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Practice

Signal and Image Processing Modules

SciPy provides powerful tools for signal processing and image processing.

You can filter time-series or spatial data, analyze frequencies, and apply transformations or basic image operations.

The two main modules are:

  • scipy.signal: Works with 1D and 2D signals like audio, time-series, and images.
  • scipy.ndimage: Provides advanced image processing functions.

Example 1: Low-Pass Filtering​

Use signal.butter() to design a low-pass filter and signal.filtfilt() to apply it without introducing phase shifts.

Butterworth Low-Pass Filter
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal, misc

# Sampling parameters
fs = 500.0  # Hz
t = np.arange(0, 1.0, 1/fs)

# Create a noisy signal
sig = np.sin(2*np.pi*5*t) + 0.5*np.sin(2*np.pi*50*t)

# Design a low-pass filter (cutoff 10 Hz)
b, a = signal.butter(N=4, Wn=10/(fs/2), btype='low')

# Apply the filter
filtered = signal.filtfilt(b, a, sig)

# Plot the results
plt.plot(t, sig, label="Original", alpha=0.5)
plt.plot(t, filtered, label="Filtered", linewidth=2)
plt.xlabel("Time [s]")
plt.ylabel("Amplitude")
plt.title("Low-Pass Butterworth Filter")
plt.legend()
plt.show()

Example 2: Spectrogram​

Use signal.spectrogram() to visualize how the frequency content of a signal changes over time.

Spectrogram
f, t_spec, Sxx = signal.spectrogram(sig, fs)
plt.pcolormesh(t_spec, f, Sxx, shading='gouraud')
plt.ylabel("Frequency [Hz]")
plt.xlabel("Time [s]")
plt.title("Spectrogram")
plt.show()

A spectrogram is useful for analyzing audio, vibrations, or any time-varying frequency patterns.


Example 3: Image Blurring​

Use signal.convolve2d() to blur an image with a Gaussian kernel.

Gaussian Blur with Convolution
# Load a sample image
face = misc.face(gray=True)

# Create a Gaussian blur kernel
kernel_size = 15
sigma = 3.0
x = np.linspace(-sigma, sigma, kernel_size)
gauss_kernel_1d = np.exp(-x**2 / (2 * sigma**2))
gauss_kernel_1d /= gauss_kernel_1d.sum()
gauss_kernel_2d = np.outer(gauss_kernel_1d, gauss_kernel_1d)

# Apply convolution
blurred_face = signal.convolve2d(face, gauss_kernel_2d, mode='same', boundary='symm')

# Plot the original and blurred images
plt.subplot(1, 2, 1)
plt.imshow(face, cmap='gray')
plt.title("Original")
plt.axis('off')

plt.subplot(1, 2, 2)
plt.imshow(blurred_face, cmap='gray')
plt.title("Blurred")
plt.axis('off')

plt.show()

Key Takeaways​

  • Use scipy.signal for working with 1D and 2D signals, including filtering and frequency analysis.
  • Use scipy.ndimage for advanced image processing tasks.

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