340 lines
14 KiB
Python
340 lines
14 KiB
Python
"""B04 지표면 분석 엔진 오케스트레이터.
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원본 LAS를 구조화하고 지면 필터를 실행한 뒤 지표면 5종 모델을 빌드하는
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동기 계산 파이프라인. 라우터에서 asyncio.to_thread로 호출한다.
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"""
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import json
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import logging
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import time
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from collections.abc import Callable
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from pathlib import Path
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from typing import Any
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import numpy as np
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from B04_wf1_Surface.B04_wf1_Surface_Engine_Ground import (
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build_ground_masks,
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run_ground_filter,
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summarize_masks,
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)
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from B04_wf1_Surface.B04_wf1_Surface_Engine_Pipeline import build_all_terrain_models
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from B04_wf1_Surface.B04_wf1_Surface_Engine_Structurize import structurize_las
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from common_util.common_util_atomic import atomic_write_npz
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from common_util.common_util_json import atomic_write_json
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from config.config_system import build_surface_model_config
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logger = logging.getLogger(__name__)
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# 진행 콜백 시그니처: (진행률 0~100, 현재 단계 키, 메시지)
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ProgressCallback = Callable[[int, str, str], None]
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GROUND_POINT_SAMPLE_LIMIT = 500_000
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GROUND_POINT_CACHE_VERSION = 2
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def _source_identity(las_path: Path) -> dict[str, Any]:
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"""입력 LAS의 정체성(이름·크기·수정시각)으로 캐시 세대를 식별한다 (PLAN B-2)."""
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stat = las_path.stat()
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return {
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"filename": las_path.name,
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"size_bytes": int(stat.st_size),
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"mtime": float(stat.st_mtime),
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}
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def _relative_to_project(project_root: Path, path: Path) -> str:
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"""프로젝트 루트 기준 posix 상대 경로 문자열."""
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return path.relative_to(project_root).as_posix()
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def cache_ground_points(
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structured_path: Path,
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filter_key: str,
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mask: np.ndarray | None = None,
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) -> Path:
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"""필터링된 지면 포인트 미리보기 캐시를 생성하고 경로를 반환한다.
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mask 미전달 시 영구 저장된 mask_{filter}.npy를 우선 재사용한다 (PLAN A-2).
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"""
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with np.load(structured_path) as structured:
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xyz = np.asarray(structured["xyz"], dtype=np.float32)
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mask_path = structured_path.parent / f"mask_{filter_key}.npy"
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if mask is None and mask_path.is_file():
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stored_mask = np.load(mask_path)
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if len(stored_mask) == len(xyz):
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mask = np.asarray(stored_mask, dtype=bool)
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if mask is None:
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mask = build_ground_masks(structured, [filter_key])[filter_key]
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np.save(mask_path, np.asarray(mask, dtype=bool))
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ground_indexes = np.flatnonzero(mask)
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ground_points = xyz[ground_indexes]
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ground_point_count = int(len(ground_points))
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if ground_point_count:
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data_bounds = np.column_stack((ground_points.min(axis=0), ground_points.max(axis=0)))
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else:
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data_bounds = np.zeros((3, 2), dtype=np.float64)
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if ground_point_count > GROUND_POINT_SAMPLE_LIMIT:
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rng = np.random.default_rng(20260717)
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sample_indexes = rng.choice(
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ground_point_count, GROUND_POINT_SAMPLE_LIMIT, replace=False
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)
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ground_indexes = ground_indexes[sample_indexes]
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points = ground_points[sample_indexes]
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else:
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points = ground_points
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arrays = {
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"xyz": points,
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"bounds": np.asarray(structured["bounds"], dtype=np.float64),
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"data_bounds": np.asarray(data_bounds, dtype=np.float64),
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"cache_version": np.asarray(GROUND_POINT_CACHE_VERSION, dtype=np.int16),
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"point_count": np.asarray(ground_point_count, dtype=np.int64),
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"sampled_count": np.asarray(len(points), dtype=np.int64),
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}
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if "rgb" in structured:
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rgb = np.asarray(structured["rgb"])
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if rgb.ndim > 0 and len(rgb) == len(xyz):
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arrays["rgb"] = rgb[ground_indexes]
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cache_path = structured_path.parent / f"ground_points_{filter_key}.npz"
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atomic_write_npz(cache_path, **arrays)
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return cache_path
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def run_surface_analysis(
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project_root: Path,
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las_path: Path,
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*,
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source_filters: list[str],
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methods: list[str],
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force: bool = False,
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on_progress: ProgressCallback | None = None,
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) -> dict[str, Any]:
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"""구조화→필터→모델 빌드를 수행하고 산출 메타데이터를 반환한다.
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반환 dict:
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- processed: {processed_file_path, converted_file_path, point_count, bounds, statistics}
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- ground_summary: 필터별 지면 포인트 요약
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- manifest: 지표면 모델 파이프라인 manifest
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- models: [{model_type, model_file_path, resolution_m, generation_params, layers}]
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"""
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def _report(percent: int, stage: str, message: str) -> None:
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if on_progress is not None:
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on_progress(percent, stage, message)
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total_started = time.monotonic()
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stage_root = project_root / "B04_wf1_Surface"
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processed_dir = stage_root / "processed"
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models_dir = stage_root / "models"
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processed_dir.mkdir(parents=True, exist_ok=True)
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models_dir.mkdir(parents=True, exist_ok=True)
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# 0. 입력 세대 검증: LAS가 바뀌었으면 모든 캐시를 재계산한다 (PLAN B-2)
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identity_path = processed_dir / "source_identity.json"
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current_identity = _source_identity(las_path)
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stored_identity: dict[str, Any] | None = None
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if identity_path.is_file():
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try:
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stored_identity = json.loads(identity_path.read_text(encoding="utf-8"))
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except (OSError, ValueError):
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stored_identity = None
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rebuild = force or stored_identity != current_identity
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# 1. LAS 구조화 (structured.npz) — 캐시가 유효하면 스킵 (PLAN B-1)
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structured_path = processed_dir / "structured.npz"
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if rebuild or not structured_path.is_file():
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_report(10, "structurize", "LAS 구조화 중")
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step_started = time.monotonic()
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structured_path = structurize_las(las_path, processed_dir)
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atomic_write_json(identity_path, current_identity)
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logger.info(
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"B04 LAS 구조화 완료: %s (%.1fs)", las_path.name, time.monotonic() - step_started
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)
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else:
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_report(10, "structurize", "구조화 캐시 재사용")
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logger.info("B04 구조화 캐시 재사용: %s", structured_path.name)
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with np.load(structured_path) as structured:
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xyz = structured["xyz"]
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bounds = structured["bounds"]
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total_points = int(len(xyz))
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stats = {
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"min_z": float(bounds[2, 0]),
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"max_z": float(bounds[2, 1]),
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"mean_z": float(np.mean(xyz[:, 2])) if total_points else None,
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}
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bounds_dict = {
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"x_min": float(bounds[0, 0]),
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"x_max": float(bounds[0, 1]),
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"y_min": float(bounds[1, 0]),
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"y_max": float(bounds[1, 1]),
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}
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data = {"xyz": xyz, "bounds": bounds}
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# 2. 지면 필터 실행 — mask_{filter}.npy 영구 캐시 우선 재사용 (PLAN A-1)
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_report(40, "ground_filter", "지면 필터 적용 중")
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masks: dict[str, np.ndarray] = {}
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for filter_key in source_filters:
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mask_path = processed_dir / f"mask_{filter_key}.npy"
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mask: np.ndarray | None = None
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if not rebuild and mask_path.is_file():
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stored_mask = np.load(mask_path)
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if len(stored_mask) == total_points:
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mask = np.asarray(stored_mask, dtype=bool)
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logger.info("B04 지면 필터 캐시 재사용: %s", mask_path.name)
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if mask is None:
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step_started = time.monotonic()
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mask = np.asarray(run_ground_filter(filter_key, data), dtype=bool)
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np.save(mask_path, mask)
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logger.info(
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"B04 지면 필터 계산 완료: %s (%.1fs)",
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filter_key,
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time.monotonic() - step_started,
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)
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masks[filter_key] = mask
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ground_summary = summarize_masks(data, masks)
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for filter_key, mask in masks.items():
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cache_path = processed_dir / f"ground_points_{filter_key}.npz"
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if not rebuild and cache_path.is_file():
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with np.load(cache_path) as cached:
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if (
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"cache_version" in cached
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and int(cached["cache_version"]) == GROUND_POINT_CACHE_VERSION
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):
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continue
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cache_ground_points(structured_path, filter_key, mask)
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# 3. 지표면 5종 모델 빌드
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_report(70, "surface_model", "지표면 모델 생성 중")
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config = build_surface_model_config()
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config["source_filters"] = list(source_filters)
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config["precompute"] = list(methods)
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step_started = time.monotonic()
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def _model_progress(percent: int, detail: str) -> None:
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_report(70 + int(percent * 0.2), "surface_model", detail)
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manifest = build_all_terrain_models(
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data, masks, models_dir, config, force=rebuild, progress=_model_progress
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)
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logger.info(
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"B04 지표면 모델 빌드 완료: status=%s (%.1fs)",
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manifest.get("status"),
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time.monotonic() - step_started,
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)
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# 3-2. VWorld 지도 및 국가 GIS 벡터 다운로드 (기존 산출물이 있으면 스킵)
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_report(90, "download_maps", "VWorld 지도 및 GIS 벡터 데이터 다운로드 중")
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try:
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# 입력 LAS와 같은 폴더의 .prj를 우선 사용 (PLAN E-1: 전체 재귀 glob 제거)
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prj_candidates = sorted(las_path.parent.glob("*.prj")) or sorted(
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project_root.glob("B03_FileInput/**/*.prj")
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)
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prj_path = prj_candidates[0] if prj_candidates else project_root / "result.prj"
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bounds_dict_for_download = {
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"x": [float(bounds[0, 0]), float(bounds[0, 1])],
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"y": [float(bounds[1, 0]), float(bounds[1, 1])],
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"z": [float(bounds[2, 0]), float(bounds[2, 1])],
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}
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from B04_wf1_Surface.B04_wf1_Surface_Engine_GisVector import download_all_gis_vectors
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from B04_wf1_Surface.B04_wf1_Surface_Engine_VWorld import download_vworld_satellite_map
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# VWorld 지도 및 GIS 데이터 저장 위치는 B04_wf1_Surface/processed에 보관.
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layers = [
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{"layer": "Satellite", "ext": "jpeg"},
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{"layer": "Hybrid", "ext": "png"},
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{"layer": "white", "ext": "png"},
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]
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for item in layers:
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meta_path = processed_dir / f"vworld_{item['layer'].lower()}_meta.json"
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if not rebuild and meta_path.is_file():
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continue
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try:
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step_started = time.monotonic()
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download_vworld_satellite_map(
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prj_path,
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bounds_dict_for_download,
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processed_dir,
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layer_name=item["layer"],
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ext=item["ext"],
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)
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logger.info(
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"B04 VWorld %s 지도 다운로드 완료 (%.1fs)",
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item["layer"],
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time.monotonic() - step_started,
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)
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except Exception as exc:
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logger.warning("B04 VWorld %s 지도 다운로드 실패: %s", item["layer"], exc)
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if rebuild or not any(processed_dir.glob("*_bounds.geojson")):
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try:
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step_started = time.monotonic()
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download_all_gis_vectors(prj_path, bounds_dict_for_download, processed_dir)
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logger.info(
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"B04 국가 GIS 벡터 다운로드 완료 (%.1fs)", time.monotonic() - step_started
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)
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except Exception as exc:
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logger.warning("B04 국가 GIS 벡터 다운로드 실패: %s", exc)
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except Exception as exc:
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logger.warning("B04 지도·GIS 다운로드 단계 실패: %s", exc)
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_report(95, "saving", "결과 저장 중")
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processed = {
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"processed_file_path": _relative_to_project(project_root, structured_path),
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"converted_file_path": None,
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"point_count": total_points,
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"bounds": bounds_dict,
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"statistics": stats,
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}
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# manifest에서 저장된 모델별 정보 추출 (필터별 대표 모델)
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models: list[dict[str, Any]] = []
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for filter_key, filter_entry in manifest.get("source_filters", {}).items():
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for method, meta in filter_entry.get("methods", {}).items():
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if meta.get("status") != "completed":
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continue
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model_file = meta.get("model_file")
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model_path = (models_dir / model_file) if model_file else None
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layers: list[dict[str, Any]] = []
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if meta.get("preview_file"):
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layers.append(
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{
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"layer_name": f"{method}_{filter_key}_preview",
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"geometry_type": "MESH" if method != "meshfree" else "POINTCLOUD",
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"file_path": _relative_to_project(
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project_root, models_dir / meta["preview_file"]
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),
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"file_format": "glb" if method != "meshfree" else "ply",
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}
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)
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models.append(
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{
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"model_type": method,
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"source_filter": filter_key,
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"representation": meta.get("representation"),
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"model_file_path": _relative_to_project(project_root, model_path)
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if model_path
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else None,
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"resolution_m": meta.get("grid_resolution_meters"),
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"generation_params": {
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"source_filter": filter_key,
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"representation": meta.get("representation"),
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"footprint_area_m2": meta.get("footprint_area_m2"),
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},
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"layers": layers,
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}
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)
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logger.info(
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"B04 WF1 분석 완료: 모델 %d개, 총 %.1fs", len(models), time.monotonic() - total_started
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)
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return {
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"processed": processed,
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"ground_summary": ground_summary,
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"manifest": manifest,
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"models": models,
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}
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