hancav's picture
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import { pipeline, env } from "https://cdn.jsdelivr.net/npm/@xenova/transformers@2.6.0";
env.allowLocalModels = false;
const fileUpload = document.getElementById("file-upload");
const imageContainer = document.getElementById("image-container");
const status = document.getElementById("status");
status.textContent = "Loading model...";
const detector = await pipeline("object-detection", "Xenova/detr-resnet-50");
status.textContent = "Ready";
fileUpload.addEventListener("change", function (e) {
const file = e.target.files[0];
if (!file) {
return;
}
const reader = new FileReader();
// Set up a callback when the file is loaded
reader.onload = function (e2) {
imageContainer.innerHTML = "";
const image = document.createElement("img");
image.src = e2.target.result;
imageContainer.appendChild(image);
detect(image); // Uncomment this line to run the model
};
reader.readAsDataURL(file);
});
async function detect(img) {
status.textContent = "Analysing...";
const output = await detector(img.src, {
threshold: 0.5,
percentage: true,
});
status.textContent = "";
console.log("output", output);
// ...
output.forEach(renderBox);
}
// Render a bounding box and label on the image
function renderBox({ box, label }) {
const { xmax, xmin, ymax, ymin } = box;
// Generate a random color for the box
const color = "#" + Math.floor(Math.random() * 0xffffff).toString(16).padStart(6, 0);
// Draw the box
const boxElement = document.createElement("div");
boxElement.className = "bounding-box";
Object.assign(boxElement.style, {
borderColor: color,
left: 100 * xmin + "%",
top: 100 * ymin + "%",
width: 100 * (xmax - xmin) + "%",
height: 100 * (ymax - ymin) + "%",
});
// Draw the label
const labelElement = document.createElement("span");
labelElement.textContent = label;
labelElement.className = "bounding-box-label";
labelElement.style.backgroundColor = color;
boxElement.appendChild(labelElement);
imageContainer.appendChild(boxElement);
}