| from __future__ import annotations |
|
|
| import sys |
| import os |
| from pathlib import Path |
|
|
| import pandas as pd |
|
|
| |
| |
| |
| PROJECT_ROOT = Path("/root/autodl-tmp/SplatAtlas") |
|
|
| |
| APP_C_SRC_FULL = PROJECT_ROOT / "outputs" / "phase2" / "task_2_3_appE_full.csv" |
| APP_C_SRC_FILT = PROJECT_ROOT / "outputs" / "phase2" / "task_2_3_appE_filtered_P01_P04.csv" |
|
|
| |
| APP_D_SRC = PROJECT_ROOT / "outputs" / "phase4" / "task_4_1_seed_variance_summary_reconstructed.csv" |
|
|
| OUT_DIR = PROJECT_ROOT / "tex" |
| OUT_DIR.mkdir(parents=True, exist_ok=True) |
| APP_C_OUT = OUT_DIR / "appC_paired_rows.tex" |
| APP_D_OUT = OUT_DIR / "appD_seed_summary_rows.tex" |
|
|
| SEED_P95 = 0.384 |
|
|
| |
| |
| |
| VARIANT_DISPLAY = { |
| "pgsr": "PGSR", |
| "erankgs": "eRankGS", |
| "lightgaussian": "LightGaussian", |
| "steepgs": "SteepGS", |
| } |
|
|
| METHOD_DISPLAY = { |
| "vanilla_3dgs": r"\texttt{vanilla\_3dgs}", |
| "analyticsplatting": r"\texttt{analyticsplatting}", |
| "erankgs": r"\texttt{erankgs}", |
| "ges": r"\texttt{ges}", |
| "lightgaussian": r"\texttt{lightgaussian}", |
| "minisplatting": r"\texttt{minisplatting}", |
| "opti3dgs": r"\texttt{opti3dgs}", |
| "pgsr": r"\texttt{pgsr}", |
| "steepgs": r"\texttt{steepgs}", |
| "3dgsmcmc": r"\texttt{3dgsmcmc}", |
| } |
|
|
| SCENE_DISPLAY = { |
| "bicycle": "Bicycle", "bonsai": "Bonsai", "counter": "Counter", |
| "flowers": "Flowers", "garden": "Garden", "kitchen": "Kitchen", |
| "room": "Room", "stump": "Stump", "treehill": "Treehill", |
| "auditorium": "Auditorium", "ballroom": "Ballroom", "barn": "Barn", |
| "caterpillar": "Caterpillar", "courtroom": "Courtroom", "lighthouse": "Lighthouse", |
| "museum": "Museum", "palace": "Palace", "playground": "Playground", |
| "temple": "Temple", "train": "Train", "truck": "Truck", |
| "drjohnson": "DrJohnson", "playroom": "Playroom", |
| "chair": "Chair", "drums": "Drums", "ficus": "Ficus", "hotdog": "Hotdog", |
| "lego": "Lego", "materials": "Materials", "mic": "Mic", "ship": "Ship", |
| } |
|
|
| def fmt_scene(key) -> str: |
| k = str(key).strip().lower() |
| name = SCENE_DISPLAY.get(k, str(key)) |
| return rf"\textsc{{{name}}}" |
|
|
| def fmt_signed(x, prec: int = 3) -> str: |
| if pd.isna(x): return "---" |
| return f"{x:+.{prec}f}" |
|
|
| def fmt_unsigned(x, prec: int = 3) -> str: |
| if pd.isna(x): return "---" |
| return f"{x:.{prec}f}" |
|
|
| |
| |
| |
| def generate_app_c() -> int: |
| if APP_C_SRC_FULL.exists(): |
| print(f"[App C] reading {APP_C_SRC_FULL}") |
| df = pd.read_csv(APP_C_SRC_FULL) |
| print(f"[App C] loaded {len(df)} rows total from FULL CSV") |
| df = df[df["pair_id"].isin(["P01", "P02", "P03", "P04"])].copy() |
| print(f"[App C] after P01-P04 filter: {len(df)} rows (expect 124)") |
| elif APP_C_SRC_FILT.exists(): |
| print(f"[App C] reading {APP_C_SRC_FILT}") |
| df = pd.read_csv(APP_C_SRC_FILT) |
| print(f"[App C] loaded {len(df)} rows total from FILTERED CSV (expect 124)") |
| else: |
| print("ERROR: missing App C source files. Searching for candidates:") |
| os.system('find /root/autodl-tmp/SplatAtlas/outputs -name "task_2_3_appE*.csv"') |
| sys.exit(1) |
|
|
| df["_scene_disp"] = df["scene"].astype(str).str.lower().map(SCENE_DISPLAY).fillna(df["scene"]) |
| df = df.sort_values(["pair_id", "_scene_disp"]).reset_index(drop=True) |
|
|
| lines: list[str] = [] |
| lines.append("% AUTOGENERATED by tools/generate_appendix_rows.py - DO NOT EDIT BY HAND.\n") |
|
|
| last_pair = None |
| n_data_rows = 0 |
| for _, row in df.iterrows(): |
| if last_pair is not None and row["pair_id"] != last_pair: |
| lines.append(r"\midrule" + "\n") |
| last_pair = row["pair_id"] |
|
|
| variant_disp = VARIANT_DISPLAY.get(row["variant"], row["variant"]) |
| scene_disp = fmt_scene(row["scene"]) |
|
|
| cells = [ |
| row["pair_id"], |
| variant_disp, |
| scene_disp, |
| fmt_signed(row["delta_psnr_full"]), |
| fmt_signed(row["delta_sh_net_effect"]), |
| fmt_signed(row["delta_sh_corruption_rate"]), |
| fmt_signed(row["delta_opacity_net_effect"]), |
| fmt_signed(row["delta_opacity_pathology_rate"]), |
| fmt_signed(row["delta_coverage_error_fraction"]), |
| fmt_signed(row["delta_residual_error"]), |
| ] |
| lines.append(" & ".join(cells) + r" \\" + "\n") |
| n_data_rows += 1 |
|
|
| APP_C_OUT.write_text("".join(lines)) |
| print(f"[App C] wrote {APP_C_OUT} ({n_data_rows} data rows)") |
| return n_data_rows |
|
|
| def generate_app_d() -> int: |
| if not APP_D_SRC.exists(): |
| print("ERROR: missing App D source files. Searching for candidates:") |
| os.system('find /root/autodl-tmp/SplatAtlas/outputs -name "*seed_variance*summary*.csv"') |
| sys.exit(1) |
|
|
| print(f"[App D] reading {APP_D_SRC}") |
| df = pd.read_csv(APP_D_SRC) |
| print(f"[App D] loaded {len(df)} rows total") |
|
|
| scene_col = "SceneNormalized" if "SceneNormalized" in df.columns else "Scene" |
| method_col = "Method" |
|
|
| out_lines: list[str] = [] |
| out_lines.append("% AUTOGENERATED by tools/generate_appendix_rows.py - DO NOT EDIT BY HAND.\n") |
|
|
| n_data_rows = 0 |
| for scene_idx, scene in enumerate(["bonsai", "lego"]): |
| if scene_idx > 0: |
| out_lines.append(r"\midrule" + "\n") |
|
|
| df_scene = df[df[scene_col].astype(str).str.lower() == scene].copy() |
| df_scene = df_scene.sort_values(method_col) |
| scene_disp = fmt_scene(scene) |
|
|
| for _, row in df_scene.iterrows(): |
| method_key = row[method_col] |
| escaped_key = str(method_key).replace("_", r"\_") |
| method_disp = METHOD_DISPLAY.get(method_key, rf"\texttt{{{escaped_key}}}") |
|
|
| psnr_mean = row["PSNR_mean"] |
| psnr_std = row["PSNR_std"] |
| psnr_min = row["PSNR_min"] |
| psnr_max = row["PSNR_max"] |
| status = str(row.get("Status", "OK")).strip().upper() |
|
|
| if status == "METHOD_FAILURE": |
| flag = r"\textsc{Failure}" |
| elif status == "OUTLIER" or (not pd.isna(psnr_std) and psnr_std > SEED_P95): |
| flag = r"\textsc{Outlier}" |
| else: |
| flag = "---" |
|
|
| cells = [ |
| method_disp, scene_disp, |
| fmt_unsigned(psnr_mean), fmt_unsigned(psnr_std), |
| fmt_unsigned(psnr_min), fmt_unsigned(psnr_max), |
| flag, |
| ] |
| out_lines.append(" & ".join(cells) + r" \\" + "\n") |
| n_data_rows += 1 |
|
|
| APP_D_OUT.write_text("".join(out_lines)) |
| print(f"[App D] wrote {APP_D_OUT} ({n_data_rows} data rows)") |
| return n_data_rows |
|
|
| |
| |
| |
| if __name__ == "__main__": |
| print("\n========================================================") |
| print("1. Generating LaTeX row fragments") |
| print("========================================================") |
| c_rows = generate_app_c() |
| d_rows = generate_app_d() |
|
|
| print("\n========================================================") |
| print("2. SANITY CHECKS") |
| print("========================================================") |
| print(f"[Check] App C output rows equals 124: {'PASS' if c_rows == 124 else 'FAIL'} ({c_rows})") |
| print(f"[Check] App D output rows equals 20: {'PASS' if d_rows == 20 else 'FAIL'} ({d_rows})") |
| |
| |
| with open(APP_D_OUT, "r") as f: |
| d_content = f.read() |
| ana_outlier = ("analyticsplatting" in d_content and "Outlier" in d_content) |
| era_failure = ("erankgs" in d_content and "Failure" in d_content) |
| print(f"[Check] analyticsplatting has \\textsc{{Outlier}}: {'PASS' if ana_outlier else 'FAIL'}") |
| print(f"[Check] erankgs has \\textsc{{Failure}}: {'PASS' if era_failure else 'FAIL'}") |
|
|
| print("\n========================================================") |
| print("Generated:") |
| print(f"- {APP_C_OUT}: {c_rows} rows") |
| print(f"- {APP_D_OUT}: {d_rows} rows") |
| print("========================================================") |
|
|
|
|
