def getPaths(self):
paths = Paths.Paths()
paths.closed_paths = self._closed_paths
for hash_lists in self._endpoints_map.values():
for info in hash_lists:
if info.point_index == 0:
paths.open_paths.append(info.path)
for distance in [100, 250]:
n = 0
while n < len(paths.open_paths):
m = self._findOpenPathCloseTo(paths.open_paths[n][-1], paths.open_paths, distance)
if m is not None and m != n:
paths.open_paths[n] += paths.open_paths[m]
paths.open_paths.pop(m)
if m < n:
n -= 1
continue
n += 1
n = 0
while n < len(paths.open_paths):
if PointUtil.length(PointUtil.sub(paths.open_paths[n][0], paths.open_paths[n][-1])) < 200:
paths.closed_paths.append(paths.open_paths[n])
paths.open_paths.pop(n)
continue
n += 1
return paths
def _findClosestIndexTo(self, point, path):
best = None
best_distance = None
for m in range(0, len(path)):
distance = PointUtil.lengthSquared(PointUtil.sub(point, path[m]))
if best_distance is None or best_distance > distance:
best_distance = distance
best = m
return best
def _orderStartPoints(self):
if len(self._result.open_paths) > 1:
start = self._result.open_paths[0][0]
for n in range(1, len(self._result.open_paths)):
if PointUtil.lengthSquared(PointUtil.sub(start, self._result.open_paths[n][0])) > PointUtil.lengthSquared(PointUtil.sub(start, self._result.open_paths[n][-1])):
self._result.open_paths[n].reverse()
start = self._result.open_paths[n][-1]
if len(self._result.closed_paths) > 1:
start = self._result.closed_paths[0][0]
for n in range(1, len(self._result.closed_paths)):
best = self._findClosestIndexTo(start, self._result.closed_paths[n])
self._result.closed_paths[n] = self._result.closed_paths[n][best:] + self._result.closed_paths[n][:best]
start = self._result.closed_paths[n][0]
best = self._findClosestIndexTo(self._result.closed_paths[1][0], self._result.closed_paths[0])
self._result.closed_paths[0] = self._result.closed_paths[0][best:] + self._result.closed_paths[0][:best]
def _findEndpoint(self, point):
best = None
best_hash = None
best_distance = 10 * 10
for hash in _hashes(point):
if hash in self._endpoints_map:
for info in self._endpoints_map[hash]:
p = info.path[info.point_index]
distance = PointUtil.lengthSquared(PointUtil.sub(p, point))
if distance < best_distance:
best_distance = distance
best_hash = hash
best = info
if best is not None:
self._endpoints_map[best_hash].remove(best)
return best
def _centerPoints(self, paths):
center_points = []
for poly in paths.closed_paths:
center = (0, 0)
for point in poly:
center = PointUtil.add(center, point)
center = (center[0] / len(poly), center[1] / len(poly))
center_points.append(center)
return center_points
def _calculateClosedOrder(self, center_points):
done = [False] * len(center_points)
start = None
for n in range(0, len(center_points)):
if start is None or center_points[start][0] > center_points[n][0]:
start = n
if start is None:
return []
done[start] = True
result = [start]
while len(result) < len(center_points):
best_distance = None
best = None
for n in range(0, len(center_points)):
if done[n]:
continue
distance = PointUtil.lengthSquared(PointUtil.sub(center_points[start], center_points[n]))
if best_distance is None or best_distance > distance:
best_distance = distance
best = n
done[best] = True
result.append(best)
start = best
return result
def _findOpenPathCloseTo(self, point, open_paths, distance):
for n in range(0, len(open_paths)):
if PointUtil.length(PointUtil.sub(open_paths[n][0], point)) < distance:
return n
return None
def _calculateOpenOrder(self, paths):
quick_finds = 0
slow_finds = 0
slow_time = 0.0
Logger.log("i", "Line order optimizer, start")
hash_map = {}
for n in range(0, len(paths)):
h = _hash(paths[n][0])
if h not in hash_map:
hash_map[h] = []
hash_map[h].append(n)
h = _hash(paths[n][-1])
if h not in hash_map:
hash_map[h] = []
hash_map[h].append(n)
todo = set(range(0, len(paths)))
start = None
for n in range(0, len(paths)):
if start is None or paths[start][0][0] > paths[n][0][0]:
start = n
if start is None:
return []
todo.remove(start)
result = [start]
while len(result) < len(paths):
best_distance = None
best = None
for h in _hashes(paths[start][0]):
if h in hash_map:
for n in hash_map[h]:
if n not in todo:
continue
distance = min(
PointUtil.lengthSquared(PointUtil.sub(paths[start][0], paths[n][0])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][0], paths[n][-1])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][-1], paths[n][0])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][-1], paths[n][-1]))
)
if best_distance is None or best_distance > distance:
best_distance = distance
best = n
for h in _hashes(paths[start][-1]):
if h in hash_map:
for n in hash_map[h]:
if n not in todo:
continue
distance = min(
PointUtil.lengthSquared(PointUtil.sub(paths[start][0], paths[n][0])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][0], paths[n][-1])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][-1], paths[n][0])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][-1], paths[n][-1]))
)
if best_distance is None or best_distance > distance:
best_distance = distance
best = n
if best is None:
slow_finds += 1
t = time.time()
for n in todo:
distance = min(
PointUtil.lengthSquared(PointUtil.sub(paths[start][0], paths[n][0])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][0], paths[n][-1])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][-1], paths[n][0])),
PointUtil.lengthSquared(PointUtil.sub(paths[start][-1], paths[n][-1]))
)
if best_distance is None or best_distance > distance:
best_distance = distance
best = n
slow_time += time.time() - t
else:
quick_finds += 1
todo.remove(best)
result.append(best)
start = best
Logger.log("i", "Line order optimizer, quick: %d, slow: %d (%f)", quick_finds, slow_finds, slow_time)
return result
