Metadata-Version: 2.4
Name: cineinfini-audit
Version: 0.4.10.1
Summary: CineInfini: Adaptive Multi-Stage Video Quality Audit Pipeline
Author-email: Salah-Eddine BENBRAHIM <benbrahim.salah.eddine.777@gmail.com>
License: MIT
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: opencv-python>=4.8
Requires-Dist: numpy>=1.24
Requires-Dist: matplotlib>=3.5
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Requires-Dist: Pillow>=9.5
Requires-Dist: torch>=2.0
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Requires-Dist: open-clip-torch>=2.20
Requires-Dist: transformers>=4.30
Requires-Dist: requests>=2.28
Requires-Dist: click>=8.1
Requires-Dist: markdown>=3.4
Requires-Dist: weasyprint>=60
Requires-Dist: plotly>=5.15
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Provides-Extra: dev
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Dynamic: license-file

# 🎬 CineInfini — Video Quality Auditor

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.9+](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/)
[![PyPI](https://img.shields.io/pypi/v/cineinfini-audit.svg)](https://pypi.org/project/cineinfini-audit/)
[![Tests](https://img.shields.io/badge/tests-272%20passing-brightgreen)](tests/)
[![Modules](https://img.shields.io/badge/modules-21-blue)](docs/STATUS.md)

**The only no-reference, modular, per-shot, multi-dimensional video auditor
that runs in under 1 GB of weights and exposes every gate to the user.**

CineInfini scores AI-generated and natural videos across 19 native metrics
and produces output compatible with **VBench**, **VideoScore**, **DOVER**,
and **FAST-VQA** — so you submit the same audit to any leaderboard.

---

## TL;DR

```bash
pip install cineinfini-audit
cineinfini bootstrap                                       # Tier-1 weights (~835 MB)
cineinfini audit my_video.mp4 --config cfg/profiles/postproduction.yaml
cineinfini score    output/                                # → 5 axes + composite
cineinfini export-vbench output/                           # → VBench JSON
```

---

## What you get from one audit run

A single `cineinfini audit` produces **all of these output formats simultaneously**:

| Format | What | Compatible with |
|---|---|---|
| `data.json` | Native gates + per-shot ACCEPT/REVIEW/REJECT | — |
| `dashboard.html` / `.pdf` / `.md` | Human-readable reports | — |
| `*.vbench.json` | 16-dim VBench-leaderboard format | [Vchitect/VBench](https://github.com/Vchitect/VBench) |
| 5-axis VideoScore + composite | Visual / Temporal / Dynamic / Alignment / Factual | [TIGER-Lab/VideoScore](https://huggingface.co/TIGER-Lab/VideoScore) |
| `dover_aesthetic`, `dover_technical` | DOVER scores | [DOVER](https://github.com/QualityAssessment/DOVER) (when wired) |
| `fastvqa_score` | FAST-VQA score | [FAST-VQA](https://github.com/VQAssessment/FAST-VQA-and-FasterVQA) (when wired) |

## Performance profiles

```bash
cineinfini audit video.mp4 --config cfg/profiles/realtime.yaml         # < 2s/min CPU
cineinfini audit video.mp4 --config cfg/profiles/ultralight.yaml       # < 5s total
cineinfini audit video.mp4 --config cfg/profiles/postproduction.yaml   # default for serious users
cineinfini audit video.mp4 --config cfg/profiles/academic.yaml         # all 21 modules
cineinfini audit video.mp4 --config cfg/profiles/low_memory.yaml       # < 4 GB VRAM
```

See [`docs/USER_MANUAL.md`](docs/USER_MANUAL.md) for the full reference and
[`docs/benchmarking/PROFILES.md`](docs/benchmarking/PROFILES.md) for performance.

## How CineInfini compares

| Capability | VMAF | DOVER | VideoScore | VBench | EvalCrafter | **CineInfini** |
|---|---|---|---|---|---|---|
| No-reference | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ |
| Per-shot diagnostic | ❌ | ❌ | ❌ | ⚙️ | ⚙️ | **✅** |
| Disk footprint | 5 MB | 200 MB | 16 GB | 10+ GB | 30+ GB | **835 MB** |
| Plug-in extensibility | ❌ | ❌ | ❌ | ❌ | ❌ | **✅** |
| YAML-driven config | ❌ | ⚙️ | ❌ | ❌ | ❌ | **✅** |
| Causal/physics modules | ❌ | ❌ | ⚙️ | VBench-2.0 only | ⚙️ | **✅** |

Full comparison: [`docs/benchmarking/COMPARISON.md`](docs/benchmarking/COMPARISON.md).

---

## Installation

```bash
# Minimal — pure-CV modules only
pip install cineinfini-audit

# With ML modules (CLIP, ArcFace, DINOv2)
cineinfini bootstrap                            # ~835 MB models

# Full — including DOVER, FAST-VQA cross-benchmark wrappers
pip install torch torchvision dover-vqa fast-vqa
cineinfini bootstrap --include-optional         # +395 MB
```

Detailed guide: [`docs/INSTALLATION.md`](docs/INSTALLATION.md).

---

## Documentation index

| Doc | What |
|---|---|
| **[`docs/USER_MANUAL.md`](docs/USER_MANUAL.md)** | Complete user manual — every CLI command, module, profile, output |
| [`docs/QUICKSTART.md`](docs/QUICKSTART.md) | 5-minute getting started |
| [`docs/INSTALLATION.md`](docs/INSTALLATION.md) | Install + competitor dependencies |
| [`docs/STATUS.md`](docs/STATUS.md) | Exhaustive status: done, not done, what to install |
| [`docs/MISSING_ASSETS.md`](docs/MISSING_ASSETS.md) | Tier 1/2/3 asset inventory + training recipes |
| [`docs/benchmarking/COMPARISON.md`](docs/benchmarking/COMPARISON.md) | Head-to-head vs 12 industrial/academic tools |
| [`docs/benchmarking/COMPETITORS.md`](docs/benchmarking/COMPETITORS.md) | One-liner install for each competitor |
| [`docs/benchmarking/PROFILES.md`](docs/benchmarking/PROFILES.md) | Per-profile performance characteristics |
| [`notebooks/`](notebooks/) | Runnable Jupyter notebooks |

---

## Citation

```bibtex
@software{cineinfini,
  author  = {Benbrahim, Salah-Eddine},
  title   = {CineInfini: Video Quality Auditor},
  year    = {2026},
  url     = {https://github.com/CineInfini/Video_Quality_Auditor},
  version = {0.4.10.1}
}
```

## License

MIT. See [LICENSE](LICENSE).
