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index.html
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<!DOCTYPE html>
<html>
<head>
<link href='https://fonts.googleapis.com/css?family=Roboto:100,300,400,500,700,900|Material+Icons' rel="stylesheet">
<link href="https://cdn.jsdelivr.net/npm/vuetify/dist/vuetify.min.css" rel="stylesheet">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1, user-scalable=no, minimal-ui">
<style>
#app {
background-color: #ffffff;
font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
h1 {
font-size: 60px;
line-height: 55px;
}
.bar {
position: absolute;
height: 5px;
width: 100%;
bottom: 0;
z-index: 1;
}
.rainbow {
background: linear-gradient(270deg, #ff0000, #f9ff00, #00ff20, #00fdff, #0011ff, #dc00ff);
background-size: 1200% 1200%;
-webkit-animation: AnimationName 26s ease infinite;
-moz-animation: AnimationName 26s ease infinite;
animation: AnimationName 26s ease infinite;
}
@-webkit-keyframes AnimationName {
0%{background-position:0% 50%}
50%{background-position:100% 50%}
100%{background-position:0% 50%}
}
@-moz-keyframes AnimationName {
0%{background-position:0% 50%}
50%{background-position:100% 50%}
100%{background-position:0% 50%}
}
@keyframes AnimationName {
0%{background-position:0% 50%}
50%{background-position:100% 50%}
100%{background-position:0% 50%}
}
.custom-loader {
animation: loader 1s infinite;
display: flex;
}
@-moz-keyframes loader {
from {
transform: rotate(0);
}
to {
transform: rotate(360deg);
}
}
@-webkit-keyframes loader {
from {
transform: rotate(0);
}
to {
transform: rotate(360deg);
}
}
@-o-keyframes loader {
from {
transform: rotate(0);
}
to {
transform: rotate(360deg);
}
}
@keyframes loader {
from {
transform: rotate(0);
}
to {
transform: rotate(360deg);
}
}
</style>
</head>
<body>
<div id="app">
<v-app>
<v-content>
<v-container>
<h1 style="position: relative" :class="$vuetify.breakpoint.smAndDown ? 'mt-5' : 'mt-0'">
<div class="rainbow bar"></div>
<div style="position: relative; z-index: 2">“Sneaker Generator”<div>
</h1>
<a class="pa-3 mr-5" href="https://github.com/98mprice/sneaker-generator" style="position: absolute; top: 0; right: 0; z-index: 5;">
<h2>fork me! 🍽</h2>
</a>
<template grid-list-sm fluid>
<v-layout row wrap>
<v-flex
v-for="n in random12"
:key="n"
xs3 sm2 lg1
d-flex
>
<v-card flat tile class="d-flex">
<v-img
:src="`github-pages/img/shoe${n + 1}.png`"
aspect-ratio="1"
class="grey lighten-2"
>
<v-layout
slot="placeholder"
fill-height
align-center
justify-center
ma-0
>
<v-progress-circular indeterminate color="grey lighten-5"></v-progress-circular>
</v-layout>
</v-img>
</v-card>
</v-flex>
</v-layout>
</template>
<h2>Neural Network that makes shoes. Pointless, but kinda fun.</h2>
<h2 class="mt-4">Try it on your machine</h2>
<p>You'll need some things installed, but you can run it either with <a href="https://github.com/98mprice/sneaker-generator/blob/master/node/predict.js">Node.js</a> or <a href="https://github.com/98mprice/sneaker-generator/blob/master/python/predict.py">Python</a>.</p>
<h2>Try it in the browser</h2>
<p class="mb-0"><b>Due to a limitation with Tensorflow.js in the browser, model prediction can only happen on the CPU.</b></p>
<p class="mb-0">Therefore, depending on the speed of your CPU, it can be pretty slow. On my 1,6 GHz i5 it takes ~10 mins.
<br>On the upside, one iteration produces 64 shoes.
</p>
<v-layout row wrap class="mt-3">
<v-flex xs12 sm6>
<div class="rainbow" style="position: relative; width: 266px; height: 266px; display: block; margin-left: auto; margin-right: auto;">
<canvas id="myCanvas" width="256" height="256" style="position: absolute; width: 256px; height: 256px; background-color: white; left: 5px; top: 5px;"></canvas>
</div>
</v-flex>
<v-flex xs12 sm6>
<v-layout column fill-height>
<v-btn
flat
:loading="loading_model"
:disabled="loading_model || model != null"
@click="downloadModel"
>
<v-icon left dark>cloud_download</v-icon>fetch model
<span slot="loader" class="custom-loader">
<v-icon light>cached</v-icon>
</span>
</v-btn>
<v-btn
flat
:loading="loading_predict"
:disabled="model == null"
@click="predict"
>
start
<span slot="loader" class="custom-loader">
<v-icon light>cached</v-icon>
</span>
</v-btn>
<v-dialog
v-model="loading_predict"
hide-overlay
persistent
width="300"
>
<v-card
class="rainbow"
>
<v-card-text>
<p>Currently {{status}}...</p>
<b>This tab might freeze!
Don't be alarmed, it's just doing the prediction.
Leave it for a while and let it do it's thing.
</b>
<v-progress-linear
indeterminate
color="black"
class="mb-0"
></v-progress-linear>
</v-card-text>
</v-card>
</v-dialog>
<v-spacer></v-spacer>
<v-slide-y-transition>
<v-layout row v-show="preds != null">
<v-flex xs6>
<v-btn flat block @click="drawShoe">
<v-icon left dark>arrow_forward</v-icon>next shoe
</v-btn>
</v-flex>
<v-flex xs6>
<v-btn flat block @click="save">
<v-icon left dark>save_alt</v-icon>save
</v-btn>
<a id="download" download="triangle.png"></a>
</v-flex>
</v-layout>
</v-slide-y-transition>
</v-layout>
</v-flex>
</v-layout>
</v-container>
</v-content>
</v-app>
</div>
<script src="https://cdn.jsdelivr.net/npm/vue/dist/vue.js"></script>
<script src="https://cdn.jsdelivr.net/npm/vuetify/dist/vuetify.js"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/[email protected]/dist/tf.min.js"> </script>
<script src="https://cdn.jsdelivr.net/gh/nicolaspanel/[email protected]/dist/numjs.min.js"></script>
<script>
new Vue({
el: '#app',
data: {
loading_model: false,
loading_predict: false,
model: null,
status: 'preparing',
count: 0,
preds: null
},
mounted: function() {
tf.setBackend('cpu')
},
computed: {
random12: function() {
let x = Array(12).fill(0).map((e,i)=>i+1)
this.shuffle(x)
return x
}
},
methods: {
shuffle: function(a) {
var j, x, i;
for (i = a.length - 1; i > 0; i--) {
j = Math.floor(Math.random() * (i + 1));
x = a[i];
a[i] = a[j];
a[j] = x;
}
return a;
},
downloadModel: async function() {
this.loading_model = true;
console.log('doing')
const model = await tf.loadModel('https://raw.githubusercontent.com/98mprice/sneaker-test/master/src/generator/model.json');
console.log('done')
this.model = model;
this.loading_model = false;
},
randn_bm: function(min, max, skew) {
var u = 0, v = 0;
while(u === 0) u = Math.random(); //Converting [0,1) to (0,1)
while(v === 0) v = Math.random();
let num = Math.sqrt( -2.0 * Math.log( u ) ) * Math.cos( 2.0 * Math.PI * v );
num = num / 10.0 + 0.5; // Translate to 0 -> 1
if (num > 1 || num < 0) num = randn_bm(min, max, skew); // resample between 0 and 1 if out of range
num = Math.pow(num, skew); // Skew
num *= max - min; // Stretch to fill range
num += min; // offset to min
return num;
},
test: function() {
this.loading_predict = true
this.status = "doing stuff"
setTimeout(() => (this.loading_predict = false), 4000)
},
save: function() {
var download = document.getElementById("download");
var image = document.getElementById("myCanvas").toDataURL("image/png").replace("image/png", "image/octet-stream");
download.setAttribute("href", image);
},
drawShoe: function() {
if (this.count >= this.preds.length) {
this.count = 0
}
var c=document.getElementById("myCanvas");
var ctx=c.getContext("2d");
var imgData=ctx.createImageData(256,256);
for (var i = 0; i<imgData.data.length; i += 4) {
r = (this.preds[this.count] + 1)*127.5
g = (this.preds[this.count+1] + 1)*127.5
b = (this.preds[this.count+2] + 1)*127.5
imgData.data[i+0]=r;
imgData.data[i+1]=b;
imgData.data[i+2]=g;
imgData.data[i+3]=255;
this.count += 3
}
ctx.putImageData(imgData,0,0);
},
predict: async function() {
this.loading_predict = true
console.log('predict')
const batch_size = 64
// Generate noise
console.log('generating noise')
this.status = "generating noise"
let noise = nj.zeros([batch_size, 1, 1, 100])
for (var i = 0; i < batch_size; i++) {
for (var j = 0; j < 100; j++) {
noise.set(i, 0, 0, j, this.randn_bm(-5, 5, 1))
}
}
let noise_tensor = tf.tensor4d(noise.tolist())
noise_tensor.print(true)
// Generate images
console.log('generating images')
this.status = "creating shoes"
await new Promise(resolve => setTimeout(resolve, 1000));
let generated_images = this.model.predict(noise_tensor)
let output_data = await generated_images.dataSync()
this.preds = Array.prototype.slice.call(output_data);
// Draw image
console.log('drawing image')
this.status = "drawing first shoe " + (this.preds == null)
this.drawShoe()
this.loading_predict = false
}
}
})
</script>
</body>
</html>