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import { env, AutoModel, AutoProcessor, RawImage } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.17.1'; | |
// 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'; | |
// Create a new object detection pipeline | |
status.textContent = 'Loading model...'; | |
const model_id = 'onnx-community/yolov10n'; | |
const model = await AutoModel.from_pretrained(model_id, { | |
quantized: false, // (Optional) Use unquantized version. | |
}); | |
const processor = await AutoProcessor.from_pretrained(model_id); | |
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(img) { | |
imageContainer.innerHTML = ''; | |
imageContainer.style.backgroundImage = `url(${img})`; | |
status.textContent = 'Analysing...'; | |
const image = await RawImage.read(img); | |
const { pixel_values } = await processor(image); | |
const { output0 } = await model({ images: pixel_values }); | |
const predictions = output0.tolist()[0]; | |
const threshold = 0.5; | |
for (const [xmin, ymin, xmax, ymax, score, id] of predictions) { | |
if (score < threshold) continue; | |
renderBox(xmin, ymin, xmax, ymax, score, model.config.id2label[id]); | |
} | |
status.textContent = ''; | |
} | |
// Render a bounding box and label on the image | |
function renderBox(xmin, ymin, xmax, ymax, score, label) { | |
// Generate a random color for the box | |
const color = '#' + Math.floor(Math.random() * 0xFFFFFF).toString(16).padStart(6, 0); | |
// Draw the box | |
const boxElement = document.createElement('div'); | |
boxElement.className = 'bounding-box'; | |
Object.assign(boxElement.style, { | |
borderColor: color, | |
left: 100 * xmin + '%', | |
top: 100 * ymin + '%', | |
width: 100 * (xmax - xmin) + '%', | |
height: 100 * (ymax - ymin) + '%', | |
}) | |
// Draw label | |
const labelElement = document.createElement('span'); | |
labelElement.textContent = `${label} (${(score * 100).toFixed(2)}%)`; | |
labelElement.className = 'bounding-box-label'; | |
labelElement.style.backgroundColor = color; | |
boxElement.appendChild(labelElement); | |
imageContainer.appendChild(boxElement); | |
} | |