feat: initial release of human_mouse v0.1.0
A small Python library for replaying real human mouse trajectories from the SapiMouse dataset onto a Playwright page. Designed for ML-based bot-detection research, behavioral biometrics prototyping, and replay-based test fixtures. Public API: load_all_segments, pick_segments, affine_warp, upsample, replay, replay_random, download_sapimouse. - src/ layout with hatchling build backend - 23 pytest tests (10 transform unit + 13 integration) - MIT license, PEP 561 py.typed marker - python -m human_mouse download for one-shot dataset fetch - examples/cloakbrowser_demo.py demonstrates end-to-end use with CloakBrowser Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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examples/cloakbrowser_demo.py
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examples/cloakbrowser_demo.py
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"""End-to-end example of human_mouse driving CloakBrowser.
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Subcommands:
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sapi one real human trajectory, replayed
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multi N trajectories overlaid on a single canvas in different hues
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Usage::
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uv run python examples/cloakbrowser_demo.py sapi
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uv run python examples/cloakbrowser_demo.py multi --n 10
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The dataset path defaults to ./sapimouse_data/sapimouse. Override with
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--data-root or call human_mouse.download_sapimouse() first.
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"""
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from __future__ import annotations
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import argparse
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import json
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import math
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import statistics
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import time
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from pathlib import Path
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from cloakbrowser import launch
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import human_mouse as hm
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HERE = Path(__file__).parent
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DEMO_HTML = HERE / "demo.html"
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DEFAULT_DATA_ROOT = HERE.parent / "sapimouse_data" / "sapimouse"
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DEFAULT_OUT = HERE.parent / "outputs"
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VIEWPORT = {"width": 1280, "height": 720}
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START = (150, 150)
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TARGET = (1060, 550)
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TARGET_DIST = math.hypot(TARGET[0] - START[0], TARGET[1] - START[1])
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def open_page(browser, mode: str):
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context = browser.new_context(viewport=VIEWPORT)
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page = context.new_page()
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page.goto(DEMO_HTML.as_uri() + f"?mode={mode}")
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page.wait_for_selector("#target")
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return page
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def save_artifacts(page, name: str, out: Path) -> tuple[Path, Path]:
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out.mkdir(parents=True, exist_ok=True)
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png, js = out / f"{name}.png", out / f"{name}.json"
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js.write_text(json.dumps(page.evaluate("window.__points"), indent=2))
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page.screenshot(path=str(png), full_page=False)
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return png, js
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def cmd_sapi(args: argparse.Namespace) -> None:
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print("loading SapiMouse segments...")
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segs = hm.load_all_segments(args.data_root)
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print(f" -> {len(segs)} segments available")
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seg = hm.pick_segments(segs, n=1,
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target_distance=TARGET_DIST,
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max_path_ratio=args.max_ratio,
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seed=args.seed)[0]
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print(f" -> {seg.user}/{seg.session} "
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f"src={len(seg.points)} dist={seg.straight_distance:.0f}px "
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f"dur={seg.duration_ms:.0f}ms ratio={seg.path_ratio:.2f}")
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dense = hm.upsample(hm.affine_warp(seg, START, TARGET), args.density)
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print("launching CloakBrowser (humanize=False)...")
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browser = launch(humanize=False, headless=False)
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page = open_page(browser, "sapi")
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time.sleep(0.3)
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t0 = time.perf_counter()
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hm.replay(page, dense)
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elapsed = (time.perf_counter() - t0) * 1000
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page.locator("#target").click()
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png, js = save_artifacts(page, "sapi", args.out)
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captured = json.loads(js.read_text())
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browser.close()
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print(f"\n sent={len(dense)} captured={len(captured)} "
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f"replay={elapsed:.0f}ms")
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print(f" -> {png}")
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def cmd_multi(args: argparse.Namespace) -> None:
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multi_dir = args.out / "multi"
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print("loading SapiMouse segments...")
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segs = hm.load_all_segments(args.data_root)
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print(f" -> {len(segs)} segments total")
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picks = hm.pick_segments(segs, n=args.n,
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target_distance=TARGET_DIST,
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max_path_ratio=args.max_ratio,
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seed=args.seed)
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print("launching CloakBrowser (humanize=False)...")
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browser = launch(humanize=False, headless=False)
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page = open_page(browser, "multi")
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runs = []
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for i, seg in enumerate(picks, 1):
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hue = round(360 * (i - 1) / len(picks))
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page.evaluate(f"window.__setHue({hue}); window.__resetTrace();")
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dense = hm.upsample(hm.affine_warp(seg, START, TARGET), args.density)
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t0 = time.perf_counter()
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hm.replay(page, dense)
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elapsed = (time.perf_counter() - t0) * 1000
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points = page.evaluate("window.__points")
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multi_dir.mkdir(parents=True, exist_ok=True)
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(multi_dir / f"run_{i:02d}.json").write_text(json.dumps(points, indent=2))
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dts = [b["t"] - a["t"] for a, b in zip(points, points[1:])]
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runs.append({
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"i": i, "hue": hue, "user": seg.user, "session": seg.session,
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"src_points": len(seg.points), "sent_points": len(dense),
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"captured_points": len(points),
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"captured_median_dt_ms": round(statistics.median(dts), 2) if dts else 0.0,
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"replay_ms": round(elapsed, 1),
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})
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print(f" run {i:>2}/{len(picks)} hue={hue:>3} "
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f"{seg.user}/{seg.session} captured={len(points)} pts")
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overlay = multi_dir / "overlay.png"
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page.screenshot(path=str(overlay), full_page=False)
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(multi_dir / "summary.json").write_text(json.dumps({
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"n": len(picks), "runs": runs,
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}, indent=2))
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browser.close()
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print(f"\n overlay -> {overlay}")
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def build_parser() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(
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description="human_mouse demo driven by CloakBrowser",
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)
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parser.add_argument("--data-root", type=Path, default=DEFAULT_DATA_ROOT,
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help="path to unzipped SapiMouse folder")
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parser.add_argument("--out", type=Path, default=DEFAULT_OUT,
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help="output directory for screenshots and JSON")
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sub = parser.add_subparsers(dest="cmd", required=True)
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p_sapi = sub.add_parser("sapi", help="one real SapiMouse trajectory replayed")
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p_sapi.add_argument("--density", type=int, default=4)
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p_sapi.add_argument("--max-ratio", type=float, default=2.0)
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p_sapi.add_argument("--seed", type=int, default=42)
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p_sapi.set_defaults(func=cmd_sapi)
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p_multi = sub.add_parser("multi", help="N trajectories overlaid in different hues")
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p_multi.add_argument("--n", type=int, default=10)
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p_multi.add_argument("--density", type=int, default=4)
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p_multi.add_argument("--max-ratio", type=float, default=2.0)
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p_multi.add_argument("--seed", type=int, default=None)
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p_multi.set_defaults(func=cmd_multi)
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return parser
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def main() -> None:
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args = build_parser().parse_args()
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args.func(args)
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if __name__ == "__main__":
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main()
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