435 lines
16 KiB
JavaScript
435 lines
16 KiB
JavaScript
/**
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* =============================================================================
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* 动态版 - 手部姿态识别与模型管理脚本 (v3.0)
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* 由人体姿态识别修改为手部姿态识别，并确保非镜像显示
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* =============================================================================
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*/
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'use strict';
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// --- 全局变量和常量 ---
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const videoElement = document.getElementById('video');
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const canvasElement = document.getElementById('canvas');
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const canvasCtx = canvasElement.getContext('2d');
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const statusElement = document.getElementById('status');
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const resultElement = document.getElementById('result-text');
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// UI元素
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const poseClassesContainer = document.getElementById('pose-classes-container');
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const addClassButton = document.getElementById('btn-add-class');
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const predictButton = document.getElementById('btn-predict');
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const exportButton = document.getElementById('btn-export');
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const importButton = document.getElementById('btn-import');
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const fileImporter = document.getElementById('file-importer');
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let detector, classifier, animationFrameId;
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let isPredicting = false;
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const appState = {
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classMap: {},
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nextClassId: 0
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};
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// --- 主应用逻辑 ---
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/**
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* 初始化应用，加载模型并设置摄像头
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*/
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async function init() {
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try {
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classifier = knnClassifier.create();
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// --- 修改点 1: 加载手部检测模型 ---
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const model = handPoseDetection.SupportedModels.MediaPipeHands;
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const detectorConfig = {
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runtime: 'mediapipe', // 推荐使用 MediaPipe runtime 获得最佳性能
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solutionPath: 'https://cdn.jsdelivr.net/npm/@mediapipe/hands' // MediaPipe solution files path
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};
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detector = await handPoseDetection.createDetector(model, detectorConfig);
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await setupCamera();
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setupEventListeners();
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mainLoop();
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statusElement.innerText = "手部模型和摄像头已就绪!";
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enableControls();
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addNewClass(); // 默认创建第一个类别
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} catch (error) {
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console.error("初始化失败:", error);
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statusElement.innerText = "初始化失败，请检查摄像头权限或刷新。";
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statusElement.style.backgroundColor = '#fce8e6';
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statusElement.style.color = '#d93025';
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}
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}
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/**
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* 设置和启动用户摄像头 (无需修改，与之前一致)
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*/
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async function setupCamera() {
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const stream = await navigator.mediaDevices.getUserMedia({ video: true });
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videoElement.srcObject = stream;
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return new Promise((resolve) => {
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videoElement.onloadedmetadata = () => {
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videoElement.play();
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// 确保 Canvas 与 Video 宽高一致，并且在这里不需要 Canvas 镜像
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canvasElement.width = videoElement.videoWidth;
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canvasElement.height = videoElement.videoHeight;
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resolve();
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};
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});
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}
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/**
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* 为所有交互式元素绑定事件监听器 (无需修改，与之前一致)
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*/
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function setupEventListeners() {
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addClassButton.addEventListener('click', addNewClass);
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predictButton.addEventListener('click', togglePrediction);
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exportButton.addEventListener('click', exportModel);
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importButton.addEventListener('click', () => fileImporter.click());
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fileImporter.addEventListener('change', importModel);
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}
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// --- 动态类别管理 (无需修改，与之前一致) ---
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/**
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* 动态创建一个新类别的UI元素并添加到页面
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* @param {number} cId - 类别的唯一ID
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* @param {string} cName - 类别的名称
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*/
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function createClassUI(cId, cName) {
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const poseClassDiv = document.createElement('div');
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poseClassDiv.className = 'pose-class';
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poseClassDiv.dataset.classId = cId;
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poseClassDiv.innerHTML = `
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<div class="class-info">
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<input type="text" class="class-name-input" value="${cName}" data-class-id="${cId}">
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<span class="sample-count">(0 样本)</span>
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</div>
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<div class="class-actions">
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<button class="btn-sample" data-class-id="${cId}">采集样本</button>
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<button class="btn-delete-class" title="删除类别" data-class-id="${cId}">×</button>
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</div>
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`;
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poseClassesContainer.appendChild(poseClassDiv);
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const nameInput = poseClassDiv.querySelector('.class-name-input');
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nameInput.addEventListener('change', (e) => {
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appState.classMap[cId] = e.target.value;
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});
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const sampleButton = poseClassDiv.querySelector('.btn-sample');
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sampleButton.addEventListener('click', () => addExample(cId));
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if (isPredicting) sampleButton.disabled = true;
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const deleteButton = poseClassDiv.querySelector('.btn-delete-class');
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deleteButton.addEventListener('click', () => deleteClass(cId));
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}
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/**
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* 添加一个新的姿态类别
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*/
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function addNewClass() {
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const classId = appState.nextClassId;
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const className = `手势 ${classId + 1}`; // 改为“手势”
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appState.classMap[classId] = className;
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appState.nextClassId++;
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createClassUI(classId, className);
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}
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/**
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* 删除一个指定的姿态类别
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* @param {number} classId - 要删除的类别的ID
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*/
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function deleteClass(classId) {
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if (confirm(`确定要删除类别 "${appState.classMap[classId]}" 吗？所有样本都将丢失。`)) {
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const elementToRemove = poseClassesContainer.querySelector(`[data-class-id="${classId}"]`);
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if (elementToRemove) elementToRemove.remove();
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delete appState.classMap[classId];
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classifier.clearClass(classId);
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updateSampleCounts();
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updatePredictionUI();
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checkExportAbility();
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}
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}
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/**
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* 采集一个姿态样本并添加到KNN分类器
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* @param {number} classId 类别的ID
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*/
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async function addExample(classId) {
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// --- 修改点 2: 使用 estimateHands 替代 estimatePoses ---
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// flipHorizontal: false 确保模型输出的坐标与原视频方向一致 (非镜像)
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const hands = await detector.estimateHands(videoElement, { flipHorizontal: false });
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if (hands && hands.length > 0) {
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// KNN 分类器通常只处理一个实例，这里我们取检测到的第一只手
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const handTensor = flattenHand(hands[0]); // 使用新的 flattenHand
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classifier.addExample(handTensor, classId);
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handTensor.dispose(); // 释放内存
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updateSampleCounts();
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checkExportAbility();
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} else {
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console.warn(`为类别 ${appState.classMap[classId]} 采集样本失败，未检测到手部。`);
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}
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}
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// --- 模型与预测逻辑 (小修改) ---
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/**
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* 开始或停止姿态预测 (少量文案修改)
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*/
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function togglePrediction() {
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if (classifier.getNumClasses() === 0) {
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alert("请先为至少一个手势采集样本后再开始预测！"); // 文案修改
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return;
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}
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isPredicting = !isPredicting;
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updatePredictionUI();
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}
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/**
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* 应用的主循环
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*/
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async function mainLoop() {
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// --- 修改点 3: 使用 estimateHands 替代 estimatePoses ---
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// flipHorizontal: false 确保模型输出的坐标与原视频方向一致 (非镜像)
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const hands = await detector.estimateHands(videoElement, { flipHorizontal: false });
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canvasCtx.clearRect(0, 0, canvasElement.width, canvasElement.height); // 清空画布
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if (hands && hands.length > 0) {
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// 通常只处理检测到的第一只手，如果有两只手，可以根据需求处理
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drawHand(hands[0]); // 使用新的 drawHand
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if (isPredicting && classifier.getNumClasses() > 0) {
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const handTensor = flattenHand(hands[0]); // 使用新的 flattenHand
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const result = await classifier.predictClass(handTensor, 3);
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handTensor.dispose();
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const confidence = Math.round(result.confidences[result.label] * 100);
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const predictedClassName = appState.classMap[result.label] || '未知手势'; // 文案修改
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resultElement.innerText = `手势: ${predictedClassName} (${confidence}%)`; // 文案修改
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}
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}
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animationFrameId = requestAnimationFrame(mainLoop);
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}
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// --- 模型管理函数 (无需修改，与之前一致) ---
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/**
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* 导出KNN模型为包含类别信息的JSON文件
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*/
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function exportModel() {
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if (classifier.getNumClasses() === 0) {
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alert('模型中还没有任何样本，无法导出！');
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return;
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}
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const dataset = classifier.getClassifierDataset();
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const datasetObj = {};
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Object.keys(dataset).forEach((key) => {
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const data = dataset[key];
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datasetObj[key] = data.arraySync();
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});
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const modelData = {
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classMap: appState.classMap,
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dataset: datasetObj
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};
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const jsonStr = JSON.stringify(modelData);
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const blob = new Blob([jsonStr], { type: "application/json" });
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const url = URL.createObjectURL(blob);
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const a = document.createElement('a');
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a.href = url;
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a.download = `hand-knn-model.json`; // 文件名改为 hand-knn-model.json
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document.body.appendChild(a);
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a.click();
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document.body.removeChild(a);
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URL.revokeObjectURL(url);
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}
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/**
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* 从JSON文件导入KNN模型并恢复类别状态 (无需修改，与之前一致)
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* @param {Event} event
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*/
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function importModel(event) {
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const file = event.target.files[0];
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if (!file) return;
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const reader = new FileReader();
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reader.onload = (e) => {
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try {
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const modelData = JSON.parse(e.target.result);
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if (!modelData.classMap || !modelData.dataset) {
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throw new Error("无效的模型文件格式。");
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}
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classifier.clearAllClasses();
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poseClassesContainer.innerHTML = '';
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appState.classMap = {};
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appState.classMap = modelData.classMap;
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const classIds = Object.keys(appState.classMap).map(Number);
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appState.nextClassId = classIds.length > 0 ? Math.max(...classIds) + 1 : 0;
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classIds.forEach(id => {
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createClassUI(id, appState.classMap[id]);
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});
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const newDataset = {};
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Object.keys(modelData.dataset).forEach((key) => {
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newDataset[key] = tf.tensor(modelData.dataset[key]);
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});
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classifier.setClassifierDataset(newDataset);
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updateSampleCounts();
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checkExportAbility();
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alert('模型导入成功！');
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} catch (error) {
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console.error("导入模型失败:", error);
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alert(`导入失败！请确保文件是正确的模型JSON文件。\n错误: ${error.message}`);
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} finally {
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fileImporter.value = '';
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}
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};
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reader.readAsText(file);
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}
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// --- 辅助和UI更新函数 ---
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/**
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* --- 修改点 4: 展平手部关键点 ---
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* 将手部关键点展平为一维张量。
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* 考虑到 MediaPipe Hands 模型的关键点总数是21个 (0-20)。
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* @param {Object} hand - 单个手部检测结果对象
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* @returns {tf.Tensor} - 展平后的关键点坐标张量
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*/
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function flattenHand(hand) {
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// 归一化关键点坐标到 [0, 1] 范围，然后展平
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const keypoints = hand.keypoints.map(p => [p.x / videoElement.videoWidth, p.y / videoElement.videoHeight]).flat();
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return tf.tensor(keypoints);
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}
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const HAND_CONNECTIONS = [
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[0, 1], [1, 2], [2, 3], [3, 4], // Thumb
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[0, 5], [5, 6], [6, 7], [7, 8], // Index finger
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[0, 9], [9, 10], [10, 11], [11, 12], // Middle finger
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[0, 13], [13, 14], [14, 15], [15, 16], // Ring finger
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[0, 17], [17, 18], [18, 19], [19, 20], // Pinky finger
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[0, 5], [5, 9], [9, 13], [13, 17], [17, 0] // Palm base connections
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];
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/**
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* --- 修改点 5: 绘制手部骨骼 ---
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* 绘制手部关键点和连接线。
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* @param {Object} hand - 单个手部检测结果对象
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*/
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function drawHand(hand) {
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if (hand.keypoints) {
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const keypoints = hand.keypoints;
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// 绘制连接线
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canvasCtx.strokeStyle = '#00FFFF'; // 青色
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canvasCtx.lineWidth = 2;
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for (const connection of HAND_CONNECTIONS) {
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const start = keypoints[connection[0]];
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const end = keypoints[connection[1]];
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// 检查关键点的 score，确保是可靠的
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if (start && end && start.score > 0.3 && end.score > 0.3) {
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canvasCtx.beginPath();
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canvasCtx.moveTo(start.x, start.y);
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canvasCtx.lineTo(end.x, end.y);
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canvasCtx.stroke();
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}
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}
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// 绘制关键点
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canvasCtx.fillStyle = '#FF0000'; // 红色
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for (const keypoint of keypoints) {
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if (keypoint.score > 0.3) { // 同样检查 score
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canvasCtx.beginPath();
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// 关键点半径设置小一点，因为手部关键点比人体姿态更密集
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canvasCtx.arc(keypoint.x, keypoint.y, 4, 0, 2 * Math.PI);
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canvasCtx.fill();
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}
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}
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}
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}
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/**
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* 更新所有类别UI上的样本数量 (无需修改，与之前一致)
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*/
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function updateSampleCounts() {
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const dataset = classifier.getClassifierDataset();
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const allClassElements = document.querySelectorAll('.pose-class');
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allClassElements.forEach(el => {
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const classId = parseInt(el.dataset.classId, 10);
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const classInfo = dataset[classId];
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// 确保 classInfo 存在，因为 classifier.clearClass(id) 后，dataset[id] 可能会是 undefined
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const count = classInfo ? classInfo.shape[0] : 0;
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el.querySelector('.sample-count').innerText = `(${count} 样本)`;
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});
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}
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/**
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* 根据状态更新UI (少量文案修改)
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*/
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function updatePredictionUI() {
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const allActionButtons = document.querySelectorAll('.btn-sample, .btn-delete-class, .btn-add-class, #btn-import');
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if (isPredicting) {
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predictButton.innerText = "停止预测";
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predictButton.classList.add('stop');
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resultElement.innerText = "正在分析手势..."; // 文案修改
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allActionButtons.forEach(btn => btn.disabled = true);
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document.querySelectorAll('.class-name-input').forEach(input => input.disabled = true);
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checkExportAbility();
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} else {
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predictButton.innerText = "开始预测";
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predictButton.classList.remove('stop');
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resultElement.innerText = "已停止";
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allActionButtons.forEach(btn => btn.disabled = false);
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document.querySelectorAll('.class-name-input').forEach(input => input.disabled = false);
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checkExportAbility();
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}
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// 只有在有类别且有样本时才能预测
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predictButton.disabled = isPredicting ? false : classifier.getNumClasses() === 0;
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}
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function enableControls() {
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[predictButton, importButton, exportButton, addClassButton].forEach(btn => btn.disabled = false);
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checkExportAbility();
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}
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/** 检查是否可以导出模型并更新按钮状态 */
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function checkExportAbility() {
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exportButton.disabled = isPredicting || classifier.getNumClasses() === 0;
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}
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// 释放 TensorFlow.js 相关的内存
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function cleanup() {
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if (detector) {
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// 对于 MediaPipe runtime，detector.dispose() 可能不是必须的，
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// 其内部会管理WebGL资源。但为保险起见可以保留。
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// 或者更彻底地，如果不再需要，可以手动清理所有tf.Tensor。
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}
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if (classifier) classifier.clearAllClasses();
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if (animationFrameId) cancelAnimationFrame(animationFrameId);
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tf.disposeAll(); // 额外添加，确保所有创建的张量都被释放，防止内存泄露
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console.log("Cleanup complete. All TensorFlow.js tensors disposed.");
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}
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// --- 启动应用 ---
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window.onbeforeunload = cleanup; // 页面关闭前清理资源
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init();
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