start

tools/extract/data/annotations.toml

@@ -38,7 +38,7 @@ box  = [[-885, 2045], [-833, 1995]]
 
 [[annotation]]
 name = "Schild 97 in B2"
-kind = "symbol"
+kind = "region"
 box  = [[-551, 2199], [-497, 2147]]
 
 [[annotation]]
@@ -153,7 +153,7 @@ box  = [[-861, 1572], [-797, 1521]]
 
 [[annotation]]
 name = "Schild 97 in B4"
-kind = "symbol"
+kind = "region"
 box  = [[-652, 1601], [-592, 1539]]
 
 [[annotation]]
@@ -238,7 +238,7 @@ box  = [[1440, 1336], [1515, 1289]]
 
 [[annotation]]
 name = "Schild 97 in A6"
-kind = "symbol"
+kind = "region"
 box  = [[-869, 1230], [-817, 1184]]
 
 [[annotation]]
@@ -278,7 +278,7 @@ box  = [[-1000, 830], [-945, 774]]
 
 [[annotation]]
 name = "Schild 97 in A7"
-kind = "symbol"
+kind = "region"
 box  = [[-945, 933], [-893, 883]]
 
 [[annotation]]
@@ -338,7 +338,7 @@ box  = [[-670, 530], [-613, 490]]
 
 [[annotation]]
 name = "Schild 404 in B8"
-kind = "symbol"
+kind = "region"
 box  = [[-626, 618], [-567, 575]]
 
 [[annotation]]
@@ -478,7 +478,7 @@ box  = [[1905, -60], [1934, -131]]
 
 [[annotation]]
 name = "Schild 397 in B12"
-kind = "symbol"
+kind = "region"
 box  = [[-685, -346], [-625, -397]]
 
 [[annotation]]
@@ -493,7 +493,7 @@ box  = [[-313, -398], [-169, -433]]
 
 [[annotation]]
 name = "Schild 397 in D12"
-kind = "symbol"
+kind = "region"
 box  = [[-146, -410], [-87, -460]]
 
 [[annotation]]

tools/extract/leonida/annotations.py

@@ -24,7 +24,7 @@ from skimage.morphology import disk
 
 from .classify import PALETTE
 from .coords import SLICE, chunk_for_world, chunk_name
-from .inpaint import RebuildStats, fill_nearest, is_thin, rebuild
+from .inpaint import RebuildStats, add_buildings, fill_nearest, is_thin, rebuild
 
 KINDS = ("text_white", "text_red", "marker", "symbol", "street_name", "region")
 MARGIN = {"text_white": 7, "text_red": 7, "marker": 3, "symbol": 4, "street_name": 1, "region": 0}
@@ -406,7 +406,7 @@ def remove_annotations(mosaic: np.ndarray, labels: np.ndarray, annotations: Anno
             owners.setdefault(int(cid), entry)
         touched.append(ids)
 
-    generated = []
+    generated, rebuilt = [], []
     h, w = labels.shape
     for cid, sl in enumerate(ndi.find_objects(components), start=1):
         if sl is None:
@@ -419,9 +419,17 @@ def remove_annotations(mosaic: np.ndarray, labels: np.ndarray, annotations: Anno
             continue
         mask = np.zeros(labels.shape, dtype=bool)
         mask[sl] = piece
-        result = rebuild(labels, mask, union)
+        result = rebuild(labels, mask, union, add=False)
         owner = owners[cid]
         owner.stats = result.stats if owner.stats is None else _merged(owner.stats, result.stats)
+        rebuilt.append((cid, sl))
+    # Ergänzt wird erst, wenn alle Masken aufgebaut sind: sonst zählten Buchstabenkonturen noch nicht
+    # aufgebauter Masken als Straße und teilten Blöcke, und das Ergebnis hinge von der Reihenfolge ab.
+    for cid, sl in rebuilt:
+        mask = np.zeros(labels.shape, dtype=bool)
+        mask[sl] = components[sl] == cid
+        result = add_buildings(labels, mask, union)
+        owners[cid].stats = _merged(owners[cid].stats, result.stats)
         generated += [Polygon([(SLICE["x0"] + c, SLICE["y1"] - r) for r, c in corners])
                       for corners in result.buildings]
     for entry, ids in zip(entries, touched):

tools/extract/leonida/inpaint.py

@@ -90,12 +90,14 @@ def is_thin(mask: np.ndarray) -> bool:
     return float(ndi.distance_transform_edt(np.pad(mask, 1)).max()) <= THIN_DEPTH
 
 
-def rebuild(labels: np.ndarray, mask: np.ndarray, blocked: np.ndarray | None = None) -> RebuildResult:
+def rebuild(labels: np.ndarray, mask: np.ndarray, blocked: np.ndarray | None = None,
+            add: bool = True) -> RebuildResult:
     """Baut das Klassenbild unter einer zusammenhängenden Maske wieder auf.
 
     blocked sind alle Pixel, die nicht als Quelle taugen — alle Beschriftungsmasken; ohne
     Angabe nur diese Maske. Reihenfolge wie in der Spec: Straßen und Wege, Flächen,
-    Gebäude schließen, Gebäude ergänzen.
+    Gebäude schließen, Gebäude ergänzen. Mit add=False endet der Aufbau nach dem Schließen;
+    ergänzt wird dann über add_buildings, sobald alle Masken aufgebaut sind.
     """
     blocked = mask if blocked is None else blocked
     stats = RebuildStats()
@@ -107,6 +109,14 @@ def rebuild(labels: np.ndarray, mask: np.ndarray, blocked: np.ndarray | None = N
         _stamp(L, M, painted, road, points, halves)
     _fill_areas(L, M & ~painted, B)
     _close_buildings(L, M, B, painted, stats)
+    corners = _add_buildings(labels, mask, blocked, stats) if add else []
+    return RebuildResult(stats, corners)
+
+
+def add_buildings(labels: np.ndarray, mask: np.ndarray, blocked: np.ndarray | None = None) -> RebuildResult:
+    """Ergänzt Gebäude unter einer schon aufgebauten Maske; die Statistik trägt nur added und note."""
+    blocked = mask if blocked is None else blocked
+    stats = RebuildStats()
     corners = _add_buildings(labels, mask, blocked, stats)
     return RebuildResult(stats, corners)
 
@@ -535,13 +545,17 @@ def _window(mask, margin):
 
 
 def _rectangle(rows, cols) -> Polygon:
-    """Gedrehtes Hüllrechteck um Pixel (Zeilen, Spalten) als Polygon in (Spalte, Zeile), mit Pixelkante."""
+    """Gedrehtes Hüllrechteck um Pixel (Zeilen, Spalten) als Polygon in (Spalte, Zeile), mit Pixelkante.
+
+    Liegen die Pixel auf einer Linie, ist das Hüllrechteck eine Linie; eckige Enden machen daraus
+    wieder ein Rechteck mit vier Ecken statt einer Kapsel.
+    """
     points = MultiPoint(np.column_stack([cols, rows]).astype(float))
     with warnings.catch_warnings():
         # Siehe buildings.regularize(): harmlose GEOS-Warnung bei achsenparallelen Formen.
         warnings.filterwarnings("ignore", message=r".*oriented_envelope.*", category=RuntimeWarning)
         envelope = points.minimum_rotated_rectangle
-    return envelope.buffer(0.5, join_style="mitre")
+    return envelope.buffer(0.5, cap_style="square", join_style="mitre")
 
 
 def _sides(rect: Polygon) -> tuple[float, float]:

tools/extract/run.py

@@ -25,6 +25,7 @@ def main() -> None:
     labels = drop_map_labels(labels)
     removal = remove_annotations(mosaic, labels, load_annotations(Path("data/annotations.toml")))
     labels = removal.labels
+    labels = drop_map_labels(labels)       # Inselreste, die erst das Entfernen im Wasser hinterlässt
     print(removal.report())
     if unknown_share > 0.05:
         raise SystemExit(f"{unknown_share:.1%} der Pixel unbekannt. Palette kalibrieren, nicht die Grenze anheben.")

tools/extract/tests/test_annotations_remove.py

@@ -1,4 +1,5 @@
 import numpy as np
+from scipy import ndimage as ndi
 from shapely.geometry import box
 
 from leonida.annotations import (Annotation, AnnotationList, Grid, annotation_mask, candidates, grid_mask,
@@ -6,7 +7,7 @@ from leonida.annotations import (Annotation, AnnotationList, Grid, annotation_ma
 from leonida.classify import CLASSES, classify
 
 PARK, ROAD, URBAN = (192, 216, 128), (80, 80, 80), (216, 216, 216)
-WHITE, OUTLINE = (248, 248, 248), (80, 88, 96)
+WHITE, OUTLINE, BUILDING = (248, 248, 248), (80, 88, 96), (176, 176, 176)
 
 
 def picture(h=200, w=300, colour=URBAN):
@@ -154,6 +155,24 @@ def test_the_grid_is_closed_before_anything_is_rebuilt():
     assert removal.grid_lines == 7                          # Spalten 0, 100, 200, 300 und Zeilen 0, 100, 200
 
 
+def test_a_grid_line_through_an_entry_leaves_no_strip_in_it():
+    img = picture(colour=PARK)
+    img[:, 98:102] = ROAD                                   # Gitterlinie, als Straße gelesen, quer durch den Schriftzug
+    letter(img, (80, 120), (60, 140))
+    annotations = AnnotationList([entry("text_white", (60, 141), (40, 161))], grid=Grid(100, 6))
+    removal = remove_annotations(img, classify(img), annotations)
+    assert (removal.labels == CLASSES["land"]).all()        # erst danach aufgebaut, liefe dort ein Straßenstreifen
+
+
+def test_a_thin_mask_takes_the_nearest_class_and_leaves_no_slit():
+    img = picture()
+    img[60:140, 100:200] = BUILDING
+    line = entry("region", (99, 102), (90, 211), name="Zeigerlinie")   # 3 m breit, quer über das Gebäude
+    removal = remove_annotations(img, classify(img), AnnotationList([line]))
+    assert (removal.labels[60:140, 100:200] == CLASSES["gebaeude"]).all()
+    assert (removal.labels[99:102, 90:100] == CLASSES["stadt"]).all()
+
+
 def test_entries_sharing_one_mask_are_reported_together():
     img = picture()
     img[:, 140:160] = ROAD
@@ -165,6 +184,37 @@ def test_entries_sharing_one_mask_are_reported_together():
     assert "#2 Rechts · A1 · Maske" in report and "gemeinsam mit #1" in report
 
 
+class ReversedComponents:
+    """scipy.ndimage für annotations.py, nur nummeriert label() die Flächen rückwärts."""
+
+    def __getattr__(self, name):
+        return getattr(ndi, name)
+
+    @staticmethod
+    def label(image, structure=None):
+        numbers, count = ndi.label(image, structure=structure)
+        return np.where(numbers > 0, count + 1 - numbers, 0), count
+
+
+def test_added_buildings_do_not_depend_on_the_order_of_the_masks(monkeypatch):
+    img = picture(300, 400)
+    img[20:40, :] = ROAD
+    img[260:280, :] = ROAD
+    img[:, 20:40] = ROAD
+    img[:, 360:380] = ROAD
+    for top in (60, 100, 140, 180):
+        for left in (60, 120):
+            img[top:top + 20, left:left + 30] = BUILDING        # links sichtbar bebaut
+    letter(img, (43, 257), (180, 190))                      # Kontur in Straßenfarbe teilt den Block bis zum Aufbau
+    annotations = AnnotationList([entry("text_white", (30, 271), (165, 206), index=1, name="Balken"),
+                                  entry("region", (60, 241), (220, 341), index=2, name="Rest")])
+    forward = remove_annotations(img, classify(img), annotations)
+    monkeypatch.setattr("leonida.annotations.ndi", ReversedComponents())
+    backward = remove_annotations(img, classify(img), annotations)
+    assert forward.generated                                # im verdeckten Teil rechts entstehen Gebäude
+    assert (forward.labels == backward.labels).all()
+
+
 def test_buildings_mostly_inside_added_rectangles_are_marked():
     heights = [{"height": 9.0, "source": "district"}, {"height": 12.0, "source": "district"}]
     marked = mark_generated([box(1, 1, 9, 9), box(6, 0, 16, 10)], heights, [box(0, 0, 10, 10)])

tools/extract/tests/test_inpaint.py

@@ -1,8 +1,9 @@
 import numpy as np
+import pytest
 from scipy import ndimage as ndi
 
 from leonida.classify import CLASSES
-from leonida.inpaint import fill_nearest, is_thin, rebuild
+from leonida.inpaint import _rectangle, _sides, fill_nearest, is_thin, rebuild
 
 STADT, PARK, WATER = CLASSES["stadt"], CLASSES["gruen"], CLASSES["wasser"]
 ROAD, PATH, BUILDING = CLASSES["strasse"], CLASSES["weg"], CLASSES["gebaeude"]
@@ -133,6 +134,18 @@ def test_a_path_stays_a_path_and_the_street_wins_the_crossing():
     assert (labels[120:138, 146:149] == PATH).all()
 
 
+def test_a_path_runs_through_to_a_stub_too_short_for_a_direction():
+    labels = city()
+    labels[:80, 145:150] = PATH
+    labels[120:134, 155:160] = PATH                          # kurzes, versetztes Stück: kein Straßenende
+    mask = box_mask(labels.shape, slice(80, 120), slice(100, 200))
+    scribble(labels, mask)
+    result = rebuild(labels, mask)
+    assert (result.stats.connected, result.stats.extended, result.stats.dead_ends) == (1, 0, 0)
+    paths, count = ndi.label(labels == PATH)
+    assert count == 1                                        # oberer Weg und Stück hängen zusammen
+
+
 def test_park_and_water_under_text_stay_what_they_are():
     labels = city()
     labels[:, :150] = PARK
@@ -203,9 +216,12 @@ def test_a_hidden_block_part_gets_the_density_of_the_visible_part():
 def test_added_buildings_keep_setback_gaps_and_stay_in_the_hidden_part():
     labels, mask = block_city()
     scribble(labels, mask)
+    before = labels.copy()
     result = rebuild(labels, mask)
+    assert (labels[~mask] == before[~mask]).all()
+    for corners in result.buildings:                          # Maske: Pixel 60–240 × 220–340 samt Pixelkante
+        assert all(59.5 <= r <= 240.5 and 219.5 <= c <= 340.5 for r, c in corners)
     new = (labels == BUILDING) & mask
-    assert not (new & ~mask).any()
     road_distance = ndi.distance_transform_edt(~np.isin(labels, [ROAD, PATH]))
     assert road_distance[new].min() >= 15                  # Straßenabstand der Nachbarn, auf 15 m begrenzt
     pieces, count = ndi.label(new)
@@ -223,6 +239,14 @@ def test_no_buildings_are_added_in_a_park():
     assert result.stats.added == 0
 
 
+def test_no_buildings_are_added_in_water():
+    labels, mask = block_city()
+    labels[40:260, 200:360] = WATER
+    scribble(labels, mask)
+    result = rebuild(labels, mask)
+    assert result.stats.added == 0
+
+
 def test_without_three_neighbours_nothing_is_added():
     labels, mask = block_city()
     labels[labels == BUILDING] = STADT
@@ -242,3 +266,9 @@ def test_rebuild_is_deterministic():
     a, b = rebuild(first, mask), rebuild(second, mask)
     assert (first == second).all()
     assert a.buildings == b.buildings
+
+
+def test_pixels_on_one_line_give_a_rectangle_with_four_corners():
+    rect = _rectangle(np.full(10, 5), np.arange(10, 20))     # 10 Pixel in einer Zeile
+    assert len(rect.exterior.coords) == 5                    # vier Ecken und der Schlusspunkt
+    assert _sides(rect) == pytest.approx((1.0, 10.0))