Cura/cura/Arranging/ShapeArray.py

# Copyright (c) 2019 Ultimaker B.V.
# Cura is released under the terms of the LGPLv3 or higher.

import numpy
import copy
from typing import Optional, Tuple, TYPE_CHECKING

from UM.Math.Polygon import Polygon

if TYPE_CHECKING:
    from UM.Scene.SceneNode import SceneNode


class ShapeArray:
    """Polygon representation as an array for use with :py:class:`cura.Arranging.Arrange.Arrange`"""

    def __init__(self, arr: numpy.array, offset_x: float, offset_y: float, scale: float = 1) -> None:
        self.arr = arr
        self.offset_x = offset_x
        self.offset_y = offset_y
        self.scale = scale

    @classmethod
    def fromPolygon(cls, vertices: numpy.array, scale: float = 1) -> "ShapeArray":
        """Instantiate from a bunch of vertices

        :param vertices:
        :param scale:  scale the coordinates
        :return: a shape array instantiated from a bunch of vertices
        """

        # 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 = numpy.array([int(numpy.amax(flip_vertices[:, 0])), int(numpy.amax(flip_vertices[:, 1]))])
        shape[numpy.where(shape == 0)] = 1
        arr = cls.arrayFromPolygon(shape, flip_vertices)
        if not numpy.ndarray.any(arr):
            # set at least 1 pixel
            arr[0][0] = 1
        return cls(arr, offset_x, offset_y)

    @classmethod
    def fromNode(cls, node: "SceneNode", min_offset: float, scale: float = 0.5, include_children: bool = False) -> Tuple[Optional["ShapeArray"], Optional["ShapeArray"]]:
        """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
        :return: A tuple containing an offset and hull shape array
        """

        transform = node._transformation
        transform_x = transform._data[0][3]
        transform_y = transform._data[2][3]
        hull_verts = node.callDecoration("getConvexHull")
        # If a model is too small then it will not contain any points
        if hull_verts is None or not hull_verts.getPoints().any():
            return None, None
        # For one_at_a_time printing you need the convex hull head.
        hull_head_verts = node.callDecoration("getConvexHullHead") or hull_verts
        if hull_head_verts is None:
            hull_head_verts = Polygon()

        # If the child-nodes are included, adjust convex hulls as well:
        if include_children:
            children = node.getAllChildren()
            if not children is None:
                for child in children:
                    # 'Inefficient' combination of convex hulls through known code rather than mess it up:
                    child_hull = child.callDecoration("getConvexHull")
                    if not child_hull is None:
                        hull_verts = hull_verts.unionConvexHulls(child_hull)
                    child_hull_head = child.callDecoration("getConvexHullHead") or child_hull
                    if not child_hull_head is None:
                        hull_head_verts = hull_head_verts.unionConvexHulls(child_hull_head)

        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

    @classmethod
    def arrayFromPolygon(cls, shape: Tuple[int, int], vertices: numpy.array) -> numpy.array:
        """Create :py:class:`numpy.ndarray` 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: `Stackoverflow - generating a filled polygon inside a numpy array <https://stackoverflow.com/questions/37117878/generating-a-filled-polygon-inside-a-numpy-array>`_

        :param shape:  numpy format shape, [x-size, y-size]
        :param vertices:
        :return: numpy array with dimensions defined by shape
        """

        base_array = numpy.zeros(shape, dtype = numpy.int32)  # 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]):
            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
        base_array[fill] = 1

        return base_array

    @classmethod
    def _check(cls, p1: numpy.array, p2: numpy.array, base_array: numpy.array) -> Optional[numpy.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: `Stackoverflow - generating a filled polygon inside a numpy array <https://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
        :return: A numpy array with indices that mark one side of the line
        """

        if p1[0] == p2[0] and p1[1] == p2[1]:
            return None
        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