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import os
import time
import pybullet as p
from surrol.tasks.ecm_env import EcmEnv, goal_distance
from surrol.utils.pybullet_utils import (
get_body_pose,
)
import random
import cv2
import pickle
from surrol.utils.robotics import (
get_euler_from_matrix,
get_matrix_from_euler
)
import torch
from surrol.utils.utils import RGB_COLOR_255, Boundary, Trajectory, get_centroid
from surrol.robots.ecm import RENDER_HEIGHT, RENDER_WIDTH, FoV
from surrol.const import ASSET_DIR_PATH
import numpy as np
from surrol.robots.psm import Psm1, Psm2
import sys
sys.path.append('/home/kejianshi/Desktop/Surgical_Robot/science_robotics/stateregress_back')
sys.path.append('/home/kejianshi/Desktop/Surgical_Robot/science_robotics/stateregress_back/utils')
from general_utils import AttrDict
sys.path.append('/home/kejianshi/Desktop/Surgical_Robot/science_robotics/ar_surrol/surrol_datagen/tasks')
from depth_anything.dpt import DepthAnything
from depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
from vmodel import vismodel
from config import opts
class ActiveTrack(EcmEnv):
"""
Active track is not a GoalEnv since the environment changes internally.
The reward is shaped.
"""
ACTION_MODE = 'cVc'
# RCM_ACTION_MODE = 'yaw'
QPOS_ECM = (0, 0, 0.02, 0)
WORKSPACE_LIMITS = ((-0.3, 0.6), (-0.4, 0.4), (0.05, 0.05))
CUBE_NUMBER = 18
def __init__(self, render_mode=None):
# to control the step
self._step = 0
self.counter=0
self.img_list={}
super(ActiveTrack, self).__init__(render_mode)
def step(self, action: np.ndarray):
obs, reward, done, info = super().step(action)
centroid = obs['observation'][-3: -1]
if not (-1.2 < centroid[0] < 1.1 and -1.1 < centroid[1] < 1.1):
# early stop if out of view
done = True
info['achieved_goal'] = centroid
return obs, reward, done, info
def compute_reward(self, achieved_goal: np.ndarray, desired_goal: np.ndarray, info):
""" Dense reward."""
centroid, wz = achieved_goal, self.ecm.wz
d = goal_distance(centroid, desired_goal) / 2
reward = 1 - (d + np.linalg.norm(wz) * 0.1) # maximum reward is 1, important for baseline DDPG
return reward
def _env_setup(self):
super(ActiveTrack, self)._env_setup()
opts.device='cuda:0'
self.v_model=vismodel(opts)
ckpt=torch.load(opts.ckpt_dir, map_location=opts.device)
self.v_model.load_state_dict(ckpt['state_dict'])
self.v_model.to(opts.device)
self.v_model.eval()
self.use_camera = True
# robot
self.ecm.reset_joint(self.QPOS_ECM)
pos_x = random.uniform(0.18, 0.24)
pos_y = random.uniform(0.21, 0.24)
pos_z = random.uniform(0.5, 0.6)
left_right = random.choice([-1, 1])
self.POSE_PSM1 = ((pos_x, left_right*pos_y, pos_z), (0, 0, -(90+ left_right*20 ) / 180 * np.pi)) #(x:0.18-0.25, y:0.21-0.24, z:0.5)
self.QPOS_PSM1 = (0, 0, 0.10, 0, 0, 0)
self.PSM_WORLSPACE_LIMITS = ((0.18+0.45,0.18+0.55), (0.24-0.29,0.24-0.19), (0.5-0.1774,0.5-0.1074))
self.PSM_WORLSPACE_LIMITS = np.asarray(self.PSM_WORLSPACE_LIMITS) \
+ np.array([0., 0., 0.0102]).reshape((3, 1))
# trajectory
traj = Trajectory(self.PSM_WORLSPACE_LIMITS, seed=None)
self.traj = traj
self.traj.set_step(self._step)
self.psm1 = Psm1(self.POSE_PSM1[0], p.getQuaternionFromEuler(self.POSE_PSM1[1]),
scaling=self.SCALING)
if left_right == 1:
self.psm1.move_joint([0.4516922970194888, -0.11590085534517788, 0.1920614431341014, -0.275713630305575, -0.025332969748983816, -0.44957632598600145])
else:
self.psm1.move_joint([0.4516922970194888, -0.11590085534517788, 0.1920614431341014, -0.275713630305575, -0.025332969748983816, -0.44957632598600145])
# target cube
init_psm_Pose = self.psm1.get_current_position(frame='world')
# print(init_psm_Pose[:3, 3])
# exit()
b = Boundary(self.PSM_WORLSPACE_LIMITS)
x, y = self.traj.step()
obj_id = p.loadURDF(os.path.join(ASSET_DIR_PATH, 'cube/cube.urdf'),
(init_psm_Pose[0, 3], init_psm_Pose[1, 3], init_psm_Pose[2, 3]),
p.getQuaternionFromEuler(np.random.uniform(np.deg2rad([0, 0, -90]),
np.deg2rad([0, 0, 90]))),
globalScaling=0.001 * self.SCALING)
# print('psm_eef:', self.psm1.get_joint_number())
color = RGB_COLOR_255[0]
p.changeVisualShape(obj_id, -1,
rgbaColor=(color[0] / 255, color[1] / 255, color[2] / 255, 0),
specularColor=(0.1, 0.1, 0.1))
self.obj_ids['fixed'].append(obj_id) # 0 (target)
self.obj_id = obj_id
b.add(obj_id, sample=False, min_distance=0.12)
# self._cid = p.createConstraint(obj_id, -1, -1, -1,
# p.JOINT_FIXED, [0, 0, 0], [0, 0, 0], [x, y, 0.05 * self.SCALING])
self._cid = p.createConstraint(
parentBodyUniqueId=self.psm1.body,
parentLinkIndex=5,
childBodyUniqueId=self.obj_id,
childLinkIndex=-1,
jointType=p.JOINT_FIXED,
jointAxis=[0, 0, 0],
parentFramePosition=[0, 0, 0],
childFramePosition=[0, 0, 0]
)
# '''
# Set up initial env
# '''
# self.psm1_eul = np.array(p.getEulerFromQuaternion(
# self.psm1.pose_rcm2world(self.psm1.get_current_position(), 'tuple')[1])) # in the world frame
# # robot
# #self.psm1_eul = np.array(p.getEulerFromQuaternion(
# # self.psm1.pose_rcm2world(self.psm1.get_current_position(), 'tuple')[1])) # in the world frame
# if self.RCM_ACTION_MODE == 'yaw':
# #self.psm1_eul = np.array([np.deg2rad(-90), 0., self.psm1_eul[2]])
# '''
# # RCM init
# #eul=np.array([np.deg2rad(-90), 0., 0.])
# print(self.psm1.wTr)
# print(self.psm1.tool_T_tip)
# init_pose=self.psm1.get_current_position()
# eul=np.array([0, 0.,np.deg2rad(-50)])
# rcm_eul=get_matrix_from_euler(eul)
# init_pose[:3,:3]=rcm_eul
# rcm_pose=self.psm1.pose_world2rcm(init_pose)
# rcm_eul=get_euler_from_matrix(rcm_pose[:3,:3])
# print('from [0, 0.,np.deg2rad(-50)] to ',rcm_eul)
# #exit()
# eul=np.array([0, 0.,np.deg2rad(-90)])
# rcm_eul=get_matrix_from_euler(eul)
# init_pose[:3,:3]=rcm_eul
# rcm_pose=self.psm1.pose_world2rcm(init_pose)
# rcm_eul=get_euler_from_matrix(rcm_pose[:3,:3])
# print('from [0, 0.,np.deg2rad(-90)] to ',rcm_eul)
# m=np.array([[ 0.93969262 ,-0.34202014 , 0. , 1.21313615],
# [ 0.34202014 , 0.93969262 , 0. ,-2.25649898],
# [ 0. , 0. , 1. ,-4.25550013],
# [ 0. , 0. , 0. , 1. ]])
# #print(m.shape)
# m=get_euler_from_matrix(m[:3,:3])
# print('m1: ',m)
# m=np.array([[ 0. ,-0.93969262 ,-0.34202014 , 1.21313615],
# [ 0. ,-0.34202014 , 0.93969262 ,-2.25649898],
# [-1. , 0. , 0. ,-4.25550013],
# [ 0. , 0. , 0. , 1. ],])
# m=get_euler_from_matrix(m[:3,:3])
# print('m2: ',m)
# exit()
# '''
# # RCM init
# eul=np.array([np.deg2rad(-90), 0., 0.])
# eul= get_matrix_from_euler(eul)
# init_pose=self.psm1.get_current_position()
# self.rcm_init_eul=np.array(get_euler_from_matrix(init_pose[:3, :3]))
# init_pose[:3,:3]=eul
# rcm_pose=self.psm1.pose_world2rcm_no_tip(init_pose)
# rcm_eul=get_euler_from_matrix(rcm_pose[:3,:3])
# #print('rcm eul: ',rcm_eul)
# #exit()
# self.rcm_init_eul[0]=rcm_eul[0]
# self.rcm_init_eul[1]=rcm_eul[1]
# print(self.rcm_init_eul)
# #exit()
# elif self.RCM_ACTION_MODE == 'pitch':
# self.psm1_eul = np.array([np.deg2rad(0), self.psm1_eul[1], np.deg2rad(-90)])
# else:
# raise NotImplementedError
# self.psm2 = None
# self._contact_constraint = None
# self._contact_approx = False
# other cubes
# b.set_boundary(self.workspace_limits + np.array([[-0.2, 0], [0, 0], [0, 0]]))
# for i in range(self.CUBE_NUMBER):
# obj_id = p.loadURDF(os.path.join(ASSET_DIR_PATH, 'cube/cube.urdf'),
# (0, 0, 0.05), (0, 0, 0, 1),
# globalScaling=0.8 * self.SCALING)
# color = RGB_COLOR_255[1 + i // 2]
# p.changeVisualShape(obj_id, -1,
# rgbaColor=(color[0] / 255, color[1] / 255, color[2] / 255, 1),
# specularColor=(0.1, 0.1, 0.1))
# # p.changeDynamics(obj_id, -1, mass=0.01)
# b.add(obj_id, min_distance=0.12)
# def _get_obs(self) -> np.ndarray:
# robot_state = self._get_robot_state()
# # may need to modify
# rgb_array, mask, depth = self.ecm.render_image()
# in_view, centroids = get_centroid(mask, self.obj_id)
# if not in_view:
# # out of view; differ when the object is on the boundary.
# pos, _ = get_body_pose(self.obj_id)
# centroids = self.ecm.get_centroid_proj(pos)
# # print(" -> Out of view! {}".format(np.round(centroids, 4)))
# observation = np.concatenate([
# robot_state, np.array(in_view).astype(np.float).ravel(),
# centroids.ravel(), np.array(self.ecm.wz).ravel() # achieved_goal.copy(),
# ])
# return observation
def _get_obs(self) -> dict:
robot_state = self._get_robot_state()
render_obs,seg, depth=self.ecm.render_image()
#cv2.imwrite('/research/d1/rshr/arlin/data/debug/depth_noise_debug/img.png',cv2.cvtColor(render_obs, cv2.COLOR_BGR2RGB))
#plt.imsave('/research/d1/rshr/arlin/data/debug/depth_noise_debug/img2.png',render_obs)
#print('depth max: ',np.max(depth))
#exit()
render_obs=cv2.resize(render_obs,(320,240))
self.counter+=1
#print(render_obs[0][0])
#exit()
#seg=np.array(seg==6).astype(int)
seg=np.array((seg==6 )| (seg==1)).astype(int)
#seg=np.array(seg==1).astype(int)
#seg=np.resize(seg,(320,240))
#plt.imsave('/research/d1/rshr/arlin/data/debug/depth_noise_debug/depth.png',depth)
#exit()
seg = cv2.resize(seg, (320,240), interpolation =cv2.INTER_NEAREST)
#plt.imsave('/research/d1/rshr/arlin/data/debug/seg_debug/noise_{}/seg.png'.format(self.curr_intensity),seg)
#exit()
depth = cv2.resize(depth, (320,240), interpolation =cv2.INTER_NEAREST)
#print(np.max(depth))
#depth = cv2.resize(depth, (320,240),interpolation=cv2.INTER_LANCZOS4)
#image=cv2.cvtColor(render_obs, cv2.COLOR_BGR2RGB) / 255.0
#plt.imsave('/home/student/code/regress_data7/seg/seg_{}.png'.format(self.counter),seg)
#image = self.transform({'image': image})['image']
#image=torch.from_numpy(image).to("cuda:0").float()
# test depth noise
#if np.random.randn()<0.5:
# instensity=np.random.randint(3,15)/100
#instensity=0.1
# depth = add_gaussian_noise(depth, instensity)
'''
if self.counter==10:
cv2.imwrite('/research/d1/rshr/arlin/data/debug/depth_noise_debug/gaussian/img.png',cv2.cvtColor(render_obs, cv2.COLOR_BGR2RGB))
plt.imsave('/research/d1/rshr/arlin/data/debug/depth_noise_debug/gaussian/depth.png',depth)
for i in [0.01,0.05,0.1,0.15,0.2]:
noisy_depth_map = add_random_noise(depth, i)
plt.imsave('/research/d1/rshr/arlin/data/debug/depth_noise_debug/gaussian/noise_{}.png'.format(i),noisy_depth_map)
exit()
'''
#noisy_segmentation_map = add_noise_to_segmentation(seg, self.seg_noise_intensity)
#noisy_depth_map = add_gaussian_noise(depth, self.curr_intensity)
#if self.counter==10:
# plt.imsave('/research/d1/rshr/arlin/data/debug/seg_debug/noise_{}/img.png'.format(self.curr_intensity),render_obs)
# plt.imsave('/research/d1/rshr/arlin/data/debug/seg_debug/noise_{}/seg.png'.format(self.curr_intensity),seg)
# plt.imsave('/research/d1/rshr/arlin/data/debug/seg_debug/noise_{}/noise_seg.png'.format(self.curr_intensity),noisy_segmentation_map)
seg=torch.from_numpy(seg).to("cuda:0").float()
depth=torch.from_numpy(depth).to("cuda:0").float()
with torch.no_grad():
v_output=self.v_model.get_obs(seg.unsqueeze(0), depth.unsqueeze(0))[0]#.cpu().data().numpy()
#print(v_output.shape)
v_output=v_output.cpu().numpy()
achieved_goal = np.array([
v_output[0], v_output[1], self.ecm.wz
])
observation = np.concatenate([
robot_state, np.array([0.0]).astype(np.float).ravel(),
v_output.ravel(), np.array(self.ecm.wz).ravel() # achieved_goal.copy(),
])
obs = {
'observation': observation.copy(),
'achieved_goal': achieved_goal.copy(),
'desired_goal': np.array([0., 0., 0.]).copy()
}
return obs
def _sample_goal(self) -> np.ndarray:
""" Samples a new goal and returns it.
"""
goal = np.array([0., 0.])
return goal.copy()
def _step_callback(self):
""" Move the target along the trajectory
"""
for _ in range(10):
x, y = self.traj.step()
self._step = self.traj.get_step()
current_PSM_position = self.psm1.get_current_position(frame='world')
new_PSM_position = current_PSM_position.copy()
new_PSM_position[0, 3] =x
new_PSM_position[1, 3] =y
new_PSM_position = self.psm1.pose_world2rcm(new_PSM_position)
self.psm1.move(new_PSM_position)
# pivot = [x, y, 0.05 * self.SCALING]
# p.changeConstraint(self._cid, pivot, maxForce=50)
p.stepSimulation()
def get_oracle_action(self, obs) -> np.ndarray:
"""
Define a human expert strategy
"""
centroid = obs['observation'][-3: -1]
cam_u = centroid[0] * RENDER_WIDTH
cam_v = centroid[1] * RENDER_HEIGHT
self.ecm.homo_delta = np.array([cam_u, cam_v]).reshape((2, 1))
if np.linalg.norm(self.ecm.homo_delta) < 8 and np.linalg.norm(self.ecm.wz) < 0.1:
# e difference is small enough
action = np.zeros(3)
else:
# print("Pixel error: {:.4f}".format(np.linalg.norm(self.ecm.homo_delta)))
# controller
fov = np.deg2rad(FoV)
fx = (RENDER_WIDTH / 2) / np.tan(fov / 2)
fy = (RENDER_HEIGHT / 2) / np.tan(fov / 2) # TODO: not sure
cz = 1.0
Lmatrix = np.array([[-fx / cz, 0., cam_u / cz],
[0., -fy / cz, cam_v / cz]])
action = 0.5 * np.dot(np.linalg.pinv(Lmatrix), self.ecm.homo_delta).flatten() / 0.01
if np.abs(action).max() > 1:
action /= np.abs(action).max()
return action
if __name__ == "__main__":
env = ActiveTrack(render_mode='human') # create one process and corresponding env
env.test(horizon=200)
env.close()
time.sleep(2)
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