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generate_meshes_distractors.py
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import argparse
import wandb
import numpy as np
import yaml
import os
import os
from os import listdir
from os.path import isfile, join
def generate_all_meshes(cfg, env, visualize=False, generate_arm_mesh=False):
import omni.isaac.core.utils.prims as prim_utils
from omni.isaac.orbit.utils.math import quat_apply
mesh_prim_paths = []
for object_name in cfg["mesh_names"]:
object_num = object_name.split("_")[-1]
mesh_prim_paths.append(f"Xform_{object_num}/Object_{object_num}")
print(mesh_prim_paths[-1])
folder = cfg["foldermeshname"] #"booknshelvemesh"
os.makedirs(folder, exist_ok=True)
# while True:
# env.step(np.array([0]))
import torch
import trimesh
from omni.isaac.core.prims import RigidPrimView
all_arm_points = []
if generate_arm_mesh:
arm_mesh_prim_path = ['panda_link0', 'panda_link1', 'panda_link2', 'panda_link3', 'panda_link4', 'panda_link5',
'panda_link6', 'panda_link7', 'panda_hand', 'panda_leftfinger', 'panda_rightfinger']
views = []
for prim_name in arm_mesh_prim_path:
view = RigidPrimView(
prim_paths_expr=f"/World/envs/env_*/Robot/{prim_name}", reset_xform_properties=False
)
view.initialize()
views.append(view)
i= 0
for prim_name in arm_mesh_prim_path:
print(prim_name)
mesh_prim_path = f'/World/envs/env_0/Robot/{prim_name}/visuals/{prim_name}' # leaf level prim
mesh_prim = prim_utils.get_prim_at_path(mesh_prim_path)
points_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(mesh_prim), 'points'))
faces_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(mesh_prim), 'faceVertexIndices')).reshape(-1, 3)
scale_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(mesh_prim), 'xformOp:scale'))
# if "184" in mesh_prim_path:
points_scaled_np = points_np * scale_np
tmesh = trimesh.Trimesh(vertices=points_scaled_np, faces=faces_np)
points = tmesh.sample(5000)
np.save(f"franka_arm_meshes/{prim_name}_pcd.npy", points)
pose = views[i].get_world_poses()
pos = pose[0]
rot = pose[1]
print(pos, rot)
rotated_points = quat_apply( rot, torch.tensor(points).to("cuda").float())
trans_points = rotated_points + pos
all_arm_points.append(trans_points.cpu().detach().numpy())
i+=1
for prim_name in mesh_prim_paths:
mesh_prim_path = f'/World/envs/env_0/Scene/{prim_name}/Geometry/Object_Geometry' # leaf level prim
mesh_prim = prim_utils.get_prim_at_path(mesh_prim_path)
points_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(mesh_prim), 'points'))
faces_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(mesh_prim), 'faceVertexIndices')).reshape(-1, 3)
# root_prim = prim_utils.get_prim_at_path(f'/World/envs/env_0/Scene/Xform_184/Object_184/Geometry/Object_Geometry')
# scale_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(root_prim), 'xformOp:scale'))
scale = [1,1,1]
if "184" in mesh_prim_path:
scale = [3,3,3]
points_scaled_np = points_np * np.array(scale)
tmesh = trimesh.Trimesh(vertices=points_scaled_np, faces=faces_np)
points = tmesh.sample(5000)
from pxr import UsdGeom
import torch
xformable = UsdGeom.Xformable(mesh_prim)
transform_matrix = xformable.GetLocalTransformation()
expand_points = np.concatenate([points, np.ones((len(points),1))], axis=1)
points_inv = ([email protected](transform_matrix))[:,:3]
data_name = prim_name.split("/")[-1]
np.save(f"{folder}/{data_name}_pcd.npy", points_inv)
data = env.base_env._env.env.scene._data
object_pos = data[data_name].root_state_w[:,:3]
object_rot = data[data_name].root_state_w[:,3:7]
from scipy.spatial.transform import Rotation as R
# rotation = R.from_quat(object_rot)
# rotated_points = rotation.apply(points)
rotated_points = quat_apply( object_rot, torch.tensor(points_inv).to("cuda").float())
trans_points = rotated_points + object_pos
all_arm_points.append(trans_points.cpu().detach().numpy())
if visualize:
import open3d as o3d
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(np.concatenate(all_arm_points))
o3d.visualization.draw_geometries([pcd])
def run(
**cfg):
if cfg["from_disk"]:
cfg["render_images"] = False
env_name = cfg["env_name"]
demo_folder = cfg["demo_folder"]
num_demos = cfg["num_demos"]
max_path_length = cfg["max_path_length"]
offset = cfg["offset"]
save_all = cfg["save_all"]
save = True
from utils import rollout_policy, visualize_trajectory, create_env, create_state_policy, create_pcd_policy
env, _ = create_env(cfg, cfg['display'], seed=cfg['seed'])
obs = env.reset()
img = env.render(["pointcloud"])
import omni.isaac.core.utils.prims as prim_utils
from omni.isaac.orbit.utils.math import quat_inv, quat_apply, quat_mul,euler_xyz_from_quat, random_orientation, sample_uniform, scale_transform, quat_from_euler_xyz
import IPython
IPython.embed()
while True:
env.step(np.array([0]))
# root_object_prim = '' # top level prim of the object
# mesh_prim_paths = [ "Xform_268/Object_268", "Xform_272/Object_272", "Xform_277/Object_277"]
# for object_name in cfg["mesh_names"]:
# object_num = object_name.split("_")[-1]
# mesh_prim_paths.append(f"Xform_{object_num}/Object_{object_num}")
# print(mesh_prim_paths[-1])
mesh_names = cfg["mesh_names"] # ["Object_268", "Object_272", "Object_277"]
all_points = []
folder = cfg["foldermeshname"] #"booknshelvemesh"
os.makedirs(folder, exist_ok=True)
os.makedirs("franka_arm_meshes", exist_ok=True)
import trimesh
# mesh_names = ["lamp_treated_fixed", "book_fixed", "helmet_fixed", "umbrella_fixed", "bottle_fixed"]
# mesh_objects = [-,-,"Xform_276"]
mesh_names = ["lamp_treated_fixed","helmet_fixed", "book_fixed", "umbrella_fixed", "bottle_fixed", ]
mesh_objects = [262,276, 265,263,263]
for prim_name, id in zip(mesh_names,mesh_objects):
mesh_prim_path = f'/World/envs/env_0/Scene/{prim_name}/Xform_{id}/Object_{id}/Geometry/Object_Geometry' # leaf level prim
mesh_prim = prim_utils.get_prim_at_path(mesh_prim_path)
points_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(mesh_prim), 'points'))
faces_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(mesh_prim), 'faceVertexIndices')).reshape(-1, 3)
# root_prim = prim_utils.get_prim_at_path(f'/World/envs/env_0/Scene/Xform_184/Object_184/Geometry/Object_Geometry')
# scale_np = np.asarray(prim_utils.get_prim_property(prim_utils.get_prim_path(root_prim), 'xformOp:scale'))
scale = [1,1,1]
points_scaled_np = points_np * np.array(scale)
tmesh = trimesh.Trimesh(vertices=points_scaled_np, faces=faces_np)
points = tmesh.sample(5000)
from pxr import UsdGeom
import torch
xformable = UsdGeom.Xformable(mesh_prim)
transform_matrix = xformable.GetLocalTransformation()
expand_points = np.concatenate([points, np.ones((len(points),1))], axis=1)
points_inv = ([email protected](transform_matrix))[:,:3]
data_name = prim_name.split("/")[-1]
if save:
np.save(f"{folder}/{data_name}_pcd.npy", points_inv)
data = env.base_env._env.env.scene._data
object_pos = data[data_name].root_state_w[:,:3]
object_rot = data[data_name].root_state_w[:,3:7]
from scipy.spatial.transform import Rotation as R
# rotation = R.from_quat(object_rot)
# rotated_points = rotation.apply(points)
rotated_points = quat_apply( object_rot, torch.tensor(points_inv).to("cuda").float())
trans_points = rotated_points + object_pos
all_arm_points.append(trans_points.cpu().detach().numpy())
import open3d as o3d
pcd = o3d.geometry.PointCloud()
pcd.points = o3d.utility.Vector3dVector(np.concatenate(all_arm_points))
# pcd2 = o3d.geometry.PointCloud()
# pcd2.points = o3d.utility.Vector3dVector(np.concatenate(all_points2)+0.01)
# pcd2.colors = o3d.utility.Vector3dVector(np.zeros_like(np.concatenate(all_points2)))
o3d.visualization.draw_geometries([pcd])
## Generate sample scene
## from state and joints
# import IPython
# IPython.embed()
# while simulation_app.is_running():
# env.base_env._env.sim.step(True)
# simulation_app.close()
def env_distance(env, state, goal):
obs = env.observation(state)
return env.compute_shaped_distance(obs, goal)
def create_video(images, video_filename):
images = np.array(images).astype(np.uint8)
images = images.transpose(0,3,1,2)
wandb.log({"demos_video_trajectories":wandb.Video(images, fps=10)})
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--gpu",type=int, default=0)
parser.add_argument("--seed",type=int, default=0)
parser.add_argument("--env_name", type=str, default='isaac-env')
parser.add_argument("--epsilon_greedy_rollout",type=float, default=None)
parser.add_argument("--task_config", type=str, default=None)
parser.add_argument("--num_demos", type=int, default=10)
parser.add_argument("--max_path_length", type=int, default=None)
parser.add_argument("--noise", type=float, default=0)
parser.add_argument("--save_all", action="store_true", default=False)
parser.add_argument("--display", action="store_true", default=False)
parser.add_argument("--usd_name",type=str, default=None)
parser.add_argument("--usd_path",type=str, default=None)
parser.add_argument("--img_width", type=int, default=None)
parser.add_argument("--img_height", type=int, default=None)
parser.add_argument("--demo_folder", type=str, default="")
parser.add_argument("--not_randomize", action="store_true", default=False)
parser.add_argument("--from_disk", action="store_true", default=False)
parser.add_argument("--offset", type=int, default=0)
parser.add_argument("--extra_params", type=str, default=None)
parser.add_argument("--datafolder", type=str, default=None)
parser.add_argument("--datafilename", type=str, default=None)
parser.add_argument("--distractors",type=str, default="distractors_fixed")
parser.add_argument("--sensors",type=str, default=None)
args = parser.parse_args()
with open("config.yaml") as file:
config = yaml.safe_load(file)
params = config["common"]
params.update(config[args.env_name])
params.update({'randomize_pos':not args.not_randomize, 'randomize_rot':not args.not_randomize})
if args.extra_params is not None:
all_extra_params = args.extra_params.split(",")
for extra_param in all_extra_params:
params.update(config[extra_param])
data_folder_name = f"{args.env_name}_"
data_folder_name = data_folder_name+"teleop"
data_folder_name = data_folder_name + str(args.seed)
params.update(config["teleop_params"])
params.update(config["teleop"])
params.update(config[args.distractors])
params["render_images"] = True
params["num_envs"] = 1
# params["sensors"] = ["synthetic_pcd"]
for key in args.__dict__:
value = args.__dict__[key]
if value is not None:
params[key] = value
params["data_folder"] = data_folder_name
del params["camera_rot"]
wandb.init(project=args.env_name+"generate_meshes", config=params, dir="/data/pulkitag/data/marcel/wandb")
run(**params)
# dd_utils.launch(run, params, mode='local', instance_type='c4.xlarge')