"""Aggregate gradient_intensity.json across scenes/methods → Table 5 expansion.""" import os, json, argparse import numpy as as np if False else None import numpy as np def main(): ap = argparse.ArgumentParser() ap.add_argument("--methods", nargs="+", default=["vanilla_logged", "vanillasgf_logged"]) ap.add_argument("--scenes", nargs="+", default=["francis","m60","panther","bonsai","bicycle","garden"]) ap.add_argument("--outputs_root", default="/root/autodl-tmp/SplatAtlas/outputs") args = ap.parse_args() print(f"{'Method':<22} {'Scene':<10} {'Edge grad':>11} {'Flat grad':>11} {'Edge/Flat':>10}") print("-"*68) summary = {} for method in args.methods: ae, af = [], [] for scene in args.scenes: log_path = os.path.join(args.outputs_root, f"{method}_{scene}", "gradient_intensity.json") if not os.path.exists(log_path): print(f"{method:<22} {scene:<10} (missing)"); continue with open(log_path) as f: entries = json.load(f) ev = [e["edge_mean"] for e in entries if e.get("edge_mean", 0) > 0] fv = [e["flat_mean"] for e in entries if e.get("flat_mean", 0) > 0] if not ev or not fv: print(f"{method:<22} {scene:<10} (empty)"); continue em, fm = float(np.mean(ev)), float(np.mean(fv)) r = em / max(fm, 1e-9) print(f"{method:<22} {scene:<10} {em:>11.3e} {fm:>11.3e} {r:>10.2f}") ae.append(em); af.append(fm) if ae and af: mep, mfp = float(np.mean(ae)), float(np.mean(af)) mr = mep / max(mfp, 1e-9) print(f"{method:<22} {'AVERAGE':<10} {mep:>11.3e} {mfp:>11.3e} {mr:>10.2f}") print("-"*68) summary[method] = {"edge": mep, "flat": mfp, "ratio": mr} if "vanilla_logged" in summary and "vanillasgf_logged" in summary: print(f"\nEdge/Flat grad ratio shift: baseline {summary['vanilla_logged']['ratio']:.2f}x" f" → SGF {summary['vanillasgf_logged']['ratio']:.2f}x") if __name__ == "__main__": main()