def worker(idx, house_id, device):
colormapFile = "../metadata/colormap_coarse.csv"
api = objrender.RenderAPI(w=args.width, h=args.height, device=device)
env = Environment(api, house_id, cfg)
N = 15000
start = time.time()
cnt = 0
env.reset()
for t in range(N):
cnt += 1
env.move_forward(random.random() * 3, random.random() * 3)
mat = env.render()
if (cnt % 50 == 0):
env.reset()
end = time.time()
print("Worker {}, speed {:.3f} fps".format(idx, N / (end - start)))
class House3DRGBD:
def __init__(self,
train_mode=True,
area_reward_scale=1,
collision_penalty=0.1,
step_penalty=0.0005,
max_depth=3.0,
render_door=False,
start_indoor=False,
ignore_collision=False,
ob_dilation_kernel=5,
large_map_size=80):
self.seed()
self.configs = get_configs()
self.configs['large_map_size'] = large_map_size
self.env = None
self.train_mode = train_mode
self.render_door = render_door
self.ignore_collision = ignore_collision
self.start_indoor = start_indoor
self.render_height = self.configs['render_height']
self.render_width = self.configs['render_width']
self.img_height = self.configs['output_height']
self.img_width = self.configs['output_width']
self.ob_dilation_kernel = ob_dilation_kernel
self.config = load_config(self.configs['path'],
prefix=self.configs['par_path'])
self.move_sensitivity = self.configs['move_sensitivity']
self.rot_sensitivity = self.configs['rot_sensitivity']
self.train_houses = self.configs['train_houses']
self.test_houses = self.configs['test_houses']
if train_mode:
self.houses_id = self.train_houses
# print("Number of traning houses:", len(self.houses_id))
else:
self.houses_id = None # self.test_houses
self.depth_threshold = (0, max_depth)
self.area_reward_scale = area_reward_scale
self.collision_penalty = collision_penalty
self.step_penalty = step_penalty
self.observation_space = [self.img_width, self.img_height, 3]
self.action_space = [6]
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed)
return [seed]
def reset(self, house_id=None, x=None, y=None, yaw=None):
if house_id is None:
house_id = self.np_random.choice(self.houses_id, 1)[0]
self.hid = house_id
if self.env is not None:
del self.api
del self.env
self.api = objrender.RenderAPI(self.render_width,
self.render_height,
device=RENDERING_GPU)
self.env = Environment(self.api,
house_id,
self.config,
GridDet=self.configs['GridDet'],
RenderDoor=self.render_door,
StartIndoor=self.start_indoor)
if not self.train_mode:
self.loc_map = self.env.gen_locmap()
obs_map = self.env.house.obsMap.T
self.obs_pos = obs_map == 1
self.traj = []
self.traj_actions = []
self.grid_traj = []
self.L_min = self.env.house.L_lo
self.L_max = self.env.house.L_hi
self.grid_size = self.env.house.grid_det
grid_num = np.array(
[self.env.house.n_row[0] + 1, self.env.house.n_row[1] + 1])
self.grids_mat = np.zeros(tuple(grid_num), dtype=np.uint8)
self.max_grid_size = np.max(grid_num)
self.max_seen_area = float(np.prod(grid_num))
self.env.reset(x=x, y=y, yaw=yaw)
self.start_pos, self.grid_start_pos = self.get_camera_grid_pos()
if not self.train_mode:
print('start pose: ', self.start_pos)
self.traj.append(self.start_pos.tolist())
self.grid_traj.append(self.grid_start_pos.tolist())
rgb, depth, extrinsics = self.get_obs()
large_loc_map, small_loc_map = self.get_loc_map()
self.seen_area = self.get_seen_area(rgb, depth, extrinsics,
self.grids_mat)
self.ep_len = 0
self.ep_reward = 0
self.collision_times = 0
ob = (self.resize_img(rgb), large_loc_map, small_loc_map)
return ob
def step(self, action):
if self.ignore_collision:
collision_flag = self.motion_primitive_no_check(action)
else:
collision_flag = self.motion_primitive(action)
rgb, depth, extrinsics = self.get_obs()
large_loc_map, small_loc_map = self.get_loc_map()
if not self.train_mode:
current_pos, grid_current_pos = self.get_camera_grid_pos()
self.traj_actions.append(int(action))
self.traj.append(current_pos.tolist())
self.grid_traj.append(grid_current_pos.tolist())
reward, seen_area, raw_reward = self.cal_reward(
rgb, depth, extrinsics, collision_flag)
self.ep_len += 1
self.ep_reward += reward
if collision_flag:
self.collision_times += 1
info = {
'reward_so_far': self.ep_reward,
'steps_so_far': self.ep_len,
'seen_area': seen_area,
'collisions': self.collision_times,
'start_pose': self.start_pos,
'house_id': self.hid,
'collision_flag': collision_flag
}
info = {**info, **raw_reward}
done = False
ob = (self.resize_img(rgb), large_loc_map, small_loc_map)
return ob, reward, done, info
def render(self):
loc_map, small_map = self.get_loc_map()
if not self.train_mode:
rad = self.env.house.robotRad / self.env.house.grid_det
x = int(loc_map.shape[0] / 2)
y = int(loc_map.shape[1] / 2)
cv2.circle(loc_map, (x, y), 1, (255, 0, 255), thickness=-1)
x = int(small_map.shape[0] / 2)
y = int(small_map.shape[1] / 2)
cv2.circle(small_map, (x, y),
int(rad) * 2, (255, 0, 255),
thickness=-1)
loc_map = cv2.resize(loc_map, (self.render_width, self.render_height),
interpolation=cv2.INTER_CUBIC)
if not self.train_mode:
x = int(loc_map.shape[0] / 2)
y = int(loc_map.shape[1] / 2)
cv2.circle(loc_map, (x, y), 2, (255, 0, 255), thickness=-1)
self.env.set_render_mode('rgb')
rgb = self.env.render()
img = np.concatenate((rgb, loc_map), axis=1)
img = img[:, :, ::-1]
img = cv2.resize(img, (img.shape[1] * 3, img.shape[0] * 3),
interpolation=cv2.INTER_CUBIC)
cv2.imshow("nav", img)
cv2.waitKey(40)
def resize_img(self, img):
return cv2.resize(img, (self.img_width, self.img_height),
interpolation=cv2.INTER_AREA)
def motion_primitive(self, action):
# 0: Forward
# 1: Turn Left
# 2: Turn Right
# 3: Strafe Left
# 4: Strafe Right
# 5: Backward
collision_flag = False
if action == 0:
if not self.train_mode:
print('Action: Forward')
if not self.env.move_forward(self.move_sensitivity):
if not self.train_mode:
print('Cannot move forward, collision!!!')
collision_flag = True
elif action == 1:
if not self.train_mode:
print('Action: Turn Left')
self.env.rotate(-self.rot_sensitivity)
elif action == 2:
if not self.train_mode:
print('Action: Turn Right')
self.env.rotate(self.rot_sensitivity)
elif action == 3:
if not self.train_mode:
print('Action: Strafe Left')
if not self.env.move_forward(dist_fwd=0,
dist_hor=-self.move_sensitivity):
if not self.train_mode:
print('Cannot strafe left, collision!!!')
collision_flag = True
elif action == 4:
if not self.train_mode:
print('Action: Strafe Right')
if not self.env.move_forward(dist_fwd=0,
dist_hor=self.move_sensitivity):
if not self.train_mode:
print('Cannot strafe right, collision!!!')
collision_flag = True
elif action == 5:
if not self.train_mode:
print('Action: Backward')
if not self.env.move_forward(-self.move_sensitivity):
if not self.train_mode:
print('Cannot move backward, collision!!!')
collision_flag = True
else:
raise ValueError('unknown action type: [{0:d}]'.format(action))
return collision_flag
def move_forward(self, dist_fwd, dist_hor=0):
"""
Move with `fwd` distance to the front and `hor` distance to the right.
Both distance are float numbers.
Ignore collision !!!
"""
pos = self.env.cam.pos
pos = pos + self.env.cam.front * dist_fwd
pos = pos + self.env.cam.right * dist_hor
self.env.cam.pos.x = pos.x
self.env.cam.pos.z = pos.z
def motion_primitive_no_check(self, action):
# motion primitive without collision checking
# 0: Forward
# 1: Turn Left
# 2: Turn Right
# 3: Strafe Left
# 4: Strafe Right
# 5: Backward
collision_flag = False
if action == 0:
self.move_forward(self.move_sensitivity)
elif action == 1:
self.env.rotate(-self.rot_sensitivity)
elif action == 2:
self.env.rotate(self.rot_sensitivity)
elif action == 3:
self.move_forward(dist_fwd=0, dist_hor=-self.move_sensitivity)
elif action == 4:
self.move_forward(dist_fwd=0, dist_hor=self.move_sensitivity)
elif action == 5:
self.move_forward(-self.move_sensitivity)
else:
raise ValueError('unknown action type: [{0:d}]'.format(action))
return collision_flag
def get_obs(self):
self.env.set_render_mode('rgb')
rgb = self.env.render()
self.env.set_render_mode('depth')
depth = self.env.render()
infmask = depth[:, :, 1]
depth = depth[:, :, 0] * (infmask == 0)
true_depth = depth.astype(np.float32) / 255.0 * 20.0
extrinsics = self.env.cam.getExtrinsicsNumpy()
return rgb, true_depth, extrinsics
def get_seen_area(self, rgb, depth, extrinsics, out_mat, inv_E=True):
points, points_colors = gen_point_cloud(
depth,
rgb,
extrinsics,
depth_threshold=self.depth_threshold,
inv_E=inv_E)
grid_locs = np.floor(
(points[:, [0, 2]] - self.L_min) / self.grid_size).astype(int)
grids_mat = np.zeros(
(self.grids_mat.shape[0], self.grids_mat.shape[1]), dtype=np.uint8)
high_filter_idx = points[:, 1] < HEIGHT_THRESHOLD[1]
low_filter_idx = points[:, 1] > HEIGHT_THRESHOLD[0]
obstacle_idx = np.logical_and(high_filter_idx, low_filter_idx)
self.safe_assign(grids_mat, grid_locs[high_filter_idx, 0],
grid_locs[high_filter_idx, 1], 2)
kernel = np.ones((3, 3), np.uint8)
grids_mat = cv2.morphologyEx(grids_mat, cv2.MORPH_CLOSE, kernel)
obs_mat = np.zeros((self.grids_mat.shape[0], self.grids_mat.shape[1]),
dtype=np.uint8)
self.safe_assign(obs_mat, grid_locs[obstacle_idx, 0],
grid_locs[obstacle_idx, 1], 1)
kernel = np.ones((self.ob_dilation_kernel, self.ob_dilation_kernel),
np.uint8)
obs_mat = cv2.morphologyEx(obs_mat, cv2.MORPH_CLOSE, kernel)
obs_idx = np.where(obs_mat == 1)
self.safe_assign(grids_mat, obs_idx[0], obs_idx[1], 1)
out_mat[np.where(grids_mat == 2)] = 2
out_mat[np.where(grids_mat == 1)] = 1
seen_area = np.sum(out_mat > 0)
return seen_area
def cal_reward(self, rgb, depth, extrinsics, collision_flag):
filled_grid_num = self.get_seen_area(rgb,
depth,
extrinsics,
self.grids_mat,
inv_E=True)
area_reward = (filled_grid_num - self.seen_area)
reward = area_reward * self.area_reward_scale
if collision_flag:
reward -= self.collision_penalty
reward -= self.step_penalty
self.seen_area = filled_grid_num
raw_reward = {'area': area_reward, 'collision_flag': collision_flag}
return reward, filled_grid_num, raw_reward
def get_loc_map(self):
top_down_map = self.grids_mat.T.copy()
half_size = max(top_down_map.shape[0], top_down_map.shape[1],
self.configs['large_map_range']) * 3
ego_map = np.ones(
(half_size * 2, half_size * 2, 3), dtype=np.uint8) * 255
loc_map = np.zeros((top_down_map.shape[0], top_down_map.shape[1], 3),
dtype=np.uint8)
loc_map[top_down_map == 0] = np.array([255, 255, 255])
loc_map[top_down_map == 1] = np.array([0, 0, 255])
loc_map[top_down_map == 2] = np.array([0, 255, 0])
current_pos, grid_current_pos = self.get_camera_grid_pos()
x_start = half_size - grid_current_pos[1]
y_start = half_size - grid_current_pos[0]
x_end = x_start + top_down_map.shape[0]
y_end = y_start + top_down_map.shape[1]
assert x_start >= 0 and y_start >= 0 and \
x_end <= ego_map.shape[0] and y_end <= ego_map.shape[1]
ego_map[x_start:x_end, y_start:y_end] = loc_map
center = (half_size, half_size)
rot_angle = self.constrain_to_pm_pi(90 + current_pos[2])
M = cv2.getRotationMatrix2D(center, rot_angle, 1.0)
ego_map = cv2.warpAffine(ego_map,
M, (ego_map.shape[1], ego_map.shape[0]),
flags=cv2.INTER_AREA,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(255, 255, 255))
start = half_size - self.configs['small_map_range']
end = half_size + self.configs['small_map_range']
small_ego_map = ego_map[start:end, start:end]
start = half_size - self.configs['large_map_range']
end = half_size + self.configs['large_map_range']
assert start >= 0
assert end <= ego_map.shape[0]
large_ego_map = ego_map[start:end, start:end]
return cv2.resize(
large_ego_map,
(self.configs['large_map_size'], self.configs['large_map_size']),
interpolation=cv2.INTER_AREA), small_ego_map
def safe_assign(self, im_map, x_idx, y_idx, value):
try:
im_map[x_idx, y_idx] = value
except IndexError:
valid_idx1 = np.logical_and(x_idx >= 0, x_idx < im_map.shape[0])
valid_idx2 = np.logical_and(y_idx >= 0, y_idx < im_map.shape[1])
valid_idx = np.logical_and(valid_idx1, valid_idx2)
im_map[x_idx[valid_idx], y_idx[valid_idx]] = value
def constrain_to_pm_pi(self, theta):
# make sure theta is within [-180, 180)
return (theta + 180) % 360 - 180
def get_camera_grid_pos(self):
current_pos = np.array([
self.env.cam.pos.x, self.env.cam.pos.z,
self.constrain_to_pm_pi(self.env.cam.yaw)
])
grid_pos = np.array(
self.env.house.to_grid(current_pos[0], current_pos[1]))
return current_pos, grid_pos
def truncated_norm(self, mu, sigma, lower_limit, upper_limit, size):
if sigma == 0:
return mu
lower_limit = (lower_limit - mu) / sigma
upper_limit = (upper_limit - mu) / sigma
r = truncnorm(lower_limit, upper_limit, loc=mu, scale=sigma)
return r.rvs(size)
class House3DEnv(gym.Env):
def __init__(self,
train_mode=True,
area_reward_scale=0.0005,
collision_penalty=0.01,
step_penalty=0.0,
max_depth=2.0,
render_door=False,
start_indoor=True,
ignore_collision=False,
ob_dilation_kernel=5,
depth_signal=True,
max_steps=500):
self.seed()
self.configs = get_configs()
self.env = None
self.train_mode = train_mode
self.render_door = render_door
self.ignore_collision = ignore_collision
self.start_indoor = start_indoor
self.render_height = self.configs['render_height']
self.render_width = self.configs['render_width']
self.ob_dilation_kernel = ob_dilation_kernel
self.config = load_config(self.configs['path'],
prefix=self.configs['par_path'])
self.move_sensitivity = self.configs['move_sensitivity']
self.rot_sensitivity = self.configs['rot_sensitivity']
self.train_houses = self.configs['train_houses']
self.test_houses = self.configs['test_houses']
if train_mode:
self.houses_id = self.train_houses
else:
self.houses_id = self.test_houses
self.depth_threshold = (0, max_depth)
self.area_reward_scale = area_reward_scale
self.collision_penalty = collision_penalty
self.step_penalty = step_penalty
self.max_step = max_steps
n_channel = 3
if depth_signal:
n_channel += 1
self.observation_shape = (self.render_width, self.render_height,
n_channel)
self.observation_space = spaces.Box(0,
255,
shape=self.observation_shape,
dtype=np.uint8)
#self.action_space = spaces.Discrete(n_discrete_actions)
self.action_space = spaces.Box(low=np.array([0.0, -1.0, -1.0]),
high=np.array([1.0, 1.0, 1.0]),
dtype=np.float32)
self.tracker = []
self.num_rotate = 0
self.right_rotate = 0
def seed(self, seed=None):
self.np_random, seed = seeding.np_random()
return [seed]
def constrain_to_pm_pi(self, theta):
return (theta + 180) % 360 - 180
def get_camera_grid_pos(self):
current_pos = np.array([
self.env.cam.pos.x, self.env.cam.pos.z,
self.constrain_to_pm_pi(self.env.cam.yaw)
])
grid_pos = np.array(
self.env.house.to_grid(current_pos[0], current_pos[1]))
return current_pos, grid_pos
def get_obs(self):
self.env.set_render_mode('rgb')
rgb = self.env.render()
self.env.set_render_mode('depth')
depth = self.env.render()
infmask = depth[:, :, 1]
depth = depth[:, :, 0] * (infmask == 0)
true_depth = depth.astype(np.float32) / 255.0 * 20.0
extrinsics = self.env.cam.getExtrinsicsNumpy()
return rgb, np.expand_dims(depth, -1), true_depth, extrinsics
def safe_assign(self, im_map, x_idx, y_idx, value):
try:
im_map[x_idx, y_idx] = value
except IndexError:
valid_idx1 = np.logical_and(x_idx >= 0, x_idx < im_map.shape[0])
valid_idx2 = np.logical_and(y_idx >= 0, y_idx < im_map.shape[1])
valid_idx = np.logical_and(valid_idx1, valid_idx2)
im_map[x_idx[valid_idx], y_idx[valid_idx]] = value
def get_seen_area(self, rgb, depth, extrinsics, out_mat, inv_E=True):
points, points_colors = gen_point_cloud(
depth,
rgb,
extrinsics,
depth_threshold=self.depth_threshold,
inv_E=inv_E)
grid_locs = np.floor(
(points[:, [0, 2]] - self.L_min) / self.grid_size).astype(int)
grids_mat = np.zeros(
(self.grids_mat.shape[0], self.grids_mat.shape[1]), dtype=np.uint8)
high_filter_idx = points[:, 1] < HEIGHT_THRESHOLD[1]
low_filter_idx = points[:, 1] > HEIGHT_THRESHOLD[0]
obstacle_idx = np.logical_and(high_filter_idx, low_filter_idx)
self.safe_assign(grids_mat, grid_locs[high_filter_idx, 0],
grid_locs[high_filter_idx, 1], 2)
kernel = np.ones((3, 3), np.uint8)
grids_mat = cv2.morphologyEx(grids_mat, cv2.MORPH_CLOSE, kernel)
obs_mat = np.zeros((self.grids_mat.shape[0], self.grids_mat.shape[1]),
dtype=np.uint8)
self.safe_assign(obs_mat, grid_locs[obstacle_idx, 0],
grid_locs[obstacle_idx, 1], 1)
kernel = np.ones((self.ob_dilation_kernel, self.ob_dilation_kernel),
np.uint8)
obs_mat = cv2.morphologyEx(obs_mat, cv2.MORPH_CLOSE, kernel)
obs_idx = np.where(obs_mat == 1)
self.safe_assign(grids_mat, obs_idx[0], obs_idx[1], 1)
out_mat[np.where(grids_mat == 2)] = 2
out_mat[np.where(grids_mat == 1)] = 1
seen_area = np.sum(out_mat > 0)
#cal_seen_area = np.sum(out_mat == 1)
return seen_area
def cal_reward(self, rgb, depth, extrinsics, collision_flag):
if collision_flag:
reward = -1.0 * self.collision_penalty
area_reward = 0.0
filled_grid_num = self.seen_area
else:
filled_grid_num = self.get_seen_area(rgb,
depth,
extrinsics,
self.grids_mat,
inv_E=True)
area_reward = (filled_grid_num - self.seen_area)
reward = area_reward * self.area_reward_scale
reward -= self.step_penalty
self.seen_area = filled_grid_num
raw_reward = {'area': area_reward, 'collision_flag': collision_flag}
return reward, filled_grid_num, raw_reward
def reset(self, house_id=None, x=None, y=None, yaw=None):
if not self.train_mode:
obs_map = self.env.house.obsMap.T
self.obs_pos = obs_map == 1
self.traj = []
self.traj_actions = []
self.grid_traj = []
if house_id is None:
house_id = self.np_random.choice(self.houses_id, 1)[0]
self.hid = house_id
if self.env is not None:
del self.api
del self.env
self.api = objrender.RenderAPI(self.render_width,
self.render_height,
device=RENDERING_GPU)
self.env = Environment(self.api,
house_id,
self.config,
GridDet=self.configs['GridDet'],
RenderDoor=self.render_door,
StartIndoor=self.start_indoor)
self.tracker = []
self.num_rotate = 0
self.right_rotate = 0
self.L_min = self.env.house.L_lo
self.L_max = self.env.house.L_hi
self.grid_size = self.env.house.grid_det
grid_num = np.array(
[self.env.house.n_row[0] + 1, self.env.house.n_row[1] + 1])
self.grids_mat = np.zeros(tuple(grid_num), dtype=np.uint8)
self.max_grid_size = np.max(grid_num)
self.max_seen_area = float(np.prod(grid_num))
self.env.reset(x=x, y=y, yaw=yaw)
self.start_pos, self.grid_start_pos = self.get_camera_grid_pos()
if not self.train_mode:
self.traj.append(self.start_pos.tolist())
self.grid_traj.append(self.grid_start_pos.tolist())
rgb, depth, true_depth, extrinsics = self.get_obs()
self.seen_area = self.get_seen_area(rgb, true_depth, extrinsics,
self.grids_mat)
self.ep_len = 0
self.ep_reward = 0
self.collision_times = 0
ret_obs = np.concatenate((rgb, depth), axis=-1)
return ret_obs
def motion_primitive(self, action):
collision_flag = False
det_fwd = np.clip(action[0] + 0.8, 0.0, 2.0) / 2.0
tmp_alpha = 0.0
if action[2] > 0:
tmp_alpha = 1.0
elif action[2] < 0:
tmp_alpha = -1.0
det_rot = np.clip(action[1] + 0.8, 0.0, 1.6) * 0.5 * tmp_alpha
move_fwd = det_fwd * self.move_sensitivity
rotation = det_rot * self.rot_sensitivity
if not self.env.move_forward(move_fwd, 0.0):
collision_flag = True
else:
self.env.rotate(rotation)
return collision_flag
def step(self, action):
collision_flag = self.motion_primitive(action)
rgb, depth, true_depth, extrinsics = self.get_obs()
current_pos, grid_current_pos = self.get_camera_grid_pos()
if not self.train_mode:
self.traj_actions.append(int(action))
self.traj.append(current_pos.tolist())
self.grid_traj.append(grid_current_pos.tolist())
self.tracker.append([
grid_current_pos[0], grid_current_pos[1], self.env.cam.front.x,
self.env.cam.front.z, self.env.cam.right.x, self.env.cam.right.z
])
if action[1] > -0.8 and action[2] > 0:
self.right_rotate += 1
if action[1] > -0.8 and action[2] != 0:
self.num_rotate += 1
reward, seen_area, raw_reward = self.cal_reward(
rgb, true_depth, extrinsics, collision_flag)
self.ep_len += 1
self.ep_reward += reward
if collision_flag:
self.collision_times += 1
info = {
'reward_so_far': self.ep_reward,
'steps_so_far': self.ep_len,
'seen_area': seen_area,
'collisions': self.collision_times,
'start_pose': self.start_pos,
'house_id': self.hid,
'collision_flag': collision_flag,
'grid_current_pos': grid_current_pos - self.grid_start_pos,
'current_rot': current_pos[2] - self.start_pos[2]
}
info = {**info, **raw_reward, **self.info}
if self.ep_len >= self.max_step:
done = True
info.update({'bad_transition': True})
else:
done = False
ret_obs = np.concatenate((rgb, depth), axis=-1)
return ret_obs, reward, done, info
@property
def house(self):
return self.env.house
@property
def info(self):
ret = self.env.info
ret['track'] = self.tracker
ret['total_rotate'] = self.num_rotate
ret['right_rotate'] = self.right_rotate
if not self.train_mode:
ret['traj_actions'] = self.traj_actions
ret['traj'] = self.traj
ret['grid_traj'] = self.grid_traj
return ret
