start

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)])