685 lines
24 KiB
HTML
685 lines
24 KiB
HTML
<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>KNN 图像分类器 - TensorFlow.js</title>
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<!-- !!!!!! 核心劫持代码：确保在任何 TF.js 库之前加载 !!!!!! -->
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<script>
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(function() {
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// 定义你的镜像服务器的公共前缀，用于存放 MobileNet 模型文件
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// 根据你提供的最新路径进行更新。
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// 确保这些文件就命名为 model.json, group1-shardXof4.bin，并直接在此目录下
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const MOBILENET_MIRROR_BASE_URL = 'https://goood-space-assets.oss-cn-beijing.aliyuncs.com/public/fetch/mobilenet/';
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// 定义需要被劫持的原始 URL 的域名模式
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// 我们观察到最终请求来自 storage.googleapis.com
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const INTERCEPT_DOMAINS = [
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'https://storage.googleapis.com/tfjs-models/', // tfjs 官方模型常用的 CDN
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'https://storage.googleapis.com/', // 更宽泛的匹配 Google Storage
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'https://tfhub.dev/', // 如果 MobileNet 也会通过 tfhub.dev 间接加载
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];
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// 备份原始的 fetch 函数
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const originalFetch = window.fetch;
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window.fetch = function(input, init) {
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let url = input;
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if (input instanceof Request) {
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url = input.url;
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}
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let newUrl = url;
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let isIntercepted = false;
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// 检查 URL 是否以我们关注的域名开头
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for (const domain of INTERCEPT_DOMAINS) {
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if (url.startsWith(domain)) {
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// 尝试从 URL 中提取文件名 (不包含查询参数)
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// 匹配 model.json 或 group1-shardXofY.bin
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const fileNameMatch = url.match(/(model\.json|group1-shard\dof\d\.bin)/);
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if (fileNameMatch) {
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const fileName = fileNameMatch[0]; // 获取匹配到的文件名
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newUrl = MOBILENET_MIRROR_BASE_URL + fileName; // 拼接新的镜像 URL
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isIntercepted = true;
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break; // 找到匹配的域名和文件，停止循环
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}
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}
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}
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if (isIntercepted) {
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console.warn(`[TFJS Fetch Intercepted] Original: ${url}`);
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console.warn(`[TFJS Fetch Intercepted] Redirecting to: ${newUrl}`);
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// 如果 input 是 Request 对象，需要创建新的 Request 对象来修改 URL
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if (input instanceof Request) {
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try {
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input = new Request(newUrl, {
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method: input.method,
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headers: input.headers,
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body: input.body,
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referrer: input.referrer,
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referrerPolicy: input.referrerPolicy,
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mode: 'cors', // 总是使用 CORS 模式
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credentials: input.credentials,
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cache: 'default',
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redirect: 'follow',
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integrity: undefined, // 移除 integrity 属性以避免校验失败
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signal: input.signal,
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});
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} catch (e) {
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console.error(`[TFJS Fetch Intercepted Error] Failed to create new Request object: ${e.message}. Falling back to URL string.`, input);
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// 如果创建 Request 对象失败，回退到直接使用 URL 字符串
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input = newUrl;
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}
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} else {
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// 如果 input 是 URL 字符串，直接替换
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input = newUrl;
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}
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}
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return originalFetch(input, init).catch(error => {
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console.error(`[TFJS Fetch Intercepted Error] Failed to load ${url} (redirected to ${newUrl || url || input}):`, error);
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throw error;
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});
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};
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// -------------------- 劫持 XMLHttpRequest API (备用安全网) --------------------
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const originalXHR = window.XMLHttpRequest;
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window.XMLHttpRequest = function() {
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const xhr = new originalXHR();
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const originalOpen = xhr.open;
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xhr.open = function(method, url, async = true, user = null, password = null) {
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let newUrl = url;
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let isIntercepted = false;
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for (const domain of INTERCEPT_DOMAINS) {
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if (url.startsWith(domain)) {
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const fileNameMatch = url.match(/(model\.json|group1-shard\dof\d\.bin)/);
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if (fileNameMatch) {
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const fileName = fileNameMatch[0];
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newUrl = MOBILENET_MIRROR_BASE_URL + fileName;
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isIntercepted = true;
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break;
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}
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}
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}
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if (isIntercepted) {
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console.warn(`[TFJS XHR Intercepted] Original: ${url}`);
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console.warn(`[TFJS XHR Intercepted] Redirecting to: ${newUrl}`);
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url = newUrl; // 修改传入 open 的 URL
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}
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// 调用原始的 open 方法
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return originalOpen.apply(this, arguments);
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};
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// 将原始 XMLHttpRequest 的所有静态属性和方法复制到劫持后的 XMLHttpRequest 构造函数
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// 这样像 XHR.UNSENT 等常量仍然可用
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for (const key in originalXHR) {
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if (typeof originalXHR[key] !== 'function' && originalXHR.hasOwnProperty(key)) {
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window.XMLHttpRequest[key] = originalXHR[key];
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}
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}
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return xhr;
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};
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})();
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</script>
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<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@latest"></script>
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<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/mobilenet@latest"></script>
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<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/knn-classifier@latest"></script>
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<style>
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* {
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}
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body {
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font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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min-height: 100vh;
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padding: 20px;
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}
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.main-container {
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max-width: 1400px;
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margin: 0 auto;
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}
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h1 {
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color: white;
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text-align: center;
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margin-bottom: 30px;
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font-size: 2.5em;
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text-shadow: 2px 2px 4px rgba(0,0,0,0.2);
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}
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.grid-container {
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display: grid;
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grid-template-columns: 1fr 1fr;
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gap: 20px;
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margin-bottom: 20px;
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}
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@media (max-width: 768px) {
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.grid-container {
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grid-template-columns: 1fr;
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}
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}
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.card {
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background: white;
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border-radius: 15px;
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padding: 25px;
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box-shadow: 0 10px 30px rgba(0,0,0,0.2);
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}
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.card h2 {
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color: #333;
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margin-bottom: 20px;
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border-bottom: 2px solid #667eea;
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padding-bottom: 10px;
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}
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.class-input {
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margin-bottom: 20px;
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padding: 15px;
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background: #f8f9fa;
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border-radius: 10px;
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}
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.class-input h3 {
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color: #555;
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margin-bottom: 10px;
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display: flex;
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align-items: center;
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gap: 10px;
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}
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.class-number {
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background: #667eea;
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color: white;
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width: 25px;
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height: 25px;
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border-radius: 50%;
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display: inline-flex;
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align-items: center;
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justify-content: center;
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font-size: 14px;
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}
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input[type="text"] {
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width: 100%;
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padding: 10px;
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border: 2px solid #e0e0e0;
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border-radius: 5px;
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margin-bottom: 10px;
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font-size: 16px;
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transition: border-color 0.3s;
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}
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input[type="text"]:focus {
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outline: none;
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border-color: #667eea;
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}
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input[type="file"] {
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display: none;
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}
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.file-label {
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display: inline-block;
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padding: 10px 20px;
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background: #667eea;
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color: white;
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border-radius: 5px;
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cursor: pointer;
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transition: background 0.3s;
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margin-right: 10px;
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}
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.file-label:hover {
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background: #5a67d8;
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}
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.btn {
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padding: 12px 30px;
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border: none;
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border-radius: 5px;
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font-size: 16px;
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cursor: pointer;
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transition: all 0.3s;
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margin: 5px;
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}
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.btn-primary {
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background: #667eea;
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color: white;
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}
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.btn-primary:hover {
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background: #5a67d8;
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transform: translateY(-2px);
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box-shadow: 0 5px 15px rgba(102, 126, 234, 0.4);
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}
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.btn-success {
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background: #48bb78;
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color: white;
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}
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.btn-success:hover {
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background: #38a169;
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}
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.btn-danger {
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background: #f56565;
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color: white;
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}
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.btn-danger:hover {
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background: #e53e3e;
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}
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.btn:disabled {
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background: #cbd5e0;
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cursor: not-allowed;
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transform: none;
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}
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#webcam-container {
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position: relative;
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width: 100%;
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max-width: 640px;
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margin: 20px auto;
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}
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#webcam {
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width: 100%;
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border-radius: 10px;
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background: #000;
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}
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.samples-count {
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display: inline-block;
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background: #edf2f7;
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padding: 2px 8px;
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border-radius: 10px;
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font-size: 12px;
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color: #4a5568;
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margin-left: 5px;
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}
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.image-preview {
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display: flex;
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flex-wrap: wrap;
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gap: 10px;
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margin-top: 10px;
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max-height: 150px;
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overflow-y: auto;
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}
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.preview-img {
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width: 60px;
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height: 60px;
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object-fit: cover;
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border-radius: 5px;
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border: 2px solid #e0e0e0;
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}
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.status-message {
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padding: 15px;
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border-radius: 5px;
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margin: 10px 0;
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text-align: center;
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font-weight: 500;
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}
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.status-success {
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background: #c6f6d5;
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color: #22543d;
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border: 1px solid #9ae6b4;
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}
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.status-error {
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background: #fed7d7;
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color: #742a2a;
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border: 1px solid #fc8181;
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}
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.status-info {
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background: #bee3f8;
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color: #2c5282;
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border: 1px solid #90cdf4;
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}
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.button-group {
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display: flex;
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gap: 10px;
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margin: 20px 0;
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flex-wrap: wrap;
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}
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.full-width {
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grid-column: 1 / -1;
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}
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.prediction-results {
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margin-top: 20px;
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padding: 20px;
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background: #f7fafc;
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border-radius: 10px;
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}
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.prediction-item {
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padding: 15px;
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margin: 10px 0;
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background: white;
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border-radius: 8px;
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border-left: 4px solid #667eea;
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box-shadow: 0 2px 4px rgba(0,0,0,0.05);
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}
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.prediction-header {
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display: flex;
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justify-content: space-between;
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align-items: center;
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margin-bottom: 8px;
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}
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.prediction-label {
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font-weight: 600;
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color: #2d3748;
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font-size: 16px;
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}
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.prediction-confidence {
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background: linear-gradient(135deg, #667eea, #764ba2);
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color: white;
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padding: 4px 12px;
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border-radius: 20px;
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font-size: 14px;
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font-weight: 500;
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min-width: 60px;
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text-align: center;
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}
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.confidence-bar-container {
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width: 100%;
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height: 24px;
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background: #e2e8f0;
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border-radius: 12px;
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overflow: hidden;
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position: relative;
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}
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.confidence-bar {
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height: 100%;
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background: linear-gradient(90deg, #667eea, #764ba2);
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border-radius: 12px;
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transition: width 0.4s cubic-bezier(0.4, 0, 0.2, 1);
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position: relative;
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min-width: 0;
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box-shadow: 0 2px 8px rgba(102, 126, 234, 0.3);
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}
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.confidence-bar::after {
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content: '';
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position: absolute;
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top: 0;
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left: 0;
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right: 0;
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bottom: 0;
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background: linear-gradient(90deg, transparent, rgba(255,255,255,0.3), transparent);
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animation: shimmer 2s infinite;
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}
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@keyframes shimmer {
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0% { transform: translateX(-100%); }
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100% { transform: translateX(100%); }
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}
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.confidence-bar.high {
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background: linear-gradient(90deg, #48bb78, #38a169);
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}
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.confidence-bar.medium {
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background: linear-gradient(90deg, #ed8936, #dd6b20);
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}
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.confidence-bar.low {
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background: linear-gradient(90deg, #f56565, #e53e3e);
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}
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.confidence-percentage {
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position: absolute;
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left: 50%;
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top: 50%;
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transform: translate(-50%, -50%);
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color: white;
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font-weight: 600;
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font-size: 12px;
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text-shadow: 0 1px 2px rgba(0,0,0,0.2);
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z-index: 1;
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}
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.top-tags {
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margin: 20px 0;
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padding: 15px;
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background: #edf2fe;
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border-radius: 10px;
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}
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.tag-item {
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display: inline-block;
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background: white;
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padding: 5px 12px;
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margin: 5px;
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border-radius: 15px;
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font-size: 14px;
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border: 1px solid #cbd5e0;
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}
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.tag-weight {
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color: #667eea;
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font-weight: bold;
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margin-left: 5px;
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}
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.k-selector {
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margin: 15px 0;
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padding: 15px;
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background: #f8f9fa;
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border-radius: 8px;
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}
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.k-selector label {
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display: block;
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margin-bottom: 10px;
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color: #555;
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font-weight: 500;
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}
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.k-value-display {
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display: inline-block;
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background: #667eea;
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color: white;
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padding: 2px 8px;
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border-radius: 5px;
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margin-left: 10px;
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}
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input[type="range"] {
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width: 100%;
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margin: 10px 0;
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}
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.model-info {
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margin-top: 20px;
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padding: 15px;
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background: #f0f4f8;
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border-radius: 8px;
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font-size: 14px;
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}
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.info-item {
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display: flex;
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justify-content: space-between;
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margin: 5px 0;
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}
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.info-label {
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color: #718096;
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}
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.info-value {
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color: #2d3748;
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font-weight: 500;
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}
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</style>
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</head>
|
||
<body>
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<div class="main-container">
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<h1>🤖 KNN 图像分类器（基于特征标签）</h1>
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<div class="grid-container">
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<!-- 数据采集卡片 -->
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<div class="card">
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<h2>📸 数据采集</h2>
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<div class="class-input">
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<h3><span class="class-number">1</span> 第一类</h3>
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<input type="text" id="class1Name" placeholder="输入类别名称（如：猫）" value="类别1">
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<label class="file-label" for="class1Images">
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选择图片
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</label>
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<input type="file" id="class1Images" multiple accept="image/*">
|
||
<span class="samples-count" id="class1Count">0 张图片</span>
|
||
<button class="btn btn-primary" onclick="captureFromWebcam(0)">从摄像头采集</button>
|
||
<div class="image-preview" id="class1Preview"></div>
|
||
</div>
|
||
|
||
<div class="class-input">
|
||
<h3><span class="class-number">2</span> 第二类</h3>
|
||
<input type="text" id="class2Name" placeholder="输入类别名称（如：狗）" value="类别2">
|
||
<label class="file-label" for="class2Images">
|
||
选择图片
|
||
</label>
|
||
<input type="file" id="class2Images" multiple accept="image/*">
|
||
<span class="samples-count" id="class2Count">0 张图片</span>
|
||
<button class="btn btn-primary" onclick="captureFromWebcam(1)">从摄像头采集</button>
|
||
<div class="image-preview" id="class2Preview"></div>
|
||
</div>
|
||
|
||
<div class="class-input">
|
||
<h3><span class="class-number">3</span> 第三类（可选）</h3>
|
||
<input type="text" id="class3Name" placeholder="输入类别名称（可选）" value="类别3">
|
||
<label class="file-label" for="class3Images">
|
||
选择图片
|
||
</label>
|
||
<input type="file" id="class3Images" multiple accept="image/*">
|
||
<span class="samples-count" id="class3Count">0 张图片</span>
|
||
<button class="btn btn-primary" onclick="captureFromWebcam(2)">从摄像头采集</button>
|
||
<div class="image-preview" id="class3Preview"></div>
|
||
</div>
|
||
|
||
<div class="button-group">
|
||
<button class="btn btn-success" id="addDataBtn">训练KNN模型</button>
|
||
<button class="btn btn-danger" id="clearDataBtn">清空数据</button>
|
||
</div>
|
||
|
||
<div id="dataStatus"></div>
|
||
</div>
|
||
|
||
<!-- KNN模型信息卡片 -->
|
||
<div class="card">
|
||
<h2>🎯 KNN 模型设置</h2>
|
||
|
||
<div class="k-selector">
|
||
<label>
|
||
K值（最近邻数量）
|
||
<span class="k-value-display" id="kValueDisplay">3</span>
|
||
</label>
|
||
<input type="range" id="kValue" min="1" max="20" value="3"
|
||
oninput="document.getElementById('kValueDisplay').textContent = this.value">
|
||
<small style="color: #718096;">K值越大，预测越保守；K值越小，对局部特征越敏感</small>
|
||
</div>
|
||
|
||
<div class="k-selector">
|
||
<label>
|
||
滤波器系数 (α)
|
||
<span class="k-value-display" id="filterAlphaDisplay">0.3</span>
|
||
</label>
|
||
<input type="range" id="filterAlpha" min="0.05" max="1.0" step="0.05" value="0.3"
|
||
oninput="document.getElementById('filterAlphaDisplay').textContent = this.value">
|
||
<small style="color: #718096;">低通滤波器系数：值越小输出越平滑(0.1-0.3推荐)，值越大响应越快</small>
|
||
</div>
|
||
|
||
<div class="k-selector">
|
||
<label>
|
||
距离阈值 (Distance Threshold)
|
||
<span class="k-value-display" id="distanceThresholdDisplay">0.5</span>
|
||
</label>
|
||
<input type="range" id="distanceThreshold" min="0.1" max="2.0" step="0.05" value="0.5"
|
||
oninput="document.getElementById('distanceThresholdDisplay').textContent = this.value">
|
||
<small style="color: #718096;">距离阈值：样本与训练数据的最大距离，超过此值判定为"未知/背景"（单品类检测关键参数）</small>
|
||
</div>
|
||
|
||
<div class="top-tags" id="topTags">
|
||
<h3 style="margin-bottom: 10px;">📊 特征标签提取预览</h3>
|
||
<div id="tagsList">等待数据...</div>
|
||
</div>
|
||
|
||
<div class="model-info">
|
||
<h3 style="margin-bottom: 10px;">ℹ️ 模型信息</h3>
|
||
<div class="info-item">
|
||
<span class="info-label">预训练模型:</span>
|
||
<span class="info-value">MobileNet v2</span>
|
||
</div>
|
||
<div class="info-item">
|
||
<span class="info-label">特征维度:</span>
|
||
<span class="info-value">1280维嵌入向量</span>
|
||
</div>
|
||
<div class="info-item">
|
||
<span class="info-label">分类器类型:</span>
|
||
<span class="info-value">K-最近邻 (KNN)</span>
|
||
</div>
|
||
<div class="info-item">
|
||
<span class="info-label">总样本数:</span>
|
||
<span class="info-value" id="totalSamples">0</span>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
</div>
|
||
|
||
<!-- 预测卡片 -->
|
||
<div class="card full-width">
|
||
<h2>📹 实时预测</h2>
|
||
|
||
<div class="button-group">
|
||
<button class="btn btn-primary" id="startWebcamBtn">启动摄像头</button>
|
||
<button class="btn btn-danger" id="stopWebcamBtn" disabled>停止摄像头</button>
|
||
<button class="btn btn-success" id="saveModelBtn">保存模型</button>
|
||
<button class="btn btn-primary" id="loadModelBtn">加载模型</button>
|
||
</div>
|
||
|
||
<div id="webcam-container">
|
||
<video id="webcam" autoplay playsinline muted></video>
|
||
</div>
|
||
|
||
<div class="prediction-results" id="predictionResults">
|
||
<h3>预测结果</h3>
|
||
<div id="predictions">等待预测...</div>
|
||
</div>
|
||
|
||
<div id="predictionStatus"></div>
|
||
</div>
|
||
</div>
|
||
|
||
<script src="knn-classifier.js"></script>
|
||
</body>
|
||
</html> |