b8e95eccbe
Ретина, ламина, медулла, лобула, грибовидное тело, веерное тело, центральный комплекс, нисходящие нейроны. Обучение памяти тоннеля и считывания MBON, оценка leave-one-bag-out, полигон дальности, 24 теста. Реальный объект на 55 м — 98.9 % кадров, ложных 7.5 трека на км, кадр обрабатывается за 33 мс на CPU.
81 lines
3.2 KiB
Python
81 lines
3.2 KiB
Python
"""Characterise the Pandar128 scan geometry inside the metro tunnel bags."""
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from __future__ import annotations
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import sys
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import numpy as np
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sys.path.insert(0, r"C:\Games\Study\AI_Lidar\tools")
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from probe_bag import frames # noqa: E402
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W, H = 7200, 128
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def as_image(p):
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"""Return (H,W) arrays: ordering is col-major with ring cycling fastest."""
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x = p["x"].reshape(W, H).T.astype(np.float32)
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y = p["y"].reshape(W, H).T.astype(np.float32)
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z = p["z"].reshape(W, H).T.astype(np.float32)
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i = p["intensity"].reshape(W, H).T.astype(np.float32)
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return x, y, z, i
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def main(db, idx=100):
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for ts, m in frames(db, limit=1, start=idx):
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x, y, z, inten = as_image(m["points"])
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valid = ~((x == 0) & (y == 0) & (z == 0))
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r = np.sqrt(x * x + y * y + z * z)
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# azimuth measured in the sensor XY plane; forward is -Y
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az = np.degrees(np.arctan2(x, -y)) # 0 = forward, + = right
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el = np.degrees(np.arcsin(np.clip(z / np.maximum(r, 1e-6), -1, 1)))
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print(f"valid {valid.sum()}/{valid.size} = {valid.mean():.1%}")
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print("\n--- azimuth per column (median over valid rings) ---")
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azc = np.where(valid, az, np.nan)
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with np.errstate(all="ignore"):
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colaz = np.nanmedian(azc, axis=0)
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good = np.isfinite(colaz)
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print("columns with any return:", good.sum())
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cols = np.arange(W)
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for c in [0, 1, 2, 1800, 3599, 3600, 3601, 5400, 7198, 7199]:
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print(f" col {c:5d}: az={colaz[c]:8.3f}")
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d = np.diff(colaz[good])
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d = d[np.abs(d) < 1.0]
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print(f" median azimuth step: {np.median(d):.4f} deg")
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print("\n--- elevation per ring (median over valid columns) ---")
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elr = np.where(valid, el, np.nan)
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with np.errstate(all="ignore"):
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ringel = np.nanmedian(elr, axis=1)
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print(" ring0..9 :", np.round(ringel[:10], 2))
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print(" ring60..69:", np.round(ringel[60:70], 2))
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print(" ring118..127:", np.round(ringel[118:], 2))
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print(f" elevation span: {np.nanmin(ringel):.2f} .. {np.nanmax(ringel):.2f}")
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print("\n--- valid-return fraction by azimuth sector ---")
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for lo, hi in [(-180, -90), (-90, -30), (-30, -10), (-10, 10), (10, 30), (30, 90), (90, 180)]:
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sel = (colaz >= lo) & (colaz < hi)
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if sel.sum() == 0:
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print(f" [{lo:4d},{hi:4d}) : no columns")
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continue
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v = valid[:, sel]
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print(f" [{lo:4d},{hi:4d}) : {sel.sum():5d} cols, valid {v.mean():.1%}")
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print("\n--- forward cone (|az|<3 deg) range distribution ---")
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fwd = np.abs(colaz) < 3.0
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rf = r[:, fwd][valid[:, fwd]]
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print(f" columns {fwd.sum()}, valid pts {rf.size}")
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print(" pct:", np.round(np.percentile(rf, [50, 90, 99, 99.9, 100]), 2))
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print("\n--- points beyond 100 m, in train gauge (|x|<1.6, -1<z<1.4) ---")
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far = valid & (r > 100)
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gauge = far & (np.abs(x) < 1.6) & (z > -1.0) & (z < 1.4)
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print(f" far {far.sum()}, of them in gauge {gauge.sum()}")
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if gauge.sum():
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print(" max distance in gauge:", np.round(r[gauge].max(), 1))
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if __name__ == "__main__":
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main(sys.argv[1], int(sys.argv[2]) if len(sys.argv) > 2 else 100)
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