一、系统架构设计
A[数据采集] --> B[预处理模块]
B --> C[特征提取]
C --> D[多模态融合]
D --> E[情绪分类]
E --> F[系统部署]
F --> G[用户界面]
二、数据准备与处理
- 数据收集
-
视频数据：FER2013（静态图像）、RAVDESS（动态视频）
-
音频数据：CREMA-D、IEMOCAP
-
自定义采集：使用OpenCV+PyAudio实现同步采集
- 数据预处理
视频处理：
import cv2
def process_video(video_path):
cap = cv2.VideoCapture(video_path)
frames = []
while cap.isOpened():
ret, frame = cap.read()
if not ret: break
人脸检测
face = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face.detectMultiScale(gray, 1.3, 5)
裁剪和归一化
if len(faces) > 0:
(x,y,w,h) = faces[0]
roi = cv2.resize(gray[y:y+h, x:x+w], (128,128))
frames.append(roi)
return np.array(frames)
音频处理：
import librosa
def extract_audio_features(audio_path):
y, sr = librosa.load(audio_path, sr=16000)
分帧处理（30ms窗口）
frames = librosa.util.frame(y, frame_length=480, hop_length=160)
提取MFCC特征
mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=40)
动态特征拼接
delta = librosa.feature.delta(mfcc)
ddelta = librosa.feature.delta(mfcc, order=2)
return np.concatenate([mfcc, delta, ddelta], axis=0)
- 数据同步策略
-
使用FFmpeg提取视频时间戳
-
动态时间规整（DTW）对齐音视频序列
-
创建时间对齐的元数据文件
三、模型设计与训练
- 视觉分支（PyTorch实现）
import torch
from torchvision.models import resnet34
class VisualNet(nn.Module):
def init(self):
super().init()
self.base = resnet34(pretrained=True)
self.base.fc = nn.Identity() # 移除全连接层
self.temporal = nn.LSTM(512, 256, bidirectional=True)
def forward(self, x):
x: (B, T, C, H, W)
B, T = x.shape[:2]
x = x.view(B*T, *x.shape[2:])
features = self.base(x) # (B*T, 512)
features = features.view(B, T, -1)
out, _ = self.temporal(features)
return out[:, -1] # 取最后时刻输出
- 音频分支
class AudioNet(nn.Module):
def init(self):
super().init()
self.conv = nn.Sequential(
nn.Conv1d(120, 64, 3, padding=1),
nn.BatchNorm1d(64),
nn.ReLU(),
nn.MaxPool1d(2))
self.lstm = nn.LSTM(64, 128, bidirectional=True)
def forward(self, x):
x: (B, T, Features)
x = x.permute(0,2,1) # (B, Features, T)
x = self.conv(x)
x = x.permute(2,0,1) # (T, B, Features)
out, _ = self.lstm(x)
return out[-1]
- 多模态融合
注意力融合层：
class FusionModule(nn.Module):
def init(self, v_dim, a_dim):
super().init()
self.v_proj = nn.Linear(v_dim, 256)
self.a_proj = nn.Linear(a_dim, 256)
self.attention = nn.MultiheadAttention(256, 4)
def forward(self, v_feat, a_feat):
v = self.v_proj(v_feat).unsqueeze(0) # (1,B,256)
a = self.a_proj(a_feat).unsqueeze(0)
combined = torch.cat([v, a], dim=0) # (2,B,256)
attn_out, _ = self.attention(combined, combined, combined)
return attn_out.mean(dim=0)
四、训练策略
- 损失函数设计
class MultimodalLoss(nn.Module):
def init(self):
super().init()
self.ce = nn.CrossEntropyLoss()
self.kl = nn.KLDivLoss()
def forward(self, pred, label, v_out, a_out):
主损失
main_loss = self.ce(pred, label)
模态一致性损失
p_v = F.log_softmax(v_out, dim=1)
p_a = F.softmax(a_out, dim=1)
consistency_loss = self.kl(p_v, p_a.detach())
return main_loss + 0.5 * consistency_loss
- 训练技巧
-
分阶段训练：先单模态预训练，再联合微调
-
数据增强策略：
-
视觉：随机遮挡、色彩抖动
-
音频：添加噪声、时移变换
-
优化器配置：
optimizer = torch.optim.AdamW([
{'params': visual_net.parameters(), 'lr': 1e-4},
{'params': audio_net.parameters(), 'lr': 3e-4},
{'params': fusion_module.parameters(), 'lr': 5e-4}
], weight_decay=1e-5)
五、实时处理与部署
- 实时处理架构
import queue
from threading import Thread
class RealTimeProcessor:
def init(self):
self.video_queue = queue.Queue(maxsize=30)
self.audio_queue = queue.Queue(maxsize=100)
def video_capture(self):
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
processed = process_frame(frame)
self.video_queue.put(processed)
def audio_capture(self):
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=1,
rate=16000, input=True,
frames_per_buffer=1024)
while True:
data = stream.read(1024)
features = extract_features(data)
self.audio_queue.put(features)
def sync_processor(self):
while True:
动态时间对齐算法
video_batch = self.get_video_window()
audio_batch = self.get_audio_window()
aligned_data = dtw_align(video_batch, audio_batch)
yield aligned_data
- 部署优化方案
- 使用TensorRT进行模型量化：
trtexec --onnx=model.onnx --saveEngine=model.engine \
--fp16 --workspace=2048
- 边缘设备优化：
import torch_tensorrt
traced_model = torch.jit.trace(model, example_input)
trt_model = torch_tensorrt.compile(traced_model,
inputs= [torch_tensorrt.Input((1, 3, 128, 128),
torch_tensorrt.Input((1, 100, 120))],
enabled_precisions= {torch.float16})
六、评估与调优
- 评估指标
from sklearn.metrics import f1_score, confusion_matrix
def evaluate(y_true, y_pred):
acc = (y_true == y_pred).mean()
f1 = f1_score(y_true, y_pred, average='macro')
cm = confusion_matrix(y_true, y_pred)
return {'accuracy': acc, 'f1': f1, 'confusion_matrix': cm}
- 模型分析工具
import shap
def explain_sample(video, audio):
explainer = shap.DeepExplainer(model)
shap_values = explainer.shap_values([video, audio])
可视化各模态贡献度
shap.image_plot(shap_values[0], video)
shap.summary_plot(shap_values[1], audio)
七、系统集成方案
- 服务端架构
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI()
class Request(BaseModel):
video_url: str
audio_url: str
@app.post("/analyze")
async def analyze(data: Request):
video = download_and_process(data.video_url)
audio = process_audio(data.audio_url)
with torch.no_grad():
prediction = model(video, audio)
return {"emotion": class_names[prediction.argmax()]}
- 前端界面示例
// React组件示例
function EmotionDetector() {
const [result, setResult] = useState(null);
const handleUpload = async (files) => {
const formData = new FormData();
formData.append('video', files[0]);
formData.append('audio', files[1]);
const res = await fetch('/analyze', {
method: 'POST',
body: formData
});
setResult(await res.json());
};
return (
<div>
<input type="file" onChange={e => handleUpload(e.target.files)} />
{result && <EmotionChart data={result}/>}
</div>
);
}
八、挑战解决方案
- 模态异步问题：
-
采用双缓冲队列+动态时间规整
-
设置最大等待时延（200ms），超时使用插值补偿
- 噪声处理：
def denoise_audio(audio):
return nr.reduce_noise(y=audio, sr=16000,
stationary=True,
prop_decrease=0.8)
def enhance_video(frame):
clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8,8))
return clahe.apply(frame)
- 资源优化：
- 使用模型蒸馏技术：
distiller = Distiller(teacher=teacher_model, student=student_model)
distiller.train_with_distillation(train_loader,
alpha=0.3,
temperature=4)
总结：
该方案完整覆盖了从数据采集到部署的全流程，重点解决了多模态系统中的关键挑战。实际部署时可根据硬件资源调整模型复杂度，推荐使用NVIDIA Jetson系列设备进行边缘部署。