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App.js
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App.js
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import debounce from 'lodash.debounce'
import React, { Component } from 'react'
import Dropzone from 'react-dropzone'
import Footer from './Footer'
import Header from './Header'
import Message from './Message'
import Results from './Results'
import sampleImg from '../img/sample.jpg'
import { FaceFinder } from '../ml/face'
import { EmotionNet } from '../ml/models'
import { readFile, nextFrame, drawBox, drawText } from '../util'
class App extends Component {
state = {
ready: false,
loading: false,
imgUrl: sampleImg,
detections: [],
faces: [],
emotions: [],
}
componentDidMount() {
this.initModels()
window.addEventListener('resize', this.handleResize)
}
componentWillUnmount() {
window.removeEventListener('resize', this.handleResize)
}
initModels = async () => {
const faceModel = new FaceFinder()
await faceModel.load()
const emotionModel = new EmotionNet()
await emotionModel.load()
this.models = { face: faceModel, emotion: emotionModel }
this.setState({ ready: true }, this.initPredict)
}
initPredict = () => {
if (!this.img || !this.img.complete) return
this.setState({ loading: true })
this.analyzeFaces()
}
handleImgLoaded = () => {
this.clearCanvas()
this.analyzeFaces()
}
handleResize = debounce(() => this.drawDetections(), 100)
handleUpload = async files => {
if (!files.length) return
const fileData = await readFile(files[0])
this.setState({
imgUrl: fileData.url,
loading: true,
detections: [],
faces: [],
emotions: [],
})
}
analyzeFaces = async () => {
await nextFrame()
if (!this.models) return
// get face bounding boxes and canvases
const faceResults = await this.models.face.findAndExtractFaces(this.img)
const { detections, faces } = faceResults
// get emotion predictions
let emotions = await Promise.all(
faces.map(async face => await this.models.emotion.classify(face))
)
this.setState(
{ loading: false, detections, faces, emotions },
this.drawDetections
)
}
clearCanvas = () => {
this.canvas.width = 0
this.canvas.height = 0
}
drawDetections = () => {
const { detections, emotions } = this.state
if (!detections.length) return
const { width, height } = this.img
this.canvas.width = width
this.canvas.height = height
const ctx = this.canvas.getContext('2d')
const detectionsResized = detections.map(d => d.forSize(width, height))
detectionsResized.forEach((det, i) => {
const { x, y } = det.box
const { emoji } = emotions[i][0].label
drawBox({ ctx, ...det.box })
drawText({ ctx, x, y, text: emoji })
})
}
render() {
const { ready, imgUrl, loading, faces, emotions } = this.state
const noFaces = ready && !loading && imgUrl && !faces.length
return (
<div className="px2 mx-auto container app">
<Header />
<main>
<div className="py1">
<Dropzone
className="btn btn-small btn-primary btn-upload bg-black h5"
accept="image/jpeg, image/png"
multiple={false}
disabled={!ready}
onDrop={this.handleUpload}
>
Upload image
</Dropzone>
</div>
{imgUrl && (
<div className="relative">
<img
ref={el => (this.img = el)}
onLoad={this.handleImgLoaded}
src={imgUrl}
alt=""
/>
<canvas
ref={el => (this.canvas = el)}
className="absolute top-0 left-0"
/>
</div>
)}
{!ready && <Message>Loading machine learning models...</Message>}
{loading && <Message>Analyzing image...</Message>}
{noFaces && (
<Message bg="red" color="white">
<strong>Sorry!</strong> No faces were detected. Please try another
image.
</Message>
)}
{faces.length > 0 && <Results faces={faces} emotions={emotions} />}
</main>
<Footer />
</div>
)
}
}
export default App