# 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 deployment env is seg_smp so the API and task wrappers share
# the same segmentation dependency stack.
conda run -n seg_smp uvicorn app.main:app --app-dir backend --host 0.0.0.0 --port 8010

# 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:8010` by default.

The web UI includes a dataset bench for creating upload workspaces, uploading
images/labels/masks, and jumping into the existing rename, PNG conversion,
resize, pair-check, label rebuild, transparent overlay, stitch, and video-frame
jobs. Segmentation previews, YOLO heatmaps, and loss/metric artifacts are
grouped on the results dashboard.

The coverage panel calls `GET /api/coverage` and verifies that the user-facing
scripts from the existing `Seg/` workspace are mapped to web jobs. MMSeg
vendored internals, docs, build outputs, converters, and config templates are
classified as supporting artifacts rather than direct web actions.

## 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
```

Weights must remain local to the deployment machine. Do not push `.pt`, `.pth`,
`.onnx`, `.engine`, or `weights/files/` into Gitea. The repository stores only
code, `weights/manifest.json`, and helper scripts. Before pushing, run:

```bash
scripts/check_no_weight_git.sh
```

If a deployment machine needs weights, run the sync command locally on that
machine after cloning the code.

## 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`,
  `dataset.yolo_check_pairs`, `dataset.yolo_stack`, `dataset.yolo_txt_sort`,
  `dataset.yolo_convert_png`, `dataset.yolo_resize`
- `segmodel.train`, `segmodel.batch_train`, `segmodel.predict`,
  `segmodel.batch_predict`, `segmodel.flops`, `segmodel.params_flops`,
  `segmodel.benchmark`, `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`
- `visual.train`, `visual.inference`, `visual.fps`,
  `visual.yolo11_heatmap_v1`, `visual.yolo11_heatmap_v2`, `visual.deal_labels`
- `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.
Use `GET /api/coverage` to inspect script-to-task coverage and task
buildability.

## Agents

Run the local evaluation and validation agents before publishing changes:

```bash
PYTHONPATH=backend conda run -n seg_smp python scripts/run_agents.py --build
```

The validation agent checks catalog coverage, the new `seg_smp` env, GPU
visibility, no-weight Git safety, backend tests, frontend build, and live
backend/frontend endpoints when the services are running.