141 lines
4.7 KiB
Python
141 lines
4.7 KiB
Python
import numpy as np
|
|
from sensor_msgs.msg import CameraInfo
|
|
from pathlib import Path
|
|
import rospy
|
|
|
|
from .visualization import Visualizer
|
|
from robot_helpers.ros import tf
|
|
from robot_helpers.ros.conversions import *
|
|
from vgn.detection import *
|
|
from vgn.perception import UniformTSDFVolume
|
|
from vgn.utils import *
|
|
|
|
|
|
class Policy:
|
|
def activate(self, bbox):
|
|
raise NotImplementedError
|
|
|
|
def update(self, img, extrinsic):
|
|
raise NotImplementedError
|
|
|
|
|
|
class BasePolicy(Policy):
|
|
def __init__(self, rate=5, filter_grasps=False):
|
|
self.rate = rate
|
|
self.filter_grasps = filter_grasps
|
|
self.load_parameters()
|
|
self.init_visualizer()
|
|
|
|
def load_parameters(self):
|
|
self.base_frame = rospy.get_param("active_grasp/base_frame_id")
|
|
self.task_frame = "task"
|
|
info_topic = rospy.get_param("active_grasp/camera/info_topic")
|
|
msg = rospy.wait_for_message(info_topic, CameraInfo, rospy.Duration(2.0))
|
|
self.intrinsic = from_camera_info_msg(msg)
|
|
self.vgn = VGN(Path(rospy.get_param("vgn/model")))
|
|
self.score_fn = lambda g: g.pose.translation[2] # TODO
|
|
|
|
def init_visualizer(self):
|
|
self.visualizer = Visualizer(self.base_frame)
|
|
|
|
def activate(self, bbox):
|
|
self.bbox = bbox
|
|
|
|
self.center = 0.5 * (bbox.min + bbox.max)
|
|
self.T_base_task = Transform.translation(self.center - np.full(3, 0.15))
|
|
tf.broadcast(self.T_base_task, self.base_frame, self.task_frame)
|
|
rospy.sleep(1.0) # wait for the transform to be published
|
|
|
|
N, self.T = 40, 10
|
|
grid_shape = (N,) * 3
|
|
|
|
self.tsdf = UniformTSDFVolume(0.3, N)
|
|
|
|
self.qual_hist = np.zeros((self.T,) + grid_shape, np.float32)
|
|
self.rot_hist = np.zeros((self.T, 4) + grid_shape, np.float32)
|
|
self.width_hist = np.zeros((self.T,) + grid_shape, np.float32)
|
|
|
|
self.viewpoints = []
|
|
self.done = False
|
|
self.best_grasp = None
|
|
|
|
self.visualizer.clear()
|
|
self.visualizer.bbox(bbox)
|
|
|
|
def integrate_img(self, img, extrinsic):
|
|
self.viewpoints.append(extrinsic.inv())
|
|
self.tsdf.integrate(img, self.intrinsic, extrinsic * self.T_base_task)
|
|
|
|
if self.filter_grasps:
|
|
out = self.vgn.predict(self.tsdf.get_grid())
|
|
t = (len(self.viewpoints) - 1) % self.T
|
|
self.qual_hist[t, ...] = out.qual
|
|
self.rot_hist[t, ...] = out.rot
|
|
self.width_hist[t, ...] = out.width
|
|
|
|
mean_qual = self.compute_mean_quality()
|
|
self.visualizer.quality(self.task_frame, self.tsdf.voxel_size, mean_qual)
|
|
|
|
self.visualizer.scene_cloud(self.task_frame, self.tsdf.get_scene_cloud())
|
|
self.visualizer.path(self.viewpoints)
|
|
|
|
def compute_best_grasp(self):
|
|
if self.filter_grasps:
|
|
qual = self.compute_mean_quality()
|
|
index_list = select_local_maxima(qual, 0.9, 3)
|
|
grasps = [g for i in index_list if (g := self.select_mean_at(i))]
|
|
else:
|
|
out = self.vgn.predict(self.tsdf.get_grid())
|
|
qual = out.qual
|
|
index_list = select_local_maxima(qual, 0.9, 3)
|
|
grasps = [select_at(out, i) for i in index_list]
|
|
|
|
grasps = [from_voxel_coordinates(g, self.tsdf.voxel_size) for g in grasps]
|
|
grasps = self.transform_and_reject(grasps)
|
|
grasps = sort_grasps(grasps, self.score_fn)
|
|
|
|
self.visualizer.quality(self.task_frame, self.tsdf.voxel_size, qual)
|
|
self.visualizer.grasps(grasps)
|
|
|
|
return grasps[0] if len(grasps) > 0 else None
|
|
|
|
def compute_mean_quality(self):
|
|
qual = np.mean(self.qual_hist, axis=0, where=self.qual_hist > 0.0)
|
|
return np.nan_to_num(qual, copy=False) # mean of empty slices returns nan
|
|
|
|
def select_mean_at(self, index):
|
|
i, j, k = index
|
|
ts = np.flatnonzero(self.qual_hist[:, i, j, k])
|
|
if len(ts) < 3:
|
|
return
|
|
ori = Rotation.from_quat([self.rot_hist[t, :, i, j, k] for t in ts])
|
|
pos = np.array([i, j, k], dtype=np.float64)
|
|
width = self.width_hist[ts, i, j, k].mean()
|
|
qual = self.qual_hist[ts, i, j, k].mean()
|
|
return Grasp(Transform(ori.mean(), pos), width, qual)
|
|
|
|
def transform_and_reject(self, grasps):
|
|
result = []
|
|
for grasp in grasps:
|
|
pose = self.T_base_task * grasp.pose
|
|
tip = pose.rotation.apply([0, 0, 0.05]) + pose.translation
|
|
if self.bbox.is_inside(tip):
|
|
grasp.pose = pose
|
|
result.append(grasp)
|
|
return result
|
|
|
|
|
|
registry = {}
|
|
|
|
|
|
def register(id, cls):
|
|
global registry
|
|
registry[id] = cls
|
|
|
|
|
|
def make(id, *args, **kwargs):
|
|
if id in registry:
|
|
return registry[id](*args, **kwargs)
|
|
else:
|
|
raise ValueError("{} policy does not exist.".format(id))
|