Metadata-Version: 2.4
Name: adaspun
Version: 0.2.1
Summary: ADASPUN research package for ADAS perception, stereo depth, risk scoring, QUBO warning selection, and NeuroSentinel V9 execution.
Author: T, Puneeth
License: ﻿Research License Placeholder
        
        Local research and academic experimentation only.
        Replace before public release.
        No warranty. Not certified for safety-critical deployment.
        
Keywords: ADAS,computer-vision,stereo-depth,risk-estimation,QUBO,NeuroSentinel
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: opencv-python
Requires-Dist: pyyaml
Requires-Dist: tqdm
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: pillow
Provides-Extra: yolo
Requires-Dist: ultralytics; extra == "yolo"
Requires-Dist: torch; extra == "yolo"
Provides-Extra: ui
Requires-Dist: gradio; extra == "ui"
Dynamic: license-file

﻿# ADASPUN

ADASPUN is a research-oriented Python package for ADAS perception, stereo depth, risk estimation, QUBO warning selection, and NeuroSentinel-4D++ V9 execution.

## Install

Install from PyPI:

    pip install adaspun

For local development with YOLO extras:

    python -m pip install -e ".[yolo]"

## Quick checks

    adaspun-doctor
    adaspun-v9-robust --help
    adaspun-native-video --help

## Robust V9 source-of-truth runner

The robust V9 command delegates to a local NeuroSentinel V9 research workspace.
A full V9 run requires local assets such as the V9 script, helper backbone, model weights, and input videos.

Example:

    adaspun-v9-robust --project-root D:\Puneeth_Adas --v9-script D:\Puneeth_Adas\final_video_pipeline\run_neurosentinel_ddpm_ensemble_v9_sota_clean_ui_DISTANCE_LABELS.py --cuda --doctor-first --copy-output D:\Puneeth_Adas\outputs\adaspun_v9_demo.mp4

## Native modular commands

    adaspun-native-video --help
    adaspun-risk-smoke --help
    adaspun-qubo-smoke --help

## Research preview note

ADASPUN is currently a research preview package.
The modular native commands are included for package development and experimentation.
The robust V9 command is a bridge to the complete local NeuroSentinel V9 source-of-truth pipeline and does not bundle model weights, datasets, videos, or private research assets.

## Citation

If you use ADASPUN in academic work, cite the repository/package metadata in CITATION.cff.

## Python API

```python
from adaspun import ADASPUNRunner

runner = ADASPUNRunner(model=r"D:\path\to\best.pt", cuda=True)
result = runner.run_video("road.mp4", "output.mp4", max_frames=120)
print(result.output_video)
```

See `docs/python_api_usage.md` for more examples.

## Hosted model weights

ADASPUN does not bundle trained `.pt` weights in the PyPI wheel. Use local `.pt` files or host weights on Hugging Face/GitHub Releases and update the model registry. See `docs/hosted_models.md`.
