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RockGlacierNet — compute per-band normalization stats for a built patch set.
The training data_generator z-score normalizes each band. Its built-in
GLOBAL_MEAN/STD were measured on the OLD glacier-centric crops and badly misfit the
whole-Sikkim region patches (e.g. Elev std 282 vs actual ~1150, NDVI off by +3.7 sigma).
This writes correct stats next to the patches; data_generator loads them automatically.
python patch_dataset/compute_norm_stats.py --patch-size 128
Output: patch_dataset/patches_{size}/metadata/norm_stats.json
"""
import os
import sys
import json
import glob
import argparse
import numpy as np
import rasterio
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
import config as C
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--patch-size", type=int, default=C.DEFAULT_PATCH)
args = ap.parse_args()
outdir = C.patches_dir(args.patch_size)
feats = sorted(glob.glob(str(outdir / "train" / "features" / "*.tif")))
if not feats:
sys.exit(f"No train features in {outdir}/train/features — build patches first.")
n = C.NUM_BANDS
# Welford-ish running sums over all pixels of all train patches.
count = 0
s1 = np.zeros(n, dtype=np.float64)
s2 = np.zeros(n, dtype=np.float64)
for fp in feats:
with rasterio.open(fp) as s:
a = s.read().astype(np.float64) # (C, H, W)
a = np.where(np.isfinite(a) & (a != C.NODATA_VALUE), a, 0.0)
flat = a.reshape(n, -1)
s1 += flat.sum(axis=1)
s2 += (flat * flat).sum(axis=1)
count += flat.shape[1]
mean = s1 / count
std = np.sqrt(np.maximum(s2 / count - mean * mean, 1e-12))
stats = {"band_names": C.BAND_NAMES,
"mean": [round(float(x), 6) for x in mean],
"std": [round(float(x), 6) for x in std],
"num_train_patches": len(feats),
"num_pixels": int(count)}
out = outdir / "metadata" / "norm_stats.json"
with open(out, "w") as f:
json.dump(stats, f, indent=2)
print(f"Computed over {len(feats)} train patches ({count:,} px/band)")
print(f"{'band':7s} {'mean':>12s} {'std':>12s}")
for nm, m, sd in zip(C.BAND_NAMES, stats["mean"], stats["std"]):
print(f"{nm:7s} {m:12.4f} {sd:12.4f}")
print(f"\nSaved -> {out}")
if __name__ == "__main__":
main()
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