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bimantoromaesa merged 1 commit into from
Mar 6, 2026
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Add root API endpoint and update README with examples #4

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12 changes: 12 additions & 0 deletions README.md
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Expand Up @@ -249,6 +249,18 @@ asyncio.run(stream())

See [docs/streaming.md](docs/streaming.md) for the full binary frame protocol specification and a complete async client example.

### Runnable example scripts

The [`examples/`](examples/) directory contains ready-to-run Python clients:

| Script | Description |
| ---------------------------------------------------------- | -------------------------------------------------- |
| [`examples/rest_infer.py`](examples/rest_infer.py) | REST inference — detect, classify, segment |
| [`examples/rest_vlm.py`](examples/rest_vlm.py) | REST inference — visual language model (VLM) |
| [`examples/ws_video_infer.py`](examples/ws_video_infer.py) | WebSocket video streaming — frame-by-frame results |

See [`examples/README.md`](examples/README.md) for full usage, argument reference, and sample output for each script.

---

## Development Setup
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257 changes: 257 additions & 0 deletions examples/README.md
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# Examples

This directory contains runnable client examples for MATA-SERVER.

| Script | Transport | Tasks covered |
| ---------------------------------------- | -------------------------------- | -------------------------------------- |
| [`rest_infer.py`](#rest_inferpy) | REST (`POST /v1/infer`) | `detect`, `classify`, `segment` |
| [`rest_vlm.py`](#rest_vlmpy) | REST (`POST /v1/infer`) | `vlm` |
| [`ws_video_infer.py`](#ws_video_inferpy) | WebSocket (`WS /v1/stream/{id}`) | `detect`, `segment`, `classify`, `vlm` |

> **Prerequisites** — a MATA-SERVER instance must be reachable before running any example.
> Start one locally with:
>
> ```bash
> MATA_SERVER_AUTH_MODE=none mataserver serve
> ```
>
> All examples default to `127.0.0.1:8110`. Pass `--host` / `--port` to override.

---

## rest_infer.py

Runs single-shot inference against the REST API using a **base64-encoded image** payload.
Covers the three classic vision tasks — object detection, image classification, and instance segmentation.

### Requirements

```bash
pip install requests
```

### Usage

```bash
# Run all three tasks with the default model set
python examples/rest_infer.py --image examples/images/coco_cat_remote.jpg

# Run a single task
python examples/rest_infer.py \
--image examples/images/coco_cat_remote.jpg \
--task detect \
--model PekingU/rtdetr_r18vd

# Zero-shot open-vocabulary detection with text prompts
python examples/rest_infer.py \
--image examples/images/coco_cat_remote.jpg \
--task detect \
--model google/owlv2-base-patch16-ensemble \
--prompts "cat,dog,remote control"
```

### Arguments

| Argument | Default | Description |
| ----------- | ------------ | -------------------------------------------------------------------- |
| `--image` | _(required)_ | Path to an image file |
| `--task` | all three | One of `detect`, `classify`, `segment` |
| `--model` | task default | HuggingFace model repo ID; defaults to the bundled task-to-model map |
| `--prompts` | — | Comma-separated text prompts for zero-shot / open-vocabulary models |
| `--host` | `127.0.0.1` | Server hostname |
| `--port` | `8110` | Server port |

### Default models

| Task | Default model |
| ---------- | ---------------------------------------------- |
| `detect` | `PekingU/rtdetr_r18vd` |
| `classify` | `google/vit-base-patch16-224` |
| `segment` | `facebook/mask2former-swin-tiny-coco-instance` |

### Sample output

```
--- Task: DETECT | Model: PekingU/rtdetr_r18vd ---
3 detection(s)
[0.94] cat bbox=[42, 10, 380, 470]
[0.81] remote bbox=[200, 300, 260, 420]
[0.57] couch bbox=[0, 250, 480, 480]
Full response keys: ['schema_version', 'task', 'model', 'timestamp', 'detections']

--- Task: CLASSIFY | Model: google/vit-base-patch16-224 ---
5 class(es)
[0.84] tabby cat
[0.07] Egyptian cat
...

--- Task: SEGMENT | Model: facebook/mask2former-swin-tiny-coco-instance ---
4 segment(s)
[0.91] cat bbox=[42, 10, 380, 470]
...
```

---

## rest_vlm.py

Sends an image and a natural-language prompt to a **Visual Language Model** (VLM) via the REST API and prints the generated response text.

### Requirements

```bash
pip install requests
```

### Usage

```bash
# Basic question about an image
python examples/rest_vlm.py \
--image examples/images/coco_cat_remote.jpg \
--prompt "What do you see in this image?"

# Control generation parameters
python examples/rest_vlm.py \
--image examples/images/coco_cat_remote.jpg \
--prompt "List every object you can identify." \
--max-tokens 256 \
--temperature 0.3

# Use a different VLM
python examples/rest_vlm.py \
--image examples/images/coco_cat_remote.jpg \
--prompt "Describe the scene in one sentence." \
--model Qwen/Qwen2.5-VL-7B-Instruct
```

### Arguments

| Argument | Default | Description |
| --------------- | ----------------------------- | ------------------------------------------------------------- |
| `--image` | _(required)_ | Path to an image file |
| `--prompt` | `"Describe this image."` | Natural-language question or instruction |
| `--model` | `Qwen/Qwen2.5-VL-3B-Instruct` | VLM model repo ID |
| `--max-tokens` | — | Maximum number of tokens to generate |
| `--temperature` | — | Sampling temperature (`0.0` = greedy, higher = more creative) |
| `--host` | `127.0.0.1` | Server hostname |
| `--port` | `8110` | Server port |

### Sample output

```
--- VLM Inference ---
Model : Qwen/Qwen2.5-VL-3B-Instruct
Prompt : 'What do you see in this image?'

Response:
The image shows a cat sitting on a couch next to a remote control. The cat appears to be relaxed and is looking towards the camera.
```

---

## ws_video_infer.py

Streams a local video file to MATA-SERVER over a **WebSocket connection** and prints inference results frame-by-frame as they arrive.
Implements the full session lifecycle:

1. `POST /v1/sessions` — create a streaming session and receive a `session_id`
2. `WS /v1/stream/{session_id}` — connect and stream binary-encoded frames
3. `DELETE /v1/sessions/{session_id}` — clean up after streaming ends

Frames are encoded using the MATA binary wire format: a 13-byte header (`frame_id` uint32 BE + `timestamp` float64 BE + `encoding` uint8) followed by JPEG bytes.

### Requirements

```bash
pip install aiohttp opencv-python
```

### Usage

```bash
# Object detection on a video
python examples/ws_video_infer.py \
--video examples/videos/cup.mp4 \
--task detect

# Limit to first 60 frames and cap the send rate
python examples/ws_video_infer.py \
--video examples/videos/cup.mp4 \
--task detect \
--max-frames 60 \
--fps-limit 15

# Use the "latest" frame policy — server always processes the newest frame
# (drops intermediate frames when inference is slower than send rate)
python examples/ws_video_infer.py \
--video examples/videos/cup.mp4 \
--model PekingU/rtdetr_r18vd \
--task detect \
--frame-policy latest

# Authenticated server
python examples/ws_video_infer.py \
--video examples/videos/cup.mp4 \
--task detect \
--api-key my-secret-key
```

### Arguments

| Argument | Default | Description |
| ---------------- | ---------------------- | ---------------------------------------------------------------------- |
| `--video` | _(required)_ | Path to a video file (`mp4`, `avi`, etc.) |
| `--task` | _(required)_ | Inference task: `detect`, `segment`, `classify`, `vlm`, etc. |
| `--model` | `PekingU/rtdetr_r18vd` | HuggingFace model repo ID |
| `--max-frames` | `0` (all) | Maximum frames to send; `0` = entire video |
| `--fps-limit` | `0` (native) | Cap send rate in frames per second; `0` = no limit |
| `--frame-policy` | `queue` | `queue` (process every frame in order) or `latest` (skip stale frames) |
| `--api-key` | — | Bearer token for authenticated servers |
| `--host` | `127.0.0.1` | Server hostname |
| `--port` | `8110` | Server port |

### Frame policies

| Policy | Behaviour | Best for |
| -------- | ------------------------------------------------------------------------------------------ | ----------------------------------------- |
| `queue` | Every frame is queued and processed in order. No frames are dropped. | Offline analysis, accuracy-critical tasks |
| `latest` | When the server is busy, older queued frames are dropped and only the most recent is kept. | Real-time / live-stream scenarios |

### Sample output

```
[1/3] Creating session model='PekingU/rtdetr_r18vd' task='detect' frame_policy='queue'
session_id : sess_a1b2c3d4e5f6

[2/3] Streaming 120/120 frames @ 30.0 fps
[frame 0] 2 detections
[frame 1] 2 detections
[frame 2] 3 detections
...
Sent 120 frames. Waiting for results…

Sent : 120 frames in 4.01s (29.9 fps)
Received: 118 results | 0 dropped | 0 errors

[3/3] Deleting session sess_a1b2c3d4e5f6
Session deleted (204)
```

---

## Sample assets

| File | Description |
| ---------------------------- | --------------------------------------------------------------------------------------- |
| `images/coco_cat_remote.jpg` | COCO-style photo with a cat and a TV remote — used by `rest_infer.py` and `rest_vlm.py` |
| `videos/cup.mp4` | Short clip of a cup — used by `ws_video_infer.py` |

---

## Further reading

- [API reference](../docs/api.md) — full endpoint specs and request/response schemas
- [Streaming protocol](../docs/streaming.md) — binary frame format and WebSocket lifecycle
- [Deployment guide](../docs/deployment.md) — Docker, GPU, and production configuration
- [Root README](../README.md) — project overview, quick-start, and CLI reference
5 changes: 5 additions & 0 deletions mataserver/main.py
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Expand Up @@ -107,6 +107,11 @@ def create_app() -> FastAPI:

app.include_router(api_router, prefix="/v1")

@app.get("/", tags=["root"], include_in_schema=False)
async def root() -> dict[str, str]:
"""Minimal liveness check — intentionally lightweight, no auth required."""
return {"status": "running", "message": "mataserver is running"}

return app


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