【深度学习模型】

# 初始化卡尔曼滤波器
kf = KalmanFilter()
kf.initialization()

while True:
    # 获取相机图像
    frame = get_camera_frame()

    # 使用深度学习模型对相机图像进行识别，得到障碍物位置和速度的估计值
    obstacle_position, obstacle_velocity = deep_learning_model(frame)

    # 更新卡尔曼滤波器的状态转移矩阵，将深度学习模型的输出作为观测量
    kf.update(observation=obstacle_position)

    # 预测障碍物的位置和速度
    obstacle_position, obstacle_velocity = kf.predict()

    # 绘制障碍物在相机图像中的位置
    draw_obstacle(frame, obstacle_position)

    # 显示图像
    show_image(frame)

pip install torch torchaudio torchvision

import torch
import torchaudio
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader

# 设置随机种子以保证可重复性
torch.manual_seed(0)

# 定义音频分类模型
class AudioClassificationModel(nn.Module):
    def __init__(self):
        super(AudioClassificationModel, self).__init__()
        self.conv1 = nn.Conv1d(1, 16, kernel_size=3, stride=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv1d(16, 32, kernel_size=3, stride=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool1d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(32 * 13, 64)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(64, 2)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        return x

# 定义数据集类
class AudioDataset(torch.utils.data.Dataset):
    def __init__(self, audio_list, label_list):
        self.audio_list = audio_list
        self.label_list = label_list
    
    def __len__(self):
        return len(self.audio_list)
    
    def __getitem__(self, index):
        audio, sample_rate = torchaudio.load(self.audio_list[index])
        label = self.label_list[index]
        return audio, label

# 设置超参数
batch_size = 32
learning_rate = 0.001
num_epochs = 10

# 创建模型和优化器
model = AudioClassificationModel()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()

# 加载数据集
train_audio_list = ["path_to_big_chord_1.wav", "path_to_big_chord_2.wav", ...]
train_label_list = [1, 1, ...]  # 1代表大和弦
train_dataset = AudioDataset(train_audio_list, train_label_list)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# 训练模型
model.train()
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (audio, label) in enumerate(train_loader):
        outputs = model(audio)
        loss = criterion(outputs, label)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 10 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item()))

# 测试模型
model.eval()
test_audio_list = ["path_to_small_chord_1.wav", "path_to_small_chord_2.wav", ...]
test_label_list = [0, 0, ...]  # 0代表小和弦
test_dataset = AudioDataset(test_audio_list, test_label_list)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

with torch.no_grad():
    correct = 0
    total = 0
    for audio, labels in test_loader:
        outputs = model(audio)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the model on the test audio: {} %'.format(100 * correct / total))