def collect_data():
robot = Robot()
cubusm = CubesManager()
for i in range(MAX_PICTURE_NUM):
cubusm.reset_cube(rand=True)
Box_position = cubusm.read_cube_pose("demo_cube")
# print "cube position:", str(Box_position)
joint, view = robot.get_state()
rgb, dep = robot.get_rgb_dep()
# b, g, r = cv2.split(rgb)
# print view[0,0,0]
# print dep
# rgb = cv2.merge([r, g, b])
# print dep
# plt.imshow(dep)
# plt.show()
rgb = cv2.resize(rgb, (224, 224))
dep = cv2.resize(dep, (224, 224))
# print dep
cv2.imwrite(
"/home/ljt/Desktop/images/rgb/" + str(Box_position) + ".png", rgb)
# cv2.imwrite("/home/ljt/Desktop/ws/src/fetch_moveit_config/images/dep/" + str(Box_position) + ".png", dep)
# a = np.array(rgb).shape
# print a
# print "camera image shape:", view.shape
np.save("/home/ljt/Desktop/images/dep/" + str(Box_position), dep)
import math
from camera import RGBD
from MofanDDPG import DDPG
from Env import Robot, CubesManager
import copy
import numpy as np
MAX_EPISODES = 900
MAX_EP_STEPS = 5
ON_TRAIN = True
if __name__ == '__main__':
# set env
robot = Robot()
cubm = CubesManager()
observation_dim = 3
action_dim = 3
action_bound = -1, 1
# set RL method (continuous)
rl = DDPG(action_dim, observation_dim, action_bound)
number = 0
steps = []
# start training
for i in range(MAX_EPISODES):
cubm.reset_cube(rand=True)
Box_position = cubm.read_cube_pose("demo_cube")
print "cube position:", Box_position
robot.Box_position = copy.deepcopy(Box_position)
now_position = robot.gripper.get_current_pose(