Decoding "Shot Marilyns" - Warhol's Color Language

Last update: 11/25/2024

Image source: The Interior Review

Overview

Andy Warhol's Shot Marilyns showcases how color serves as a powerful language, evoking emotions and responses from viewers. Our research delves into how Warhol transformed a single iconic image into five distinct masterpieces. By analyzing color composition and distribution, particularly through methods like relative conditional entropy, we reveal intricate relationships among colors in key areas such as backgrounds, hair, eyeshadow, and face.

Detail about this research: https://shotmarilyns.gd.edu.kg/

Authors

Name	Institution	Email
Hengyuan Liu	University of California, Los Angeles	hengyuanliu [at] ucla [dot] edu
Li Yuan	Swiss Federal Institute of Technology Zurich	liyuan1 [at] ethz [dot] ch
Kathy Mo	University of California, Los Angeles	kathymo24 [at] ucla [dot] edu
Xinhui Luo	Tufts University	xinhui.luo [at] tufts [dot] edu
Weilin Cheng	University of California, Davis	wncheng [at] ucdavis [dot] edu
Erick Arenas	University of California, Davis	esarenas [at] ucdavis [dot] edu

How to Reproduce the Analysis

Option 1: Using Google Colaboratory (the easiest way)

For an easier setup, you can run the analysis in Google Colaboratory. Click the button below to open the notebook in Google Colaboratory.

Possible drawbacks: Google Colaboratory may have different versions of the machine system, Python, and libraries installed. If any errors occur due to incompatible software versions, please use the Docker option to reproduce the analysis.

Option 2: Using Docker (the most stable way)

Step 1: Download the Repository

You can download the repository in two ways:

• Using Git Command:

  git clone https://github.com/GitData-GA/shot-marilyns-analysis.git shot-marilyns-analysis-main

  Make sure you have installed Git.

• Direct Download:

  Download the ZIP file using the following button and unzip it:

  Download

Step 2: Navigate to the Directory

Open your Docker terminal and change to the shot-marilyns-analysis-main directory (change the file path if necessary):

cd shot-marilyns-analysis-main

Step 3: Prepare for Analysis

Run the following commands to create a directory for the analysis plots, build the Docker image, start the Docker container, and execute the analysis script:

mkdir img; docker build -t shot-marilyns-analysis .; docker run -it --rm -v "$(pwd)/img:/img" shot-marilyns-analysis

Step 4: Review the Results

The analysis log will be displayed in the terminal, and all output plots will be saved in the img folder within shot-marilyns-analysis-main.

Computational Detail

Operating System: Ubuntu 22.04.3 LTS.

CPU Information: Intel(R) Xeon(R) CPU @ 2.20GHz, 2 cores

Memory: Recommended at least 4GB.

Disk: Recommended at least 4GB free space if using Docker.

Python Version: Python 3.10.12.

Python packages and versions:

• matplotlib==3.8.0
• networkx==3.4.2
• numpy==1.26.4
• opencv-python==4.10.0.84
• pandas==2.2.2
• pytz==2024.2
• requests==2.32.3
• scikit-image==0.24.0
• scikit-learn==1.5.2