) {
// When it is found, the process seen in 'pop' is repeated
// to fill up the hole.
var end = this.content.pop();
if (i != len - 1) {
this.content[i] = end;
if (this.scoreFunction(end) < this.scoreFunction(node))
this.bubbleUp(i);
else
this.sinkDown(i);
}
return;
}
}
throw new Error("Node not found.");
},
size: function() {
return this.content.length;
},
bubbleUp: function(n) {
// Fetch the element that has to be moved.
var element = this.content[n];
// When at 0, an element can not go up any further.
while (n > 0) {
// Compute the parent element's index, and fetch it.
var parentN = Math.floor((n + 1) / 2) - 1,
parent = this.content[parentN];
// Swap the elements if the parent is greater.
if (this.scoreFunction(element) < this.scoreFunction(parent)) {
this.content[parentN] = element;
this.content[n] = parent;
// Update 'n' to continue at the new position.
n = parentN;
}
// Found a parent that is less, no need to move it further.
else {
break;
}
}
},
sinkDown: function(n) {
// Look up the target element and its score.
var length = this.content.length,
element = this.content[n],
elemScore = this.scoreFunction(element);
while(true) {
// Compute the indices of the child elements.
var child2N = (n + 1) * 2, child1N = child2N - 1;
// This is used to store the new position of the element,
// if any.
var swap = null;
// If the first child exists (is inside the array)...
if (child1N < length) {
// Look it up and compute its score.
var child1 = this.content[child1N],
child1Score = this.scoreFunction(child1);
// If the score is less than our element's, we need to swap.
if (child1Score < elemScore)
swap = child1N;
}
// Do the same checks for the other child.
if (child2N < length) {
var child2 = this.content[child2N],
child2Score = this.scoreFunction(child2);
if (child2Score < (swap == null ? elemScore : child1Score)){
swap = child2N;
}
}
// If the element needs to be moved, swap it, and continue.
if (swap != null) {
this.content[n] = this.content[swap];
this.content[swap] = element;
n = swap;
}
// Otherwise, we are done.
else {
break;
}
}
}
};
this.kdTree = kdTree;
exports.kdTree = kdTree;
exports.BinaryHeap = BinaryHeap;
}));
Raw
sample_images.json
[
{
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{"right_eye": [[0.5669913111895505, -0.8175985529301849], [0.8042524498506649, -0.9297693884966264], [1.0501467785627774, -0.9008754876192222], [1.2347879024803765, -0.7559349109169072], [1.0350538547476638, -0.7485936676187326], [0.8003766095921957, -0.7537614546300253]], "left_eyebrow": [[-1.6309647234043476, -1.2299254808273112], [-1.3074323193297848, -1.5280293870641748], [-0.9403235277814689, -1.6725595265477258], [-0.5520725158726878, -1.7109681270101866], [-0.17891442777902022, -1.5970949074721579]], "chin": [[-2.560159004441164, -0.6164512882515883], [-2.4419388723293705, -0.12078679056889419], [-2.3237187402175774, 0.3748777071138002], [-2.170555410683029, 0.8830512351059617], [-1.8862524815084556, 1.300196147892147], [-1.4820270362505021, 1.6025863316062445], [-1.0165483861980351, 1.788929839495431], [-0.5273436222794566, 1.964056263827973], [-0.035554964855231685, 2.021844065582781], [0.41265730509523957, 1.9260581005842765], [0.7474067927264463, 1.6516803082135243], [1.0485050296877203, 1.3061241742444376], [1.2797168718034835, 0.9355499796564165], [1.429825235517091, 0.5162316105833493], [1.5087552576323644, 0.1305644921802145], [1.5639591658815262, -0.24388554266627616], [1.5492766792851769, -0.6433536381317015]], "right_eyebrow": [[0.5350424444912053, -1.4991961212833014], [0.7947377502656083, -1.56391472911752], [1.110518473823232, -1.5100027676211814], [1.353828909029698, -1.363770244166043], [1.4897258584622508, -1.1377261890615726]], "left_eye": [[-1.191796080723638, -0.9150262668037465], [-0.9420259117530563, -1.0621402997929439], [-0.6724054691748325, -1.044463482472184], [-0.4528211478344778, -0.8870138754604014], [-0.7000074232994133, -0.8572384650489384], [-0.9696278658776372, -0.8749152823696983]], "nose_bridge": [[0.2326522607771081, -1.0949706760253477], [0.25121058763192694, -0.8715105144265235], [0.2934950283528572, -0.6592674363843436], [0.34699655263043166, -0.42329824447605224]], "bottom_lip": [[0.8556411529378879, 0.4683689456323621], [0.6705895918015246, 0.8751782843959621], [0.4083103925214754, 1.0572355148079147], [0.1723412006131841, 1.1107370390854892], [-0.07355312809892828, 1.081843138208085], [-0.5165976110381071, 0.9429518580511216], [-1.0006345879453928, 0.5331481885631111], [-0.8608617982543706, 0.5831843098009803], [-0.011007976551591858, 0.9071271510943072], [0.21245218504723226, 0.8885688242394882], [0.4596384605121678, 0.8587934138280252], [0.7495196139167979, 0.4895111659928272]], "nose_tip": [[-0.25866532433182204, -0.10792795813719287], [-0.03649710948582112, -0.06781697370314466], [0.2081052724734681, 0.019746238463126495], [0.38540515309289247, -0.03504723256727112], [0.5627050337123167, -0.08984070359766869]], "top_lip": [[-1.0006345879453928, 0.5331481885631111], [-0.5498384244892753, 0.3200236009868722], [-0.07660809391986935, 0.15435124114285628], [0.14556012092613158, 0.1944622255769045], [0.36902028252495567, 0.17590389872208567], [0.6610748922164679, 0.2410329437750685], [0.8556411529378879, 0.4683689456323621], [0.7495196139167979, 0.4895111659928272], [0.3427102751531979, 0.30445960485646395], [0.09552399968826236, 0.3342350152679269], [-0.11542713160109441, 0.3178501446999902], [-0.8608617982543706, 0.5831843098009803]], "transform": {"center": [166.15277777777777, 177.93055555555554], "scale": 38.103839503080835, "angle": 0.44163034041047217}},
{"right_eye": [[0.4151434248116892, -0.9747116017375426], [0.6164530502082548, -1.1177643511780764], [0.840374908534021, -1.115304567222966], [1.0812559415566874, -1.0037103832211225], [0.8629871414632213, -0.9697920338273219], [0.6083402080208444, -0.9302206262012211]], "left_eyebrow": [[-1.3575459734645086, -1.5933991816579023], [-1.228992614765765, -1.7251458146338354], [-1.04710194802121, -1.753411105795336], [-0.8118739732308435, -1.6781950551424039], [-0.6437492069059991, -1.5552947546759603]], "chin": [[-1.2808629421674176, -0.8601834564473818], [-1.3278137016588496, -0.44306481491246047], [-1.3020081944524593, -0.037252289842139324], [-1.1977933623159471, 0.3936322521124927], [-1.0208222634816126, 0.8132106776025245], [-0.8438511646472786, 1.2327891030925562], [-0.6361549906963333, 1.6103363370013768], [-0.39208068339647717, 1.9822305126778974], [0.0056189994864335735, 2.1439687304483623], [0.47853473302227667, 2.070478973428461], [0.9595633087455303, 1.8094454914317046], [1.3565295013063619, 1.4869618592017262], [1.7001583858213816, 1.0609968851573148], [1.8926216787084575, 0.6212660106931931], [2.001022588433111, 0.1200849859958496], [2.109423498157765, -0.381096038701494], [2.139415082952296, -0.9073490802831485]], "right_eyebrow": [[0.17918195969924341, -1.5341495023909184], [0.5316571767237532, -1.6634363514117416], [0.8954385102128634, -1.7199669337347425], [1.2511070015145629, -1.5889537910808884], [1.5146002674664296, -1.331847073683401]], "left_eye": [[-1.127237566584363, -1.0703394143534375], [-0.9986842078856193, -1.2020860473293709], [-0.7691092913275532, -1.1632481300253494], [-0.5589533334214976, -1.009622754442295], [-0.7828751917472637, -1.0120825383974055], [-1.0067970500730299, -1.0145423223525156]], "nose_bridge": [[-0.23720319151359864, -1.096878411881907], [-0.28980700923733055, -0.7161379036958966], [-0.3424108269610625, -0.3353973955098862], [-0.3642895695681836, 0.0033119210949131075]], "bottom_lip": [[1.0220062622897037, 0.5330175499426296], [0.6702645355872733, 1.1465262489638959], [0.21996103498063055, 1.3655285393794412], [-0.07106403181065749, 1.410753005237842], [-0.3426701399499349, 1.3411900128172096], [-0.6481945974830128, 1.0533582203031115], [-0.8898091208277586, 0.4575421863008249], [-0.7693686043164254, 0.5133392783017465], [-0.34586341422712463, 1.0808900211425327], [-0.07991036432014736, 1.1140748802142542], [0.18038962735452982, 1.1108816059370643], [0.9492499955918816, 0.5443236664072298]], "nose_tip": [[-0.6189365030105607, 0.042883328721013844], [-0.4815368118023271, 0.20781482076866853], [-0.24630883701196074, 0.283030871421601], [-0.0029680200341839318, 0.1707031970976782], [0.209647721826982, 0.10040671435496656]], "top_lip": [[-0.8898091208277586, 0.4575421863008249], [-0.6771933789665927, 0.38724570355811316], [-0.4112403290596155, 0.42043056262983447], [-0.14528727915263823, 0.4536154217015558], [0.1457377876386498, 0.40839095584315493], [0.5935815042901821, 0.4133105237533757], [1.0220062622897037, 0.5330175499426296], [0.9492499955918816, 0.5443236664072298], [0.12631882898663913, 0.5231784141221881], [-0.17035929603694905, 0.5320247466316779], [-0.39993421259501527, 0.49318682932765656], [-0.7693686043164254, 0.5133392783017465]], "transform": {"center": [388.7916666666667, 118.43055555555556], "scale": 27.163027335191224, "angle": 0.15416410878170614}}
],
"url": "z_selfie.jpg"}
]
Raw
sketch.html
<head>
<style> body {padding: 0; margin: 0;} </style>
</head>
<body style="background-color:white">
<img src="same_pose.jpg" width="960" height="500"/><br>
Same Pose
<hr>
<img src="same_subject.jpg" width="960" height="500"/><br>
Same Subject
<p>
<a href="index.html">program</a>
</body>
Raw
system_runner.js
// recent work from my @VicUniWgtn 3rd year media design students that combines @nulhom's dlib face library with @p5xjs
var canvasWidth = 960;
var canvasHeight = 500;
var button;
var curRandomSeed;
var mainFace;
var faceImages = [];
var curFaceIndex = 0;
var curTrainIndex = 0;
var curValidIndex = 0;
var main_canvas;
var faceSelector;
var drawFaceCheckbox;
var faceGuideCheckbox;
var facePointsCheckbox;
var sliders = [];
var NUM_SLIDERS = 12;
var sliderTint;
var selfieData = []
var trainData = {}
var trainDataKeys = []
var trainValues = {}
var validData = {}
var validDataKeys = []
var faceMapping = null;
var loadedSelfieData = [];
function preload () {
loadedSelfieData = loadJSON('sample_images.json');
trainData = loadJSON('training_images.json');
trainValues = loadJSON('training_values.json');
validData = loadJSON('testing_images.json');
}
var allEmbeddingsTree;
var allEmbeddings = [];
var embeddingDimensions;
var curNeighbors = [];
function squaredDistance(a, b) {
var sum = 0;
var length = 128;
for(var i=0; i<128; i++) {
var diff = a[i] - b[i];
sum += diff * diff;
}
// print(a.length,diff);
// print(sum, a==b);
return sum;
}
var haveStarted = false;
function setup () {
var loadedSelfieDataArray = Object.values(loadedSelfieData);
Array.prototype.unshift.apply(selfieData, loadedSelfieDataArray);
// create the drawing canvas, save the canvas element
main_canvas = createCanvas(canvasWidth, canvasHeight);
main_canvas.parent('canvasContainer');
curRandomSeed = int(focusedRandom(0, 100));
mainFace = new Face();
littleFace = new Face();
for(var i=0; i<selfieData.length; i++) {
var data = selfieData[i];
data.image = loadImage(data.url);
}
trainDataKeys = Object.keys(trainData);
for(var i=0; i<trainDataKeys.length; i++) {
var curKey = trainDataKeys[i];
var data = trainData[curKey];
var curEmbedding = data.embedding[0];
if(curEmbedding.length == 128) {
curEmbedding.push(curKey);
allEmbeddings.push(curEmbedding);
}
else {
print("rejected embedding ", curEmbedding);
}
data.image = loadImage(data.url);
}
validDataKeys = Object.keys(validData);
for(var i=0; i<validDataKeys.length; i++) {
var curKey = validDataKeys[i];
var data = validData[curKey];
data.image = loadImage(data.url);
}
// print("Length: ", allEmbeddings.length);
// setup k-d tree
var N = allEmbeddings[0].length - 1;
embeddingDimensions = Array.apply(null, {length: N}).map(Number.call, Number);
// print(embeddingDimensions)
allEmbeddingsTree = new kdTree(allEmbeddings, squaredDistance, embeddingDimensions);
// print(allEmbeddingsTree)
faceSelector = createSelect();
faceSelector.option('Faces');
faceSelector.option('Train');
faceSelector.option('NearestNeighbors');
faceSelector.option('TrainingQuiz');
faceSelector.option('InterpolationQuiz');
faceSelector.value('Faces');
faceSelector.parent('selector1Container');
/* create the sliders */
for(i=1; i<=NUM_SLIDERS; i++) {
var slider = createSlider(0, 100, 50);
var parentStr = 'slider' + i + 'Container';
slider.parent(parentStr);
sliders.push(slider);
}
drawFaceCheckbox = createCheckbox('', true);
drawFaceCheckbox.parent('checkbox1Container');
faceGuideCheckbox = createCheckbox('', true);
faceGuideCheckbox.parent('checkbox2Container');
facePointsCheckbox = createCheckbox('', false);
facePointsCheckbox.parent('checkbox3Container');
sliderTint = createSlider(0, 100, 10);
sliderTint.parent("sliderTintContainer");
/* and the buttons */
var loadButton = createButton('load');
loadButton.mousePressed(loadCurrentSettings);
loadButton.parent('button1Container');
var interpButton = createButton('interpolate');
interpButton.mousePressed(interpolateCurrent);
interpButton.parent('button1Container');
var saveButton = createButton('save');
saveButton.mousePressed(saveCurrentSettings);
saveButton.parent('button2Container');
var getValuesButton = createButton('get values');
getValuesButton.mousePressed(getSingleJson);
getValuesButton.parent('button3Container');
var randButton = createButton('get all values');
randButton.mousePressed(getAllJson);
randButton.parent('button4Container');
updateSlidersForTraining();
// rotation in degrees
angleMode(DEGREES);
haveStarted = true;
}
function saveCurrentSettings() {
var curKey = trainDataKeys[curTrainIndex];
obj = mainFace.getProperties();
trainValues[curKey] = obj;
// for(var i=0; i<obj.length; i++) {
// trainData[curKey][i] = obj[i];
// }
var text = select('#output');
text.html("Storing values for " + curKey);
// getAllJson();
}
function getSingleJson() {
obj = mainFace.getProperties();
var text = select('#output');
var json = JSON.stringify(obj, null, 2);
text.html(json)
}
function getAllJson() {
obj = trainValues;
var text = select('#output');
var json = JSON.stringify(obj, null, 2);
// alert(json);
text.html(json)
}
// global variables for colors
var bg_color1 = [50, 50, 50];
var lastSwapTime = 0;
var millisPerSwap = 5000;
function changeRandomSeed() {
curRandomSeed = curRandomSeed + 1;
lastSwapTime = millis();
}
function mouseClicked() {
// changeRandomSeed();
}
var quiz_done = false;
var guessed_answer = 0;
function draw () {
var mode = faceSelector.value();
if(millis() > lastSwapTime + millisPerSwap) {
lastSwapTime = millis();
// changeRandomSeed();
}
resetFocusedRandom(curRandomSeed);
noStroke();
background(bg_color1);
var textDisplay = "unknown";
var params = [];
for(var i=0; i<NUM_SLIDERS; i++) {
params.push(sliders[i].value());
}
if (mode == 'Faces' || mode == 'FacePair' || mode == 'Train') {
var do_train = (mode == 'Train');
var is_face = (mode == 'Faces');
var show_face_guide = faceGuideCheckbox.checked();
var show_face_points = facePointsCheckbox.checked();
var do_draw_face = drawFaceCheckbox.checked();
if(do_train) {
// var keys = Object.keys(trainData);
var curKey = trainDataKeys[curTrainIndex];
var data = trainData[curKey];
}
else {
var data = selfieData[curFaceIndex];
}
// Displays the image at its actual size at point (0,0)
var img = data.image
if (is_face) {
x2 = 0;
y1 = 0;
x1 = 0;
var im_w = 960;
var im_h = 500;
var rect_w = 960;
var rect_h = 540;
image(img, x2, y1, im_w, im_h);
var is_face = true;
}
else {
var x1 = (width/4-400/2);
var x2 = (3*width/4-400/2);
var y1 = (height/2-400/2);
var rect_w = 400;
var rect_h = 400;
var im_w = 400;
var im_h = 400;
image(img, x1, y1, 400, 400);
var is_face = false;
}
if(do_train) {
if (curKey in trainValues) {
fill(0, 200, 0);
}
else {
fill(200, 0, 0);
}
ellipse(x1+400/2, y1+400+15, 10, 10);
}
noStroke();
var curSliderTintValue = sliderTint.value();
var overlayAlpha = map(curSliderTintValue, 0, 100, 255, 0);
fill(bg_color1[0], bg_color1[1], bg_color1[2], overlayAlpha);
rect(x2, y1, rect_w, rect_h);
stroke(0);
fill(255);
for(var i=0; i<data.landmarks.length; i++) {
// get array of face marker positions [x, y] format
var positions = data.landmarks[i];
var shifted_positions = JSON.parse(JSON.stringify(positions))
var data_mean = [0.0, 0.0];
var data_scale = 1.0;
var data_angle = 0.0;
if ('transform' in positions) {
data_mean = positions.transform.center;
data_scale = positions.transform.scale;
data_angle = positions.transform.angle;
delete shifted_positions.transform
}
var scale_x = im_w / img.width;
var scale_y = im_h / img.height;
push();
translate(x1, y1)
translate(scale_x*data_mean[0], scale_y*data_mean[1]);
scale(scale_x*data_scale, scale_y*data_scale);
rotate(degrees(data_angle));
stroke(0);
fill(255);
strokeWeight(1/data_scale);
Object.keys(positions).forEach(function(key) {
if (key=='transform') {
return;
}
var curSection = positions[key];
var shiftedSection = shifted_positions[key];
for (var i=0; i<curSection.length; i++) {
var cur_x = curSection[i][0];
var cur_y = curSection[i][1];
if (show_face_points) {
ellipse(cur_x, cur_y, 5/data_scale, 5/data_scale);
}
// get ready for drawing the face
shiftedSection[i][0] = cur_x;
shiftedSection[i][1] = cur_y;
}
});
noFill();
if(show_face_guide) {
stroke(0, 0, 255);
ellipse(0, 0, 4, 4);
line(0, -2, 0, 2);
line(-2, 0, 2, 0);
}
// ellipse(x1+data_mean[0], y1+data_mean[1], 4*data_scale, 4*data_scale);
// line(x1+data_mean[0], y1+data_mean[1]-2*data_scale, x1+data_mean[0], y1+data_mean[1]+2*data_scale);
pop();
var settings = params;
// NOT NOW
// if ((typeof data.embedding !== 'undefined') &&
// (data.embedding != null) &&
// (data.embedding.length > i) &&
// (data.embedding[i] != null) &&
// (typeof data.embedding[i].length !== 'undefined') &&
// (data.embedding[i].length == 128)) {
// // print("Using embedding ", i)
// var curEmbedding = data.embedding[i];
// results = getAverageSettingsFrom(curEmbedding);
// settings = results[0];
// }
push();
translate(x2, y1)
translate(scale_x*data_mean[0], scale_y*data_mean[1]);
scale(scale_x*data_scale, scale_y*data_scale);
rotate(degrees(data_angle));
strokeWeight(1/data_scale);
mainFace.setProperties(settings);
if (do_draw_face) {
mainFace.draw(shifted_positions);
}
pop();
}
if(do_train) {
textDisplay = "Train: " + curKey;
}
else {
textDisplay = "";
}
}
else if (mode == 'NearestNeighbors') {
// var keys = Object.keys(trainData);
var curKey = trainDataKeys[curTrainIndex];
var data = trainData[curKey];
// Displays the image at its actual size at point (0,0)
var img = data.image
var x1 = (width/4-250/2);
var y1 = (height/3-250/2);
image(img, x1, y1, 250, 250);
if (curKey in trainValues) {
fill(0, 200, 0);
}
else {
fill(200, 0, 0);
}
ellipse(x1+250/2, y1+250+15, 10, 10);
var y2 = (3*height/4-80/2);
for(var i=0; i<4; i++) {
// var keys = Object.keys(trainData);
var curKey = curNeighbors[i];
var nearData = trainData[curKey];
// Displays the image at its actual size at point (0,0)
var img = nearData.image
var x2 = (width/4 - 200 + i*100);
image(img, x2, y2, 80, 80);
}
for(var i=0; i<1; i++) {
// get array of face marker positions [x, y] format
var positions = data.landmarks[i];
var shifted_positions = JSON.parse(JSON.stringify(positions))
var data_mean = [0.0, 0.0];
var data_scale = 1.0;
var data_angle = 0.0;
if ('transform' in positions) {
data_mean = positions.transform.center;
data_scale = positions.transform.scale;
data_angle = positions.transform.angle;
delete shifted_positions.transform
}
var scale_x = 400.0 / img.width;
var scale_y = 400.0 / img.height;
Object.keys(positions).forEach(function(key) {
if (key=='transform') {
return;
}
var curSection = positions[key];
var shiftedSection = shifted_positions[key];
for (var i=0; i<curSection.length; i++) {
var cur_x = curSection[i][0];
var cur_y = curSection[i][1];
// get ready for drawing the face
shiftedSection[i][0] = cur_x;
shiftedSection[i][1] = cur_y;
}
});
var scale_x = 250.0 / img.width;
var scale_y = 250.0 / img.height;
var x2 = (3*width/4-250/2);
push();
translate(x2, y1);
translate(scale_x*data_mean[0], scale_y*data_mean[1]);
scale(scale_x*data_scale, scale_y*data_scale);
rotate(degrees(data_angle));
strokeWeight(1/data_scale);
mainFace.setProperties(params);
mainFace.draw(shifted_positions);
pop();
var scale_x = 80.0 / img.width;
var scale_y = 80.0 / img.height;
for(var j=0; j<4; j++) {
// var keys = Object.keys(trainData);
var curKey = curNeighbors[j];
var x2 = (3*width/4 - 200 + j*100);
push();
translate(x2, y2);
if (curKey in trainValues) {
var settings = trainValues[curKey]
translate(scale_x*data_mean[0], scale_y*data_mean[1]);
scale(scale_x*data_scale, scale_y*data_scale);
rotate(degrees(data_angle));
strokeWeight(1/data_scale);
littleFace.setProperties(settings);
littleFace.draw(shifted_positions);
}
else {
noFill();
stroke(100);
rect(10, 10, 70, 70);
}
pop();
}
}
textDisplay = "Neighbors: " + trainDataKeys[curTrainIndex];
}
else if (mode == 'TrainingQuiz' || mode == 'InterpolationQuiz') {
var curKey = trainDataKeys[curTrainIndex];
var data = trainData[curKey];
var valid_mode = false;
if (mode == 'InterpolationQuiz') {
valid_mode = true;
curKey = validDataKeys[curValidIndex];
data = validData[curKey];
}
// Displays the image at its actual size at point (0,0)
var img = data.image
var x1 = (width/2-200/2);
var y1 = (height/3-300/2);
image(img, x1, y1, 200, 200);
if(valid_mode) {
fill(0, 0, 200);
}
else if (curKey in trainValues) {
fill(0, 200, 0);
}
else {
fill(200, 0, 0);
}
ellipse(x1+200/2, y1+200+15, 10, 10);
var y2 = (3*height/5-80/2);
var y3 = (4*height/5-80/2);
/*
for(var i=0; i<4; i++) {
// var keys = Object.keys(trainData);
var curKey = curNeighbors[i];
var nearData = trainData[curKey];
// Displays the image at its actual size at point (0,0)
var img = nearData.image
var x2 = (width/4 - 200 + i*100);
image(img, x2, y2, 80, 80);
}
*/
for(var i=0; i<1; i++) {
// get array of face marker positions [x, y] format
var positions = data.landmarks[i];
var shifted_positions = JSON.parse(JSON.stringify(positions))
var data_mean = [0.0, 0.0];
var data_scale = 1.0;
var data_angle = 0.0;
if ('transform' in positions) {
data_mean = positions.transform.center;
data_scale = positions.transform.scale;
data_angle = positions.transform.angle;
delete shifted_positions.transform
}
var scale_x = 400.0 / img.width;
var scale_y = 400.0 / img.height;
Object.keys(positions).forEach(function(key) {
if (key=='transform') {
return;
}
var curSection = positions[key];
var shiftedSection = shifted_positions[key];
for (var i=0; i<curSection.length; i++) {
var cur_x = curSection[i][0];
var cur_y = curSection[i][1];
// get ready for drawing the face
shiftedSection[i][0] = cur_x;
shiftedSection[i][1] = cur_y;
}
});
/*
var scale_x = 250.0 / img.width;
var scale_y = 250.0 / img.height;
var x2 = (3*width/4-250/2);
push();
translate(x2, y1);
translate(scale_x*data_mean[0], scale_y*data_mean[1]);
scale(scale_x*data_scale, scale_y*data_scale);
rotate(degrees(data_angle));
strokeWeight(1/data_scale);
mainFace.setProperties(params);
mainFace.draw(shifted_positions);
pop();
*/
var scale_x = 80.0 / img.width;
var scale_y = 80.0 / img.height;
var otherKeys = Object.keys(trainValues);
var index = otherKeys.indexOf(trainDataKeys[curTrainIndex]);
if(index >= 0) {
otherKeys.splice(index, 1);
}
var answerSlot = int(focusedRandom(0, 4));
var answerKeys = Array(4);
for(var j=0; j<4; j++) {
if(j == answerSlot) {
curKey = trainDataKeys[curTrainIndex];
}
else {
var guess = int(focusedRandom(0, otherKeys.length));
// if(otherKeys.length > j+2) {
// while(answerKeys.indexOf(guess) == -1) {
// guess = int(focusedRandom(0, otherKeys.length));
// }
// }
curKey = otherKeys[guess];
}
answerKeys[j] = curKey;
// print("Answer", j, " is ", curKey);
var x2 = (width/2 - 200 + j*100);
var settings = params;
if (valid_mode && j == answerSlot) {
var curEmbedding = data.embedding[0];
results = getAverageSettingsFrom(curEmbedding);
settings = results[0];
var validTrainKeys = results[1];
}
else if (curKey in trainValues) {
settings = trainValues[curKey];
}
push();
translate(x2, y2);
translate(scale_x*data_mean[0], scale_y*data_mean[1]);
scale(scale_x*data_scale, scale_y*data_scale);
rotate(degrees(data_angle));
strokeWeight(1/data_scale);
if (typeof settings !== 'undefined') {
littleFace.setProperties(settings);
}
littleFace.draw(shifted_positions);
pop();
if(quiz_done && guessed_answer == (j+1)) {
push();
translate(x2, y2);
noFill();
strokeWeight(4);
if(guessed_answer == (answerSlot+1)) {
stroke(0, 100, 0);
}
else {
stroke(100, 0, 0);
}
rect(-10, -10, 100, 100);
pop();
}
}
if(quiz_done) {
for(var j=0; j<4; j++) {
if (valid_mode && (answerSlot+1) == (j+1)) {
for(var k=0; k<4; k++) {
var curKey = validTrainKeys[k];
var nearData = trainData[curKey];
// Displays the image at its actual size at point (0,0)
var img = nearData.image
var x2 = (width/2 - 200 + j*100 + (k%2)*40);
var y4 = y3 + (int(k/2))*40;
image(img, x2, y4, 40, 40);
}
}
else {
var curKey = answerKeys[j];
var nearData = trainData[curKey];
// Displays the image at its actual size at point (0,0)
if (typeof nearData !== 'undefined') {
var img = nearData.image
var x2 = (width/2 - 200 + j*100);
image(img, x2, y3, 80, 80);
}
}
}
}
}
if(valid_mode) {
if(quiz_done) {
textDisplay = "InterpolationQuiz: hit spacebar to continue";
}
else {
textDisplay = "InterpolationQuiz: hit 1, 2, 3, or 4 to guess";
}
}
else {
if(quiz_done) {
textDisplay = "TrainingQuiz: hit spacebar to continue";
}
else {
textDisplay = "TrainingQuiz: hit 1, 2, 3, or 4 to guess";
}
}
}
fill(255);
textSize(32);
textAlign(CENTER);
text(textDisplay, width/2, height-12);
}
function keyTyped() {
if(!haveStarted) {
return;
}
var mode = faceSelector.value();
if (key == 'q' && mode != 'Faces') {
faceSelector.value('Faces');
}
else if (key == 'w' && mode != 'Train') {
faceSelector.value('Train');
}
else if (key == 'e' && mode != 'NearestNeighbors') {
faceSelector.value('NearestNeighbors');
}
else if (key == 'r' && mode != 'TrainingQuiz') {
faceSelector.value('TrainingQuiz');
}
else if (key == 't' && mode != 'InterpolationQuiz') {
faceSelector.value('InterpolationQuiz');
}
if (key == ' ' &&
(mode == 'TrainingQuiz' || mode == 'InterpolationQuiz') && quiz_done) {
quiz_done = false;
if(mode == 'TrainingQuiz') {
curTrainIndex = (curTrainIndex + 1) % trainDataKeys.length;
}
else {
curValidIndex = (curValidIndex + 1) % validDataKeys.length;
}
changeRandomSeed();
}
else if ((mode == 'TrainingQuiz' || mode == 'InterpolationQuiz') && quiz_done == false) {
if(key >= '1' && key <= '4') {
guessed_answer = key - '0';
quiz_done = true;
}
}
if (key == 's') {
saveCurrentSettings();
}
else if (key == 'i') {
interpolateCurrent();
}
else if (key == 'l') {
loadCurrentSettings();
}
if (key == '!') {
saveBlocksImages();
}
else if (key == '@') {
saveBlocksImages(true);
}
}
function interpolateCurrent() {
var curNeighborSettings = [];
for(var i=0; i<4; i++) {
neighborKey = curNeighbors[i]
if(neighborKey in trainValues) {
curNeighborSettings.push(trainValues[neighborKey]);
}
}
for(var i=0; i<NUM_SLIDERS; i++) {
sliders[i].value(50);
}
if(curNeighborSettings.length > 0) {
settings = curNeighborSettings[0];
for(var i=0; i<settings.length; i++) {
var sum = 0;
for(j=0; j<curNeighborSettings.length; j++) {
sum += curNeighborSettings[j][i];
}
var avg = int(sum / curNeighborSettings.length)
sliders[i].value(avg);
}
}
}
function loadCurrentSettings() {
var curKey = trainDataKeys[curTrainIndex];
for(var i=0; i<NUM_SLIDERS; i++) {
sliders[i].value(50);
}
if (curKey in trainValues) {
var settings = trainValues[curKey]
for(var i=0; i<settings.length; i++) {
sliders[i].value(settings[i]);
}
}
}
function updateSlidersForTraining() {
var mode = faceSelector.value();
var curKey = trainDataKeys[curTrainIndex];
// first find the closest neighbors
var nearest = allEmbeddingsTree.nearest(trainData[curKey].embedding[0], 5);
curNeighbors = [];
curNeighborSettings = [];
for(var i=0; i<5; i++) {
if(nearest[i][0][128] != curKey) {
var neighborKey = nearest[i][0][128];
curNeighbors.push(neighborKey);
if(neighborKey in trainValues) {
curNeighborSettings.push(trainValues[neighborKey]);
}
}
}
loadCurrentSettings();
// if(mode == 'NearestNeighbors') {
// interpolateCurrent();
// }
// else {
// loadCurrentSettings();
// }
}
function getAverageSettingsFrom(e) {
// first find the closest neighbors
var nearest = allEmbeddingsTree.nearest(e, 4);
curNeighbors = [];
curNeighborSettings = [];
for(var i=0; i<4; i++) {
var neighborKey = nearest[i][0][128];
curNeighbors.push(neighborKey);
if(neighborKey in trainValues) {
curNeighborSettings.push(trainValues[neighborKey]);
}
}
for(var i=0; i<4; i++) {
neighborKey = curNeighbors[i]
if(neighborKey in trainValues) {
curNeighborSettings.push(trainValues[neighborKey]);
}
}
var trainValueKeys = Object.keys(trainValues);
var props = trainValues[trainValueKeys[0]];
if(curNeighborSettings.length > 0) {
settings = curNeighborSettings[0];
for(var i=0; i<settings.length; i++) {
var sum = 0;
for(j=0; j<curNeighborSettings.length; j++) {
sum += curNeighborSettings[j][i];
}
var avg = int(sum / curNeighborSettings.length)
props[i] = avg;
}
}
return [props, curNeighbors];
}
function keyPressed() {
if(!haveStarted) {
return;
}
var mode = faceSelector.value();
if (mode == 'Faces') {
if (keyCode == LEFT_ARROW || keyCode == UP_ARROW) {
curFaceIndex = (curFaceIndex + selfieData.length - 1) % selfieData.length;
} else if (keyCode === RIGHT_ARROW || keyCode == DOWN_ARROW) {
curFaceIndex = (curFaceIndex + 1) % selfieData.length;
}
}
else if (mode == 'Train' || mode == 'NearestNeighbors') {
if (keyCode == LEFT_ARROW || keyCode == UP_ARROW) {
curTrainIndex = (curTrainIndex + trainDataKeys.length - 1) % trainDataKeys.length;
updateSlidersForTraining();
} else if (keyCode == RIGHT_ARROW || keyCode == DOWN_ARROW) {
curTrainIndex = (curTrainIndex + 1) % trainDataKeys.length;
updateSlidersForTraining();
}
}
}
Raw
testing_images.json
{
"162771": {"landmarks": [{"chin": [[-1.607575262062853, -1.0016724859194308], [-1.6351091555381696, -0.5396664325705911], [-1.5394992491248505, -0.07852759428757607], [-1.4131033927393726, 0.38239444022898283], [-1.2869243401203507, 0.8125305247733828], [-1.0378182913791492, 1.2110134442797993], [-0.7583599001987015, 1.5477076600754416], [-0.4175464128403919, 1.8531823183660128], [0.04467644427490394, 1.9115021618134882], [0.5370348400629903, 1.8772473515780308], [1.0280924132523397, 1.6582768415096198], [1.457361282730915, 1.4089539890019627], [1.7938386947601013, 1.0987096478493557], [2.006305091834827, 0.6661887218739381], [2.094977277721549, 0.14217716104786826], [2.1528635136361123, -0.3816175960117454], [2.180180603344973, -0.874409599332744]], "left_eye": [[-1.2385774699298584, -1.0658460310612226], [-1.0549457889291858, -1.2210766035207539], [-0.7776554354132997, -1.1922418874467005], [-0.5606361592429941, -0.9782578640067813], [-0.8067069552538093, -0.9457374839029732], 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Raw
training_images.json
This file has been truncated, but you can view the full file.
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