def getcorrectedpcd(self, pcdarray):
"""
convert a poind cloud from its sensor frame to global frame
:param pcdarray:
:return:
author: weiwei
date: 20191229osaka
"""
return rm.homotransformpointarray(self.sensorhomomat, pcdarray)
def register_tube(self, tgtpcdnp, type=1, toggledebug=False):
"""
allow user to register a tube
:param tgtpcdnp:
:param type: 1 -- large; 2 -- small
:return:
author: weiwei
date: 20200318
"""
elearray = np.zeros((5, 10))
eleconfidencearray = np.zeros((5, 10))
tgtpcdnp = o3dh.remove_outlier(tgtpcdnp,
downsampling_voxelsize=None,
nb_points=90,
radius=5)
# transform back to the local frame of the tubestand
tgtpcdnp_normalized = rm.homotransformpointarray(
rm.homoinverse(self.tubestandhomomat), tgtpcdnp)
if toggledebug:
cm.CollisionModel(tgtpcdnp_normalized).reparentTo(base.render)
if self.__directory is None:
tscm2 = cm.CollisionModel("./objects/tubestand.stl")
else:
tscm2 = cm.CollisionModel(self.__directory +
"/objects/tubestand.stl")
tscm2.reparentTo(base.render)
for i in range(5):
for j in range(10):
holepos = self.tubeholecenters[i][j]
tmppcd = self._crop_pcd_overahole(tgtpcdnp_normalized,
holepos[0], holepos[1])
return tmppcd
def findtubes(self, tubestand_homomat, tgtpcdnp, toggledebug=False):
"""
:param tgtpcdnp:
:return:
author: weiwei
date: 20200317
"""
elearray = np.zeros((5, 10))
eleconfidencearray = np.zeros((5, 10))
tgtpcdnp = o3dh.remove_outlier(tgtpcdnp,
downsampling_voxelsize=None,
nb_points=90,
radius=5)
# transform back to the local frame of the tubestand
tgtpcdnp_normalized = rm.homotransformpointarray(
rm.homoinverse(tubestand_homomat), tgtpcdnp)
if toggledebug:
cm.CollisionModel(tgtpcdnp_normalized).reparentTo(base.render)
tscm2 = copy.deepcopy(self.tubestandcm)
tscm2.reparentTo(base.render)
for i in range(5):
for j in range(10):
holepos = self.tubeholecenters[i][j]
tmppcd = self._crop_pcd_overahole(tgtpcdnp_normalized,
holepos[0], holepos[1])
if len(tmppcd) > 50:
if toggledebug:
print(
"------more than 50 raw points, start a new test------"
)
tmppcdover100 = tmppcd[tmppcd[:, 2] > 100]
tmppcdbelow90 = tmppcd[tmppcd[:, 2] < 90]
tmppcdlist = [tmppcdover100, tmppcdbelow90]
if toggledebug:
print("rotate around...")
rejflaglist = [False, False]
allminstdlist = [[], []]
newtmppcdlist = [None, None]
minstdlist = [None, None]
for k in range(2):
if toggledebug:
print("checking over 100 and below 90, now: ", j)
if len(tmppcdlist[k]) < 10:
rejflaglist[k] = True
continue
for angle in np.linspace(0, 180, 10):
tmphomomat = np.eye(4)
tmphomomat[:3, :3] = rm.rodrigues(
tubestand_homomat[:3, 2], angle)
newtmppcdlist[k] = rm.homotransformpointarray(
tmphomomat, tmppcdlist[k])
minstdlist[k] = np.min(
np.std(newtmppcdlist[k][:, :2], axis=0))
if toggledebug:
print(minstdlist[k])
allminstdlist[k].append(minstdlist[k])
if minstdlist[k] < 1.5:
rejflaglist[k] = True
if toggledebug:
print("rotate round done")
print("minstd ",
np.min(np.asarray(allminstdlist[k])))
if all(item for item in rejflaglist):
continue
elif all(not item for item in rejflaglist):
print("CANNOT tell if the tube is big or small")
raise ValueError()
else:
tmppcd = tmppcdbelow90 if rejflaglist[
0] else tmppcdover100
candidatetype = 2 if rejflaglist[0] else 1
tmpangles = np.arctan2(tmppcd[:, 1], tmppcd[:, 0])
tmpangles[
tmpangles < 0] = 360 + tmpangles[tmpangles < 0]
if toggledebug:
print(np.std(tmpangles))
print("ACCEPTED! ID: ", i, j)
elearray[i][j] = candidatetype
eleconfidencearray[i][j] = 1
if toggledebug:
# normalized
objnp = p3dh.genpointcloudnodepath(tmppcd,
pntsize=5)
rgb = np.random.rand(3)
objnp.setColor(rgb[0], rgb[1], rgb[2], 1)
objnp.reparentTo(base.render)
stick = p3dh.gendumbbell(
spos=np.array([holepos[0], holepos[1], 10]),
epos=np.array([holepos[0], holepos[1], 60]))
stick.setColor(rgb[0], rgb[1], rgb[2], 1)
stick.reparentTo(base.render)
# original
tmppcd_tr = rm.homotransformpointarray(
tubestand_homomat, tmppcd)
objnp_tr = p3dh.genpointcloudnodepath(tmppcd_tr,
pntsize=5)
objnp_tr.setColor(rgb[0], rgb[1], rgb[2], 1)
objnp_tr.reparentTo(base.render)
spos_tr = rm.homotransformpoint(
tubestand_homomat,
np.array([holepos[0], holepos[1], 0]))
stick_tr = p3dh.gendumbbell(
spos=np.array([spos_tr[0], spos_tr[1], 10]),
epos=np.array([spos_tr[0], spos_tr[1], 60]))
stick_tr.setColor(rgb[0], rgb[1], rgb[2], 1)
stick_tr.reparentTo(base.render)
# box normalized
center, bounds = rm.get_aabb(tmppcd)
boxextent = np.array([
bounds[0, 1] - bounds[0, 0],
bounds[1, 1] - bounds[1, 0],
bounds[2, 1] - bounds[2, 0]
])
boxhomomat = np.eye(4)
boxhomomat[:3, 3] = center
box = p3dh.genbox(
extent=boxextent,
homomat=boxhomomat,
rgba=np.array([rgb[0], rgb[1], rgb[2], .3]))
box.reparentTo(base.render)
# box original
center_r = rm.homotransformpoint(
tubestand_homomat, center)
boxhomomat_tr = copy.deepcopy(tubestand_homomat)
boxhomomat_tr[:3, 3] = center_r
box_tr = p3dh.genbox(
extent=boxextent,
homomat=boxhomomat_tr,
rgba=np.array([rgb[0], rgb[1], rgb[2], .3]))
box_tr.reparentTo(base.render)
if toggledebug:
print("------the new test is done------")
return elearray, eleconfidencearray
def findtubes(self, tubestand_homomat, tgtpcdnp, toggledebug=False):
"""
:param tubestand_homomat:
:param tgtpcdnp:
:return:
"""
elearray = np.zeros((5, 10))
eleconfidencearray = np.zeros((5, 10))
tgtpcdo3d = o3dh.nparray_to_o3dpcd(tgtpcdnp)
tgtpcdo3d_removed = o3dh.remove_outlier(tgtpcdo3d,
downsampling_voxelsize=None,
nb_points=90,
radius=5)
tgtpcdnp = o3dh.o3dpcd_to_parray(tgtpcdo3d_removed)
# transform tgtpcdnp back
tgtpcdnp_normalized = rm.homotransformpointarray(
rm.homoinverse(tubestand_homomat), tgtpcdnp)
if toggledebug:
cm.CollisionModel(tgtpcdnp_normalized).reparentTo(base.render)
tscm2 = cm.CollisionModel("../objects/tubestand.stl")
tscm2.reparentTo(base.render)
for i in range(5):
for j in range(10):
holepos = self.tubeholecenters[i][j]
# squeeze the hole size by half
tmppcd = tgtpcdnp_normalized[
tgtpcdnp_normalized[:, 0] < holepos[0] +
self.tubeholesize[0] / 1.9]
tmppcd = tmppcd[tmppcd[:, 0] > holepos[0] -
self.tubeholesize[0] / 1.9]
tmppcd = tmppcd[tmppcd[:, 1] < holepos[1] +
self.tubeholesize[1] / 1.9]
tmppcd = tmppcd[tmppcd[:, 1] > holepos[1] -
self.tubeholesize[1] / 1.9]
tmppcd = tmppcd[tmppcd[:, 2] > 70]
if len(tmppcd) > 100:
print(
"------more than 100 raw points, start a new test------"
)
# use core tmppcd to decide tube types (avoid noises)
coretmppcd = tmppcd[tmppcd[:, 0] < holepos[0] +
self.tubeholesize[0] / 4]
coretmppcd = coretmppcd[coretmppcd[:, 0] > holepos[0] -
self.tubeholesize[0] / 4]
coretmppcd = coretmppcd[coretmppcd[:, 1] < holepos[1] +
self.tubeholesize[1] / 4]
coretmppcd = coretmppcd[coretmppcd[:, 1] > holepos[1] -
self.tubeholesize[1] / 4]
print("testing the number of core points...")
print(len(coretmppcd[:, 2]))
if len(coretmppcd[:, 2]) < 10:
print("------the new test is done------")
continue
if np.max(tmppcd[:, 2]) > 100:
candidatetype = 1
tmppcd = tmppcd[tmppcd[:, 2] >
100] # crop tmppcd for better charge
else:
candidatetype = 2
tmppcd = tmppcd[tmppcd[:, 2] < 90]
if len(tmppcd) < 10:
continue
print("passed the core points test, rotate around...")
rejflag = False
for angle in np.linspace(0, 180, 20):
tmphomomat = np.eye(4)
tmphomomat[:3, :3] = rm.rodrigues(
tubestand_homomat[:3, 2], angle)
newtmppcd = rm.homotransformpointarray(
tmphomomat, tmppcd)
minstd = np.min(np.std(newtmppcd[:, :2], axis=0))
print(minstd)
if minstd < 1.3:
rejflag = True
print("rotate round done")
if rejflag:
continue
else:
tmpangles = np.arctan2(tmppcd[:, 1], tmppcd[:, 0])
tmpangles[
tmpangles < 0] = 360 + tmpangles[tmpangles < 0]
print(np.std(tmpangles))
print("ACCEPTED! ID: ", i, j)
elearray[i][j] = candidatetype
eleconfidencearray[i][j] = 1
if toggledebug:
# normalized
objnp = p3dh.genpointcloudnodepath(tmppcd, pntsize=5)
rgb = np.random.rand(3)
objnp.setColor(rgb[0], rgb[1], rgb[2], 1)
objnp.reparentTo(base.render)
stick = p3dh.gendumbbell(
spos=np.array([holepos[0], holepos[1], 10]),
epos=np.array([holepos[0], holepos[1], 60]))
stick.setColor(rgb[0], rgb[1], rgb[2], 1)
stick.reparentTo(base.render)
# original
tmppcd_tr = rm.homotransformpointarray(
tubestand_homomat, tmppcd)
objnp_tr = p3dh.genpointcloudnodepath(tmppcd_tr,
pntsize=5)
objnp_tr.setColor(rgb[0], rgb[1], rgb[2], 1)
objnp_tr.reparentTo(base.render)
spos_tr = rm.homotransformpoint(
tubestand_homomat,
np.array([holepos[0], holepos[1], 0]))
stick_tr = p3dh.gendumbbell(
spos=np.array([spos_tr[0], spos_tr[1], 10]),
epos=np.array([spos_tr[0], spos_tr[1], 60]))
stick_tr.setColor(rgb[0], rgb[1], rgb[2], 1)
stick_tr.reparentTo(base.render)
# box normalized
center, bounds = rm.get_aabb(tmppcd)
boxextent = np.array([
bounds[0, 1] - bounds[0, 0],
bounds[1, 1] - bounds[1, 0],
bounds[2, 1] - bounds[2, 0]
])
boxhomomat = np.eye(4)
boxhomomat[:3, 3] = center
box = p3dh.genbox(extent=boxextent,
homomat=boxhomomat,
rgba=np.array(
[rgb[0], rgb[1], rgb[2], .3]))
box.reparentTo(base.render)
# box original
center_r = rm.homotransformpoint(
tubestand_homomat, center)
boxhomomat_tr = copy.deepcopy(tubestand_homomat)
boxhomomat_tr[:3, 3] = center_r
box_tr = p3dh.genbox(extent=boxextent,
homomat=boxhomomat_tr,
rgba=np.array(
[rgb[0], rgb[1], rgb[2], .3]))
box_tr.reparentTo(base.render)
print("------the new test is done------")
return elearray, eleconfidencearray
def phoxi_calib_refinewithmodel(yhx, pxc, rawamat, armname):
"""
The performance of this refining method using cad model is not good.
The reason is probably a precise mobdel is needed.
:param yhx: an instancde of YumiHelperX
:param pxc: phoxi client
:param armname:
:return:
author: weiwei
date: 20191228
"""
handpalmtemplate = pickle.load(
open(
os.path.join(yhx.root, "dataobjtemplate",
"handpalmtemplatepcd.pkl"), "rb"))
newhomomatlist = []
lastarmjnts = yhx.robot_s.initrgtjnts
eerot = np.array([[1, 0, 0], [0, 0, -1],
[0, 1, 0]]).T # horizontal, facing right
for x in [300, 360, 420]:
for y in range(-200, 201, 200):
for z in [70, 90, 130, 200]:
armjnts = yhx.movetoposrotmsc(eepos=np.array([x, y, z]),
eerot=eerot,
msc=lastarmjnts,
armname=armname)
if armjnts is not None and not yhx.pcdchecker.isSelfCollided(
yhx.robot_s):
lastarmjnts = armjnts
yhx.movetox(armjnts, armname=armname)
tcppos, tcprot = yhx.robot_s.gettcp()
initpos = tcppos + tcprot[:, 2] * 7
initrot = tcprot
inithomomat = rm.homobuild(initpos, initrot)
pxc.triggerframe()
pcd = pxc.getpcd()
realpcd = rm.homotransformpointarray(rawamat, pcd)
minx = tcppos[0] - 100
maxx = tcppos[0] + 100
miny = tcppos[1]
maxy = tcppos[1] + 140
minz = tcppos[2]
maxz = tcppos[2] + 70
realpcdcrop = o3dh.crop_nx3_nparray(
realpcd, [minx, maxx], [miny, maxy], [minz, maxz])
if len(realpcdcrop) < len(handpalmtemplate) / 2:
continue
# yhx.rbtmesh.genmnp(yhx.robot_s).reparentTo(base.render)
# yhx.p3dh.genframe(tcppos, tcprot, thickness=10). reparentTo(base.render)
# yhx.p3dh.gensphere([minx, miny, minz], radius=10).reparentTo(base.render)
# yhx.p3dh.gensphere([maxx, maxy, maxz], radius=10).reparentTo(base.render)
# yhx.p3dh.genpointcloudnodepath(realpcd).reparentTo(base.render)
# yhx.p3dh.genpointcloudnodepath(realpcdcrop, colors=[1,1,0,1]).reparentTo(base.render)
# yhx.p3dh.genpointcloudnodepath(rm.homotransformpointarray(inithomomat, handpalmtemplate), colors=[.5,1,.5,1]).reparentTo(base.render)
# yhx.base.run()
hto3d = o3dh.nparray_to_o3dpcd(
rm.homotransformpointarray(inithomomat,
handpalmtemplate))
rpo3d = o3dh.nparray_to_o3dpcd(realpcdcrop)
inlinnerrmse, newhomomat = o3dh.registration_icp_ptpt(
hto3d,
rpo3d,
np.eye(4),
maxcorrdist=2,
toggledebug=False)
print(inlinnerrmse, ", one round is done!")
newhomomatlist.append(rm.homoinverse(newhomomat))
newhomomat = rm.homomat_average(newhomomatlist, denoise=False)
refinedamat = np.dot(newhomomat, rawamat)
pickle.dump(
refinedamat,
open(os.path.join(yhx.root, "datacalibration", "refinedcalibmat.pkl"),
"wb"))
print(rawamat)
print(refinedamat)
return refinedamat
