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/yolov10m'; const processor = await AutoProcessor.from_pretrained(model_id); const model = await AutoModel.from_pretrained(model_id, { quantized: true }); 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)})`.replaceAll(' ', '\u00a0'); labelElement.className = 'bounding-box-label'; labelElement.style.backgroundColor = color; boxElement.appendChild(labelElement); imageContainer.appendChild(boxElement); }