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import { env, AutoProcessor, AutoModel, RawImage } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.17.2';
// Since we will download the model from the Hugging Face Hub, we can skip the local model check
env.allowLocalModels = false;
// Reference the elements that we will need
const status = document.getElementById('status');
const fileUpload = document.getElementById('upload');
const imageContainer = document.getElementById('container');
const example = document.getElementById('example');
const EXAMPLE_URL = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/city-streets.jpg';
const THRESHOLD = 0.2;
// Create a new object detection pipeline
status.textContent = 'Loading model...';
const model_id = 'onnx-community/yolov10s';
const processor = await AutoProcessor.from_pretrained(model_id);
const model = await AutoModel.from_pretrained(model_id, { quantized: false });
status.textContent = 'Ready';
example.addEventListener('click', (e) => {
e.preventDefault();
detect(EXAMPLE_URL);
});
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 = e2 => detect(e2.target.result);
reader.readAsDataURL(file);
});
// Detect objects in the image
async function detect(url) {
// Update UI
imageContainer.innerHTML = '';
// Read image
const image = await RawImage.fromURL(url);
// Set container width and height depending on the image aspect ratio
const ar = image.width / image.height;
const [cw, ch] = (ar > 1) ? [640, 640 / ar] : [640 * ar, 640];
imageContainer.style.width = `${cw}px`;
imageContainer.style.height = `${ch}px`;
imageContainer.style.backgroundImage = `url(${url})`;
status.textContent = 'Analysing...';
// Preprocess image
const inputs = await processor(image);
// Predict bounding boxes
const { output0 } = await model({ images: inputs.pixel_values });
status.textContent = '';
const sizes = inputs.reshaped_input_sizes[0].reverse();
output0.tolist()[0].forEach(x => renderBox(x, sizes));
}
// Render a bounding box and label on the image
function renderBox([xmin, ymin, xmax, ymax, score, id], [w, h]) {
if (score < THRESHOLD) return; // Skip boxes with low confidence
// Generate a random color for the box
const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0);
// Draw the box
const boxElement = document.createElement('div');
boxElement.className = 'bounding-box';
Object.assign(boxElement.style, {
borderColor: color,
left: 100 * xmin / w + '%',
top: 100 * ymin / h + '%',
width: 100 * (xmax - xmin) / w + '%',
height: 100 * (ymax - ymin) / h + '%',
})
// Draw label
const labelElement = document.createElement('span');
labelElement.textContent = `${model.config.id2label[id]} (${score.toFixed(2)})`;
labelElement.className = 'bounding-box-label';
labelElement.style.backgroundColor = color;
boxElement.appendChild(labelElement);
imageContainer.appendChild(boxElement);
}
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