SeetaPsych Lib

A Computer Vision Toolkit for Face-based Psychological Measurement

SeetaPsych Lib is a Python library for face and body-based psychology analysis. It provides a modular Pipeline/Runner runtime and an optional Streamlit WebUI.

Requirements

• Python >= 3.10
• (Recommended) uv package manager: https://github.com/astral-sh/uv

Installation

Create Virtual Environment

It is recommended to use an isolated virtual environment before installing dependencies.

Using uv (recommended)

# Create a virtual environment at .venv
uv venv

# Activate (bash/zsh)
source .venv/bin/activate

# Activate (PowerShell)
.venv\Scripts\Activate.ps1

# Activate (Windows CMD)
.venv\Scripts\activate.bat

Using standard venv

python -m venv .venv

# bash/zsh
source .venv/bin/activate

# PowerShell
.venv\Scripts\Activate.ps1

# Windows CMD
.venv\Scripts\activate.bat

Using conda

conda create -n seetapsych python=3.10
conda activate seetapsych

Install Dependencies

Install the required dependencies:

• seetapsych-lib
• seetapsych-attributes
• seetapsych-configs

To run the WebUI, you need to install the seetapsych-lib[webui] package.

Using uv (recommended)

uv pip install 'seetapsych-lib[webui]' seetapsych-attributes seetapsych-configs

Using pip

pip install seetapsych-lib[webui] seetapsych-attributes seetapsych-configs

Install Default Configs

# download default configs
seetapsych-manager download
# install each module requirements
seetapsych-manager setup
# download each model
seetapsych-manager cache

The setup and cache commands can be skipped. When you use the WebUI or call the library programmatically later, seetapsych-lib can install dependencies and download necessary models on demand.

Public Resources

The default modules installed with seetapsych-lib are published and maintained at https://github.com/seetapsych/seetapsych-configs.

To update the built-in modules to their latest versions, upgrade the configs package and re-download:

uv pip compile --upgrade-package seetapsych-configs
seetapsych-manager download -f

Algorithm inputs and execution outputs are defined via Attributes.

The full Attributes specification is available at https://github.com/seetapsych/seetapsych-attributes.

Quick Start

Run WebUI (Streamlit)

seetapsych-webui --log INFO

or

python -m seetapsych_lib.webui --log INFO

A local browser window will open automatically, or you can manually navigate to: http://localhost:8501.

Common arguments:

• --dirs <DIR...>: load modules from directories
• --files <FILE...>: load modules from local config files
• --urls <URL...>: load modules from remote URLs
• --disable-builtin: disable builtin modules
• --disable-default: disable default modules
• --cache-dir <DIR>: model cache directory
• --upload-dir <DIR>: upload directory
• --log <LEVEL>: log level (e.g., DEBUG, INFO, WARNING, or an integer like 10)

Programmatic Usage

# -*- coding: utf-8 -*-

import json
import cv2

from seetapsych_lib.runtime.factory import Factory
from seetapsych_lib.runtime.pipeline import Pipeline
from seetapsych_lib.runtime.runner import Runner
from seetapsych_lib.runtime.parallel_runner import ParallelRunner


def main():
    # All installed algorithm modules are loaded by default during initialization
    # You can use the `load_xxx_module(s)` methods to load specific algorithm modules
    factory = Factory()

    # Quickly build a workflow and declare the attribute to compute as the face feature 'face/detection'
    # You can view all available attributes of installed algorithms using the `seetapsych-manager show` command
    # Result fields for attributes can be found at https://github.com/seetapsych/seetapsych-attributes
    pipeline = Pipeline(factory, attributes=["face/detection"])

    # Check for dependencies or missing issues that need to be resolved with solve()
    print(pipeline.problem())
    # Resolve workflow dependencies, automatically add face detection and corresponding models
    pipeline.solve()

    # Check for runtime environment issues that require installation or download to fix
    print(pipeline.satisfied())
    # Install missing dependencies required for the current pipeline to run
    pipeline.install_requirements()
    # Download missing models required for the pipeline to run
    pipeline.cache_models()

    # Create a basic executor
    runner = Runner(pipeline)
    # Or create a parallel executor
    # runner = ParallelRunner(pipeline)

    # Run the algorithm
    report = runner.run(data={"default": cv2.imread("image.jpg")})

    # Print the execution results
    print(json.dumps(report, indent=2, ensure_ascii=False))


if __name__ == "__main__":
    main()

Configuration

Environment Variables

The following environment variables are supported: