File size: 6,037 Bytes
fea0eb7 70a55a5 4a6e12d fea0eb7 70a55a5 fea0eb7 70a55a5 fea0eb7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Image Classification - Hugging Face Transformers.js</title>
<script type="module">
// Import the library
import { pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]';
// Make it available globally
window.pipeline = pipeline;
</script>
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet">
<link rel="stylesheet" href="css/styles.css">
</head>
<body>
<div class="container-main">
<!-- 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>Image Classification</h4>
</div>
<!-- Actual Content of this page -->
<div id="image-classification-container" class="container mt-4">
<h5>Classify an Image:</h5>
<div class="d-flex align-items-center">
<label for="imageClassificationURLText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter
image URL:</label>
<input type="text" class="form-control flex-grow-1" id="imageClassificationURLText"
value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg"
placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;">
<button id="ClassifyButton" class="btn btn-primary" onclick="classifyImage()">Classify</button>
</div>
<div class="mt-4">
<h4>Output:</h4>
<pre id="outputArea"></pre>
</div>
</div>
<hr> <!-- Line Separator -->
<div id="image-classification-local-container" class="container mt-4">
<h5>Classify a Local Image:</h5>
<div class="d-flex align-items-center">
<label for="imageClassificationLocalFile" class="mb-0 text-nowrap"
style="margin-right: 15px;">Select Local Image:</label>
<input type="file" id="imageClassificationLocalFile" accept="image/*" />
<button id="ClassifyButtonLocal" class="btn btn-primary"
onclick="classifyImageLocal()">Classify</button>
</div>
<div class="mt-4">
<h4>Output:</h4>
<pre id="outputAreaLocal"></pre>
</div>
</div>
<hr> <!-- Line Separator -->
<div id="image-classification-top-container" class="container mt-4">
<h5>Classify an Image and Return Top n Classes:</h5>
<div class="d-flex align-items-center">
<label for="imageClassificationTopURLText" class="mb-0 text-nowrap" style="margin-right: 15px;">Enter
image URL:</label>
<input type="text" class="form-control flex-grow-1" id="imageClassificationTopURLText"
value="https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg"
placeholder="Enter image" style="margin-right: 15px; margin-left: 15px;">
<button id="ClassifyTopButton" class="btn btn-primary" onclick="classifyTopImage()">Classify</button>
</div>
<div class="mt-4">
<h4>Output:</h4>
<pre id="outputAreaTop"></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 classifier;
// Initialize the sentiment analysis model
async function initializeModel() {
// TO-Do: pipeline() 함수를 사용하여 ViT 모델 인스턴스를 classifier라는 이름으로 생성하십시오/
}
async function classifyImage() {
const textFieldValue = document.getElementById("imageClassificationURLText").value.trim();
const result = await classifier(textFieldValue);
document.getElementById("outputArea").innerText = JSON.stringify(result, null, 2);
}
async function classifyImageLocal() {
const fileInput = document.getElementById("imageClassificationLocalFile");
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);
// classifier에 url을 입력하여 출력되는 결과를 result에 저장하십시오.
// 힌트: cont result = ???
// HTML코드 중 element Id가 'outputAreaLocal'인 요소에 resul의 값을 JSON string 형태로 text로 출력하십시오.
// 힌트: document.getElementById와 JSON.stringify 이용
}
async function classifyTopImage() {
// 코드 삭제됨
}
// Initialize the model after the DOM is completely loaded
window.addEventListener("DOMContentLoaded", initializeModel);
</script>
</body>
</html> |