enczoo 0.1.0

Map images (as `PIL.Images`) to intermediate representations (as `np.ndarray`) from off-the-shelf vision models.

enczoo: a zoo of encoding models for images

enczoo is a Python library with a single goal: to map images (as PIL.Images) to intermediate representations (as np.ndarray) from off-the-shelf vision models, such as AlexNet and ResNet50.

This library is meant for those who just need to compute off-the-shelf image features once for their project (and perhaps cache them elsewhere).

Installation

enczoo requires Python 3.12 or above, and may be installed using uv with the following command:

uv add enczoo

Usage

import enczoo
import PIL.Image
image = PIL.Image.open('my-image.png')

model = enczoo.ResNet50(layer_name='avgpool') # try layer4, layer3, ...
features = model.compute_features(images=[image]) # np.ndarray

# Want another layer? Check out: print(enczoo.ResNet50.layer_names)

Things enczoo handles

enczoo aims to "just work" by solving several tiny problems which collectively make computing image features a bit annoying. enczoo handles:

• performing model-specific image normalization ("was it -1 to 1, 0 to 1, 0-255...? ImageNet channel normalization...?"),
• correctly encoding images ("my image was in mode L, not RGB!")
• turning off any batch normalization ("was the model in training mode...?")
• extracting intermediate layers by name ("how do I do that forward hook thing again...?")
• turning off autograd, and returning tensors as np.ndarray (no more .cpu().numpy())
• image cropping to fit input tensor shape (default: center cropping. no black bars!)
• and more!