import imutils import time import cv2 import numpy as np from kalman import * import matplotlib.pyplot as plt
时间: 2023-07-21 19:01:17 浏览: 49
您好!您的代码看起来是在使用Python编写一个计算机视觉相关的程序。根据您的代码,您可能需要安装一些额外的库来运行它。在Ubuntu上进行Python编程,可以按照以下步骤安装和配置所需的库:
1. 安装pip:打开终端,输入以下命令安装pip(Python包管理工具):
```
sudo apt-get install python-pip
```
2. 安装所需的库:根据您的代码,您可能需要安装以下库:imutils、OpenCV、numpy和matplotlib。
```
pip install imutils
pip install opencv-python
pip install numpy
pip install matplotlib
```
3. 验证安装:运行以下命令验证库是否成功安装:
```
python -c "import imutils; import cv2; import numpy; import matplotlib"
```
如果没有报错,说明库已成功安装。
请注意,这只是安装所需库的基本步骤。根据您的具体环境和需求,可能需要进行其他配置或安装其他依赖项。希望这可以帮助您在Ubuntu上进行Python编程!
相关问题
优化:import numpy as np import scipy.signal as signal import scipy.io.wavfile as wavfile import pywt import matplotlib.pyplot as plt def wiener_filter(x, fs, cutoff): # 维纳滤波函数 N = len(x) freqs, Pxx = signal.periodogram(x, fs=fs) H = np.zeros(N) H[freqs <= cutoff] = 1 Pxx_smooth = np.maximum(Pxx, np.max(Pxx) * 1e-6) H_smooth = np.maximum(H, np.max(H) * 1e-6) G = H_smooth / (H_smooth + 1 / Pxx_smooth) y = np.real(np.fft.ifft(np.fft.fft(x) * G)) return y def kalman_filter(x): # 卡尔曼滤波函数 Q = np.diag([0.01, 1]) R = np.diag([1, 0.1]) A = np.array([[1, 1], [0, 1]]) H = np.array([[1, 0], [0, 1]]) x_hat = np.zeros((2, len(x))) P = np.zeros((2, 2, len(x))) x_hat[:, 0] = np.array([x[0], 0]) P[:, :, 0] = np.eye(2) for k in range(1, len(x)): x_hat[:, k] = np.dot(A, x_hat[:, k-1]) P[:, :, k] = np.dot(np.dot(A, P[:, :, k-1]), A.T) + Q K = np.dot(np.dot(P[:, :, k], H.T), np.linalg.inv(np.dot(np.dot(H, P[:, :, k]), H.T) + R)) x_hat[:, k] += np.dot(K, x[k] - np.dot(H, x_hat[:, k])) P[:, :, k] = np.dot(np.eye(2) - np.dot(K, H), P[:, :, k]) y = x_hat[0, :] return y # 读取含有噪声的语音信号 rate, data = wavfile.read("shengyin.wav") data = data.astype(float) / 32767.0 # 维纳滤波 y_wiener = wiener_filter(data, fs=rate, cutoff=1000) # 卡尔曼滤波 y_kalman = kalman_filter(data) # 保存滤波后的信号到文件中 wavfile.write("wiener_filtered.wav", rate, np.int32(y_wiener * 32767.0)) wavfile.write("kalman_filtered.wav", rate, np.int32(y_kalman * 32767.0))
这段代码的优化可以从以下几个方面入手:
1. 合理使用numpy中的函数,避免使用Python内置的循环语句,以提高运行效率。
2. 在函数中进行参数检查,以确保参数的类型和取值范围符合要求,避免不必要的错误和异常。
3. 对于频繁使用的函数,可以将其封装成单独的函数,以提高代码的可读性和重用性。
4. 可以使用并行化的技术来提高运行效率,例如多线程或者异步编程。
下面是对代码的优化改进:
import numpy as np
import scipy.signal as signal
import scipy.io.wavfile as wavfile
import pywt
import matplotlib.pyplot as plt
from typing import Tuple
def periodogram(x: np.ndarray, fs: int) -> Tuple[np.ndarray, np.ndarray]:
freqs, Pxx = signal.periodogram(x, fs=fs)
return freqs, Pxx
def wiener_filter(x: np.ndarray, fs: int, cutoff: float) -> np.ndarray:
# 维纳滤波函数
N = len(x)
freqs, Pxx = periodogram(x, fs=fs)
H = np.zeros(N)
H[freqs <= cutoff] = 1
Pxx_smooth = np.maximum(Pxx, np.max(Pxx) * 1e-6)
H_smooth = np.maximum(H, np.max(H) * 1e-6)
G = H_smooth / (H_smooth + 1 / Pxx_smooth)
y = np.real(np.fft.ifft(np.fft.fft(x) * G))
return y
def kalman_filter(x: np.ndarray) -> np.ndarray:
# 卡尔曼滤波函数
Q = np.diag([0.01, 1])
R = np.diag([1, 0.1])
A = np.array([[1, 1], [0, 1]])
H = np.array([[1, 0], [0, 1]])
x_hat = np.zeros((2, len(x)))
P = np.zeros((2, 2, len(x)))
x_hat[:, 0] = np.array([x[0], 0])
P[:, :, 0] = np.eye(2)
for k in range(1, len(x)):
x_hat[:, k] = np.dot(A, x_hat[:, k-1])
P[:, :, k] = np.dot(np.dot(A, P[:, :, k-1]), A.T) + Q
K = np.dot(np.dot(P[:, :, k], H.T), np.linalg.inv(np.dot(np.dot(H, P[:, :, k]), H.T) + R))
x_hat[:, k] += np.dot(K, x[k] - np.dot(H, x_hat[:, k]))
P[:, :, k] = np.dot(np.eye(2) - np.dot(K, H), P[:, :, k])
y = x_hat[0, :]
return y
def filter_wav_file(in_file: str, out_file: str, filter_func) -> None:
# 读取含有噪声的语音信号
rate, data = wavfile.read(in_file)
data = data.astype(float) / 32767.0
# 进行滤波
y_filtered = filter_func(data)
# 保存滤波后的信号到文件中
wavfile.write(out_file, rate, np.int32(y_filtered * 32767.0))
# 维纳滤波
filter_wav_file("shengyin.wav", "wiener_filtered.wav", lambda x: wiener_filter(x, fs=rate, cutoff=1000))
# 卡尔曼滤波
filter_wav_file("shengyin.wav", "kalman_filtered.wav", kalman_filter)
#https://pysource.com/2021/10/29/kalman-filter-predict-the-trajectory-of-an-object/ import cv2 import numpy as np import matplotlib.pyplot as plt class KalmanFilter: #实例属性 kf = cv2.KalmanFilter(4, 2) #其值为4,因为状态转移矩阵transitionMatrix有4个维度 #需要观测的维度为2 kf.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32) #创建测量矩阵 kf.transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 0.7, 0], [0, 0, 0, 0.7]], np.float32) #创建状态转移矩阵 # 创建一个0-99的一维矩阵 z = [i for i in range(100)] z_watch = np.mat(z) # 创建一个方差为1的高斯噪声,精确到小数点后两位 noise = np.round(np.random.normal(0, 1, 100), 2) noise_mat = np.mat(noise) # 将z的观测值和噪声相加 z_mat = z_watch + noise_mat # 定义x的初始状态,即位置和速度 x_mat = np.mat([[0, ], [0, ]]) y_mat = np.mat([[0, ], [0, ]]) def predict(self, coordX, coordY): #实例方法,自己实现一个predict ''' This function estimates the position of the object''' measured = np.array([[np.float32(coordX)], [np.float32(coordY)]]) self.kf.correct(measured) #结合观测值更新状态值,correct为卡尔曼滤波器自带函数 predicted = self.kf.predict() #调用卡尔曼滤波器自带的预测函数 x, y = int(predicted[0]), int(predicted[1]) #得到预测后的坐标值 # 绘制结果 plt.plot(measured[0], 'k+', label='Measured_x') plt.plot(x, 'b-', label='Kalman Filter_x') #plt.plot(real_state, 'g-', label='Real state') plt.legend(loc='upper left') plt.title('Kalman Filter Results') plt.xlabel('Time (s)') plt.ylabel('Position (m)') plt.show() return x, y predict(self,x_mat,y_mat)优化这段python代码,随机生成x和y并实现对x和y的输入值的预测,并画出图像,实现可视化
# 导入必要的库
import cv2
import numpy as np
import matplotlib.pyplot as plt
class KalmanFilter:
def __init__(self):
# 创建卡尔曼滤波器
self.kf = cv2.KalmanFilter(4, 2)
# 创建测量矩阵
self.kf.measurementMatrix = np.array([[1, 0, 0, 0], [0, 1, 0, 0]], np.float32)
# 创建状态转移矩阵
self.kf.transitionMatrix = np.array([[1, 0, 1, 0], [0, 1, 0, 1], [0, 0, 0.7, 0], [0, 0, 0, 0.7]], np.float32)
# 创建过程噪声协方差矩阵
self.kf.processNoiseCov = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0.5, 0], [0, 0, 0, 0.5]], np.float32)
# 定义x的初始状态,即位置和速度
self.x = np.array([[0], [0], [0], [0]], np.float32)
def predict(self, coordX, coordY):
# 将输入值转换为测量值
measured = np.array([[np.float32(coordX)], [np.float32(coordY)]])
# 预测下一时刻的状态值
self.x = self.kf.predict()
# 结合观测值更新状态值
self.x = self.kf.correct(measured)
# 得到预测后的坐标值
x, y = int(self.x[0]), int(self.x[1])
return x, y
# 创建KalmanFilter实例
kf = KalmanFilter()
# 随机生成x和y
x = np.array([i for i in range(1, 101)])
y = np.array([i for i in range(1, 101)])
# 添加随机噪声
x = x + np.random.normal(0, 10, 100)
y = y + np.random.normal(0, 10, 100)
# 预测输入值的位置
predicted_x = []
predicted_y = []
for i in range(100):
px, py = kf.predict(x[i], y[i])
predicted_x.append(px)
predicted_y.append(py)
# 绘制结果
plt.plot(x, y, 'k+', label='Measured')
plt.plot(predicted_x, predicted_y, 'b-', label='Kalman Filter')
plt.legend(loc='upper left')
plt.title('Kalman Filter Results')
plt.xlabel('X')
plt.ylabel('Y')
plt.show()