# Seg Data Server Net

Modular web control plane for the existing Seg image segmentation workspace.

The platform keeps the current training and analysis scripts as the compute
core, then adds:

- a FastAPI backend for catalog discovery, job orchestration, logs, results,
  GPU status, and weight management;
- a React/Vite frontend for launching jobs and inspecting progress;
- a unified `weights/` area with a generated manifest for `.pt`, `.pth`,
  `.onnx`, and `.engine` assets.

## Layout

```text
Seg_Data_Server_Net/
  backend/            FastAPI API, job runner, module wrappers
  frontend/           React + Vite operator UI
  scripts/            helper scripts for running services and syncing weights
  weights/            copied model weights and manifest.json
```

## Quick Start

```bash
cd Seg_Data_Server_Net
cp .env.example .env

# Backend. The existing machine already has a seg_server env with FastAPI.
conda run -n seg_server uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8000

# Frontend.
cd frontend
npm install
npm run dev -- --host 0.0.0.0
```

Open the Vite URL shown in the terminal. The frontend expects the backend at
`http://localhost:8000` by default.

## Weight Sync

The current workspace contains tens of GB of pretrained and trained weights.
They are copied into `weights/files/<original-relative-path>` and indexed in
`weights/manifest.json`.

```bash
cd Seg_Data_Server_Net
python scripts/sync_weights.py --mode copy --hash
```

For repository storage, use Git LFS or a Gitea release/package store:

```bash
git lfs install
git lfs track "*.pt" "*.pth" "*.onnx" "*.engine"
```

If Git LFS is not available on the host or server, keep the copied weights on
the deployment volume and commit only `weights/manifest.json`.

## Job Types

The backend exposes all current Seg capabilities as job types. Examples:

- `dataset.rename`, `dataset.resize`, `dataset.pair`, `dataset.rebuild_labels`,
  `dataset.stack`, `dataset.stitch`, `dataset.video_frames`
- `segmodel.train`, `segmodel.batch_train`, `segmodel.predict`,
  `segmodel.batch_predict`, `segmodel.flops`, `segmodel.raw_mask_check`
- `yolo.train`, `yolo.batch_train`, `yolo.predict`, `yolo.batch_predict`,
  `yolo.heatmap`, `yolo.compare`, `yolo.raw_mask_check`, `yolo.video_visible`
- `mmseg.generate_data`, `mmseg.generate_alg`, `mmseg.train`,
  `mmseg.metrics`, `mmseg.flops_fps`, `mmseg.draw`, `mmseg.extract_loss_miou`
- `analysis.all`, `system.backup`, `mock.echo`

Use `GET /api/catalog` to inspect supported models, algorithms, datasets, and
task types discovered from the existing `Seg/` workspace.