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