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How to Perform Image Processing in Python Using scipy.ndimage?

Image Processing using scipy.ndimage

Condition for Image Processing using scipy.ndimage in Python

  • Description: The `scipy.ndimage` module provides algorithms for multidimensional image processing, including filtering, morphology, segmentation, and geometric transformations.
Why Should We Choose scipy.ndimage?
  • Versatility: Supports a wide range of tasks like smoothing, edge detection, dilation, and erosion.
  • Efficient Implementation: Built on NumPy, it handles large datasets efficiently.
  • Integration with SciPy Ecosystem: Easily integrates with visualization and advanced image processing libraries.
  • Multi-dimensional Support: Suitable for 3D image analysis or multi-channel data.
Step-by-Step Process
  • Import Required Libraries: Load necessary libraries like NumPy, Matplotlib, and SciPy.
  • Load Image: Use `data` from skimage or load custom images using PIL or imageio.
  • Apply Filters/Operations: Perform edge detection, smoothing, and other operations using scipy.ndimage functions.
  • Analyze Results: Visualize the processed images using Matplotlib.
Sample Source Code
  • # Sobel Edge Detection
    import numpy as np
    import matplotlib.pyplot as plt
    from scipy import ndimage
    from skimage import data

    # Load example image
    image = data.camera()
    sx = ndimage.sobel(image, axis=0)
    sy = ndimage.sobel(image, axis=1)
    edges = np.hypot(sx, sy)

    # Plot
    plt.figure(figsize=(10, 5))
    plt.subplot(1, 2, 1)
    plt.imshow(image, cmap='gray')
    plt.title('Original Image')
    plt.axis('off')
    plt.subplot(1, 2, 2)
    plt.imshow(edges, cmap='gray')
    plt.title('Edge Detection')
    plt.axis('off')
    plt.show()
Screenshots
  • Image Processing Screenshot