260705_2

2026-07-05 21:27:23 +09:00
parent 23d907265a
commit 3abc2edba6
83 changed files with 10351 additions and 1217 deletions
@@ -0,0 +1,125 @@
+"""B04 지표면 분석 엔진 오케스트레이터.
+
+원본 LAS를 구조화하고 지면 필터를 실행한 뒤 지표면 5종 모델을 빌드하는
+동기 계산 파이프라인. 라우터에서 asyncio.to_thread로 호출한다.
+"""
+
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+
+from B04_wf1_Surface.B04_wf1_Surface_Engine_Ground import build_ground_masks, summarize_masks
+from B04_wf1_Surface.B04_wf1_Surface_Engine_Pipeline import build_all_terrain_models
+from B04_wf1_Surface.B04_wf1_Surface_Engine_Structurize import structurize_las
+from config.config_system import build_surface_model_config
+
+
+def _relative_to_project(project_root: Path, path: Path) -> str:
+    """프로젝트 루트 기준 posix 상대 경로 문자열."""
+    return path.relative_to(project_root).as_posix()
+
+
+def run_surface_analysis(
+    project_root: Path,
+    las_path: Path,
+    *,
+    source_filters: list[str],
+    methods: list[str],
+    force: bool = False,
+) -> dict[str, Any]:
+    """구조화→필터→모델 빌드를 수행하고 산출 메타데이터를 반환한다.
+
+    반환 dict:
+      - processed: {processed_file_path, converted_file_path, point_count, bounds, statistics}
+      - ground_summary: 필터별 지면 포인트 요약
+      - manifest: 지표면 모델 파이프라인 manifest
+      - models: [{model_type, model_file_path, resolution_m, generation_params, layers}]
+    """
+    stage_root = project_root / "B04_wf1_Surface"
+    processed_dir = stage_root / "processed"
+    models_dir = stage_root / "models"
+    processed_dir.mkdir(parents=True, exist_ok=True)
+    models_dir.mkdir(parents=True, exist_ok=True)
+
+    # 1. LAS 구조화 (structured.npz)
+    structured_path = structurize_las(las_path, processed_dir)
+    with np.load(structured_path) as structured:
+        xyz = structured["xyz"]
+        bounds = structured["bounds"]
+        total_points = int(len(xyz))
+        stats = {
+            "min_z": float(bounds[2, 0]),
+            "max_z": float(bounds[2, 1]),
+            "mean_z": float(np.mean(xyz[:, 2])) if total_points else None,
+        }
+        bounds_dict = {
+            "x_min": float(bounds[0, 0]),
+            "x_max": float(bounds[0, 1]),
+            "y_min": float(bounds[1, 0]),
+            "y_max": float(bounds[1, 1]),
+        }
+        data = {"xyz": xyz, "bounds": bounds}
+
+        # 2. 지면 필터 실행
+        masks = build_ground_masks(data, source_filters)
+        ground_summary = summarize_masks(data, masks)
+
+        # 3. 지표면 5종 모델 빌드
+        config = build_surface_model_config()
+        config["source_filters"] = list(source_filters)
+        config["precompute"] = list(methods)
+        manifest = build_all_terrain_models(data, masks, models_dir, config, force=force)
+
+    processed = {
+        "processed_file_path": _relative_to_project(project_root, structured_path),
+        "converted_file_path": None,
+        "point_count": total_points,
+        "bounds": bounds_dict,
+        "statistics": stats,
+    }
+
+    # manifest에서 저장된 모델별 정보 추출 (필터별 대표 모델)
+    models: list[dict[str, Any]] = []
+    for filter_key, filter_entry in manifest.get("source_filters", {}).items():
+        for method, meta in filter_entry.get("methods", {}).items():
+            if meta.get("status") != "completed":
+                continue
+            model_file = meta.get("model_file")
+            model_path = (models_dir / model_file) if model_file else None
+            layers: list[dict[str, Any]] = []
+            if meta.get("preview_file"):
+                layers.append(
+                    {
+                        "layer_name": f"{method}_{filter_key}_preview",
+                        "geometry_type": "MESH" if method != "meshfree" else "POINTCLOUD",
+                        "file_path": _relative_to_project(
+                            project_root, models_dir / meta["preview_file"]
+                        ),
+                        "file_format": "glb" if method != "meshfree" else "ply",
+                    }
+                )
+            models.append(
+                {
+                    "model_type": method,
+                    "source_filter": filter_key,
+                    "representation": meta.get("representation"),
+                    "model_file_path": _relative_to_project(project_root, model_path)
+                    if model_path
+                    else None,
+                    "resolution_m": meta.get("grid_resolution_meters"),
+                    "generation_params": {
+                        "source_filter": filter_key,
+                        "representation": meta.get("representation"),
+                        "footprint_area_m2": meta.get("footprint_area_m2"),
+                    },
+                    "layers": layers,
+                }
+            )
+
+    return {
+        "processed": processed,
+        "ground_summary": ground_summary,
+        "manifest": manifest,
+        "models": models,
+    }