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Data Grinder

A simple script and service for running an image through third party computer vision services that extract faces, text, colors, and tags.

Requirements

  • Python 3.9.*
  • Virtualenv

Setup

Clone the repository

> git clone https://github.com/harvardartmuseums/data-grinder.git

Create a virtual environment

We recommend creating a virtual environment with Virtualenv and running everything within it.

> cd data-grinder
> virtualenv venv

Activate the virtual environment

> venv\Scripts\activate.bat

Install dependencies

> pip install -r requirements.txt

Set configuration values

  • Clone .env-template to .env
  • Open .env in a text editor.
  • Enter API keys and credentials for the services you want to use.
  • Google Vision requires a oauth credentials for authentication. Generate a sevice account key through your Google account API dashboard. Read more at https://cloud.google.com/vision/docs/common/auth. Paste the key as json into the .env file.

Services Implemented

HAM Color Service: extract colors
Clarifai: tag features, extract colors
Imagga: tag features, extract colors, categorize
Google Vision: tag features, find faces, find text
Microsoft Cognitive Services: categories, tags, description, faces, color
AWS Rekognition: labels, faces, text
Azure OpenAI GPT-4: description
Azure OpenAI GPT-4o: description
Anthropic Claude Haiku on AWS Bedrock: description
Anthropic Claude Sonnet on AWS Bedrock: description

API Tools-Data-Process Diagrams

Usage

Run as a script from the command line:

$ python main.py -url https://some.image/url

OR

Run as a local service:

$ flask run

Then open a browser to http://127.0.0.1:5000/extract

Parameters Values
url Any Harvard NRS URL that resolves to a IIIF compatible image
services (optional, default uses all services) One or more from the list of valid services separated by spaces imagga, gv, mcs, clarifai, color, aws, hash, openai, claude, gpt-4, gpt-4o, claude-3-haiku, claude-3-5-sonnet

Example response:

{
    "lastupdated": "2018-02-07 21:58:28",
    "drsstatus": "ok",
    "width": 581,
    "height": 768,
    "widthFull": 775,
    "heightFull": 1024,
    "scalefactor": 1.333907056798623,    
    "url": "https://nrs.harvard.edu/urn-3:huam:75033B_dynmc",    
    "iiifbaseuri": "https://ids.lib.harvard.edu/ids/iiif/14178676",
    "idsid": "14178676",
    "colors": [],
    "hashes": {},
    "clarifai": {},
    "microsoftvision": {},
    "googlevision": {},    
    "imagga": {},
    "aws": {},
    "openai": {},
    "gpt-4": {},
    "gpt-4o": {},
    "claude": {},
    "claude-3-haiku": {},
    "claude-3-5-sonnet": {}
}

Data in the response:
url: The original image URL passed in to this script. This must be in the form of a NRS URL for an image in the Harvard DRS.
drsstatus: The response from the DRS. The value will be "ok" or "bad".
width: The width in pixels of the image supplied on the command line.
height: The height in pixels of the image supplied on the command line.
widthFull: The width in pixels of the full image file in the DRS.
heightFull: The height in pixels of the full image file in the DRS.
scalefactor: The propotional difference between teh supplied image and the full image. In the example response, the full image is 1.334 times larger than the supplied image.
iiifbaseuri: A fully formed IIIF URI for the image in the DRS.
idsid: The image file ID in the DRS returned when requesting the original image URL.
colors:
clarifai:
microsoftvision:
googlevision:
imagga:
aws:
openai: