class Checker:
def __init__(self):
self.env = Env()
def remainder(self, file):
time = self.env.getTime()
if time > 17:
self.env.playWavFile(file)
return self.env.wasWavPlayed(file)
else:
return self.env.resetWav(file)
def init_server() -> grpc.Server:
pool = futures.ThreadPoolExecutor(max_workers=Env.NUM_THREADS)
server = grpc.server(pool)
_init_route(server)
_init_reflection(server)
server.add_insecure_port(Env.address())
return server
from src.franka import Franka
from pyrep.objects.shape import Shape
import numpy as np
import cv2
def scene(scene_file_name):
'''
return abs dir of scene file
'''
return scene_path + scene_file_name
if __name__ == '__main__':
# open v-rep and launch BaseScene.ttt file
env = Env(scene('Kine_picking.ttt'))
# start simulation
env.start()
# franka
franka = Franka()
# set franka to home joints
franka.home(env)
# TODO: generate a series of waypoints form the letters in "KINEMATICS"
x, y, z = franka.get_position()
'''
rotate along Y axis 45 degrees
and move to robot frame
'''
rotation = np.array([[np.cos(np.pi / 4), 0,
return b
parser = argparse.ArgumentParser()
parser.add_argument('--nb-steps', type=int, default=1000)
parser.add_argument('--test', type=int, default=0)
parser.add_argument('--weights', type=str, default=None)
args = parser.parse_args()
np.random.seed(0)
nb_steps = args.nb_steps
nb_users = 40
state_size = 3
learning_rate = 1e-2
baseline = generate_baseline(nb_users=nb_users)
env = Env(baseline=baseline, state_size=state_size)
nb_actions = env.action_space.shape[-1]
model = Sequential()
model.add(Dense(10, input_dim=state_size, activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(nb_actions, activation='linear'))
model.compile(loss='mse', optimizer=Adam(lr=learning_rate))
model.summary()
agent = DQNAgent(model=model,
nb_users=nb_users,
nb_actions=nb_actions,
state_size=state_size,
gamma=.99,
import sys
sys.path.append("../")
import pickle
from datetime import datetime, date, timedelta
from src.env import Env
from src.utils.order import Order
# The initial status of the simulator: 3000 stations and 8000 cars assigned to these stations
simulator = Env(station_count=3000, car_count=8000)
f = open('data/orders_one_week.pkl', 'rb')
simulator._new_orders = pickle.load(f)
f.close()
while simulator._current_time < datetime(2020,4,7, 23, 50, 0):
# Get the orders going to be finished
processing_orders = simulator.step_1()
for processing_order in processing_orders:
station_id = processing_order.get_end_st()
station = simulator._station_dict[station_id]
car = simulator._car_dict[processing_order.get_car_id()]
simulator.parking_car(station,car._car_id)
img = cv2.imread('img.png')
env = Env(scene('Tic_tac_toe.ttt'))
env.start()
cam = Camera()
depth_image = np.load('depth_img.npy')
O_chesses, X_chesses, chessboard = cv_get_position(img,depth_image)
print(chessboard)
env.stop()
env.shutdown()
cv2.imshow('img',img)
cv2.waitKey()
'''
if __name__ == "__main__":
env = Env(scene('Tic_tac_toe.ttt'))
env.start()
# franka
franka = Franka()
# set franka to home joints
franka.home(env)
# initiate the camera and get an color image and depth image
cam = Camera()
img = cam.capture_bgr()
depth_img = cam.capture_depth(in_meters=True)
# TODO: detect the location of the chessboard and the chess pieces from the image
O_chesses_pos, X_chesses_pos, chessboard_pos = cv_get_position(
img, depth_img)
def __init__(self):
self.env = Env()
from pyrep.objects.shape import Shape
from pyrep.const import PrimitiveShape
import numpy as np
import cv2
def scene(scene_file_name):
'''
return abs dir of scene file
'''
return scene_path + scene_file_name
if __name__ == '__main__':
# open v-rep and launch BaseScene.ttt file
env = Env(scene('BaseScene.ttt'))
# start simulation
env.start()
# franka
franka = Franka()
# set franka to home joints
path = franka.home(env)
# cam
cam = Camera()
# find the path to target
target = Shape.create(type=PrimitiveShape.SPHERE,
size=[0.05, 0.05, 0.05],
color=[1.0, 0.1, 0.1],
static=True,
from src.franka import Franka
from pyrep.objects.shape import Shape
import numpy as np
import cv2
def scene(scene_file_name):
'''
return abs dir of scene file
'''
return scene_path + scene_file_name
if __name__ == '__main__':
# open v-rep and launch BaseScene.ttt file
env = Env(scene('Kine_picking.ttt'))
# start simulation
env.set_simulation_timestep(0.1)
env.step_ui()
env.start()
# franka
franka = Franka()
# set franka to home joints
franka.home(env)
# cam
cam = Camera()
# target_plane, cylinder, cubic
target_plane = Shape('plane')
def test_is_production():
os.environ['MODE'] = 'development'
assert Env().is_production() == False
os.environ["MODE"] = 'production'
assert Env().is_production() == True