116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
import cv_bridge
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import numpy as np
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from pathlib import Path
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import rospy
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from sensor_msgs.msg import CameraInfo, Image, PointCloud2
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from .visualization import Visualizer
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from robot_helpers.ros import tf
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from robot_helpers.ros.conversions import *
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from vgn.detection import VGN, compute_grasps
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from vgn.perception import UniformTSDFVolume
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from vgn.utils import *
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class Policy:
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def activate(self, bbox):
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raise NotImplementedError
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def update(self):
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raise NotImplementedError
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class BasePolicy(Policy):
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def __init__(self):
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self.cv_bridge = cv_bridge.CvBridge()
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self.vgn = VGN(Path(rospy.get_param("vgn/model")))
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self.finger_depth = 0.05
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self.rate = 5
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self._load_parameters()
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self._lookup_transforms()
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self._init_camera_stream()
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self._init_publishers()
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self._init_visualizer()
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def _load_parameters(self):
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self.task_frame = rospy.get_param("~frame_id")
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self.base_frame = rospy.get_param("~base_frame_id")
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self.ee_frame = rospy.get_param("~ee_frame_id")
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self.cam_frame = rospy.get_param("~camera/frame_id")
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self.info_topic = rospy.get_param("~camera/info_topic")
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self.depth_topic = rospy.get_param("~camera/depth_topic")
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def _lookup_transforms(self):
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self.T_B_task = tf.lookup(self.base_frame, self.task_frame)
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self.T_EE_cam = tf.lookup(self.ee_frame, self.cam_frame)
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def _init_camera_stream(self):
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msg = rospy.wait_for_message(self.info_topic, CameraInfo, rospy.Duration(2.0))
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self.intrinsic = from_camera_info_msg(msg)
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rospy.Subscriber(self.depth_topic, Image, self._sensor_cb, queue_size=1)
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def _sensor_cb(self, msg):
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self.img = self.cv_bridge.imgmsg_to_cv2(msg).astype(np.float32)
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self.extrinsic = tf.lookup(self.cam_frame, self.task_frame, msg.header.stamp)
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def _init_publishers(self):
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self.scene_cloud_pub = rospy.Publisher("scene_cloud", PointCloud2, queue_size=1)
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def _init_visualizer(self):
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self.visualizer = Visualizer(self.task_frame)
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def activate(self, bbox):
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self.bbox = bbox
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self.tsdf = UniformTSDFVolume(0.3, 40)
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self.viewpoints = []
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self.done = False
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self.best_grasp = None # grasp pose defined w.r.t. the robot's base frame
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self.visualizer.clear()
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self.visualizer.bbox(bbox)
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def _integrate_latest_image(self):
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self.viewpoints.append(self.extrinsic.inv())
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self.tsdf.integrate(
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self.img,
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self.intrinsic,
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self.extrinsic,
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)
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self._publish_scene_cloud()
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self.visualizer.path(self.viewpoints)
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def _publish_scene_cloud(self):
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cloud = self.tsdf.get_scene_cloud()
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msg = to_cloud_msg(self.task_frame, np.asarray(cloud.points))
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self.scene_cloud_pub.publish(msg)
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def _predict_best_grasp(self):
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tsdf_grid = self.tsdf.get_grid()
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out = self.vgn.predict(tsdf_grid)
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score_fn = lambda g: g.pose.translation[2]
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grasps = compute_grasps(self.tsdf.voxel_size, out, score_fn, max_filter_size=3)
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grasps = self._select_grasps_on_target_object(grasps)
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return self.T_B_task * grasps[0].pose if len(grasps) > 0 else None
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def _select_grasps_on_target_object(self, grasps):
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result = []
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for g in grasps:
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tip = g.pose.rotation.apply([0, 0, 0.05]) + g.pose.translation
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if self.bbox.is_inside(tip):
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result.append(g)
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return result
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registry = {}
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def register(id, cls):
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global registry
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registry[id] = cls
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def make(id):
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if id in registry:
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return registry[id]()
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else:
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raise ValueError("{} policy does not exist.".format(id))
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