# 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
- **Live Preview**: MJPEG streaming — watch processing in real-time (15+ FPS at 640p)
- **Multiple Models**: Run multiple model configurations on the same video for comparison
- **Model Groups**: Models grouped by stem (e.g., `best.pt`, `best.engine`) with format dropdown
- **Class Filtering**: Choose which classes to count (sack, box, truck)
- **Auto Truck Detection**: Automatically loads `truck-detector` when selected model lacks truck class
- **Video Reuse**: Re-analyze previously uploaded videos without re-uploading
- **Annotated Output**: Download MP4 videos with detection overlays for human review
- **Async Processing**: Background job queue — upload and poll status
- **Abort**: Cancel running jobs mid-processing

## 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**: Drag-and-drop or click to upload video, or select from previously uploaded videos
- **Model Selection**: Grouped cards with format dropdown (`.engine` / `.pt` / `.onnx`) and class filter
- **Live Preview**: Real-time MJPEG stream during processing
- **Job Status**: Auto-refreshing progress page with abort button
- **Download**: Annotated MP4 per model result

## API Endpoints

| Endpoint | Method | Description |
|---|---|---|
| `/` | GET | Upload form with model groups |
| `/upload` | POST | Start processing job |
| `/upload/reuse` | POST | Re-analyze an existing uploaded video |
| `/status/<job_id>` | GET | Job status with results |
| `/jobs` | GET | All jobs listing |
| `/download/<job_id>/<filename>` | GET | Download output video |
| `/preview/thumb/<job_id>` | GET | Thumbnail JPEG for a job's video |
| `/api/preview/<job_id>` | GET | MJPEG live stream during processing |
| `/api/models` | GET | List model groups (JSON) |
| `/api/videos` | GET | List uploaded videos (JSON) |
| `/api/videos/<filename>` | DELETE | Delete an uploaded video |
| `/api/jobs` | GET | List all jobs (JSON) |
| `/api/jobs/<job_id>` | GET | Job detail (JSON) |
| `/api/jobs/<job_id>/cancel` | POST | Cancel a running job |
| `/api/jobs/<job_id>/samples` | GET | Sample frame paths (JSON) |

## Configuration

| Variable | Default | Description |
|----------|---------|-------------|
| `WEB_HOST` | `0.0.0.0` | Flask bind address |
| `WEB_PORT` | `9000` | Flask port |
| `FLASK_DEBUG` | `false` | Flask debug mode |
| `MODELS_DIR` | `./models` | Model weights directory |
| `UPLOAD_DIR` | `./uploads` | Uploaded videos directory |
| `OUTPUT_DIR` | `./output` | Annotated output directory |

## 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, grouping, class metadata
├── pipeline.py           # Video processing pipeline
├── preview.py            # Video probing, thumbnails, sample frames
└── job.py                # Async job queue
```

## Model Formats

Models are grouped by stem name. Each group has available formats:

| Format | Extension | Speed | Notes |
|--------|-----------|-------|-------|
| TensorRT | `.engine` | Fastest | Preferred on Jetson (GPU-optimized) |
| PyTorch | `.pt` | Medium | Works everywhere |
| ONNX | `.onnx` | Medium | Requires `onnxruntime` (not available on Jetson) |

Format preference order: `.engine` > `.pt` > `.onnx`

## Truck Detection

The pipeline requires truck detection to start counting. If the selected model lacks a "truck" class (e.g., `best.engine` which only has "sack"), the system automatically loads the `truck-detector` model for truck detection.

Models with "truck" class: `v4-best`, `model_karung_truk`
Models without "truck" class: `best`, `karung-dimuat`, `yolo11n-bbox` (auto-loads truck-detector)