551 lines
17 KiB
HTML
551 lines
17 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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<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>
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<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/*">
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<span class="samples-count" id="class1Count">0 张图片</span>
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<button class="btn btn-primary" onclick="captureFromWebcam(0)">从摄像头采集</button>
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<div class="image-preview" id="class1Preview"></div>
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</div>
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<div class="class-input">
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<h3><span class="class-number">2</span> 第二类</h3>
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<input type="text" id="class2Name" placeholder="输入类别名称（如：狗）" value="类别2">
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<label class="file-label" for="class2Images">
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选择图片
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</label>
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<input type="file" id="class2Images" multiple accept="image/*">
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<span class="samples-count" id="class2Count">0 张图片</span>
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<button class="btn btn-primary" onclick="captureFromWebcam(1)">从摄像头采集</button>
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<div class="image-preview" id="class2Preview"></div>
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</div>
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<div class="class-input">
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<h3><span class="class-number">3</span> 第三类（可选）</h3>
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<input type="text" id="class3Name" placeholder="输入类别名称（可选）" value="">
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<label class="file-label" for="class3Images">
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选择图片
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</label>
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<input type="file" id="class3Images" multiple accept="image/*">
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<span class="samples-count" id="class3Count">0 张图片</span>
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<button class="btn btn-primary" onclick="captureFromWebcam(2)">从摄像头采集</button>
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<div class="image-preview" id="class3Preview"></div>
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</div>
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<div class="button-group">
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<button class="btn btn-success" id="addDataBtn">训练KNN模型</button>
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<button class="btn btn-danger" id="clearDataBtn">清空数据</button>
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</div>
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<div id="dataStatus"></div>
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</div>
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<!-- KNN模型信息卡片 -->
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<div class="card">
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<h2>🎯 KNN 模型设置</h2>
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<div class="k-selector">
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<label>
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K值（最近邻数量）
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<span class="k-value-display" id="kValueDisplay">3</span>
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</label>
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<input type="range" id="kValue" min="1" max="20" value="3"
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oninput="document.getElementById('kValueDisplay').textContent = this.value">
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<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>
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</div>
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<div class="k-selector">
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<label>
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滤波器系数 (α)
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<span class="k-value-display" id="filterAlphaDisplay">0.3</span>
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</label>
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<input type="range" id="filterAlpha" min="0.05" max="1.0" step="0.05" value="0.3"
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oninput="document.getElementById('filterAlphaDisplay').textContent = this.value">
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<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>
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</div>
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<div class="top-tags" id="topTags">
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<h3 style="margin-bottom: 10px;">📊 特征标签提取预览</h3>
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<div id="tagsList">等待数据...</div>
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</div>
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<div class="model-info">
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<h3 style="margin-bottom: 10px;">ℹ️ 模型信息</h3>
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<div class="info-item">
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<span class="info-label">预训练模型:</span>
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<span class="info-value">MobileNet v2</span>
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</div>
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<div class="info-item">
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<span class="info-label">特征维度:</span>
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<span class="info-value">1000个标签</span>
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</div>
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<div class="info-item">
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<span class="info-label">分类器类型:</span>
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<span class="info-value">K-最近邻 (KNN)</span>
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</div>
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<div class="info-item">
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<span class="info-label">总样本数:</span>
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<span class="info-value" id="totalSamples">0</span>
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</div>
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</div>
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</div>
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</div>
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<!-- 预测卡片 -->
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<div class="card full-width">
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<h2>📹 实时预测</h2>
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<div class="button-group">
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<button class="btn btn-primary" id="startWebcamBtn">启动摄像头</button>
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<button class="btn btn-danger" id="stopWebcamBtn" disabled>停止摄像头</button>
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<button class="btn btn-success" id="saveModelBtn">保存模型</button>
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<button class="btn btn-primary" id="loadModelBtn">加载模型</button>
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</div>
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<div id="webcam-container">
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<video id="webcam" autoplay playsinline muted></video>
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</div>
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<div class="prediction-results" id="predictionResults">
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<h3>预测结果</h3>
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<div id="predictions">等待预测...</div>
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</div>
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<div id="predictionStatus"></div>
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</div>
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</div>
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<script src="knn-classifier.js"></script>
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</body>
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</html> |