mirror of
https://git.mirrors.martin98.com/https://github.com/Ultimaker/Cura
synced 2025-04-29 23:34:32 +08:00
118 lines
4.9 KiB
Python
Executable File
118 lines
4.9 KiB
Python
Executable File
import numpy
|
|
import copy
|
|
|
|
from UM.Math.Polygon import Polygon
|
|
|
|
|
|
## Polygon representation as an array for use with Arrange
|
|
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
|
|
|
|
## Instantiate from a bunch of vertices
|
|
# \param vertices
|
|
# \param scale scale the coordinates
|
|
@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)
|
|
|
|
## Instantiate an offset and hull ShapeArray from a scene node.
|
|
# \param node source node where the convex hull must be present
|
|
# \param min_offset offset for the offset ShapeArray
|
|
# \param scale scale the coordinates
|
|
@classmethod
|
|
def fromNode(cls, node, min_offset, scale = 0.5):
|
|
transform = node._transformation
|
|
transform_x = transform._data[0][3]
|
|
transform_y = transform._data[2][3]
|
|
hull_verts = node.callDecoration("getConvexHull")
|
|
# For one_at_a_time printing you need the convex hull head.
|
|
hull_head_verts = node.callDecoration("getConvexHullHead") or hull_verts
|
|
|
|
# If a model is to small then it will not contain any points
|
|
if not hull_verts.getPoints().any():
|
|
return None, None
|
|
|
|
offset_verts = hull_head_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
|
|
# \param shape numpy format shape, [x-size, y-size]
|
|
# \param vertices
|
|
@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 arrayFromPolygon
|
|
# 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
|
|
# \param p1 2-tuple with x, y for point 1
|
|
# \param p2 2-tuple with x, y for point 2
|
|
# \param base_array boolean array to project the line on
|
|
@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
|
|
|