def __init__(self,
TARGETS = ['SM_Plant_7','SM_Plant_8'],# you can change it for other targets
DOCKER = True,
ENV_NAME='RealisticRendering',
cam_id = 0,
MAX_STEPS = 100):
self.cam_id = cam_id
self.target_list = TARGETS
if DOCKER:
self.docker = run_docker.RunDocker()
env_ip, env_dir = self.docker.start(ENV_NAME = ENV_NAME)
self.unrealcv = UnrealCv(self.cam_id, ip=env_ip, targets=self.target_list, env=env_dir)
else:
self.docker = False
#you need run the environment previously
env_ip = '127.0.0.1'
self.unrealcv = UnrealCv(self.cam_id, ip=env_ip, targets=self.target_list)
print env_ip
self.reward_th = 0.2
self.trigger_th = 0.9
self.origin = [
(-106.195, 437.424, 40),
( 27.897, -162.437, 40),
( -66.601, -4.503, 40),
( 10.832, 135.126, 40),
( 67.903, 26.995, 40),
( -23.558, -187.354, 40),
( -80.312, 254.579, 40),
]
self.count_steps = 0
self.max_steps = MAX_STEPS
self.action = (0,0,0) #(velocity,angle,trigger)
self.targets_pos = self.unrealcv.get_objects_pos(self.target_list)
self.trajectory = []
state = self.unrealcv.read_image(self.cam_id, 'lit')
#self.action_space = spaces.Discrete(len(self.ACTION_LIST))
self.action_space = spaces.Box(low = 0,high = 100, shape = self.action)
self.observation_space = spaces.Box(low=0, high=255, shape=state.shape)
self.trigger_count = 0
def __init__(
self,
ENV_NAME='RealisticRendering' # if use your own environment,please change it
):
self.cam_id = 0
# run virtual enrionment in docker container
self.docker = run_docker.RunDocker()
env_ip, env_dir = self.docker.start(ENV_NAME=ENV_NAME)
# connect unrealcv client to server
self.unrealcv = UnrealCv(self.cam_id, ip=env_ip, env=env_dir)
self.startpose = self.unrealcv.get_pose()
# ACTION: (linear velocity ,angle velocity)
self.ACTION_LIST = [
(20, 0),
(20, 15),
(20, -15),
(20, 30),
(20, -30),
]
self.count_steps = 0
self.max_steps = 100
self.target_pos = (-60, 0, 50)
self.action_space = gym.spaces.Discrete(len(self.ACTION_LIST))
state = self.unrealcv.read_image(self.cam_id, 'lit')
self.observation_space = gym.spaces.Box(low=0,
high=255,
shape=state.shape)
def __init__(
self,
setting_file,
test, # if True will use the test_xy as start point
timeDependent,
action_type, # 'discrete', 'continuous'
observation_type, # 'color', 'depth', 'rgbd'
reward_type # distance, bbox, bbox_distance,
):
self.show = False
self.collision = False
self.timeDependent = timeDependent
self.target = None
# self.dis2target_now= 100
# self.isTargetChanged = False
print("Reading setting file: ", setting_file)
self.test = test
self.action_type = action_type
self.observation_type = observation_type
self.reward_type = reward_type
assert self.action_type == 'discrete' or self.action_type == 'continuous'
assert self.observation_type == 'depth'
setting = self.load_env_setting(setting_file)
# connect UnrealCV
self.unrealcv = UnrealCv(9000, self.cam_id, self.env_ip,
self.message_handler)
print("Camera_id: ", self.cam_id)
print("Environment ip: ", self.env_ip)
print("Observation type: ", observation_type)
print("action_type: ", action_type)
print("Reward type: ", reward_type)
print("timeDependent: ", str(self.timeDependent))
self.unrealcv.notify_connection()
self.count_steps = 0
# action
if self.action_type == 'discrete':
self.action_space = spaces.Discrete(len(self.discrete_angular))
elif self.action_type == 'continuous':
self.action_space = spaces.Box(
low=np.array(self.continous_actions['low']),
high=np.array(self.continous_actions['high']))
# get start position
self.startPoint = self.unrealcv.get_pose()
self.unrealcv.get_target_point()
while (self.target == None):
time.sleep(0.5)
# for reset point generation
self.trajectory = []
self.start_id = 0
def __init__(
self,
setting_file,
test, # if True will use the test_xy as start point
action_type, # 'discrete', 'continuous'
observation_type, # 'color', 'depth', 'rgbd'
reward_type # distance, bbox, bbox_distance,
):
self.show = True
print("Reading setting file: ", setting_file)
self.test = test
self.action_type = action_type
self.observation_type = observation_type
self.reward_type = reward_type
assert self.action_type == 'discrete' or self.action_type == 'continuous'
assert self.observation_type == 'depth'
setting = self.load_env_setting(setting_file)
# connect UnrealCV
self.unrealcv = UnrealCv(9000, self.cam_id, self.env_ip)
print("Camera_id: ", self.cam_id)
print("Environment ip: ", self.env_ip)
print("Observation type: ", observation_type)
print("action_type: ", action_type)
print("Reward type: ", reward_type)
self.unrealcv.notify_connection()
self.unrealcv.declare_step_by_step()
self.count_steps = 0
# self.targets_pos = self.target_points[0]
# print ("Target points: ",self.target_points)
# print ("The target pose: ",self.targets_pos)
# action
if self.action_type == 'discrete':
self.action_space = spaces.Discrete(len(self.discrete_actions))
elif self.action_type == 'continuous':
self.action_space = spaces.Box(
low=np.array(self.continous_actions['low']),
high=np.array(self.continous_actions['high']))
# get start position
current_pose = self.unrealcv.get_pose()
# self.dis2target_now = self.get_distance(current_pose, self.targets_pos)
# for reset point generation
self.trajectory = []
self.start_id = 0