# human_mouse

Replay real human mouse trajectories from the [SapiMouse](https://www.ms.sapientia.ro/~manyi/sapimouse/sapimouse.html)
dataset onto a Playwright page.

Useful when you need mouse movement that is statistically indistinguishable
from a real user — for ML-based bot-detection research, behavioral biometrics
prototyping, or replay-based test fixtures.

## Install

```bash
uv add human_mouse              # or: pip install human_mouse
uv add "human_mouse[demo]"      # also installs cloakbrowser for the example
```

## One-time dataset download

```python
import human_mouse as hm
hm.download_sapimouse("./data")   # ~8 MB, no registration
```

This unzips SapiMouse to `./data/sapimouse/userN/...`.

## Quick start

```python
import human_mouse as hm
from playwright.sync_api import sync_playwright

with sync_playwright() as pw:
    browser = pw.chromium.launch(headless=False)
    page = browser.new_page()
    page.goto("https://example.com")

    # one-shot: pick a real human trajectory matching the distance and replay it
    seg = hm.replay_random(
        page,
        start=(100, 100),
        end=(900, 500),
        data_root="./data/sapimouse",
        density=4,        # 4x spatial upsample for smoother visuals
        seed=42,
    )
    print(f"replayed {seg.user}/{seg.session}, {len(seg.points)} source points")
```

## Step-by-step API

For full control over which segment is picked and how it's processed:

```python
import human_mouse as hm

segs = hm.load_all_segments("./data/sapimouse")

seg = hm.pick_segments(
    segs,
    n=1,
    target_distance=1000,     # only match segments ~1000 px long
    distance_tolerance=0.3,   # ±30 %
    max_path_ratio=2.0,       # skip erratic meanderers
    seed=42,
)[0]

points = hm.affine_warp(seg, (100, 100), (900, 500))
points = hm.upsample(points, factor=4)
hm.replay(page, points, speed=1.0)
```

## Public API

| Symbol | Purpose |
|---|---|
| `Segment` | dataclass with `.points`, `.start`, `.end`, `.straight_distance`, `.path_length`, `.path_ratio`, `.duration_ms` |
| `load_all_segments(data_root)` | scan SapiMouse, return every continuous Move-only segment ≥ 50 pts |
| `pick_segments(segments, n, ...)` | filter by distance / path-ratio / distinct-session, randomly choose `n` |
| `affine_warp(seg, start, end)` | translate + rotate + uniform-scale onto new endpoints (shape preserved) |
| `upsample(points, factor)` | linearly interpolate `factor-1` sub-points between every adjacent pair |
| `replay(page, points, speed=1.0)` | drive `page.mouse.move(...)` honoring the recorded `dt` |
| `replay_random(page, start, end, data_root, ...)` | one-shot: pick + warp + upsample + replay |
| `download_sapimouse(dest)` | download and unzip the dataset |

## Running the bundled example

The `examples/` folder ships a Playwright canvas page (`demo.html`) plus a
runner script that uses [CloakBrowser](https://github.com/CloakHQ/CloakBrowser):

```bash
uv add "human_mouse[demo]"
python -m human_mouse download ./sapimouse_data            # one-time, ~8 MB
uv run python examples/cloakbrowser_demo.py sapi           # one trajectory
uv run python examples/cloakbrowser_demo.py multi --n 10   # 10 overlaid
```

Outputs land in `./outputs/`:

- `sapi.png` + `sapi.json` — single trajectory screenshot and per-event coords
- `multi/overlay.png` + per-run JSONs + `summary.json`

## What `replay` is and isn't

**Is**: a deterministic replay of one real human's mouse path, warped to your
endpoints. The shape (curvature, hesitation, end-point fumbling) and the
timing (`dt` distribution) come straight from a real recording.

**Isn't**: a generator. It samples from a fixed dataset of ~4 000 segments.
For per-call novelty, randomize the seed; for true synthesis, look at
trajectory generative models (e.g. SapiAgent, DMTG).

## Dataset attribution

If you use SapiMouse data in published work, cite:

> Antal, M. et al. *SapiMouse: Mouse Dynamics-based User Authentication Using
> Deep Feature Learning*. IEEE SACI 2021.

## License

MIT.