def RunAndSaveImage(self, ax, plt):
start_time = time.time()
start_point = random_map.Point(0, 0)
self.open_set.append(start_point)
while True:
p = self.SelectPointInOpenList()
if not p:
print('No path found, algorithm failed!!!')
return
if (not (p.x == 0 and p.y == 0)) and (not (
p.x == self.map.size - 1 and p.y == self.map.size - 1)):
rec = Rectangle((p.x, p.y), 1, 1, color='c')
ax.add_patch(rec)
# plt.draw()
plt.pause(0.05)
if self.IsEndPoint(p):
return self.BuildPath(p, ax, plt, start_time, baseName="DFS")
self.close_set.append(p)
# Process all neighbors
x = p.x
y = p.y
# 启发函数为曼哈顿距离，遍历下左上右4个点
# 因为终点在右上方，所以优先向上和右遍历
self.ProcessPoint(x, y - 1, p)
self.ProcessPoint(x - 1, y, p)
self.ProcessPoint(x + 1, y, p)
self.ProcessPoint(x, y + 1, p)
def ProcessPoint(self, x, y, parent):
# 不合法的点
if not self.IsValidPoint(x, y):
return # Do nothing for invalid point
p = random_map.Point(x, y)
# 邻点在close_set中，跳过
if self.IsInCloseList(p):
return # Do nothing for visited point
# 邻点p在open_set，比较g(n)是否比原来更小，如果更小则更新其g(n)、优先级f(n)和其父节点
# 这里可以删除open_set中的对应点，修改后重新加入；也可以调用函数返回这个点然后对其修改
open_p = self.IsInOpenList(p)
if open_p:
p = open_p
# 比较该点前一次遍历的点和该点的父节点的g_cost，前一个遍历点小则更新父节点为前一个遍历点
if parent.g_cost < p.parent.g_cost:
p.parent = parent
p.g_cost = self.BaseCost(p)
# p.g_cost = parent.g_cost + 1
p.f_cost = self.TotalCost(p)
# 邻点p既不在open_set，也不在close_set中，设置节点p的parent为节点n，计算节点p的优先级f(n)，将节点m加入open_set中
else:
p.parent = parent
p.g_cost = self.BaseCost(p)
# p.g_cost = parent.g_cost + 1
p.f_cost = self.TotalCost(p)
self.open_set.append(p)
print('Process Point [', p.x, ',', p.y, ']', ', f_cost: ', p.f_cost)
def ProcessPoint(self, x, y, parent):
# 不合法的点，不做处理
if not self.IsValidPoint(x, y):
return
p = random_map.Point(x, y)
# 邻点在close_set和open_set中，跳过
if self.IsInCloseList(p) or self.IsInOpenList(p):
return # Do nothing for visited point
else:
p.parent = parent
self.open_set.append(p)
print('Process Point [', p.x, ',', p.y, ']')
def ProcessPoint(self, x, y, parent):
# 不合法的点，不做处理
if not self.IsValidPoint(x, y):
return
p = random_map.Point(x, y)
# 邻点在close_set，跳过
if self.IsInCloseList(p):
return # Do nothing for visited point
open_p = self.IsInOpenList(p)
if open_p:
p = open_p
if p.parent and parent.g_cost < p.parent.g_cost:
p.parent = parent
# p.g_cost = parent.g_cost + 1
p.g_cost = self.BaseCost(p)
else:
p.parent = parent
p.g_cost = self.BaseCost(p)
self.open_set.append(p)
print('Process Point [', p.x, ',', p.y, ']')
def RunAndSaveImage(self, ax, plt):
start_time = time.time()
start_point = random_map.Point(0, 0)
start_point.g_cost = 0
start_point.f_cost = 0
self.open_set.append(start_point)
while True:
index = self.SelectPointInOpenList()
if index < 0:
print('No path found, algorithm failed!!!')
return
p = self.open_set[index]
if (not (p.x == 0 and p.y == 0)) and (not (
p.x == self.map.size - 1 and p.y == self.map.size - 1)):
rec = Rectangle((p.x, p.y), 1, 1, color='c')
ax.add_patch(rec)
# plt.draw()
plt.pause(0.05)
# self.SaveImage(plt)
if self.IsEndPoint(p):
return self.BuildPath(p, ax, plt, start_time, baseName="AStar")
del self.open_set[index]
self.close_set.append(p)
# Process all neighbors
x = p.x
y = p.y
# 启发函数为曼哈顿距离，可以遍历上右下左4个点
self.ProcessPoint(x + 1, y, p)
self.ProcessPoint(x, y + 1, p)
self.ProcessPoint(x - 1, y, p)
self.ProcessPoint(x, y - 1, p)