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Copy pathDepth.js
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143 lines (128 loc) · 5.28 KB
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import React from 'react'
import logo from './logo.svg';
import './App.css';
class App extends React.Component {
constructor(props){
super(props);
this.inited = false;
}
init = async () => {
let tf = window.tf;
if (this.inited) {
return;
}
this.inited = true;
let textElement = document.getElementById('but')
textElement.innerHTML = 'started'
let model = await tf.loadLayersModel('/NormalMapTFJS/model/model.json');
model.inputs[0].shape = [null,null,null,3]
model.inputLayers[0].batchInputShape = [null,null,null,3]
model.inputLayers[0].inputSpec[0].shape = [null,null,null,3]
model.feedInputShapes[0] = [null,null,null,3]
model.internalInputShapes[0] = [null,null,null,3]
textElement.innerHTML = 'loaded'
let stream = await navigator.mediaDevices.getUserMedia({ video: {width:512, height:512},facingMode: "environment" })
textElement.innerHTML = 'capture'
let video = document.getElementById('video')
video.srcObject = stream;
await video.play()
let canvas = document.getElementById('result')
let ctx = canvas.getContext('2d');
let update = () => {
let result = tf.tidy(() => {
textElement.innerHTML = 'req'
try {
let image = tf.browser.fromPixels(video).expandDims().div(255)
//if(image.shape[2]!==1024|| image.shape[1]!==768){
image = tf.image.resizeBilinear(image, [512, 512])
//}
textElement.innerHTML = 'req1'
let result = model.predict(image);
textElement.innerHTML = 'req2'
result = result.reshape(result.shape.slice(1)).mul(0.5).add(0.5).maximum(0).minimum(1)
return result;
} catch (err) {
textElement.innerHTML = err.message
}
})
tf.browser.toPixels(result, canvas).then(()=>{
textElement.innerHTML = 'req3'
result.dispose();
requestAnimationFrame(update)
}).catch(err=>{
textElement.innerHTML = err.message
})
}
requestAnimationFrame(update)
}
initDepth = async () => {
let tf = window.tf;
if (this.inited) {
return;
}
this.inited = true;
let textElement = document.getElementById('but')
textElement.innerHTML = 'started'
let model = await tf.loadLayersModel('/NormalMapTFJS/depth/model.json');
// model.inputs[0].shape = [null,null,null,3]
// model.inputLayers[0].batchInputShape = [null,null,null,3]
// model.inputLayers[0].inputSpec[0].shape = [null,null,null,3]
// model.feedInputShapes[0] = [null,null,null,3]
// model.internalInputShapes[0] = [null,null,null,3]
textElement.innerHTML = 'loaded'
let stream = await navigator.mediaDevices.getUserMedia({ video: {width:256, height:192},facingMode: "environment" })
textElement.innerHTML = 'capture'
let video = document.getElementById('video')
video.srcObject = stream;
await video.play()
let canvas = document.getElementById('result')
let ctx = canvas.getContext('2d');
tf.enableProdMode()
let update = () => {
let result = tf.tidy(() => {
textElement.innerHTML = 'req'
try {
let time = new Date()
let image = tf.browser.fromPixels(video).expandDims().div(255)
//if(image.shape[2]!==1024|| image.shape[1]!==768){192,256
image = tf.image.resizeBilinear(image, [192, 256])
//}
textElement.innerHTML = 'req1'
let result = model.predict(image);
textElement.innerHTML = 'req2'
let start = [0,8,8,0]
let end = [1,result.shape[1]-16,result.shape[2]-16,1]
let minV = result.slice(start,end).min().dataSync()[0]
let maxV = result.slice(start,end).max().dataSync()[0]-minV
result = result.reshape(result.shape.slice(1)).sub(minV).div(maxV).maximum(0).minimum(1)
console.log('minV: ', minV, maxV, new Date()-time);
return result;
} catch (err) {
console.log('err: ', err);
textElement.innerHTML = err.message
}
})
tf.browser.toPixels(result, canvas).then(()=>{
textElement.innerHTML = 'req3'
result.dispose();
requestAnimationFrame(update)
}).catch(err=>{
textElement.innerHTML = err.message
})
}
requestAnimationFrame(update)
}
render(){
console.log(this);
return (
<div className="App">
<video id='video'></video>
<canvas id='result'></canvas>
<div id="but" onClick={()=>{
this.initDepth().catch(err=>{console.error(err);document.body.innerText= err.message})
}}>Play</div>
</div>
);
}
}
export default App;