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低通滤波器实验代码,这是参考别人网上的代码,所以自己也分享一下,共同进步
# -*- coding: utf-8 -*- import numpy as np from scipy.signal import butter, lfilter, freqz import matplotlib.pyplot as plt def butter_lowpass(cutoff, fs, order=5): nyq = 0.5 * fs normal_cutoff = cutoff / nyq b, a = butter(order, normal_cutoff, btype='low', analog=False) return b, a def butter_lowpass_filter(data, cutoff, fs, order=5): b, a = butter_lowpass(cutoff, fs, order=order) y = lfilter(b, a, data) return y # Filter requirements. order = 6 fs = 30.0 # sample rate, Hz cutoff = 3.667 # desired cutoff frequency of the filter, Hz # Get the filter coefficients so we can check its frequency response. b, a = butter_lowpass(cutoff, fs, order) # Plot the frequency response. w, h = freqz(b, a, worN=800) plt.subplot(2, 1, 1) plt.plot(0.5*fs*w/np.pi, np.abs(h), 'b') plt.plot(cutoff, 0.5*np.sqrt(2), 'ko') plt.axvline(cutoff, color='k') plt.xlim(0, 0.5*fs) plt.title("Lowpass Filter Frequency Response") plt.xlabel('Frequency [Hz]') plt.grid() # Demonstrate the use of the filter. # First make some data to be filtered. T = 5.0 # seconds n = int(T * fs) # total number of samples t = np.linspace(0, T, n, endpoint=False) # "Noisy" data. We want to recover the 1.2 Hz signal from this. data = np.sin(1.2*2*np.pi*t) + 1.5*np.cos(9*2*np.pi*t) + 0.5*np.sin(12.0*2*np.pi*t) # Filter the data, and plot both the original and filtered signals. y = butter_lowpass_filter(data, cutoff, fs, order) plt.subplot(2, 1, 2) plt.plot(t, data, 'b-', label='data') plt.plot(t, y, 'g-', linewidth=2, label='filtered data') plt.xlabel('Time [sec]') plt.grid() plt.legend() plt.subplots_adjust(hspace=0.35) plt.show()
实际代码,没有整理,可以读取txt文本文件,然后进行低通滤波,并将滤波前后的波形和FFT变换都显示出来
# -*- coding: utf-8 -*- import numpy as np from scipy.signal import butter, lfilter, freqz import matplotlib.pyplot as plt import os def butter_lowpass(cutoff, fs, order=5): nyq = 0.5 * fs normal_cutoff = cutoff / nyq b, a = butter(order, normal_cutoff, btype='low', analog=False) return b, a def butter_lowpass_filter(data, cutoff, fs, order=5): b, a = butter_lowpass(cutoff, fs, order=order) y = lfilter(b, a, data) return y # Filter requirements. order = 5 fs = 100000.0 # sample rate, Hz cutoff = 1000 # desired cutoff frequency of the filter, Hz # Get the filter coefficients so we can check its frequency response. # b, a = butter_lowpass(cutoff, fs, order) # Plot the frequency response. # w, h = freqz(b, a, worN=1000) # plt.subplot(3, 1, 1) # plt.plot(0.5*fs*w/np.pi, np.abs(h), 'b') # plt.plot(cutoff, 0.5*np.sqrt(2), 'ko') # plt.axvline(cutoff, color='k') # plt.xlim(0, 1000) # plt.title("Lowpass Filter Frequency Response") # plt.xlabel('Frequency [Hz]') # plt.grid() # Demonstrate the use of the filter. # First make some data to be filtered. # T = 5.0 # seconds # n = int(T * fs) # total number of samples # t = np.linspace(0, T, n, endpoint=False) # "Noisy" data. We want to recover the 1.2 Hz signal from this. # # data = np.sin(1.2*2*np.pi*t) + 1.5*np.cos(9*2*np.pi*t) + 0.5*np.sin(12.0*2*np.pi*t) # Filter the data, and plot both the original and filtered signals. path = "*****" for file in os.listdir(path): if file.endswith("txt"): data=[] filePath = os.path.join(path, file) with open(filePath, 'r') as f: lines = f.readlines()[8:] for line in lines: # print(line) data.append(float(line)*100) # print(len(data)) t1=[i*10 for i in range(len(data))] plt.subplot(231) # plt.plot(range(len(data)), data) plt.plot(t1, data, linewidth=2,label='original data') # plt.title('ori wave', fontsize=10, color='#F08080') plt.xlabel('Time [us]') plt.legend() # filter_data = data[30000:35000] # filter_data=data[60000:80000] # filter_data2=data[60000:80000] # filter_data = data[80000:100000] # filter_data = data[100000:120000] filter_data = data[120000:140000] filter_data2=filter_data t2=[i*10 for i in range(len(filter_data))] plt.subplot(232) plt.plot(t2, filter_data, linewidth=2,label='cut off wave before filter') plt.xlabel('Time [us]') plt.legend() # plt.title('cut off wave', fontsize=10, color='#F08080') # filter_data=zip(range(1,len(data),int(fs/len(data))),data) # print(filter_data) n1 = len(filter_data) Yamp1 = abs(np.fft.fft(filter_data) / (n1 / 2)) Yamp1 = Yamp1[range(len(Yamp1) // 2)] # x_axis=range(0,n//2,int(fs/len # 计算最大赋值点频率 max1 = np.max(Yamp1) max1_index = np.where(Yamp1 == max1) if (len(max1_index[0]) == 2): print((max1_index[0][0] )* fs / n1, (max1_index[0][1]) * fs / n1) else: Y_second = Yamp1 Y_second = np.sort(Y_second) print(np.where(Yamp1 == max1)[0] * fs / n1, (np.where(Yamp1 == Y_second[-2])[0]) * fs / n1) N1 = len(Yamp1) # print(N1) x_axis1 = [i * fs / n1 for i in range(N1)] plt.subplot(233) plt.plot(x_axis1[:300], Yamp1[:300], linewidth=2,label='FFT data') plt.xlabel('Frequence [Hz]') # plt.title('FFT', fontsize=10, color='#F08080') plt.legend() # plt.savefig(filePath.replace("txt", "png")) # plt.close() # plt.show() Y = butter_lowpass_filter(filter_data2, cutoff, fs, order) n3 = len(Y) t3 = [i * 10 for i in range(n3)] plt.subplot(235) plt.plot(t3, Y, linewidth=2, label='cut off wave after filter') plt.xlabel('Time [us]') plt.legend() Yamp2 = abs(np.fft.fft(Y) / (n3 / 2)) Yamp2 = Yamp2[range(len(Yamp2) // 2)] # x_axis = range(0, n // 2, int(fs / len(Yamp))) max2 = np.max(Yamp2) max2_index = np.where(Yamp2 == max2) if (len(max2_index[0]) == 2): print(max2, max2_index[0][0] * fs / n3, max2_index[0][1] * fs / n3) else: Y_second2 = Yamp2 Y_second2 = np.sort(Y_second2) print((np.where(Yamp2 == max2)[0]) * fs / n3, (np.where(Yamp2 == Y_second2[-2])[0]) * fs / n3) N2=len(Yamp2) # print(N2) x_axis2 = [i * fs / n3 for i in range(N2)] plt.subplot(236) plt.plot(x_axis2[:300], Yamp2[:300],linewidth=2, label='FFT data after filter') plt.xlabel('Frequence [Hz]') # plt.title('FFT after low_filter', fontsize=10, color='#F08080') plt.legend() # plt.show() plt.savefig(filePath.replace("txt", "png")) plt.close() print('*'*50) # plt.subplot(3, 1, 2) # plt.plot(range(len(data)), data, 'b-', linewidth=2,label='original data') # plt.grid() # plt.legend() # # plt.subplot(3, 1, 3) # plt.plot(range(len(y)), y, 'g-', linewidth=2, label='filtered data') # plt.xlabel('Time') # plt.grid() # plt.legend() # plt.subplots_adjust(hspace=0.35) # plt.show() ''' # Y_fft = Y[60000:80000] Y_fft = Y # Y_fft = Y[80000:100000] # Y_fft = Y[100000:120000] # Y_fft = Y[120000:140000] n = len(Y_fft) Yamp = np.fft.fft(Y_fft)/(n/2) Yamp = Yamp[range(len(Yamp)//2)] max = np.max(Yamp) # print(max, np.where(Yamp == max)) Y_second = Yamp Y_second=np.sort(Y_second) print(float(np.where(Yamp == max)[0])* fs / len(Yamp),float(np.where(Yamp==Y_second[-2])[0])* fs / len(Yamp)) # print(float(np.where(Yamp == max)[0]) * fs / len(Yamp)) '''
补充拓展:浅谈opencv的理想低通滤波器和巴特沃斯低通滤波器
低通滤波器
1.理想的低通滤波器
其中,D0表示通带的半径。D(u,v)的计算方式也就是两点间的距离,很简单就能得到。
使用低通滤波器所得到的结果如下所示。低通滤波器滤除了高频成分,所以使得图像模糊。由于理想低通滤波器的过度特性过于急峻,所以会产生了振铃现象。
2.巴特沃斯低通滤波器
同样的,D0表示通带的半径,n表示的是巴特沃斯滤波器的次数。随着次数的增加,振铃现象会越来越明显。
void ideal_Low_Pass_Filter(Mat src){ Mat img; cvtColor(src, img, CV_BGR2GRAY); imshow("img",img); //调整图像加速傅里叶变换 int M = getOptimalDFTSize(img.rows); int N = getOptimalDFTSize(img.cols); Mat padded; copyMakeBorder(img, padded, 0, M - img.rows, 0, N - img.cols, BORDER_CONSTANT, Scalar::all(0)); //记录傅里叶变换的实部和虚部 Mat planes[] = { Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F) }; Mat complexImg; merge(planes, 2, complexImg); //进行傅里叶变换 dft(complexImg, complexImg); //获取图像 Mat mag = complexImg; mag = mag(Rect(0, 0, mag.cols & -2, mag.rows & -2));//这里为什么&上-2具体查看opencv文档 //其实是为了把行和列变成偶数 -2的二进制是11111111.......10 最后一位是0 //获取中心点坐标 int cx = mag.cols / 2; int cy = mag.rows / 2; //调整频域 Mat tmp; Mat q0(mag, Rect(0, 0, cx, cy)); Mat q1(mag, Rect(cx, 0, cx, cy)); Mat q2(mag, Rect(0, cy, cx, cy)); Mat q3(mag, Rect(cx, cy, cx, cy)); q0.copyTo(tmp); q3.copyTo(q0); tmp.copyTo(q3); q1.copyTo(tmp); q2.copyTo(q1); tmp.copyTo(q2); //Do为自己设定的阀值具体看公式 double D0 = 60; //处理按公式保留中心部分 for (int y = 0; y < mag.rows; y++){ double* data = mag.ptr<double>(y); for (int x = 0; x < mag.cols; x++){ double d = sqrt(pow((y - cy),2) + pow((x - cx),2)); if (d <= D0){ } else{ data[x] = 0; } } } //再调整频域 q0.copyTo(tmp); q3.copyTo(q0); tmp.copyTo(q3); q1.copyTo(tmp); q2.copyTo(q1); tmp.copyTo(q2); //逆变换 Mat invDFT, invDFTcvt; idft(mag, invDFT, DFT_SCALE | DFT_REAL_OUTPUT); // Applying IDFT invDFT.convertTo(invDFTcvt, CV_8U); imshow("理想低通滤波器", invDFTcvt); } void Butterworth_Low_Paass_Filter(Mat src){ int n = 1;//表示巴特沃斯滤波器的次数 //H = 1 / (1+(D/D0)^2n) Mat img; cvtColor(src, img, CV_BGR2GRAY); imshow("img", img); //调整图像加速傅里叶变换 int M = getOptimalDFTSize(img.rows); int N = getOptimalDFTSize(img.cols); Mat padded; copyMakeBorder(img, padded, 0, M - img.rows, 0, N - img.cols, BORDER_CONSTANT, Scalar::all(0)); Mat planes[] = { Mat_<float>(padded), Mat::zeros(padded.size(), CV_32F) }; Mat complexImg; merge(planes, 2, complexImg); dft(complexImg, complexImg); Mat mag = complexImg; mag = mag(Rect(0, 0, mag.cols & -2, mag.rows & -2)); int cx = mag.cols / 2; int cy = mag.rows / 2; Mat tmp; Mat q0(mag, Rect(0, 0, cx, cy)); Mat q1(mag, Rect(cx, 0, cx, cy)); Mat q2(mag, Rect(0, cy, cx, cy)); Mat q3(mag, Rect(cx, cy, cx, cy)); q0.copyTo(tmp); q3.copyTo(q0); tmp.copyTo(q3); q1.copyTo(tmp); q2.copyTo(q1); tmp.copyTo(q2); double D0 = 100; for (int y = 0; y < mag.rows; y++){ double* data = mag.ptr<double>(y); for (int x = 0; x < mag.cols; x++){ //cout << data[x] << endl; double d = sqrt(pow((y - cy), 2) + pow((x - cx), 2)); //cout << d << endl; double h = 1.0 / (1 + pow(d / D0, 2 * n)); if (h <= 0.5){ data[x] = 0; } else{ //data[x] = data[x]*0.5; //cout << h << endl; } //cout << data[x] << endl; } } q0.copyTo(tmp); q3.copyTo(q0); tmp.copyTo(q3); q1.copyTo(tmp); q2.copyTo(q1); tmp.copyTo(q2); //逆变换 Mat invDFT, invDFTcvt; idft(complexImg, invDFT, DFT_SCALE | DFT_REAL_OUTPUT); // Applying IDFT invDFT.convertTo(invDFTcvt, CV_8U); imshow("巴特沃斯低通滤波器", invDFTcvt); }
以上这篇python实现低通滤波器代码就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持亿速云。
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