PixPlot

This repository contains code that can be used to visualize tens of thousands of images in a two-dimensional projection within which similar images are clustered together. The image analysis uses Tensorflow's Inception bindings, and the visualization layer uses a custom WebGL viewer.

Dependencies

To install the Python dependencies, you can run (ideally in a virtual environment):

pip install pixplot

The HTML viewer requires a WebGL-enabled browser.

Quickstart

If you have a WebGL-enabled browser and a directory full of images to process, you can prepare the data for the viewer by installing the dependencies above then running:

pixplot --images "path/to/images/*.jpg"

To see the results of this process, you can start a web server by running:

# for python 3.x
python -m http.server 5000

# for python 2.x
python -m SimpleHTTPServer 5000

The visualization will then be available at http://localhost:5000/output.

Sample Data

To acquire some sample data with which to build a plot, feel free to use some data prepared by Yale's DHLab:

pip install image_datasets

Then in a Python script:

import image_datasets
image_datasets.oslomini.download()

The .download() command will make a directory named datasets in your current working directory. That datasets directory will contain a subdirectory named 'oslomini', which contains a directory of images and another directory with a CSV file of image metadata. Using that data, we can next build a plot:

pixplot --images "datasets/oslomini/images/*" --metadata "datasets/oslomini/metadata/metadata.csv"

Creating Massive Plots

If you need to plot more than 100,000 images but don't have an expensive graphics card with which to visualize huge WebGL displays, you might want to specify a smaller "cell_size" parameter when building your plot. The "cell_size" argument controls how large each image is in the atlas files; smaller values require fewer textures to be rendered, which decreases the GPU RAM required to view a plot:

pixplot --images "path/to/images/*.jpg" --cell_size 10

Controlling UMAP Layout

The UMAP algorithm is particularly sensitive to three hyperparemeters:

--min_distance: determines the minimum distance between points in the embedding

--n_neighbors: determines the tradeoff between local and global clusters

--metric: determines the distance metric to use when positioning points

UMAP's creator, Leland McInnes, has written up a helpful overview of these hyperparameters. To specify the value for one or more of these hyperparameters when building a plot, one may use the flags above, e.g.:

pixplot --images "path/to/images/*.jpg" --n_neighbors 2

Curating Automatic Hotspots

PixPlot uses Hierarchical density-based spatial clustering of applications with noise, a refinement of the earlier DBSCAN algorithm, to find hotspots in the visualization. You may be interested in consulting this explanation of how HDBSCAN works.

Adding Metadata

If you have metadata associated with each of your images, you can pass in that metadata when running the data processing script. Doing so will allow the PixPlot viewer to display the metadata associated with an image when a user clicks on that image.

To specify the metadata for your image collection, you can add --metadata=path/to/metadata.csv to the command you use to call the processing script. For example, you might specify:

pixplot --images "path/to/images/*.jpg" --metadata "path/to/metadata.csv"

Your metadata should be in a comma-separated value file (CSV), should contain one row for each of your input images, and should contain exactly the following columns in the following order.

If desired, you can add a fifth column "year" that will be used to create a date-based layout of your images:

Year-column values should contain a year integer for each image, e.g.:

IIIF Images

If you would like to process images that are hosted on a IIIF server, you can specify a newline-delimited list of IIIF image manifests as the --images argument. For example, the following could be saved as manifest.txt:

https://manifests.britishart.yale.edu/manifest/40005
https://manifests.britishart.yale.edu/manifest/40006
https://manifests.britishart.yale.edu/manifest/40007
https://manifests.britishart.yale.edu/manifest/40008
https://manifests.britishart.yale.edu/manifest/40009

One could then specify these images as input by running pixplot --images manifest.txt --n_clusters 2

Demonstrations (Developed with PixPlot 2.0 codebase)

Acknowledgements

The DHLab would like to thank Cyril Diagne and Nicolas Barradeau, lead developers of the spectacular Google Arts Experiments TSNE viewer, for generously sharing ideas on optimization techniques used in this viewer, and Lillianna Marie for naming this viewer PixPlot.