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fall_detection_data_driven.py
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fall_detection_data_driven.py
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import torchvision.models as models
import torch
import numpy as np
import matplotlib.pyplot as plt
device = torch.device("cpu" if torch.cuda.is_available() else "cpu")
import torch.nn as nn
import torch.nn.functional as F
# 模型定义
class SimpleCNN(nn.Module):
def __init__(self, num_classes):
super(SimpleCNN, self).__init__()
self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.fc1 = nn.Linear(64 * 25 * 13, 512)
self.fc2 = nn.Linear(512, num_classes)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 64 * 25 * 13)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def fall_recognition(lock, csi_amplitude_array,csi_shape,result_lock,action_array,model_path="F:\SRIBD\ESP32-Realtime-System\Falldataset_20240225\dataset_20240225.pth",task_action=3):
model_action=SimpleCNN(num_classes=task_action)
model_action.load_state_dict(torch.load(model_path,map_location=device))
model_action=model_action.to(device)
model_action.eval()
torch.set_grad_enabled(False)
csi_arr=np.frombuffer(csi_amplitude_array, dtype=np.float32).reshape(csi_shape)
action_arr=np.frombuffer(action_array, dtype=np.float32).reshape(task_action)
while True:
with lock:
data=torch.from_numpy(csi_arr)
data=data.reshape([1,1,csi_shape[0],csi_shape[1]]).to(device)
# data=data[:,:,:,:52]
# data = data / torch.norm(data, dim=-2, keepdim=True)
action=model_action(data)
action=torch.softmax(action,dim=-1)
with result_lock:
action_arr[:]=action.detach().cpu().squeeze().numpy()
action_name=["fall","sit","walk"]
def fall_recognition_plot(action_array,task_action=3):
action_arr = np.frombuffer(action_array, dtype=np.float32).reshape(task_action)
plt.ion() # 打开交互模式
fig, ax1 = plt.subplots()
ax1.set_ylim(0, 1)
ax1.set_ylabel("Action")
# ax1.set_xticklabels(action_name, rotation='vertical', fontsize=7, fontweight='bold') # 将横坐标标签纵向显示
bar_action = ax1.bar(action_name, action_arr) # 创建柱状图
while True:
for bar, h in zip(bar_action, action_arr):
bar.set_height(h) # 更新柱状图的高度
ax1.set_title("Action: "+action_name[np.argmax(action_arr)])
plt.draw()
plt.pause(0.3) # 暂停一段时间,使得图形有动态效果