Datasets:

KCBtheone
/

splatatlas-core

import json
import glob
import os
import pandas as pd
import numpy as np

# 1. 基础配置
methods = [
    "vanilla_3dgs", "analyticsplatting", "erankgs", "ges", 
    "lightgaussian", "minisplatting", "opti3dgs", "pgsr", 
    "steepgs", "3dgsmcmc"
]
scene_mapping = {"bonsai": "bonsai", "lego": "Lego"}
seeds = {"seed0": "", "seed1": "_seed1", "seed2": "_seed2"}

raw_records = []

# 2. 扫描数据
print("========================================================")
print("开始扫描 Seed 目录...")
for method in methods:
    for norm_scene, dir_scene in scene_mapping.items():
        for seed_id, seed_suffix in seeds.items():
            dir_name = f"{method}{seed_suffix}_{dir_scene}"
            search_dir = f"outputs/{dir_name}"

            # vanilla_3dgs 特殊 fallback 逻辑
            if method == "vanilla_3dgs" and seed_id == "seed0":
                if not os.path.exists(search_dir) or not glob.glob(f"{search_dir}/metrics_test_iter*.json"):
                    search_dir = f"outputs/{method}_{dir_scene}_bak"

            json_files = sorted(glob.glob(f"{search_dir}/metrics_test_iter*.json"))
            psnr, ssim, lpips = np.nan, np.nan, np.nan
            source_file = "MISSING"
            status = "MISSING"

            if json_files:
                target_file = json_files[-1]  # 默认取排序后的最后一个
                for jf in json_files:
                    if "30000" in jf:
                        target_file = jf
                        break

                source_file = target_file
                try:
                    with open(target_file, 'r') as f:
                        data = json.load(f)
                        # 兼容不同 JSON 层级
                        photo = data.get("photometric", data) 
                        psnr = photo.get("PSNR", photo.get("psnr", np.nan))
                        ssim = photo.get("SSIM", photo.get("ssim", np.nan))
                        lpips = photo.get("LPIPS", photo.get("lpips", np.nan))
                        status = "OK"
                except Exception:
                    pass

            # 基于业务逻辑的 Status 调整
            if method == "erankgs" and norm_scene == "lego" and psnr < 20:  # ~17dB
                status = "METHOD_FAILURE"

            raw_records.append({
                "SceneNormalized": norm_scene,
                "SceneDirName": dir_scene,
                "Method": method,
                "Seed": seed_id,
                "PSNR": psnr,
                "SSIM": ssim,
                "LPIPS": lpips,
                "Status": status,
                "SourceFile": source_file
            })

# 输出 Raw Table
os.makedirs("outputs/phase4", exist_ok=True)
df_raw = pd.DataFrame(raw_records)
raw_path = "outputs/phase4/task_4_1_seed_variance_raw_reconstructed.csv"
df_raw.to_csv(raw_path, index=False)

# 3. 生成 Summary Table
summary_records = []
for (norm_scene, method), group in df_raw.groupby(["SceneNormalized", "Method"]):
    psnrs = group["PSNR"].dropna()
    n_seeds = len(psnrs)
    psnr_mean = psnrs.mean() if n_seeds > 0 else np.nan
    psnr_std = psnrs.std(ddof=1) if n_seeds > 1 else np.nan
    psnr_min = psnrs.min() if n_seeds > 0 else np.nan
    psnr_max = psnrs.max() if n_seeds > 0 else np.nan
    psnr_range = (psnr_max - psnr_min) if n_seeds > 0 else np.nan

    statuses = group["Status"].tolist()
    if "METHOD_FAILURE" in statuses:
        final_status = "METHOD_FAILURE"
    elif method == "analyticsplatting" and norm_scene == "lego" and psnr_std > 1.0:
        final_status = "OUTLIER"
    elif "MISSING" in statuses and n_seeds < 3:
        final_status = "MISSING_SEEDS"
    else:
        final_status = "OK"

    summary_records.append({
        "Scene": norm_scene,
        "Method": method,
        "NSeeds": n_seeds,
        "PSNR_mean": psnr_mean,
        "PSNR_std": psnr_std,
        "PSNR_min": psnr_min,
        "PSNR_max": psnr_max,
        "PSNR_range": psnr_range,
        "Status": final_status
    })

df_summary = pd.DataFrame(summary_records)
sum_path = "outputs/phase4/task_4_1_seed_variance_summary_reconstructed.csv"
df_summary.to_csv(sum_path, index=False)

# 4. 生成 Kendall Tau Checked CSV
tau_file = "outputs/phase4/task_4_2_kendall_tau.json"
tau_path = "outputs/phase4/task_4_2_kendall_tau_checked.csv"
if os.path.exists(tau_file):
    with open(tau_file, 'r') as f:
        tau_data = json.load(f)
    tau_records = []
    for scene in ["bonsai", "Lego"]:
        if scene in tau_data:
            sc_data = tau_data[scene]
            tau_records.append({
                "Scene": "lego" if scene == "Lego" else "bonsai",
                "default_vs_seed1_tau": sc_data.get("default_vs_seed1", {}).get("tau"),
                "default_vs_seed1_pval": sc_data.get("default_vs_seed1", {}).get("pval"),
                "default_vs_seed2_tau": sc_data.get("default_vs_seed2", {}).get("tau"),
                "default_vs_seed2_pval": sc_data.get("default_vs_seed2", {}).get("pval"),
                "seed1_vs_seed2_tau": sc_data.get("seed1_vs_seed2", {}).get("tau"),
                "seed1_vs_seed2_pval": sc_data.get("seed1_vs_seed2", {}).get("pval"),
                "median_tau": sc_data.get("median_tau"),
                "min_tau": sc_data.get("min_tau"),
                "valid_methods_count": len(sc_data.get("valid_methods", []))
            })
    df_tau = pd.DataFrame(tau_records)
    df_tau.to_csv(tau_path, index=False)
else:
    print(f"⚠️ 警告: Kendall Tau 源文件缺失 {tau_file}")


# 5. Sanity Checks 输出
print("\n========================================================")
print("SANITY CHECKS")
print("========================================================")

# 基础计数
print(f"每个 scene 找到的 method 数量: bonsai = {len(df_summary[df_summary['Scene'] == 'bonsai'])}, lego = {len(df_summary[df_summary['Scene'] == 'lego'])}")
print(f"每个 scene 找到的 raw seed rows: bonsai = {len(df_raw[df_raw['SceneNormalized'] == 'bonsai'])}, lego = {len(df_raw[df_raw['SceneNormalized'] == 'lego'])}")

# 特殊 Cell 验证
try:
    ana_lego = df_summary[(df_summary['Method'] == 'analyticsplatting') & (df_summary['Scene'] == 'lego')].iloc[0]
    print(f"\n[验证] analyticsplatting x Lego")
    print(f"  - PSNR_mean:  {ana_lego['PSNR_mean']:.3f}")
    print(f"  - PSNR_std:   {ana_lego['PSNR_std']:.3f} (预期 ~1.33)")
    print(f"  - PSNR_range: {ana_lego['PSNR_range']:.3f} (预期 ~2.34)")
    print(f"  - Status:     {ana_lego['Status']} (预期 OUTLIER)")
except Exception as e:
    print("\n[错误] 未能定位 analyticsplatting x Lego 的聚合数据")

try:
    era_lego = df_summary[(df_summary['Method'] == 'erankgs') & (df_summary['Scene'] == 'lego')].iloc[0]
    print(f"\n[验证] erankgs x Lego")
    print(f"  - PSNR_mean:  {era_lego['PSNR_mean']:.3f} (预期 ~17)")
    print(f"  - PSNR_std:   {era_lego['PSNR_std']:.3f}")
    print(f"  - PSNR_range: {era_lego['PSNR_range']:.3f}")
    print(f"  - Status:     {era_lego['Status']} (预期 METHOD_FAILURE)")
except Exception as e:
    print("\n[错误] 未能定位 erankgs x Lego 的聚合数据")

# 队列级中位数与 P95 (排除 METHOD_FAILURE)
valid_stds_incl = df_summary[df_summary["Status"].isin(["OK", "OUTLIER"])]["PSNR_std"].dropna()
valid_stds_excl = df_summary[df_summary["Status"] == "OK"]["PSNR_std"].dropna()

med_incl = valid_stds_incl.median()
p95_incl = valid_stds_incl.quantile(0.95)
med_excl = valid_stds_excl.median()
p95_excl = valid_stds_excl.quantile(0.95)

print("\n[验证] Cohort PSNR Std 统计")
print(f"  - Median (including outlier): {med_incl:.4f}")
print(f"  - P95    (including outlier): {p95_incl:.4f}")
print(f"  - Median (excluding outlier): {med_excl:.4f}")
print(f"  - P95    (excluding outlier): {p95_excl:.4f}")

if abs(p95_incl - 0.384) > 0.005 and abs(p95_excl - 0.384) > 0.005:
    print(f"\n⚠️ 论文一致性警告:")
    print(f"不一致：论文中声明 P95=0.384 dB，但重算结果为包含Outlier: {p95_incl:.3f}, 排除Outlier: {p95_excl:.3f}")
else:
    print(f"\n✅ P95=0.384 dB 与论文声明一致")

# Kendall Tau
if 'df_tau' in locals():
    print("\n[验证] Kendall Tau 值")
    b_tau = df_tau[df_tau["Scene"] == "bonsai"]["median_tau"].iloc[0]
    l_tau = df_tau[df_tau["Scene"] == "lego"]["median_tau"].iloc[0]
    print(f"  - bonsai median_tau: {b_tau:.4f} (预期: 0.7778)")
    print(f"  - Lego median_tau:   {l_tau:.4f} (预期: 0.8571)")

print("\n========================================================")
print("生成的文件清单:")
print(f"1. {raw_path}")
print(f"2. {sum_path}")
print(f"3. {tau_path}")
print("========================================================")