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gifshot.js
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gifshot.js
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/*Copyrights for code authored by Yahoo Inc. is licensed under the following terms:
MIT License
Copyright 2017 Yahoo Inc.
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*/
;(function(window, document, navigator, undefined) {
"use strict";
/*
utils.js
========
*/
/* Copyright 2017 Yahoo Inc.
* Copyrights licensed under the MIT License. See the accompanying LICENSE file for terms.
*/
var utils = {
URL: window.URL || window.webkitURL || window.mozURL || window.msURL,
getUserMedia: function () {
var getUserMedia = navigator.getUserMedia || navigator.webkitGetUserMedia || navigator.mozGetUserMedia || navigator.msGetUserMedia;
return getUserMedia ? getUserMedia.bind(navigator) : getUserMedia;
}(),
requestAnimFrame: window.requestAnimationFrame || window.webkitRequestAnimationFrame || window.mozRequestAnimationFrame || window.oRequestAnimationFrame || window.msRequestAnimationFrame,
requestTimeout: function requestTimeout(callback, delay) {
callback = callback || utils.noop;
delay = delay || 0;
if (!utils.requestAnimFrame) {
return setTimeout(callback, delay);
}
var start = new Date().getTime();
var handle = new Object();
var requestAnimFrame = utils.requestAnimFrame;
var loop = function loop() {
var current = new Date().getTime();
var delta = current - start;
delta >= delay ? callback.call() : handle.value = requestAnimFrame(loop);
};
handle.value = requestAnimFrame(loop);
return handle;
},
Blob: window.Blob || window.BlobBuilder || window.WebKitBlobBuilder || window.MozBlobBuilder || window.MSBlobBuilder,
btoa: function () {
var btoa = window.btoa || function (input) {
var output = '';
var i = 0;
var l = input.length;
var key = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=';
var chr1 = void 0;
var chr2 = void 0;
var chr3 = void 0;
var enc1 = void 0;
var enc2 = void 0;
var enc3 = void 0;
var enc4 = void 0;
while (i < l) {
chr1 = input.charCodeAt(i++);
chr2 = input.charCodeAt(i++);
chr3 = input.charCodeAt(i++);
enc1 = chr1 >> 2;
enc2 = (chr1 & 3) << 4 | chr2 >> 4;
enc3 = (chr2 & 15) << 2 | chr3 >> 6;
enc4 = chr3 & 63;
if (isNaN(chr2)) {
enc3 = enc4 = 64;
} else if (isNaN(chr3)) {
enc4 = 64;
}
output = output + key.charAt(enc1) + key.charAt(enc2) + key.charAt(enc3) + key.charAt(enc4);
}
return output;
};
return btoa ? btoa.bind(window) : utils.noop;
}(),
isObject: function isObject(obj) {
return obj && Object.prototype.toString.call(obj) === '[object Object]';
},
isEmptyObject: function isEmptyObject(obj) {
return utils.isObject(obj) && !Object.keys(obj).length;
},
isArray: function isArray(arr) {
return arr && Array.isArray(arr);
},
isFunction: function isFunction(func) {
return func && typeof func === 'function';
},
isElement: function isElement(elem) {
return elem && elem.nodeType === 1;
},
isString: function isString(value) {
return typeof value === 'string' || Object.prototype.toString.call(value) === '[object String]';
},
isSupported: {
canvas: function canvas() {
var el = document.createElement('canvas');
return el && el.getContext && el.getContext('2d');
},
webworkers: function webworkers() {
return window.Worker;
},
blob: function blob() {
return utils.Blob;
},
Uint8Array: function Uint8Array() {
return window.Uint8Array;
},
Uint32Array: function Uint32Array() {
return window.Uint32Array;
},
videoCodecs: function () {
var testEl = document.createElement('video');
var supportObj = {
'mp4': false,
'h264': false,
'ogv': false,
'ogg': false,
'webm': false
};
try {
if (testEl && testEl.canPlayType) {
// Check for MPEG-4 support
supportObj.mp4 = testEl.canPlayType('video/mp4; codecs="mp4v.20.8"') !== '';
// Check for h264 support
supportObj.h264 = (testEl.canPlayType('video/mp4; codecs="avc1.42E01E"') || testEl.canPlayType('video/mp4; codecs="avc1.42E01E, mp4a.40.2"')) !== '';
// Check for Ogv support
supportObj.ogv = testEl.canPlayType('video/ogg; codecs="theora"') !== '';
// Check for Ogg support
supportObj.ogg = testEl.canPlayType('video/ogg; codecs="theora"') !== '';
// Check for Webm support
supportObj.webm = testEl.canPlayType('video/webm; codecs="vp8, vorbis"') !== -1;
}
} catch (e) {}
return supportObj;
}()
},
noop: function noop() {},
each: function each(collection, callback) {
var x = void 0;
var len = void 0;
if (utils.isArray(collection)) {
x = -1;
len = collection.length;
while (++x < len) {
if (callback(x, collection[x]) === false) {
break;
}
}
} else if (utils.isObject(collection)) {
for (x in collection) {
if (collection.hasOwnProperty(x)) {
if (callback(x, collection[x]) === false) {
break;
}
}
}
}
},
mergeOptions: function mergeOptions(defaultOptions, userOptions) {
if (!utils.isObject(defaultOptions) || !utils.isObject(userOptions) || !Object.keys) {
return;
}
var newObj = {};
utils.each(defaultOptions, function (key, val) {
newObj[key] = defaultOptions[key];
});
utils.each(userOptions, function (key, val) {
var currentUserOption = userOptions[key];
if (!utils.isObject(currentUserOption)) {
newObj[key] = currentUserOption;
} else {
if (!defaultOptions[key]) {
newObj[key] = currentUserOption;
} else {
newObj[key] = utils.mergeOptions(defaultOptions[key], currentUserOption);
}
}
});
return newObj;
},
setCSSAttr: function setCSSAttr(elem, attr, val) {
if (!utils.isElement(elem)) {
return;
}
if (utils.isString(attr) && utils.isString(val)) {
elem.style[attr] = val;
} else if (utils.isObject(attr)) {
utils.each(attr, function (key, val) {
elem.style[key] = val;
});
}
},
removeElement: function removeElement(node) {
if (!utils.isElement(node)) {
return;
}
if (node.parentNode) {
node.parentNode.removeChild(node);
}
},
createWebWorker: function createWebWorker(content) {
if (!utils.isString(content)) {
return {};
}
try {
var blob = new utils.Blob([content], {
'type': 'text/javascript'
});
var objectUrl = utils.URL.createObjectURL(blob);
var worker = new Worker(objectUrl);
return {
'objectUrl': objectUrl,
'worker': worker
};
} catch (e) {
return '' + e;
}
},
getExtension: function getExtension(src) {
return src.substr(src.lastIndexOf('.') + 1, src.length);
},
getFontSize: function getFontSize() {
var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};
if (!document.body || options.resizeFont === false) {
return options.fontSize;
}
var text = options.text;
var containerWidth = options.gifWidth;
var fontSize = parseInt(options.fontSize, 10);
var minFontSize = parseInt(options.minFontSize, 10);
var div = document.createElement('div');
var span = document.createElement('span');
div.setAttribute('width', containerWidth);
div.appendChild(span);
span.innerHTML = text;
span.style.fontSize = fontSize + 'px';
span.style.textIndent = '-9999px';
span.style.visibility = 'hidden';
document.body.appendChild(span);
while (span.offsetWidth > containerWidth && fontSize >= minFontSize) {
span.style.fontSize = --fontSize + 'px';
}
document.body.removeChild(span);
return fontSize + 'px';
},
webWorkerError: false
};
var utils$2 = Object.freeze({
default: utils
});
/*
error.js
========
*/
/* Copyright 2017 Yahoo Inc.
* Copyrights licensed under the MIT License. See the accompanying LICENSE file for terms.
*/
// Dependencies
var error = {
validate: function validate(skipObj) {
skipObj = utils.isObject(skipObj) ? skipObj : {};
var errorObj = {};
utils.each(error.validators, function (indece, currentValidator) {
var errorCode = currentValidator.errorCode;
if (!skipObj[errorCode] && !currentValidator.condition) {
errorObj = currentValidator;
errorObj.error = true;
return false;
}
});
delete errorObj.condition;
return errorObj;
},
isValid: function isValid(skipObj) {
var errorObj = error.validate(skipObj);
var isValid = errorObj.error !== true ? true : false;
return isValid;
},
validators: [{
condition: utils.isFunction(utils.getUserMedia),
errorCode: 'getUserMedia',
errorMsg: 'The getUserMedia API is not supported in your browser'
}, {
condition: utils.isSupported.canvas(),
errorCode: 'canvas',
errorMsg: 'Canvas elements are not supported in your browser'
}, {
condition: utils.isSupported.webworkers(),
errorCode: 'webworkers',
errorMsg: 'The Web Workers API is not supported in your browser'
}, {
condition: utils.isFunction(utils.URL),
errorCode: 'window.URL',
errorMsg: 'The window.URL API is not supported in your browser'
}, {
condition: utils.isSupported.blob(),
errorCode: 'window.Blob',
errorMsg: 'The window.Blob File API is not supported in your browser'
}, {
condition: utils.isSupported.Uint8Array(),
errorCode: 'window.Uint8Array',
errorMsg: 'The window.Uint8Array function constructor is not supported in your browser'
}, {
condition: utils.isSupported.Uint32Array(),
errorCode: 'window.Uint32Array',
errorMsg: 'The window.Uint32Array function constructor is not supported in your browser'
}],
messages: {
videoCodecs: {
errorCode: 'videocodec',
errorMsg: 'The video codec you are trying to use is not supported in your browser'
}
}
};
var error$2 = Object.freeze({
default: error
});
/*
defaultOptions.js
=================
*/
/* Copyright 2017 Yahoo Inc.
* Copyrights licensed under the MIT License. See the accompanying LICENSE file for terms.
*/
// Helpers
var noop = function noop() {};
var defaultOptions = {
sampleInterval: 10,
numWorkers: 2,
filter: '',
gifWidth: 200,
gifHeight: 200,
interval: 0.1,
numFrames: 10,
frameDuration: 1,
keepCameraOn: false,
images: [],
video: null,
webcamVideoElement: null,
cameraStream: null,
text: '',
fontWeight: 'normal',
fontSize: '16px',
minFontSize: '10px',
resizeFont: false,
fontFamily: 'sans-serif',
fontColor: '#ffffff',
textAlign: 'center',
textBaseline: 'bottom',
textXCoordinate: null,
textYCoordinate: null,
progressCallback: noop,
completeCallback: noop,
saveRenderingContexts: false,
savedRenderingContexts: [],
showFrameText: true,
crossOrigin: 'Anonymous',
waterMark: null,
waterMarkHeight: null,
waterMarkWidth: null,
waterMarkXCoordinate: 1,
waterMarkYCoordinate: 1
};
var defaultOptions$2 = Object.freeze({
default: defaultOptions
});
/*
isSupported.js
==============
*/
/* Copyright 2017 Yahoo Inc.
* Copyrights licensed under the MIT License. See the accompanying LICENSE file for terms.
*/
// Dependencies
function isSupported() {
return error.isValid();
}
/*
isWebCamGIFSupported.js
=======================
*/
/* Copyright 2017 Yahoo Inc.
* Copyrights licensed under the MIT License. See the accompanying LICENSE file for terms.
*/
function isWebCamGIFSupported() {
return error.isValid();
}
/*
isSupported.js
==============
*/
/* Copyright 2017 Yahoo Inc.
* Copyrights licensed under the MIT License. See the accompanying LICENSE file for terms.
*/
// Dependencies
function isSupported$1() {
var options = {
getUserMedia: true
};
return error.isValid(options);
}
/*
isExistingVideoGIFSupported.js
==============================
*/
/* Copyright 2017 Yahoo Inc.
* Copyrights licensed under the MIT License. See the accompanying LICENSE file for terms.
*/
// Dependencies
function isExistingVideoGIFSupported(codecs) {
var hasValidCodec = false;
if (utils.isArray(codecs) && codecs.length) {
utils.each(codecs, function (indece, currentCodec) {
if (utils.isSupported.videoCodecs[currentCodec]) {
hasValidCodec = true;
}
});
if (!hasValidCodec) {
return false;
}
} else if (utils.isString(codecs) && codecs.length) {
if (!utils.isSupported.videoCodecs[codecs]) {
return false;
}
}
return error.isValid({
'getUserMedia': true
});
}
/*
NeuQuant.js
===========
*/
/*
* NeuQuant Neural-Net Quantization Algorithm
* ------------------------------------------
*
* Copyright (c) 1994 Anthony Dekker
*
* NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See
* "Kohonen neural networks for optimal colour quantization" in "Network:
* Computation in Neural Systems" Vol. 5 (1994) pp 351-367. for a discussion of
* the algorithm.
*
* Any party obtaining a copy of these files from the author, directly or
* indirectly, is granted, free of charge, a full and unrestricted irrevocable,
* world-wide, paid up, royalty-free, nonexclusive right and license to deal in
* this software and documentation files (the "Software"), including without
* limitation the rights to use, copy, modify, merge, publish, distribute,
* sublicense, and/or sell copies of the Software, and to permit persons who
* receive copies from any such party to do so, with the only requirement being
* that this copyright notice remain intact.
*/
/*
* This class handles Neural-Net quantization algorithm
* @author Kevin Weiner (original Java version - [email protected])
* @author Thibault Imbert (AS3 version - bytearray.org)
* @version 0.1 AS3 implementation
* @version 0.2 JS->AS3 "translation" by antimatter15
* @version 0.3 JS clean up + using modern JS idioms by sole - http://soledadpenades.com
* Also implement fix in color conversion described at http://stackoverflow.com/questions/16371712/neuquant-js-javascript-color-quantization-hidden-bug-in-js-conversion
*/
function NeuQuant() {
var netsize = 256; // number of colours used
// four primes near 500 - assume no image has a length so large
// that it is divisible by all four primes
var prime1 = 499;
var prime2 = 491;
var prime3 = 487;
var prime4 = 503;
// minimum size for input image
var minpicturebytes = 3 * prime4;
// Network Definitions
var maxnetpos = netsize - 1;
var netbiasshift = 4; // bias for colour values
var ncycles = 100; // no. of learning cycles
// defs for freq and bias
var intbiasshift = 16; // bias for fractions
var intbias = 1 << intbiasshift;
var gammashift = 10; // gamma = 1024
var gamma = 1 << gammashift;
var betashift = 10;
var beta = intbias >> betashift; // beta = 1/1024
var betagamma = intbias << gammashift - betashift;
// defs for decreasing radius factor
// For 256 colors, radius starts at 32.0 biased by 6 bits
// and decreases by a factor of 1/30 each cycle
var initrad = netsize >> 3;
var radiusbiasshift = 6;
var radiusbias = 1 << radiusbiasshift;
var initradius = initrad * radiusbias;
var radiusdec = 30;
// defs for decreasing alpha factor
// Alpha starts at 1.0 biased by 10 bits
var alphabiasshift = 10;
var initalpha = 1 << alphabiasshift;
var alphadec;
// radbias and alpharadbias used for radpower calculation
var radbiasshift = 8;
var radbias = 1 << radbiasshift;
var alpharadbshift = alphabiasshift + radbiasshift;
var alpharadbias = 1 << alpharadbshift;
// Input image
var thepicture;
// Height * Width * 3
var lengthcount;
// Sampling factor 1..30
var samplefac;
// The network itself
var network;
var netindex = [];
// for network lookup - really 256
var bias = [];
// bias and freq arrays for learning
var freq = [];
var radpower = [];
function NeuQuantConstructor(thepic, len, sample) {
var i;
var p;
thepicture = thepic;
lengthcount = len;
samplefac = sample;
network = new Array(netsize);
for (i = 0; i < netsize; i++) {
network[i] = new Array(4);
p = network[i];
p[0] = p[1] = p[2] = (i << netbiasshift + 8) / netsize | 0;
freq[i] = intbias / netsize | 0; // 1 / netsize
bias[i] = 0;
}
}
function colorMap() {
var map = [];
var index = new Array(netsize);
for (var i = 0; i < netsize; i++) {
index[network[i][3]] = i;
}var k = 0;
for (var l = 0; l < netsize; l++) {
var j = index[l];
map[k++] = network[j][0];
map[k++] = network[j][1];
map[k++] = network[j][2];
}
return map;
}
// Insertion sort of network and building of netindex[0..255]
// (to do after unbias)
function inxbuild() {
var i;
var j;
var smallpos;
var smallval;
var p;
var q;
var previouscol;
var startpos;
previouscol = 0;
startpos = 0;
for (i = 0; i < netsize; i++) {
p = network[i];
smallpos = i;
smallval = p[1]; // index on g
// find smallest in i..netsize-1
for (j = i + 1; j < netsize; j++) {
q = network[j];
if (q[1] < smallval) {
// index on g
smallpos = j;
smallval = q[1]; // index on g
}
}
q = network[smallpos];
// swap p (i) and q (smallpos) entries
if (i != smallpos) {
j = q[0];
q[0] = p[0];
p[0] = j;
j = q[1];
q[1] = p[1];
p[1] = j;
j = q[2];
q[2] = p[2];
p[2] = j;
j = q[3];
q[3] = p[3];
p[3] = j;
}
// smallval entry is now in position i
if (smallval != previouscol) {
netindex[previouscol] = startpos + i >> 1;
for (j = previouscol + 1; j < smallval; j++) {
netindex[j] = i;
}
previouscol = smallval;
startpos = i;
}
}
netindex[previouscol] = startpos + maxnetpos >> 1;
for (j = previouscol + 1; j < 256; j++) {
netindex[j] = maxnetpos; // really 256
}
}
// Main Learning Loop
function learn() {
var i;
var j;
var b;
var g;
var r;
var radius;
var rad;
var alpha;
var step;
var delta;
var samplepixels;
var p;
var pix;
var lim;
if (lengthcount < minpicturebytes) {
samplefac = 1;
}
alphadec = 30 + (samplefac - 1) / 3;
p = thepicture;
pix = 0;
lim = lengthcount;
samplepixels = lengthcount / (3 * samplefac);
delta = samplepixels / ncycles | 0;
alpha = initalpha;
radius = initradius;
rad = radius >> radiusbiasshift;
if (rad <= 1) {
rad = 0;
}
for (i = 0; i < rad; i++) {
radpower[i] = alpha * ((rad * rad - i * i) * radbias / (rad * rad));
}
if (lengthcount < minpicturebytes) {
step = 3;
} else if (lengthcount % prime1 !== 0) {
step = 3 * prime1;
} else {
if (lengthcount % prime2 !== 0) {
step = 3 * prime2;
} else {
if (lengthcount % prime3 !== 0) {
step = 3 * prime3;
} else {
step = 3 * prime4;
}
}
}
i = 0;
while (i < samplepixels) {
b = (p[pix + 0] & 0xff) << netbiasshift;
g = (p[pix + 1] & 0xff) << netbiasshift;
r = (p[pix + 2] & 0xff) << netbiasshift;
j = contest(b, g, r);
altersingle(alpha, j, b, g, r);
if (rad !== 0) {
// Alter neighbours
alterneigh(rad, j, b, g, r);
}
pix += step;
if (pix >= lim) {
pix -= lengthcount;
}
i++;
if (delta === 0) {
delta = 1;
}
if (i % delta === 0) {
alpha -= alpha / alphadec;
radius -= radius / radiusdec;
rad = radius >> radiusbiasshift;
if (rad <= 1) {
rad = 0;
}
for (j = 0; j < rad; j++) {
radpower[j] = alpha * ((rad * rad - j * j) * radbias / (rad * rad));
}
}
}
}
// Search for BGR values 0..255 (after net is unbiased) and return colour index
function map(b, g, r) {
var i;
var j;
var dist;
var a;
var bestd;
var p;
var best;
// Biggest possible distance is 256 * 3
bestd = 1000;
best = -1;
i = netindex[g]; // index on g
j = i - 1; // start at netindex[g] and work outwards
while (i < netsize || j >= 0) {
if (i < netsize) {
p = network[i];
dist = p[1] - g; // inx key
if (dist >= bestd) {
i = netsize; // stop iter
} else {
i++;
if (dist < 0) {
dist = -dist;
}
a = p[0] - b;
if (a < 0) {
a = -a;
}
dist += a;
if (dist < bestd) {
a = p[2] - r;
if (a < 0) {
a = -a;
}
dist += a;
if (dist < bestd) {
bestd = dist;
best = p[3];
}
}
}
}
if (j >= 0) {
p = network[j];
dist = g - p[1]; // inx key - reverse dif
if (dist >= bestd) {
j = -1; // stop iter
} else {
j--;
if (dist < 0) {
dist = -dist;
}
a = p[0] - b;
if (a < 0) {
a = -a;
}
dist += a;
if (dist < bestd) {
a = p[2] - r;
if (a < 0) {
a = -a;
}
dist += a;
if (dist < bestd) {
bestd = dist;
best = p[3];
}
}
}
}
}
return best;
}
function process() {
learn();
unbiasnet();
inxbuild();
return colorMap();
}
// Unbias network to give byte values 0..255 and record position i
// to prepare for sort
function unbiasnet() {
var i;
var j;
for (i = 0; i < netsize; i++) {
network[i][0] >>= netbiasshift;
network[i][1] >>= netbiasshift;
network[i][2] >>= netbiasshift;
network[i][3] = i; // record colour no
}
}
// Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2))
// in radpower[|i-j|]
function alterneigh(rad, i, b, g, r) {
var j;
var k;
var lo;
var hi;
var a;
var m;
var p;
lo = i - rad;
if (lo < -1) {
lo = -1;
}
hi = i + rad;
if (hi > netsize) {
hi = netsize;
}
j = i + 1;
k = i - 1;
m = 1;
while (j < hi || k > lo) {
a = radpower[m++];
if (j < hi) {
p = network[j++];
try {
p[0] -= a * (p[0] - b) / alpharadbias | 0;
p[1] -= a * (p[1] - g) / alpharadbias | 0;
p[2] -= a * (p[2] - r) / alpharadbias | 0;
} catch (e) {}
}
if (k > lo) {
p = network[k--];
try {
p[0] -= a * (p[0] - b) / alpharadbias | 0;
p[1] -= a * (p[1] - g) / alpharadbias | 0;
p[2] -= a * (p[2] - r) / alpharadbias | 0;
} catch (e) {}
}
}
}
// Move neuron i towards biased (b,g,r) by factor alpha
function altersingle(alpha, i, b, g, r) {
// alter hit neuron
var n = network[i];
var alphaMult = alpha / initalpha;
n[0] -= alphaMult * (n[0] - b) | 0;
n[1] -= alphaMult * (n[1] - g) | 0;