image_utilities 0.0.1.dev1

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 formats.

For example, 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 this specific input shape/dtype/range combination, but [almost] any combination.

Features

• Supports NumPy arrays, PyTorch Tensors, and PIL Images
• Handles arbitrary shapes [..., H, W, C] or [..., C, H, W] and preserves the input shape, batching all necessary transformations.
• Handles all common data types [Float, Integer, Boolean], and Ranges [0, 1], [0, 255]
• Vertical/Horizontal concatenation of images with automatic padding, device conversion, and even batching
• Writing text on images
• Video encoding [mp4, gif] of a sequence of images
• Image normalization, resizing, 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, adding as a submodule, or even just taking the src/image_utils/im.py file as it works standalone!

This package is not currently on PyPI. To install, use the git url:

pip install git+https://github.com/alexanderswerdlow/image_utils.git

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!") # Writes the text on all 20 images
img = img.scale(2) # Scales image, preserving aspect ratio. Use resize(), scale_to_width(), or scale_to_width() for more control.
img = img.crop(200, 300, 0, 100)

# The Im class supports regular array slicing and unpacking! Here we concatenate the two [10, ...] into a single [10, ...] sequence of images
img = Im.concat_horizontal(*img, spacing=15) # Concatenation even works with varying shapes with automatic padding!
img.save() # Batched images are saved as a grid by default. Uses a timestamp for the name and PNG format by default. 
img.save_video() # We now have a 10 frame video!

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!

When you should use image_utils

If you need to quickly visualize and work with images in a flexible way and aren't concerned with maximum efficiency

When you shouldn't use image_utils

Currently, you shouldn't use image_utils in your pre-processing pipeline for a machine learning model. There is no guarantee that a given operation [e.g., resize] will have bit-perfect consistency between versions. Furthermore, image_utils focuses on flexibility over a wide-range of formats which in practice means frequent internal conversions and thus incurs additional overhead.

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