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from typing import Callable, List
from omegaconf import DictConfig, OmegaConf
from enum import Enum
import torch
import numpy as np
import multiprocess as mp
import time
import threading
import cv2
import pygame
import os
import pickle
import subprocess
from pathlib import Path
from copy import deepcopy
import open3d as o3d
from pgnd.utils import get_root, mkdir
root: Path = get_root(__file__)
from camera.multi_realsense import MultiRealsense
from camera.single_realsense import SingleRealsense
from modules_planning.segment_perception import SegmentPerception
from modules_planning.planning_module import PlanningModule
from modules_planning.xarm_controller import XarmController
from modules_planning.udp_util import udpReceiver, udpSender
from modules_planning.common.communication import XARM_STATE_PORT, XARM_CONTROL_PORT, XARM_CONTROL_PORT_L, XARM_CONTROL_PORT_R
from modules_planning.common.xarm import GRIPPER_OPEN_MIN, GRIPPER_OPEN_MAX, POSITION_UPDATE_INTERVAL, COMMAND_CHECK_INTERVAL
class EnvEnum(Enum):
NONE = 0
INFO = 1
DEBUG = 2
VERBOSE = 3
class RobotPlanningEnv(mp.Process):
def __init__(
self,
cfg: DictConfig,
exp_root: str,
ckpt_path: Path | str,
realsense: MultiRealsense | SingleRealsense | None = None,
shm_manager: mp.managers.SharedMemoryManager | None = None,
serial_numbers: list[str] | None= None,
resolution: tuple[int, int] = (1280, 720),
capture_fps: int = 30,
enable_depth: bool = True,
enable_color: bool = True,
record_fps: int | None = 0,
record_time: float | None = 60 * 10, # 10 minutes
text_prompts: str = "white cotton rope.",
show_annotation: bool = True,
use_robot: bool = False,
robot_ip: str = '192.168.1.196',
gripper_enable: bool = False,
calibrate_result_dir: Path = root / "log" / "latest_calibration",
debug: bool | int | None = False,
bimanual: bool = False,
bimanual_robot_ip: List[str] = ['192.168.1.196', '192.168.1.224'],
construct_target: bool = False,
) -> None:
# Debug level
self.debug = 0 if debug is None else (2 if debug is True else debug)
self.cfg = cfg
self.exp_root = Path(exp_root)
if os.path.exists(self.exp_root.parent / "target" / "target.pcd"):
mkdir(self.exp_root / "target", overwrite=False, resume=True)
os.system(f"cp {self.exp_root.parent / 'target' / 'target.pcd'} {self.exp_root / 'target'}")
else:
assert construct_target, "target.pcd not found"
self.construct_target = construct_target
if construct_target:
mkdir(self.exp_root.parent / "target", overwrite=False, resume=False)
self.torch_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.bimanual = bimanual
if self.bimanual:
self.bbox = np.array([[0.0, 0.7], [-0.35, 0.45 + 0.75], [-0.8, 0.0]])
else:
self.bbox = np.array([[0.0, 0.7], [-0.35, 0.45], [-0.8, 0.0]])
self.text_prompts = text_prompts
self.show_annotation = show_annotation
self.capture_fps = capture_fps
self.record_fps = record_fps
self.state = mp.Manager().dict() # should be main explict exposed variable to the child class / process
# Realsense camera setup
# camera is always required for real env
if realsense is not None:
assert isinstance(realsense, MultiRealsense) or isinstance(realsense, SingleRealsense)
self.realsense = realsense
self.serial_numbers = list(self.realsense.cameras.keys())
else:
self.realsense = MultiRealsense(
shm_manager=shm_manager,
serial_numbers=serial_numbers,
resolution=resolution,
capture_fps=capture_fps,
enable_depth=enable_depth,
enable_color=enable_color,
verbose=self.debug > EnvEnum.VERBOSE.value
)
self.serial_numbers = list(self.realsense.cameras.keys())
# manual
self.realsense.set_exposure(exposure=60, gain=60) # 100: bright, 60: dark
self.realsense.set_white_balance(3800)
# base calibration
self.calibrate_result_dir = calibrate_result_dir
with open(f'{self.calibrate_result_dir}/base.pkl', 'rb') as f:
base = pickle.load(f)
if self.bimanual:
R_leftbase2board = base['R_leftbase2world']
t_leftbase2board = base['t_leftbase2world']
R_rightbase2board = base['R_rightbase2world']
t_rightbase2board = base['t_rightbase2world']
leftbase2world_mat = np.eye(4)
leftbase2world_mat[:3, :3] = R_leftbase2board
leftbase2world_mat[:3, 3] = t_leftbase2board
self.state["b2w_l"] = leftbase2world_mat
rightbase2world_mat = np.eye(4)
rightbase2world_mat[:3, :3] = R_rightbase2board
rightbase2world_mat[:3, 3] = t_rightbase2board
self.state["b2w_r"] = rightbase2world_mat
else:
R_base2board = base['R_base2world']
t_base2board = base['t_base2world']
base2world_mat = np.eye(4)
base2world_mat[:3, :3] = R_base2board
base2world_mat[:3, 3] = t_base2board
self.state["b2w"] = base2world_mat
# camera calibration
extr_list = []
with open(f'{self.calibrate_result_dir}/rvecs.pkl', 'rb') as f:
rvecs = pickle.load(f)
with open(f'{self.calibrate_result_dir}/tvecs.pkl', 'rb') as f:
tvecs = pickle.load(f)
for i in range(len(self.serial_numbers)):
device = self.serial_numbers[i]
R_world2cam = cv2.Rodrigues(rvecs[device])[0]
t_world2cam = tvecs[device][:, 0]
extr_mat = np.eye(4)
extr_mat[:3, :3] = R_world2cam
extr_mat[:3, 3] = t_world2cam
extr_list.append(extr_mat)
self.state["extr"] = np.stack(extr_list)
# save calibration
mkdir(self.exp_root / "calibration", overwrite=False, resume=False)
subprocess.run(f'cp -r {self.calibrate_result_dir}/* {str(self.exp_root)}/calibration', shell=True)
# Perception setup
self.perception = SegmentPerception(
realsense=self.realsense,
capture_fps=self.realsense.capture_fps, # mush be the same as realsense capture fps
record_fps=record_fps,
record_time=record_time,
text_prompts=self.text_prompts,
show_annotation=self.show_annotation,
bbox=self.bbox,
device=self.torch_device,
verbose=self.debug > EnvEnum.VERBOSE.value)
# Robot setup
self.use_robot = use_robot
if use_robot:
if bimanual:
self.left_xarm_controller = XarmController(
start_time=time.time(),
ip=bimanual_robot_ip[0],
gripper_enable=gripper_enable,
mode='3D',
command_mode='cartesian',
robot_id=0,
verbose=True,
)
self.right_xarm_controller = XarmController(
start_time=time.time(),
ip=bimanual_robot_ip[1],
gripper_enable=gripper_enable,
mode='3D',
command_mode='cartesian',
robot_id=1,
verbose=True,
)
self.xarm_controller = None
else:
self.xarm_controller = XarmController(
start_time=time.time(),
ip=robot_ip,
gripper_enable=gripper_enable,
mode='3D',
command_mode='cartesian',
verbose=True,
)
self.left_xarm_controller = None
self.right_xarm_controller = None
else:
self.left_xarm_controller = None
self.right_xarm_controller = None
self.xarm_controller = None
# subprocess can only start a process object created by current process
self._real_alive = mp.Value('b', False)
self.start_time = 0
mp.Process.__init__(self)
self._alive = mp.Value('b', False)
# pygame
# Initialize a separate Pygame window for image display
img_w, img_h = 848, 480
col_num = 2
self.screen_width, self.screen_height = img_w * col_num, img_h * len(self.realsense.serial_numbers)
self.image_window = None
# Shared memory for image data
self.image_data = mp.Array('B', np.zeros((self.screen_height, self.screen_width, 3), dtype=np.uint8).flatten())
# robot eef
self.eef_point = np.array([[0.0, 0.0, 0.175]]) # the eef point in the gripper frame
# planning_module setup
self.planning_module = PlanningModule(
cfg=cfg,
exp_root=self.exp_root,
ckpt_path=ckpt_path,
device=self.torch_device,
bimanual=bimanual,
bbox=self.bbox,
eef_point=self.eef_point,
debug=self.debug
)
self.planning_module.set_target(str(self.exp_root / "target" / "target.pcd"))
self.command = []
self.command_sender = None
self.command_sender_left = None
self.command_sender_right = None
def real_start(self, start_time) -> None:
self._real_alive.value = True
print("starting real env")
# Realsense camera setup
self.realsense.start()
self.realsense.restart_put(start_time + 1)
time.sleep(2)
# Perception setup
if self.perception is not None:
self.perception.start()
self.perception.update_extrinsics(self.state["extr"])
# Robot setup
if self.use_robot:
if self.bimanual:
assert self.left_xarm_controller is not None
assert self.right_xarm_controller is not None
self.left_xarm_controller.start()
self.right_xarm_controller.start()
else:
assert self.xarm_controller is not None
self.xarm_controller.start()
while not self.real_alive:
self._real_alive.value = True
print(".", end="")
time.sleep(0.5)
# get intrinsics
intrs = self.get_intrinsics()
intrs = np.array(intrs)
np.save(self.exp_root / "calibration" / "intrinsics.npy", intrs)
print("real env started")
self.update_real_state_t = threading.Thread(name="update_real_state", target=self.update_real_state)
self.update_real_state_t.start()
def real_stop(self, wait=False) -> None:
self._real_alive.value = False
if self.use_robot:
if self.bimanual and self.left_xarm_controller and self.left_xarm_controller.is_controller_alive:
self.left_xarm_controller.stop()
if self.bimanual and self.right_xarm_controller and self.right_xarm_controller.is_controller_alive:
self.right_xarm_controller.stop()
if not self.bimanual and self.xarm_controller and self.xarm_controller.is_controller_alive:
self.xarm_controller.stop()
if self.perception is not None and self.perception.alive.value:
self.perception.stop()
self.realsense.stop(wait=False)
self.image_display_thread.join()
self.update_real_state_t.join()
print("real env stopped")
@property
def real_alive(self) -> bool:
alive = self._real_alive.value
if self.perception is not None:
alive = alive and self.perception.alive.value
if self.use_robot:
controller_alive = \
(self.bimanual and self.left_xarm_controller and self.left_xarm_controller.is_controller_alive \
and self.right_xarm_controller and self.right_xarm_controller.is_controller_alive) \
or (not self.bimanual and self.xarm_controller and self.xarm_controller.is_controller_alive)
alive = alive and controller_alive
self._real_alive.value = alive
return self._real_alive.value
def _update_perception(self) -> None:
if self.perception.alive.value and self.perception.do_process.value:
if not self.perception.perception_q.empty():
self.state["perception_out"] = {
"time": time.time(),
"value": self.perception.perception_q.get()
}
return
def _update_robot(self) -> None:
if self.bimanual:
assert self.left_xarm_controller is not None
assert self.right_xarm_controller is not None
if self.left_xarm_controller.is_controller_alive and self.right_xarm_controller.is_controller_alive:
if not self.left_xarm_controller.cur_trans_q.empty() and not self.right_xarm_controller.cur_trans_q.empty():
self.state["robot_out"] = {
"time": time.time(),
"left_value": self.left_xarm_controller.cur_trans_q.get(),
"right_value": self.right_xarm_controller.cur_trans_q.get()
}
if not self.left_xarm_controller.cur_gripper_q.empty() and not self.right_xarm_controller.cur_trans_q.empty():
self.state["gripper_out"] = {
"time": time.time(),
"left_value": self.left_xarm_controller.cur_gripper_q.get(),
"right_value": self.right_xarm_controller.cur_gripper_q.get()
}
else:
assert self.xarm_controller is not None
if self.xarm_controller.is_controller_alive:
if not self.xarm_controller.cur_trans_q.empty():
self.state["robot_out"] = {
"time": time.time(),
"value": self.xarm_controller.cur_trans_q.get()
}
if not self.xarm_controller.cur_gripper_q.empty():
self.state["gripper_out"] = {
"time": time.time(),
"value": self.xarm_controller.cur_gripper_q.get()
}
return
def update_real_state(self) -> None:
while self.real_alive:
try:
if self.use_robot:
self._update_robot()
if self.perception is not None:
self._update_perception()
except BaseException as e:
print(f"Error in update_real_state: {e.with_traceback()}")
break
print("update_real_state stopped")
def display_image(self):
pygame.init()
self.image_window = pygame.display.set_mode((self.screen_width, self.screen_height))
pygame.display.set_caption('Image Display Window')
while self._alive.value:
# Extract image data from the shared array
image = np.frombuffer(self.image_data.get_obj(), dtype=np.uint8).reshape((self.screen_height, self.screen_width, 3))
pygame_image = pygame.surfarray.make_surface(image.swapaxes(0, 1))
# Blit the image to the window
self.image_window.blit(pygame_image, (0, 0))
pygame.display.update()
# Handle events (e.g., close window)
for event in pygame.event.get():
if event.type == pygame.QUIT:
self.stop()
pygame.quit()
return
time.sleep(1 / self.realsense.capture_fps) # 30 FPS
print("Image display stopped")
def start_image_display(self):
# Start a thread for the image display loop
self.image_display_thread = threading.Thread(name="display_image", target=self.display_image)
self.image_display_thread.start()
def run(self) -> None:
robot_record_dir = None
if self.bimanual:
self.command_sender_left = udpSender(port=XARM_CONTROL_PORT_L)
self.command_sender_right = udpSender(port=XARM_CONTROL_PORT_R)
else:
self.command_sender = udpSender(port=XARM_CONTROL_PORT)
# initialize images
rgbs = [] # bgr
depths = []
resolution = self.realsense.resolution
for i in range(len(self.realsense.serial_numbers)):
rgbs.append(np.zeros((resolution[1], resolution[0], 3), np.uint8))
depths.append(np.zeros((resolution[1], resolution[0]), np.uint16))
fps = self.record_fps if self.record_fps and self.record_fps > 0 else self.realsense.capture_fps # visualization fps
idx = 0
while self.alive:
try:
tic = time.time()
state = deepcopy(self.state)
if self.bimanual:
b2w_l = state["b2w_l"]
b2w_r = state["b2w_r"]
b2w = None
else:
b2w = state["b2w"]
b2w_l = None
b2w_r = None
# update images from realsense to shared memory
perception_out = state.get("perception_out", None)
robot_out = state.get("robot_out", None)
gripper_out = state.get("gripper_out", None)
# clear previous images
if perception_out is not None:
self.state["perception_out"] = None
intrinsics = self.get_intrinsics()
state["intr"] = intrinsics
self.state["intr"] = intrinsics
self.perception.update_intrinsics(intrinsics)
update_perception = True
if update_perception and perception_out is not None:
print("update image display")
for k in range(len(perception_out['value']['color'])):
rgbs[k] = perception_out['value']['color'][k].copy()
depths[k] = perception_out['value']['depth'][k].copy()
intr = intrinsics[k]
l = 0.1
origin = state["extr"][k] @ np.array([0, 0, 0, 1])
x_axis = state["extr"][k] @ np.array([l, 0, 0, 1])
y_axis = state["extr"][k] @ np.array([0, l, 0, 1])
z_axis = state["extr"][k] @ np.array([0, 0, l, 1])
origin = origin[:3] / origin[2]
x_axis = x_axis[:3] / x_axis[2]
y_axis = y_axis[:3] / y_axis[2]
z_axis = z_axis[:3] / z_axis[2]
origin = intr @ origin
x_axis = intr @ x_axis
y_axis = intr @ y_axis
z_axis = intr @ z_axis
cv2.line(rgbs[k], (int(origin[0]), int(origin[1])), (int(x_axis[0]), int(x_axis[1])), (255, 0, 0), 2)
cv2.line(rgbs[k], (int(origin[0]), int(origin[1])), (int(y_axis[0]), int(y_axis[1])), (0, 255, 0), 2)
cv2.line(rgbs[k], (int(origin[0]), int(origin[1])), (int(z_axis[0]), int(z_axis[1])), (0, 0, 255), 2)
if self.use_robot:
eef_points = np.concatenate([self.eef_point, np.ones((self.eef_point.shape[0], 1))], axis=1) # (n, 4)
eef_colors = [(0, 255, 255)]
eef_axis = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]) # (3, 4)
eef_axis_colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0)]
if robot_out is not None:
assert gripper_out is not None
eef_points_world_vis = []
eef_points_vis = []
if self.bimanual:
left_eef_world_list = []
right_eef_world_list = []
assert b2w_l is not None
assert b2w_r is not None
for val, b2w, eef_world_list in zip(["left_value", "right_value"], [b2w_l, b2w_r], [left_eef_world_list, right_eef_world_list]):
e2b = robot_out[val] # (4, 4)
eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3] # (n, 3)
eef_points_vis.append(eef_points)
eef_points_world_vis.append(eef_points_world)
eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T # (3, 3)
eef_world = np.concatenate([eef_points_world, eef_orientation_world], axis=0) # (n+3, 3)
eef_world_list.append(eef_world)
left_eef_world = np.concatenate(left_eef_world_list, axis=0) # (n+3, 3)
right_eef_world = np.concatenate(right_eef_world_list, axis=0) # (n+3, 3)
eef_world = np.concatenate([left_eef_world, right_eef_world], axis=0) # (2n+6, 3)
else:
assert b2w is not None
e2b = robot_out["value"] # (4, 4)
eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3] # (n, 3)
eef_points_vis.append(eef_points)
eef_points_world_vis.append(eef_points_world)
eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T # (3, 3)
eef_world = np.concatenate([eef_points_world, eef_orientation_world], axis=0) # (n+3, 3)
# add gripper
if self.bimanual:
left_gripper = gripper_out["left_value"]
right_gripper = gripper_out["right_value"]
gripper_world = np.array([left_gripper, right_gripper, 0.0])[None, :] # (1, 3)
else:
gripper = gripper_out["value"]
gripper_world = np.array([gripper, 0.0, 0.0])[None, :] # (1, 3)
eef_world = np.concatenate([eef_world, gripper_world], axis=0) # (n+4, 3) or (2n+7, 3)
if robot_record_dir is not None:
np.savetxt(robot_record_dir / f"{robot_out['time']:.3f}.txt", eef_world, fmt="%.6f")
eef_points_vis = np.concatenate(eef_points_vis, axis=0)
eef_points_world_vis = np.concatenate(eef_points_world_vis, axis=0)
eef_points_world_vis = np.concatenate([eef_points_world_vis, np.ones((eef_points_world_vis.shape[0], 1))], axis=1) # (n, 4)
eef_colors = eef_colors * eef_points_world_vis.shape[0]
if self.bimanual:
for point_orig, point, color, val, b2w in zip(eef_points_vis, eef_points_world_vis, eef_colors, ["left_value", "right_value"], [b2w_l, b2w_r]):
e2b = robot_out[val] # (4, 4)
point = state["extr"][k] @ point
point = point[:3] / point[2]
point = intr @ point
cv2.circle(rgbs[k], (int(point[0]), int(point[1])), 2, color, -1)
# draw eef axis
for axis, color in zip(eef_axis, eef_axis_colors):
eef_point_axis = point_orig + 0.1 * axis
eef_point_axis_world = (b2w @ e2b @ eef_point_axis).T
eef_point_axis_world = state["extr"][k] @ eef_point_axis_world
eef_point_axis_world = eef_point_axis_world[:3] / eef_point_axis_world[2]
eef_point_axis_world = intr @ eef_point_axis_world
cv2.line(rgbs[k],
(int(point[0]), int(point[1])),
(int(eef_point_axis_world[0]), int(eef_point_axis_world[1])),
color, 2)
else:
point_orig, point, color, val, b2w = eef_points_vis[0], eef_points_world_vis[0], eef_colors[0], "value", b2w
e2b = robot_out[val] # (4, 4)
point = state["extr"][k] @ point
point = point[:3] / point[2]
point = intr @ point
cv2.circle(rgbs[k], (int(point[0]), int(point[1])), 2, color, -1)
# draw eef axis
for axis, color in zip(eef_axis, eef_axis_colors):
eef_point_axis = point_orig + 0.1 * axis
eef_point_axis_world = (b2w @ e2b @ eef_point_axis).T
eef_point_axis_world = state["extr"][k] @ eef_point_axis_world
eef_point_axis_world = eef_point_axis_world[:3] / eef_point_axis_world[2]
eef_point_axis_world = intr @ eef_point_axis_world
cv2.line(rgbs[k],
(int(point[0]), int(point[1])),
(int(eef_point_axis_world[0]), int(eef_point_axis_world[1])),
color, 2)
row_imgs = []
for row in range(len(self.realsense.serial_numbers)):
row_imgs.append(
np.hstack(
(cv2.cvtColor(rgbs[row], cv2.COLOR_BGR2RGB),
cv2.applyColorMap(cv2.convertScaleAbs(depths[row], alpha=0.03), cv2.COLORMAP_JET))
)
)
combined_img = np.vstack(row_imgs)
combined_img = cv2.resize(combined_img, (self.screen_width,self.screen_height))
np.copyto(
np.frombuffer(self.image_data.get_obj(), dtype=np.uint8).reshape((self.screen_height, self.screen_width, 3)),
combined_img
)
# save target
if self.construct_target and perception_out is not None and robot_out is not None:
assert os.path.exists(self.exp_root.parent / "target")
save_dir = str(self.exp_root.parent / "target" / "target.pcd")
pts = perception_out['value']['pts']
print(f"target pts: {pts}")
pts = np.concatenate(pts, axis=0)
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(pts)
o3d.io.write_point_cloud(save_dir, pcd)
mkdir(self.exp_root.parent / "target" / "vis", overwrite=False, resume=True)
eef_points_world_vis = []
eef_points = np.concatenate([self.eef_point, np.ones((self.eef_point.shape[0], 1))], axis=1) # (n, 4)
eef_axis = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]) # (3, 4)
if self.bimanual:
left_eef_world_list = []
right_eef_world_list = []
assert b2w_l is not None
assert b2w_r is not None
for val, b2w, eef_world_list in zip(["left_value", "right_value"], [b2w_l, b2w_r], [left_eef_world_list, right_eef_world_list]):
e2b = robot_out[val] # (4, 4)
eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3] # (n, 3)
eef_points_world_vis.append(eef_points_world)
eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T # (3, 3)
eef_world = np.concatenate([eef_points_world, eef_orientation_world], axis=0) # (n+3, 3)
eef_world_list.append(eef_world)
left_eef_world = np.concatenate(left_eef_world_list, axis=0) # (n+3, 3)
right_eef_world = np.concatenate(right_eef_world_list, axis=0) # (n+3, 3)
eef_world = np.concatenate([left_eef_world, right_eef_world], axis=0) # (2n+6, 3)
else:
assert b2w is not None
e2b = robot_out["value"] # (4, 4)
eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3] # (n, 3)
eef_points_world_vis.append(eef_points_world)
eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T # (3, 3)
eef_world = np.concatenate([eef_points_world, eef_orientation_world], axis=0) # (n+3, 3)
np.savetxt(str(self.exp_root.parent / "target" / "vis" / f"robot.txt"), eef_world, fmt="%.6f")
for k in range(len(perception_out['value']['color'])):
rgbs[k] = perception_out['value']['color'][k]
depths[k] = perception_out['value']['depth'][k]
rgb_save_dir = str(self.exp_root.parent / "target" / "vis" / f"rgb_{k}.png")
depth_save_dir = str(self.exp_root.parent / "target" / "vis" / f"depth_{k}.png")
cv2.imwrite(rgb_save_dir, rgbs[k])
cv2.imwrite(depth_save_dir, depths[k])
intr_list = []
extr_list = []
for k in range(len(perception_out['value']['color'])):
intr = intrinsics[k]
extr = state["extr"][k]
intr_list.append(intr)
extr_list.append(extr)
np.save(str(self.exp_root.parent / "target" / "vis" / "intrinsics.npy"), np.stack(intr_list))
np.save(str(self.exp_root.parent / "target" / "vis" / "extrinsics.npy"), np.stack(extr_list))
print(f"target saved to {save_dir}")
time.sleep(5)
continue
# do planning
if perception_out is not None and robot_out is not None and gripper_out is not None:
if len(perception_out['value']['pts']) > 0:
self.perception.do_process.value = False # pause perception
command = self.planning_module.get_command(state, save_dir=f'{self.exp_root}/interaction_{idx:02d}', is_first_iter=(idx == 0))
if self.bimanual:
self.command_sender_left.send(command[0])
self.command_sender_right.send(command[1])
else:
self.command_sender.send(command[0])
idx += 1
time.sleep(10) # TODO execution time
self.perception.do_process.value = True # resume perception
else:
print(f'no points detected in perception_out: {perception_out["value"]["pts"]}')
else:
print(f'perception_out is None: {perception_out is None}', end=' ')
print(f'robot_out is None: {robot_out is None}', end=' ')
print(f'gripper_out is None: {gripper_out is None}')
time.sleep(max(0, 1 / fps - (time.time() - tic)))
except BaseException as e:
print(f"Error in robot planning env: {e.with_traceback()}")
break
if self.bimanual:
assert self.command_sender_left is not None
assert self.command_sender_right is not None
self.command_sender_left.close()
self.command_sender_right.close()
else:
assert self.command_sender is not None
self.command_sender.close()
self.stop()
print("RealEnv process stopped")
def get_intrinsics(self):
return self.realsense.get_intrinsics()
def get_extrinsics(self):
return self.state["extr"]
@property
def alive(self) -> bool:
alive = self._alive.value and self.real_alive
self._alive.value = alive
return alive
def start(self) -> None:
self.start_time = time.time()
self._alive.value = True
self.real_start(time.time())
self.start_image_display()
super().start()
def stop(self) -> None:
self._alive.value = False
self.real_stop()
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