Cura/cura/Arranging/Arrange.py

# Copyright (c) 2018 Ultimaker B.V.
# Cura is released under the terms of the LGPLv3 or higher.
from typing import List

from UM.Scene.Iterator.DepthFirstIterator import DepthFirstIterator
from UM.Logger import Logger
from UM.Math.Polygon import Polygon
from UM.Math.Vector import Vector
from UM.Scene.SceneNode import SceneNode
from cura.Arranging.ShapeArray import ShapeArray
from cura.Scene import ZOffsetDecorator

from collections import namedtuple

import numpy
import copy


##  Return object for  bestSpot
LocationSuggestion = namedtuple("LocationSuggestion", ["x", "y", "penalty_points", "priority"])


##  The Arrange classed is used together with ShapeArray. Use it 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.
#
#   Note: Make sure the scale is the same between ShapeArray objects and the Arrange instance.
class Arrange:
    build_volume = None

    def __init__(self, x, y, offset_x, offset_y, scale= 0.5):
        self._scale = scale  # convert input coordinates to arrange coordinates
        world_x, world_y = int(x * self._scale), int(y * self._scale)
        self._shape = (world_y, world_x)
        self._priority = numpy.zeros((world_y, world_x), dtype=numpy.int32)  # beware: these are indexed (y, x)
        self._priority_unique_values = []
        self._occupied = numpy.zeros((world_y, world_x), dtype=numpy.int32)  # beware: these are indexed (y, x)
        self._offset_x = int(offset_x * self._scale)
        self._offset_y = int(offset_y * self._scale)
        self._last_priority = 0
        self._is_empty = True

    ##  Helper to create an Arranger instance
    #
    #   Either fill in scene_root and create will find all sliceable nodes by itself,
    #   or use fixed_nodes to provide the nodes yourself.
    #   \param scene_root   Root for finding all scene nodes
    #   \param fixed_nodes  Scene nodes to be placed
    @classmethod
    def create(cls, scene_root = None, fixed_nodes = None, scale = 0.5, x = 350, y = 250, min_offset = 8):
        arranger = Arrange(x, y, x // 2, y // 2, scale = scale)
        arranger.centerFirst()

        if fixed_nodes is None:
            fixed_nodes = []
            for node_ in DepthFirstIterator(scene_root):
                # Only count sliceable objects
                if node_.callDecoration("isSliceable"):
                    fixed_nodes.append(node_)

        # Place all objects fixed nodes
        for fixed_node in fixed_nodes:
            vertices = fixed_node.callDecoration("getConvexHullHead") or fixed_node.callDecoration("getConvexHull")
            if not vertices:
                continue
            vertices = vertices.getMinkowskiHull(Polygon.approximatedCircle(min_offset))
            points = copy.deepcopy(vertices._points)
            shape_arr = ShapeArray.fromPolygon(points, scale = scale)
            arranger.place(0, 0, shape_arr)

        # If a build volume was set, add the disallowed areas
        if Arrange.build_volume:
            disallowed_areas = Arrange.build_volume.getDisallowedAreasNoBrim()
            for area in disallowed_areas:
                points = copy.deepcopy(area._points)
                shape_arr = ShapeArray.fromPolygon(points, scale = scale)
                arranger.place(0, 0, shape_arr, update_empty = False)
        return arranger

    ##  This resets the optimization for finding location based on size
    def resetLastPriority(self):
        self._last_priority = 0

    ##  Find placement for a node (using offset shape) and place it (using hull shape)
    #   return the nodes that should be placed
    #   \param node
    #   \param offset_shape_arr ShapeArray with offset, for placing the shape
    #   \param hull_shape_arr ShapeArray without offset, used to find location
    def findNodePlacement(self, node: SceneNode, offset_shape_arr: ShapeArray, hull_shape_arr: ShapeArray, step = 1):
        best_spot = self.bestSpot(
            hull_shape_arr, start_prio = self._last_priority, step = step)
        x, y = best_spot.x, best_spot.y

        # Save the last priority.
        self._last_priority = best_spot.priority

        # Ensure that the object is above the build platform
        node.removeDecorator(ZOffsetDecorator.ZOffsetDecorator)
        bbox = node.getBoundingBox()
        if bbox:
            center_y = node.getWorldPosition().y - bbox.bottom
        else:
            center_y = 0

        if x is not None:  # We could find a place
            node.setPosition(Vector(x, center_y, y))
            found_spot = True
            self.place(x, y, offset_shape_arr)  # place the object in arranger
        else:
            Logger.log("d", "Could not find spot!"),
            found_spot = False
            node.setPosition(Vector(200, center_y, 100))
        return found_spot

    ##  Fill priority, center is best. Lower value is better
    #   This is a strategy for the arranger.
    def centerFirst(self):
        # Square distance: creates a more round shape
        self._priority = numpy.fromfunction(
            lambda j, i: (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()

    ##  Fill priority, back is best. Lower value is better
    #   This is a strategy for the arranger.
    def backFirst(self):
        self._priority = numpy.fromfunction(
            lambda j, i: 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()

    ##  Return the amount of "penalty points" for polygon, which is the sum of priority
    #   None if occupied
    #   \param x x-coordinate to check shape
    #   \param y y-coordinate
    #   \param shape_arr the ShapeArray object to place
    def checkShape(self, x, y, shape_arr):
        x = int(self._scale * x)
        y = int(self._scale * y)
        offset_x = x + self._offset_x + shape_arr.offset_x
        offset_y = y + self._offset_y + shape_arr.offset_y
        if offset_x < 0 or offset_y < 0:
            return None  # out of bounds in self._occupied
        occupied_x_max = offset_x + shape_arr.arr.shape[1]
        occupied_y_max = offset_y + shape_arr.arr.shape[0]
        if occupied_x_max > self._occupied.shape[1] + 1 or occupied_y_max > self._occupied.shape[0] + 1:
            return None  # out of bounds in self._occupied
        occupied_slice = self._occupied[
            offset_y:occupied_y_max,
            offset_x:occupied_x_max]
        try:
            if numpy.any(occupied_slice[numpy.where(shape_arr.arr == 1)]):
                return None
        except IndexError:  # out of bounds if you try to place an object outside
            return None
        prio_slice = self._priority[
            offset_y:offset_y + shape_arr.arr.shape[0],
            offset_x:offset_x + shape_arr.arr.shape[1]]
        return numpy.sum(prio_slice[numpy.where(shape_arr.arr == 1)])

    ##  Find "best" spot for ShapeArray
    #   Return namedtuple with properties x, y, penalty_points, priority.
    #   \param shape_arr ShapeArray
    #   \param start_prio Start with this priority value (and skip the ones before)
    #   \param step Slicing value, higher = more skips = faster but less accurate
    def bestSpot(self, shape_arr, start_prio = 0, step = 1):
        start_idx_list = numpy.where(self._priority_unique_values == start_prio)
        if start_idx_list:
            start_idx = start_idx_list[0][0]
        else:
            start_idx = 0
        for priority in self._priority_unique_values[start_idx::step]:
            tryout_idx = numpy.where(self._priority == priority)
            for idx in range(len(tryout_idx[0])):
                x = tryout_idx[1][idx]
                y = tryout_idx[0][idx]
                projected_x = int((x - self._offset_x) / self._scale)
                projected_y = int((y - self._offset_y) / self._scale)

                penalty_points = self.checkShape(projected_x, projected_y, shape_arr)
                if penalty_points is not None:
                    return LocationSuggestion(x = projected_x, y = projected_y, penalty_points = penalty_points, priority = priority)
        return LocationSuggestion(x = None, y = None, penalty_points = None, priority = priority)  # No suitable location found :-(

    ##  Place the object.
    #   Marks the locations in self._occupied and self._priority
    #   \param x x-coordinate
    #   \param y y-coordinate
    #   \param shape_arr ShapeArray object
    #   \param update_empty updates the _is_empty, used when adding disallowed areas
    def place(self, x, y, shape_arr, update_empty = True):
        x = int(self._scale * x)
        y = int(self._scale * y)
        offset_x = x + self._offset_x + shape_arr.offset_x
        offset_y = y + self._offset_y + shape_arr.offset_y
        shape_y, shape_x = self._occupied.shape

        min_x = min(max(offset_x, 0), shape_x - 1)
        min_y = min(max(offset_y, 0), shape_y - 1)
        max_x = min(max(offset_x + shape_arr.arr.shape[1], 0), shape_x - 1)
        max_y = min(max(offset_y + shape_arr.arr.shape[0], 0), shape_y - 1)
        occupied_slice = self._occupied[min_y:max_y, min_x:max_x]
        # we use a slice of shape because it can be out of bounds
        new_occupied = numpy.where(shape_arr.arr[
            min_y - offset_y:max_y - offset_y, min_x - offset_x:max_x - offset_x] == 1)
        if update_empty and new_occupied:
            self._is_empty = False
        occupied_slice[new_occupied] = 1

        # Set priority to low (= high number), so it won't get picked at trying out.
        prio_slice = self._priority[min_y:max_y, min_x:max_x]
        prio_slice[new_occupied] = 999

        # If you want to see how the rasterized arranger build plate looks like, uncomment this code
        # numpy.set_printoptions(linewidth=500, edgeitems=200)
        # print(self._occupied.shape)
        # print(self._occupied)

    @property
    def isEmpty(self):
        return self._is_empty