Spaces:
Running
Running
| import { env, AutoProcessor, AutoModel, RawImage } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.15.1'; | |
| // 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'; | |
| const THRESHOLD = 0.25; | |
| // Create a new object detection pipeline | |
| status.textContent = 'Loading model...'; | |
| const processor = await AutoProcessor.from_pretrained('Xenova/yolov9-c_all'); | |
| // For this demo, we resize the image so that its shortest edge is 256px | |
| processor.feature_extractor.size = { shortest_edge: 256 } | |
| const model = await AutoModel.from_pretrained('Xenova/yolov9-c_all'); | |
| 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 { outputs } = await model(inputs); | |
| status.textContent = ''; | |
| const sizes = inputs.reshaped_input_sizes[0].reverse(); | |
| outputs.tolist().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]; | |
| labelElement.className = 'bounding-box-label'; | |
| labelElement.style.backgroundColor = color; | |
| boxElement.appendChild(labelElement); | |
| imageContainer.appendChild(boxElement); | |
| } | |