def get_demo_to_exp_tool_transform(verb_data_accessor, tool_stage_info, exp_tool_cloud, transform_type):
tool_stage_data = verb_data_accessor.get_demo_stage_data(tool_stage_info.stage_name)
demo_tool_cloud = tool_stage_data["object_cloud"][tool_stage_info.item]["xyz"]
demo_tool_cloud_down = voxel_downsample(demo_tool_cloud, .02)
exp_tool_cloud_down = voxel_downsample(exp_tool_cloud, .02)
demo_to_exp_tool_transform = get_warping_transform(demo_tool_cloud_down,
exp_tool_cloud_down,
transform_type)
return demo_to_exp_tool_transform
def get_demo_to_exp_tool_transform(verb_data_accessor, tool_stage_info, exp_tool_cloud, transform_type):
tool_stage_data = verb_data_accessor.get_demo_stage_data(tool_stage_info.stage_name)
demo_tool_cloud = tool_stage_data["object_cloud"][tool_stage_info.item]["xyz"]
demo_tool_cloud_down = voxel_downsample(demo_tool_cloud, .02)
exp_tool_cloud_down = voxel_downsample(exp_tool_cloud, .02)
demo_to_exp_tool_transform = get_warping_transform(demo_tool_cloud_down,
exp_tool_cloud_down,
transform_type)
return demo_to_exp_tool_transform
def test_registration_rope_images():
library = trajectory_library.TrajectoryLibrary("lm.h5","read")
plt.ion()
segment_group = library.root["segments"]
n_segs = len(segment_group.keys())
n_rows = int(round(np.sqrt(n_segs)))
n_cols = int(np.ceil(n_segs / n_rows))
print n_rows, n_cols
sim_ropes = []
for (i,(name, data)) in enumerate(segment_group.items()):
plt.subplot(n_rows, n_cols, i+1)
if "object_points" in segment_group:
points_n3 = data["object_points"][0]
else:
print "rope field is deprecated. use object points"
points_n3 = data["rope"][0]
plt.plot(points_n3[:,1], points_n3[:,0],'.')
plt.title(name)
sim_ropes.append(points_n3)
i=int(sys.argv[1])
rope = np.dot(np.squeeze(np.loadtxt("/home/joschu/Data/rope/rope%i.txt"%i)),np.diag([1,-1,-1]))
rope = voxel_downsample(rope,.03)
#f = registration.tps_rpm
tps = registration.tps_rpm(rope[:,:2], sim_ropes[i][:,:2],plotting=10,reg_init=1,reg_final=.025,n_iter=200)
def preproc(xyz1):
xyz1 = xyz1.reshape(-1,3)
xyz1 = np.dot(xyz1, np.diag([1,-1,-1]))
xyz1 = xyz1[(xyz1[:,2] > .02) & (xyz1[:,2] < .2)
& (np.abs(xyz1[:,0]) < .15) & (np.abs(xyz1[:,1]) < .3)]
xyz1 = voxel_downsample(xyz1, .015)
return xyz1
def check_that_nr_fit_runs():
from jds_image_proc.clouds import voxel_downsample
#from brett2.ros_utils import RvizWrapper
#import lfd.registration as lr
##import lfd.warping as lw
#if rospy.get_name() == "/unnamed":
#rospy.init_node("test_rigidity", disable_signals=True)
#from time import sleep
#sleep(1)
#rviz = RvizWrapper.create()
pts0 = np.loadtxt("../test/rope_control_points.txt")
pts1 = np.loadtxt("../test/bad_rope.txt")
pts_rigid = voxel_downsample(pts0[:10], .02)
#lr.Globals.setup()
np.seterr(all='ignore')
np.set_printoptions(suppress=True)
lin_ag, trans_g, w_eg, x_ea = tps.tps_nr_fit_enhanced(pts0,
pts1,
0.01,
pts_rigid,
0.001,
method="newton",
plotting=0)
#lin_ag2, trans_g2, w_ng2 = tps_fit(pts0, pts1, .01, .01)
#assert np.allclose(w_ng, w_ng2)
def eval_partial(x_ma):
return tps_eval(x_ma, lin_ag, trans_g, w_eg, x_ea)
#lr.plot_orig_and_warped_clouds(eval_partial, pts0, pts1, res=.008)
#handles = lw.draw_grid(rviz, eval_partial, pts0.min(axis=0), pts0.max(axis=0), 'base_footprint')
grads = tps.tps_grad(pts_rigid, lin_ag, trans_g, w_eg, x_ea)
def plot_original_and_warped_demo_and_spec_pt(best_demo, warped_demo, spec_pt_traj, warped_spec_pt_traj, traj):
arms_used = best_demo["arms_used"]
if arms_used in "lb":
pose_array = conversions.array_to_pose_array(asarray(best_demo["l_gripper_tool_frame"]["position"]), 'base_footprint', asarray(best_demo["l_gripper_tool_frame"]["orientation"]))
Globals.handles.append(Globals.rviz.draw_traj_points(pose_array, rgba = (1,0,0,1),ns = "make_verb_traj_service"))
pose_array = conversions.array_to_pose_array(asarray(warped_demo["l_gripper_tool_frame"]["position"]), 'base_footprint', asarray(warped_demo["l_gripper_tool_frame"]["orientation"]))
Globals.handles.append(Globals.rviz.draw_traj_points(pose_array, rgba = (0,1,0,1),ns = "make_verb_traj_service"))
Globals.handles.extend(Globals.rviz.draw_trajectory(traj.l_gripper_poses, traj.l_gripper_angles, ns = "make_verb_traj_service_grippers"))
if arms_used in "rb":
pose_array = conversions.array_to_pose_array(asarray(best_demo["r_gripper_tool_frame"]["position"]), 'base_footprint', asarray(best_demo["r_gripper_tool_frame"]["orientation"]))
Globals.handles.append(Globals.rviz.draw_traj_points(pose_array, rgba = (1,0,0,1),ns = "make_verb_traj_service"))
pose_array = conversions.array_to_pose_array(asarray(warped_demo["r_gripper_tool_frame"]["position"]), 'base_footprint', asarray(warped_demo["r_gripper_tool_frame"]["orientation"]))
Globals.handles.append(Globals.rviz.draw_traj_points(pose_array, rgba = (0,1,0,1),ns = "make_verb_traj_service"))
Globals.handles.extend(Globals.rviz.draw_trajectory(traj.r_gripper_poses, traj.r_gripper_angles, ns = "make_verb_traj_service_grippers"))
for (clouds,rgba) in [(sorted_values(best_demo["object_clouds"]),(1,0,0,.5)),
(sorted_values(warped_demo["object_clouds"]),(0,1,0,.5))]:
cloud = []
for subcloud in clouds:
cloud.extend(np.asarray(subcloud["xyz"]).reshape(-1,3))
cloud = np.array(cloud)
cloud = voxel_downsample(cloud, .02)
pose_array = conversions.array_to_pose_array(cloud, 'base_footprint')
Globals.handles.append(Globals.rviz.draw_curve(pose_array, rgba = rgba,width=.01,type=Marker.CUBE_LIST))
def check_that_nr_fit_runs():
from jds_image_proc.clouds import voxel_downsample
#from brett2.ros_utils import RvizWrapper
#import lfd.registration as lr
##import lfd.warping as lw
#if rospy.get_name() == "/unnamed":
#rospy.init_node("test_rigidity", disable_signals=True)
#from time import sleep
#sleep(1)
#rviz = RvizWrapper.create()
pts0 = np.loadtxt("../test/rope_control_points.txt")
pts1 = np.loadtxt("../test/bad_rope.txt")
pts_rigid = voxel_downsample(pts0[:10], .02)
#lr.Globals.setup()
np.seterr(all='ignore')
np.set_printoptions(suppress=True)
lin_ag, trans_g, w_eg, x_ea = tps.tps_nr_fit_enhanced(pts0, pts1, 0.01, pts_rigid, 0.001, method="newton",plotting=1)
#lin_ag2, trans_g2, w_ng2 = tps_fit(pts0, pts1, .01, .01)
#assert np.allclose(w_ng, w_ng2)
def eval_partial(x_ma):
return tps_eval(x_ma, lin_ag, trans_g, w_eg, x_ea)
#lr.plot_orig_and_warped_clouds(eval_partial, pts0, pts1, res=.008)
#handles = lw.draw_grid(rviz, eval_partial, pts0.min(axis=0), pts0.max(axis=0), 'base_footprint')
grads = tps.tps_grad(pts_rigid, lin_ag, trans_g, w_eg, x_ea)
def save_simple_pc(demo_dir, demo_name, stage_num, file_name):
verb_data_accessor = multi_item_verbs.VerbDataAccessor(test_info_dir="test/multi_item/empty_move_data/%s"%demo_dir)
stage_info = verb_data_accessor.get_stage_info(demo_name, stage_num)
stage_data = verb_data_accessor.get_demo_stage_data(stage_info.stage_name)
stage_pc = stage_data["object_cloud"][stage_info.item]["xyz"]
pc_down = voxel_downsample(stage_pc, .02)
os.chdir(osp.join(osp.dirname(__file__), "test_pcs"))
np.savetxt("%s.pc" % (stage_info.item if file_name is None else file_name), pc_down)
def downsample(xyz):
if DS_METHOD == "voxel":
xyz_ds, ds_inds = voxel_downsample(xyz, DS_LENGTH, return_inds=True)
elif DS_METHOD == "hull":
xyz = np.squeeze(xyz)
_, inds = get_concave_hull(xyz[:, 0:2], .05)
xyz_ds = xyz[inds]
ds_inds = [[i] for i in inds]
return xyz_ds, ds_inds
def setup_obj_rave(obj_cloud_xyz, obj_name):
rave_env = Globals.pr2.env
pc_down = voxel_downsample(obj_cloud_xyz, 0.02)
body = rave.RaveCreateKinBody(rave_env, "")
body.SetName(obj_name) # might want to change this name later
delaunay = scipy.spatial.Delaunay(pc_down)
body.InitFromTrimesh(rave.TriMesh(delaunay.points, delaunay.convex_hull), True)
rave_env.Add(body)
return body
def downsample(xyz):
if DS_METHOD == "voxel":
xyz_ds, ds_inds = voxel_downsample(xyz, DS_LENGTH, return_inds = True)
elif DS_METHOD == "hull":
xyz = np.squeeze(xyz)
_, inds = get_concave_hull(xyz[:,0:2],.05)
xyz_ds = xyz[inds]
ds_inds = [[i] for i in inds]
return xyz_ds, ds_inds
def setup_obj_rave(obj_cloud_xyz, obj_name):
rave_env = Globals.pr2.env
pc_down = voxel_downsample(obj_cloud_xyz, .02)
body = rave.RaveCreateKinBody(rave_env, '')
body.SetName(obj_name) #might want to change this name later
delaunay = scipy.spatial.Delaunay(pc_down)
body.InitFromTrimesh(rave.TriMesh(delaunay.points, delaunay.convex_hull),
True)
rave_env.Add(body)
return body
def save_simple_pc(demo_dir, demo_name, stage_num, file_name):
verb_data_accessor = multi_item_verbs.VerbDataAccessor(
test_info_dir="test/multi_item/empty_move_data/%s" % demo_dir)
stage_info = verb_data_accessor.get_stage_info(demo_name, stage_num)
stage_data = verb_data_accessor.get_demo_stage_data(stage_info.stage_name)
stage_pc = stage_data["object_cloud"][stage_info.item]["xyz"]
pc_down = voxel_downsample(stage_pc, .02)
os.chdir(osp.join(osp.dirname(__file__), "test_pcs"))
np.savetxt("%s.pc" % (stage_info.item if file_name is None else file_name),
pc_down)
def filter_pc2(cloud_pc2):
cloud_xyz = (pc2xyzrgb(cloud_pc2)[0]).reshape(-1,3)
cloud_xyz_down = voxel_downsample(cloud_xyz, .02)
graph = ri.points_to_graph(cloud_xyz_down, .03)
cc = ri.largest_connected_component(graph)
good_xyzs = np.array([graph.node[node_id]["xyz"] for node_id in cc.nodes()])
pose_array = juc.array_to_pose_array(good_xyzs, "base_footprint")
Globals.handles.append(Globals.rviz.draw_curve(pose_array, rgba = (1,1,0,1), type=Marker.CUBE_LIST, width=.001, ns="segmentation"))
raw_input("press enter when done looking")
del Globals.handles[:]
return xyz2pc(good_xyzs, cloud_pc2.header.frame_id)
def test_plate():
import rospy, itertools, glob
from jds_utils.colorize import colorize
from jds_image_proc.pcd_io import load_xyzrgb
import h5py
if rospy.get_name() == "/unnamed": rospy.init_node('test_registration_3d',disable_signals=True)
data_dir = "/home/joschu/python/lfd/data"
f = h5py.File(osp.join(data_dir, "images/pickup-plate0.seg.h5"),"r")
xyz1, rgb1 = np.asarray(f["plate"]["xyz"]), np.asarray(f["plate"]["rgb"])
xyz1 = voxel_downsample(xyz1, .02)
from numpy import sin, cos, pi
xyz_target = xyz1.mean(axis=0)[None,:] + 1.5*np.dot(xyz1 - xyz1.mean(axis=0)[None,:], np.array([[cos(pi/3), sin(pi/3), 0], [-sin(pi/3), cos(pi/3), 0], [0, 0, 1]]))
f = registration.tps_rpm_zrot(xyz1, xyz_target, plotting=200,reg_init=2,reg_final=.5,n_iter=8, verbose=False)
def test_zrot1():
#def test_plate():
import rospy, itertools, glob
from jds_utils.colorize import colorize
from jds_image_proc.pcd_io import load_xyzrgb
import h5py
if rospy.get_name() == "/unnamed": rospy.init_node('test_registration_3d',disable_signals=True)
data_dir = "/home/joschu/python/lfd/data"
f = h5py.File(osp.join(data_dir, "images/pickup-plate0.seg.h5"),"r")
xyz1, rgb1 = np.asarray(f["plate"]["xyz"]), np.asarray(f["plate"]["rgb"])
xyz1 = voxel_downsample(xyz1, .02)
from numpy import sin, cos, pi
print "true angle", pi/3
xyz_target = xyz1.mean(axis=0)[None,:] + 1.5*np.dot(xyz1 - xyz1.mean(axis=0)[None,:], np.array([[cos(pi/3), sin(pi/3), 0], [-sin(pi/3), cos(pi/3), 0], [0, 0, 1]]))
f = registration.tps_rpm_zrot(xyz1, xyz_target, plotting=1,reg_init=2,reg_final=.5,n_iter=8, verbose=False, cost_per_radian=2)
def test_registration_rope_images():
library = trajectory_library.TrajectoryLibrary("lm.h5","read")
plt.ion()
segment_group = library.root["segments"]
n_segs = len(segment_group.keys())
n_rows = int(round(np.sqrt(n_segs)))
n_cols = int(np.ceil(n_segs / n_rows))
print n_rows, n_cols
sim_ropes = []
for (i,(name, data)) in enumerate(segment_group.items()):
plt.subplot(n_rows, n_cols, i+1)
if "object_points" in segment_group:
points_n3 = data["object_points"][0]
else:
print "rope field is deprecated. use object points"
points_n3 = data["rope"][0]
plt.plot(points_n3[:,1], points_n3[:,0],'.')
plt.title(name)
sim_ropes.append(points_n3)
#win_name = "get points"
#cv2.namedWindow(win_name)
#im = np.zeros((100,100), 'uint8')
#im.fill(150)
#xys = np.array(roi.get_polyline(im, win_name),float)/100
#curve = curves.unif_resample(xys,100,tol=.1)
print np.squeeze(np.loadtxt("/home/joschu/Data/rope/rope2.txt")).shape
rope = np.dot(np.squeeze(np.loadtxt("/home/joschu/Data/rope/rope1.txt")),np.diag([1,-1,-1]))
rope = voxel_downsample(rope,.03)
reg = registration_matlab.NonrigidRegistration(display=True)
reg.fit_transformation_icp(rope[:,:2], sim_ropes[3][:,:2])
def test_registration_rope_images():
library = trajectory_library.TrajectoryLibrary("lm.h5", "read")
plt.ion()
segment_group = library.root["segments"]
n_segs = len(segment_group.keys())
n_rows = int(round(np.sqrt(n_segs)))
n_cols = int(np.ceil(n_segs / n_rows))
print n_rows, n_cols
sim_ropes = []
for (i, (name, data)) in enumerate(segment_group.items()):
plt.subplot(n_rows, n_cols, i + 1)
if "object_points" in segment_group:
points_n3 = data["object_points"][0]
else:
print "rope field is deprecated. use object points"
points_n3 = data["rope"][0]
plt.plot(points_n3[:, 1], points_n3[:, 0], '.')
plt.title(name)
sim_ropes.append(points_n3)
#win_name = "get points"
#cv2.namedWindow(win_name)
#im = np.zeros((100,100), 'uint8')
#im.fill(150)
#xys = np.array(roi.get_polyline(im, win_name),float)/100
#curve = curves.unif_resample(xys,100,tol=.1)
print np.squeeze(np.loadtxt("/home/joschu/Data/rope/rope2.txt")).shape
rope = np.dot(np.squeeze(np.loadtxt("/home/joschu/Data/rope/rope1.txt")),
np.diag([1, -1, -1]))
rope = voxel_downsample(rope, .03)
reg = registration_matlab.NonrigidRegistration(display=True)
reg.fit_transformation_icp(rope[:, :2], sim_ropes[3][:, :2])
def save_pcs(clouds_pc2, save_file):
os.chdir(osp.join(osp.dirname(__file__), "exp_pcs"))
for i, cloud_pc2 in enumerate(clouds_pc2):
np.savetxt("%s.pc%i" % (save_file, i), voxel_downsample(pc2xyzrgb(cloud_pc2)[0], 0.02))
from os.path import join, basename, splitext
from glob import glob
from point_clouds.features import cloud_shape_context
from jds_image_proc.clouds import voxel_downsample
import numpy as np
DATA_DIR = '/home/joschu/Data/rope'
for fname in glob(join(DATA_DIR, "*.txt")):
if "feats" in fname: continue
print fname
xyz = np.loadtxt(fname)
if "sim" in fname: xyz = np.dot(xyz, np.diag([1, -1, 1]))
else: xyz = voxel_downsample(xyz, .03)
features = np.c_[xyz, cloud_shape_context(xyz, 2)]
feat_fname = splitext(fname)[0] + ".feats.txt"
np.savetxt(feat_fname, features)
def voxel_downsample(xyz, s, return_inds=False):
xyz = xyz.reshape(-1, 3)
xyz = xyz[np.isfinite(xyz[:, 0])]
pts = []
keys = set([])
for (i, pt) in enumerate(xyz):
x, y, z = pt
key = (int(x // s), int(y // s), int(z // s))
if key not in keys:
keys.add(key)
pts.append(pt)
return np.array(pts)
X = voxel_downsample(ctrl_pts, .02)
y = dists = ssd.cdist(X, good_pts).min(axis=1)**2
#X = array([
#(10,0),
#(3,4),
#(4,3),
#(5,0),
#(4,-3),
#(3,-4)])
#y = array([
#25,
#0,0,0,0,0])
def downsample(xyz): from jds_image_proc.clouds import voxel_downsample DS_LENGTH = .025 xyz_ds, ds_inds = voxel_downsample(xyz, DS_LENGTH, return_inds = True) return xyz_ds, ds_inds
def make_traj(req):
"""
Generate a trajectory using warping
See MakeTrajectory service description
(TODO) should be able to specify a specific demo
"""
assert isinstance(req, MakeTrajectoryRequest)
new_object_clouds = [pc2xyzrgb(cloud)[0] for cloud in req.object_clouds]
scene_info = "PLACEHOLDER"
best_demo_name, best_demo_info = verbs.get_closest_demo(
req.verb, scene_info)
best_demo_data = verbs.get_demo_data(best_demo_name)
transform_type = "tps"
old_object_clouds = [
best_demo_data["object_clouds"][obj_name]["xyz"]
for obj_name in best_demo_data["object_clouds"].keys()
]
if len(old_object_clouds) > 1:
raise Exception("i don't know what to do with multiple object clouds")
x_nd = voxel_downsample(old_object_clouds[0], .02)
y_md = voxel_downsample(new_object_clouds[0], .02)
if transform_type == "tps":
#warp = registration.tps_rpm_zrot(x_nd, y_md, plotting=2,reg_init=2,reg_final=.05, n_iter=10, verbose=False)
warp = registration.tps_rpm(x_nd,
y_md,
plotting=2,
reg_init=2,
reg_final=.05,
n_iter=10,
verbose=False)
elif transform_type == "translation2d":
warp = registration.Translation2d()
warp.fit(x_nd, y_md)
elif transform_type == "rigid2d":
warp = registration.Rigid2d()
warp.fit(x_nd, y_md)
else:
raise Exception("transform type %s is not yet implemented" %
transform_type)
l_offset, r_offset = np.zeros(3), np.zeros(3)
#if "r_tool" in best_demo_info:
#r_offset = asarray(tool_info[this_verb_info["r_tool"]]["translation"])
#if "l_tool" in best_demo_info:
#l_offset = asarray(tool_info[this_verb_info["l_tool"]]["translation"])
arms_used = best_demo_info["arms_used"]
warped_demo_data = warping.transform_verb_demo(warp, best_demo_data)
resp = MakeTrajectoryResponse()
traj = resp.traj
traj.arms_used = arms_used
if arms_used in "lb":
traj.l_gripper_poses.poses = juc.xyzs_quats_to_poses(
warped_demo_data["l_gripper_tool_frame"]["position"],
warped_demo_data["l_gripper_tool_frame"]["orientation"])
traj.l_gripper_angles = warped_demo_data["l_gripper_joint"]
traj.l_gripper_poses.header.frame_id = req.object_clouds[
0].header.frame_id
if "l_tool" in best_demo_info: traj.l_gripper_angles *= 0
if arms_used in "rb":
traj.r_gripper_poses.poses = juc.xyzs_quats_to_poses(
warped_demo_data["r_gripper_tool_frame"]["position"],
warped_demo_data["r_gripper_tool_frame"]["orientation"])
traj.r_gripper_angles = warped_demo_data["r_gripper_joint"]
traj.r_gripper_poses.header.frame_id = req.object_clouds[
0].header.frame_id
if "r_tool" in best_demo_info: traj.r_gripper_angles *= 0
Globals.handles = []
plot_original_and_warped_demo(best_demo_data, warped_demo_data, traj)
pose_array = conversions.array_to_pose_array(y_md, 'base_footprint')
Globals.handles.append(
Globals.rviz.draw_curve(pose_array,
rgba=(0, 0, 1, 1),
width=.01,
type=Marker.CUBE_LIST))
return resp
return np.any(np.all((pt[None,:] == arr), axis=1))
def get_good_inds(orig_xyz, good_xyz):
good_inds = []
for i, xyz in enumerate(orig_xyz):
if array_contains(good_xyzs, xyz):
good_inds.append(i)
return good_inds
for object_name in object_names:
pc_sel = seg_svc.call(ProcessCloudRequest(cloud_in = pc)).cloud_out
xyz, rgb = ros_utils.pc2xyzrgb(pc_sel)
xyz = xyz.reshape(-1,3)
rgb = rgb.reshape(-1,3)
if args.do_filtering:
xyz_down = voxel_downsample(xyz, .02)
graph = ri.points_to_graph(xyz_down, .03)
cc = ri.largest_connected_component(graph)
good_xyzs = np.array([graph.node[node_id]["xyz"] for node_id in cc.nodes()])
good_inds = get_good_inds(xyz, good_xyzs)
good_rgbs = rgb[good_inds]
else:
good_xyzs, good_rgbs = xyz, rgb
outfile.create_group(object_name)
outfile[object_name]["xyz"] = good_xyzs
#outfile[object_name]["rgb"] = good_rgbs
if args.plotting:
rviz = ros_utils.RvizWrapper.create()
rospy.sleep(.5)
def get_demo_to_exp_target_transform(demo_target_cloud, exp_target_cloud, transform_type):
demo_target_cloud_down = voxel_downsample(demo_target_cloud, .02)
exp_target_cloud_down = voxel_downsample(exp_target_cloud, .02)
demo_to_exp_target_transform = get_warping_transform(demo_target_cloud_down, exp_target_cloud_down, transform_type)
return demo_to_exp_target_transform
def get_demo_to_exp_target_transform(demo_target_cloud, exp_target_cloud, transform_type):
demo_target_cloud_down = voxel_downsample(demo_target_cloud, .02)
exp_target_cloud_down = voxel_downsample(exp_target_cloud, .02)
demo_to_exp_target_transform = get_warping_transform(demo_target_cloud_down, exp_target_cloud_down, transform_type)
return demo_to_exp_target_transform
def make_traj(req):
"""
Generate a trajectory using warping
See MakeTrajectory service description
(TODO) should be able to specify a specific demo
"""
assert isinstance(req, MakeTrajectoryRequest)
new_object_clouds = [pc2xyzrgb(cloud)[0] for cloud in req.object_clouds]
scene_info = "PLACEHOLDER"
best_demo_name, best_demo_info = verbs.get_closest_demo(req.verb, scene_info)
best_demo_data = verbs.get_demo_data(best_demo_name)
transform_type = "tps"
old_object_clouds = [best_demo_data["object_clouds"][obj_name]["xyz"]
for obj_name in best_demo_data["object_clouds"].keys()]
if len(old_object_clouds) > 1:
raise Exception("i don't know what to do with multiple object clouds")
x_nd = voxel_downsample(old_object_clouds[0],.02)
y_md = voxel_downsample(new_object_clouds[0],.02)
if transform_type == "tps":
#warp = registration.tps_rpm_zrot(x_nd, y_md, plotting=2,reg_init=2,reg_final=.05, n_iter=10, verbose=False)
warp = registration.tps_rpm(x_nd, y_md, plotting=2,reg_init=2,reg_final=.05, n_iter=10, verbose=False)
elif transform_type == "translation2d":
warp = registration.Translation2d()
warp.fit(x_nd, y_md)
elif transform_type == "rigid2d":
warp = registration.Rigid2d()
warp.fit(x_nd, y_md)
else:
raise Exception("transform type %s is not yet implemented"%transform_type)
l_offset,r_offset = np.zeros(3), np.zeros(3)
#if "r_tool" in best_demo_info:
#r_offset = asarray(tool_info[this_verb_info["r_tool"]]["translation"])
#if "l_tool" in best_demo_info:
#l_offset = asarray(tool_info[this_verb_info["l_tool"]]["translation"])
arms_used = best_demo_info["arms_used"]
warped_demo_data = warping.transform_verb_demo(warp, best_demo_data)
resp = MakeTrajectoryResponse()
traj = resp.traj
traj.arms_used = arms_used
if arms_used in "lb":
traj.l_gripper_poses.poses = xyzs_quats_to_poses(warped_demo_data["l_gripper_tool_frame"]["position"], warped_demo_data["l_gripper_tool_frame"]["orientation"])
traj.l_gripper_angles = warped_demo_data["l_gripper_joint"]
traj.l_gripper_poses.header.frame_id = req.object_clouds[0].header.frame_id
if "l_tool" in best_demo_info: traj.l_gripper_angles *= 0
if arms_used in "rb":
traj.r_gripper_poses.poses = xyzs_quats_to_poses(warped_demo_data["r_gripper_tool_frame"]["position"], warped_demo_data["r_gripper_tool_frame"]["orientation"])
traj.r_gripper_angles = warped_demo_data["r_gripper_joint"]
traj.r_gripper_poses.header.frame_id = req.object_clouds[0].header.frame_id
if "r_tool" in best_demo_info: traj.r_gripper_angles *= 0
Globals.handles = []
plot_original_and_warped_demo(best_demo_data, warped_demo_data, traj)
pose_array = conversions.array_to_pose_array(y_md, 'base_footprint')
Globals.handles.append(Globals.rviz.draw_curve(pose_array, rgba = (0,0,1,1),width=.01,type=Marker.CUBE_LIST))
return resp
def execute(self,userdata):
"""
- lookup closest trajectory from database
- if it's a terminal state, we're done
- warp it based on the current rope
returns: done, not_done, failure
"""
xyz_new = np.squeeze(np.asarray(userdata.points))
#if args.obj == "cloth": xyz_new = voxel_downsample(xyz_new, .025)
xyz_new_ds, ds_inds = downsample(xyz_new)
dists_new = recognition.calc_geodesic_distances_downsampled_old(xyz_new,xyz_new_ds, ds_inds)
ELOG.log('SelectTrajectory', 'xyz_new', xyz_new)
ELOG.log('SelectTrajectory', 'xyz_new_ds', xyz_new_ds)
ELOG.log('SelectTrajectory', 'dists_new', dists_new)
if args.count_steps: candidate_demo_names = self.count2segnames[Globals.stage]
else: candidate_demo_names = demos.keys()
global last_selected_segment
print 'Last selected segment:', last_selected_segment
if args.human_select_demo:
best_name = None
while best_name not in demos:
print 'Select demo from', demos.keys()
best_name = raw_input('> ')
else:
from joblib import parallel
costs_names = parallel.Parallel(n_jobs=-2)(parallel.delayed(calc_seg_cost)(seg_name, xyz_new_ds, dists_new) for seg_name in candidate_demo_names)
#costs_names = [calc_seg_cost(seg_name, xyz_new_ds, dists_new) for seg_name in candidate_demo_names]
#costs_names = [calc_seg_cost(seg_name) for seg_name in candidate_demo_names]
ELOG.log('SelectTrajectory', 'costs_names', costs_names)
_, best_name = min(costs_names)
ELOG.log('SelectTrajectory', 'best_name', best_name)
best_demo = demos[best_name]
if best_demo["done"]:
rospy.loginfo("best demo was a 'done' state")
return "done"
best_demo = demos[best_name]
rospy.loginfo("best segment name: %s", best_name)
last_selected_segment = best_name
xyz_demo_ds = best_demo["cloud_xyz_ds"]
ELOG.log('SelectTrajectory', 'xyz_demo_ds', xyz_demo_ds)
if args.test: n_iter = 21
else: n_iter = 101
if args.use_rigid:
self.f = registration.Translation2d()
self.f.fit(xyz_demo_ds, xyz_new_ds)
ELOG.log('SelectTrajectory', 'f', self.f)
else:
self.f, info = registration.tps_rpm(xyz_demo_ds, xyz_new_ds, plotting = 20, reg_init=1,reg_final=.01,n_iter=n_iter,verbose=False, return_full=True)#, interactive=True)
ELOG.log('SelectTrajectory', 'f', self.f)
ELOG.log('SelectTrajectory', 'f_info', info)
if args.use_nr:
rospy.loginfo('Using nonrigidity costs')
from lfd import tps
import scipy.spatial.distance as ssd
pts_grip = []
for lr in "lr":
if best_demo["arms_used"] in ["b", lr]:
pts_grip.extend(best_demo["%s_gripper_tool_frame"%lr]["position"])
pts_grip = np.array(pts_grip)
dist_to_rope = ssd.cdist(pts_grip, xyz_demo_ds).min(axis=1)
pts_grip_near_rope = pts_grip[dist_to_rope < .04,:]
pts_rigid = voxel_downsample(pts_grip_near_rope, .01)
self.f.lin_ag, self.f.trans_g, self.f.w_ng, self.f.x_na = tps.tps_nr_fit_enhanced(info["x_Nd"], info["targ_Nd"], 0.01, pts_rigid, 0.001, method="newton", plotting=5)
# print 'correspondences', self.f.corr_nm
#################### Generate new trajectory ##################
#### Plot original and warped point clouds #######
# orig_pose_array = conversions.array_to_pose_array(np.squeeze(best_demo["cloud_xyz_ds"]), "base_footprint")
# warped_pose_array = conversions.array_to_pose_array(self.f.transform_points(np.squeeze(best_demo["cloud_xyz_ds"])), "base_footprint")
# Globals.handles.append(Globals.rviz.draw_curve(orig_pose_array,rgba=(1,0,0,1),id=19024,type=Marker.CUBE_LIST))
# Globals.handles.append(Globals.rviz.draw_curve(warped_pose_array,rgba=(0,1,0,1),id=2983,type=Marker.CUBE_LIST))
#### Plot grid ########
mins = np.squeeze(best_demo["cloud_xyz"]).min(axis=0)
maxes = np.squeeze(best_demo["cloud_xyz"]).max(axis=0)
mins[2] -= .1
maxes[2] += .1
grid_handle = warping.draw_grid(Globals.rviz, self.f.transform_points, mins, maxes, 'base_footprint')
Globals.handles.append(grid_handle)
#### Actually generate the trajectory ###########
warped_demo = warping.transform_demo_with_fingertips(self.f, best_demo)
# if yes_or_no('dump warped demo?'):
# import pickle
# fname = '/tmp/warped_demo_' + str(np.random.randint(9999999999)) + '.pkl'
# with open(fname, 'w') as f:
# pickle.dump(warped_demo, f)
# print 'saved to', fname
ELOG.log('SelectTrajectory', 'warped_demo', warped_demo)
def make_traj(warped_demo, inds=None, xyz_offset=0, feas_check_only=False):
traj = {}
total_feas_inds = 0
total_inds = 0
for lr in "lr":
leftright = {"l":"left","r":"right"}[lr]
if best_demo["arms_used"] in [lr, "b"]:
if args.hard_table:
clipinplace(warped_demo["l_gripper_tool_frame"]["position"][:,2],Globals.table_height+.032,np.inf)
clipinplace(warped_demo["r_gripper_tool_frame"]["position"][:,2],Globals.table_height+.032,np.inf)
pos = warped_demo["%s_gripper_tool_frame"%lr]["position"] + xyz_offset
ori = warped_demo["%s_gripper_tool_frame"%lr]["orientation"]
if inds is not None:
pos, ori = pos[inds], ori[inds]
if feas_check_only:
feas_inds = lfd_traj.compute_feas_inds(
pos,
ori,
Globals.pr2.robot.GetManipulator("%sarm"%leftright),
"%s_gripper_tool_frame"%lr,
check_collisions=True)
traj["%s_arm_feas_inds"%lr] = feas_inds
else:
arm_traj, feas_inds = lfd_traj.make_joint_traj_by_graph_search(
pos,
ori,
Globals.pr2.robot.GetManipulator("%sarm"%leftright),
"%s_gripper_tool_frame"%lr,
check_collisions=True)
traj["%s_arm"%lr] = arm_traj
traj["%s_arm_feas_inds"%lr] = feas_inds
total_feas_inds += len(feas_inds)
total_inds += len(pos)
rospy.loginfo("%s arm: %i of %i points feasible", leftright, len(feas_inds), len(pos))
return traj, total_feas_inds, total_inds
# Check if we need to move the base for reachability
base_offset = np.array([0, 0, 0])
if args.use_base:
# First figure out how much we need to move the base to maximize feasible points
OFFSET = 0.1
XYZ_OFFSETS = np.array([[0., 0., 0.], [-OFFSET, 0, 0], [OFFSET, 0, 0], [0, -OFFSET, 0], [0, OFFSET, 0]])
inds_to_check = lfd_traj.where_near_rope(best_demo, xyz_demo_ds, add_other_points=30)
need_to_move_base = False
best_feas_inds, best_xyz_offset = -1, None
for xyz_offset in XYZ_OFFSETS:
_, n_feas_inds, n_total_inds = make_traj(warped_demo, inds=inds_to_check, xyz_offset=xyz_offset, feas_check_only=True)
rospy.loginfo('Cloud offset %s has feas inds %d', str(xyz_offset), n_feas_inds)
if n_feas_inds > best_feas_inds:
best_feas_inds, best_xyz_offset = n_feas_inds, xyz_offset
if n_feas_inds >= 0.99*n_total_inds: break
if np.linalg.norm(best_xyz_offset) > 0.01:
need_to_move_base = True
base_offset = -best_xyz_offset
rospy.loginfo('Best base offset: %s, with %d feas inds', str(base_offset), best_feas_inds)
# Move the base
if need_to_move_base:
rospy.loginfo('Will move base.')
userdata.base_offset = base_offset
return 'move_base'
else:
rospy.loginfo('Will not move base.')
Globals.pr2.update_rave()
# calculate joint trajectory using IK
trajectory = make_traj(warped_demo)[0]
# fill in gripper/grab stuff
for lr in "lr":
leftright = {"l":"left","r":"right"}[lr]
if best_demo["arms_used"] in [lr, "b"]:
if len(trajectory["%s_arm_feas_inds"%lr]) == 0: return "failure"
trajectory["%s_grab"%lr] = best_demo["%s_gripper_joint"%lr] < .07
trajectory["%s_gripper"%lr] = warped_demo["%s_gripper_joint"%lr]
trajectory["%s_gripper"%lr][trajectory["%s_grab"%lr]] = 0
# smooth any discontinuities in the arm traj
for lr in "lr":
leftright = {"l":"left","r":"right"}[lr]
if best_demo["arms_used"] in [lr, "b"]:
trajectory["%s_arm"%lr], discont_times, n_steps = lfd_traj.smooth_disconts(
trajectory["%s_arm"%lr],
Globals.pr2.env,
Globals.pr2.robot.GetManipulator("%sarm"%leftright),
"%s_gripper_tool_frame"%lr,
ignore_inds=[1] # ignore the 0--1 discontinuity, which is usually just moving from rest to the traj starting pose
)
# after smoothing the arm traj, we need to fill in all other trajectories (in both arms)
other_lr = 'r' if lr == 'l' else 'l'
if best_demo["arms_used"] in [other_lr, "b"]:
trajectory["%s_arm"%other_lr] = lfd_traj.fill_stationary(trajectory["%s_arm"%other_lr], discont_times, n_steps)
for tmp_lr in 'lr':
if best_demo["arms_used"] in [tmp_lr, "b"]:
trajectory["%s_grab"%tmp_lr] = lfd_traj.fill_stationary(trajectory["%s_grab"%tmp_lr], discont_times, n_steps)
trajectory["%s_gripper"%tmp_lr] = lfd_traj.fill_stationary(trajectory["%s_gripper"%tmp_lr], discont_times, n_steps)
trajectory["%s_gripper"%tmp_lr][trajectory["%s_grab"%tmp_lr]] = 0
# plotting
for lr in "lr":
leftright = {"l":"left","r":"right"}[lr]
if best_demo["arms_used"] in [lr, "b"]:
# plot warped trajectory
Globals.handles.append(Globals.rviz.draw_curve(
conversions.array_to_pose_array(
alternate(warped_demo["%s_gripper_l_finger_tip_link"%lr]["position"], warped_demo["%s_gripper_r_finger_tip_link"%lr]["position"]),
"base_footprint"
),
width=.001, rgba = (1,0,1,.4), type=Marker.LINE_LIST,
ns='warped_finger_traj'
))
# plot original trajectory
Globals.handles.append(Globals.rviz.draw_curve(
conversions.array_to_pose_array(
alternate(best_demo["%s_gripper_l_finger_tip_link"%lr]["position"], best_demo["%s_gripper_r_finger_tip_link"%lr]["position"]),
"base_footprint"
),
width=.001, rgba = (0,1,1,.4), type=Marker.LINE_LIST,
ns='demo_finger_traj'
))
ELOG.log('SelectTrajectory', 'trajectory', trajectory)
userdata.trajectory = trajectory
if args.prompt_before_motion:
consent = yes_or_no("trajectory ok?")
else:
consent = True
if consent: return "not_done"
else: return "failure"
from jds_utils.colorize import colorize
import h5py
import matplotlib.pyplot as plt
h5file = h5py.File(
osp.join(osp.dirname(lfd.__file__), "data/knot_segments.h5"), "r")
seg_names = h5file.keys()
results = []
dists = []
ropes = []
for i in xrange(6):
rope = np.squeeze(h5file[seg_names[i]]["cloud_xyz"])
if args.downsample:
rope_ds, ds_inds = voxel_downsample(rope, .03, return_inds=True)
dist = recognition.calc_geodesic_distances_downsampled(
rope, rope_ds, ds_inds)
dists.append(dist)
ropes.append(rope_ds)
else:
dist = recognition.calc_geodesic_distances(rope)
dists.append(dist)
ropes.append(rope)
for i0 in xrange(6):
rope0 = ropes[i0]
n0 = len(rope0)
#pairs = zip(xrange(n0), np.random.randint(0,n0,n0))
dists0 = dists[i0]
def make_traj_multi_stage_do_work(current_stage_info, cur_exp_clouds, clouds_frame_id, stage_num, prev_stage_info, prev_exp_clouds, verb_data_accessor, to_gripper_frame_func=None):
arms_used = current_stage_info.arms_used
verb_stage_data = verb_data_accessor.get_demo_data(current_stage_info.stage_name)
if stage_num == 0:
# don't do any extra transformation for the first stage
prev_demo_to_exp_grip_transform_lin_rigid = np.identity(4)
# no special point translation for first stage since no tool yet
special_point_translation = np.identity(4)
elif stage_num > 0:
# make sure that the tool stage only uses one arm (the one with the tool)
assert arms_used in ['r', 'l']
prev_stage_data = verb_data_accessor.get_demo_data(prev_stage_info.stage_name)
prev_demo_pc = prev_stage_data["object_clouds"][prev_stage_info.item]["xyz"]
prev_exp_pc = prev_exp_clouds[0]
prev_demo_pc_down = voxel_downsample(prev_demo_pc, .02)
prev_exp_pc_down = voxel_downsample(prev_exp_pc, .02)
gripper_data_key = "%s_gripper_tool_frame" % (arms_used)
# transform point cloud in base frame to gripper frame
# assume right hand has the tool for now
# use the last pose of the gripper in the stage to figure out the point cloud of the tool in the gripper frame when the tool was grabbed
prev_demo_gripper_pos = prev_stage_data[gripper_data_key]["position"][-1]
prev_demo_gripper_orien = prev_stage_data[gripper_data_key]["orientation"][-1]
prev_demo_gripper_to_base_transform = juc.trans_rot_to_hmat(prev_demo_gripper_pos, prev_demo_gripper_orien)
prev_demo_base_to_gripper_transform = np.linalg.inv(prev_demo_gripper_to_base_transform)
prev_demo_pc_in_gripper_frame = np.array([apply_transform(prev_demo_base_to_gripper_transform, point) for point in prev_demo_pc_down])
# get the new point cloud in the new gripper frame
# prev_exp_pc_in_gripper_frame = [apply_transform(prev_exp_base_to_gripper_transform, point) for point in prev_exp_pc_down]
if to_gripper_frame_func is None:
prev_exp_pc_in_gripper_frame = to_gripper_frame_tf_listener(prev_exp_pc_down, gripper_data_key)
else:
prev_exp_pc_in_gripper_frame = to_gripper_frame_func(prev_exp_pc_down, gripper_data_key)
# get the transformation from the new point cloud to the old point cloud for the previous stage
prev_demo_to_exp_grip_transform = get_tps_transform(prev_demo_pc_in_gripper_frame, prev_exp_pc_in_gripper_frame)
# transforms gripper trajectory point into special point trajectory point
if prev_stage_info.special_point is None:
# if there is no special point, linearize at origin
prev_demo_to_exp_grip_transform_lin_rigid = lin_rigid_tps_transform(prev_demo_to_exp_grip_transform, np.zeros(3))
# don't do a special point translation if there is no specified special point
special_point_translation = np.identity(4)
else:
prev_demo_to_exp_grip_transform_lin_rigid = lin_rigid_tps_transform(prev_demo_to_exp_grip_transform, np.array(prev_stage_info.special_point))
# translation from gripper pose in world frame to special point pose in world frame
special_point_translation = jut.translation_matrix(np.array(prev_stage_info.special_point))
if arms_used != 'b':
arms_used_list = [arms_used]
else:
arms_used_list = ['r', 'l']
warped_stage_data = group_to_dict(verb_stage_data) # deep copy it
resp = MakeTrajectoryResponse()
traj = resp.traj
traj.arms_used = arms_used
for arm in arms_used_list:
gripper_data_key = "%s_gripper_tool_frame" % (arm)
# find the special point trajectory before the target transformation
cur_demo_gripper_traj_xyzs = verb_stage_data[gripper_data_key]["position"]
cur_demo_gripper_traj_oriens = verb_stage_data[gripper_data_key]["orientation"]
cur_demo_gripper_traj_mats = [juc.trans_rot_to_hmat(trans, orien) for (trans, orien) in zip(cur_demo_gripper_traj_xyzs, cur_demo_gripper_traj_oriens)]
cur_mid_spec_pt_traj_mats = [np.dot(gripper_mat, special_point_translation) for gripper_mat in cur_demo_gripper_traj_mats]
# find the transformation from the new special point to the gripper frame
cur_exp_inv_special_point_transformation = np.linalg.inv(np.dot(prev_demo_to_exp_grip_transform_lin_rigid, special_point_translation))
# save the demo special point traj for plotting
plot_spec_pt_traj = []
for gripper_mat in cur_demo_gripper_traj_mats:
spec_pt_xyz, spec_pt_orien = juc.hmat_to_trans_rot(np.dot(gripper_mat, special_point_translation))
plot_spec_pt_traj.append(spec_pt_xyz)
print 'grip transform:'
print prev_demo_to_exp_grip_transform_lin_rigid
print 'special point translation:'
print special_point_translation
print 'inverse special point translation:'
print cur_exp_inv_special_point_transformation
# find the target transformation for the experiment scene
demo_object_clouds = [verb_stage_data["object_clouds"][obj_name]["xyz"] for obj_name in verb_stage_data["object_clouds"].keys()]
if len(demo_object_clouds) > 1:
raise Exception("i don't know what to do with multiple object clouds")
x_nd = voxel_downsample(demo_object_clouds[0], .02)
y_md = voxel_downsample(cur_exp_clouds[0], .02)
# transformation from old target object to new target object in world frame
cur_demo_to_exp_transform = get_tps_transform(x_nd, y_md)
# apply the target warping transformation to the special point trajectory
cur_mid_spec_pt_traj_xyzs, cur_mid_spec_pt_traj_oriens = [], []
for cur_mid_spec_pt_traj_mat in cur_mid_spec_pt_traj_mats:
cur_mid_spec_pt_traj_xyz, cur_mid_spec_pt_traj_orien = juc.hmat_to_trans_rot(cur_mid_spec_pt_traj_mat)
cur_mid_spec_pt_traj_xyzs.append(cur_mid_spec_pt_traj_xyz)
cur_mid_spec_pt_traj_oriens.append(juc.quat2mat(cur_mid_spec_pt_traj_orien))
cur_exp_spec_pt_traj_xyzs, cur_exp_spec_pt_traj_oriens = cur_demo_to_exp_transform.transform_frames(np.array(cur_mid_spec_pt_traj_xyzs), np.array(cur_mid_spec_pt_traj_oriens))
plot_warped_spec_pt_traj = cur_exp_spec_pt_traj_xyzs #save the special point traj for plotting
cur_exp_spec_pt_traj_mats = [juc.trans_rot_to_hmat(cur_exp_spec_pt_traj_xyz, mat2quat(cur_exp_spec_pt_traj_orien)) for cur_exp_spec_pt_traj_xyz, cur_exp_spec_pt_traj_orien in zip(cur_exp_spec_pt_traj_xyzs, cur_exp_spec_pt_traj_oriens)]
# transform the warped special point trajectory back to a gripper trajectory in the experiment
cur_exp_gripper_traj_mats = [np.dot(spec_pt_mat, cur_exp_inv_special_point_transformation) for spec_pt_mat in cur_exp_spec_pt_traj_mats]
warped_stage_data[gripper_data_key]["position"] = []
warped_stage_data[gripper_data_key]["orientation"] = []
for exp_traj_mat in cur_exp_gripper_traj_mats:
warped_pos, warped_orien = juc.hmat_to_trans_rot(exp_traj_mat)
warped_stage_data[gripper_data_key]["position"].append(warped_pos)
warped_stage_data[gripper_data_key]["orientation"].append(warped_orien)
if arm == 'r':
traj.r_gripper_poses.poses = xyzs_quats_to_poses(warped_stage_data[gripper_data_key]["position"], warped_stage_data[gripper_data_key]["orientation"])
print "poses: ", len(traj.r_gripper_poses.poses)
traj.r_gripper_angles = warped_stage_data["r_gripper_joint"]
traj.r_gripper_poses.header.frame_id = clouds_frame_id
elif arm == 'l':
traj.l_gripper_poses.poses = xyzs_quats_to_poses(warped_stage_data[gripper_data_key]["position"], warped_stage_data[gripper_data_key]["orientation"])
print "poses: ", len(traj.l_gripper_poses.poses)
traj.l_gripper_angles = warped_stage_data["l_gripper_joint"]
traj.l_gripper_poses.header.frame_id = clouds_frame_id
Globals.handles = []
plot_original_and_warped_demo_and_spec_pt(verb_stage_data, warped_stage_data, plot_spec_pt_traj, plot_warped_spec_pt_traj, traj)
pose_array = conversions.array_to_pose_array(y_md, 'base_footprint')
Globals.handles.append(Globals.rviz.draw_curve(pose_array, rgba = (0,0,1,1),width=.01,type=Marker.CUBE_LIST))
return resp
if 1:
if rospy.get_name() == "/unnamed":
rospy.init_node("test_rigidity", disable_signals=True)
from time import sleep
sleep(1)
rviz = RvizWrapper.create()
np.random.seed(1)
if 1:
pts0 = np.loadtxt("rope_control_points.txt")
pts1 = np.loadtxt("bad_rope.txt")
pts_rigid = voxel_downsample(pts0, .02)
#pts_rigid += np.random.randn(*pts_rigid.shape)
gN = len(pts0)
gM = len(pts_rigid)
#pts0 = pts0 + np.random.randn(*pts0.shape)*.1
#pts1 = pts1 + np.random.randn(*pts1.shape)*.1
if 0:
gN = 100
gM = 20
pts0 = np.random.randn(gN,3)
pts1 = np.random.randn(gN,3)
from lfd.tps import *
from jds_utils.colorize import colorize
if 1:
if rospy.get_name() == "/unnamed":
rospy.init_node("test_rigidity", disable_signals=True)
from time import sleep
sleep(1)
rviz = RvizWrapper.create()
np.random.seed(1)
if 1:
pts0 = np.loadtxt("rope_control_points.txt")
pts1 = np.loadtxt("bad_rope.txt")
pts_rigid = voxel_downsample(pts0, .02)
#pts_rigid += np.random.randn(*pts_rigid.shape)
gN = len(pts0)
gM = len(pts_rigid)
#pts0 = pts0 + np.random.randn(*pts0.shape)*.1
#pts1 = pts1 + np.random.randn(*pts1.shape)*.1
if 0:
gN = 100
gM = 20
pts0 = np.random.randn(gN, 3)
pts1 = np.random.randn(gN, 3)
from lfd.tps import *
from os.path import join, basename, splitext
from glob import glob
from point_clouds.features import cloud_shape_context
from jds_image_proc.clouds import voxel_downsample
import numpy as np
DATA_DIR = '/home/joschu/Data/rope'
for fname in glob(join(DATA_DIR,"*.txt")):
if "feats" in fname: continue
print fname
xyz = np.loadtxt(fname)
if "sim" in fname: xyz = np.dot(xyz, np.diag([1,-1,1]))
else: xyz = voxel_downsample(xyz, .03)
features = np.c_[xyz, cloud_shape_context(xyz, 2)]
feat_fname = splitext(fname)[0] + ".feats.txt"
np.savetxt(feat_fname, features)
def plot_original_and_warped_demo(best_demo, warped_demo, traj):
arms_used = best_demo["arms_used"]
if arms_used in "lb":
pose_array = conversions.array_to_pose_array(
asarray(best_demo["l_gripper_tool_frame"]["position"]),
'base_footprint')
Globals.handles.append(
Globals.rviz.draw_curve(pose_array,
rgba=(1, 0, 0, 1),
ns="make_verb_traj_service"))
pose_array = conversions.array_to_pose_array(
asarray(warped_demo["l_gripper_tool_frame"]["position"]),
'base_footprint')
Globals.handles.append(
Globals.rviz.draw_curve(pose_array,
rgba=(0, 1, 0, 1),
ns="make_verb_traj_service"))
if arms_used in "rb":
pose_array = conversions.array_to_pose_array(
asarray(best_demo["r_gripper_tool_frame"]["position"]),
'base_footprint')
Globals.handles.append(
Globals.rviz.draw_curve(pose_array,
rgba=(1, 0, 0, 1),
ns="make_verb_traj_service"))
pose_array = conversions.array_to_pose_array(
asarray(warped_demo["r_gripper_tool_frame"]["position"]),
'base_footprint')
Globals.handles.append(
Globals.rviz.draw_curve(pose_array,
rgba=(0, 1, 0, 1),
ns="make_verb_traj_service"))
Globals.handles.extend(
Globals.rviz.draw_trajectory(traj.l_gripper_poses,
traj.l_gripper_angles,
ns="make_verb_traj_service_grippers"))
if arms_used == 'b':
Globals.handles.extend(
Globals.rviz.draw_trajectory(traj.r_gripper_poses,
traj.r_gripper_angles,
ns="make_verb_traj_service_grippers"))
for (clouds, rgba) in [
(sorted_values(best_demo["object_clouds"]), (1, 0, 0, .5)),
(sorted_values(warped_demo["object_clouds"]), (0, 1, 0, .5))
]:
cloud = []
for subcloud in clouds:
cloud.extend(np.asarray(subcloud["xyz"]).reshape(-1, 3))
cloud = np.array(cloud)
cloud = voxel_downsample(cloud, .02)
pose_array = conversions.array_to_pose_array(cloud, 'base_footprint')
Globals.handles.append(
Globals.rviz.draw_curve(pose_array,
rgba=rgba,
width=.01,
type=Marker.CUBE_LIST))
def get_good_inds(orig_xyz, good_xyz):
good_inds = []
for i, xyz in enumerate(orig_xyz):
if array_contains(good_xyzs, xyz):
good_inds.append(i)
return good_inds
for object_name in object_names:
pc_sel = seg_svc.call(ProcessCloudRequest(cloud_in=pc)).cloud_out
xyz, rgb = ros_utils.pc2xyzrgb(pc_sel)
xyz = xyz.reshape(-1, 3)
rgb = rgb.reshape(-1, 3)
if args.do_filtering:
xyz_down = voxel_downsample(xyz, .02)
graph = ri.points_to_graph(xyz_down, .03)
cc = ri.largest_connected_component(graph)
good_xyzs = np.array(
[graph.node[node_id]["xyz"] for node_id in cc.nodes()])
good_inds = get_good_inds(xyz, good_xyzs)
good_rgbs = rgb[good_inds]
else:
good_xyzs, good_rgbs = xyz, rgb
outfile.create_group(object_name)
outfile[object_name]["xyz"] = good_xyzs
#outfile[object_name]["rgb"] = good_rgbs
if args.plotting:
rviz = ros_utils.RvizWrapper.create()
if rospy.get_name() == "/unnamed":
rospy.init_node("blah", disable_signals=True)
h5file = h5py.File(osp.join(osp.dirname(lfd.__file__), "data/knot_segments.h5"), "r")
seg_names = h5file.keys()
results = []
dists = []
ropes = []
for i in xrange(6):
rope = np.squeeze(h5file[seg_names[i]]["cloud_xyz"])
if args.downsample:
rope_ds, ds_inds = voxel_downsample(rope, 0.03, return_inds=True)
dist = recognition.calc_geodesic_distances_downsampled(rope, rope_ds, ds_inds)
dists.append(dist)
ropes.append(rope_ds)
else:
dist = recognition.calc_geodesic_distances(rope)
dists.append(dist)
ropes.append(rope)
for i0 in xrange(6):
rope0 = ropes[i0]
n0 = len(rope0)
# pairs = zip(xrange(n0), np.random.randint(0,n0,n0))
dists0 = dists[i0]
for i1 in xrange(6):
def voxel_downsample(xyz,s, return_inds = False):
xyz = xyz.reshape(-1,3)
xyz = xyz[np.isfinite(xyz[:,0])]
pts = []
keys = set([])
for (i,pt) in enumerate(xyz):
x,y,z = pt
key = (int(x//s),int(y//s),int(z//s))
if key not in keys:
keys.add(key)
pts.append(pt)
return np.array(pts)
X = voxel_downsample(ctrl_pts, .02)
y = dists = ssd.cdist(X, good_pts).min(axis=1)**2
#X = array([
#(10,0),
#(3,4),
#(4,3),
#(5,0),
#(4,-3),
#(3,-4)])
#y = array([
def select_trajectory(points, curr_robot_joint_vals, curr_step):
"""
- lookup closest trajectory from database
- if it's a terminal state, we're done
- warp it based on the current rope
returns: done, not_done, failure
"""
xyz_new = np.squeeze(np.asarray(points))
#if args.obj == "cloth": xyz_new = voxel_downsample(xyz_new, .025)
xyz_new_ds, ds_inds = downsample(xyz_new)
# xyz_new_ds, ds_inds = xyz_new.reshape(-1,3), np.arange(0, len(xyz_new)).reshape(-1, 1)
dists_new = recognition.calc_geodesic_distances_downsampled_old(xyz_new,xyz_new_ds, ds_inds)
candidate_demo_names = Globals.demos.keys()
#from joblib import parallel
#costs_names = parallel.Parallel(n_jobs = 4)(parallel.delayed(calc_seg_cost)(seg_name, xyz_new_ds, dists_new) for seg_name in candidate_demo_names)
costs_names = [calc_seg_cost(seg_name, xyz_new_ds, dists_new) for seg_name in sorted(candidate_demo_names)]
_, best_name = min(costs_names)
print "choices: ", candidate_demo_names
best_name = None
while best_name not in Globals.demos:
best_name = raw_input("type name of trajectory you want to use\n")
rospy.loginfo('costs_names %s', costs_names)
#matcher = recognition.CombinedNNMatcher(recognition.DataSet.LoadFromDict(Globals.demos), [recognition.GeodesicDistMatcher, recognition.ShapeContextMatcher], [1, 0.1])
#best_name, best_cost = matcher.match(xyz_new)
best_demo = Globals.demos[best_name]
if best_demo["done"]:
rospy.loginfo("best demo was a 'done' state")
return {'status': 'success'}
rospy.loginfo("best segment name: %s", best_name)
xyz_demo_ds = best_demo["cloud_xyz_ds"]
# print 'arms used', best_demo['arms_used']
# overlap_ctl_pts = []
# grabbing_pts = []
# for lr in 'lr':
# # look at points around gripper when grabbing
# grabbing = map(bool, list(best_demo["%s_gripper_joint"%lr] < .07))
# grabbing_pts.extend([p for i, p in enumerate(best_demo["%s_gripper_l_finger_tip_link"%lr]["position"]) if grabbing[i] and (i == 0 or not grabbing[i-1])])
# grabbing_pts.extend([p for i, p in enumerate(best_demo["%s_gripper_r_finger_tip_link"%lr]["position"]) if grabbing[i] and (i == 0 or not grabbing[i-1])])
# overlap_ctl_pts = [p for p in xyz_demo_ds if any(np.linalg.norm(p - g) < 0.1 for g in grabbing_pts)]
# overlap_ctl_pts = xyz_demo_ds
#rviz_draw_points(overlap_ctl_pts,rgba=(1,1,1,1),type=Marker.CUBE_LIST)
# rviz_draw_points(grabbing_pts,rgba=(.5,.5,.5,1),type=Marker.CUBE_LIST)
n_iter = 101
#warping_map = registration.tps_rpm_with_overlap_control(xyz_demo_ds, xyz_new_ds, overlap_ctl_pts, reg_init=1,reg_final=.01,n_iter=n_iter,verbose=False, plotting=20)
warping_map,info = registration.tps_rpm(xyz_demo_ds, xyz_new_ds, reg_init=1,reg_final=.01,n_iter=n_iter,verbose=False, plotting=20,return_full=True)
from lfd import tps
import scipy.spatial.distance as ssd
f = warping_map
pts_grip = []
for lr in "lr":
if best_demo["arms_used"] in ["b", lr]:
pts_grip.extend(best_demo["%s_gripper_tool_frame"%lr]["position"])
pts_grip = np.array(pts_grip)
dist_to_rope = ssd.cdist(pts_grip, xyz_demo_ds).min(axis=1)
pts_grip_near_rope = pts_grip[dist_to_rope < .04,:]
pts_rigid = voxel_downsample(pts_grip_near_rope, .01)
Globals.handles = []
registration.Globals.handles = []
f.lin_ag, f.trans_g, f.w_ng, f.x_na = tps.tps_nr_fit_enhanced(info["x_Nd"], info["targ_Nd"], 0.01, pts_rigid, 0.001, method="newton", plotting=5)
#if plotting:
#plot_orig_and_warped_clouds(f.transform_points, x_nd, y_md)
#targ_pose_array = conversions.array_to_pose_array(targ_Nd, 'base_footprint')
#Globals.handles.append(Globals.rviz.draw_curve(targ_pose_array,rgba=(1,1,0,1),type=Marker.CUBE_LIST))
#raw_input('Press enter to continue:')
#################### Generate new trajectory ##################
#### Plot original and warped point clouds #######
# orig_pose_array = conversions.array_to_pose_array(np.squeeze(best_demo["cloud_xyz_ds"]), "base_footprint")
# warped_pose_array = conversions.array_to_pose_array(warping_map.transform_points(np.squeeze(best_demo["cloud_xyz_ds"])), "base_footprint")
# Globals.handles.append(Globals.rviz.draw_curve(orig_pose_array,rgba=(1,0,0,1),id=19024,type=Marker.CUBE_LIST, ns='demo_cloud'))
# Globals.handles.append(Globals.rviz.draw_curve(warped_pose_array,rgba=(0,1,0,1),id=2983,type=Marker.CUBE_LIST, ns='warped_cloud'))
#### Plot grid ########
mins = np.squeeze(best_demo["cloud_xyz"]).min(axis=0)
maxes = np.squeeze(best_demo["cloud_xyz"]).max(axis=0)
mins[2] -= .1
maxes[2] += .1
grid_handle = warping.draw_grid(Globals.rviz, warping_map.transform_points, mins, maxes, 'base_footprint')
Globals.handles.append(grid_handle)
#### Actually generate the trajectory ###########
warped_demo = warping.transform_demo_with_fingertips(warping_map, best_demo)
Globals.pr2.update_rave_without_ros(curr_robot_joint_vals)
trajectory = {}
trajectory['seg_name'] = best_name
trajectory['demo'] = best_demo
if 'tracked_states' in best_demo:
trajectory['orig_tracked_states'] = best_demo['tracked_states']
trajectory['tracked_states'], Globals.offset_trans = warping.transform_tracked_states(warping_map, best_demo, Globals.offset_trans)
steps = 0
for lr in "lr":
leftright = {"l":"left","r":"right"}[lr]
if best_demo["arms_used"] in [lr, "b"]:
#if args.hard_table:
# clipinplace(warped_demo["l_gripper_tool_frame"]["position"][:,2],Globals.table_height+.032,np.inf)
# clipinplace(warped_demo["r_gripper_tool_frame"]["position"][:,2],Globals.table_height+.032,np.inf)
rospy.loginfo("calculating joint trajectory...")
#arm_traj, feas_inds = lfd_traj.make_joint_traj(
# warped_demo["%s_gripper_tool_frame"%lr]["position"],
# warped_demo["%s_gripper_tool_frame"%lr]["orientation"],
# best_demo["%sarm"%leftright],
# Globals.pr2.robot.GetManipulator("%sarm"%leftright),
# "base_footprint","%s_gripper_tool_frame"%lr,
# 1+2+16
#)
arm_traj, feas_inds = lfd_traj.make_joint_traj_by_graph_search(
warped_demo["%s_gripper_tool_frame"%lr]["position"],
warped_demo["%s_gripper_tool_frame"%lr]["orientation"],
Globals.pr2.robot.GetManipulator("%sarm"%leftright),
"%s_gripper_tool_frame"%lr,
check_collisions=True
)
if len(feas_inds) == 0: return {'status': "failure"}
trajectory["%s_arm"%lr] = arm_traj
trajectory["%s_steps"%lr] = steps = len(arm_traj)
rospy.loginfo("%s arm: %i of %i points feasible", leftright, len(feas_inds), len(arm_traj))
trajectory["%s_grab"%lr] = map(bool, list(best_demo["%s_gripper_joint"%lr] < .02))
trajectory["%s_gripper"%lr] = warped_demo["%s_gripper_joint"%lr]
trajectory["%s_gripper"%lr][trajectory["%s_grab"%lr]] = 0
# plot warped trajectory
Globals.handles.append(Globals.rviz.draw_curve(
conversions.array_to_pose_array(
alternate(warped_demo["%s_gripper_l_finger_tip_link"%lr]["position"], warped_demo["%s_gripper_r_finger_tip_link"%lr]["position"]),
"base_footprint"
),
width=.001, rgba = (1,0,1,.4), type=Marker.LINE_LIST,
ns='warped_finger_traj'
))
# plot original trajectory
Globals.handles.append(Globals.rviz.draw_curve(
conversions.array_to_pose_array(
alternate(best_demo["%s_gripper_l_finger_tip_link"%lr]["position"], best_demo["%s_gripper_r_finger_tip_link"%lr]["position"]),
"base_footprint"
),
width=.001, rgba = (0,1,1,.4), type=Marker.LINE_LIST,
ns='demo_finger_traj'
))
assert 'l_steps' not in trajectory or steps == trajectory['l_steps']
assert 'r_steps' not in trajectory or steps == trajectory['r_steps']
trajectory['steps'] = steps
#raw_input('Press enter to continue:')
return {'status': 'not_done', 'trajectory': trajectory}
def downsample(xyz): from jds_image_proc.clouds import voxel_downsample DS_LENGTH = .025 xyz_ds, ds_inds = voxel_downsample(xyz, DS_LENGTH, return_inds = True) return xyz_ds, ds_inds
