你說:
有沒有可能用代碼控制圖片圖層,運行圖片組成的動畫
是有可能用代碼來控制圖片圖層並實現動畫的,常見的方法是使用編程語言結合圖像處理庫來進行。以下是一些常用的技術和庫來達成這個目標:
Python + PIL (Pillow) + Pygame:
HTML + CSS + JavaScript (Canvas API):
Unity or Unreal Engine (C# / C++):
GIF 動畫:
如果你想要使用 Python,以下是用 Pillow 來合併圖層的簡單例子:
pythonfrom PIL import Image, ImageDraw
# 打開圖片作為圖層
background = Image.open("background.png")
foreground = Image.open("foreground.png").convert("RGBA")
# 改變前景圖層的透明度
alpha = 128
foreground.putalpha(alpha)
# 將前景疊加到背景上
composite = Image.alpha_composite(background.convert("RGBA"), foreground)
# 保存合併後的圖片
composite.save("output.png")
# 可以在每一幀改變 alpha 或前景位置來做動畫效果
如果你想進一步控制圖片動畫,使用 Pygame 則可以讓你動態控制圖片層和動畫進程。
確實可以通過代碼來控制多個圖層,實現複雜的角色動畫,包括各種部件(如嘴巴、表情、頭髮、手、身體等)的變化。以下是一些推薦的方法和工具,以及具體的實現思路,幫助你達成這一目標:
這兩款工具專門用於2D骨骼動畫,允許你將角色拆分為多個部件(圖層),並通過骨骼和動畫設計來控制它們的運動。
Spine
DragonBones
實現步驟:
Unity 是一款功能強大的遊戲引擎,支持2D和3D動畫,並且具有豐富的API來控制多個圖層和部件。
實現思路:
範例代碼(C#):
csharpusing UnityEngine;
public class CharacterAnimator : MonoBehaviour
{
public GameObject mouth;
public GameObject hair;
public GameObject hands;
// 其他部件
void Update()
{
// 示例:簡單的嘴巴動作
if (Input.GetKeyDown(KeyCode.Space))
{
StartCoroutine(OpenMouth());
}
}
IEnumerator OpenMouth()
{
// 改變嘴巴的顯示狀態或位置
mouth.SetActive(true);
yield return new WaitForSeconds(0.5f);
mouth.SetActive(false);
}
// 其他動畫控制方法
}
Godot 是一個開源的遊戲引擎,支持2D和3D動畫,並使用 GDScript 進行編程控制。
實現思路: 類似於 Unity,將角色部件作為獨立節點導入,並使用 GDScript 腳本來控制它們的動畫和屬性變化。
資源:
如果你希望在網頁上實現複雜的角色動畫,可以考慮使用 JavaScript 結合 HTML5 Canvas 或 WebGL,並利用一些動畫庫來輔助。
Pixi.js 是一個高性能的2D渲染引擎,適合用於複雜的動畫和交互。
實現思路:
範例代碼:
javascriptconst app = new PIXI.Application({ width: 800, height: 600 });
document.body.appendChild(app.view);
// 加載資源
PIXI.Loader.shared
.add(['mouth.png', 'hair.png', 'hands.png'])
.load(setup);
function setup() {
const mouth = new PIXI.Sprite(PIXI.Loader.shared.resources['mouth.png'].texture);
const hair = new PIXI.Sprite(PIXI.Loader.shared.resources['hair.png'].texture);
const hands = new PIXI.Sprite(PIXI.Loader.shared.resources['hands.png'].texture);
// 設置初始位置
mouth.position.set(100, 100);
hair.position.set(100, 50);
hands.position.set(100, 150);
// 添加到舞台
app.stage.addChild(hair);
app.stage.addChild(mouth);
app.stage.addChild(hands);
// 使用 Tween.js 進行動畫
// 假設已經引入 Tween.js
new TWEEN.Tween(mouth.position)
.to({ y: 120 }, 500)
.yoyo(true)
.repeat(Infinity)
.start();
app.ticker.add((delta) => {
TWEEN.update();
});
}
如果需要更高級的效果,可以使用 Three.js 結合 WebGL 來實現,但這需要更多的圖形學知識。
資源:
雖然 Python 在動畫方面的資源相對較少,但仍有一些庫可以實現複雜的動畫控制。
Pygame 是一個流行的Python遊戲開發庫,支持圖層管理和動畫。
實現思路:
範例代碼:
pythonimport pygame
import sys
pygame.init()
screen = pygame.display.set_mode((800, 600))
clock = pygame.time.Clock()
# 加載圖像
mouth_img = pygame.image.load("mouth.png").convert_alpha()
hair_img = pygame.image.load("hair.png").convert_alpha()
hands_img = pygame.image.load("hands.png").convert_alpha()
# 設置初始位置
mouth_rect = mouth_img.get_rect(topleft=(100, 100))
hair_rect = hair_img.get_rect(topleft=(100, 50))
hands_rect = hands_img.get_rect(topleft=(100, 150))
# 動畫參數
mouth_open = False
mouth_timer = 0
while True:
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
sys.exit()
# 控制嘴巴動畫
mouth_timer += clock.get_time()
if mouth_timer > 500:
mouth_open = not mouth_open
mouth_timer = 0
# 清屏
screen.fill((255, 255, 255))
# 繪製圖層
screen.blit(hair_img, hair_rect)
if mouth_open:
screen.blit(pygame.image.load("mouth_open.png").convert_alpha(), mouth_rect)
else:
screen.blit(mouth_img, mouth_rect)
screen.blit(hands_img, hands_rect)
pygame.display.flip()
clock.tick(60)
如果你的角色是基於矢量圖形(SVG)的,可以利用 JavaScript 和 CSS 來實現複雜的動畫效果。
SVG 支持將不同部件作為獨立的元素,可以通過 JavaScript 操控這些元素的屬性來實現動畫。
實現思路:
transform
、opacity
、d
路徑等)來實現動畫。範例代碼:
html<!DOCTYPE html>
<html lang="zh-TW">
<head>
<meta charset="UTF-8">
<title>SVG Animation</title>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.12.1/gsap.min.js"></script>
</head>
<body>
<svg width="800" height="600">
<!-- 頭髮 -->
<g id="hair">
<path d="M..." fill="brown"/>
</g>
<!-- 嘴巴 -->
<g id="mouth">
<path d="M..." fill="red"/>
</g>
<!-- 手 -->
<g id="hands">
<path d="M..." fill="peachpuff"/>
</g>
</svg>
<script>
// 使用 GSAP 進行動畫
gsap.to("#mouth", {
scaleY: 1.2,
duration: 0.5,
yoyo: true,
repeat: -1
});
gsap.to("#hair", {
rotation: 10,
transformOrigin: "center",
duration: 1,
yoyo: true,
repeat: -1
});
gsap.to("#hands", {
x: 10,
duration: 0.5,
yoyo: true,
repeat: -1
});
</script>
</body>
</html>
根據你的需求和技術背景,選擇最適合的工具和框架:
如果你偏向於使用專業的動畫設計工具並結合代碼控制,推薦使用 Spine 或 DragonBones,這些工具提供了豐富的動畫設計功能和良好的代碼集成支持。
如果你需要更高的自定義性和靈活性,並且熟悉遊戲引擎,Unity 或 Godot 是不錯的選擇,這些引擎提供了強大的圖層管理和動畫控制功能。
如果你的應用主要是網頁端的,可以考慮使用 Pixi.js 結合 GSAP 來實現複雜的2D動畫效果。
如果你偏好使用Python,並且動畫需求不至於過於複雜,Pygame 也是一個可行的選擇。
無論選擇哪種工具,合理組織和管理圖層是實現複雜動畫的關鍵:
複雜的動畫開發是一個持續迭代的過程,建議你在實現過程中不斷測試和優化,確保動畫效果流暢且符合需求。
希望以上建議能幫助你實現複雜的角色動畫。如果有更多具體問題,歡迎隨時提問!
實現一個結合機器學習的虛擬聊天角色,能夠根據語音、表情與反應動作進行互動,是一個多領域交叉的複雜項目。這需要整合自然語言處理、語音合成、計算機視覺、動畫技術以及實時系統整合等多種技術。以下是詳細的實現建議,包括可用技術、推薦工具以及整體流程。
自然語言處理(NLP):
語音合成(TTS):
口型同步(Lip-Sync):
表情識別與生成:
反應動作與場景互動:
動畫與渲染:
整合與同步:
工具與模型:
實現方式:
工具與服務:
實現方式:
工具與技術:
實現方式:
技術與模型:
實現方式:
工具與引擎:
實現方式:
工具:
實現方式:
技術與框架:
實現方式:
角色設計:
動畫準備:
選擇動畫引擎:
導入角色模型與動畫:
設置語言模型:
API 整合:
選擇 TTS 服務:
集成 TTS:
選擇口型同步工具:
處理音頻:
應用到角色:
情感分析:
映射表情:
實現方式:
情境判斷:
動作觸發:
互動效果:
協調各模塊:
實時運行:
優化性能:
csharpusing System.Collections;
using UnityEngine;
using UnityEngine.Networking;
public class ChatbotController : MonoBehaviour
{
public string openAIApiKey = "YOUR_API_KEY";
public string ttsApiEndpoint = "YOUR_TTS_API_ENDPOINT";
public AudioSource audioSource;
public IEnumerator GetChatResponse(string userInput, System.Action<string> callback)
{
// 構建 OpenAI API 請求
var jsonData = JsonUtility.ToJson(new
{
prompt = userInput,
max_tokens = 150
});
using (UnityWebRequest request = new UnityWebRequest("https://api.openai.com/v1/engines/gpt-4/completions", "POST"))
{
byte[] bodyRaw = System.Text.Encoding.UTF8.GetBytes(jsonData);
request.uploadHandler = new UploadHandlerRaw(bodyRaw);
request.downloadHandler = new DownloadHandlerBuffer();
request.SetRequestHeader("Content-Type", "application/json");
request.SetRequestHeader("Authorization", "Bearer " + openAIApiKey);
yield return request.SendWebRequest();
if (request.result == UnityWebRequest.Result.Success)
{
// 解析回應
// 假設回應結構中有 "choices" -> [0] -> "text"
var response = JsonUtility.FromJson<OpenAIResponse>(request.downloadHandler.text);
callback(response.choices[0].text);
}
else
{
Debug.LogError("NLP API Error: " + request.error);
callback("抱歉,我無法回應。");
}
}
}
[System.Serializable]
public class OpenAIResponse
{
public Choice[] choices;
}
[System.Serializable]
public class Choice
{
public string text;
}
// TTS 實現(假設 TTS API 接受文本並返回音頻 URL)
public IEnumerator ConvertTextToSpeech(string text, System.Action<AudioClip> callback)
{
var jsonData = JsonUtility.ToJson(new { text = text });
using (UnityWebRequest request = new UnityWebRequest(ttsApiEndpoint, "POST"))
{
byte[] bodyRaw = System.Text.Encoding.UTF8.GetBytes(jsonData);
request.uploadHandler = new UploadHandlerRaw(bodyRaw);
request.downloadHandler = new DownloadHandlerBuffer();
request.SetRequestHeader("Content-Type", "application/json");
yield return request.SendWebRequest();
if (request.result == UnityWebRequest.Result.Success)
{
// 假設回應包含音頻文件的 URL
string audioUrl = JsonUtility.FromJson<TTSResponse>(request.downloadHandler.text).audioUrl;
using (UnityWebRequest audioRequest = UnityWebRequestMultimedia.GetAudioClip(audioUrl, AudioType.WAV))
{
yield return audioRequest.SendWebRequest();
if (audioRequest.result == UnityWebRequest.Result.Success)
{
AudioClip clip = DownloadHandlerAudioClip.GetContent(audioRequest);
callback(clip);
}
else
{
Debug.LogError("TTS Audio Download Error: " + audioRequest.error);
callback(null);
}
}
}
else
{
Debug.LogError("TTS API Error: " + request.error);
callback(null);
}
}
}
[System.Serializable]
public class TTSResponse
{
public string audioUrl;
}
}
生成嘴型動畫:
應用到 Unity:
csharpusing System.Collections;
using UnityEngine;
using UnityEngine.Networking;
public class EmotionController : MonoBehaviour
{
public string emotionAnalysisApi = "YOUR_EMOTION_ANALYSIS_API_ENDPOINT";
public IEnumerator AnalyzeEmotion(string text, System.Action<string> callback)
{
var jsonData = JsonUtility.ToJson(new { text = text });
using (UnityWebRequest request = new UnityWebRequest(emotionAnalysisApi, "POST"))
{
byte[] bodyRaw = System.Text.Encoding.UTF8.GetBytes(jsonData);
request.uploadHandler = new UploadHandlerRaw(bodyRaw);
request.downloadHandler = new DownloadHandlerBuffer();
request.SetRequestHeader("Content-Type", "application/json");
yield return request.SendWebRequest();
if (request.result == UnityWebRequest.Result.Success)
{
var response = JsonUtility.FromJson<EmotionResponse>(request.downloadHandler.text);
callback(response.emotion);
}
else
{
Debug.LogError("Emotion Analysis API Error: " + request.error);
callback("neutral");
}
}
}
[System.Serializable]
public class EmotionResponse
{
public string emotion; // e.g., happy, sad, angry, neutral
}
public void SetEmotion(string emotion)
{
Animator animator = GetComponent<Animator>();
switch (emotion)
{
case "happy":
animator.SetTrigger("Happy");
break;
case "sad":
animator.SetTrigger("Sad");
break;
case "angry":
animator.SetTrigger("Angry");
break;
default:
animator.SetTrigger("Neutral");
break;
}
}
}
csharpusing System.Collections;
using UnityEngine;
public class ReactionController : MonoBehaviour
{
public Animator animator;
public void TriggerReaction(string reaction)
{
switch (reaction)
{
case "nod":
animator.SetTrigger("Nod");
break;
case "shake_head":
animator.SetTrigger("ShakeHead");
break;
case "wave":
animator.SetTrigger("Wave");
break;
// 添加更多反應動作
default:
break;
}
}
}
csharpusing System.Collections;
using UnityEngine;
public class VirtualAvatarController : MonoBehaviour
{
public ChatbotController chatbotController;
public EmotionController emotionController;
public ReactionController reactionController;
public Animator animator;
void Start()
{
// 初始化或載入
}
public void UserInput(string input)
{
StartCoroutine(ProcessInput(input));
}
private IEnumerator ProcessInput(string input)
{
// 獲取 NLP 回應
string responseText = "";
yield return chatbotController.GetChatResponse(input, (response) => { responseText = response; });
// 語音合成
AudioClip speechClip = null;
yield return chatbotController.ConvertTextToSpeech(responseText, (clip) => { speechClip = clip; });
if (speechClip != null)
{
// 播放語音
GetComponent<AudioSource>().clip = speechClip;
GetComponent<AudioSource>().Play();
// 生成嘴型動畫(假設有相應的嘴型數據生成流程)
// 例如調用 Wav2Lip 模型並應用嘴型數據
// 分析情感並設置表情
string emotion = "";
yield return emotionController.AnalyzeEmotion(responseText, (e) => { emotion = e; });
emotionController.SetEmotion(emotion);
// 根據情感或上下文觸發反應動作
if (emotion == "happy")
{
reactionController.TriggerReaction("wave");
}
else if (emotion == "sad")
{
reactionController.TriggerReaction("nod");
}
// 根據具體需求添加更多條件
}
}
}
Unity 官方文檔:
機器學習模型:
口型同步工具:
TTS 服務:
情感分析:
動畫與表情控制:
模塊化設計:
逐步實現與測試:
資源管理與優化:
用戶體驗優先:
持續迭代與改進:
實現這樣一個複雜的系統需要跨領域的知識和持續的開發努力。建議你先從各個模塊的基本實現入手,逐步完善和整合,並充分利用現有的工具和資源來加速開發過程。如果在實現過程中遇到具體問題,歡迎隨時提問,我將竭誠協助!
抱歉之前的回答沒有完全理解你的需求。你希望通過代碼控制2D動畫的各個圖層,並利用大型語言模型(LLM)來驅動這些動畫,以實現一個能夠根據用戶對話進行說話、表情變化和反應動作的虛擬角色。下面將詳細介紹如何實現這一目標,包括使用的技術、具體流程以及實現細節。
用戶交互界面:
自然語言處理(NLP):
語音合成(TTS):
口型同步(Lip-Sync):
表情變化:
反應動作:
2D動畫圖層控制:
Web技術(HTML5, CSS3, JavaScript):
JavaScript動畫庫:
角色設計:
圖層導出:
html<!DOCTYPE html>
<html lang="zh-TW">
<head>
<meta charset="UTF-8">
<title>虛擬聊天角色</title>
<style>
/* 基本樣式 */
#character-container {
position: relative;
width: 400px;
height: 600px;
margin: 0 auto;
background-color: #f0f0f0;
}
.layer {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
}
</style>
</head>
<body>
<div id="character-container">
<img id="body" class="layer" src="body.png" alt="身體">
<img id="hair" class="layer" src="hair.png" alt="頭髮">
<img id="eyes" class="layer" src="eyes_neutral.png" alt="眼睛">
<img id="mouth" class="layer" src="mouth_closed.png" alt="嘴巴">
<img id="hands" class="layer" src="hands.png" alt="手臂">
<!-- 添加更多部件 -->
</div>
<input type="text" id="user-input" placeholder="輸入訊息...">
<button id="send-button">發送</button>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.11.4/gsap.min.js"></script>
<script src="app.js"></script>
</body>
</html>
javascript// app.js
document.getElementById('send-button').addEventListener('click', () => {
const userInput = document.getElementById('user-input').value;
if (userInput.trim() !== '') {
sendMessage(userInput);
document.getElementById('user-input').value = '';
}
});
async function sendMessage(message) {
// 發送用戶訊息到後端,並獲取回應
const response = await fetch('/api/chat', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ message })
});
const data = await response.json();
const reply = data.reply;
const emotion = data.emotion;
// 控制動畫
animateMouth();
changeExpression(emotion);
// 語音合成和播放
playTTS(reply);
}
function animateMouth() {
// 簡單的嘴巴開合動畫
const mouth = document.getElementById('mouth');
gsap.to(mouth, { opacity: 0, duration: 0.1, yoyo: true, repeat: 3 });
}
function changeExpression(emotion) {
const eyes = document.getElementById('eyes');
switch(emotion) {
case 'happy':
eyes.src = 'eyes_happy.png';
break;
case 'sad':
eyes.src = 'eyes_sad.png';
break;
case 'angry':
eyes.src = 'eyes_angry.png';
break;
default:
eyes.src = 'eyes_neutral.png';
}
}
function playTTS(text) {
// 調用TTS API並播放語音
fetch('/api/tts', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ text })
})
.then(response => response.blob())
.then(blob => {
const url = URL.createObjectURL(blob);
const audio = new Audio(url);
audio.play();
})
.catch(err => console.error('TTS Error:', err));
}
javascript// server.js
const express = require('express');
const bodyParser = require('body-parser');
const fetch = require('node-fetch');
const app = express();
const port = 3000;
app.use(bodyParser.json());
app.use(express.static('public')); // 假設前端文件在public資料夾
// 聊天API
app.post('/api/chat', async (req, res) => {
const userMessage = req.body.message;
// 調用OpenAI GPT-4 API獲取回應
const gptResponse = await fetch('https://api.openai.com/v1/chat/completions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer YOUR_OPENAI_API_KEY`
},
body: JSON.stringify({
model: "gpt-4",
messages: [{ role: "user", content: userMessage }]
})
});
const gptData = await gptResponse.json();
const reply = gptData.choices[0].message.content.trim();
// 調用情感分析API(這裡假設使用同一個GPT模型進行情感分析)
const emotion = await analyzeEmotion(reply);
res.json({ reply, emotion });
});
// TTS API
app.post('/api/tts', async (req, res) => {
const text = req.body.text;
// 調用Google TTS API(示例)
const ttsResponse = await fetch('https://texttospeech.googleapis.com/v1/text:synthesize?key=YOUR_GOOGLE_TTS_API_KEY', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
input: { text },
voice: { languageCode: 'zh-TW', ssmlGender: 'NEUTRAL' },
audioConfig: { audioEncoding: 'MP3' }
})
});
const ttsData = await ttsResponse.json();
const audioContent = ttsData.audioContent;
// 將Base64音頻數據轉換為二進制
const buffer = Buffer.from(audioContent, 'base64');
res.set('Content-Type', 'audio/mpeg');
res.send(buffer);
});
// 情感分析函數
async function analyzeEmotion(text) {
// 這裡可以使用Hugging Face的情感分析API,或自行訓練模型
// 以下為假設的情感分析結果
// 實際應根據情感分析API的返回結果進行解析
// 示例:簡單的情感判斷
if (text.includes('高興') || text.includes('快樂')) {
return 'happy';
} else if (text.includes('難過') || text.includes('傷心')) {
return 'sad';
} else if (text.includes('生氣') || text.includes('憤怒')) {
return 'angry';
} else {
return 'neutral';
}
}
app.listen(port, () => {
console.log(`Server running at http://localhost:${port}`);
});
在專案目錄下運行以下命令:
bashnpm init -y npm install express body-parser node-fetch
嘴型同步:
表情變化:
反應動作:
如果希望支持語音輸入,可以利用Web Speech API:
javascript// 添加語音輸入按鈕
<button id="voice-button">說話</button>
<script>
// 語音輸入功能
document.getElementById('voice-button').addEventListener('click', () => {
const recognition = new (window.SpeechRecognition || window.webkitSpeechRecognition)();
recognition.lang = 'zh-TW';
recognition.onresult = (event) => {
const transcript = event.results[0][0].transcript;
document.getElementById('user-input').value = transcript;
sendMessage(transcript);
};
recognition.start();
});
</script>
在前述的animateMouth
函數中,簡單地通過改變嘴型的透明度來實現嘴巴的開合效果。這是一種基礎的實現方式,可以根據音頻的長度和節奏進行更精細的控制。
進階實現:
javascriptfunction animateMouthWithTiming(audioDuration) {
const mouth = document.getElementById('mouth');
const totalCycles = Math.floor(audioDuration / 0.5); // 每0.5秒切換一次嘴型
let currentCycle = 0;
const interval = setInterval(() => {
if (currentCycle % 2 === 0) {
mouth.src = 'mouth_open.png';
} else {
mouth.src = 'mouth_closed.png';
}
currentCycle++;
if (currentCycle >= totalCycles) {
clearInterval(interval);
mouth.src = 'mouth_closed.png';
}
}, 500);
}
然後在playTTS
函數中調用:
javascriptfunction playTTS(text) {
fetch('/api/tts', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ text })
})
.then(response => response.blob())
.then(blob => {
const url = URL.createObjectURL(blob);
const audio = new Audio(url);
// 獲取音頻持續時間
audio.addEventListener('loadedmetadata', () => {
animateMouthWithTiming(audio.duration);
});
audio.play();
})
.catch(err => console.error('TTS Error:', err));
}
在changeExpression
函數中,根據情感結果切換眼睛的圖片。可以使用動畫庫(如GSAP)來增加平滑的過渡效果。
javascriptfunction changeExpression(emotion) {
const eyes = document.getElementById('eyes');
let newSrc = 'eyes_neutral.png';
switch(emotion) {
case 'happy':
newSrc = 'eyes_happy.png';
break;
case 'sad':
newSrc = 'eyes_sad.png';
break;
case 'angry':
newSrc = 'eyes_angry.png';
break;
default:
newSrc = 'eyes_neutral.png';
}
gsap.to(eyes, { opacity: 0, duration: 0.2, onComplete: () => {
eyes.src = newSrc;
gsap.to(eyes, { opacity: 1, duration: 0.2 });
}});
}
根據情感或特定關鍵詞觸發角色的手臂動作等。
步驟:
準備動作圖像:
控制手臂圖層的切換:
javascriptfunction triggerReaction(emotion) {
const hands = document.getElementById('hands');
let newSrc = 'hands.png'; // 默認姿勢
switch(emotion) {
case 'happy':
newSrc = 'hands_wave.png';
break;
case 'sad':
newSrc = 'hands_sad.png';
break;
case 'angry':
newSrc = 'hands_angry.png';
break;
default:
newSrc = 'hands.png';
}
gsap.to(hands, { opacity: 0, duration: 0.2, onComplete: () => {
hands.src = newSrc;
gsap.to(hands, { opacity: 1, duration: 0.2 });
}});
}
然後在sendMessage
函數中調用:
javascriptfunction sendMessage(message) {
// ...之前的代碼
changeExpression(emotion);
triggerReaction(emotion);
// ...後續代碼
}
更多表情和動作:
使用動畫過渡:
加載動畫:
音頻與動畫同步:
響應式設計:
使用音頻分析:
使用機器學習模型:
情境觸發的動作:
場景變換:
以下是一個簡化的完整範例,展示如何通過代碼控制2D動畫圖層並結合LLM進行互動。
index.html:
html<!DOCTYPE html>
<html lang="zh-TW">
<head>
<meta charset="UTF-8">
<title>虛擬聊天角色</title>
<style>
#character-container {
position: relative;
width: 400px;
height: 600px;
margin: 0 auto;
background-color: #f0f0f0;
}
.layer {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
transition: opacity 0.2s;
}
</style>
</head>
<body>
<div id="character-container">
<img id="body" class="layer" src="body.png" alt="身體">
<img id="hair" class="layer" src="hair.png" alt="頭髮">
<img id="eyes" class="layer" src="eyes_neutral.png" alt="眼睛">
<img id="mouth" class="layer" src="mouth_closed.png" alt="嘴巴">
<img id="hands" class="layer" src="hands.png" alt="手臂">
</div>
<input type="text" id="user-input" placeholder="輸入訊息...">
<button id="send-button">發送</button>
<button id="voice-button">語音輸入</button>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.11.4/gsap.min.js"></script>
<script src="app.js"></script>
</body>
</html>
app.js:
javascript// app.js
document.getElementById('send-button').addEventListener('click', () => {
const userInput = document.getElementById('user-input').value;
if (userInput.trim() !== '') {
sendMessage(userInput);
document.getElementById('user-input').value = '';
}
});
document.getElementById('voice-button').addEventListener('click', () => {
const recognition = new (window.SpeechRecognition || window.webkitSpeechRecognition)();
recognition.lang = 'zh-TW';
recognition.onresult = (event) => {
const transcript = event.results[0][0].transcript;
document.getElementById('user-input').value = transcript;
sendMessage(transcript);
};
recognition.start();
});
async function sendMessage(message) {
// 發送用戶訊息到後端,並獲取回應
const response = await fetch('/api/chat', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ message })
});
const data = await response.json();
const reply = data.reply;
const emotion = data.emotion;
// 控制動畫
animateMouth();
changeExpression(emotion);
triggerReaction(emotion);
// 語音合成和播放
playTTS(reply);
}
function animateMouth() {
// 簡單的嘴巴開合動畫
const mouth = document.getElementById('mouth');
gsap.to(mouth, { opacity: 0, duration: 0.1, yoyo: true, repeat: 3 });
}
function changeExpression(emotion) {
const eyes = document.getElementById('eyes');
let newSrc = 'eyes_neutral.png';
switch(emotion) {
case 'happy':
newSrc = 'eyes_happy.png';
break;
case 'sad':
newSrc = 'eyes_sad.png';
break;
case 'angry':
newSrc = 'eyes_angry.png';
break;
default:
newSrc = 'eyes_neutral.png';
}
gsap.to(eyes, { opacity: 0, duration: 0.2, onComplete: () => {
eyes.src = newSrc;
gsap.to(eyes, { opacity: 1, duration: 0.2 });
}});
}
function triggerReaction(emotion) {
const hands = document.getElementById('hands');
let newSrc = 'hands.png'; // 默認姿勢
switch(emotion) {
case 'happy':
newSrc = 'hands_wave.png';
break;
case 'sad':
newSrc = 'hands_sad.png';
break;
case 'angry':
newSrc = 'hands_angry.png';
break;
default:
newSrc = 'hands.png';
}
gsap.to(hands, { opacity: 0, duration: 0.2, onComplete: () => {
hands.src = newSrc;
gsap.to(hands, { opacity: 1, duration: 0.2 });
}});
}
function playTTS(text) {
// 調用TTS API並播放語音
fetch('/api/tts', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ text })
})
.then(response => response.blob())
.then(blob => {
const url = URL.createObjectURL(blob);
const audio = new Audio(url);
// 獲取音頻持續時間
audio.addEventListener('loadedmetadata', () => {
animateMouthWithTiming(audio.duration);
});
audio.play();
})
.catch(err => console.error('TTS Error:', err));
}
function animateMouthWithTiming(audioDuration) {
const mouth = document.getElementById('mouth');
const totalCycles = Math.floor(audioDuration / 0.5); // 每0.5秒切換一次嘴型
let currentCycle = 0;
const interval = setInterval(() => {
if (currentCycle % 2 === 0) {
mouth.src = 'mouth_open.png';
} else {
mouth.src = 'mouth_closed.png';
}
currentCycle++;
if (currentCycle >= totalCycles) {
clearInterval(interval);
mouth.src = 'mouth_closed.png';
}
}, 500);
}
server.js:
javascript// server.js
const express = require('express');
const bodyParser = require('body-parser');
const fetch = require('node-fetch');
const app = express();
const port = 3000;
app.use(bodyParser.json());
app.use(express.static('public')); // 假設前端文件在public資料夾
// 聊天API
app.post('/api/chat', async (req, res) => {
const userMessage = req.body.message;
// 調用OpenAI GPT-4 API獲取回應
const gptResponse = await fetch('https://api.openai.com/v1/chat/completions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': `Bearer YOUR_OPENAI_API_KEY`
},
body: JSON.stringify({
model: "gpt-4",
messages: [{ role: "user", content: userMessage }]
})
});
const gptData = await gptResponse.json();
const reply = gptData.choices[0].message.content.trim();
// 調用情感分析函數
const emotion = await analyzeEmotion(reply);
res.json({ reply, emotion });
});
// TTS API
app.post('/api/tts', async (req, res) => {
const text = req.body.text;
// 調用Google TTS API(示例)
const ttsResponse = await fetch('https://texttospeech.googleapis.com/v1/text:synthesize?key=YOUR_GOOGLE_TTS_API_KEY', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({
input: { text },
voice: { languageCode: 'zh-TW', ssmlGender: 'NEUTRAL' },
audioConfig: { audioEncoding: 'MP3' }
})
});
const ttsData = await ttsResponse.json();
const audioContent = ttsData.audioContent;
// 將Base64音頻數據轉換為二進制
const buffer = Buffer.from(audioContent, 'base64');
res.set('Content-Type', 'audio/mpeg');
res.send(buffer);
});
// 情感分析函數
async function analyzeEmotion(text) {
// 使用簡單的情感分析邏輯,實際應使用更精確的模型
if (text.includes('高興') || text.includes('快樂') || text.includes('喜歡')) {
return 'happy';
} else if (text.includes('難過') || text.includes('傷心') || text.includes('不開心')) {
return 'sad';
} else if (text.includes('生氣') || text.includes('憤怒') || text.includes('不滿')) {
return 'angry';
} else {
return 'neutral';
}
}
app.listen(port, () => {
console.log(`Server running at http://localhost:${port}`);
});
確保你的專案目錄結構如下:
javaproject/
│
├── public/
│ ├── index.html
│ ├── app.js
│ ├── body.png
│ ├── hair.png
│ ├── eyes_neutral.png
│ ├── eyes_happy.png
│ ├── eyes_sad.png
│ ├── eyes_angry.png
│ ├── mouth_closed.png
│ ├── mouth_open.png
│ ├── hands.png
│ ├── hands_wave.png
│ ├── hands_sad.png
│ ├── hands_angry.png
│ └── ...其他圖像
│
├── server.js
├── package.json
└── package-lock.json
在專案根目錄下運行:
bashnode server.js
然後在瀏覽器中訪問http://localhost:3000
,應該能看到你的虛擬聊天角色,並可以通過輸入文字或語音與之互動。
如果前端需求複雜,可以考慮使用React、Vue或Angular等前端框架,便於組織代碼和管理狀態。
目前的口型同步是基於定時器的簡單實現,可以進一步通過音頻分析獲取更精確的嘴型變化。
目前的情感分析基於簡單的關鍵詞判斷,可以考慮集成更先進的情感分析API或模型(如Hugging Face的情感分析模型),提升判斷的準確性。
使用高質量的TTS服務,並根據需要調整語音參數(如語速、語調),提升用戶體驗。
利用動畫庫(如GSAP)提供更多動畫效果,如縮放、旋轉、淡入淡出等,提升虛擬角色的表現力。
確保所有動畫和音頻處理在前端高效運行,避免卡頓或延遲。可以通過壓縮圖像、使用CDN等方式提升加載速度。
Web動畫與圖層控制:
Node.js與Express:
OpenAI API:
Google Text-to-Speech API:
Web Speech API:
情感分析模型:
實現一個基於2D圖層控制並由LLM驅動的虛擬聊天角色,是一個多技術融合的項目。以下是一些關鍵建議:
模塊化設計:
逐步實現與測試:
重用和擴展:
用戶體驗優先:
持續優化和迭代:
希望以上詳細的實現方案能夠幫助你實現2D動畫圖層控制並由LLM驅動的虛擬聊天角色。如果在實現過程中遇到具體問題,歡迎隨時提問,我將竭誠協助!
理解你的需求後,以下將詳細說明如何將後端的多個機器人系統與前端的2D動畫圖層結合運作,實現一個互動性強、動作豐富的虛擬聊天角色。這涉及後端機器人系統的整合、前端動畫控制的實現以及兩者之間的通信協調。以下是完整的實現方案:
主機器人(Main Robot):
分析機器人(Analysis Robot):
背景機器人(Background Robot):
主機器人(Main Robot):
分析機器人(Analysis Robot):
背景機器人(Background Robot):
設置WebSocket服務:
後端事件推送:
HTML結構:
<img>
或<div>
)。JavaScript動畫控制:
用戶輸入處理:
後端處理與回應:
前端動畫與語音回應:
a. 設置WebSocket服務
使用Socket.io來建立WebSocket服務,實現前後端的即時通信。
javascript// server.js
const express = require('express');
const http = require('http');
const socketIo = require('socket.io');
const bodyParser = require('body-parser');
const fetch = require('node-fetch'); // 如果需要調用外部API
const app = express();
const server = http.createServer(app);
const io = socketIo(server);
app.use(bodyParser.json());
app.use(express.static('public')); // 前端文件放在public資料夾
// 假設有三個機器人:主機器人、分析機器人、背景機器人
// 可以用不同的API端點模擬這些機器人
// 主機器人API
app.post('/api/main-robot', async (req, res) => {
const userMessage = req.body.message;
// 1. 發送訊息到分析機器人
const analysisResponse = await fetch('http://localhost:3000/api/analysis-robot', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ message: userMessage })
});
const analysisData = await analysisResponse.json();
// 2. 發送訊息到背景機器人
const backgroundResponse = await fetch('http://localhost:3000/api/background-robot', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ query: userMessage })
});
const backgroundData = await backgroundResponse.json();
// 3. 綜合分析結果,生成回應memo
const reply = generateReply(analysisData, backgroundData);
// 4. 推送回應給前端
io.emit('bot-response', { reply, emotion: analysisData.emotion });
res.json({ status: 'success' });
});
// 分析機器人API
app.post('/api/analysis-robot', (req, res) => {
const message = req.body.message;
// 進行情感分析,這裡用簡單的關鍵詞判斷
let emotion = 'neutral';
if (message.includes('高興') || message.includes('快樂') || message.includes('喜歡')) {
emotion = 'happy';
} else if (message.includes('難過') || message.includes('傷心') || message.includes('不開心')) {
emotion = 'sad';
} else if (message.includes('生氣') || message.includes('憤怒') || message.includes('不滿')) {
emotion = 'angry';
}
res.json({ emotion });
});
// 背景機器人API
app.post('/api/background-robot', (req, res) => {
const query = req.body.query;
// 根據query從Redis中搜尋相關資料,這裡用簡單的模擬
const data = searchRedis(query);
res.json({ data });
});
// 搜尋Redis的模擬函數
function searchRedis(query) {
// 實際應使用Redis客戶端進行查詢
const mockData = {
'你好': '你好!有什麼我可以幫助你的嗎?',
'天氣': '今天的天氣晴朗,氣溫適中。',
// 添加更多模擬資料
};
return mockData[query] || '抱歉,我不太明白你的意思。';
}
// 生成回應函數
function generateReply(analysisData, backgroundData) {
// 根據情感和背景資料生成回應
let baseReply = backgroundData.data || '抱歉,我不太明白你的意思。';
// 可以根據情感調整回應語氣
switch (analysisData.emotion) {
case 'happy':
baseReply += ' 很高興能幫助你!';
break;
case 'sad':
baseReply += ' 希望你一切都好。';
break;
case 'angry':
baseReply += ' 抱歉讓你感到不快。';
break;
default:
baseReply += ' 有什麼我可以幫助你的嗎?';
}
return baseReply;
}
// 啟動WebSocket連接
io.on('connection', (socket) => {
console.log('新用戶連接');
socket.on('disconnect', () => {
console.log('用戶離線');
});
});
// 啟動伺服器
const PORT = process.env.PORT || 3000;
server.listen(PORT, () => {
console.log(`Server running on port ${PORT}`);
});
b. 安裝所需的Node.js包
在專案根目錄下運行:
bashnpm init -y npm install express socket.io body-parser node-fetch
a. HTML結構
確保每個圖層都有獨立的HTML元素,並且重疊顯示。
html<!-- public/index.html -->
<!DOCTYPE html>
<html lang="zh-TW">
<head>
<meta charset="UTF-8">
<title>虛擬聊天角色</title>
<style>
/* 基本樣式 */
#character-container {
position: relative;
width: 400px;
height: 600px;
margin: 0 auto;
background-color: #f0f0f0;
}
.layer {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
transition: opacity 0.2s;
}
#controls {
text-align: center;
margin-top: 20px;
}
</style>
</head>
<body>
<div id="character-container">
<img id="body" class="layer" src="body.png" alt="身體">
<img id="hair" class="layer" src="hair.png" alt="頭髮">
<img id="eyes" class="layer" src="eyes_neutral.png" alt="眼睛">
<img id="mouth" class="layer" src="mouth_closed.png" alt="嘴巴">
<img id="hands" class="layer" src="hands.png" alt="手臂">
<!-- 添加更多部件 -->
</div>
<div id="controls">
<input type="text" id="user-input" placeholder="輸入訊息...">
<button id="send-button">發送</button>
<button id="voice-button">語音輸入</button>
</div>
<!-- 引入GSAP動畫庫和Socket.io客戶端庫 -->
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.11.4/gsap.min.js"></script>
<script src="/socket.io/socket.io.js"></script>
<script src="app.js"></script>
</body>
</html>
b. JavaScript控制動畫和通信
使用Socket.io客戶端來接收後端推送的回應,並根據回應控制動畫圖層。
javascript// public/app.js
// 連接WebSocket
const socket = io();
// 事件監聽器
document.getElementById('send-button').addEventListener('click', () => {
const userInput = document.getElementById('user-input').value;
if (userInput.trim() !== '') {
sendMessage(userInput);
document.getElementById('user-input').value = '';
}
});
document.getElementById('voice-button').addEventListener('click', () => {
const recognition = new (window.SpeechRecognition || window.webkitSpeechRecognition)();
recognition.lang = 'zh-TW';
recognition.onresult = (event) => {
const transcript = event.results[0][0].transcript;
document.getElementById('user-input').value = transcript;
sendMessage(transcript);
};
recognition.start();
});
function sendMessage(message) {
// 發送用戶訊息到後端主機器人API
fetch('/api/main-robot', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ message })
})
.then(response => response.json())
.then(data => {
// 回應已由後端通過WebSocket推送,無需前端處理
})
.catch(err => console.error('Main Robot Error:', err));
}
// 接收後端推送的機器人回應
socket.on('bot-response', (data) => {
const { reply, emotion } = data;
console.log('Bot Reply:', reply, 'Emotion:', emotion);
// 控制動畫
animateMouth();
changeExpression(emotion);
triggerReaction(emotion);
// 播放語音合成
playTTS(reply);
});
function animateMouth() {
// 簡單的嘴巴開合動畫
const mouth = document.getElementById('mouth');
gsap.to(mouth, { opacity: 0, duration: 0.1, yoyo: true, repeat: 3 });
}
function changeExpression(emotion) {
const eyes = document.getElementById('eyes');
let newSrc = 'eyes_neutral.png';
switch(emotion) {
case 'happy':
newSrc = 'eyes_happy.png';
break;
case 'sad':
newSrc = 'eyes_sad.png';
break;
case 'angry':
newSrc = 'eyes_angry.png';
break;
default:
newSrc = 'eyes_neutral.png';
}
gsap.to(eyes, { opacity: 0, duration: 0.2, onComplete: () => {
eyes.src = newSrc;
gsap.to(eyes, { opacity: 1, duration: 0.2 });
}});
}
function triggerReaction(emotion) {
const hands = document.getElementById('hands');
let newSrc = 'hands.png'; // 默認姿勢
switch(emotion) {
case 'happy':
newSrc = 'hands_wave.png';
break;
case 'sad':
newSrc = 'hands_sad.png';
break;
case 'angry':
newSrc = 'hands_angry.png';
break;
default:
newSrc = 'hands.png';
}
gsap.to(hands, { opacity: 0, duration: 0.2, onComplete: () => {
hands.src = newSrc;
gsap.to(hands, { opacity: 1, duration: 0.2 });
}});
}
function playTTS(text) {
// 調用TTS API並播放語音
fetch('/api/tts', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ text })
})
.then(response => response.blob())
.then(blob => {
const url = URL.createObjectURL(blob);
const audio = new Audio(url);
// 在播放前進行嘴型同步
audio.addEventListener('loadedmetadata', () => {
animateMouthWithTiming(audio.duration);
});
audio.play();
})
.catch(err => console.error('TTS Error:', err));
}
function animateMouthWithTiming(audioDuration) {
const mouth = document.getElementById('mouth');
const totalCycles = Math.floor(audioDuration / 0.5); // 每0.5秒切換一次嘴型
let currentCycle = 0;
const interval = setInterval(() => {
if (currentCycle % 2 === 0) {
mouth.src = 'mouth_open.png';
} else {
mouth.src = 'mouth_closed.png';
}
currentCycle++;
if (currentCycle >= totalCycles) {
clearInterval(interval);
mouth.src = 'mouth_closed.png';
}
}, 500);
}
確保後端的server.js
與前端的index.html
及app.js
正確連接,並將所有圖層圖像放在public
資料夾中。
WebSocket通信:主機器人處理完成後,通過Socket.io推送bot-response
事件給前端,前端根據事件數據觸發相應的動畫。
動畫觸發邏輯:前端收到bot-response
後,根據情感分析結果改變表情、手臂動作等,同時進行口型同步和語音播放。
精確的嘴型同步:根據音頻播放的具體時間和音節,進一步細化嘴型的切換頻率和樣式。可考慮增加更多嘴型圖片(如笑、閉、嘟等)以提升自然度。
表情和動作過渡:使用GSAP的動畫功能來平滑過渡不同的表情和動作,提升動畫流暢性。
javascriptfunction changeExpression(emotion) {
const eyes = document.getElementById('eyes');
let newSrc = 'eyes_neutral.png';
switch(emotion) {
case 'happy':
newSrc = 'eyes_happy.png';
break;
case 'sad':
newSrc = 'eyes_sad.png';
break;
case 'angry':
newSrc = 'eyes_angry.png';
break;
default:
newSrc = 'eyes_neutral.png';
}
// 平滑過渡
gsap.to(eyes, { opacity: 0, duration: 0.2, onComplete: () => {
eyes.src = newSrc;
gsap.to(eyes, { opacity: 1, duration: 0.2 });
}});
}
更多動畫圖層:根據需要添加更多的動畫圖層,如眉毛、臉頰等,並根據情感和對話內容進行細節控制。
反應動作的多樣性:根據不同情感或對話內容,觸發不同的反應動作,如點頭、搖頭、揮手等。
javascriptfunction triggerReaction(emotion) {
const hands = document.getElementById('hands');
let newSrc = 'hands.png'; // 默認姿勢
switch(emotion) {
case 'happy':
newSrc = 'hands_wave.png';
break;
case 'sad':
newSrc = 'hands_sad.png';
break;
case 'angry':
newSrc = 'hands_angry.png';
break;
default:
newSrc = 'hands.png';
}
gsap.to(hands, { opacity: 0, duration: 0.2, onComplete: () => {
hands.src = newSrc;
gsap.to(hands, { opacity: 1, duration: 0.2 });
}});
}
性能測試:確保動畫流暢運行,避免因高負載或大量圖層導致的性能問題。可以優化圖像大小和格式,使用CDN加速資源加載。
用戶體驗測試:根據用戶反饋不斷調整動畫效果和語音同步,提升互動體驗。
錯誤處理:添加錯誤處理機制,確保在後端機器人失敗或通信中斷時,前端能夠穩定運行並給出適當的提示。
index.html
html<!DOCTYPE html>
<html lang="zh-TW">
<head>
<meta charset="UTF-8">
<title>虛擬聊天角色</title>
<style>
#character-container {
position: relative;
width: 400px;
height: 600px;
margin: 0 auto;
background-color: #f0f0f0;
}
.layer {
position: absolute;
top: 0;
left: 0;
width: 100%;
height: 100%;
transition: opacity 0.2s;
}
#controls {
text-align: center;
margin-top: 20px;
}
</style>
</head>
<body>
<div id="character-container">
<img id="body" class="layer" src="body.png" alt="身體">
<img id="hair" class="layer" src="hair.png" alt="頭髮">
<img id="eyes" class="layer" src="eyes_neutral.png" alt="眼睛">
<img id="mouth" class="layer" src="mouth_closed.png" alt="嘴巴">
<img id="hands" class="layer" src="hands.png" alt="手臂">
</div>
<div id="controls">
<input type="text" id="user-input" placeholder="輸入訊息...">
<button id="send-button">發送</button>
<button id="voice-button">語音輸入</button>
</div>
<script src="https://cdnjs.cloudflare.com/ajax/libs/gsap/3.11.4/gsap.min.js"></script>
<script src="/socket.io/socket.io.js"></script>
<script src="app.js"></script>
</body>
</html>
app.js
javascript// public/app.js
// 連接WebSocket
const socket = io();
// 事件監聽器
document.getElementById('send-button').addEventListener('click', () => {
const userInput = document.getElementById('user-input').value;
if (userInput.trim() !== '') {
sendMessage(userInput);
document.getElementById('user-input').value = '';
}
});
document.getElementById('voice-button').addEventListener('click', () => {
const recognition = new (window.SpeechRecognition || window.webkitSpeechRecognition)();
recognition.lang = 'zh-TW';
recognition.onresult = (event) => {
const transcript = event.results[0][0].transcript;
document.getElementById('user-input').value = transcript;
sendMessage(transcript);
};
recognition.start();
});
function sendMessage(message) {
// 發送用戶訊息到後端主機器人API
fetch('/api/main-robot', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ message })
})
.then(response => response.json())
.then(data => {
// 回應已由後端通過WebSocket推送,無需前端處理
})
.catch(err => console.error('Main Robot Error:', err));
}
// 接收後端推送的機器人回應
socket.on('bot-response', (data) => {
const { reply, emotion } = data;
console.log('Bot Reply:', reply, 'Emotion:', emotion);
// 控制動畫
animateMouth();
changeExpression(emotion);
triggerReaction(emotion);
// 播放語音合成
playTTS(reply);
});
function animateMouth() {
// 簡單的嘴巴開合動畫
const mouth = document.getElementById('mouth');
gsap.to(mouth, { opacity: 0, duration: 0.1, yoyo: true, repeat: 3 });
}
function changeExpression(emotion) {
const eyes = document.getElementById('eyes');
let newSrc = 'eyes_neutral.png';
switch(emotion) {
case 'happy':
newSrc = 'eyes_happy.png';
break;
case 'sad':
newSrc = 'eyes_sad.png';
break;
case 'angry':
newSrc = 'eyes_angry.png';
break;
default:
newSrc = 'eyes_neutral.png';
}
gsap.to(eyes, { opacity: 0, duration: 0.2, onComplete: () => {
eyes.src = newSrc;
gsap.to(eyes, { opacity: 1, duration: 0.2 });
}});
}
function triggerReaction(emotion) {
const hands = document.getElementById('hands');
let newSrc = 'hands.png'; // 默認姿勢
switch(emotion) {
case 'happy':
newSrc = 'hands_wave.png';
break;
case 'sad':
newSrc = 'hands_sad.png';
break;
case 'angry':
newSrc = 'hands_angry.png';
break;
default:
newSrc = 'hands.png';
}
gsap.to(hands, { opacity: 0, duration: 0.2, onComplete: () => {
hands.src = newSrc;
gsap.to(hands, { opacity: 1, duration: 0.2 });
}});
}
function playTTS(text) {
// 調用TTS API並播放語音
fetch('/api/tts', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ text })
})
.then(response => response.blob())
.then(blob => {
const url = URL.createObjectURL(blob);
const audio = new Audio(url);
// 在播放前進行嘴型同步
audio.addEventListener('loadedmetadata', () => {
animateMouthWithTiming(audio.duration);
});
audio.play();
})
.catch(err => console.error('TTS Error:', err));
}
function animateMouthWithTiming(audioDuration) {
const mouth = document.getElementById('mouth');
const totalCycles = Math.floor(audioDuration / 0.5); // 每0.5秒切換一次嘴型
let currentCycle = 0;
const interval = setInterval(() => {
if (currentCycle % 2 === 0) {
mouth.src = 'mouth_open.png';
} else {
mouth.src = 'mouth_closed.png';
}
currentCycle++;
if (currentCycle >= totalCycles) {
clearInterval(interval);
mouth.src = 'mouth_closed.png';
}
}, 500);
}
server.js
javascript// server.js
const express = require('express');
const http = require('http');
const socketIo = require('socket.io');
const bodyParser = require('body-parser');
const fetch = require('node-fetch'); // 如果需要調用外部API
const app = express();
const server = http.createServer(app);
const io = socketIo(server);
app.use(bodyParser.json());
app.use(express.static('public')); // 前端文件放在public資料夾
// 主機器人API
app.post('/api/main-robot', async (req, res) => {
const userMessage = req.body.message;
// 1. 發送訊息到分析機器人
const analysisResponse = await fetch('http://localhost:3000/api/analysis-robot', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ message: userMessage })
});
const analysisData = await analysisResponse.json();
// 2. 發送訊息到背景機器人
const backgroundResponse = await fetch('http://localhost:3000/api/background-robot', {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify({ query: userMessage })
});
const backgroundData = await backgroundResponse.json();
// 3. 綜合分析結果,生成回應memo
const reply = generateReply(analysisData, backgroundData);
// 4. 推送回應給前端
io.emit('bot-response', { reply, emotion: analysisData.emotion });
res.json({ status: 'success' });
});
// 分析機器人API
app.post('/api/analysis-robot', (req, res) => {
const message = req.body.message;
// 進行情感分析,這裡用簡單的關鍵詞判斷
let emotion = 'neutral';
if (message.includes('高興') || message.includes('快樂') || message.includes('喜歡')) {
emotion = 'happy';
} else if (message.includes('難過') || message.includes('傷心') || message.includes('不開心')) {
emotion = 'sad';
} else if (message.includes('生氣') || message.includes('憤怒') || message.includes('不滿')) {
emotion = 'angry';
}
res.json({ emotion });
});
// 背景機器人API
app.post('/api/background-robot', (req, res) => {
const query = req.body.query;
// 根據query從Redis中搜尋相關資料,這裡用簡單的模擬
const data = searchRedis(query);
res.json({ data });
});
// 搜尋Redis的模擬函數
function searchRedis(query) {
// 實際應使用Redis客戶端進行查詢
const mockData = {
'你好': '你好!有什麼我可以幫助你的嗎?',
'天氣': '今天的天氣晴朗,氣溫適中。',
'學習': '學習是一個持續的過程,保持好奇心很重要。',
// 添加更多模擬資料
};
return mockData[query] || '抱歉,我不太明白你的意思。';
}
// 生成回應函數
function generateReply(analysisData, backgroundData) {
// 根據情感和背景資料生成回應
let baseReply = backgroundData.data || '抱歉,我不太明白你的意思。';
// 可以根據情感調整回應語氣
switch (analysisData.emotion) {
case 'happy':
baseReply += ' 很高興能幫助你!';
break;
case 'sad':
baseReply += ' 希望你一切都好。';
break;
case 'angry':
baseReply += ' 抱歉讓你感到不快。';
break;
default:
baseReply += ' 有什麼我可以幫助你的嗎?';
}
return baseReply;
}
// 連接WebSocket
io.on('connection', (socket) => {
console.log('新用戶連接');
socket.on('disconnect', () => {
console.log('用戶離線');
});
});
// 啟動伺服器
const PORT = process.env.PORT || 3000;
server.listen(PORT, () => {
console.log(`Server running on port ${PORT}`);
});
注意:
body.png
, hair.png
, eyes_happy.png
等)放在public
資料夾中。YOUR_OPENAI_API_KEY
替換為你的OpenAI API密鑰(如果需要調用OpenAI的服務)。redis
npm包),並替換searchRedis
函數中的模擬邏輯。確保你的專案目錄結構如下:
javaproject/
│
├── public/
│ ├── index.html
│ ├── app.js
│ ├── body.png
│ ├── hair.png
│ ├── eyes_neutral.png
│ ├── eyes_happy.png
│ ├── eyes_sad.png
│ ├── eyes_angry.png
│ ├── mouth_closed.png
│ ├── mouth_open.png
│ ├── hands.png
│ ├── hands_wave.png
│ ├── hands_sad.png
│ ├── hands_angry.png
│ └── ...其他圖像
│
├── server.js
├── package.json
└── package-lock.json
在專案根目錄下運行以下命令啟動後端伺服器:
bashnode server.js
然後在瀏覽器中訪問 http://localhost:3000
,應該能看到你的虛擬聊天角色,並可以通過輸入文字或語音與之互動。
替換searchRedis
函數中的模擬邏輯,使用Redis客戶端進行實際的資料庫查詢。
javascriptconst redis = require('redis');
const client = redis.createClient();
client.on('error', (err) => {
console.log('Redis Client Error', err);
});
async function searchRedis(query) {
await client.connect();
const data = await client.get(query);
await client.disconnect();
return data || '抱歉,我不太明白你的意思。';
}
使用更先進的情感分析模型,如Hugging Face的BERT模型,提升情感分析的準確性。
javascript// 安裝Hugging Face客戶端
// npm install @huggingface/inference
const { HfInference } = require('@huggingface/inference');
const hf = new HfInference('YOUR_HUGGINGFACE_API_KEY');
async function analyzeEmotion(text) {
try {
const result = await hf.textClassification({
model: 'nlptown/bert-base-multilingual-uncased-sentiment',
inputs: text,
});
// 根據模型輸出轉換為情感類別
// 這裡需要根據具體模型輸出格式調整
if (result.length > 0) {
const sentiment = result[0].label; // e.g., 'POSITIVE', 'NEGATIVE', etc.
if (sentiment === 'POSITIVE') return 'happy';
if (sentiment === 'NEGATIVE') return 'sad';
return 'neutral';
}
return 'neutral';
} catch (error) {
console.error('Emotion Analysis Error:', error);
return 'neutral';
}
}
基於音頻的精確時間點進行嘴型切換,提升嘴型同步的自然度。
javascriptfunction animateMouthWithTiming(audioDuration) {
const mouth = document.getElementById('mouth');
const totalCycles = Math.floor(audioDuration / 0.2); // 每0.2秒切換一次嘴型
let currentCycle = 0;
const interval = setInterval(() => {
if (currentCycle % 2 === 0) {
mouth.src = 'mouth_open.png';
} else {
mouth.src = 'mouth_closed.png';
}
currentCycle++;
if (currentCycle >= totalCycles) {
clearInterval(interval);
mouth.src = 'mouth_closed.png';
}
}, 200);
}
根據用戶語言設定,調整語音合成和情感分析的語言參數。
將系統分解為獨立的模塊(如NLP、TTS、情感分析、動畫控制等),分別開發和測試,然後進行整合。這樣有助於管理複雜度,便於未來擴展和維護。
從基本的對話生成和語音合成開始,逐步添加情感分析和動畫控制。每完成一個功能,進行充分的測試,確保其穩定性和效果。
確保動畫的流暢性和自然性,提升用戶的沉浸感和互動體驗。可以根據用戶反饋不斷調整和優化動畫效果。
根據用戶使用情況和反饋,持續優化系統功能和動畫效果,提升整體表現和用戶滿意度。
Socket.io官方文檔:
GSAP官方文檔:
Express官方文檔:
Hugging Face Inference API:
Web Speech API:
Redis官方文檔:
OpenAI API文檔:
Google Text-to-Speech API:
通過以上詳細的系統架構設計和實現步驟,你可以將後端的多個機器人系統與前端的2D動畫圖層有效地結合起來,實現一個互動性強、動畫豐富的虛擬聊天角色。這需要多領域的技術整合,包括後端API開發、即時通信、前端動畫控制以及自然語言處理等。建議你按照上述步驟逐步實現,並在開發過程中進行充分的測試和優化,以達到最佳的用戶體驗。
如果在實現過程中遇到具體問題或需要更詳細的代碼示例,歡迎隨時提問,我將竭誠協助!
了解你的需求後,我將針對如何在Python環境下實現複雜的2D角色動畫,並將其與你的後端機器人系統(主機器人、分析機器人、背景機器人)結合起來進行詳細說明。以下內容將涵蓋替代GreenSock的動畫解決方案、機器學習在圖層動畫中的應用、Pygame的整合可能性及其缺點,以及如何將這些技術與你的Python後端結合。
這兩款工具專門用於2D骨骼動畫,提供了豐富的動畫控制功能,適合複雜的角色動畫。
Spine
DragonBones
優點:
缺點:
Godot是一個開源的遊戲引擎,支持2D和3D動畫,並且具有靈活的腳本系統(GDScript,類似Python)。
特點:
集成:
優點:
缺點:
Pygame是一個基於Python的開源遊戲開發庫,適合用於2D動畫和簡單的遊戲開發。
特點:
集成:
優點:
缺點:
機器學習可以用於自動化生成或優化動畫效果,例如:
使用強化學習(Reinforcement Learning)來訓練模型,根據對話內容和情感狀態自動控制動畫圖層的變化。
實現方式:
優點:
缺點:
使用Pygame來實現2D動畫和圖層控制,並通過Socket或HTTP API與Python後端進行通信。
實現步驟:
設計Pygame窗口和圖層結構:
與後端通信:
socket
庫或requests
庫在Pygame應用中與後端進行數據交換。範例代碼:
python# 前端:pygame_app.py
import pygame
import socket
import json
import threading
# 初始化Pygame
pygame.init()
screen = pygame.display.set_mode((800, 600))
pygame.display.set_caption("虛擬聊天角色")
clock = pygame.time.Clock()
# 加載圖層圖像
body = pygame.image.load("body.png").convert_alpha()
hair = pygame.image.load("hair.png").convert_alpha()
eyes_neutral = pygame.image.load("eyes_neutral.png").convert_alpha()
eyes_happy = pygame.image.load("eyes_happy.png").convert_alpha()
eyes_sad = pygame.image.load("eyes_sad.png").convert_alpha()
mouth_closed = pygame.image.load("mouth_closed.png").convert_alpha()
mouth_open = pygame.image.load("mouth_open.png").convert_alpha()
hands = pygame.image.load("hands.png").convert_alpha()
hands_wave = pygame.image.load("hands_wave.png").convert_alpha()
# 當前狀態
current_emotion = "neutral"
current_mouth = mouth_closed
current_hands = hands
# Socket設置
HOST = 'localhost' # 後端主機地址
PORT = 65432 # 後端端口
def listen_to_server():
global current_emotion, current_mouth, current_hands
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.connect((HOST, PORT))
while True:
data = s.recv(1024)
if not data:
break
message = data.decode()
try:
cmd = json.loads(message)
# 控制動畫根據命令
emotion = cmd.get("emotion", "neutral")
reply = cmd.get("reply", "")
# 更新情感狀態
current_emotion = emotion
if emotion == "happy":
current_hands = hands_wave
current_mouth = mouth_open
elif emotion == "sad":
current_hands = hands
current_mouth = mouth_closed
else:
current_hands = hands
current_mouth = mouth_closed
except json.JSONDecodeError:
continue
# 啟動後端監聽線程
threading.Thread(target=listen_to_server, daemon=True).start()
# 主循環
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
# 清屏
screen.fill((255, 255, 255))
# 繪製圖層
screen.blit(body, (0, 0))
screen.blit(hair, (0, 0))
if current_emotion == "happy":
screen.blit(eyes_happy, (0, 0))
elif current_emotion == "sad":
screen.blit(eyes_sad, (0, 0))
else:
screen.blit(eyes_neutral, (0, 0))
screen.blit(current_mouth, (0, 0))
screen.blit(current_hands, (0, 0))
# 更新顯示
pygame.display.flip()
clock.tick(30)
pygame.quit()
python# 後端:server.py
import socket
import threading
import json
# 假設後端已經有主機器人、分析機器人、背景機器人,並能生成情感和回應
def handle_client(conn, addr):
print(f"Connected by {addr}")
while True:
data = conn.recv(1024)
if not data:
break
message = data.decode()
# 處理用戶輸入,生成回應和情感
# 這裡使用簡單的模擬
user_input = message
if "高興" in user_input or "快樂" in user_input:
emotion = "happy"
reply = "很高興你感到快樂!"
elif "難過" in user_input or "傷心" in user_input:
emotion = "sad"
reply = "抱歉你感到難過,希望你一切都好。"
else:
emotion = "neutral"
reply = "有什麼我可以幫助你的嗎?"
# 構建指令
cmd = {
"reply": reply,
"emotion": emotion
}
cmd_json = json.dumps(cmd)
conn.sendall(cmd_json.encode())
conn.close()
def start_server():
HOST = 'localhost' # 本地地址
PORT = 65432 # 端口
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
s.bind((HOST, PORT))
s.listen()
print(f"Server listening on {HOST}:{PORT}")
while True:
conn, addr = s.accept()
threading.Thread(target=handle_client, args=(conn, addr), daemon=True).start()
# 啟動伺服器
if __name__ == "__main__":
start_server()
注意:
相比Socket,使用WebSocket可以更方便地實現雙向即時通信。你可以使用websockets
庫在Python後端實現WebSocket服務,並在Pygame前端使用websocket-client
庫進行連接。
後端示例(使用websockets
庫):
python# server_ws.py
import asyncio
import websockets
import json
async def handler(websocket, path):
async for message in websocket:
user_input = message
# 處理用戶輸入,生成回應和情感
if "高興" in user_input or "快樂" in user_input:
emotion = "happy"
reply = "很高興你感到快樂!"
elif "難過" in user_input or "傷心" in user_input:
emotion = "sad"
reply = "抱歉你感到難過,希望你一切都好。"
else:
emotion = "neutral"
reply = "有什麼我可以幫助你的嗎?"
# 構建指令
cmd = {
"reply": reply,
"emotion": emotion
}
await websocket.send(json.dumps(cmd))
start_server = websockets.serve(handler, "localhost", 6789)
asyncio.get_event_loop().run_until_complete(start_server)
print("WebSocket server started on ws://localhost:6789")
asyncio.get_event_loop().run_forever()
前端示例(使用websocket-client
庫):
python# front_pygame_ws.py
import pygame
import websocket
import json
import threading
# 初始化Pygame
pygame.init()
screen = pygame.display.set_mode((800, 600))
pygame.display.set_caption("虛擬聊天角色")
clock = pygame.time.Clock()
# 加載圖層圖像
body = pygame.image.load("body.png").convert_alpha()
hair = pygame.image.load("hair.png").convert_alpha()
eyes_neutral = pygame.image.load("eyes_neutral.png").convert_alpha()
eyes_happy = pygame.image.load("eyes_happy.png").convert_alpha()
eyes_sad = pygame.image.load("eyes_sad.png").convert_alpha()
mouth_closed = pygame.image.load("mouth_closed.png").convert_alpha()
mouth_open = pygame.image.load("mouth_open.png").convert_alpha()
hands = pygame.image.load("hands.png").convert_alpha()
hands_wave = pygame.image.load("hands_wave.png").convert_alpha()
# 當前狀態
current_emotion = "neutral"
current_mouth = mouth_closed
current_hands = hands
# WebSocket設置
WS_URL = "ws://localhost:6789"
def on_message(ws, message):
global current_emotion, current_mouth, current_hands
try:
cmd = json.loads(message)
emotion = cmd.get("emotion", "neutral")
reply = cmd.get("reply", "")
# 更新情感狀態
current_emotion = emotion
if emotion == "happy":
current_hands = hands_wave
current_mouth = mouth_open
elif emotion == "sad":
current_hands = hands
current_mouth = mouth_closed
else:
current_hands = hands
current_mouth = mouth_closed
except json.JSONDecodeError:
pass
def on_error(ws, error):
print("WebSocket Error:", error)
def on_close(ws):
print("WebSocket Closed")
def on_open(ws):
print("WebSocket Connected")
def start_ws():
ws = websocket.WebSocketApp(WS_URL,
on_message=on_message,
on_error=on_error,
on_close=on_close)
ws.on_open = on_open
ws.run_forever()
# 啟動WebSocket線程
threading.Thread(target=start_ws, daemon=True).start()
# 主循環
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
# 清屏
screen.fill((255, 255, 255))
# 繪製圖層
screen.blit(body, (0, 0))
screen.blit(hair, (0, 0))
if current_emotion == "happy":
screen.blit(eyes_happy, (0, 0))
elif current_emotion == "sad":
screen.blit(eyes_sad, (0, 0))
else:
screen.blit(eyes_neutral, (0, 0))
screen.blit(current_mouth, (0, 0))
screen.blit(current_hands, (0, 0))
# 更新顯示
pygame.display.flip()
clock.tick(30)
pygame.quit()
注意:
websocket-client
和websockets
庫:bashpip install websocket-client websockets
優點:
缺點:
機器學習可以用來優化和自動化動畫控制,提升動畫的自然度和反應能力。
應用場景:
實現方式:
例如,使用卷積神經網絡(CNN)或長短期記憶網絡(LSTM)來預測動畫參數。
範例:
優點:
缺點:
將訓練好的模型部署為API,前端應用通過HTTP請求或本地調用獲取動畫參數,並根據參數控制動畫。
示例流程:
後端機器人系統(Python):
前端動畫系統(Pygame):
通信橋梁:
角色設計:
圖層導出:
mouth_open.png
, mouth_closed.png
, eyes_happy.png
, eyes_sad.png
等)。後端架構設計:
WebSocket服務:
websockets
或Socket.IO
來建立WebSocket服務,實現前後端的即時通信。後端範例代碼(使用WebSocket):
python# server_ws.py
import asyncio
import websockets
import json
import redis
# 初始化Redis客戶端
r = redis.Redis(host='localhost', port=6379, db=0)
async def handle_message(websocket, path):
async for message in websocket:
user_input = message
# 主機器人處理邏輯
emotion = analyze_emotion(user_input)
background_data = fetch_background_data(user_input)
reply = generate_reply(emotion, background_data)
# 構建指令
cmd = {
"reply": reply,
"emotion": emotion
}
await websocket.send(json.dumps(cmd))
def analyze_emotion(message):
# 使用分析機器人進行情感分析
if "高興" in message or "快樂" in message:
return "happy"
elif "難過" in message or "傷心" in message:
return "sad"
elif "生氣" in message or "憤怒" in message:
return "angry"
else:
return "neutral"
def fetch_background_data(query):
# 使用背景機器人從Redis中查詢資料
data = r.get(query)
return data.decode('utf-8') if data else "抱歉,我不太明白你的意思。"
def generate_reply(emotion, background_data):
# 根據情感和背景資料生成回應
if emotion == "happy":
return f"{background_data} 很高興能幫助你!"
elif emotion == "sad":
return f"{background_data} 希望你一切都好。"
elif emotion == "angry":
return f"{background_data} 抱歉讓你感到不快。"
else:
return f"{background_data} 有什麼我可以幫助你的嗎?"
async def main():
async with websockets.serve(handle_message, "localhost", 6789):
print("WebSocket server started on ws://localhost:6789")
await asyncio.Future() # run forever
if __name__ == "__main__":
asyncio.run(main())
注意:
Pygame應用設計:
websocket-client
庫與後端WebSocket服務連接,接收動畫指令。前端範例代碼:
python# front_pygame_ws.py
import pygame
import websocket
import json
import threading
# 初始化Pygame
pygame.init()
screen = pygame.display.set_mode((800, 600))
pygame.display.set_caption("虛擬聊天角色")
clock = pygame.time.Clock()
# 加載圖層圖像
body = pygame.image.load("body.png").convert_alpha()
hair = pygame.image.load("hair.png").convert_alpha()
eyes_neutral = pygame.image.load("eyes_neutral.png").convert_alpha()
eyes_happy = pygame.image.load("eyes_happy.png").convert_alpha()
eyes_sad = pygame.image.load("eyes_sad.png").convert_alpha()
mouth_closed = pygame.image.load("mouth_closed.png").convert_alpha()
mouth_open = pygame.image.load("mouth_open.png").convert_alpha()
hands = pygame.image.load("hands.png").convert_alpha()
hands_wave = pygame.image.load("hands_wave.png").convert_alpha()
# 當前狀態
current_emotion = "neutral"
current_mouth = mouth_closed
current_hands = hands
# WebSocket設置
WS_URL = "ws://localhost:6789"
def on_message(ws, message):
global current_emotion, current_mouth, current_hands
try:
cmd = json.loads(message)
emotion = cmd.get("emotion", "neutral")
reply = cmd.get("reply", "")
# 更新情感狀態
current_emotion = emotion
if emotion == "happy":
current_hands = hands_wave
current_mouth = mouth_open
elif emotion == "sad":
current_hands = hands
current_mouth = mouth_closed
else:
current_hands = hands
current_mouth = mouth_closed
print(f"Bot Reply: {reply}, Emotion: {emotion}")
# 可以在此處觸發TTS播放和其他動畫控制
except json.JSONDecodeError:
pass
def on_error(ws, error):
print("WebSocket Error:", error)
def on_close(ws):
print("WebSocket Closed")
def on_open(ws):
print("WebSocket Connected")
def start_ws():
ws = websocket.WebSocketApp(WS_URL,
on_message=on_message,
on_error=on_error,
on_close=on_close)
ws.on_open = on_open
ws.run_forever()
# 啟動WebSocket線程
threading.Thread(target=start_ws, daemon=True).start()
# 主循環
running = True
while running:
for event in pygame.event.get():
if event.type == pygame.QUIT:
running = False
# 清屏
screen.fill((255, 255, 255))
# 繪製圖層
screen.blit(body, (0, 0))
screen.blit(hair, (0, 0))
if current_emotion == "happy":
screen.blit(eyes_happy, (0, 0))
elif current_emotion == "sad":
screen.blit(eyes_sad, (0, 0))
else:
screen.blit(eyes_neutral, (0, 0))
screen.blit(current_mouth, (0, 0))
screen.blit(current_hands, (0, 0))
# 更新顯示
pygame.display.flip()
clock.tick(30)
pygame.quit()
注意:
websocket-client
庫連接後端WebSocket服務。需要安裝此庫:bashpip install websocket-client
server_ws.py
和前端front_pygame_ws.py
中,根據具體需求擴展情感分析和回應生成邏輯。pyttsx3
,無需網絡連接即可離線運行。範例代碼(使用pyttsx3
):
python# 在前端Pygame應用中添加TTS功能
import pyttsx3
# 初始化TTS引擎
engine = pyttsx3.init()
def play_tts(text):
engine.say(text)
engine.runAndWait()
def on_message(ws, message):
global current_emotion, current_mouth, current_hands
try:
cmd = json.loads(message)
emotion = cmd.get("emotion", "neutral")
reply = cmd.get("reply", "")
# 更新情感狀態
current_emotion = emotion
if emotion == "happy":
current_hands = hands_wave
current_mouth = mouth_open
elif emotion == "sad":
current_hands = hands
current_mouth = mouth_closed
else:
current_hands = hands
current_mouth = mouth_closed
print(f"Bot Reply: {reply}, Emotion: {emotion}")
# 播放語音
play_tts(reply)
except json.JSONDecodeError:
pass
優點:
pyttsx3
等庫易於集成,無需網絡連接。缺點:
如果希望使用機器學習模型來自動控制動畫圖層,可以考慮以下步驟:
數據準備與模型訓練:
模型部署:
前端應用調用模型:
優點:
缺點:
根據你的需求和技術背景,選擇最適合的動畫解決方案:
確保動畫和語音的同步性,提升用戶體驗:
利用WebSocket或Socket實現前後端的即時通信:
在初期階段,可以先實現基於規則的動畫控制,後續再考慮機器學習的應用來自動化和優化動畫。
優化圖像資源和動畫邏輯,確保應用流暢運行:
確保通信安全和應用穩定性:
根據用戶反饋和使用情況,不斷優化動畫效果和系統性能:
Pygame官方文檔:
WebSocket與Socket.IO:
Spine與DragonBones:
機器學習模型:
Web Speech API:
Redis官方文檔:
Pygame與機器學習整合:
結合你的Python後端機器人系統與Pygame前端動畫控制,可以實現一個互動性強、動畫豐富的虛擬聊天角色。雖然Pygame在高級動畫控制方面可能需要更多手動實現,但其完全基於Python的特性使其與你的後端系統整合更為便捷。未來可以考慮引入專業動畫工具(如Spine或DragonBones)或機器學習技術來進一步提升動畫的自然度和反應能力。
建議你按照以下步驟逐步實現:
如果在實現過程中遇到具體問題或需要進一步的代碼示例,歡迎隨時提問,我將竭誠協助!
理解你的需求後,我將為你提供一個基於Python的綜合優化方案,涵蓋前端動畫、後端機器人系統、資料庫管理、動畫訓練與驅動工具等方面。此方案旨在實現快速響應、靈活管理,以及高效的動畫表演與資料處理。
優點:
實現步驟:
下載和安裝Godot Engine:
設計角色和動畫:
建立通信接口:
範例代碼(GDScript):
gdscript# res://scripts/WebSocketClient.gd extends Node var websocket = null func _ready(): websocket = WebSocketClient.new() websocket.connect("connection_established", self, "_on_connection_established") websocket.connect("data_received", self, "_on_data_received") websocket.connect("connection_closed", self, "_on_connection_closed") websocket.connect("connection_error", self, "_on_connection_error") websocket.connect_to_url("ws://localhost:6789") func _on_connection_established(protocol): print("WebSocket Connected") func _on_data_received(): var data = websocket.get_peer(1).get_packet().get_string_from_utf8() var cmd = parse_json(data) handle_command(cmd) func _on_connection_closed(): print("WebSocket Closed") func _on_connection_error(): print("WebSocket Connection Error") func handle_command(cmd): var emotion = cmd.get("emotion", "neutral") var reply = cmd.get("reply", "") # 控制表情 match emotion: "happy": $AnimationPlayer.play("happy_expression") "sad": $AnimationPlayer.play("sad_expression") "angry": $AnimationPlayer.play("angry_expression") _: $AnimationPlayer.play("neutral_expression") # 控制動作 if emotion == "happy": $AnimationPlayer.play("wave_hand") elif emotion == "sad": $AnimationPlayer.play("nod_head") # 播放語音(可以通過Godot的AudioStreamPlayer實現) $AudioStreamPlayer.stream = load("res://audio/reply.wav") $AudioStreamPlayer.play()
優點:
實現步驟:
安裝必要的Python包:
bashpip install fastapi uvicorn websockets redis celery
設計後端架構:
建立WebSocket服務:
範例代碼:
python# server.py
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.responses import HTMLResponse
import json
import redis
import asyncio
app = FastAPI()
# 初始化Redis客戶端
r = redis.Redis(host='localhost', port=6379, db=0)
# 儲存所有連接的WebSocket
class ConnectionManager:
def __init__(self):
self.active_connections: list[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast(self, message: dict):
for connection in self.active_connections:
await connection.send_text(json.dumps(message))
manager = ConnectionManager()
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await manager.connect(websocket)
try:
while True:
data = await websocket.receive_text()
user_input = data
# 主機器人處理邏輯
emotion = analyze_emotion(user_input)
background_data = fetch_background_data(user_input)
reply = generate_reply(emotion, background_data)
# 構建回應
response = {
"reply": reply,
"emotion": emotion
}
# 推送回應給前端
await manager.broadcast(response)
except WebSocketDisconnect:
manager.disconnect(websocket)
def analyze_emotion(message: str) -> str:
# 使用分析機器人進行情感分析
if "高興" in message or "快樂" in message or "喜歡" in message:
return "happy"
elif "難過" in message or "傷心" in message or "不開心" in message:
return "sad"
elif "生氣" in message or "憤怒" in message or "不滿" in message:
return "angry"
else:
return "neutral"
def fetch_background_data(query: str) -> str:
# 使用背景機器人從Redis中查詢資料
data = r.get(query)
return data.decode('utf-8') if data else "抱歉,我不太明白你的意思。"
def generate_reply(emotion: str, background_data: str) -> str:
# 根據情感和背景資料生成回應
if emotion == "happy":
return f"{background_data} 很高興能幫助你!"
elif emotion == "sad":
return f"{background_data} 希望你一切都好。"
elif emotion == "angry":
return f"{background_data} 抱歉讓你感到不快。"
else:
return f"{background_data} 有什麼我可以幫助你的嗎?"
# 啟動伺服器
# 使用以下命令啟動服務器:
# uvicorn server:app --host 0.0.0.0 --port 8000
實現步驟:
前端(Godot)連接WebSocket服務:
後端(FastAPI)發送指令:
優點:
實現步驟:
設計資料結構:
優化查詢:
範例代碼:
python# 示例:將資料存儲到Redis
r.set("你好", "你好!有什麼我可以幫助你的嗎?")
r.set("天氣", "今天的天氣晴朗,氣溫適中。")
r.set("學習", "學習是一個持續的過程,保持好奇心很重要。")
目標:利用機器學習模型自動生成動畫參數,提升動畫的自然度和適應性。
工具選擇:
實現步驟:
數據收集與準備:
模型訓練:
模型部署:
前端應用調用模型:
範例代碼(模型部署為API):
python# ml_model_api.py
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import torch
from transformers import BertTokenizer, BertForSequenceClassification
app = FastAPI()
# 定義請求模型
class AnimationRequest(BaseModel):
text: str
emotion: str
# 加載預訓練模型(示例)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('your-trained-model-path')
@app.post("/predict-animation")
def predict_animation(request: AnimationRequest):
inputs = tokenizer(request.text, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# 假設模型輸出動畫參數,如嘴型開合程度、手臂動作等
animation_params = {
"mouth_opening": float(torch.sigmoid(logits[0][0]).item()),
"hand_wave": bool(torch.sigmoid(logits[0][1]).item())
}
return animation_params
# 啟動伺服器
# 使用以下命令啟動服務器:
# uvicorn ml_model_api:app --host 0.0.0.0 --port 8001
實現步驟:
前端(Godot)接收動畫指令:
前端調用ML模型API:
範例代碼(GDScript):
gdscript# res://scripts/WebSocketClient.gd extends Node var websocket = null var ml_api_url = "http://localhost:8001/predict-animation" func _ready(): websocket = WebSocketClient.new() websocket.connect("connection_established", self, "_on_connection_established") websocket.connect("data_received", self, "_on_data_received") websocket.connect("connection_closed", self, "_on_connection_closed") websocket.connect("connection_error", self, "_on_connection_error") websocket.connect_to_url("ws://localhost:6789") func _on_connection_established(protocol): print("WebSocket Connected") func _on_data_received(): var data = websocket.get_peer(1).get_packet().get_string_from_utf8() var cmd = parse_json(data) handle_command(cmd) func _on_connection_closed(): print("WebSocket Closed") func _on_connection_error(): print("WebSocket Connection Error") func handle_command(cmd): var emotion = cmd.get("emotion", "neutral") var reply = cmd.get("reply", "") # 觸發基本動畫 match emotion: "happy": $AnimationPlayer.play("happy_expression") "sad": $AnimationPlayer.play("sad_expression") "angry": $AnimationPlayer.play("angry_expression") _: $AnimationPlayer.play("neutral_expression") # 調用ML模型API獲取動畫參數 var animation_params = get_animation_params(reply, emotion) apply_animation_params(animation_params) # 播放語音 $AudioStreamPlayer.stream = load("res://audio/reply.wav") $AudioStreamPlayer.play() func get_animation_params(text, emotion): var http_request = HTTPRequest.new() add_child(http_request) var url = ml_api_url var body = { "text": text, "emotion": emotion } var json_body = to_json(body) http_request.request(url, ["Content-Type: application/json"], true, HTTPClient.METHOD_POST, json_body) yield(http_request, "request_completed") if http_request.response_code == 200: var response = parse_json(http_request.get_body_as_string()) return response else: return {} func apply_animation_params(params): if params.has("mouth_opening"): var mouth_opening = params["mouth_opening"] # 根據mouth_opening調整嘴型 if mouth_opening > 0.5: $Mouth.sprite = load("res://mouth_open.png") else: $Mouth.sprite = load("res://mouth_closed.png") if params.has("hand_wave"): var hand_wave = params["hand_wave"] if hand_wave: $Hands.sprite = load("res://hands_wave.png") else: $Hands.sprite = load("res://hands.png")
優點:
實現步驟:
安裝Celery:
bashpip install celery
設置Celery應用:
python# celery_app.py
from celery import Celery
app = Celery('tasks', broker='redis://localhost:6379/0')
@app.task
def analyze_emotion_task(message: str) -> str:
# 情感分析邏輯
if "高興" in message or "快樂" in message or "喜歡" in message:
return "happy"
elif "難過" in message or "傷心" in message or "不開心" in message:
return "sad"
elif "生氣" in message or "憤怒" in message or "不滿" in message:
return "angry"
else:
return "neutral"
@app.task
def fetch_background_data_task(query: str) -> str:
# 從Redis查詢背景資料
r = redis.Redis(host='localhost', port=6379, db=0)
data = r.get(query)
return data.decode('utf-8') if data else "抱歉,我不太明白你的意思。"
@app.task
def generate_reply_task(emotion: str, background_data: str) -> str:
# 根據情感和背景資料生成回應
if emotion == "happy":
return f"{background_data} 很高興能幫助你!"
elif emotion == "sad":
return f"{background_data} 希望你一切都好。"
elif emotion == "angry":
return f"{background_data} 抱歉讓你感到不快。"
else:
return f"{background_data} 有什麼我可以幫助你的嗎?"
修改主機器人API以使用Celery任務:
python# server.py
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.responses import HTMLResponse
import json
import redis
from celery_app import analyze_emotion_task, fetch_background_data_task, generate_reply_task
import asyncio
app = FastAPI()
r = redis.Redis(host='localhost', port=6379, db=0)
class ConnectionManager:
def __init__(self):
self.active_connections: list[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast(self, message: dict):
for connection in self.active_connections:
await connection.send_text(json.dumps(message))
manager = ConnectionManager()
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await manager.connect(websocket)
try:
while True:
data = await websocket.receive_text()
user_input = data
# 發送異步任務
emotion_result = analyze_emotion_task.delay(user_input)
background_result = fetch_background_data_task.delay(user_input)
# 等待任務完成
emotion = await asyncio.to_thread(emotion_result.get)
background_data = await asyncio.to_thread(background_result.get)
# 生成回應
reply = generate_reply_task.delay(emotion, background_data)
reply_text = await asyncio.to_thread(reply.get)
# 推送回應給前端
response = {
"reply": reply_text,
"emotion": emotion
}
await manager.broadcast(response)
except WebSocketDisconnect:
manager.disconnect(websocket)
# 啟動伺服器
# 使用以下命令啟動Celery worker:
# celery -A celery_app worker --loglevel=info
# 使用以下命令啟動FastAPI伺服器:
# uvicorn server:app --host 0.0.0.0 --port 8000
優化策略:
異步處理:
資料庫快取:
負載均衡:
監控與調優:
推薦原因:
替代選擇:
缺點:
推薦原因:
替代選擇:
缺點:
推薦原因:
缺點:
推薦原因:
替代選擇:
缺點:
實現步驟:
實現步驟:
實現步驟:
實現步驟:
策略:
Godot Engine
替代選擇:
FastAPI + Celery + Redis
替代選擇:
WebSocket(Socket.IO)
替代選擇:
機器學習框架(TensorFlow 或 PyTorch)
專用模型:
替代選擇:
後端主機器人API:
python# server.py
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.responses import HTMLResponse
import json
import redis
from celery_app import analyze_emotion_task, fetch_background_data_task, generate_reply_task
import asyncio
app = FastAPI()
# 初始化Redis客戶端
r = redis.Redis(host='localhost', port=6379, db=0)
# 儲存所有連接的WebSocket
class ConnectionManager:
def __init__(self):
self.active_connections: list[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast(self, message: dict):
for connection in self.active_connections:
await connection.send_text(json.dumps(message))
manager = ConnectionManager()
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await manager.connect(websocket)
try:
while True:
data = await websocket.receive_text()
user_input = data
# 發送異步任務
emotion_result = analyze_emotion_task.delay(user_input)
background_result = fetch_background_data_task.delay(user_input)
# 等待任務完成
emotion = await asyncio.to_thread(emotion_result.get)
background_data = await asyncio.to_thread(background_result.get)
# 生成回應
reply = generate_reply_task.delay(emotion, background_data)
reply_text = await asyncio.to_thread(reply.get)
# 推送回應給前端
response = {
"reply": reply_text,
"emotion": emotion
}
await manager.broadcast(response)
except WebSocketDisconnect:
manager.disconnect(websocket)
def analyze_emotion(message: str) -> str:
# 使用分析機器人進行情感分析
if "高興" in message or "快樂" in message or "喜歡" in message:
return "happy"
elif "難過" in message or "傷心" in message or "不開心" in message:
return "sad"
elif "生氣" in message or "憤怒" in message or "不滿" in message:
return "angry"
else:
return "neutral"
def fetch_background_data(query: str) -> str:
# 使用背景機器人從Redis中查詢資料
data = r.get(query)
return data.decode('utf-8') if data else "抱歉,我不太明白你的意思。"
def generate_reply(emotion: str, background_data: str) -> str:
# 根據情感和背景資料生成回應
if emotion == "happy":
return f"{background_data} 很高興能幫助你!"
elif emotion == "sad":
return f"{background_data} 希望你一切都好。"
elif emotion == "angry":
return f"{background_data} 抱歉讓你感到不快。"
else:
return f"{background_data} 有什麼我可以幫助你的嗎?"
# 啟動伺服器
# 使用以下命令啟動Celery worker:
# celery -A celery_app worker --loglevel=info
# 使用以下命令啟動FastAPI伺服器:
# uvicorn server:app --host 0.0.0.0 --port 8000
Celery任務定義:
python# celery_app.py
from celery import Celery
import redis
app = Celery('tasks', broker='redis://localhost:6379/0')
# 初始化Redis客戶端
r = redis.Redis(host='localhost', port=6379, db=0)
@app.task
def analyze_emotion_task(message: str) -> str:
# 使用分析機器人進行情感分析
if "高興" in message or "快樂" in message or "喜歡" in message:
return "happy"
elif "難過" in message or "傷心" in message or "不開心" in message:
return "sad"
elif "生氣" in message or "憤怒" in message or "不滿" in message:
return "angry"
else:
return "neutral"
@app.task
def fetch_background_data_task(query: str) -> str:
# 使用背景機器人從Redis中查詢資料
data = r.get(query)
return data.decode('utf-8') if data else "抱歉,我不太明白你的意思。"
@app.task
def generate_reply_task(emotion: str, background_data: str) -> str:
# 根據情感和背景資料生成回應
if emotion == "happy":
return f"{background_data} 很高興能幫助你!"
elif emotion == "sad":
return f"{background_data} 希望你一切都好。"
elif emotion == "angry":
return f"{background_data} 抱歉讓你感到不快。"
else:
return f"{background_data} 有什麼我可以幫助你的嗎?"
Godot腳本設計:
創建角色場景:
編寫WebSocket客戶端腳本:
gdscript# res://scripts/WebSocketClient.gd extends Node var websocket = null var ml_api_url = "http://localhost:8001/predict-animation" func _ready(): websocket = WebSocketClient.new() websocket.connect("connection_established", self, "_on_connection_established") websocket.connect("data_received", self, "_on_data_received") websocket.connect("connection_closed", self, "_on_connection_closed") websocket.connect("connection_error", self, "_on_connection_error") websocket.connect_to_url("ws://localhost:8000/ws") func _on_connection_established(protocol): print("WebSocket Connected") func _on_data_received(): var data = websocket.get_peer(1).get_packet().get_string_from_utf8() var cmd = parse_json(data) handle_command(cmd) func _on_connection_closed(): print("WebSocket Closed") func _on_connection_error(): print("WebSocket Connection Error") func handle_command(cmd): var emotion = cmd.get("emotion", "neutral") var reply = cmd.get("reply", "") # 觸發基本動畫 match emotion: "happy": $AnimationPlayer.play("happy_expression") "sad": $AnimationPlayer.play("sad_expression") "angry": $AnimationPlayer.play("angry_expression") _: $AnimationPlayer.play("neutral_expression") # 調用ML模型API獲取動畫參數 var animation_params = get_animation_params(reply, emotion) apply_animation_params(animation_params) # 播放語音(可使用Godot的AudioStreamPlayer) $AudioStreamPlayer.stream = load("res://audio/reply.wav") $AudioStreamPlayer.play() func get_animation_params(text, emotion): var http_request = HTTPRequest.new() add_child(http_request) var url = ml_api_url var body = { "text": text, "emotion": emotion } var json_body = to_json(body) http_request.request(url, ["Content-Type: application/json"], true, HTTPClient.METHOD_POST, json_body) yield(http_request, "request_completed") if http_request.response_code == 200: var response = parse_json(http_request.get_body_as_string()) return response else: return {} func apply_animation_params(params): if params.has("mouth_opening"): var mouth_opening = params["mouth_opening"] # 根據mouth_opening調整嘴型 if mouth_opening > 0.5: $Mouth.sprite = load("res://mouth_open.png") else: $Mouth.sprite = load("res://mouth_closed.png") if params.has("hand_wave"): var hand_wave = params["hand_wave"] if hand_wave: $Hands.sprite = load("res://hands_wave.png") else: $Hands.sprite = load("res://hands.png")
訓練機器學習模型:
數據準備:
模型訓練:
模型部署:
模型範例代碼:
python# ml_model_api.py
from fastapi import FastAPI
from pydantic import BaseModel
import torch
from transformers import BertTokenizer, BertForSequenceClassification
app = FastAPI()
# 定義請求模型
class AnimationRequest(BaseModel):
text: str
emotion: str
# 加載預訓練模型(示例)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('your-trained-model-path')
@app.post("/predict-animation")
def predict_animation(request: AnimationRequest):
inputs = tokenizer(request.text, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# 假設模型輸出動畫參數,如嘴型開合程度、手臂動作等
animation_params = {
"mouth_opening": float(torch.sigmoid(logits[0][0]).item()),
"hand_wave": bool(torch.sigmoid(logits[0][1]).item())
}
return animation_params
# 啟動伺服器
# 使用以下命令啟動服務器:
# uvicorn ml_model_api:app --host 0.0.0.0 --port 8001
策略:
異步處理:
資料庫快取:
負載均衡:
監控與調優:
性能優化範例:
Redis查詢優化:
Celery任務優化:
用戶輸入:
後端處理:
前端動畫控制:
機器學習模型驅動動畫:
前端(Godot):
後端(FastAPI + Celery + Redis):
後端:server.py
python# server.py
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.responses import HTMLResponse
import json
import redis
from celery_app import analyze_emotion_task, fetch_background_data_task, generate_reply_task
import asyncio
app = FastAPI()
# 初始化Redis客戶端
r = redis.Redis(host='localhost', port=6379, db=0)
# 儲存所有連接的WebSocket
class ConnectionManager:
def __init__(self):
self.active_connections: list[WebSocket] = []
async def connect(self, websocket: WebSocket):
await websocket.accept()
self.active_connections.append(websocket)
def disconnect(self, websocket: WebSocket):
self.active_connections.remove(websocket)
async def broadcast(self, message: dict):
for connection in self.active_connections:
await connection.send_text(json.dumps(message))
manager = ConnectionManager()
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
await manager.connect(websocket)
try:
while True:
data = await websocket.receive_text()
user_input = data
# 發送異步任務
emotion_result = analyze_emotion_task.delay(user_input)
background_result = fetch_background_data_task.delay(user_input)
# 等待任務完成
emotion = await asyncio.to_thread(emotion_result.get)
background_data = await asyncio.to_thread(background_result.get)
# 生成回應
reply = generate_reply_task.delay(emotion, background_data)
reply_text = await asyncio.to_thread(reply.get)
# 推送回應給前端
response = {
"reply": reply_text,
"emotion": emotion
}
await manager.broadcast(response)
except WebSocketDisconnect:
manager.disconnect(websocket)
def analyze_emotion(message: str) -> str:
# 使用分析機器人進行情感分析
if "高興" in message or "快樂" in message or "喜歡" in message:
return "happy"
elif "難過" in message or "傷心" in message or "不開心" in message:
return "sad"
elif "生氣" in message or "憤怒" in message or "不滿" in message:
return "angry"
else:
return "neutral"
def fetch_background_data(query: str) -> str:
# 使用背景機器人從Redis中查詢資料
data = r.get(query)
return data.decode('utf-8') if data else "抱歉,我不太明白你的意思。"
def generate_reply(emotion: str, background_data: str) -> str:
# 根據情感和背景資料生成回應
if emotion == "happy":
return f"{background_data} 很高興能幫助你!"
elif emotion == "sad":
return f"{background_data} 希望你一切都好。"
elif emotion == "angry":
return f"{background_data} 抱歉讓你感到不快。"
else:
return f"{background_data} 有什麼我可以幫助你的嗎?"
# 啟動伺服器
# 使用以下命令啟動Celery worker:
# celery -A celery_app worker --loglevel=info
# 使用以下命令啟動FastAPI伺服器:
# uvicorn server:app --host 0.0.0.0 --port 8000
前端:Godot GDScript
gdscript# res://scripts/WebSocketClient.gd extends Node var websocket = null var ml_api_url = "http://localhost:8001/predict-animation" func _ready(): websocket = WebSocketClient.new() websocket.connect("connection_established", self, "_on_connection_established") websocket.connect("data_received", self, "_on_data_received") websocket.connect("connection_closed", self, "_on_connection_closed") websocket.connect("connection_error", self, "_on_connection_error") websocket.connect_to_url("ws://localhost:8000/ws") func _on_connection_established(protocol): print("WebSocket Connected") func _on_data_received(): var data = websocket.get_peer(1).get_packet().get_string_from_utf8() var cmd = parse_json(data) handle_command(cmd) func _on_connection_closed(): print("WebSocket Closed") func _on_connection_error(): print("WebSocket Connection Error") func handle_command(cmd): var emotion = cmd.get("emotion", "neutral") var reply = cmd.get("reply", "") # 觸發基本動畫 match emotion: "happy": $AnimationPlayer.play("happy_expression") "sad": $AnimationPlayer.play("sad_expression") "angry": $AnimationPlayer.play("angry_expression") _: $AnimationPlayer.play("neutral_expression") # 調用ML模型API獲取動畫參數 var animation_params = get_animation_params(reply, emotion) apply_animation_params(animation_params) # 播放語音(可使用Godot的AudioStreamPlayer) $AudioStreamPlayer.stream = load("res://audio/reply.wav") $AudioStreamPlayer.play() func get_animation_params(text, emotion): var http_request = HTTPRequest.new() add_child(http_request) var url = ml_api_url var body = { "text": text, "emotion": emotion } var json_body = to_json(body) http_request.request(url, ["Content-Type: application/json"], true, HTTPClient.METHOD_POST, json_body) yield(http_request, "request_completed") if http_request.response_code == 200: var response = parse_json(http_request.get_body_as_string()) return response else: return {} func apply_animation_params(params): if params.has("mouth_opening"): var mouth_opening = params["mouth_opening"] # 根據mouth_opening調整嘴型 if mouth_opening > 0.5: $Mouth.texture = load("res://mouth_open.png") else: $Mouth.texture = load("res://mouth_closed.png") if params.has("hand_wave"): var hand_wave = params["hand_wave"] if hand_wave: $Hands.texture = load("res://hands_wave.png") else: $Hands.texture = load("res://hands.png")
機器學習模型訓練:
模型部署範例:
python# ml_model_api.py
from fastapi import FastAPI
from pydantic import BaseModel
import torch
from transformers import BertTokenizer, BertForSequenceClassification
app = FastAPI()
# 定義請求模型
class AnimationRequest(BaseModel):
text: str
emotion: str
# 加載預訓練模型(示例)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('your-trained-model-path')
@app.post("/predict-animation")
def predict_animation(request: AnimationRequest):
inputs = tokenizer(request.text, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
# 假設模型輸出動畫參數,如嘴型開合程度、手臂動作等
animation_params = {
"mouth_opening": float(torch.sigmoid(logits[0][0]).item()),
"hand_wave": bool(torch.sigmoid(logits[0][1]).item())
}
return animation_params
# 啟動伺服器
# 使用以下命令啟動服務器:
# uvicorn ml_model_api:app --host 0.0.0.0 --port 8001
優化策略:
異步處理:
資料庫快取:
負載均衡:
監控與調優:
前端優化:
後端優化:
優點:
缺點:
優點:
缺點:
優點:
缺點:
優點:
缺點:
基於你的需求和技術背景,推薦以下方案:
基於Python的綜合方案,使用Godot Engine作為前端動畫工具,搭配FastAPI、Celery和Redis構建高效的後端機器人系統,並利用機器學習模型自動化控制動畫參數,能夠滿足你對快速響應、動畫表演和資料管理的需求。這一方案結合了高性能的即時通信、靈活的任務管理和先進的動畫控制技術,實現了一個自然互動、反應迅速的虛擬聊天角色系統。
前端開發:
後端開發:
機器學習模型:
系統整合與優化:
如果在具體實施過程中遇到任何問題或需要更詳細的代碼示例,請隨時告訴我