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Split ShapeArray from Arranger. CURA-3239

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Jack Ha 2017-04-03 14:48:31 +02:00
parent d6cd37626b
commit a83b1dd638
5 changed files with 131 additions and 118 deletions
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129
cura/Arrange.py
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@ -1,110 +1,15 @@
import numpy
from UM.Math.Polygon import Polygon
## Polygon representation as an array
#
class ShapeArray:
def __init__(self, arr, offset_x, offset_y, scale = 1):
self.arr = arr
self.offset_x = offset_x
self.offset_y = offset_y
self.scale = scale
@classmethod
def fromPolygon(cls, vertices, scale = 1):
# scale
vertices = vertices * scale
# flip y, x -> x, y
flip_vertices = numpy.zeros((vertices.shape))
flip_vertices[:, 0] = vertices[:, 1]
flip_vertices[:, 1] = vertices[:, 0]
flip_vertices = flip_vertices[::-1]
# offset, we want that all coordinates have positive values
offset_y = int(numpy.amin(flip_vertices[:, 0]))
offset_x = int(numpy.amin(flip_vertices[:, 1]))
flip_vertices[:, 0] = numpy.add(flip_vertices[:, 0], -offset_y)
flip_vertices[:, 1] = numpy.add(flip_vertices[:, 1], -offset_x)
shape = [int(numpy.amax(flip_vertices[:, 0])), int(numpy.amax(flip_vertices[:, 1]))]
arr = cls.arrayFromPolygon(shape, flip_vertices)
return cls(arr, offset_x, offset_y)
## Return an offset and hull ShapeArray from a scenenode.
@classmethod
def fromNode(cls, node, min_offset, scale = 0.5):
# hacky way to undo transformation
transform = node._transformation
transform_x = transform._data[0][3]
transform_y = transform._data[2][3]
hull_verts = node.callDecoration("getConvexHull")
offset_verts = hull_verts.getMinkowskiHull(Polygon.approximatedCircle(min_offset))
offset_points = copy.deepcopy(offset_verts._points) # x, y
offset_points[:, 0] = numpy.add(offset_points[:, 0], -transform_x)
offset_points[:, 1] = numpy.add(offset_points[:, 1], -transform_y)
offset_shape_arr = ShapeArray.fromPolygon(offset_points, scale = scale)
hull_points = copy.deepcopy(hull_verts._points)
hull_points[:, 0] = numpy.add(hull_points[:, 0], -transform_x)
hull_points[:, 1] = numpy.add(hull_points[:, 1], -transform_y)
hull_shape_arr = ShapeArray.fromPolygon(hull_points, scale = scale) # x, y
return offset_shape_arr, hull_shape_arr
## Create np.array with dimensions defined by shape
# Fills polygon defined by vertices with ones, all other values zero
# Only works correctly for convex hull vertices
# Originally from: http://stackoverflow.com/questions/37117878/generating-a-filled-polygon-inside-a-numpy-array
@classmethod
def arrayFromPolygon(cls, shape, vertices):
base_array = numpy.zeros(shape, dtype=float) # Initialize your array of zeros
fill = numpy.ones(base_array.shape) * True # Initialize boolean array defining shape fill
# Create check array for each edge segment, combine into fill array
for k in range(vertices.shape[0]):
fill = numpy.all([fill, cls._check(vertices[k - 1], vertices[k], base_array)], axis=0)
# Set all values inside polygon to one
base_array[fill] = 1
return base_array
## Return indices that mark one side of the line, used by array_from_polygon
# Uses the line defined by p1 and p2 to check array of
# input indices against interpolated value
# Returns boolean array, with True inside and False outside of shape
# Originally from: http://stackoverflow.com/questions/37117878/generating-a-filled-polygon-inside-a-numpy-array
@classmethod
def _check(cls, p1, p2, base_array):
if p1[0] == p2[0] and p1[1] == p2[1]:
return
idxs = numpy.indices(base_array.shape) # Create 3D array of indices
p1 = p1.astype(float)
p2 = p2.astype(float)
if p2[0] == p1[0]:
sign = numpy.sign(p2[1] - p1[1])
return idxs[1] * sign
if p2[1] == p1[1]:
sign = numpy.sign(p2[0] - p1[0])
return idxs[1] * sign
# Calculate max column idx for each row idx based on interpolated line between two points
max_col_idx = (idxs[0] - p1[0]) / (p2[0] - p1[0]) * (p2[1] - p1[1]) + p1[1]
sign = numpy.sign(p2[0] - p1[0])
return idxs[1] * sign <= max_col_idx * sign
from UM.Scene.Iterator.DepthFirstIterator import DepthFirstIterator
from UM.Logger import Logger
from cura.ShapeArray import ShapeArray
import numpy
import copy
## The Arrange classed is used together with ShapeArray. The class tries to find
# good locations for objects that you try to put on a build place.
# Different priority schemes can be defined so it alters the behavior while using
# the same logic.
class Arrange:
def __init__(self, x, y, offset_x, offset_y, scale=1):
self.shape = (y, x)
@ -166,16 +71,18 @@ class Arrange:
## Fill priority, take offset as center. lower is better
def centerFirst(self):
# Distance x + distance y
#self._priority = np.fromfunction(
# lambda i, j: abs(self._offset_x-i)+abs(self._offset_y-j), self.shape, dtype=np.int32)
# Square distance
# self._priority = np.fromfunction(
# lambda i, j: abs(self._offset_x-i)**2+abs(self._offset_y-j)**2, self.shape, dtype=np.int32)
# Distance x + distance y: creates diamond shape
#self._priority = numpy.fromfunction(
# lambda i, j: abs(self._offset_x-i)+abs(self._offset_y-j), self.shape, dtype=numpy.int32)
# Square distance: creates a more round shape
self._priority = numpy.fromfunction(
lambda i, j: abs(self._offset_x-i)**3+abs(self._offset_y-j)**3, self.shape, dtype=numpy.int32)
# self._priority = np.fromfunction(
# lambda i, j: max(abs(self._offset_x-i), abs(self._offset_y-j)), self.shape, dtype=np.int32)
lambda i, j: (self._offset_x - i) ** 2 + (self._offset_y - j) ** 2, self.shape, dtype=numpy.int32)
self._priority_unique_values = numpy.unique(self._priority)
self._priority_unique_values.sort()
def backFirst(self):
self._priority = numpy.fromfunction(
lambda i, j: 10 * j + abs(self._offset_x - i), self.shape, dtype=numpy.int32)
self._priority_unique_values = numpy.unique(self._priority)
self._priority_unique_values.sort()