Add MSH geometry export and preview rendering tools

- Implemented msh_export_obj.py for exporting NGI MSH geometry to Wavefront OBJ format, including model selection and geometry extraction. - Added msh_preview_renderer.py for rendering NGI MSH models to binary PPM images, featuring a primitive software renderer with customizable parameters. - Both tools utilize the same NRes parsing logic and provide command-line interfaces for listing models and exporting or rendering geometry.
2026-02-10 23:27:43 +00:00
parent ba1789f106
commit 5035d02220
8 changed files with 3268 additions and 393 deletions
--- a/tools/msh_export_obj.py
+++ b/tools/msh_export_obj.py
@@ -0,0 +1,357 @@
+#!/usr/bin/env python3
+"""
+Export NGI MSH geometry to Wavefront OBJ.
+
+The exporter is intended for inspection/debugging and uses the same
+batch/slot selection logic as msh_preview_renderer.py.
+"""
+
+from __future__ import annotations
+
+import argparse
+import math
+import struct
+from pathlib import Path
+from typing import Any
+
+import archive_roundtrip_validator as arv
+
+MAGIC_NRES = b"NRes"
+
+
+def _entry_payload(blob: bytes, entry: dict[str, Any]) -> bytes:
+    start = int(entry["data_offset"])
+    end = start + int(entry["size"])
+    return blob[start:end]
+
+
+def _parse_nres(blob: bytes, source: str) -> dict[str, Any]:
+    if blob[:4] != MAGIC_NRES:
+        raise RuntimeError(f"{source}: not an NRes payload")
+    return arv.parse_nres(blob, source=source)
+
+
+def _by_type(entries: list[dict[str, Any]]) -> dict[int, list[dict[str, Any]]]:
+    out: dict[int, list[dict[str, Any]]] = {}
+    for row in entries:
+        out.setdefault(int(row["type_id"]), []).append(row)
+    return out
+
+
+def _get_single(by_type: dict[int, list[dict[str, Any]]], type_id: int, label: str) -> dict[str, Any]:
+    rows = by_type.get(type_id, [])
+    if not rows:
+        raise RuntimeError(f"missing resource type {type_id} ({label})")
+    return rows[0]
+
+
+def _pick_model_payload(archive_path: Path, model_name: str | None) -> tuple[bytes, str]:
+    root_blob = archive_path.read_bytes()
+    parsed = _parse_nres(root_blob, str(archive_path))
+
+    msh_entries = [row for row in parsed["entries"] if str(row["name"]).lower().endswith(".msh")]
+    if msh_entries:
+        chosen: dict[str, Any] | None = None
+        if model_name:
+            model_l = model_name.lower()
+            for row in msh_entries:
+                name_l = str(row["name"]).lower()
+                if name_l == model_l:
+                    chosen = row
+                    break
+            if chosen is None:
+                for row in msh_entries:
+                    if str(row["name"]).lower().startswith(model_l):
+                        chosen = row
+                        break
+        else:
+            chosen = msh_entries[0]
+
+        if chosen is None:
+            names = ", ".join(str(row["name"]) for row in msh_entries[:12])
+            raise RuntimeError(
+                f"model '{model_name}' not found in {archive_path}. Available: {names}"
+            )
+        return _entry_payload(root_blob, chosen), str(chosen["name"])
+
+    by_type = _by_type(parsed["entries"])
+    if all(k in by_type for k in (1, 2, 3, 6, 13)):
+        return root_blob, archive_path.name
+
+    raise RuntimeError(
+        f"{archive_path} does not contain .msh entries and does not look like a direct model payload"
+    )
+
+
+def _extract_geometry(
+    model_blob: bytes,
+    *,
+    lod: int,
+    group: int,
+    max_faces: int,
+    all_batches: bool,
+) -> tuple[list[tuple[float, float, float]], list[tuple[int, int, int]], dict[str, int]]:
+    parsed = _parse_nres(model_blob, "<model>")
+    by_type = _by_type(parsed["entries"])
+
+    res1 = _get_single(by_type, 1, "Res1")
+    res2 = _get_single(by_type, 2, "Res2")
+    res3 = _get_single(by_type, 3, "Res3")
+    res6 = _get_single(by_type, 6, "Res6")
+    res13 = _get_single(by_type, 13, "Res13")
+
+    pos_blob = _entry_payload(model_blob, res3)
+    if len(pos_blob) % 12 != 0:
+        raise RuntimeError(f"Res3 size is not divisible by 12: {len(pos_blob)}")
+    vertex_count = len(pos_blob) // 12
+    positions = [struct.unpack_from("<3f", pos_blob, i * 12) for i in range(vertex_count)]
+
+    idx_blob = _entry_payload(model_blob, res6)
+    if len(idx_blob) % 2 != 0:
+        raise RuntimeError(f"Res6 size is not divisible by 2: {len(idx_blob)}")
+    index_count = len(idx_blob) // 2
+    indices = list(struct.unpack_from(f"<{index_count}H", idx_blob, 0))
+
+    batch_blob = _entry_payload(model_blob, res13)
+    if len(batch_blob) % 20 != 0:
+        raise RuntimeError(f"Res13 size is not divisible by 20: {len(batch_blob)}")
+    batch_count = len(batch_blob) // 20
+    batches: list[tuple[int, int, int, int]] = []
+    for i in range(batch_count):
+        off = i * 20
+        idx_count = struct.unpack_from("<H", batch_blob, off + 8)[0]
+        idx_start = struct.unpack_from("<I", batch_blob, off + 10)[0]
+        base_vertex = struct.unpack_from("<I", batch_blob, off + 16)[0]
+        batches.append((idx_count, idx_start, base_vertex, i))
+
+    res2_blob = _entry_payload(model_blob, res2)
+    if len(res2_blob) < 0x8C:
+        raise RuntimeError("Res2 is too small (< 0x8C)")
+    slot_blob = res2_blob[0x8C:]
+    if len(slot_blob) % 68 != 0:
+        raise RuntimeError(f"Res2 slot area is not divisible by 68: {len(slot_blob)}")
+    slot_count = len(slot_blob) // 68
+    slots: list[tuple[int, int, int, int]] = []
+    for i in range(slot_count):
+        off = i * 68
+        tri_start, tri_count, batch_start, slot_batch_count = struct.unpack_from("<4H", slot_blob, off)
+        slots.append((tri_start, tri_count, batch_start, slot_batch_count))
+
+    res1_blob = _entry_payload(model_blob, res1)
+    node_stride = int(res1["attr3"])
+    node_count = int(res1["attr1"])
+    node_slot_indices: list[int] = []
+    if not all_batches and node_stride >= 38 and len(res1_blob) >= node_count * node_stride:
+        if lod < 0 or lod > 2:
+            raise RuntimeError(f"lod must be 0..2 (got {lod})")
+        if group < 0 or group > 4:
+            raise RuntimeError(f"group must be 0..4 (got {group})")
+        matrix_index = lod * 5 + group
+        for n in range(node_count):
+            off = n * node_stride + 8 + matrix_index * 2
+            slot_idx = struct.unpack_from("<H", res1_blob, off)[0]
+            if slot_idx == 0xFFFF:
+                continue
+            if slot_idx >= slot_count:
+                continue
+            node_slot_indices.append(slot_idx)
+
+    faces: list[tuple[int, int, int]] = []
+    used_batches = 0
+    used_slots = 0
+
+    def append_batch(batch_idx: int) -> None:
+        nonlocal used_batches
+        if batch_idx < 0 or batch_idx >= len(batches):
+            return
+        idx_count, idx_start, base_vertex, _ = batches[batch_idx]
+        if idx_count < 3:
+            return
+        end = idx_start + idx_count
+        if end > len(indices):
+            return
+        used_batches += 1
+        tri_count = idx_count // 3
+        for t in range(tri_count):
+            i0 = indices[idx_start + t * 3 + 0] + base_vertex
+            i1 = indices[idx_start + t * 3 + 1] + base_vertex
+            i2 = indices[idx_start + t * 3 + 2] + base_vertex
+            if i0 >= vertex_count or i1 >= vertex_count or i2 >= vertex_count:
+                continue
+            faces.append((i0, i1, i2))
+            if len(faces) >= max_faces:
+                return
+
+    if node_slot_indices:
+        for slot_idx in node_slot_indices:
+            if len(faces) >= max_faces:
+                break
+            _tri_start, _tri_count, batch_start, slot_batch_count = slots[slot_idx]
+            used_slots += 1
+            for bi in range(batch_start, batch_start + slot_batch_count):
+                append_batch(bi)
+                if len(faces) >= max_faces:
+                    break
+    else:
+        for bi in range(batch_count):
+            append_batch(bi)
+            if len(faces) >= max_faces:
+                break
+
+    if not faces:
+        raise RuntimeError("no faces selected for export")
+
+    meta = {
+        "vertex_count": vertex_count,
+        "index_count": index_count,
+        "batch_count": batch_count,
+        "slot_count": slot_count,
+        "node_count": node_count,
+        "used_slots": used_slots,
+        "used_batches": used_batches,
+        "face_count": len(faces),
+    }
+    return positions, faces, meta
+
+
+def _compute_vertex_normals(
+    positions: list[tuple[float, float, float]],
+    faces: list[tuple[int, int, int]],
+) -> list[tuple[float, float, float]]:
+    acc = [[0.0, 0.0, 0.0] for _ in positions]
+    for i0, i1, i2 in faces:
+        p0 = positions[i0]
+        p1 = positions[i1]
+        p2 = positions[i2]
+        ux = p1[0] - p0[0]
+        uy = p1[1] - p0[1]
+        uz = p1[2] - p0[2]
+        vx = p2[0] - p0[0]
+        vy = p2[1] - p0[1]
+        vz = p2[2] - p0[2]
+        nx = uy * vz - uz * vy
+        ny = uz * vx - ux * vz
+        nz = ux * vy - uy * vx
+        acc[i0][0] += nx
+        acc[i0][1] += ny
+        acc[i0][2] += nz
+        acc[i1][0] += nx
+        acc[i1][1] += ny
+        acc[i1][2] += nz
+        acc[i2][0] += nx
+        acc[i2][1] += ny
+        acc[i2][2] += nz
+
+    normals: list[tuple[float, float, float]] = []
+    for nx, ny, nz in acc:
+        ln = math.sqrt(nx * nx + ny * ny + nz * nz)
+        if ln <= 1e-12:
+            normals.append((0.0, 1.0, 0.0))
+        else:
+            normals.append((nx / ln, ny / ln, nz / ln))
+    return normals
+
+
+def _write_obj(
+    output_path: Path,
+    object_name: str,
+    positions: list[tuple[float, float, float]],
+    faces: list[tuple[int, int, int]],
+) -> None:
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+    normals = _compute_vertex_normals(positions, faces)
+
+    with output_path.open("w", encoding="utf-8", newline="\n") as out:
+        out.write("# Exported by msh_export_obj.py\n")
+        out.write(f"o {object_name}\n")
+        for x, y, z in positions:
+            out.write(f"v {x:.9g} {y:.9g} {z:.9g}\n")
+        for nx, ny, nz in normals:
+            out.write(f"vn {nx:.9g} {ny:.9g} {nz:.9g}\n")
+        for i0, i1, i2 in faces:
+            a = i0 + 1
+            b = i1 + 1
+            c = i2 + 1
+            out.write(f"f {a}//{a} {b}//{b} {c}//{c}\n")
+
+
+def cmd_list_models(args: argparse.Namespace) -> int:
+    archive_path = Path(args.archive).resolve()
+    blob = archive_path.read_bytes()
+    parsed = _parse_nres(blob, str(archive_path))
+    rows = [row for row in parsed["entries"] if str(row["name"]).lower().endswith(".msh")]
+    print(f"Archive: {archive_path}")
+    print(f"MSH entries: {len(rows)}")
+    for row in rows:
+        print(f"- {row['name']}")
+    return 0
+
+
+def cmd_export(args: argparse.Namespace) -> int:
+    archive_path = Path(args.archive).resolve()
+    output_path = Path(args.output).resolve()
+
+    model_blob, model_label = _pick_model_payload(archive_path, args.model)
+    positions, faces, meta = _extract_geometry(
+        model_blob,
+        lod=int(args.lod),
+        group=int(args.group),
+        max_faces=int(args.max_faces),
+        all_batches=bool(args.all_batches),
+    )
+    obj_name = Path(model_label).stem or "msh_model"
+    _write_obj(output_path, obj_name, positions, faces)
+
+    print(f"Exported model : {model_label}")
+    print(f"Output OBJ     : {output_path}")
+    print(f"Object name    : {obj_name}")
+    print(
+        "Geometry       : "
+        f"vertices={meta['vertex_count']}, faces={meta['face_count']}, "
+        f"batches={meta['used_batches']}/{meta['batch_count']}, slots={meta['used_slots']}/{meta['slot_count']}"
+    )
+    print(
+        "Mode           : "
+        f"lod={args.lod}, group={args.group}, all_batches={bool(args.all_batches)}"
+    )
+    return 0
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Export NGI MSH geometry to Wavefront OBJ."
+    )
+    sub = parser.add_subparsers(dest="command", required=True)
+
+    list_models = sub.add_parser("list-models", help="List .msh entries in an NRes archive.")
+    list_models.add_argument("--archive", required=True, help="Path to archive (e.g. animals.rlb).")
+    list_models.set_defaults(func=cmd_list_models)
+
+    export = sub.add_parser("export", help="Export one model to OBJ.")
+    export.add_argument("--archive", required=True, help="Path to NRes archive or direct model payload.")
+    export.add_argument(
+        "--model",
+        help="Model entry name (*.msh) inside archive. If omitted, first .msh is used.",
+    )
+    export.add_argument("--output", required=True, help="Output .obj path.")
+    export.add_argument("--lod", type=int, default=0, help="LOD index 0..2 (default: 0).")
+    export.add_argument("--group", type=int, default=0, help="Group index 0..4 (default: 0).")
+    export.add_argument("--max-faces", type=int, default=120000, help="Face limit (default: 120000).")
+    export.add_argument(
+        "--all-batches",
+        action="store_true",
+        help="Ignore slot matrix selection and export all batches.",
+    )
+    export.set_defaults(func=cmd_export)
+
+    return parser
+
+
+def main() -> int:
+    parser = build_parser()
+    args = parser.parse_args()
+    return int(args.func(args))
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())