How to Efficiently Crop Random 16x16 Patches from Multiple Images Using Numpy Slices?

Efficient Random Image Cropping with Multiple Numpy Slices

Given a 4-D Numpy array representing color images, the goal is to efficiently select random 16x16 crops from each image, with unique crop locations for each image.

A naive approach using a for loop incurs unnecessary memory overhead and computation. To optimize this process, we leverage the np.lib.stride_tricks.as_strided method or the scikit-image's view_as_windows function.

Using view_as_windows

The view_as_windows function creates overlapping windows within an input array, effectively creating views into the original data without additional memory allocation. By specifying a window shape of (1, 16, 16, 1), we create sliding windows along the second and third axes (width and height) with a step size of 1.

To index the specific windows based on random offset pairs (x, y), we use the following steps:

1. Generate the window array: w = view_as_windows(X, (1,16,16,1))[...,0,:,:,0]
2. Index the windows based on the random offsets: out = w[np.arange(X.shape[0]), x, y]
3. Transpose the result to match the desired output format: out = out.transpose(0,2,3,1)

This method provides an efficient approach to crop multiple images with varying offsets, reducing memory overhead and computation time compared to the iterative approach.

The above is the detailed content of How to Efficiently Crop Random 16x16 Patches from Multiple Images Using Numpy Slices?. For more information, please follow other related articles on the PHP Chinese website!