image_utilities 0.0.3

A utility library for common image and video operations.

Image Utils

Are you tired of having to constantly switch between NumPy arrays, PyTorch Tensors, and PIL images? Simply wrap your NumPy array, PyTorch Tensor, or PIL image with Im() and let it handle conversions between shapes, formats, etc.

We can replace this:

numpy_img = (torch.rand(10, 3, 256, 256).permute(0, 2, 3, 1).detach().float().cpu().numpy() * 255).astype(np.uint8)
Image.fromarray(numpy_img[0]).save('output.png')

With this:

Im(torch.rand(10, 3, 256, 256)).save()

The powerful part is not that this works for a specific input shape/dtype/range, but [almost] any combination.

Features

• Supports NumPy arrays, PyTorch Tensors, and PIL Images
• All operations support batching over arbitrary shapes [..., H, W, C] or [..., C, H, W] which are preserved through (most) transformations.
• Handles all common data types [Float, Integer, Boolean], and Ranges [0, 1], [0, 255] with automatic detection (e.g., no .permute() is required).
• Vertical/Horizontal concatenation of images of varying shapes with automatic padding, device conversion, etc.
• Many common transformations unified across formats (resize / crop / square / scale / normalize / denormalize).
• Video support (encodes a batched sequence to [mp4, gif]).
• Minimal dependencies (primarily just NumPy & PIL).
• Writing text on images, and much more!

Installation

Warning: The library is currently in alpha and the API is subject to change. If you use this library as part of another application, consider pinning to a specific commit. Moreover, you can copy the src/image_utils/im.py file as it works standalone!

To install from PyPI:

pip install image_utilities

Usage

Below is an example of using the primary Im class:

from image_utils import Im

img = np.random.randint(0, 256, (2, 10, 256, 256, 3), np.uint8)
img = Im(img)
img = img.write_text("Hello World!")
img = img.scale(2) 
img = img.crop(200, 300, 0, 100)

img = Im.concat_horizontal(*img, spacing=15)
img.save()
img.save_video(fps=2) # We now have a 10 frame video!

This does the following:

• Initializes 20 (2 x 10) random images
• Writes "Hello World!" on all images
• Scales all images, preserving aspect ratio. (Use resize(), scale_to_width(), or scale_to_width() for more control.)
• Crops all images
• Takes [2, 10, ...] and horizontally concats along the first dim to get an output of [10, ...] (where each image has ~2x the height). This is an example of the unpacking operator (on the Im object!), but it also supports regular array slicing notation (e.g., Im()[:1])
• Saves the batch of [10, ...] to an image (viewed as a grid by default). Uses a timestamp for the name and PNG format by default.
• Saves the batch of [10, ...] as a video. Uses a timestamp for the name and MP4 format by default.

For additional usage examples, see the test suite.

When you should use image_utils

• If you need to quickly visualize and work with images/videos.
• If your code frequently switches between NumPy, PyTorch, and PIL.

When you shouldn't use image_utils

• You shouldn't use image_utils in a pre-processing pipeline. There is no guarantee that a given operation [e.g., resize] will have exact consistency between versions.

• If you are concerned with performance, you also shouldn't use image_utils as it focuses on flexibility over performance. We focus on unified operations which sometimes requires internal conversions which could otherwise be avoided.

Extra Goodies

Another handy feature is provided by library_ops. This overrides the __repr__ for NumPy arrays and PyTorch Tensors. For example:

>>> import torch
>>> torch.randn(5, 5)
tensor([[-0.5524,  1.2306,  1.3209,  0.0336, -0.2458],
        [ 0.0448, -0.5564,  1.7019,  1.3689, -2.7115],
        [ 0.3842, -0.9593, -1.3799,  0.8625, -0.4071],
        [ 1.1263,  0.8479, -0.0585,  0.2687, -1.1983],
        [-0.5371, -0.5553, -0.7780, -0.8373,  0.2803]])
>>> from image_utils import library_ops
>>> torch.randn(5, 5)
[5,5] torch.float32 cpu finite
elems: 25, avg: 0.306, min: -1.399, max: 2.467
tensor([[ 0.782,  1.755,  0.975,  2.467, -0.646],
        [ 0.899,  2.344, -1.178, -0.291, -1.399],
        [ 0.676,  1.095,  0.289,  0.104, -0.294],
        [-0.152,  1.120, -0.844,  0.698,  0.647],
        [ 0.158, -0.048,  0.338, -0.838, -1.008]])
[5,5] torch.float32 cpu finite

Instead of only seeing the array contents, we can now view the shape, dtype, device, and more. finite or infinite signifies whether the array contains any NaN of Inf values.

If you want a dedicated library for this, check out lovely-tensors!

Tests

Note: If you want to know more about how to use specific methods or which formats we test on, check out tests/test_im_utils.py.

• To run all tests, simply run: pytest
• To break with pdb on error, use: pytest --pdb -s
• To run a specific test use: pytest -k 'test_concat' --pdb -s

Development

Local Installation

To install locally in a self-contained environment with UV:

git clone https://github.com/alexanderswerdlow/image_utils.git
uv sync --extra video --extra dev --extra cpu

Build Docs

uv pip install myst_parser sphinx sphinx-rtd-theme
cd docs
make html