smartdownsample 0.1.1

Smart image downsampling for image classification datasets

smartdownsample

Efficient downsampling for image classification datasets

SmartDownsample selects the most diverse images from large collections, ideal for reducing dataset size while preserving visual variability.

Installation

pip install smartdownsample

Usage

from smartdownsample import select_distinct

# Example list of image paths
my_image_list = [
    "path/to/img1.jpg",
    "path/to/img2.jpg",
    "path/to/img3.jpg",
    "path/to/img4.jpg"
]

# Simple selection - get 100 most diverse images
selected = select_distinct(
    image_paths=my_image_list,
    target_count=100
)

# With visual verification to see excluded images in context
selected = select_distinct(
    image_paths=my_image_list,
    target_count=100,
    show_verification=True
)

print(f"Selected {len(selected)} images")

Parameters

Parameter	Default	Description
image_paths	Required	List of image file paths (str or Path objects)
target_count	Required	Exact number of images to select
window_size	100	Rolling window size (larger = better quality, slower)
random_seed	42	Random seed for reproducible results
show_progress	True	Whether to display progress bars
show_verification	False	Show visual verification comparing excluded vs included images

Step by Step

1. Sort paths by directory. Within each folder, files are naturally ordered (e.g., img1.jpg, img2.jpg, img10.jpg) so related images remain grouped.
2. Compute perceptual hashes for all valid image paths.
3. Apply rolling window selection on the hash array to choose indices of the most diverse images. This runs in O(n) time, scales to large classes of 100k+ images, and compares each candidate only to a sliding window of recent selections.
4. Return results as [valid_paths[i] for i in selected_indices].
5. Optional verification plot: If show_verification=True, the algorithm displays a visual check of 18 randomly selected excluded images and their included counterpart. The visualization opens automatically in your default image viewer without saving files to disk.

License

MIT License – see LICENSE file.