低通滤波器(Low-pass Filter):允许低频信号通过,抑制高频信号。 高通滤波器(High-pass Filter):允许高频信号通过,抑制低频信号。 带通滤波器(Band-pass Filter):允许特定频率范围内的信号通过,抑制其他频率的信号。 带阻滤波器(Band-stop Filter):抑制特定频率范围内的信号,允许其他频率的信号通过。 滤波器的设计和...
首先,我们需要导入numpy和scipy库: importnumpyasnpfromscipy.signalimportfirwin,lfilter 1. 2. 接下来,我们定义一个函数apply_lowpass_filter来应用FIR低通滤波器: defapply_lowpass_filter(signal,cutoff_freq,sampling_freq,filter_order):# 计算滤波器的系数nyquist_freq=0.5*sampling_freq normalized_cutoff_freq=...
img_filter=cv2.filter2D(img_original,-1,kernel,delta=0) img_filter_delta=cv2.filter2D(img_original,-1,kernel,delta=50) #显示结果 cv2.imshow('originial',img_original) cv2.imshow('filtered',img_filter) cv2.imshow('filtered+delta',img_filter_delta) cv2.waitKey() cv2.destroyAllWindows() ...
(1)以下为低通(高通滤波器类似这样设计)滤波器的一个demo: importnumpyasnpfromscipy.signalimportbutter,filtfiltimportmatplotlib.pyplotaspltdefbutter_lowpass_filter(data,cutoff,fs,order=4):nyq=0.5*fsnormal_cutoff=cutoff/nyqb,a=butter(order,normal_cutoff,btype='low',analog=False)#高通滤波器btype='...
# design the lowpass filter nyquist = 0.5 * fs low = f / nyquist b, a = signal.butter(10, low, 'low') # apply the filter to the data y_lowpass = signal.filtfilt(b, a, y) 这是一段带通滤波器的代码,其中使用了Butterworth滤波器设计和 filtfilt函数进行滤波。
low_pass_filter[center_row - bandwidth // 2 : center_row + bandwidth // 2, center_col - bandwidth // 2 : center_col + bandwidth // 2] = 1 # 进行滤波 f_transform_shifted_filtered = f_transform_shifted * gauss_filter 过滤完后,在看下频谱 矩形滤波只保留了中心频谱,然后用逆傅立叶变...
plt.imshow(lowPassFilter(img,60),cmap="gray") 高通滤波器 高通滤波器同低通滤波器非常类似,只不过二者通过的波正好是相反的 H(u,v)={0,ifD(u,v)≤D01,ifD(u,v)≥D0H(u,v)={0,ifD(u,v)≤D01,ifD(u,v)≥D0 defhighPassFilter(image,d): ...
# plot the first image in the top-left subplotim1 = ax[0, 0].imshow(np.abs(idealFilterLP(50, img.shape)), cmap='gray')ax[0, 0].set_title('Low Pass Filter of Diameter 50 px')ax[0, 0].set_xticks([])ax[0, 0].set_yticks(...
= ax[0, 0].imshow(np.abs(idealFilterLP(50, img.shape)), cmap='gray')ax[0, 0].set_title('Low Pass Filter of Diameter 50 px')ax[0, 0].set_xticks([])ax[0, 0].set_yticks([])# plot the second image in the top-right subplotim2 = ax[0, 1].imshow(np.abs(idealFilterHP...
defmake_lowpass(frequency:int, samplerate: int, q_factor: float = 1 / sqrt(2))-> IIRFilter:w0=tau * frequency / samplerate_sin=sin(w0)_cos=cos(w0)alpha=_sin / (2 * q_factor) b0=(1 - _cos) / 2b1=1 - _cos a0=1 + ...