| |
| |
| """Quickstart: the model card's Python snippet on the shipped sample, on one Blackhole p150. |
| |
| pip install -e . # once, from the repo root, on top of an environment that has ttnn (tt-metal) |
| python examples/quickstart.py [sample.json] [--out-dir examples/output] [--ego-pose] |
| |
| Writes <out-dir>/quickstart.json (the same JSON as POST /predict) and <out-dir>/quickstart_bev.png (a bird's-eye view |
| of the detections). The input is a sample manifest (six camera images + a calibration preset, |
| ``code/tt_bevdet/samples/*.json``); the default, the shipped synthetic test pattern, is found relative to this file |
| (runs from any directory); an input given on the command line is relative to the current directory. ``--ego-pose`` |
| passes the manifest's ``stream`` (with ``T_world_from_ego``) for the motion-compensated history mode. |
| """ |
| import argparse |
| import json |
| import math |
| from pathlib import Path |
|
|
| REPO = Path(__file__).resolve().parents[1] |
| ap = argparse.ArgumentParser() |
| ap.add_argument("input", nargs="?", default=str(REPO / "code" / "tt_bevdet" / "samples" / "synthetic_6cam.json")) |
| ap.add_argument("--out-dir", default=str(REPO / "examples" / "output")) |
| ap.add_argument("--device-id", type=int, default=0) |
| ap.add_argument("--ego-pose", action="store_true", help="use the manifest's stream (ego pose), if it has one") |
| args = ap.parse_args() |
| out_dir = Path(args.out_dir) |
| out_dir.mkdir(parents=True, exist_ok=True) |
|
|
| |
| from tt_bevdet import BEVDet, load_sample |
|
|
| with BEVDet.from_pretrained(device_id=args.device_id) as model: |
| out = model(**load_sample(args.input, with_stream=args.ego_pose)) |
|
|
| for d in out.to_dicts(): |
| print(f'{d["label"]:13s} {d["autoware_label"]:10s} {d["score"]:.3f} centre {d["center"]} size {d["size"]} ' |
| f'yaw {d["yaw"]:+.2f} v {d["velocity"]}') |
| |
|
|
| (out_dir / "quickstart.json").write_text(json.dumps(out.to_dict(), indent=1)) |
|
|
|
|
| def bev_png(dets, path, rng=40.0, size=640): |
| """Bird's-eye view of the detections around the ego vehicle (x forward = up, y left = left), 10 m rings.""" |
| from PIL import Image, ImageDraw |
|
|
| colours = {"car": (57, 135, 229), "truck": (57, 135, 229), "bus": (57, 135, 229), "trailer": (57, 135, 229), |
| "construction_vehicle": (57, 135, 229), "pedestrian": (217, 89, 38), "bicycle": (25, 158, 112), |
| "motorcycle": (25, 158, 112)} |
| im = Image.new("RGB", (size, size), (26, 26, 25)) |
| d = ImageDraw.Draw(im, "RGBA") |
| s = size / (2 * rng) |
|
|
| def px(x, y): |
| return ((rng - y) * s, (rng - x) * s) |
|
|
| for r in range(10, int(rng) + 1, 10): |
| (u0, v0), (u1, v1) = px(r, r), px(-r, -r) |
| d.ellipse([u0, v0, u1, v1], outline=(44, 44, 42), width=1) |
| for det in dets: |
| x, y, _ = det["center"] |
| length, width, _ = det["size"] |
| c, sn = math.cos(det["yaw"]), math.sin(det["yaw"]) |
| q = [px(x + c * a * length - sn * b * width, y + sn * a * length + c * b * width) |
| for a, b in ((0.5, 0.5), (0.5, -0.5), (-0.5, -0.5), (-0.5, 0.5))] |
| col = colours.get(det["label"], (195, 194, 183)) |
| d.polygon(q, fill=col + (70,), outline=col) |
| d.line([px(x, y), px(x + c * length / 2, y + sn * length / 2)], fill=col, width=2) |
| d.text((q[0][0] + 3, q[0][1] - 12), f'{det["label"]} {det["score"]:.2f}', fill=(195, 194, 183)) |
| ego = [px(a * 2.05, b * 0.9) for a, b in ((1, 1), (1, -1), (-1, -1), (-1, 1))] |
| d.polygon(ego, fill=(255, 255, 255)) |
| d.text((6, size - 16), f"{2 * rng:.0f} m x {2 * rng:.0f} m, rings every 10 m, up = forward (+x)", fill=(137, 135, 129)) |
| im.save(path) |
|
|
|
|
| bev_png(out.to_dicts(), out_dir / "quickstart_bev.png") |
| print(f"{len(out)} detections -> {out_dir / 'quickstart.json'}, {out_dir / 'quickstart_bev.png'} " |
| f"timing_ms={ {k: round(v, 2) for k, v in out.timing_ms.items()} }") |
|
|
