TransformersJsBase/object-detection.html at main · hielenagrin/TransformersJsBase · GitHub

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<!DOCTYPE html>
<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>