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Editing: polysimplify.py
# coding=utf-8 # https://github.com/Permafacture/Py-Visvalingam-Whyatt """ Visvalingam-Whyatt method of poly-line vertex reduction Visvalingam, M and Whyatt J D (1993) "Line Generalisation by Repeated Elimination of Points", Cartographic J., 30 (1), 46 - 51 Described here: http://web.archive.org/web/20100428020453/http://www2.dcs.hull.ac.uk/CISRG/publications/DPs/DP10/DP10.html ========================================= The MIT License (MIT) Copyright (c) 2014 Elliot Hallmark Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================ """ from typing import Iterable, List import numpy as np def triangle_area(p1, p2, p3): """ calculates the area of a triangle given its vertices """ return abs(p1[0] * (p2[1] - p3[1]) + p2[0] * (p3[1] - p1[1]) + p3[0] * (p1[1] - p2[1])) / 2. def triangle_areas_from_array(arr): """ take an (N,2) array of points and return an (N,1) array of the areas of those triangles, where the first and last areas are np.inf see triangle_area for algorithm """ result = np.empty((len(arr),), arr.dtype) result[0] = np.inf result[-1] = np.inf p1 = arr[:-2] p2 = arr[1:-1] p3 = arr[2:] # an accumulators to avoid unnecessary intermediate arrays accr = result[1:-1] # Accumulate directly into result acc1 = np.empty_like(accr) np.subtract(p2[:, 1], p3[:, 1], out=accr) np.multiply(p1[:, 0], accr, out=accr) np.subtract(p3[:, 1], p1[:, 1], out=acc1) np.multiply(p2[:, 0], acc1, out=acc1) np.add(acc1, accr, out=accr) np.subtract(p1[:, 1], p2[:, 1], out=acc1) np.multiply(p3[:, 0], acc1, out=acc1) np.add(acc1, accr, out=accr) np.abs(accr, out=accr) accr /= 2. # Notice: accr was writing into result, so the answer is in there return result # the final value in thresholds is np.inf, which will never be # the min value. So, I am safe in "deleting" an index by # just shifting the array over on top of it def remove(s, i): """ Quick trick to remove an item from a numpy array without creating a new object. Rather than the array shape changing, the final value just gets repeated to fill the space. ~3.5x faster than numpy.delete """ s[i:-1] = s[i + 1:] class VWSimplifier(object): def __init__(self, pts: Iterable[List[float]]) -> None: """ Initialize with points. takes some time to build the thresholds but then all threshold filtering later is ultra fast """ self.pts = np.array(pts, dtype=np.float32) self.thresholds = self.build_thresholds() self.ordered_thresholds = sorted(self.thresholds, reverse=True) def build_thresholds(self): """ compute the area value of each vertex, which one would use to mask an array of points for any threshold value. returns a numpy.array (length of pts) of the areas. """ pts = self.pts nmax = len(pts) real_areas = triangle_areas_from_array(pts) real_indices = list(range(nmax)) # destructable copies # ARG! areas=real_areas[:] doesn't make a copy! areas = np.copy(real_areas) i = real_indices[:] # pick first point and set up for loop min_vert = np.argmin(areas) this_area = areas[min_vert] # areas and i are modified for each point finished remove(areas, min_vert) # faster # areas = np.delete(areas, min_vert) # slower # noinspection PyTypeChecker i.pop(min_vert) # cntr = 3 while this_area < np.inf: # min_vert was removed from areas and i. Now, # adjust the adjacent areas and remove the new # min_vert. # Now that min_vert was filtered out, min_vert points # to the point after the deleted point. skip = False # modified area may be the next minvert try: right_area = triangle_area(pts[i[min_vert - 1]], pts[i[min_vert]], pts[i[min_vert + 1]]) except IndexError: # trying to update area of endpoint. Don't do it pass else: right_idx = i[min_vert] if right_area <= this_area: # even if the point now has a smaller area, # it ultimately is not more significant than # the last point, which needs to be removed # first to justify removing this point. # Though this point is the next most significant right_area = this_area # min_vert refers to the point to the right of # the previous min_vert, so we can leave it # unchanged if it is still the min_vert skip = min_vert # update both collections of areas real_areas[right_idx] = right_area areas[min_vert] = right_area if min_vert > 1: # cant try/except because 0-1=-1 is a valid index left_area = triangle_area(pts[i[min_vert - 2]], pts[i[min_vert - 1]], pts[i[min_vert]]) if left_area <= this_area: # same justification as above left_area = this_area skip = min_vert - 1 real_areas[i[min_vert - 1]] = left_area areas[min_vert - 1] = left_area # only np.argmin if we have too. min_vert = skip or np.argmin(areas) i.pop(min_vert) this_area = areas[min_vert] # areas = np.delete(areas,min_vert) #slower remove(areas, min_vert) # f aster return real_areas def from_threshold(self, threshold) -> List[List[float]]: return (self.pts[self.thresholds >= threshold]).tolist() def from_number(self, n) -> List[List[float]]: thresholds = self.ordered_thresholds try: threshold = thresholds[int(n)] except IndexError: result = self.pts else: result = self.pts[self.thresholds > threshold] return result.tolist() def from_ratio(self, r) -> List[List[float]]: if r <= 0 or r > 1: raise ValueError("Ratio must be 0<r<=1") else: return self.from_number(r * len(self.thresholds))
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