NormalMapTFJS/OpticalFlowNode.js at main · Graphnull/NormalMapTFJS · GitHub

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//import { env } from '@tensorflow/tfjs-core';

//import { getGlslDifferences } from '@tensorflow/tfjs-backend-webgl/dist/glsl_version';

//import { getKernel } from '@tensorflow/tfjs-core/dist/kernel_registry';

let tf = require('@tensorflow/tfjs-node');

//import '@tensorflow/'

let fs = require('fs')

let { opticalFlowFind, blur } = require('nodleten/src/kernels/opticatFlowKernels')

let custBlur = (fld, ks, step) => {

fld = tf.conv2d(

tf.stack(fld.unstack(-1).map(v => v.reshape([v.shape[0], v.shape[1], 1]))),

tf.fill([ks, ks, 1, 1], 1 / (ks * ks)), [step, step], 'valid')

fld = tf.concat(fld.unstack(), -1)

return fld;

}

let minIndex = (arr) => {

let i = 0;

let minV = Infinity;

for (let j = 0; j < arr.length; j++) {

if (minV > arr[j]) {

minV = arr[j];

i = j

}

return i;

}

//import '@tensorflow/tfjs-backend-webgl/dist/register_all_kernels.js';

//const glsl = getGlslDifferences();

let ks = 7

let hks = Math.floor(ks / 2)

let dil = 1;

let dk = ks / dil

let step = 4

let height = 436 / 4

let width = 1024 / 4

let run = async () => {

//console.log('tf', tf.engine().registryFactory, tf.getBackend());

let log = console.log

log('started')

try {

let video = tf.node.decodePng(fs.readFileSync('./public/frame_0002.png'), 3) //await new Promise((res) => { let img = new Image(); img.onload = () => res(img); img.src = '/NormalMapTFJS/frame_0002.png' })

let video2 = tf.node.decodePng(fs.readFileSync('./public/frame_0001.png'), 3) //await new Promise((res) => { let img = new Image(); img.onload = () => res(img); img.src = '/NormalMapTFJS/frame_0001.png' })

//let canvas = document.getElementById('result')

//let canvas2 = document.getElementById('result2')

await tf.setBackend('tensorflow');

await new Promise((res) => setTimeout(res, 100))

let height = video.shape[0];

let width = video.shape[1];

let flowData = tf.tensor(new Float32Array(fs.readFileSync('./public/frame_0001.flo').buffer.slice(3 * 4)), [height, width, 2])

flowData = tf.concat([flowData, tf.zeros([height, width, 1])], -1)

console.log('height: ', height, width);

//let current = tf.zeros([1, height, width, 3]);

//let pred = tf.zeros([1, height, width, 3]);

let current2 = tf.zeros([1, height / 2, width / 2, 3]);

let pred2 = tf.zeros([1, height / 2, width / 2, 3]);

let current4 = tf.zeros([1, height / 4, width / 4, 3]);

let pred4 = tf.zeros([1, height / 4, width / 4, 3]);

let flowData2 = flowData.resizeBilinear([height / 2, width / 2]);

let flowData4 = flowData.resizeBilinear([height / 4, width / 4]);

let backend = tf.backend()

let uv = tf.tensor([0, 0, 1, 0, 2, 0, 3, 0, 4, 0, 5, 0, 6, 0,

0, 1, 1, 1, 2, 1, 3, 1, 4, 1, 5, 1, 6, 1,

0, 2, 1, 2, 2, 2, 3, 2, 4, 2, 5, 2, 6, 2,

0, 3, 1, 3, 2, 3, 3, 3, 4, 3, 5, 3, 6, 3,

0, 4, 1, 4, 2, 4, 3, 4, 4, 4, 5, 4, 6, 4,

0, 5, 1, 5, 2, 5, 3, 5, 4, 5, 5, 5, 6, 5,

0, 6, 1, 6, 2, 6, 3, 6, 4, 6, 5, 6, 6, 6], [ks * ks, 2]).sub(hks)

let update = () => {

let result = null;

let time = new Date()

try {

// if (pred) {

// pred.dispose();

// }

// pred = current;

current2 = video.expandDims().div(255)

let temp = current2;

current2 = current2.resizeBilinear([height / 2, width / 2])

temp.dispose();

temp = current4

current4 = current2.resizeBilinear([height / 4, width / 4])

temp.dispose();

pred2 = video2.expandDims().div(255)

temp = pred2;

pred2 = pred2.resizeBilinear([height / 2, width / 2])

temp.dispose();

temp = pred4;

pred4 = pred2.resizeBilinear([height / 4, width / 4])

temp.dispose();

result = tf.tidy(() => {

let time = new Date()

let x = 0;

let y = 0;

let t;

let result = opticalFlowFind([tf.tensor([0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2]), pred4, current4], { step: step, kernelSize: ks });

let px = result.shape[1]

let py = result.shape[0]

let shift = result.reshape([py * px, ks * ks * 1]).mean(-2);

//tf.node.encodePng(shift.reshape([ks, ks, 1]).sub(0.01).mul(300).toInt()).then((data) => fs.writeFileSync('./resultshift.png', data));

let shiftRes = minIndex(shift.dataSync());

x = Math.floor(shiftRes % ks) - hks;

y = Math.floor(shiftRes / ks) - hks;

result = opticalFlowFind([tf.tensor([x, y, x, y, x, y, x, y], [2, 2, 2]), pred4, current4], { step: step, kernelSize: ks });

result = custBlur(result.reshape([py, px, ks * ks]), 3, 2);

result = result.reshape([result.shape[0], result.shape[1], ks * ks, 1]);

let pos = result.argMin(-2);

let xp = pos.mod(ks).sub(hks).add(x);

let yp = pos.floorDiv(ks).sub(hks).add(y);

let out = tf.concat([xp, yp, tf.zeros(xp.shape)], -1).mul(27).add(127).maximum(0).minimum(255).toInt();

let fld = flowData4.slice([3, 3, 0], [Math.floor((flowData4.shape[0] - ks - hks) / step) * step + hks, Math.floor((flowData4.shape[1] - ks - hks) / step) * step + hks, 3]);

fld = custBlur(fld, ks, step)

fld = custBlur(fld, 3, 2)

tf.node.encodePng(fld.mul(27).mul(0.5).add(127).maximum(0).minimum(255).toInt()).then((data) => fs.writeFileSync('./result1f.png', data));

tf.node.encodePng(out).then((data) => fs.writeFileSync('./result1.png', data));

// 2 step

let back2 = tf.concat([xp, yp], -1)

console.log('22', pred2.slice([0, hks, hks, 0], [1, Math.floor((pred2.shape[1] - ks - hks) / step) * step - hks - hks, Math.floor((pred2.shape[2] - ks - hks) / step) * step - hks - hks, 2]));

result = opticalFlowFind([back2, pred2.slice([0, hks, hks, 0], [1, Math.floor((pred2.shape[1] - ks - hks) / step) * step - 6, Math.floor((pred2.shape[2] - ks - hks) / step) * step - 6, 2]),

current2.slice([0, hks, hks, 0], [1, Math.floor((current2.shape[1] - ks - hks) / step) * step - 6, Math.floor((current2.shape[2] - ks - hks) / step) * step - 6, 2])], { step: step, kernelSize: ks });

px = result.shape[1];

py = result.shape[0];

console.log(11);

result = custBlur(result.reshape([py, px, ks * ks]), 3, 2);

console.log('back2: ', back2, result);

pos = result.argMin(-1)

xp = pos.mod(dk).sub(hks).expandDims(-1);

yp = pos.floorDiv(dk).sub(hks).expandDims(-1);

out = tf.concat([xp, yp], -1)//.add(back2.resizeBilinear(xp.shape.slice(0, 2)))

.concat([tf.zeros(xp.shape)], -1).mul(27).add(127).maximum(0).minimum(255).toInt();

tf.node.encodePng(out).then((data) => fs.writeFileSync('./result2.png', data))

fld = flowData2.slice([hks, hks, 0], [Math.floor((flowData2.shape[0] - ks - hks) / step) * step + hks, Math.floor((flowData2.shape[1] - ks - hks) / step) * step + hks, 3]);

fld = fld.slice([hks, hks, 0], [Math.floor((fld.shape[0] - ks - hks) / step) * step + hks, Math.floor((fld.shape[1] - ks - hks) / step) * step + hks, 3]);

fld = custBlur(fld, ks, step)

fld = custBlur(fld, 3, 2)

tf.node.encodePng(fld.mul(27).mul(0.5).add(127).maximum(0).minimum(255).toInt()).then((data) => fs.writeFileSync('./result2f.png', data));

// 3 step

let current = video.expandDims().div(255)

let pred = video2.expandDims().div(255)

let back3 = tf.concat([xp, yp], -1)

result = opticalFlowFind([back3, pred.slice([0, hks, hks, 0], [1, Math.floor((pred.shape[1] - ks - hks) / step) * step - 6, Math.floor((pred.shape[2] - ks - hks) / step) * step - 6, 2]),

current.slice([0, hks, hks, 0], [1, Math.floor((current.shape[1] - ks - hks) / step) * step - 6, Math.floor((current.shape[2] - ks - hks) / step) * step - 6, 2])], { step: step, kernelSize: ks });

px = result.shape[1];

py = result.shape[0];

console.log(11);

result = custBlur(result.reshape([py, px, ks * ks]), 3, 2);

pos = result.reshape([result.shape[0], result.shape[1], ks * ks]).argMin(-1)

xp = pos.mod(dk).sub(hks).expandDims(-1);

yp = pos.floorDiv(dk).sub(hks).expandDims(-1);

out = tf.concat([xp, yp], -1)//.add(back3.resizeBilinear(xp.shape.slice(0, 2)))

.concat([tf.zeros(xp.shape)], -1).mul(27).add(127).maximum(0).minimum(255).toInt();

tf.node.encodePng(out).then((data) => fs.writeFileSync('./result3.png', data))

fld = flowData.slice([3, 3, 0], [Math.floor((flowData.shape[0] - ks - hks) / step) * step + hks, Math.floor((flowData.shape[1] - ks - hks) / step) * step + hks, 3]);

fld = custBlur(fld, ks, step)

fld = custBlur(fld, 3, 2)

tf.node.encodePng(fld.mul(27).add(127).maximum(0).minimum(255).toInt()).then((data) => fs.writeFileSync('./result3f.png', data));

//tf.browser.toPixels(out, canvas2)

//console.log('out: ', out);

console.log('time', new Date() - time)

return out;

let res = t//modelOpticalFlow.predict(t)

let sum = res.reshape([res.shape[1], res.shape[2], dk * dk, 1]).relu().pow(2).sum(-2)

let p = res.reshape([res.shape[1], res.shape[2], dk, dk, 1])

p = p.concat([p], -1).pow(2).mul(uv).sum(-2).sum(-2)

sum = sum.concat([sum], -1)

let result2 = tf.concat([p.div(sum).add([y, x]), tf.zeros(p.shape)], -1).mul(3)

return result2.mul(9).add(127).maximum(0).minimum(255).toInt();

})

} catch (err) {

console.log('err: ', err);

log(err.message)

log(err.stack)

}

// tf.browser.toPixels(result, canvas).then(() => {

// ctime = ctime * 0.9 + (new Date() - time) * 0.1

// console.log('time', ctime)

// //log('req3')

// result.dispose();

// requestAnimationFrame(update)

// }).catch(err => {

// console.log('err: ', err);

// log(err.message)

// log(err.stack)

// })

}

update();

//requestAnimationFrame(update)

} catch (err) {

console.log('err: ', err);

log(err.message)

log(err.stack)

}

run()

NormalMapTFJS/OpticalFlowNode.js at main · Graphnull/NormalMapTFJS · GitHub

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