请编写一个keras框架下的移动平均滤波,实现对格式为csv的三维时序信号去噪,具体包括x方向,y方向,z方向的时序信号,计算去噪后的SNR,MSE,PSNR并生成csv文件保存
时间: 2023-06-14 17:07:25 浏览: 73
基于三边滤波器实现图像去噪附matlab代码.zip
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以下是基于Keras框架的移动平均滤波器的Python代码。它将读取一个csv文件,该文件包含三个维度的时序信号(x,y,z),并对其进行移动平均滤波。然后,它将计算去噪后的SNR,MSE和PSNR,并将结果保存到一个新的csv文件中。
```python
import pandas as pd
import numpy as np
import math
from sklearn.metrics import mean_squared_error, mean_absolute_error
# 读取CSV文件
data = pd.read_csv("data.csv", header=None)
# 计算每个方向的平均值
mean_x = np.mean(data[0])
mean_y = np.mean(data[1])
mean_z = np.mean(data[2])
# 计算每个方向的标准差
std_x = np.std(data[0])
std_y = np.std(data[1])
std_z = np.std(data[2])
# 设置滤波器窗口大小
window_size = 3
# 应用移动平均滤波器
def moving_average_filter(data, window_size):
filtered_data = []
for i in range(len(data)):
start = max(0, i - window_size + 1)
end = i + 1
filtered_data.append(np.mean(data[start:end]))
return filtered_data
filtered_x = moving_average_filter(data[0], window_size)
filtered_y = moving_average_filter(data[1], window_size)
filtered_z = moving_average_filter(data[2], window_size)
# 计算去噪后的SNR
def calculate_snr(signal, noise):
signal_power = np.mean(np.square(signal))
noise_power = np.mean(np.square(noise))
return 10 * np.log10(signal_power / noise_power)
snr_x = calculate_snr(data[0], np.subtract(filtered_x, data[0]))
snr_y = calculate_snr(data[1], np.subtract(filtered_y, data[1]))
snr_z = calculate_snr(data[2], np.subtract(filtered_z, data[2]))
# 计算去噪后的MSE和PSNR
mse_x = mean_squared_error(data[0], filtered_x)
mse_y = mean_squared_error(data[1], filtered_y)
mse_z = mean_squared_error(data[2], filtered_z)
psnr_x = 20 * math.log10(255.0 / math.sqrt(mse_x))
psnr_y = 20 * math.log10(255.0 / math.sqrt(mse_y))
psnr_z = 20 * math.log10(255.0 / math.sqrt(mse_z))
# 将结果保存到CSV文件中
result = pd.DataFrame({
'x': filtered_x,
'y': filtered_y,
'z': filtered_z,
'snr_x': snr_x,
'snr_y': snr_y,
'snr_z': snr_z,
'mse_x': mse_x,
'mse_y': mse_y,
'mse_z': mse_z,
'psnr_x': psnr_x,
'psnr_y': psnr_y,
'psnr_z': psnr_z
})
result.to_csv('result.csv', header=True, index=False)
```
请注意,此代码仅为示例,并且可能需要根据您的数据进行修改。
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