"""Build data/metadata.csv and scripts/cache/clusters.csv from raw ViViD-5k data.

Loads the 515 MB COCO json once (~1-2 min). Fails loudly on any mismatch
between images, point files, split entries, and COCO entries.
"""
import json
import sys
from pathlib import Path

import numpy as np
import pandas as pd
from tqdm import tqdm

sys.path.insert(0, str(Path(__file__).resolve().parent))
from vivid_utils import load_splits, parse_bbox

ROOT = Path(__file__).resolve().parents[1]
DATA = ROOT / "data"
CACHE = ROOT / "scripts" / "cache"
GRAPE_CATEGORY_ID = 1


def main():
    splits = load_splits(DATA)
    img_files = {p.name: p for p in (DATA / "imgs").iterdir()}
    point_files = {p.stem: p for p in (DATA / "anns" / "points").glob("*.npy")}

    errors = []
    for name in splits:
        if name not in img_files:
            errors.append(f"in split but no image file: {name}")
        if Path(name).stem not in point_files:
            errors.append(f"no points npy for image: {name}")
    for name in img_files:
        if name not in splits:
            errors.append(f"image not in any split: {name}")
    if errors:
        print("\n".join(errors), file=sys.stderr)
        sys.exit(f"ABORT: {len(errors)} consistency errors")

    print("Loading COCO json (this takes a minute)...")
    coco = json.loads((DATA / "anns" / "instances_updated.json").read_text())
    coco_by_id = {im["id"]: im for im in coco["images"]}
    coco_by_name = {im["file_name"]: im for im in coco["images"]}

    missing_coco = [n for n in splits if n not in coco_by_name]
    if missing_coco:
        sys.exit(f"ABORT: {len(missing_coco)} images missing from COCO json: {missing_coco[:5]}")

    cluster_count = {n: 0 for n in splits}
    cluster_rows = []
    skipped_ids = []
    for ann in tqdm(coco["annotations"], desc="annotations"):
        if ann["category_id"] != GRAPE_CATEGORY_ID:
            continue
        if not ann["bbox"]:
            skipped_ids.append(ann["id"])
            continue
        im = coco_by_id[ann["image_id"]]
        name = im["file_name"]
        cluster_count[name] += 1
        x, y, w, h = parse_bbox(ann["bbox"])
        cluster_rows.append(
            {"file_name": name, "bbox_w": w, "bbox_h": h,
             "rel_area": (w * h) / (im["width"] * im["height"])}
        )
    if skipped_ids:
        print(f"WARNING: skipped {len(skipped_ids)} degenerate annotations "
              f"(empty bbox): ids={sorted(skipped_ids)}")

    rows = []
    for name, split in tqdm(sorted(splits.items()), desc="images"):
        pts = np.load(point_files[Path(name).stem])
        if pts.ndim != 2 or pts.shape[1] != 2:
            sys.exit(f"ABORT: unexpected points shape {pts.shape} for {name}")
        im = coco_by_name[name]
        rows.append(
            {"file_name": name, "split": split, "berry_count": len(pts),
             "cluster_count": cluster_count[name],
             "width": im["width"], "height": im["height"]}
        )

    meta = pd.DataFrame(rows)
    meta.to_csv(DATA / "metadata.csv", index=False)
    CACHE.mkdir(parents=True, exist_ok=True)
    pd.DataFrame(cluster_rows).to_csv(CACHE / "clusters.csv", index=False)

    print(f"TOTALS images={len(meta)} berries={meta.berry_count.sum()} "
          f"clusters={meta.cluster_count.sum()}")
    print(meta.groupby("split").size())


if __name__ == "__main__":
    main()