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Fix arranging of nested group nodes causing a crash

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CURA-6688
This commit is contained in:
Jaime van Kessel 2019-08-06 14:03:59 +02:00
parent 91d582622f
commit 5a714f3a24
1 changed files with 8 additions and 5 deletions
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13
cura/Arranging/ShapeArray.py
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@ -1,5 +1,5 @@
#Copyright (c) 2019 Ultimaker B.V. # Copyright (c) 2019 Ultimaker B.V.
#Cura is released under the terms of the LGPLv3 or higher. # Cura is released under the terms of the LGPLv3 or higher.
import numpy import numpy
import copy import copy
@ -10,6 +10,7 @@ from UM.Math.Polygon import Polygon
if TYPE_CHECKING: if TYPE_CHECKING:
from UM.Scene.SceneNode import SceneNode from UM.Scene.SceneNode import SceneNode
## Polygon representation as an array for use with Arrange ## Polygon representation as an array for use with Arrange
class ShapeArray: class ShapeArray:
def __init__(self, arr: numpy.array, offset_x: float, offset_y: float, scale: float = 1) -> None: def __init__(self, arr: numpy.array, offset_x: float, offset_y: float, scale: float = 1) -> None:
@ -101,7 +102,9 @@ class ShapeArray:
# Create check array for each edge segment, combine into fill array # Create check array for each edge segment, combine into fill array
for k in range(vertices.shape[0]): for k in range(vertices.shape[0]):
fill = numpy.all([fill, cls._check(vertices[k - 1], vertices[k], base_array)], axis=0) check_array = cls._check(vertices[k - 1], vertices[k], base_array)
if check_array is not None:
fill = numpy.all([fill, check_array], axis=0)
# Set all values inside polygon to one # Set all values inside polygon to one
base_array[fill] = 1 base_array[fill] = 1
@ -117,9 +120,9 @@ class ShapeArray:
# \param p2 2-tuple with x, y for point 2 # \param p2 2-tuple with x, y for point 2
# \param base_array boolean array to project the line on # \param base_array boolean array to project the line on
@classmethod @classmethod
def _check(cls, p1: numpy.array, p2: numpy.array, base_array: numpy.array) -> bool: def _check(cls, p1: numpy.array, p2: numpy.array, base_array: numpy.array) -> Optional[numpy.array]:
if p1[0] == p2[0] and p1[1] == p2[1]: if p1[0] == p2[0] and p1[1] == p2[1]:
return False return None
idxs = numpy.indices(base_array.shape) # Create 3D array of indices idxs = numpy.indices(base_array.shape) # Create 3D array of indices
p1 = p1.astype(float) p1 = p1.astype(float)