import { pipeline, env } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.10.1'; // 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...'; // To-Do #1 pipeline API를 사용하여 detr-resnet-50 object detection 모델의 instance를 detector라는 이름을 붙여 생성하십시오. // DETR 모델 참고 문서 https://huggingface.co/facebook/detr-resnet-50 const detector = await pipeline ("objectDetection", "detr-resnet-50"); 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...'; // To-Do #2 객체 탐지를 위한 오브젝트에 threshold를 0.5, percentage를 true로 지정하고 그 결과를 output에 저장하십시오 // threshold 값을 지정하고 쉼표를 붙이시오 // percentage 지정 const output = await detector(img.src, { threshold: 0.5, percentage: true, }; status.textContent = ''; 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 label const labelElement = document.createElement('span'); labelElement.textContent = label; labelElement.className = 'bounding-box-label'; labelElement.style.backgroundColor = color; boxElement.appendChild(labelElement); imageContainer.appendChild(boxElement); }