How to Enhance Moving Average Filter Implementation with Stride Tricks?

Using Stride Tricks for Efficient Moving Average Filter Implementation

This inquiry explores the use of stride tricks to develop a more efficient moving average filter than the convolution-based method described in a previous post. The goal is to filter large floating-point arrays for tasks where scipy.ndimage.filters.convolve is relatively slow.

The initial approach employed stride tricks to generate a series of arrays representing the top, middle, and bottom rows of a 3x3 filter kernel. These arrays were then summed and averaged to produce the filter output for each pixel. However, the questioner sought a more efficient method that could directly obtain the sum or individual values of the kernel elements for the entire array.

Improved Approach Using Multidimensional Stride Tricks

The enhanced solution involves applying multidimensional stride tricks to create a view of the original array where each element represents a moving window of the desired size. This allows for applying an arbitrary function to the last axis multiple times, effectively calculating the moving average or other desired statistical measures over a sliding window.

Code Example

<code class="python">import numpy as np

def rolling_window(array, window):
    """Multidimensional moving window function"""
    # Validate window dimensions
    if not hasattr(window, '__iter__'):
        return rolling_window_lastaxis(array, window)
    for i, win in enumerate(window):
        if win > 1:
            array = array.swapaxes(i, -1)
            array = rolling_window_lastaxis(array, win)
            array = array.swapaxes(-2, i)
    return array

filtsize = (3, 3)
array = np.arange(100).reshape((10, 10))
windowed_array = rolling_window(array, filtsize)
blurred_array = windowed_array.mean(axis=-1).mean(axis=-1)</code>

Advantages of Enhanced Solution

• Direct generation of the moving window using stride tricks, eliminating the need for intermediate steps.
• Applicability to multidimensional arrays, allowing for efficient filtering along multiple axes.
• Vectorization of moving window operations, resulting in significantly reduced computational time.

Limitations

Despite these advantages, it's important to note that using stride tricks for multidimensional moving windows can lead to increased memory usage. Therefore, careful consideration should be given to the available resources and the size of the input array.

Comparison with scipy.ndimage

While stride tricks offer flexibility and vectorization benefits, scipy.ndimage functions are generally more memory-efficient and optimized for multidimensional image processing tasks. For large arrays, scipy.ndimage.uniform_filter is recommended as a faster and more robust option for applying moving average filters.

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