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 findtubestand_obb(self, tgtpcdnp, toggledebug=False):
"""
match self.tstpcd from tgtpcdnp
NOTE: tgtpcdnp must be in global frame, use getglobalpcd to convert if local
:param tgtpcdnp:
:return:
author: weiwei
date: 20191229osaka
"""
tgtpcdnp = o3dh.remove_outlier(tgtpcdnp, nb_points=20, radius=10)
tgtpcdnp2d = tgtpcdnp[:, :2] # TODO clip using sensor z
ca = np.cov(tgtpcdnp2d, y=None, rowvar=0, bias=1)
v, vect = np.linalg.eig(ca)
tvect = np.transpose(vect)
# use the inverse of the eigenvectors as a rotation matrix and
# rotate the points so they align with the x and y axes
ar = np.dot(tgtpcdnp2d, np.linalg.inv(tvect))
# get the minimum and maximum x and y
mina = np.min(ar, axis=0)
maxa = np.max(ar, axis=0)
diff = (maxa - mina) * 0.5
# the center is just half way between the min and max xy
center = mina + diff
# get the 4 corners by subtracting and adding half the bounding boxes height and width to the center
corners = np.array([
center + [-diff[0], -diff[1]], center + [diff[0], -diff[1]],
center + [diff[0], diff[1]], center + [-diff[0], diff[1]],
center + [-diff[0], -diff[1]]
])
# use the the eigenvectors as a rotation matrix and
# rotate the corners and the centerback
corners = np.dot(corners, tvect)
center = np.dot(center, tvect)
if toggledebug:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111)
ax.scatter(tgtpcdnp2d[:, 0], tgtpcdnp2d[:, 1])
ax.scatter([center[0]], [center[1]])
ax.plot(corners[:, 0], corners[:, 1], '-')
plt.axis('equal')
plt.show()
axind = np.argsort(v)
homomat = np.eye(4)
homomat[:3, axind[0]] = np.array([vect[0, 0], vect[1, 0], 0])
homomat[:3, axind[1]] = np.array([vect[0, 1], vect[1, 1], 0])
homomat[:3, 2] = np.array([0, 0, 1])
if np.cross(homomat[:3, 0], homomat[:3, 1])[2] < -.5:
homomat[:3, 1] = -homomat[:3, 1]
homomat[:3, 3] = np.array([center[0], center[1], -15])
return homomat
def capturecorrectedpcd(self, pxc, ncapturetimes=1, id=1):
"""
capture a poind cloud and transform it from its sensor frame to global frame
:param pcdnp:
:return:
author: weiwei
date: 20200108
"""
bgdepth = self.bgdepth1
if id == 2:
bgdepth = self.bgdepth2
elif id == 3:
bgdepth = self.bgdepth3
elif id == 4:
bgdepth = self.bgdepth4
objpcdmerged = None
for i in range(ncapturetimes):
pxc.triggerframe()
fgdepth = pxc.getdepthimg()
fgpcd = pxc.getpcd()
substracteddepth = bgdepth - fgdepth
substracteddepth = substracteddepth.clip(50, 600)
substracteddepth[substracteddepth == 50] = 0
substracteddepth[substracteddepth == 600] = 0
substracteddepth[:100, :] = 0 # 300, 1700 for high resolution
substracteddepth[1000:, :] = 0
substracteddepth[:, :100] = 0
substracteddepth[:, 1000:] = 0
tempdepth = substracteddepth.flatten()
objpcd = fgpcd[np.nonzero(tempdepth)]
objpcd = self.getcorrectedpcd(objpcd)
if objpcdmerged is None:
objpcdmerged = objpcd
else:
objpcdmerged = np.vstack((objpcdmerged, objpcd))
tgtpcdo3d = o3dh.nparray_to_o3dpcd(objpcdmerged)
tgtpcdo3d_removed = o3dh.remove_outlier(tgtpcdo3d,
nb_points=50,
radius=10)
tgtpcdnp = o3dh.o3dpcd_to_parray(tgtpcdo3d_removed)
return tgtpcdnp
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
