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Arranger: moved functions, split Arrange into Arrange All and Arrange Selection. CURA-3239
This commit is contained in:
Jack Ha
parent
9db816b0fc
commit
abb5d1e76e
4 changed files with 181 additions and 159 deletions
160
cura/Arrange.py
160
cura/Arrange.py
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@ -1,6 +1,9 @@
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import numpy as np
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import numpy
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from UM.Math.Polygon import Polygon
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## Some polygon converted to an array
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## Polygon representation as an array
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#
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class ShapeArray:
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def __init__(self, arr, offset_x, offset_y, scale = 1):
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self.arr = arr
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@ -9,39 +12,59 @@ class ShapeArray:
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self.scale = scale
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@classmethod
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def from_polygon(cls, vertices, scale = 1):
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def fromPolygon(cls, vertices, scale = 1):
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# scale
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vertices = vertices * scale
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# flip y, x -> x, y
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flip_vertices = np.zeros((vertices.shape))
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flip_vertices = numpy.zeros((vertices.shape))
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flip_vertices[:, 0] = vertices[:, 1]
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flip_vertices[:, 1] = vertices[:, 0]
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flip_vertices = flip_vertices[::-1]
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# offset, we want that all coordinates have positive values
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offset_y = int(np.amin(flip_vertices[:, 0]))
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offset_x = int(np.amin(flip_vertices[:, 1]))
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flip_vertices[:, 0] = np.add(flip_vertices[:, 0], -offset_y)
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flip_vertices[:, 1] = np.add(flip_vertices[:, 1], -offset_x)
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shape = [int(np.amax(flip_vertices[:, 0])), int(np.amax(flip_vertices[:, 1]))]
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arr = cls.array_from_polygon(shape, flip_vertices)
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offset_y = int(numpy.amin(flip_vertices[:, 0]))
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offset_x = int(numpy.amin(flip_vertices[:, 1]))
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flip_vertices[:, 0] = numpy.add(flip_vertices[:, 0], -offset_y)
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flip_vertices[:, 1] = numpy.add(flip_vertices[:, 1], -offset_x)
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shape = [int(numpy.amax(flip_vertices[:, 0])), int(numpy.amax(flip_vertices[:, 1]))]
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arr = cls.arrayFromPolygon(shape, flip_vertices)
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return cls(arr, offset_x, offset_y)
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## Return an offset and hull ShapeArray from a scenenode.
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@classmethod
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def fromNode(cls, node, min_offset, scale = 0.5):
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# hacky way to undo transformation
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transform = node._transformation
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transform_x = transform._data[0][3]
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transform_y = transform._data[2][3]
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hull_verts = node.callDecoration("getConvexHull")
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offset_verts = hull_verts.getMinkowskiHull(Polygon.approximatedCircle(min_offset))
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offset_points = copy.deepcopy(offset_verts._points) # x, y
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offset_points[:, 0] = numpy.add(offset_points[:, 0], -transform_x)
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offset_points[:, 1] = numpy.add(offset_points[:, 1], -transform_y)
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offset_shape_arr = ShapeArray.fromPolygon(offset_points, scale = scale)
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hull_points = copy.deepcopy(hull_verts._points)
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hull_points[:, 0] = numpy.add(hull_points[:, 0], -transform_x)
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hull_points[:, 1] = numpy.add(hull_points[:, 1], -transform_y)
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hull_shape_arr = ShapeArray.fromPolygon(hull_points, scale = scale) # x, y
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return offset_shape_arr, hull_shape_arr
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## Create np.array with dimensions defined by shape
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# Fills polygon defined by vertices with ones, all other values zero
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# Only works correctly for convex hull vertices
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# Originally from: http://stackoverflow.com/questions/37117878/generating-a-filled-polygon-inside-a-numpy-array
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@classmethod
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def array_from_polygon(cls, shape, vertices):
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"""
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Creates np.array with dimensions defined by shape
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Fills polygon defined by vertices with ones, all other values zero
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def arrayFromPolygon(cls, shape, vertices):
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base_array = numpy.zeros(shape, dtype=float) # Initialize your array of zeros
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Only works correctly for convex hull vertices
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"""
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base_array = np.zeros(shape, dtype=float) # Initialize your array of zeros
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fill = np.ones(base_array.shape) * True # Initialize boolean array defining shape fill
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fill = numpy.ones(base_array.shape) * True # Initialize boolean array defining shape fill
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# Create check array for each edge segment, combine into fill array
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for k in range(vertices.shape[0]):
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fill = np.all([fill, cls._check(vertices[k - 1], vertices[k], base_array)], axis=0)
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fill = numpy.all([fill, cls._check(vertices[k - 1], vertices[k], base_array)], axis=0)
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# Set all values inside polygon to one
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base_array[fill] = 1
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@ -51,43 +74,102 @@ class ShapeArray:
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## Return indices that mark one side of the line, used by array_from_polygon
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# Uses the line defined by p1 and p2 to check array of
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# input indices against interpolated value
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# Returns boolean array, with True inside and False outside of shape
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# Originally from: http://stackoverflow.com/questions/37117878/generating-a-filled-polygon-inside-a-numpy-array
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@classmethod
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def _check(cls, p1, p2, base_array):
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if p1[0] == p2[0] and p1[1] == p2[1]:
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return
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idxs = np.indices(base_array.shape) # Create 3D array of indices
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idxs = numpy.indices(base_array.shape) # Create 3D array of indices
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p1 = p1.astype(float)
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p2 = p2.astype(float)
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if p2[0] == p1[0]:
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sign = np.sign(p2[1] - p1[1])
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sign = numpy.sign(p2[1] - p1[1])
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return idxs[1] * sign
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if p2[1] == p1[1]:
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sign = np.sign(p2[0] - p1[0])
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sign = numpy.sign(p2[0] - p1[0])
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return idxs[1] * sign
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# Calculate max column idx for each row idx based on interpolated line between two points
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max_col_idx = (idxs[0] - p1[0]) / (p2[0] - p1[0]) * (p2[1] - p1[1]) + p1[1]
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sign = np.sign(p2[0] - p1[0])
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sign = numpy.sign(p2[0] - p1[0])
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return idxs[1] * sign <= max_col_idx * sign
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from UM.Scene.Iterator.DepthFirstIterator import DepthFirstIterator
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from UM.Logger import Logger
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import copy
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class Arrange:
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def __init__(self, x, y, offset_x, offset_y, scale=1):
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self.shape = (y, x)
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self._priority = np.zeros((x, y), dtype=np.int32)
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self._priority = numpy.zeros((x, y), dtype=numpy.int32)
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self._priority_unique_values = []
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self._occupied = np.zeros((x, y), dtype=np.int32)
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self._occupied = numpy.zeros((x, y), dtype=numpy.int32)
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self._scale = scale # convert input coordinates to arrange coordinates
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self._offset_x = offset_x
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self._offset_y = offset_y
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## Helper to create an Arranger instance
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#
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# Either fill in scene_root and create will find all sliceable nodes by itself,
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# or use fixed_nodes to provide the nodes yourself.
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# \param scene_root
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# \param fixed_nodes
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@classmethod
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def create(cls, scene_root = None, fixed_nodes = None, scale = 0.5):
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arranger = Arrange(220, 220, 110, 110, scale = scale)
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arranger.centerFirst()
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if fixed_nodes is None:
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fixed_nodes = []
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for node_ in DepthFirstIterator(scene_root):
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# Only count sliceable objects
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if node_.callDecoration("isSliceable"):
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fixed_nodes.append(node_)
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# place all objects fixed nodes
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for fixed_node in fixed_nodes:
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Logger.log("d", " # Placing [%s]" % str(fixed_node))
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vertices = fixed_node.callDecoration("getConvexHull")
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points = copy.deepcopy(vertices._points)
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shape_arr = ShapeArray.fromPolygon(points, scale = scale)
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arranger.place(0, 0, shape_arr)
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Logger.log("d", "Current buildplate: \n%s" % str(arranger._occupied[::10, ::10]))
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return arranger
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## Find placement for a node and place it
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#
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def findNodePlacements(self, node, offset_shape_arr, hull_shape_arr, count = 1, step = 1):
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# offset_shape_arr, hull_shape_arr, arranger -> nodes, arranger
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nodes = []
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start_prio = 0
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for i in range(count):
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new_node = copy.deepcopy(node)
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Logger.log("d", " # Finding spot for %s" % new_node)
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x, y, penalty_points, start_prio = self.bestSpot(
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offset_shape_arr, start_prio = start_prio, step = step)
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transformation = new_node._transformation
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if x is not None: # We could find a place
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transformation._data[0][3] = x
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transformation._data[2][3] = y
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Logger.log("d", "Best place is: %s %s (points = %s)" % (x, y, penalty_points))
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self.place(x, y, hull_shape_arr) # take place before the next one
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Logger.log("d", "New buildplate: \n%s" % str(self._occupied[::10, ::10]))
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else:
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Logger.log("d", "Could not find spot!")
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transformation._data[0][3] = 200
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transformation._data[2][3] = -100 + i * 20
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nodes.append(new_node)
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return nodes
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## Fill priority, take offset as center. lower is better
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def centerFirst(self):
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# Distance x + distance y
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@ -96,16 +178,16 @@ class Arrange:
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# Square distance
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# self._priority = np.fromfunction(
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# lambda i, j: abs(self._offset_x-i)**2+abs(self._offset_y-j)**2, self.shape, dtype=np.int32)
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self._priority = np.fromfunction(
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lambda i, j: abs(self._offset_x-i)**3+abs(self._offset_y-j)**3, self.shape, dtype=np.int32)
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self._priority = numpy.fromfunction(
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lambda i, j: abs(self._offset_x-i)**3+abs(self._offset_y-j)**3, self.shape, dtype=numpy.int32)
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# self._priority = np.fromfunction(
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# lambda i, j: max(abs(self._offset_x-i), abs(self._offset_y-j)), self.shape, dtype=np.int32)
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self._priority_unique_values = np.unique(self._priority)
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self._priority_unique_values = numpy.unique(self._priority)
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self._priority_unique_values.sort()
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## Return the amount of "penalty points" for polygon, which is the sum of priority
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# 999999 if occupied
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def check_shape(self, x, y, shape_arr):
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def checkShape(self, x, y, shape_arr):
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x = int(self._scale * x)
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y = int(self._scale * y)
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offset_x = x + self._offset_x + shape_arr.offset_x
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@ -114,24 +196,24 @@ class Arrange:
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offset_y:offset_y + shape_arr.arr.shape[0],
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offset_x:offset_x + shape_arr.arr.shape[1]]
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try:
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if np.any(occupied_slice[np.where(shape_arr.arr == 1)]):
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if numpy.any(occupied_slice[numpy.where(shape_arr.arr == 1)]):
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return 999999
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except IndexError: # out of bounds if you try to place an object outside
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return 999999
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prio_slice = self._priority[
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offset_y:offset_y + shape_arr.arr.shape[0],
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offset_x:offset_x + shape_arr.arr.shape[1]]
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return np.sum(prio_slice[np.where(shape_arr.arr == 1)])
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return numpy.sum(prio_slice[numpy.where(shape_arr.arr == 1)])
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## Find "best" spot
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## Find "best" spot for ShapeArray
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def bestSpot(self, shape_arr, start_prio = 0, step = 1):
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start_idx_list = np.where(self._priority_unique_values == start_prio)
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start_idx_list = numpy.where(self._priority_unique_values == start_prio)
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if start_idx_list:
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start_idx = start_idx_list[0]
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else:
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start_idx = 0
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for prio in self._priority_unique_values[start_idx::step]:
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tryout_idx = np.where(self._priority == prio)
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tryout_idx = numpy.where(self._priority == prio)
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for idx in range(len(tryout_idx[0])):
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x = tryout_idx[0][idx]
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y = tryout_idx[1][idx]
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@ -139,7 +221,7 @@ class Arrange:
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projected_y = y - self._offset_y
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# array to "world" coordinates
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penalty_points = self.check_shape(projected_x, projected_y, shape_arr)
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penalty_points = self.checkShape(projected_x, projected_y, shape_arr)
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if penalty_points != 999999:
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return projected_x, projected_y, penalty_points, prio
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return None, None, None, prio # No suitable location found :-(
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@ -158,10 +240,10 @@ class Arrange:
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max_y = min(max(offset_y + shape_arr.arr.shape[0], 0), shape_y - 1)
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occupied_slice = self._occupied[min_y:max_y, min_x:max_x]
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# we use a slice of shape because it can be out of bounds
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occupied_slice[np.where(shape_arr.arr[
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occupied_slice[numpy.where(shape_arr.arr[
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min_y - offset_y:max_y - offset_y, min_x - offset_x:max_x - offset_x] == 1)] = 1
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# Set priority to low (= high number), so it won't get picked at trying out.
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prio_slice = self._priority[min_y:max_y, min_x:max_x]
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prio_slice[np.where(shape_arr.arr[
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prio_slice[numpy.where(shape_arr.arr[
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min_y - offset_y:max_y - offset_y, min_x - offset_x:max_x - offset_x] == 1)] = 999
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