def compute_action_planning(grid, start, goal):
path = astar(grid, start, goal)
action_planning = []
for i in range(len(path) - 1):
pos = path[i]
next_pos = path[i + 1]
# mouvement = (-1, 0), (1, 0), (0, -1), (0, 1)
mouvement = tuple(map(operator.sub, next_pos, pos))
action_planning.append(ACTION[mouvement])
return path, action_planning
def generate_data():
imsize = 128
im_size = [imsize, imsize]
matlab_data = sio.loadmat('data/data.mat')
map_data = np.transpose(matlab_data["Data_mat"].astype('float32'),
[2, 0, 1])
im_data = (255 - np.transpose(matlab_data["Img_mat"].astype('float32'),
[2, 0, 1])) / 255
num = len(map_data)
rand_idx = np.random.permutation(np.arange(num))
value_data = np.zeros((num, imsize, imsize), 'float32')
X_data = np.zeros((num, imsize, imsize, 3), 'float32')
start_data = np.zeros((num, 1, 2), 'int32')
goal_data = np.zeros((num, 1, 2), 'int32')
grid_data = np.zeros((num, 32, 32), 'int32')
action_data = []
statex_data = []
statey_data = []
# when tseting, num=10
for i, index in enumerate(rand_idx):
count = 0
flagg = 0
End = True
while (End):
count += 1
if count > 5:
index -= 1
count = 0
for m in range(32):
for n in range(32):
grid_data[i, m,
n] = np.max(map_data[index, 4 * m:4 * m + 4,
4 * n:4 * n + 4])
flagg = index
#print(i, count, index)
#grid_data[i,0,:] = 1
#grid_data[i,31,:] = 1
#grid_data[i,:,0] = 1
#grid_data[i,:,31] = 1
flag = 1
while (flag):
goal_data[i, 0, 0] = np.random.randint(30) + 1
goal_data[i, 0, 1] = np.random.randint(30) + 1
if grid_data[i, goal_data[i, 0, 0], goal_data[i, 0, 1]] != 0:
flag = 1
else:
flag = 0
flag = 1
while (flag):
start_data[i, 0, 0] = np.random.randint(30) + 1
start_data[i, 0, 1] = np.random.randint(30) + 1
if grid_data[i, start_data[i, 0, 0], start_data[i, 0, 1]] == 0:
flag = 0
X = start_data[i, 0, 0]
Y = start_data[i, 0, 1]
GX = goal_data[i, 0, 0]
GY = goal_data[i, 0, 1]
start = list(zip(np.array([X]), np.array([Y])))
goal = list(zip(np.array([GX]), np.array([GY])))
S = random.Random(None).sample(start, 1)
G = random.Random(None).sample(goal, 1)
action = astar(grid_data[i], S[0], G[0])
if action != False and len(action) > 2:
End = False
s_len = len(action)
statex = np.zeros((s_len - 1), 'int32')
statey = np.zeros((s_len - 1), 'int32')
ylabel = np.zeros((s_len - 1), 'int32')
for j in range(s_len - 1):
statex[j] = action[j][0]
statey[j] = action[j][1]
ylabel[j] = cord2action(action[j + 1][0] - action[j][0],
action[j + 1][1] - action[j][1])
statex_data.append(statex)
statey_data.append(statey)
action_data.append(ylabel)
if flagg - index > 0:
print("hhh")
value_data[i, goal_data[i, 0, 0] * 4 + 2,
goal_data[i, 0, 1] * 4 + 2] = 10
X_data[i, :, :, 0] = im_data[index, :, :]
X_data[i, :, :, 1] = map_data[index, :, :]
X_data[i, :, :, 2] = value_data[i, :, :]
#print(np.where(X_data[i,:,:,1]==100.),goal_data[i]*4+2)
all_training_samples = int(6 / 7 * num)
training_samples = all_training_samples
Xtrain = X_data[0:training_samples]
S1train = statex_data[0:training_samples]
S2train = statey_data[0:training_samples]
ytrain = action_data[0:training_samples]
gtrain = grid_data[0:training_samples]
Gtrain = goal_data[0:training_samples]
Xtest = X_data[all_training_samples:]
S1test = statex_data[all_training_samples:]
S2test = statey_data[all_training_samples:]
ytest = action_data[all_training_samples:]
gtest = grid_data[all_training_samples:]
Gtest = goal_data[all_training_samples:]
sio.savemat(
'data/dataset0.mat', {
'Xtrain': Xtrain,
'S1train': S1train,
'S2train': S2train,
'ytrain': ytrain,
'gtrain': gtrain,
'Gtrain': Gtrain,
'Xtest': Xtest,
'S1test': S1test,
'S2test': S2test,
'ytest': ytest,
'gtest': gtest,
'Gtest': Gtest
})