# Feedmill Recounter

AI video analysis tool for counting objects (sacks, boxes) in feedmill videos.
Built on top of [karung_counter_semarang](https://git.proit.id/andrew/karung-counting-feedmill-semarang).

## Features

- **CLI**: Process videos from the command line with any model + class filter
- **Web UI**: Upload videos, select models, download annotated output on port 9000
- **Multiple Models**: Run multiple model configurations on the same video for comparison
- **Class Filtering**: Choose which classes to count (sack, box, truck)
- **Annotated Output**: Download MP4 videos with detection overlays for human review
- **Async Processing**: Background job queue — upload and poll status

## Quick Start

```bash
pip install -e ".[dev]"

# List available models
recounter --list-models --models-dir ./models

# Process a single video via CLI
recounter --video input.mp4 --model v4-best.pt --filter sack --output-dir ./output

# Start web UI
recounter-web
# Open http://localhost:9000
```

## CLI Reference

```
recounter --video PATH          Input video file
         --model NAME           Model filename (repeatable for multiple)
         --all-models           Run all discovered models
         --list-models          List available models and exit
         --filter NAME          Class filter (repeatable): sack, box, truck
         --sack-conf FLOAT      Sack confidence threshold (default: 0.4)
         --truck-conf FLOAT     Truck confidence threshold (default: 0.5)
          --output PATH          Output path (single model only)
         --output-dir DIR       Output directory (default: ./output)
         --models-dir DIR       Models directory (default: ./models)
```

## Web UI

- **Port**: 9000 (configurable via `WEB_PORT` env)
- **Upload**: Select video file
- **Model Selection**: Checkboxes for each model, dropdown for class filter
- **Job Status**: Auto-refreshing progress page
- **Download**: Annotated MP4 per model result

## API Endpoints

| Endpoint | Method | Description |
|---|---|---|
| `/` | GET | Upload form with model selection |
| `/upload` | POST | Start processing job |
| `/status/<job_id>` | GET | Job status with results |
| `/jobs` | GET | All jobs listing |
| `/download/<job_id>/<filename>` | GET | Download output video |
| `/api/models` | GET | List available models |
| `/api/jobs` | GET | List all jobs (JSON) |
| `/api/jobs/<job_id>` | GET | Job detail (JSON) |

## Project Structure

```
src/
├── interfaces.py         # Detection dataclass + protocols
├── detection.py          # YOLO detectors with class filtering
├── tracking.py           # ByteTrack/FastTrack tracker
├── stabilizer.py         # Bbox smoothing + occlusion hold
├── truck_roi.py          # Truck ROI detection + EMA smoothing
├── counting.py           # Line-crossing counter
├── batch.py              # Batch lifecycle state machine
├── dashboard.py          # Frame annotation overlay
├── video_writer.py       # Annotated video writer
├── model_registry.py     # Model discovery + class metadata
├── pipeline.py           # Video processing pipeline
└── job.py                # Async job queue
```