336 lines
14 KiB
Python
336 lines
14 KiB
Python
"""B04 등고선 추출 엔진.
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5종 표현(regular_grid/triangular_mesh/bspline_surface/local_rbf_height_field/
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meshfree_surfels)의 npz 모델에서 표고 격자를 환원하고, marching squares로
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지정 간격 등고선 라인을 추출한다. DTM valid_mask를 footprint로 사용해
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경계 누출을 차단한다.
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"""
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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 scipy.interpolate import RBFInterpolator, RectBivariateSpline
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from skimage import measure
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# 등고선 캐시 형식/추출 규칙이 바뀔 때 증가시킨다.
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CONTOUR_EXTRACTOR_VERSION = 3
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def extract_contours_from_grid(
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x_coords: np.ndarray,
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y_coords: np.ndarray,
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z_grid: np.ndarray,
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valid_mask: np.ndarray | None,
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interval: float,
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min_interval: float = 0.5,
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scene_center: tuple[float, float, float] | None = None,
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) -> list[dict[str, Any]]:
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"""정규 표고 격자로부터 등고선 라인을 추출한다."""
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interval = max(interval, min_interval)
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finite_mask = np.isfinite(z_grid)
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if valid_mask is not None:
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finite_mask &= valid_mask
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if not finite_mask.any():
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return []
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z_min = float(np.min(z_grid[finite_mask]))
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z_max = float(np.max(z_grid[finite_mask]))
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start_level = np.ceil(z_min / interval) * interval
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levels = np.arange(start_level, z_max, interval)
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if len(levels) == 0:
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return []
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if len(levels) > 500:
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new_interval = (z_max - z_min) / 100.0
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levels = np.arange(np.ceil(z_min / new_interval) * new_interval, z_max, new_interval)
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interval = new_interval
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contours_geojson_list: list[dict[str, Any]] = []
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# marching squares의 NaN 문제 예방: 무효 영역을 sentinel(z_min-1000)로 채운다.
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z_grid_masked = z_grid.copy()
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if valid_mask is not None:
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z_grid_masked[~valid_mask] = z_min - 1000.0
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invalid_mask = ~np.isfinite(z_grid_masked)
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if invalid_mask.any():
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z_grid_masked[invalid_mask] = z_min - 1000.0
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cx, cy, cz = scene_center if scene_center is not None else (0.0, 0.0, 0.0)
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for level in levels:
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for contour in measure.find_contours(z_grid_masked, level):
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current_segment: list[list[float]] = []
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for y_idx, x_idx in contour:
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x_idx_c = np.clip(x_idx, 0, len(x_coords) - 1)
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y_idx_c = np.clip(y_idx, 0, len(y_coords) - 1)
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x_0, x_1 = int(np.floor(x_idx_c)), int(np.ceil(x_idx_c))
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y_0, y_1 = int(np.floor(y_idx_c)), int(np.ceil(y_idx_c))
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is_valid = True
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if valid_mask is not None and not (
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valid_mask[y_0, x_0]
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and valid_mask[y_0, x_1]
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and valid_mask[y_1, x_0]
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and valid_mask[y_1, x_1]
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):
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is_valid = False
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if not is_valid:
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if len(current_segment) >= 2:
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mid_idx = len(current_segment) // 2
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contours_geojson_list.append(
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{
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"level": float(level),
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"coordinates": current_segment,
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"label_position": current_segment[mid_idx],
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}
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)
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current_segment = []
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continue
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tx = x_idx_c - x_0
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ty = y_idx_c - y_0
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x_val = (1.0 - tx) * x_coords[x_0] + tx * x_coords[x_1]
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y_val = (1.0 - ty) * y_coords[y_0] + ty * y_coords[y_1]
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if scene_center is not None:
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current_segment.append(
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[
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round(float(x_val - cx), 3),
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round(float(level - cz), 3),
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round(float(-(y_val - cy)), 3),
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]
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)
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else:
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current_segment.append(
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[round(float(x_val), 3), round(float(y_val), 3), round(float(level), 3)]
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)
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if len(current_segment) >= 2:
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mid_idx = len(current_segment) // 2
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contours_geojson_list.append(
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{
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"level": float(level),
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"coordinates": current_segment,
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"label_position": current_segment[mid_idx],
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}
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)
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return contours_geojson_list
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def _load_footprint_mask(
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model_npz_path: Path, x_coords: np.ndarray, y_coords: np.ndarray
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) -> np.ndarray | None:
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"""같은 source filter의 DTM valid_mask를 현재 격자에 최근접 리샘플한다."""
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stem = Path(model_npz_path).stem
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if stem.endswith("_smooth"):
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stem = stem[:-7]
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parts = stem.split("_", 1)
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if len(parts) < 2:
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return None
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filter_key = parts[1]
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dtm_path = Path(model_npz_path).parent / f"dtm_{filter_key}.npz"
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if not dtm_path.exists():
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return None
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try:
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d = np.load(dtm_path)
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dtm_x = np.asarray(d["x"]).ravel()
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dtm_y = np.asarray(d["y"]).ravel()
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dtm_mask = np.asarray(d["valid_mask"], dtype=bool)
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except Exception:
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return None
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if len(dtm_x) < 2 or len(dtm_y) < 2:
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return None
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def _nearest_idx(axis: np.ndarray, coords: np.ndarray) -> np.ndarray:
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ascending = bool(axis[0] <= axis[-1])
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a = axis if ascending else axis[::-1]
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idx = np.clip(np.searchsorted(a, coords), 1, len(a) - 1)
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idx = np.where(np.abs(a[idx - 1] - coords) <= np.abs(a[idx] - coords), idx - 1, idx)
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return idx if ascending else (len(axis) - 1 - idx)
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xi = _nearest_idx(dtm_x, np.asarray(x_coords, dtype=np.float64))
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yi = _nearest_idx(dtm_y, np.asarray(y_coords, dtype=np.float64))
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return dtm_mask[np.ix_(yi, xi)]
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def _apply_footprint(
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model_npz_path: Path, x_coords: np.ndarray, y_coords: np.ndarray, valid_mask: np.ndarray
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) -> np.ndarray:
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"""valid_mask에 DTM footprint를 교집합으로 적용한다 (형상 다르면 최근접 리샘플)."""
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fp = _load_footprint_mask(model_npz_path, x_coords, y_coords)
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if fp is not None:
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if fp.shape == valid_mask.shape:
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return valid_mask & fp
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from scipy.ndimage import zoom
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zoom_y = valid_mask.shape[0] / fp.shape[0]
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zoom_x = valid_mask.shape[1] / fp.shape[1]
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fp_resized = zoom(fp.astype(float), (zoom_y, zoom_x), order=0) > 0.5
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if fp_resized.shape == valid_mask.shape:
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return valid_mask & fp_resized
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return valid_mask
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def _tin_face_coverage_mask(
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vertices: np.ndarray, faces: np.ndarray, xx: np.ndarray, yy: np.ndarray
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) -> np.ndarray:
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"""저장된 TIN 면이 실제로 덮는 XY 영역만 True로 반환한다."""
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vertices = np.asarray(vertices)
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faces = np.asarray(faces, dtype=np.int64)
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if vertices.ndim != 2 or vertices.shape[1] < 2 or not len(faces):
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return np.zeros(xx.shape, dtype=bool)
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edges = np.vstack((faces[:, [0, 1]], faces[:, [1, 2]], faces[:, [2, 0]]))
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edges = np.sort(edges, axis=1)
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unique_edges, counts = np.unique(edges, axis=0, return_counts=True)
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boundary_edges = unique_edges[counts == 1]
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if not len(boundary_edges):
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return np.zeros(xx.shape, dtype=bool)
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from shapely import get_parts, intersects_xy, linestrings, polygonize, union_all
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boundary_lines = linestrings(vertices[boundary_edges, :2])
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polygons = list(get_parts(polygonize(boundary_lines)))
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if not polygons:
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return np.zeros(xx.shape, dtype=bool)
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coverage = union_all(polygons)
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xx_flat = np.asarray(xx, dtype=np.float64).ravel()
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yy_flat = np.asarray(yy, dtype=np.float64).ravel()
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res_flat = np.asarray(intersects_xy(coverage, xx_flat, yy_flat), dtype=bool)
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return res_flat.reshape(xx.shape)
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def _grid_axes(x_min: float, x_max: float, y_min: float, y_max: float, target_grid_m: float):
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cols = max(2, int(np.ceil((x_max - x_min) / target_grid_m)) + 1)
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rows = max(2, int(np.ceil((y_max - y_min) / target_grid_m)) + 1)
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x_coords = np.linspace(x_min, x_max, cols, dtype=np.float32)
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y_coords = np.linspace(y_min, y_max, rows, dtype=np.float32)
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return x_coords, y_coords
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def extract_contours(
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model_npz_path: Path,
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representation: str,
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interval: float,
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target_grid_m: float = 1.0,
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scene_center: tuple[float, float, float] | None = None,
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) -> list[dict[str, Any]]:
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"""표현별 npz 모델에서 표고 격자를 환원한 뒤 등고선 리스트를 추출한다."""
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model_npz_path = Path(model_npz_path)
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if not model_npz_path.exists():
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raise FileNotFoundError(f"모델 파일이 존재하지 않습니다: {model_npz_path}")
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data = np.load(model_npz_path)
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if representation == "regular_grid":
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x_coords, y_coords, z_grid, valid_mask = (
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data["x"],
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data["y"],
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data["z"],
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data["valid_mask"],
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)
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current_res = (x_coords[-1] - x_coords[0]) / (len(x_coords) - 1)
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step = max(1, int(round(target_grid_m / current_res)))
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if step > 1:
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return extract_contours_from_grid(
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x_coords[::step],
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y_coords[::step],
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z_grid[::step, ::step],
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valid_mask[::step, ::step],
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interval,
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scene_center=scene_center,
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)
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return extract_contours_from_grid(
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x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
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)
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if representation == "triangular_mesh":
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from scipy.interpolate import griddata
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vertices, faces = data["vertices"], data["faces"]
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x_min, x_max = float(np.min(vertices[:, 0])), float(np.max(vertices[:, 0]))
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y_min, y_max = float(np.min(vertices[:, 1])), float(np.max(vertices[:, 1]))
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x_coords, y_coords = _grid_axes(x_min, x_max, y_min, y_max, target_grid_m)
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xx, yy = np.meshgrid(x_coords, y_coords)
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z_grid = griddata(vertices[:, :2], vertices[:, 2], (xx, yy), method="linear")
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face_mask = _tin_face_coverage_mask(vertices, faces, xx, yy)
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valid_mask = np.isfinite(z_grid) & face_mask
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valid_mask = _apply_footprint(model_npz_path, x_coords, y_coords, valid_mask)
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return extract_contours_from_grid(
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x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
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)
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if representation == "bspline_surface":
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control_x, control_y, control_z = data["control_x"], data["control_y"], data["control_z"]
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degree = int(data["degree"][0])
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spline = RectBivariateSpline(
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control_y,
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control_x,
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control_z,
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kx=min(degree, len(control_y) - 1),
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ky=min(degree, len(control_x) - 1),
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s=float(len(control_x) * len(control_y)) * 0.01,
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)
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x_coords, y_coords = _grid_axes(
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float(control_x[0]),
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float(control_x[-1]),
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float(control_y[0]),
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float(control_y[-1]),
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target_grid_m,
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)
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z_grid = np.asarray(spline(y_coords, x_coords), dtype=np.float32)
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valid_mask = _apply_footprint(
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model_npz_path, x_coords, y_coords, np.ones_like(z_grid, dtype=bool)
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)
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return extract_contours_from_grid(
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x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
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)
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if representation == "local_rbf_height_field":
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centers_xy, center_z = data["centers_xy"], data["center_z"]
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smoothing = float(data["smoothing"][0])
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interpolator = RBFInterpolator(
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centers_xy.astype(np.float64),
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center_z.astype(np.float64),
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neighbors=min(64, len(centers_xy)),
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smoothing=smoothing,
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kernel="thin_plate_spline",
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)
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x_min, x_max = float(np.min(centers_xy[:, 0])), float(np.max(centers_xy[:, 0]))
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y_min, y_max = float(np.min(centers_xy[:, 1])), float(np.max(centers_xy[:, 1]))
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x_coords, y_coords = _grid_axes(x_min, x_max, y_min, y_max, target_grid_m)
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xx, yy = np.meshgrid(x_coords, y_coords)
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z_values = interpolator(np.column_stack([xx.ravel(), yy.ravel()])).astype(np.float32)
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z_grid = z_values.reshape(len(y_coords), len(x_coords))
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valid_mask = _apply_footprint(
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model_npz_path, x_coords, y_coords, np.ones_like(z_grid, dtype=bool)
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)
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return extract_contours_from_grid(
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x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
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)
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if representation == "meshfree_surfels":
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from scipy.interpolate import griddata
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from scipy.spatial import Delaunay
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points = data["points"]
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x_min, x_max = float(np.min(points[:, 0])), float(np.max(points[:, 0]))
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y_min, y_max = float(np.min(points[:, 1])), float(np.max(points[:, 1]))
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x_coords, y_coords = _grid_axes(x_min, x_max, y_min, y_max, target_grid_m)
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xx, yy = np.meshgrid(x_coords, y_coords)
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z_grid = griddata(points[:, :2], points[:, 2], (xx, yy), method="linear")
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valid_mask = np.isfinite(z_grid)
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try:
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tri = Delaunay(points[:, :2])
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hull_inside = tri.find_simplex(np.column_stack([xx.ravel(), yy.ravel()])) >= 0
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valid_mask = valid_mask & hull_inside.reshape(xx.shape)
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except Exception:
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pass
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valid_mask = _apply_footprint(model_npz_path, x_coords, y_coords, valid_mask)
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return extract_contours_from_grid(
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x_coords, y_coords, z_grid, valid_mask, interval, scene_center=scene_center
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)
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raise ValueError(f"지원하지 않는 표현 방식입니다: {representation}")
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