def calculate(self):
height = len(self.grid)
width = len(self.grid[0])
self.value = grids.generate(width, height, 0, 0)
a = AStar(lambda n: grids.neighbor_nodes(self.grid, n),
lambda n1, n2: self.grid[n1[0]][n1[1]], lambda n, e: 0)
for y in xrange(height):
for x in xrange(width):
a.search((y, x), None)
for leaf_node in find_leaf_nodes(a.came_from):
path = reconstruct_path(a.came_from, leaf_node)
path_distance = sum(self.grid[node[0]][node[1]]
for node in path[1:])
distance_to = 0
for path_node in path[1:]:
distance_to += self.grid[path_node[0]][path_node[1]]
distance_from = path_distance - distance_to
path_reach = min(distance_to, distance_from)
if path_reach > self.value[path_node[0]][path_node[1]]:
self.value[path_node[0]][path_node[1]] = path_reach
def calculate(self):
height = len(self.grid)
width = len(self.grid[0])
self.value = grids.generate(width, height, 0, 0)
a = AStar(
lambda n: grids.neighbor_nodes(self.grid, n),
lambda n1, n2: self.grid[n1[0]][n1[1]],
lambda n, e: 0)
for y in xrange(height):
for x in xrange(width):
a.search((y,x), None)
for leaf_node in find_leaf_nodes(a.came_from):
path = reconstruct_path(a.came_from, leaf_node)
path_distance = sum(self.grid[node[0]][node[1]] for node in path[1:])
distance_to = 0
for path_node in path[1:]:
distance_to += self.grid[path_node[0]][path_node[1]]
distance_from = path_distance - distance_to
path_reach = min(distance_to, distance_from)
if path_reach > self.value[path_node[0]][path_node[1]]:
self.value[path_node[0]][path_node[1]] = path_reach
def main():
random.seed(45)
width = 30
height = 20
minCost = 10
maxCost = 40
start = (6,8)
end = (16,20)
g = grids.generate(width, height, minCost, maxCost, random)
#grids.set_row(g, 5, 5)
#grids.set_col(g, 5, 5)
#grids.set_col(g, 15, 5)
def cost_callback(y, x):
v = g[y][x]
v = int(float(v) / maxCost * 127)
return (v, v, v)
a = searchers.AStar(
lambda n: grids.neighbor_nodes(g, n),
lambda n1, n2: g[n2[0]][n2[1]])
a.search(start, end)
def searched_callback(y, x):
v = cost_callback(y, x)
if (y,x) in a.path:
v = (v[0], v[1], v[2] + 127)
elif (y,x) in a.closed_set:
v = (v[0], v[1] + 127, v[2])
elif (y,x) in a.open_set:
v = (v[0] + 127, v[1], v[2])
return v
e_l = searchers.Landmark(g, end)
p_h = lambda n, e: e_l.cost_to_l[n]
max_cost_to_e = max(e_l.cost_to_l.itervalues())
def perfect_h_callback(y, x):
v = float(p_h((y,x), None)) / max_cost_to_l * 255
return (v, v, v)
a_p = searchers.AStar(
lambda n: grids.neighbor_nodes(g, n),
lambda n1, n2: g[n2[0]][n2[1]],
h=p_h)
a_p.search(start, end)
def searched_perfect_callback(y, x):
v = cost_callback(y, x)
if (y,x) in a_p.path:
v = (v[0], v[1], v[2] + 127)
elif (y,x) in a_p.closed_set:
v = (v[0], v[1] + 127, v[2])
elif (y,x) in a_p.open_set:
v = (v[0] + 127, v[1], v[2])
return v
l = searchers.Landmark(g, (height-1, width-1))
l_h = lambda n, e: max(l.cost_to_l[n] - l.cost_to_l[e], l.cost_from_l[e] - l.cost_from_l[n], 0)
max_cost_to_l = max(l.cost_to_l.itervalues())
def landmark_callback(y, x):
v = float(l.cost_to_l[(y,x)]) / max_cost_to_l * 255
return (v, v, v)
def landmark_h_callback(y, x):
n = (y, x)
h_n = l_h(n, end)
v = float(h_n) / max_cost_to_l * 255
return (v, v, v)
a_l = searchers.AStar(
lambda n: grids.neighbor_nodes(g, n),
lambda n1, n2: g[n2[0]][n2[1]],
h=l_h)
a_l.search(start, end)
def searched_landmark_callback(y, x):
v = cost_callback(y, x)
if (y,x) in a_l.path:
v = (v[0], v[1], v[2] + 127)
elif (y,x) in a_l.closed_set:
v = (v[0], v[1] + 127, v[2])
elif (y,x) in a_l.open_set:
v = (v[0] + 127, v[1], v[2])
return v
#r = searchers.Reach(g)
#max_reach = max([max([r.value[y][x] for x in xrange(width)]) for y in xrange(height)])
def reach_callback(y, x):
v = float(r.value[y][x]) / max_reach * 255
return (v, v, v)
a_r = searchers.AStar(
lambda n: grids.neighbor_nodes(g, n),
lambda n1, n2: g[n2[0]][n2[1]],
r=lambda n: r.value[n[0]][n[1]])
#a_r.search(start, end)
def searched_reach_callback(y, x):
v = cost_callback(y, x)
if (y,x) in a_r.path:
v = (v[0], v[1], v[2] + 127)
elif (y,x) in a_r.closed_set:
v = (v[0], v[1] + 127, v[2])
elif (y,x) in a_r.open_set:
v = (v[0] + 127, v[1], v[2])
return v
bmp.write_many('output.bmp', width, height,
[
[cost_callback, searched_callback, perfect_h_callback, searched_perfect_callback],
[landmark_callback, landmark_h_callback, searched_landmark_callback],
#[reach_callback, None, searched_reach_callback]
])
def main():
random.seed(45)
width = 30
height = 20
minCost = 10
maxCost = 40
start = (6, 8)
end = (16, 20)
g = grids.generate(width, height, minCost, maxCost, random)
#grids.set_row(g, 5, 5)
#grids.set_col(g, 5, 5)
#grids.set_col(g, 15, 5)
def cost_callback(y, x):
v = g[y][x]
v = int(float(v) / maxCost * 127)
return (v, v, v)
a = searchers.AStar(lambda n: grids.neighbor_nodes(g, n),
lambda n1, n2: g[n2[0]][n2[1]])
a.search(start, end)
def searched_callback(y, x):
v = cost_callback(y, x)
if (y, x) in a.path:
v = (v[0], v[1], v[2] + 127)
elif (y, x) in a.closed_set:
v = (v[0], v[1] + 127, v[2])
elif (y, x) in a.open_set:
v = (v[0] + 127, v[1], v[2])
return v
e_l = searchers.Landmark(g, end)
p_h = lambda n, e: e_l.cost_to_l[n]
max_cost_to_e = max(e_l.cost_to_l.itervalues())
def perfect_h_callback(y, x):
v = float(p_h((y, x), None)) / max_cost_to_l * 255
return (v, v, v)
a_p = searchers.AStar(lambda n: grids.neighbor_nodes(g, n),
lambda n1, n2: g[n2[0]][n2[1]],
h=p_h)
a_p.search(start, end)
def searched_perfect_callback(y, x):
v = cost_callback(y, x)
if (y, x) in a_p.path:
v = (v[0], v[1], v[2] + 127)
elif (y, x) in a_p.closed_set:
v = (v[0], v[1] + 127, v[2])
elif (y, x) in a_p.open_set:
v = (v[0] + 127, v[1], v[2])
return v
l = searchers.Landmark(g, (height - 1, width - 1))
l_h = lambda n, e: max(l.cost_to_l[n] - l.cost_to_l[e], l.cost_from_l[e] -
l.cost_from_l[n], 0)
max_cost_to_l = max(l.cost_to_l.itervalues())
def landmark_callback(y, x):
v = float(l.cost_to_l[(y, x)]) / max_cost_to_l * 255
return (v, v, v)
def landmark_h_callback(y, x):
n = (y, x)
h_n = l_h(n, end)
v = float(h_n) / max_cost_to_l * 255
return (v, v, v)
a_l = searchers.AStar(lambda n: grids.neighbor_nodes(g, n),
lambda n1, n2: g[n2[0]][n2[1]],
h=l_h)
a_l.search(start, end)
def searched_landmark_callback(y, x):
v = cost_callback(y, x)
if (y, x) in a_l.path:
v = (v[0], v[1], v[2] + 127)
elif (y, x) in a_l.closed_set:
v = (v[0], v[1] + 127, v[2])
elif (y, x) in a_l.open_set:
v = (v[0] + 127, v[1], v[2])
return v
#r = searchers.Reach(g)
#max_reach = max([max([r.value[y][x] for x in xrange(width)]) for y in xrange(height)])
def reach_callback(y, x):
v = float(r.value[y][x]) / max_reach * 255
return (v, v, v)
a_r = searchers.AStar(lambda n: grids.neighbor_nodes(g, n),
lambda n1, n2: g[n2[0]][n2[1]],
r=lambda n: r.value[n[0]][n[1]])
#a_r.search(start, end)
def searched_reach_callback(y, x):
v = cost_callback(y, x)
if (y, x) in a_r.path:
v = (v[0], v[1], v[2] + 127)
elif (y, x) in a_r.closed_set:
v = (v[0], v[1] + 127, v[2])
elif (y, x) in a_r.open_set:
v = (v[0] + 127, v[1], v[2])
return v
bmp.write_many(
'output.bmp',
width,
height,
[
[
cost_callback, searched_callback, perfect_h_callback,
searched_perfect_callback
],
[
landmark_callback, landmark_h_callback,
searched_landmark_callback
],
#[reach_callback, None, searched_reach_callback]
])
