CommunityScripts/plugins/LocalVisage

Local Performer Recognition

https://discourse.stashapp.cc/t/local-visage/2478

A plugin for recognizing performers from their images using DeepFace. This plugin integrates seamlessly with Stash and enables automatic facial recognition by building or updating a local model trained from your existing image collection.

🔍 Features

• Rebuild Face Recognition Model
  Completely rebuild the local facial recognition model using available images per performer.

• Update Face Recognition Model
  Incrementally updates the model if performers have fewer images than the configured target count.

• Automatic Server Control
  Easily start or stop the recognition server as needed—automatically starts when an image is queried.

• Identify
  Click on the new icon next to an image to trigger performer identification.

📦 Requirements

• Python 3.10.11 (temporarily, see instructions below)
• PythonDepManager
• stashUserscriptLibrary7djx1qp (add repo https://7djx1qp.github.io/stash-plugins)
• git need to be installed and in the PATH

Docker Usage

Because this plugin relies on tensorflow (the AI framework used), the official Stash docker image can't be used. See this post for details.

A Dockerfile has been created as a replacement to use compatible dependencies. You can find it in the plugin repository.

You have two options to use this image:

• A working docker compose can be found in the plugin folder.
  • Only changes are the build section to use the modified Dockerfile, and a new port to expose that is the deepface server port (7860)
  • Use this command to rebuild your docker container: docker compose up -d --build --pull always
• Build the image yourself
  • Run this to build it inside the plugin repo folder: docker build --pull -t stashapp/stash:LocalVisage -f Dockerfile .
  • Update your stash container configuration to expose a new port: docker run -p 7860:7860 ... stashapp/stash:LocalVisage ...

Once this image is built and running, you can skip the "Set Python Path" step below and enjoy the plugin!

Note

If you use a reverse proxy, you may encounter difficulties setting up the redirection to the deepface server.

URL used by the plugin is hardcoded inside the frontend.js file:
window.location.protocol + "//" + window.location.hostname + ":7860"

e.g. using https://stash.example.org will send requests to https://stash.example.org:7860.
But worse, using https://example.com/stash will use https://example.com:7860

⚙️ Tasks

Task	Description
Rebuild Face Recognition Model	Fully rebuild the DeepFace model for all performers.
Update Face Recognition Model	Add more images for performers with less than the target image count.
Start Server	Start the local DeepFace server if it's not already running.
Stop Server	Gracefully stop the running recognition server.

🔧 Settings

Setting	Description
Target image count per voy	Number of images to use per performer when training the model. Default is 15.

🚀 Installation & Setup

1. Set Python Path to 3.10.11

To ensure compatibility with DeepFace and the plugin’s dependency resolution process:

• Temporarily set the Python path in your system/environment to Python 3.10.11.

2. Rebuild the Model

Run the "Rebuild Face Recognition Model" task. This will:

• Set up a virtual environment
• Install all necessary Python dependencies (DeepFace, etc.)
• Build the recognition model

3. Restore Python Path (Optional)

Once setup is complete, you can revert your Python path to its original version. The plugin will continue working with the generated virtual environment.

🖼 Usage

1. Once the model is built, navigate to an image in your Stash UI.
2. Click the Performer Recognition icon overlaying the image.
3. The plugin will:
   • Automatically start the recognition server if it's not already running
   • Query the server to identify the performer
   • Display the matched performer from the trained database