taocore-human 0.3.0

Image & video pipelines for human behavior and emotion signals using TaoCore

taocore-human

Image & video pipelines for human behavior and emotion signals using TaoCore.

Philosophy

This package processes photos and videos into interpretable, bounded claims about behavior and emotion signals. It does not claim to read minds or determine truth about internal states.

Key principles:

• Outputs are "signals" and "patterns", not definitive judgments
• Uncertainty is always explicit
• Non-convergence is meaningful (signals may conflict)
• Conservative by default for human/emotion inference

Installation

# Basic (stub extractors only)
pip install taocore-human

# With image support
pip install taocore-human[image]

# With video support
pip install taocore-human[video]

# With ML models (PyTorch)
pip install taocore-human[ml]

Quick Start

Photo Folder Analysis

from taocore_human import PhotoFolderPipeline

# Process a folder of photos
pipeline = PhotoFolderPipeline("/path/to/photos")
result = pipeline.run()

# Check if interpretation is allowed
if result.interpretation_allowed:
    print(result.report.summary)
    print(result.report.behavioral_summary)
else:
    print("Interpretation declined:", result.rejection_reasons)
    print(result.report.structural_summary)  # Still available

# Export to JSON
print(pipeline.to_json(result))

Video Interaction Analysis

from taocore_human import VideoInteractionPipeline

# Process a video
pipeline = VideoInteractionPipeline("/path/to/video.mp4")
result = pipeline.run()

# Temporal patterns
print(result.temporal_patterns)

# Report
print(result.report.summary)

Architecture

Media → Feature Extraction → Nodes/Edges → Graph(s)
                                              ↓
                            Metrics (balance/flow/clusters/hubs)
                                              ↓
                                    Equilibrium Solver
                                              ↓
                                      Decider Rules
                                              ↓
                                  Uncertainty-Aware Report

What This System Does NOT Do

• Diagnose mental health or medical conditions
• Produce definitive judgments about personality or intent
• Act as a lie detector or "truth machine"
• Replace human interpretation in sensitive contexts

Components

Nodes

• PersonNode - Tracked individual with aggregated features
• FrameNode / WindowNode - Time slices for temporal analysis
• ContextNode - Scene context (lighting, quality, etc.)

Extractors (Pluggable)

• FaceExtractor - Face detection and expression signals
• PoseExtractor - Body pose estimation
• GazeExtractor - Attention/gaze estimation
• SceneExtractor - Scene-level features
• StubExtractor - Random data for testing

Pipelines

• PhotoFolderPipeline - Process folder of images
• VideoInteractionPipeline - Process video with temporal analysis

Output Example

{
  "interpretation_allowed": true,
  "confidence_level": "moderate",
  "num_persons_analyzed": 3,
  "summary": "Analysis of 3 individuals completed with moderate confidence. Signal patterns show convergent stability.",
  "limitations": [
    "Expression and emotion signals are probabilistic estimates, not ground truth about internal states."
  ],
  "recommendations": [
    "Findings may inform further investigation but should not be treated as definitive.",
    "Never use these signals for high-stakes decisions without human oversight."
  ]
}

License

MIT

CLI Quick Start

1. Install editable (dev):

cd /Users/dadatoto/taocore-human
python -m pip install -e .

2. Analyze a folder of photos:

taocore-human photo-folder /path/to/photos

3. Analyze a video:

taocore-human video /path/to/video.mp4

4. Save JSON output to a file:

taocore-human photo-folder /path/to/photos --output /tmp/taocore_result.json