How to Efficiently Crop Multiple Images Using Numpy Slices?

Efficiently Using Multiple Numpy Slices for Random Image Cropping

This question seeks an efficient way to perform random image cropping from a numpy array containing 4-dimensional images. The goal is to extract 16x16 window crops from each of the four images, ensuring different crops for each image.

One proposed solution employs a straightforward for loop to generate random offsets for each image and apply those offsets to numpy slices. However, to achieve optimal efficiency without sacrificing memory overhead, alternative approaches are explored.

Leveraging Strides and Fancy Indexing

One approach involves leveraging numpy's stride_tricks.as_strided to create sliding windows that act as views into the input array. This avoids memory duplication and provides a virtual free operation.

scikit-image's view_as_windows function simplifies this process by creating sliding windows with a specified window size and sliding axis along other dimensions. Using this function, we can efficiently extract the desired 16x16 window crops, ensuring random offsets for each image.

Implementation

The provided Python code demonstrates the application of view_as_windows to achieve random image cropping while maintaining an efficient memory footprint:

<code class="python">from skimage.util.shape import view_as_windows

# Obtain sliding windows
w = view_as_windows(X, (1,16,16,1))[...,0,:,:,0]

# Retrieve specific windows using random offsets
out = w[np.arange(X.shape[0]), x, y]

# Rearrange to match the output format of the loop-based approach
out = out.transpose(0,2,3,1)</code>

This approach offers a more efficient solution for random image cropping compared to the loop-based method while effectively addressing the memory overhead concerns.

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