#!/usr/bin/env python3
"""Convert HCC-TACE-Seg DICOM CT + DICOM-SEG to training-ready NPZ caches."""

from __future__ import annotations

import argparse
from collections import Counter
from pathlib import Path

import numpy as np
import pandas as pd
import pydicom
import SimpleITK as sitk


HU_MIN = -150.0
HU_MAX = 400.0


def rel(path: Path, root: Path) -> str:
    path = path if path.is_absolute() else path.absolute()
    root = root if root.is_absolute() else root.absolute()
    try:
        return str(path.relative_to(root))
    except ValueError:
        return str(path)


def parse_spacing(value: str) -> tuple[float, float, float]:
    parts = [float(x) for x in value.split(",")]
    if len(parts) == 1:
        return (parts[0], parts[0], parts[0])
    if len(parts) != 3:
        raise ValueError("--spacing must be a single value or three comma-separated values")
    return tuple(parts)


def normalize_ct(array: np.ndarray) -> np.ndarray:
    array = np.clip(array.astype(np.float32), HU_MIN, HU_MAX)
    return ((array - HU_MIN) / (HU_MAX - HU_MIN)).astype(np.float16)


def bbox_from_mask(mask: np.ndarray, margin_vox: tuple[int, int, int]) -> tuple[slice, slice, slice]:
    coords = np.argwhere(mask > 0)
    if coords.size == 0:
        return tuple(slice(0, s) for s in mask.shape)  # type: ignore[return-value]
    lo = coords.min(axis=0)
    hi = coords.max(axis=0) + 1
    for axis in range(3):
        lo[axis] = max(0, lo[axis] - margin_vox[axis])
        hi[axis] = min(mask.shape[axis], hi[axis] + margin_vox[axis])
    return slice(lo[0], hi[0]), slice(lo[1], hi[1]), slice(lo[2], hi[2])


def read_meta(path: Path) -> pydicom.Dataset:
    return pydicom.dcmread(str(path), stop_before_pixels=True, force=True)


def get_frame_position(frame_group: pydicom.Dataset) -> tuple[float, float, float] | None:
    if hasattr(frame_group, "PlanePositionSequence") and frame_group.PlanePositionSequence:
        return tuple(float(x) for x in frame_group.PlanePositionSequence[0].ImagePositionPatient)
    return None


def get_frame_source_uid(frame_group: pydicom.Dataset) -> str | None:
    for deriv in getattr(frame_group, "DerivationImageSequence", []):
        for source in getattr(deriv, "SourceImageSequence", []):
            uid = getattr(source, "ReferencedSOPInstanceUID", None)
            if uid:
                return str(uid)
    return None


def collect_ct_sibling_files(seg_dir: Path) -> dict[str, list[Path]]:
    study_dir = seg_dir.parent
    series_files: dict[str, list[Path]] = {}
    for series_dir in sorted(p for p in study_dir.iterdir() if p.is_dir() and p != seg_dir):
        files = sorted(series_dir.glob("*.dcm"))
        if not files:
            continue
        try:
            meta = read_meta(files[0])
        except Exception:
            continue
        if getattr(meta, "Modality", "") != "CT":
            continue
        series_files[str(series_dir)] = files
    return series_files


def choose_ct_series(seg_ds: pydicom.Dataset, seg_dir: Path) -> tuple[str, list[Path], dict[str, Path]]:
    source_uids = []
    frame_z = []
    for frame_group in getattr(seg_ds, "PerFrameFunctionalGroupsSequence", []):
        uid = get_frame_source_uid(frame_group)
        if uid:
            source_uids.append(uid)
        position = get_frame_position(frame_group)
        if position is not None:
            frame_z.append(round(float(position[2]), 2))

    best_series = ""
    best_files: list[Path] = []
    best_uid_to_file: dict[str, Path] = {}
    best_score = (-1, -1, -1)
    for series_key, files in collect_ct_sibling_files(seg_dir).items():
        uid_to_file = {}
        ct_z = []
        for path in files:
            try:
                ds = read_meta(path)
            except Exception:
                continue
            uid_to_file[str(ds.SOPInstanceUID)] = path
            if hasattr(ds, "ImagePositionPatient"):
                ct_z.append(round(float(ds.ImagePositionPatient[2]), 2))
        source_score = sum(uid in uid_to_file for uid in set(source_uids))
        z_score = len(set(frame_z) & set(ct_z))
        # Some TCIA SEG objects have absent or sparse SourceImageSequence. In
        # that case, z-position overlap is more reliable than a single UID hit.
        source_rank = source_score if source_score > 1 else 0
        score = (source_rank, z_score, len(files))
        if score > best_score:
            best_score = score
            best_series = series_key
            best_files = files
            best_uid_to_file = uid_to_file

    if not best_files:
        raise RuntimeError(f"No sibling CT series found for {seg_dir}")
    return best_series, best_files, best_uid_to_file


def load_ct_volume(files: list[Path]) -> tuple[np.ndarray, list[pydicom.Dataset], np.ndarray, tuple[float, float, float], tuple[float, ...]]:
    records = []
    for path in files:
        ds = pydicom.dcmread(str(path), force=True)
        position = np.asarray([float(x) for x in ds.ImagePositionPatient], dtype=np.float64)
        orientation = np.asarray([float(x) for x in ds.ImageOrientationPatient], dtype=np.float64)
        row_cos = orientation[:3]
        col_cos = orientation[3:]
        normal = np.cross(row_cos, col_cos)
        projection = float(np.dot(position, normal))
        records.append((projection, path, ds, position, orientation, normal))
    records.sort(key=lambda x: x[0])

    arrays = []
    metas = []
    positions = []
    for _, _, ds, position, _, _ in records:
        arr = ds.pixel_array.astype(np.float32)
        slope = float(getattr(ds, "RescaleSlope", 1.0))
        intercept = float(getattr(ds, "RescaleIntercept", 0.0))
        arrays.append(arr * slope + intercept)
        metas.append(ds)
        positions.append(position)

    first = records[0]
    spacing_y, spacing_x = [float(x) for x in metas[0].PixelSpacing]
    if len(records) > 1:
        spacing_z = float(np.median(np.diff([r[0] for r in records])))
        spacing_z = abs(spacing_z) if spacing_z else float(getattr(metas[0], "SliceThickness", 1.0))
    else:
        spacing_z = float(getattr(metas[0], "SliceThickness", 1.0))
    spacing_xyz = (spacing_x, spacing_y, spacing_z)
    row_cos = first[4][:3]
    col_cos = first[4][3:]
    normal = first[5]
    direction = (
        float(row_cos[0]), float(col_cos[0]), float(normal[0]),
        float(row_cos[1]), float(col_cos[1]), float(normal[1]),
        float(row_cos[2]), float(col_cos[2]), float(normal[2]),
    )
    return np.stack(arrays, axis=0), metas, np.stack(positions, axis=0), spacing_xyz, direction


def make_sitk_image(array_zyx: np.ndarray, metas: list[pydicom.Dataset], spacing_xyz: tuple[float, float, float], direction: tuple[float, ...], pixel_type: int) -> sitk.Image:
    image = sitk.GetImageFromArray(array_zyx.astype(np.float32 if pixel_type == sitk.sitkFloat32 else np.uint8))
    image.SetSpacing(spacing_xyz)
    image.SetOrigin(tuple(float(x) for x in metas[0].ImagePositionPatient))
    image.SetDirection(direction)
    return image


def make_reference_grid(image: sitk.Image, spacing_xyz: tuple[float, float, float]) -> sitk.Image:
    original_spacing = image.GetSpacing()
    original_size = image.GetSize()
    size = [max(1, int(round(original_size[i] * original_spacing[i] / spacing_xyz[i]))) for i in range(3)]
    ref = sitk.Image(size, image.GetPixelID())
    ref.SetOrigin(image.GetOrigin())
    ref.SetSpacing(spacing_xyz)
    ref.SetDirection(image.GetDirection())
    return ref


def resample(image: sitk.Image, ref: sitk.Image, interpolator: int, default: float, pixel_type: int) -> sitk.Image:
    return sitk.Resample(image, ref, sitk.Transform(), interpolator, default, pixel_type)


def segment_label_map(seg_ds: pydicom.Dataset) -> dict[int, int]:
    mapping = {}
    for seg in getattr(seg_ds, "SegmentSequence", []):
        number = int(seg.SegmentNumber)
        label = str(getattr(seg, "SegmentLabel", "")).lower()
        if "liver" in label:
            mapping[number] = 1
        elif any(token in label for token in ["mass", "tumor", "tumour", "lesion"]):
            mapping[number] = 2
    return mapping


def build_label_volume(seg_ds: pydicom.Dataset, metas: list[pydicom.Dataset], uid_to_index: dict[str, int]) -> np.ndarray:
    label = np.zeros((len(metas), int(seg_ds.Rows), int(seg_ds.Columns)), dtype=np.uint8)
    seg_map = segment_label_map(seg_ds)
    pixel = seg_ds.pixel_array
    if pixel.ndim == 2:
        pixel = pixel[None, ...]

    position_to_index = {}
    z_to_index = {}
    for idx, meta in enumerate(metas):
        key = tuple(round(float(x), 3) for x in meta.ImagePositionPatient)
        position_to_index[key] = idx
        z_to_index[round(float(meta.ImagePositionPatient[2]), 2)] = idx

    for frame_idx, frame_group in enumerate(seg_ds.PerFrameFunctionalGroupsSequence):
        seg_num = int(frame_group.SegmentIdentificationSequence[0].ReferencedSegmentNumber)
        target_label = seg_map.get(seg_num)
        if target_label is None:
            continue

        slice_index = None
        source_uid = get_frame_source_uid(frame_group)
        if source_uid and source_uid in uid_to_index:
            slice_index = uid_to_index[source_uid]
        if slice_index is None:
            position = get_frame_position(frame_group)
            if position is not None:
                key = tuple(round(float(x), 3) for x in position)
                slice_index = position_to_index.get(key)
                if slice_index is None:
                    slice_index = z_to_index.get(round(float(position[2]), 2))
        if slice_index is None:
            continue

        mask = pixel[frame_idx] > 0
        if target_label == 1:
            label[slice_index][mask] = np.maximum(label[slice_index][mask], 1)
        elif target_label == 2:
            label[slice_index][mask] = 2
    return label


def process_seg_case(
    row: pd.Series,
    root: Path,
    out_dir: Path,
    spacing_xyz: tuple[float, float, float],
    margin_mm: float,
    overwrite: bool,
    compressed: bool,
) -> dict[str, object]:
    seg_dir = root / str(row["dicom_series_dir"])
    seg_file = next(seg_dir.glob("*.dcm"))
    out_path = out_dir / f"{row['patient_id']}_{row.name:03d}.npz"
    if out_path.exists() and not overwrite:
        with np.load(out_path) as data:
            image_shape = data["image"].shape
            has_liver = bool((data["label"] == 1).any())
            has_tumor = bool((data["label"] == 2).any())
        return {
            "dataset": "hcc_tace_seg",
            "patient_id": row["patient_id"],
            "case_id": row["case_id"],
            "npz_path": rel(out_path, root),
            "shape_c": image_shape[0],
            "shape_z": image_shape[1],
            "shape_y": image_shape[2],
            "shape_x": image_shape[3],
            "has_liver_mask": int(has_liver),
            "has_tumor_mask": int(has_tumor),
            "skipped_existing": 1,
        }

    seg_ds = pydicom.dcmread(str(seg_file), force=True)
    ct_series_key, ct_files, uid_to_file = choose_ct_series(seg_ds, seg_dir)
    ct_array, metas, _, ct_spacing, direction = load_ct_volume(ct_files)
    uid_to_index = {str(ds.SOPInstanceUID): i for i, ds in enumerate(metas)}
    label = build_label_volume(seg_ds, metas, uid_to_index)

    image_sitk = make_sitk_image(ct_array, metas, ct_spacing, direction, sitk.sitkFloat32)
    label_sitk = make_sitk_image(label, metas, ct_spacing, direction, sitk.sitkUInt8)
    ref = make_reference_grid(image_sitk, spacing_xyz)
    image_resampled = resample(image_sitk, ref, sitk.sitkLinear, HU_MIN, sitk.sitkFloat32)
    label_resampled = resample(label_sitk, ref, sitk.sitkNearestNeighbor, 0, sitk.sitkUInt8)
    image_arr = normalize_ct(sitk.GetArrayFromImage(image_resampled))
    label_arr = sitk.GetArrayFromImage(label_resampled).astype(np.uint8)

    margin_vox = tuple(max(1, int(round(margin_mm / s))) for s in spacing_xyz[::-1])
    crop = bbox_from_mask(label_arr > 0, margin_vox)
    image_arr = image_arr[crop][None, ...].astype(np.float16)
    label_arr = label_arr[crop].astype(np.uint8)

    out_path.parent.mkdir(parents=True, exist_ok=True)
    saver = np.savez_compressed if compressed else np.savez
    saver(
        out_path,
        image=image_arr,
        label=label_arr,
        liver_mask=(label_arr == 1).astype(np.uint8),
        tumor_mask=(label_arr == 2).astype(np.uint8),
        spacing=np.asarray(spacing_xyz, dtype=np.float32),
        crop_start=np.asarray([crop[0].start, crop[1].start, crop[2].start], dtype=np.int32),
        crop_stop=np.asarray([crop[0].stop, crop[1].stop, crop[2].stop], dtype=np.int32),
    )

    seg_counts = Counter(int(x.ReferencedSegmentNumber) for x in [
        fg.SegmentIdentificationSequence[0] for fg in seg_ds.PerFrameFunctionalGroupsSequence
    ])
    return {
        "dataset": "hcc_tace_seg",
        "patient_id": row["patient_id"],
        "case_id": row["case_id"],
        "series_uid": row["series_uid"],
        "npz_path": rel(out_path, root),
        "ct_series_dir": rel(Path(ct_series_key), root),
        "seg_series_dir": row["dicom_series_dir"],
        "spacing_x": spacing_xyz[0],
        "spacing_y": spacing_xyz[1],
        "spacing_z": spacing_xyz[2],
        "shape_c": image_arr.shape[0],
        "shape_z": image_arr.shape[1],
        "shape_y": image_arr.shape[2],
        "shape_x": image_arr.shape[3],
        "has_liver_mask": int((label_arr == 1).any()),
        "has_tumor_mask": int((label_arr == 2).any()),
        "segment_frame_counts": dict(seg_counts),
        "skipped_existing": 0,
    }


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--project-root", type=Path, default=Path(__file__).resolve().parents[1])
    parser.add_argument("--spacing", default="2.0")
    parser.add_argument("--margin-mm", type=float, default=20.0)
    parser.add_argument("--limit", type=int, default=0)
    parser.add_argument("--overwrite", action="store_true")
    parser.add_argument("--compressed", action="store_true")
    parser.add_argument("--progress-every", type=int, default=10)
    args = parser.parse_args()

    root = args.project_root.resolve()
    spacing_xyz = parse_spacing(args.spacing)
    manifest = pd.read_csv(root / "manifests" / "hcc_tace_seg_series_manifest.csv")
    seg_rows = manifest[manifest["modality"] == "SEG"].reset_index(drop=True)
    out_dir = root / "data" / "processed_training" / "hcc_tace_seg_npz"
    rows = []
    errors = []
    for idx, row in seg_rows.iterrows():
        if args.limit and len(rows) >= args.limit:
            break
        try:
            result = process_seg_case(row, root, out_dir, spacing_xyz, args.margin_mm, args.overwrite, args.compressed)
            rows.append(result)
        except Exception as exc:
            errors.append({"patient_id": row.get("patient_id", ""), "seg_series_dir": row.get("dicom_series_dir", ""), "error": repr(exc)})
        if (idx + 1) % args.progress_every == 0:
            print(f"HCC cached {len(rows)}/{len(seg_rows)} errors={len(errors)}")

    out_manifest = pd.DataFrame(rows)
    out_path = root / "manifests" / "hcc_tace_seg_training_manifest.csv"
    out_manifest.to_csv(out_path, index=False)
    print(f"Wrote {out_path} ({len(out_manifest)} rows)")
    if errors:
        err_path = root / "logs" / "hcc_tace_seg_cache_errors.csv"
        pd.DataFrame(errors).to_csv(err_path, index=False)
        print(f"Wrote {err_path} ({len(errors)} errors)")


if __name__ == "__main__":
    main()