Can Numpy Slices Be Used for Efficient Random Image Cropping?

Efficient Image Cropping with Numpy Slices

Seeking an optimized approach to crop images using Numpy slices, one user questioned the feasibility of a stride-based method for generating random crops in a single pass.

Stride-based Patch Extraction

The response proposes leveraging np.lib.stride_tricks.as_strided or scikit-image's view_as_windows for creating a sliding window view of the input array. This method maintains memory efficiency without incurring any additional overhead.

View_as_Windows Explanation

view_as_windows allows users to specify a window_shape argument, representing the sliding window size along each dimension of the input array. Axes not intended for slicing receive values of 1. After slicing, the resulting array contains views with singleton dimensions (length 1) corresponding to these 1 values.

Solution

The suggested solution involves utilizing view_as_windows to extract sliding windows and then indexing into these windows based on randomly generated offsets:

<code class="python"># Get sliding windows
w = view_as_windows(X, (1, 16, 16, 1))[..., 0, :, :, 0]

# Index and get specific windows
out = w[np.arange(X.shape[0]), x, y]

# Reshape to match loopy code format (optional)
out = out.transpose(0, 2, 3, 1)</code>

This approach enables efficient cropping of images with distinct random offsets for each image, eliminating the need for a for loop.

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