/
oja_pick.py
executable file
·145 lines (123 loc) · 6.47 KB
/
oja_pick.py
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import os
import sys
import yaml
import numpy as np
from PIL import Image
import pybullet as pyb
from tqdm import trange
from collections import namedtuple
from oja import Oja
arg = yaml.load(open(sys.argv[1], 'r'), yaml.Loader)
arg = namedtuple('arg', arg.keys())(**arg)
class OjaPick:
def __init__(self, dir_dataset, data_size):
pyb.connect(pyb.GUI)
pyb.configureDebugVisualizer(shadowMapResolution=16384)
pyb.resetSimulation()
pyb.setTimeStep(sum(arg.time_step) / 2.)
# object spawning sequence
self.robot = Oja(arg.time_step)
self.obj = pyb.loadURDF("model/object/{}.urdf".format(arg.object), useFixedBase=True)
self.dir_dataset = dir_dataset
self.data_size = data_size
self.pose_tcp, self.cnt_obj = None, None
self.label_tmp = []
self.i_sample = 0
with open(os.path.join(self.dir_dataset, 'gt.yaml'), 'w') as f:
yaml.dump([], f, Dumper=yaml.Dumper)
def __del__(self):
pyb.disconnect()
def reset(self):
while True:
pos_obj = (np.random.rand(3) - 0.5) * arg.obj_pos_rag + arg.obj_pos_ctr
rot_obj = pyb.getQuaternionFromEuler((np.random.rand(3) - 0.5) * arg.obj_rot_rag + arg.obj_rot_ctr)
pyb.resetBasePositionAndOrientation(self.obj, pos_obj, rot_obj)
pos_tcp, rot_tcp = pyb.multiplyTransforms(pos_obj, pyb.getQuaternionFromEuler(arg.obj_rot_ctr),
arg.tcp_pos_offset,
pyb.getQuaternionFromEuler(arg.tcp_rot_offset))
self.robot.home()
if self.robot.set_tcp_pose(pos_tcp, rot_tcp, True):
break
self.robot.set_gripper_joint(arg.gripper_joint, True)
self.pose_tcp = (pos_tcp, rot_tcp)
def rollout(self):
timestep = np.random.uniform(*arg.time_step)
while True:
vel = (np.random.rand(6) - 0.5) * (arg.rollout_vel_lin_rag + arg.rollout_vel_rot_rag) \
+ (arg.rollout_vel_lin_ctr + arg.rollout_vel_rot_ctr)
vel = vel / np.linalg.norm(vel)
vel_ = vel[:3].tolist() + (arg.rot_mul * vel[3:]).tolist()
num_roll = 0
for i in range(arg.rollout_step):
pyb.stepSimulation()
if len(pyb.getClosestPoints(self.robot.robotUid, self.obj, 0)) > 0:
break
imgs = self.get_images()
if self.cnt_obj < arg.th_obj_pixel and arg.th_obj_pixel > 0:
break
self.save_images(i, imgs)
self.pose_tcp = self.robot.apply_speed_tcp(vel_[:3], vel_[3:], self.pose_tcp, True, True, timestep)
self.robot.set_gripper_joint(arg.gripper_joint, True)
num_roll = i
if num_roll >= 2:
break
self.reset()
self.save_label((vel * -1.).tolist() + [timestep])
def get_images(self):
shift_xyz = (np.random.rand(3) - 0.5) * arg.cam_pos_rag + arg.cam_pos_ctr
shift_rpy = (np.random.rand(3) - 0.5) * arg.cam_rot_rag + arg.cam_rot_ctr
# get complete images - Left
fov = np.random.uniform(*arg.fov)
color_l, depth_l, segme_l = self.robot.get_image(shift_xyz=shift_xyz, shift_rpy=shift_rpy, fov=fov,
size=arg.img_size, clip=arg.cam_clip, random_lighting=True)
# get complete images - Right
color_r, depth_r, segme_r = self.robot.get_image(shift_xyz=shift_xyz, shift_rpy=shift_rpy, fov=fov,
baseline=[(0, -np.random.uniform(*arg.baseline), 0), (0, 0, 0)],
size=arg.img_size, clip=arg.cam_clip, random_lighting=True)
self.cnt_obj = ((segme_l == self.obj).sum() + (segme_r == self.obj).sum()) / 2.
return color_l, depth_l, segme_l, color_r, depth_r, segme_r
def save_images(self, i_rollout, img):
Image.fromarray(img[0].astype(np.uint8)).save(os.path.join(
self.dir_dataset, 'left/color/{:05d}_{:02d}.png'.format(self.i_sample - 1, i_rollout)))
Image.fromarray(img[3].astype(np.uint8)).save(os.path.join(
self.dir_dataset, 'right/color/{:05d}_{:02d}.png'.format(self.i_sample - 1, i_rollout)))
Image.fromarray((img[1] * 255).astype(np.uint8)).save(os.path.join(
self.dir_dataset, 'left/depth/{:05d}_{:02d}.png'.format(self.i_sample - 1, i_rollout)))
Image.fromarray((img[2] + 1).astype(np.uint8)).save(os.path.join(
self.dir_dataset, 'left/segme/{:05d}_{:02d}.png'.format(self.i_sample - 1, i_rollout)))
Image.fromarray((img[4] * 255).astype(np.uint8)).save(os.path.join(
self.dir_dataset, 'right/depth/{:05d}_{:02d}.png'.format(self.i_sample - 1, i_rollout)))
Image.fromarray((img[5] + 1).astype(np.uint8)).save(os.path.join(
self.dir_dataset, 'right/segme/{:05d}_{:02d}.png'.format(self.i_sample - 1, i_rollout)))
def save_label(self, label=None):
if label is not None:
self.label_tmp.append(label)
if len(self.label_tmp) > 500 or \
((label is None) and (len(self.label_tmp) > 0)):
with open(os.path.join(self.dir_dataset, 'gt.yaml'), 'r') as f:
labels = yaml.load(f, Loader=yaml.Loader)
labels.extend(self.label_tmp)
with open(os.path.join(self.dir_dataset, 'gt.yaml'), 'w') as f:
yaml.dump(labels, f, Dumper=yaml.Dumper)
self.label_tmp = []
def run(self):
print('collecting {} data...'.format(self.data_size))
for _ in trange(self.data_size):
self.i_sample += 1
self.reset()
self.rollout()
self.save_label()
if __name__ == '__main__':
dir_base = os.path.dirname(os.path.realpath(__file__))
# training and testing data
for i in range(2):
dir_dataset = os.path.join(dir_base, arg.dir_dataset[i])
if os.path.exists(dir_dataset):
os.system('rm -r {}'.format(dir_dataset))
os.makedirs(os.path.join(dir_dataset, 'left/color'))
os.makedirs(os.path.join(dir_dataset, 'left/depth'))
os.makedirs(os.path.join(dir_dataset, 'left/segme'))
os.makedirs(os.path.join(dir_dataset, 'right/color'))
os.makedirs(os.path.join(dir_dataset, 'right/depth'))
os.makedirs(os.path.join(dir_dataset, 'right/segme'))
OjaPick(dir_dataset, arg.data_size[i]).run()