README.md

A library for style similarity search for art and design images.

Contents

• analysis.ipynb: a walkthrough of the the EDA, development, and analysis of my similarity search method. Note that there is some dynamic inheritence modificaiton and method declaration (monkey patching) to enhance the story-like flow of the notebook, which is not included in the analysts.py class versions, and would not be included in production code.
• analysts.py: classes used in analysis and EDA
• style_stack.py: primary similarity search classes for production

Installation

To install the package above, pleae run:

GPU

conda install faiss-gpu -c pytorch
conda install requirements.txt

CPU

conda install faiss-cpu -c pytorch
conda install requirements.txt

Requisites

• conda

Dependencies

• faiss
• joblib
• keras
• matplotlib
• numpy
• pillow
• scikit-learn

Usage

Build GramStack

Import GramStack and choose a model from keras.applications

from keras.applications.vgg16 import VGG16
from stylestack.gram_stack import GramStack

Set up arguments

image_dir = '../data/my_data'
model = VGG16(weights='imagenet', include_top=False)
layer_range = ('block1_conv1', 'block2_pool')

Build GramStack

stack = GramStack.build(image_dir, model, layer_range)

Query GramStack

Set weighting for embedding layers in similarity search. Any layers not specified will be weighted as 0, or all layers can be used by specifying None.

embedding_weights = {
    'block1_conv1': 1,
    'block3_conv2': 0.5,
    'block3_pool': .25
}

Set other arguments. Use write_output to output results JSON to /output/.

image_path = '../data/my_data/cat_painting.jpg'
n_results = 5
write_output = True

Query GramStack

results = stack.query(image_path, embedding_weights, n_results, write_output)

Save to disk

stack.save(lib_name='my_data')

Load from disk

GramStack.load(lib_name='my_data')

Once the GramStack is loaded, it can be queried and behaves the same as when it was built.