Spaces:
Running
Running
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title>Object Detection - Hugging Face Transformers.js</title> | |
| <script type="module"> | |
| // Import the library | |
| import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/transformers@2.5.4'; | |
| // Make it available globally | |
| window.pipeline = pipeline; | |
| </script> | |
| <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.0/dist/css/bootstrap.min.css" rel="stylesheet"> | |
| <link rel="stylesheet" href="css/styles.css"> | |
| </head> | |
| <body> | |
| <div class="container-main"> | |
| <!-- Page Header --> | |
| <div class="header"> | |
| <div class="header-logo"> | |
| <img src="images/logo.png" alt="logo"> | |
| </div> | |
| <div class="header-main-text"> | |
| <h1>Hugging Face Transformers.js</h1> | |
| </div> | |
| <div class="header-sub-text"> | |
| <h3>Free AI Models for JavaScript Web Development</h3> | |
| </div> | |
| </div> | |
| <hr> <!-- Separator --> | |
| <!-- Back to Home button --> | |
| <div class="row mt-5"> | |
| <div class="col-md-12 text-center"> | |
| <a href="index.html" class="btn btn-outline-secondary" | |
| style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a> | |
| </div> | |
| </div> | |
| <!-- Content --> | |
| <div class="container mt-5"> | |
| <!-- Centered Titles --> | |
| <div class="text-center"> | |
| <h2>Computer Vision</h2> | |
| <h4>Object Detection</h4> | |
| </div> | |
| <!-- Actual Content of this page --> | |
| <div id="object-detection-container" class="container mt-4"> | |
| <h5>Run Object Detection with facebook/detr-resnet-50:</h5> | |
| <div class="d-flex align-items-center"> | |
| <label for="objectDetectionURLText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter | |
| image URL:</label> | |
| <input type="text" class="form-control flex-grow-1" id="objectDetectionURLText" | |
| value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg" | |
| placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;"> | |
| <button id="DetectButton" class="btn btn-primary" onclick="detectImage()">Detect</button> | |
| </div> | |
| <div class="mt-4"> | |
| <h4>Output:</h4> | |
| <pre id="outputArea"></pre> | |
| </div> | |
| </div> | |
| <hr> <!-- Line Separator --> | |
| <div id="object-detection-local-container" class="container mt-4"> | |
| <h5>Detect a Local Image:</h5> | |
| <div class="d-flex align-items-center"> | |
| <label for="objectDetectionLocalFile" class="mb-0 text-nowrap" | |
| style="margin-right: 15px;">Select Local Image:</label> | |
| <input type="file" id="objectDetectionLocalFile" accept="image/*" /> | |
| <button id="DetectButtonLocal" class="btn btn-primary" | |
| onclick="detectImageLocal()">Detect</button> | |
| </div> | |
| <div class="mt-4"> | |
| <h4>Output:</h4> | |
| <pre id="outputAreaLocal"></pre> | |
| </div> | |
| </div> | |
| <!-- Back to Home button --> | |
| <div class="row mt-5"> | |
| <div class="col-md-12 text-center"> | |
| <a href="index.html" class="btn btn-outline-secondary" | |
| style="color: #3c650b; border-color: #3c650b;">Back to Main Page</a> | |
| </div> | |
| </div> | |
| </div> | |
| </div> | |
| <script> | |
| let detector; | |
| // Initialize the sentiment analysis model | |
| async function initializeModel() { | |
| detector = await pipeline('object-detection', 'Xenova/detr-resnet-50'); | |
| } | |
| async function detectImage() { | |
| const textFieldValue = document.getElementById("objectDetectionURLText").value.trim(); | |
| const result = await detector(textFieldValue, { threshold: 0.9 }); | |
| document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2); | |
| } | |
| async function detectImageLocal() { | |
| const fileInput = document.getElementById("objectDetectionLocalFile"); | |
| const file = fileInput.files[0]; | |
| if (!file) { | |
| alert('Please select an image file first.'); | |
| return; | |
| } | |
| // Create a Blob URL from the file | |
| const url = URL.createObjectURL(file); | |
| const result = await detector(url, { threshold: 0.9 }); | |
| document.getElementById("outputAreaLocal").innerText = JSON.stringify(result, null, 2); | |
| } | |
| // Initialize the model after the DOM is completely loaded | |
| window.addEventListener("DOMContentLoaded", initializeModel); | |
| </script> | |
| </body> | |
| </html> |