moved_repo_first_update

2026-02-24 10:39:48 +01:00
commit 9894a23f09
98 changed files with 35387 additions and 0 deletions
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+"""Build manifest for U-Net segmenter combining REFUGE and Papila annotations."""
+
+from __future__ import annotations
+
+import argparse
+import random
+from pathlib import Path
+from typing import Optional
+
+import sys
+
+import pandas as pd
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.append(str(ROOT))
+
+from classes.refuge_preprocessing import RefugePreprocessing
+
+REFUGE_ROOT = Path("REFUGE")
+PAPILA_IMAGES = Path("FundusImages")
+PAPILA_CONTOURS = Path("Papila/ExpertsSegmentations/Contours")
+DEFAULT_OUTPUT = Path("Papila/analysis_data/unet_manifest.csv")
+
+
+def pick_contour(base: str, kind: str) -> Optional[Path]:
+    """Return contour path for Papila image (disc/cup)."""
+    candidates = [
+        PAPILA_CONTOURS / f"{base}_{kind}_exp2.txt",
+        PAPILA_CONTOURS / f"{base}_{kind}_exp1.txt",
+    ]
+    for path in candidates:
+        if path.exists():
+            return path
+    return None
+
+
+def collect_refuge() -> pd.DataFrame:
+    pre = RefugePreprocessing(REFUGE_ROOT)
+    samples = []
+    for sample in pre.build_manifest(refresh=True):
+        if sample.mask_path is None:
+            continue
+        split = sample.split
+        if split == "test":
+            split = "holdout"
+        samples.append(
+            {
+                "sample_id": sample.sample_id,
+                "dataset": "refuge",
+                "image_path": sample.image_path.resolve(),
+                "annotation_disc": sample.mask_path.resolve(),
+                "annotation_cup": sample.mask_path.resolve(),
+                "annotation_type_disc": "mask",
+                "annotation_type_cup": "mask",
+                "split": split,
+            }
+        )
+    return pd.DataFrame(samples)
+
+
+def collect_papila() -> pd.DataFrame:
+    samples = []
+    if not PAPILA_IMAGES.exists():
+        return pd.DataFrame(samples)
+    for img_path in sorted(PAPILA_IMAGES.glob("RET*")):
+        base = img_path.stem
+        disc = pick_contour(base, "disc")
+        cup = pick_contour(base, "cup")
+        if disc is None or cup is None:
+            continue
+        samples.append(
+            {
+                "sample_id": f"papila_{base}",
+                "dataset": "papila",
+                "image_path": img_path.resolve(),
+                "annotation_disc": disc.resolve(),
+                "annotation_cup": cup.resolve(),
+                "annotation_type_disc": "contour",
+                "annotation_type_cup": "contour",
+            }
+        )
+    return pd.DataFrame(samples)
+
+
+def assign_splits(df: pd.DataFrame, holdout_ratio: float, seed: int) -> pd.DataFrame:
+    rng = random.Random(seed)
+    df = df.copy()
+    if "split" not in df.columns:
+        df["split"] = None
+    for dataset, group in df.groupby("dataset"):
+        indices = list(group.index)
+
+        # Preserve provided splits (e.g., REFUGE train/val/test); only populate
+        # missing entries with "train" so downstream code has a default.
+        split_series = df.loc[indices, "split"]
+        missing = split_series.isna() | (split_series.astype(str).str.strip() == "")
+        if missing.any():
+            df.loc[missing[missing].index, "split"] = "train"
+            split_series = df.loc[indices, "split"]
+
+        if dataset != "papila":
+            continue
+
+        if holdout_ratio <= 0:
+            continue
+
+        desired_holdout = max(1, int(len(indices) * holdout_ratio))
+        split_series = df.loc[indices, "split"]
+        current_holdout_mask = split_series == "holdout"
+        current_holdout = int(current_holdout_mask.sum())
+        remaining = desired_holdout - current_holdout
+        if remaining <= 0:
+            continue
+
+        candidate_indices = list(split_series[split_series == "train"].index)
+        rng.shuffle(candidate_indices)
+        selected = candidate_indices[:remaining]
+        df.loc[selected, "split"] = "holdout"
+    return df
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Build U-Net manifest")
+    parser.add_argument("--holdout", type=float, default=0.05)
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT)
+    args = parser.parse_args()
+
+    refuge_df = collect_refuge()
+    papila_df = collect_papila()
+    combined = pd.concat([refuge_df, papila_df], ignore_index=True)
+    combined = assign_splits(combined, holdout_ratio=args.holdout, seed=args.seed)
+    combined.to_csv(args.output, index=False)
+    print(f"Manifest saved to {args.output} with {len(combined)} entries")
+
+
+if __name__ == "__main__":
+    main()