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| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <title>Image Classification - Hugging Face Transformers.js</title> | |
| <script type="module"> | |
| // 허깅페이스의 pipeline 모듈을 import하십시오. | |
| // To-Do: ??? | |
| // 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"> | |
| <!-- Page Header --> | |
| <div class="header"> | |
| <div class="header-main-text"> | |
| <h1>Hugging Face Transformers.js</h1> | |
| </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>Mobilevit 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() { | |
| // pipeline 함수를 이용하여 Xenova/mobilevit-small 모델의 인스턴스를 생성하여 이를 classifier에 저정하십시오. 인스턴스 생성 시 quantized 파라미터의 값을 false로 설정하십시오. | |
| // To-Do: ??? | |
| } | |
| 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() { | |
| // HTML DOM의 element Id가 imageClassificationLocalFile인 element의 값을 fileInput으로 저장하십시오. | |
| // To-Do: const fileInput = ??? | |
| const file = fileInput.files[0]; | |
| if (!file) { | |
| alert('Please select an image file first.'); | |
| return; | |
| } | |
| const url = URL.createObjectURL(file); | |
| // classifier에 url을 입력하여 출력된 결과를 result에 저장하십시오. | |
| // To-Do: ??? | |
| document.getElementById("outputAreaLocal").innerText = JSON.stringify(result, null, 2); | |
| } | |
| async function classifyTopImage() { | |
| const textFieldValue = document.getElementById("imageClassificationTopURLText").value.trim(); | |
| // classifier에 textFieldValue를 입력 변수로, topk 파라미터 값을 3으로 설정하여 classifer를 수행하고 그 결과를 result에 저장하십시오. | |
| // To-Do: ??? | |
| document.getElementById("outputAreaTop").innerText = JSON.stringify(result, null, 2); | |
| } | |
| // Initialize the model after the DOM is completely loaded | |
| window.addEventListener("DOMContentLoaded", initializeModel); | |
| </script> | |
| </body> | |
| </html> |