https://github.com/centre-for-humanities-computing/pixplot
docker container for pixplot
https://github.com/centre-for-humanities-computing/pixplot
Last synced: 12 months ago
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docker container for pixplot
- Host: GitHub
- URL: https://github.com/centre-for-humanities-computing/pixplot
- Owner: centre-for-humanities-computing
- License: mit
- Created: 2019-10-11T13:39:09.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-03-25T00:04:44.000Z (about 3 years ago)
- Last Synced: 2025-04-19T21:25:29.259Z (about 1 year ago)
- Language: JavaScript
- Homepage:
- Size: 2.54 MB
- Stars: 6
- Watchers: 1
- Forks: 3
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# CHC Pix plot docker container
A fork of [DHLab's pix plot repository](https://github.com/YaleDHLab/pix-plot) for demonstrations with custom image collections
## About 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
You need to [install Docker](https://docs.docker.com/install/). If you are on Windows 7 or earlier, you may need to install [Docker Toolbox](https://docs.docker.com/toolbox/toolbox_install_windows/) instead.
The html viewer requires a WebGL-enabled browser.
## Setup
1) set a title in the index.html file
1) copy jpg files into
```
./data/images
```
## How To Generate the Pixplot
Download this repository by clicking the green "Clone or download" button and then "Download ZIP".
Unpack the zip file.
Start a terminal, cd into the folder that contains this README file.
*Below steps each have numbered commands for later reference.*
Generate the environment for your pixplot within a docker container (command 1):
```bash
# command 1:
# build the docker container
docker build --tag pixplot --file Dockerfile .
```
Process your collection into a pix plot (command 2).
*Depending on the size of your image collection, this can take several hours. In our hackathon it took Max around 3.5 hours.*
```
# command 2:
# process images from the VM collection
# use the `-v` flag to mount directories from outside
# the container into the container
docker run \
-v "$(pwd)/output:/pixplot/output" \
-v "$(pwd)/data/images:/pixplot/images" \
pixplot \
bash -c "cd pixplot && python3.6 utils/process_images.py images/*"
```
You now have generated your pixplot. The next step will start a web server to host your plot on http://localhost:5000
```
# command3:
# run the web server
docker run \
-v "$(pwd)/output:/pixplot/output" \
-p 5000:5000 \
pixplot \
bash -c "cd pixplot && python3.6 -m http.server 5000"
```
## Curating Automatic Hotspots
By default, PixPlot uses [*k*-means clustering](https://en.wikipedia.org/wiki/K-means_clustering) to find twenty hotspots in the visualization. You can adjust the number of discovered hotspots by changing the `n_clusters` value in `utils/process_images.py` and re-running the script.
After processing, you can curate the discovered hotspots by editing the resulting `output/plot_data.json` file. (This file can be unwieldy in large datasets -- you may wish to disable syntax highlighting and automatic wordwrap in your text editor.) The hotspots will be listed at the very end of the JSON data, each containing a label (by default 'Cluster *N*') and the name of an image that represents the centroid of the discovered hotspot.
You can add, remove or re-order these, change the labels to make them more meaningful, and/or adjust the image that symbolizes each hotspot in the left-hand **Hotspots** menu. *Hint: to get the name of an image that you feel better reflects the cluster, click on it in the visualization and it will appear suffixed to the URL.*
## Project Adaptations ##
1. SMK 2020
2. Artistic Exchange 21-24
## Acknowledgements
The DHLab would like to thank [Cyril Diagne](http://cyrildiagne.com/), a lead developer on the spectacular [Google Arts Experiments TSNE viewer](https://artsexperiments.withgoogle.com/tsnemap/), for generously sharing ideas on optimization techniques used in this viewer.