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RGB-Depthify.html
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RGB-Depthify.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<title>RGB-Depthify</title>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@1.7.4/dist/tf.min.js"></script>
<style>
body {
margin: 0;
}
div {
text-align: center;
width: 100vw;
height: 100vh;
font-family: Sans-Serif;
}
.highlight {
background-color: #51cb20;
}
</style>
</head>
<body>
<div id="dropArea">
<input type="file" id="imageLoader" accept="image/*" /><br />
<p id="welcome">Choose (or drag&drop) a photo you wish to RGB-Depthify.</p>
<p id="running" style="display: none;">Running. Please wait.</p>
<canvas id="resultCanvas" style="max-width: 50%; max-height: 50%;"></canvas>
</div>
<script>
let dropArea = document.getElementById("dropArea");
dropArea.addEventListener("drop", runInference, false);
document.getElementById("imageLoader").addEventListener("change", switchDisplay, false);
["dragenter", "dragover", "dragleave", "drop"].forEach((eventName) => {
dropArea.addEventListener(eventName, preventDefaults, false);
});
["dragenter", "dragover"].forEach((eventName) => {
dropArea.addEventListener(eventName, highlight, false);
});
["dragleave", "drop"].forEach((eventName) => {
dropArea.addEventListener(eventName, unhighlight, false);
});
function preventDefaults(e) {
e.preventDefault();
e.stopPropagation();
}
function highlight(e) {
dropArea.classList.add("highlight");
}
function unhighlight(e) {
dropArea.classList.remove("highlight");
}
function switchDisplay(e){
document.getElementById("welcome").style.display = "none";
document.getElementById("running").style.display = "block";
setTimeout(function () {
runInference(e);
}, 350);
}
let dropName;
if (!("createImageBitmap" in window)) {
window.createImageBitmap = async function (data) {
return new Promise((resolve, reject) => {
let dataURL;
const canvas = document.createElement("canvas");
const ctx = canvas.getContext("2d");
canvas.width = data.width;
canvas.height = data.height;
ctx.putImageData(data, 0, 0);
dataURL = canvas.toDataURL();
const img = document.createElement("img");
img.addEventListener("load", function () {
resolve(this);
});
img.src = dataURL;
});
};
}
class Pydnet {
async init(urls) {
const MODEL = "https://raw.githubusercontent.com/FilippoAleotti/demo_live/master/assets/js/pydnet.json";
this.model = await tf.loadGraphModel(MODEL);
this.height = 384;
this.width = 640;
return this;
}
async predict(img) {
const [data, resizeInputData] = tf.tidy(() => {
var raw_input = tf.browser.fromPixels(img);
var upsampledraw_input = tf.image.resizeBilinear(raw_input, [this.height, this.width]);
var preprocessedInput = upsampledraw_input.expandDims();
preprocessedInput = tf.div(preprocessedInput, 255.0);
var result = this.model.predict(preprocessedInput);
result = this.prepareOutput(result, img.width, img.height);
upsampledraw_input = tf.cast(upsampledraw_input, "int32");
const data = result.dataSync();
const resizeInputData = upsampledraw_input.dataSync();
return [data, resizeInputData];
});
await tf.nextFrame();
return [data, resizeInputData];
}
prepareOutput(tensor, width, height) {
return tf.tidy(() => {
tensor = tf.relu(tensor);
tensor = tf.squeeze(tensor);
var min_value = tf.min(tensor);
var max_value = tf.max(tensor);
tensor = tf.div(tf.sub(tensor, min_value), tf.sub(max_value, min_value));
tensor = tf.mul(tensor, 255.0);
tensor = tf.cast(tensor, "int32");
return tensor;
});
}
}
async function runInference(e) {
document.getElementById("welcome").style.display = "none";
document.getElementById("running").style.display = "block";
var fr = new FileReader();
fr.onload = function () {
var img = new Image();
img.onload = function () {
display_result(img);
};
img.src = fr.result;
};
try {
fr.readAsDataURL(e.target.files[0]);
} catch (x) {
dropName = e.dataTransfer.files[0].name;
fr.readAsDataURL(e.dataTransfer.files[0]);
}
}
async function run_inference(img) {
var outputs = await model.predict(img);
return outputs;
}
async function display_result(img) {
var results = await run_inference(img);
var canvas = document.getElementById("resultCanvas");
var ctx = canvas.getContext("2d");
canvas.width = img.width * 2;
canvas.height = img.height;
ctx.drawImage(img, 0, 0);
var buffer = new Uint8ClampedArray(model.width * model.height * 4);
var i = 0;
for (var y = 0; y < model.height; y++) {
for (var x = 0; x < model.width; x++) {
var index = y * model.width + x;
var depth = results[0][index];
buffer[i] = results[0][index];
buffer[i + 1] = results[0][index];
buffer[i + 2] = results[0][index];
buffer[i + 3] = 255.0;
i += 4;
}
}
const imageData = new ImageData(buffer, model.width, model.height);
createImageBitmap(imageData).then((renderer) => ctx.drawImage(renderer, img.width, 0, img.width, img.height));
document.getElementById("welcome").style.display = "block";
document.getElementById("running").style.display = "none";
setTimeout(function () {
try {
var ulName = document.getElementById("imageLoader").files.item(0).name;
var dlName = ulName.substr(0, ulName.lastIndexOf(".")) + "_RGBD.jpg";
} catch (x) {
var dlName = dropName.substr(0, dropName.lastIndexOf(".")) + "_RGBD.jpg";
}
var dlLink = document.createElement("a");
dlLink.download = dlName;
dlLink.href = canvas.toDataURL("image/jpeg");
document.body.appendChild(dlLink);
dlLink.click();
document.body.removeChild(dlLink);
}, 350);
}
async function setupPydnet() {
model = await new Pydnet().init();
}
setupPydnet();
</script>
</body>
</html>