feat(extract): Beschriftungsmasken, Gitternetz und Entfernen mit Bericht
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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@@ -9,16 +9,35 @@ gemeldet und übersprungen.
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"""
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from __future__ import annotations
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import math
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import tomllib
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from dataclasses import dataclass, field
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from pathlib import Path
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import numpy as np
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from scipy import ndimage as ndi
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from shapely.geometry import Polygon, box
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from shapely.strtree import STRtree
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from skimage.color import rgb2hsv, rgb2lab
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from skimage.draw import polygon as fill_polygon
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from skimage.morphology import disk
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from .classify import PALETTE
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from .coords import SLICE, chunk_for_world, chunk_name
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from .inpaint import RebuildStats, fill_nearest, is_thin, rebuild
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KINDS = ("text_white", "text_red", "marker", "symbol", "street_name", "region")
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MARGIN = {"text_white": 7, "text_red": 7, "marker": 3, "symbol": 4, "street_name": 1, "region": 0}
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RED = (248, 112, 112)
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HOLE_MAX_AREA = 400 # m²: größere eingeschlossene Flächen sind echte Karte
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MIN_BOX_SIDE = 2.0 # m
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FULL_SHARE = 0.60 # randvoll ab diesem Anteil der Farbpixel (vor dem Wachsen) am Rahmen
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CUT_MIN_PX = 20 # angeschnitten ab einem verworfenen Kandidaten dieser Größe; nicht bei street_name,
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# dessen Farbregel auch die Stadtfläche neben der Fahrbahn trifft
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GENERATED_SHARE = 0.5 # Anteil der Grundfläche in ergänzten Rechtecken für source = "annotation"
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THIN_WINDOW = 20 # m Rand um eine dünne Maske beim Auffüllen
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GRID_STRIP = 10 # m beiderseits einer Gitterlinie beim Auffüllen
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EIGHT = np.ones((3, 3), dtype=bool)
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class AnnotationError(ValueError):
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@@ -193,3 +212,238 @@ def _parse_grid(value) -> Grid | None:
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if not (_number(value[key]) and value[key] > 0):
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fail(f"Feld {key!r} muss eine Zahl größer als 0 sein, ist {value[key]!r}")
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return Grid(float(value["spacing"]), float(value["width"]))
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# --- Masken -------------------------------------------------------------------------------------
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@dataclass
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class FoundMask:
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rows: slice
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cols: slice
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mask: np.ndarray # bool, Größe des Rahmens
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applied: bool
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warning: str | None = None
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def _lab(rgb) -> np.ndarray:
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return rgb2lab(np.array([[rgb]], dtype=np.float32) / 255.0)[0, 0]
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def candidates(rgb: np.ndarray, kind: str) -> np.ndarray:
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"""Kandidatenpixel der Farbregel einer Art in einem RGB-Ausschnitt (Tabelle in der Spec)."""
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values = rgb.astype(np.int16)
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low, high = values.min(axis=2), values.max(axis=2)
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white = (low >= 236) & (high - low <= 14)
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if kind == "text_white":
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return white
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if kind == "street_name":
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return (low >= 200) & (high - low <= 20) # helle Schrift; Gebäudegrau (176) bleibt
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lab = rgb2lab(rgb.astype(np.float32) / 255.0)
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if kind == "text_red":
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return np.linalg.norm(lab - _lab(RED), axis=2) < 18
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if kind in ("marker", "symbol"):
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hsv = rgb2hsv(rgb)
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palette = np.stack([_lab(colour) for colour in PALETTE.values()])
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far = np.linalg.norm(lab[:, :, None, :] - palette[None, None], axis=3).min(axis=2) >= 25
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marker = ((hsv[..., 1] >= 0.6) & (hsv[..., 2] >= 0.5) & far) | (high <= 45)
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return marker | white if kind == "symbol" else marker
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raise ValueError(f"Art ohne Farbregel: {kind}")
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def _pixel_bounds(annotation: Annotation, shape) -> tuple[int, int, int, int]:
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"""Zeilen und Spalten, deren Pixelmitte im Hüllrechteck liegt; Pixelmitte (r, c) = Welt (x0 + c, y1 − r)."""
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h, w = shape
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x_min, y_min, x_max, y_max = annotation.outline.bounds
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c0, c1 = max(0, math.ceil(x_min - SLICE["x0"])), min(w, math.floor(x_max - SLICE["x0"]) + 1)
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r0, r1 = max(0, math.ceil(SLICE["y1"] - y_max)), min(h, math.floor(SLICE["y1"] - y_min) + 1)
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return r0, r1, c0, c1
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def annotation_mask(mosaic: np.ndarray, annotation: Annotation) -> FoundMask:
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"""Maske eines Eintrags in fünf Schritten (Spec, Abschnitt „Erkennung je Art")."""
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h, w = mosaic.shape[:2]
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r0, r1, c0, c1 = _pixel_bounds(annotation, (h, w))
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rows, cols = slice(r0, r1), slice(c0, c1)
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shape = (r1 - r0, c1 - c0)
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if annotation.kind == "region":
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mask = np.zeros(shape, dtype=bool)
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xs, ys = annotation.outline.exterior.coords.xy
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rr, cc = fill_polygon(SLICE["y1"] - np.asarray(ys) - r0, np.asarray(xs) - SLICE["x0"] - c0, shape=shape)
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mask[rr, cc] = True
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if not mask.any():
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return FoundMask(rows, cols, mask, False, "leer: das Vieleck trifft keine Pixelmitte")
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return FoundMask(rows, cols, mask, True)
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found = candidates(mosaic[rows, cols], annotation.kind)
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comps, count = ndi.label(found, structure=EIGHT)
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border = np.zeros(shape, dtype=bool)
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border[0, :], border[-1, :] = r0 > 0, r1 < h # Kanten auf dem Ausschnittsrand zählen nicht
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border[:, 0] |= c0 > 0
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border[:, -1] |= c1 < w
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touching = np.unique(comps[border & found])
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touching = touching[touching > 0]
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cut = int(max(ndi.sum(found, comps, touching))) if touching.size else 0
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keep = found & ~np.isin(comps, touching)
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if not keep.any():
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return FoundMask(rows, cols, keep, False, f"leer: keine Pixel der Art {annotation.kind} im Rahmen")
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grown = ndi.binary_dilation(keep, structure=disk(MARGIN[annotation.kind]).astype(bool))
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holes = ndi.binary_fill_holes(grown) & ~grown
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hole_ids, hole_count = ndi.label(holes)
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if hole_count:
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sizes = ndi.sum(holes, hole_ids, np.arange(1, hole_count + 1))
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grown |= np.isin(hole_ids, 1 + np.flatnonzero(sizes <= HOLE_MAX_AREA))
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share = float(keep.mean())
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if share > FULL_SHARE:
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return FoundMask(rows, cols, grown, False,
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f"randvoll: Farbpixel füllen {round(share * 100)} % des Rahmens, Rahmen zu eng oder Art falsch")
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cut_off = cut >= CUT_MIN_PX and annotation.kind != "street_name"
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return FoundMask(rows, cols, grown, True, "angeschnitten: Rahmen prüfen" if cut_off else None)
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def grid_mask(shape: tuple[int, int], grid: Grid) -> tuple[np.ndarray, int]:
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"""Streifen der Breite grid.width mittig auf jeder Gitterlinie; dazu die Zahl der Linien."""
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h, w = shape
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mask = np.zeros(shape, dtype=bool)
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lines = 0
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for size, axis in ((w, 1), (h, 0)):
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k = 0
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while k * grid.spacing <= size:
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lo = max(0, int(round(k * grid.spacing - grid.width / 2)))
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hi = min(size, int(round(k * grid.spacing + grid.width / 2)))
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if lo < hi:
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if axis == 1:
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mask[:, lo:hi] = True
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else:
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mask[lo:hi, :] = True
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lines += 1
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k += 1
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return mask, lines
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def _grid_strips(shape: tuple[int, int], grid: Grid):
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"""Streifen von GRID_STRIP Metern beiderseits jeder Gitterlinie, damit nie das ganze Bild gefüllt wird."""
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h, w = shape
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for size, axis in ((w, 1), (h, 0)):
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k = 0
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while k * grid.spacing <= size:
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lo = max(0, int(round(k * grid.spacing - grid.width / 2 - GRID_STRIP)))
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hi = min(size, int(round(k * grid.spacing + grid.width / 2 + GRID_STRIP)))
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if lo < hi:
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yield (slice(None), slice(lo, hi)) if axis == 1 else (slice(lo, hi), slice(None))
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k += 1
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# --- Entfernen, Bericht, Markierung ----------------------------------------------------------
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@dataclass
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class EntryReport:
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annotation: Annotation
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applied: bool
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warning: str | None = None
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mask_area: int = 0
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stats: RebuildStats | None = None
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shared_with: int | None = None
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@dataclass
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class Removal:
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labels: np.ndarray
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generated: list[Polygon]
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entries: list[EntryReport]
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grid_lines: int
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def report(self) -> str:
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applied = sum(1 for e in self.entries if e.applied)
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lines = [f"Beschriftungen: {applied} angewendet, {len(self.entries) - applied} übersprungen"
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f" · Gitternetz: {self.grid_lines} Linien"]
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for e in self.entries:
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if not e.applied:
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continue
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a = e.annotation
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parts = [f"#{a.index} {a.name}", a.chunk, f"Maske {e.mask_area:,} m²".replace(",", ".")]
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if e.shared_with is not None:
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parts.append(f"gemeinsam mit #{e.shared_with}")
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elif e.stats is not None:
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s = e.stats
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parts.append(f"Straßen {s.connected} verbunden, {s.extended} weitergeführt, {s.dead_ends} Sackgassen")
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parts.append(f"Gebäude {s.closed} geschlossen, {s.added} ergänzt" + (f" ({s.note})" if s.note else ""))
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lines.append(" " + " · ".join(parts))
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warned = [e for e in self.entries if e.warning]
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if warned:
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lines.append("Warnungen:")
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lines += [f" #{e.annotation.index} {e.annotation.name} · {e.annotation.chunk} · {e.warning}" for e in warned]
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return "\n".join(lines)
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def remove_annotations(mosaic: np.ndarray, labels: np.ndarray, annotations: AnnotationList) -> Removal:
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"""Entfernt alle Einträge und das Gitternetz aus einer Kopie des Klassenbilds."""
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labels = labels.copy()
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union = np.zeros(labels.shape, dtype=bool)
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entries, found = [], []
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for annotation in annotations.annotations:
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result = annotation_mask(mosaic, annotation)
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entry = EntryReport(annotation, result.applied, result.warning)
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if result.applied:
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union[result.rows, result.cols] |= result.mask
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entry.mask_area = int(result.mask.sum())
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entries.append(entry)
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found.append(result)
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grid_lines = 0
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if annotations.grid is not None:
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grid, grid_lines = grid_mask(labels.shape, annotations.grid)
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blocked = grid | union
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for strip in _grid_strips(labels.shape, annotations.grid):
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fill_nearest(labels[strip], (grid & ~union)[strip], blocked[strip])
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components, _ = ndi.label(union, structure=EIGHT)
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owners: dict[int, EntryReport] = {} # Maske -> erster Eintrag in Dateireihenfolge
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touched = []
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for entry, result in zip(entries, found):
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ids = np.unique(components[result.rows, result.cols][result.mask]) if entry.applied else np.array([], int)
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ids = ids[ids > 0]
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for cid in ids:
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owners.setdefault(int(cid), entry)
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touched.append(ids)
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generated = []
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h, w = labels.shape
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for cid, sl in enumerate(ndi.find_objects(components), start=1):
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if sl is None:
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continue
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piece = components[sl] == cid
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if is_thin(piece):
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win = (slice(max(0, sl[0].start - THIN_WINDOW), min(h, sl[0].stop + THIN_WINDOW)),
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slice(max(0, sl[1].start - THIN_WINDOW), min(w, sl[1].stop + THIN_WINDOW)))
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fill_nearest(labels[win], components[win] == cid, union[win])
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continue
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mask = np.zeros(labels.shape, dtype=bool)
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mask[sl] = piece
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result = rebuild(labels, mask, union)
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owner = owners[cid]
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owner.stats = result.stats if owner.stats is None else _merged(owner.stats, result.stats)
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generated += [Polygon([(SLICE["x0"] + c, SLICE["y1"] - r) for r, c in corners])
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for corners in result.buildings]
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for entry, ids in zip(entries, touched):
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if entry.applied and entry.stats is None and ids.size and owners[int(ids[0])] is not entry:
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entry.shared_with = owners[int(ids[0])].annotation.index
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return Removal(labels, generated, entries, grid_lines)
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def _merged(a: RebuildStats, b: RebuildStats) -> RebuildStats:
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return RebuildStats(a.connected + b.connected, a.extended + b.extended, a.dead_ends + b.dead_ends,
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a.closed + b.closed, a.added + b.added, "; ".join(dict.fromkeys(n for n in (a.note, b.note) if n)))
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def mark_generated(polygons: list[Polygon], heights: list[dict], generated: list[Polygon]) -> int:
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"""Setzt source = "annotation" bei Gebäuden, die zu mindestens der Hälfte in ergänzten Rechtecken liegen."""
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if not generated:
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return 0
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tree = STRtree(generated)
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marked = 0
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for polygon, height in zip(polygons, heights):
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covered = sum(polygon.intersection(generated[k]).area for k in tree.query(polygon))
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if covered >= GENERATED_SHARE * polygon.area:
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height["source"] = "annotation"
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marked += 1
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return marked
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