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