add dataset viewer: parquet files with embedded images and keypoints

README.md

@@ -11,6 +11,13 @@ tags:
 pretty_name: Fly Anipose (Lightning Pose subset)
 size_categories:
   - 1K<n<10K
+configs:
+  - config_name: default
+    data_files:
+      - split: ind
+        path: data/ind-train-*.parquet
+      - split: ood
+        path: data/ood-test-*.parquet
 ---
 
 # Fly Anipose — Lightning Pose Multiview Dataset

data/ind-train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d83bf5168f9f171a4af691553d730d676a1bc65bc1dd82eb5b2e7e5ed31947ab
+size 443199363

data/ood-test-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a614e672cd0ea04a0faabed8aa50441e77519067313eacc1bf697ba54187f813
+size 349177834

scripts/build_parquet.py

@@ -0,0 +1,113 @@
+"""
+Build Parquet files for HuggingFace dataset viewer.
+
+Reads Lightning Pose CSVs and corresponding PNG frames, then writes
+Parquet files with embedded images to data/ for the HF viewer.
+
+Usage (from repo root):
+    python scripts/build_parquet.py
+"""
+
+import csv
+import io
+from pathlib import Path
+
+import datasets
+from datasets import Dataset, Features, Image, Value, Sequence
+
+REPO_ROOT = Path(__file__).parent.parent
+DATA_OUT = REPO_ROOT / "data"
+
+VIEWS = ["Cam-A", "Cam-B", "Cam-C", "Cam-D", "Cam-E", "Cam-F"]
+
+KEYPOINTS = [
+    "L1A", "L1B", "L1C", "L1D", "L1E",
+    "L2A", "L2B", "L2C", "L2D", "L2E",
+    "L3A", "L3B", "L3C", "L3D", "L3E",
+    "R1A", "R1B", "R1C", "R1D", "R1E",
+    "R2A", "R2B", "R2C", "R2D", "R2E",
+    "R3A", "R3B", "R3C", "R3D", "R3E",
+]
+
+FEATURES = Features(
+    {
+        "image": Image(),
+        "session": Value("string"),
+        "view": Value("string"),
+        "split": Value("string"),
+        "frame": Value("string"),
+        **{f"{kp}_x": Value("float32") for kp in KEYPOINTS},
+        **{f"{kp}_y": Value("float32") for kp in KEYPOINTS},
+    }
+)
+
+
+def parse_csv(csv_path: Path, view: str, split: str) -> list[dict]:
+    rows = []
+    with open(csv_path) as f:
+        reader = csv.reader(f)
+        # Skip 3-row header: scorer, bodyparts, coords
+        next(reader)
+        next(reader)
+        next(reader)
+        for row in reader:
+            img_rel_path = row[0]
+            img_path = REPO_ROOT / img_rel_path
+            if not img_path.exists():
+                print(f"  WARNING: missing {img_path}, skipping")
+                continue
+
+            coords = row[1:]  # 60 values: x0,y0,x1,y1,...
+
+            record: dict = {
+                "image": {"path": None, "bytes": img_path.read_bytes()},
+                "session": "_".join(Path(img_rel_path).parent.name.split("_")[:-1]),
+                "view": view,
+                "split": split,
+                "frame": Path(img_rel_path).name,
+            }
+
+            for i, kp in enumerate(KEYPOINTS):
+                x_str = coords[i * 2]
+                y_str = coords[i * 2 + 1]
+                record[f"{kp}_x"] = float(x_str) if x_str else float("nan")
+                record[f"{kp}_y"] = float(y_str) if y_str else float("nan")
+
+            rows.append(record)
+    return rows
+
+
+def build_split(csv_suffix: str, split_name: str) -> list[dict]:
+    all_rows = []
+    for view in VIEWS:
+        csv_path = REPO_ROOT / f"CollectedData_{view}{csv_suffix}.csv"
+        if not csv_path.exists():
+            print(f"Skipping missing {csv_path}")
+            continue
+        print(f"  Reading {csv_path.name} ...")
+        rows = parse_csv(csv_path, view, split_name)
+        print(f"    {len(rows)} rows")
+        all_rows.extend(rows)
+    return all_rows
+
+
+def main():
+    DATA_OUT.mkdir(exist_ok=True)
+
+    print("Building InD split ...")
+    ind_rows = build_split("", "ind")
+    ind_ds = Dataset.from_list(ind_rows, features=FEATURES)
+    out = DATA_OUT / "ind-train-00000-of-00001.parquet"
+    ind_ds.to_parquet(str(out))
+    print(f"Wrote {out} ({out.stat().st_size / 1e6:.1f} MB, {len(ind_rows)} rows)")
+
+    print("Building OOD split ...")
+    ood_rows = build_split("_new", "ood")
+    ood_ds = Dataset.from_list(ood_rows, features=FEATURES)
+    out = DATA_OUT / "ood-test-00000-of-00001.parquet"
+    ood_ds.to_parquet(str(out))
+    print(f"Wrote {out} ({out.stat().st_size / 1e6:.1f} MB, {len(ood_rows)} rows)")
+
+
+if __name__ == "__main__":
+    main()