def multiwindow_template(world):
"""Tests multiple windows and views."""
vis.add("world",world)
vp = vis.getViewport()
vp.w,vp.h = 800,800
vis.setViewport(vp)
vis.setWindowTitle("vis.spin test: will close in 5 seconds...")
vis.spin(5.0)
#Now testing ability to re-launch windows
vis.setWindowTitle("Shown again. Close me to proceed.")
vis.spin(float('inf'))
vis.setWindowTitle("Dialog test. Close me to proceed.")
vp = vis.getViewport()
vp.w,vp.h = 400,600
vis.setViewport(vp)
vis.dialog()
vp.w,vp.h = 640,480
vis.setViewport(vp)
for i in range(3):
widgets = GLWidgetPlugin()
widgets.addWidget(RobotPoser(world.robot(0)))
vis.addPlugin(widgets)
vis.setWindowTitle("Split screen test")
vis.spin(float('inf'))
vis.setPlugin(None)
vis.setWindowTitle("Back to normal. Close me to quit.")
vis.dialog()
vis.kill()
def show_friction_cone(folder="FrictionCones/StanfordMicroSpineUnit"):
V = Volume()
V.load(folder)
for C in V.convex_decomposition:
print "Convex decomposition"
print np.amin(C.points,axis=0)
print np.amax(C.points,axis=0)
Chull = np.vstack([V.convex_hull.points[u] for u in V.convex_hull.vertices])
#print Chull
Chull *= 0.05
for i,C in enumerate(V.convex_decomposition):
if len(V.convex_decomposition) == 1:
g = 0
else:
g = float(i)/(len(V.convex_decomposition)-1)
#for j,v in enumerate(C.vertices):
# vis.add("pt%d,%d"%(i,j),vectorops.mul(C.points[v,:].tolist(),0.05))
# vis.setColor("pt%d,%d"%(i,j),1,g,0)
# vis.hideLabel("pt%d,%d"%(i,j))
for j,pt in enumerate(C.points):
v = j
vis.add("pt%d,%d"%(i,j),vectorops.mul(C.points[v,:].tolist(),0.05))
vis.setColor("pt%d,%d"%(i,j),1,g,0)
vis.hideLabel("pt%d,%d"%(i,j))
for i in xrange(Chull.shape[0]):
vis.add("CH%d"%(i,),Chull[i,:].tolist())
vis.hideLabel("CH%d"%(i,))
vis.add("origin",(0,0,0))
vis.setColor("origin",0,1,0)
vis.dialog()
def multiwindow_template(world):
"""Tests multiple windows and views."""
vis.add("world", world)
vp = vis.getViewport()
vp.w, vp.h = 400, 600
vis.setViewport(vp)
vis.addText("label1", "This is Window 1", (20, 20))
vis.setWindowTitle("Window 1")
vis.show()
id1 = vis.getWindow()
print("First window ID:", id1)
id2 = vis.createWindow("Window 2")
vis.add("Lone point", [0, 0, 0])
vis.setViewport(vp)
vis.addText("label1", "This is Window 2", (20, 20))
print("Second window ID:", vis.getWindow())
vis.setWindow(id2)
vis.spin(float('inf'))
#restore back to 1 window, clear the text
vis.setWindow(id1)
vis.clearText()
vp = vis.getViewport()
vp.w, vp.h = 800, 800
vis.setViewport(vp)
vis.setWindowTitle("vis.spin test: will close in 5 seconds...")
vis.spin(5.0)
#Now testing ability to re-launch windows
vis.setWindowTitle("Shown again. Close me to proceed.")
vis.spin(float('inf'))
vis.setWindowTitle("Dialog test. Close me to proceed.")
vp = vis.getViewport()
vp.w, vp.h = 400, 600
vis.setViewport(vp)
vis.dialog()
vp.w, vp.h = 640, 480
vis.setViewport(vp)
for i in range(3):
widgets = GLWidgetPlugin()
widgets.addWidget(RobotPoser(world.robot(0)))
vis.addPlugin(widgets)
vis.setWindowTitle("Split screen test")
vis.spin(float('inf'))
vis.setPlugin(None)
vis.setWindowTitle("Back to normal. Close me to quit.")
vis.dialog()
vis.kill()
def edit_config():
global world, robot
resource.setDirectory("resources/")
qhands = [r.getConfig() for r in hand_subrobots.values()]
q = resource.get("robosimian_body_stability.config",
default=robot.getConfig(),
doedit=False)
robot.setConfig(q)
for r, qhand in zip(hand_subrobots.values(), qhands):
r.setConfig(qhand)
vis.add("world", world)
vis.edit(("world", robot.getName()), True)
vis.dialog()
vis.edit(("world", robot.getName()), False)
resource.set("robosimian_body_stability.config", robot.getConfig())
def vis_interaction_test(world):
"""Run some tests of visualization module interacting with the resource module"""
print("Showing robot in modal dialog box")
vis.add("robot", world.robot(0))
vis.add("ee", world.robot(0).link(11).getTransform())
vis.dialog()
config = resource.get("resourcetest1.config",
description="Should show up without a hitch...",
doedit=True,
editor='visual',
world=world)
import time
if MULTITHREADED:
print(
"Showing threaded visualization (this will fail on GLUT or Mac OS)"
)
vis.show()
for i in range(3):
vis.lock()
q = world.robot(0).getConfig()
q[9] = 3.0
world.robot(0).setConfig(q)
vis.unlock()
time.sleep(1.0)
if not vis.shown():
break
vis.lock()
q = world.robot(0).getConfig()
q[9] = -1.0
world.robot(0).setConfig(q)
vis.unlock()
time.sleep(1.0)
if not vis.shown():
break
print("Hiding visualization window")
vis.show(False)
else:
print("Showing single-threaded visualization for 5s")
vis.spin(5.0)
config = resource.get("resourcetest1.config",
description="Should show up without a hitch...",
doedit=True,
editor='visual',
world=world)
def debug_stable_faces(obj, faces):
from klampt import vis, Geometry3D, GeometricPrimitive
from klampt.math import se3
import random
vis.createWindow()
obj.setTransform(*se3.identity())
vis.add("object", obj)
for i, f in enumerate(faces):
gf = GeometricPrimitive()
gf.setPolygon(np.stack(f).flatten())
color = (1, 0.5 + 0.5 * random.random(), 0.5 + 0.5 * random.random(),
0.5)
vis.add("face{}".format(i),
Geometry3D(gf),
color=color,
hide_label=True)
vis.setWindowTitle("Stable faces")
vis.dialog()
#add the world elements individually to the visualization
vis.add("robot", robot)
for i in range(1, world.numRobots()):
vis.add("robot" + str(i), world.robot(i))
for i in range(world.numRigidObjects()):
vis.add("rigidObject" + str(i), world.rigidObject(i))
for i in range(world.numTerrains()):
vis.add("terrain" + str(i), world.terrain(i))
#if you want to just see the robot in a pop up window...
if DO_SIMPLIFY and DEBUG_SIMPLIFY:
print("#########################################")
print("Showing the simplified robot")
print("#########################################")
vis.setWindowTitle("Simplified robot")
vis.dialog()
#Automatic construction of space
if not CLOSED_LOOP_TEST:
if not MANUAL_SPACE_CREATION:
space = robotplanning.makeSpace(world=world,
robot=robot,
edgeCheckResolution=1e-3,
movingSubset='all')
else:
#Manual construction of space
collider = WorldCollider(world)
space = robotcspace.RobotCSpace(robot, collider)
space.eps = 1e-3
space.setup()
else:
def calibrate_xform_camera(camera_link_transforms,
marker_link_transforms,
marker_observations,
marker_ids,
observation_relative_errors=None,
camera_initial_guess=None,
marker_initial_guess=None,
regularizationFactor=0,
maxIters=100,
tolerance=1e-7):
"""Single camera calibration function for a camera and markers on some
set of rigid bodies.
Given body transforms and a list of estimated calibration marker observations
in the camera frame, estimates both the camera transform relative to the
camera link as well as the marker transforms relative to their links.
M: is the set of m markers. By default there is at most one marker per link.
Markers can either be point markers (e.g., a mocap ball), or transform
markers (e.g., an AR tag or checkerboard pattern).
O: is the set of n observations, consisting of a reading (Tc_i,Tm_i,o_i,l_i)
where Tc_i is the camera link's transform, Tm_i is the marker link's
transform, o_i is the reading which consists of either a point or transform
estimate in the camera frame, and l_i is the ID of the marker (by default,
just its link)
Output: a tuple (err,Tc,marker_dict) where err is the norm of the
reconstruction residual, Tc is the estimated camera transform relative to the
camera's link, and marker_dict is a dict mapping each marker id to its
estimated position or transform on the marker's link.
Arguments:
- camera_link: an integer index or a RobotModelLink instance on which
the camera lies.
- calibration_configs: a list of the RobotModel configurations q_1,...,q_n
that generated the marker_observations list.
- marker_observations: a list of estimated positions or transformations
of calibration markers o_1,...,o_n, given in the camera's reference
frame (z forward, x right, y down).
If o_i is a 3-vector, the marker is considered to be a point marker.
If a se3 element (R,t) is given, the marker is considered to be
a transform marker. You may not mix point and transform observations
for a single marker ID.
- marker_ids: a list of marker ID #'s l_1,...,l_n corresponding to
each observation, e.g., the link index on which each marker lies.
- observation_relative_errors: if you have an idea of the magnitude of
each observation error, it can be placed into this list. Must be
a list of n floats, 3-lists (point markers), or 6-lists (transform
markers). By default, errors will be set proportionally to the
observed distance between the camera and marker.
- camera_initial_guess: if not None, an initial guess for the camera transform
- marker_initial_guess: if not None, a dictionary containing initial guesses
for the marker transforms
- regularizationFactor: if nonzero, the optimization penalizes deviation
of the estimated camera transform and marker transforms from zero
proportionally to this factor.
- maxIters: maximum number of iterations for optimization.
- tolerance: optimization convergence tolerance. Stops when the change of
estimates falls below this threshold
"""
if len(camera_link_transforms) != len(marker_ids):
raise ValueError("Must provide the same number of marker IDs as camera transforms")
if len(marker_link_transforms) != len(marker_ids):
raise ValueError("Must provide the same number of marker IDs as marker transforms")
if len(marker_observations) != len(marker_ids):
raise ValueError("Must provide the same number of marker observations as marker transforms")
#get all unique marker ids
marker_id_list = list(set(marker_ids))
#detect marker types
marker_types = dict((v,None) for v in marker_id_list)
for i,(obs,id) in enumerate(zip(marker_observations,marker_ids)):
if len(obs)==3:
if marker_types[id] == 't':
raise ValueError("Provided both point and transform observations for observation #%d, id %s\n"%(i,str(id)))
marker_types[id] = 'p'
elif len(obs)==2 and len(obs[0])==9 and len(obs[1])==3:
if marker_types[id] == 'p':
raise ValueError("Provided both point and transform observations for observation #%d, id %s\n"%(i,str(id)))
marker_types[id] = 't'
else:
raise ValueError("Invalid observation for observation #%d, id %s\n"%(i,str(id)))
n = len(marker_observations)
m = len(marker_id_list)
#get all the observation weights
observation_weights = []
if observation_relative_errors is None:
#default weights: proportional to distance
for obs in marker_observations:
if len(obs) == 3:
observation_weights.append(1.0/vectorops.norm(obs))
else:
observation_weights.append(1.0/vectorops.norm(obs[1]))
observation_weights = [1.0]*len(observation_weights)
else:
if len(observation_relative_errors) != n:
raise ValueError("Invalid length of observation errors")
for err in observation_relative_errors:
if hasattr(err,'__iter__'):
observation_weights.append([1.0/v for v in err])
else:
observation_weights.append(1.0/err)
#initial guesses
if camera_initial_guess == None:
camera_initial_guess = se3.identity()
if any(v == 't' for v in marker_types.itervalues()):
#estimate camera rotation from point estimates because rotations are more prone to initialization failures
point_observations = []
marker_point_rel = []
for i,obs in enumerate(marker_observations):
if len(obs)==2:
point_observations.append(obs[1])
else:
point_observations.append(obs)
marker_point_rel.append(se3.mul(se3.inv(camera_link_transforms[i]),marker_link_transforms[i])[1])
camera_initial_guess = (point_fit_rotation_3d(point_observations,marker_point_rel),[0.0]*3)
print "Estimated camera rotation from points:",camera_initial_guess
if marker_initial_guess == None:
marker_initial_guess = dict((l,(se3.identity() if marker_types[l]=='t' else [0.0]*3)) for l in marker_id_list)
else:
marker_initial_guess = marker_initial_guess.copy()
for l in marker_id_list:
if l not in marker_initial_guess:
marker_initial_guess[l] = (se3.identity() if marker_types[l]=='t' else [0.0]*3)
camera_transform = camera_initial_guess
marker_transforms = marker_initial_guess.copy()
if DO_VISUALIZATION:
rgroup = coordinates.addGroup("calibration")
rgroup.addFrame("camera link",worldCoordinates=camera_link_transforms[-1])
rgroup.addFrame("marker link",worldCoordinates=marker_link_transforms[-1])
rgroup.addFrame("camera estimate",parent="camera link",relativeCoordinates=camera_transform)
rgroup.addFrame("marker estimate",parent="marker link",relativeCoordinates=marker_transforms.values()[0])
for i,obs in enumerate(marker_observations):
rgroup.addFrame("obs"+str(i)+" estimate",parent="camera estimate",relativeCoordinates=obs)
vis.add("coordinates",rgroup)
vis.dialog()
point_observations_only = all(marker_types[marker] == 'p' for marker in marker_id_list)
xform_observations_only = all(marker_types[marker] == 't' for marker in marker_id_list)
if not point_observations_only and not xform_observations_only:
raise NotImplementedError("Can't calibrate camera from mixed point/transform markers yet")
for iters in range(maxIters):
#attempt to minimize the error on the following over all observations i
#camera_link_transform(q_i)*camera_transform*observation_i = marker_link_transform(l_i,q_i)*marker_transform(l_i)
#first, we'll assume the camera transform is fixed and then optimize the marker transforms.
#then, we'll assume the marker transforms are fixed and then optimize the camera transform.
#finally we'll check the error to detect convergence
#1. Estimate marker transforms from current camera transform
new_marker_transforms = dict((l,(TransformStats(zero=marker_initial_guess[l],prior=regularizationFactor) if marker_types[l]=='t' else VectorStats(value,zero=[0.0]*3,prior=RegularizationFactor))) for l in marker_id_list)
for i in xrange(n):
marker = marker_ids[i]
Tclink = camera_link_transforms[i]
Tmlink = marker_link_transforms[i]
obs = marker_observations[i]
Trel = se3.mul(se3.inv(Tmlink),se3.mul(Tclink,camera_transform))
if marker_types[marker] == 't':
estimate = se3.mul(Trel,obs)
else:
estimate = se3.apply(Trel,obs)
new_marker_transforms[marker].add(estimate,observation_weights[i])
print "ITERATION",iters
#print " ESTIMATED MARKER TRANSFORMS:",dict((k,v.average) for (k,v) in new_marker_transforms.iteritems())
#2. Estimate camera transform from current marker transforms
new_camera_transform = TransformStats(zero=camera_initial_guess,prior=regularizationFactor)
if point_observations_only:
#TODO: weighted point fitting
relative_points = []
for i in xrange(n):
marker = marker_ids[i]
Tclink = camera_link_transforms[i]
Tmlink = marker_link_transforms[i]
obs = marker_observations[i]
pRel = se3.apply(se3.inv(Tclink),se3.apply(Tmlink,new_marker_transforms[marker].average))
relative_points.append(pRel)
new_camera_transform.add(point_fit_xform_3d(marker_observations,relative_points),sum(observation_weights))
else:
for i in xrange(n):
marker = marker_ids[i]
Tclink = camera_link_transforms[i]
Tmlink = marker_link_transforms[i]
obs = marker_observations[i]
Trel = se3.mul(se3.inv(Tclink),se3.mul(Tmlink,new_marker_transforms[marker].average))
estimate = se3.mul(Trel,se3.inv(obs))
new_camera_transform.add(estimate,observation_weights[i])
#print " ESTIMATED CAMERA TRANSFORMS:",new_camera_transform.average
#3. compute difference between last and current estimates
diff = 0.0
diff += vectorops.normSquared(se3.error(camera_transform,new_camera_transform.average))
for marker in marker_id_list:
if marker_types[marker]=='t':
diff += vectorops.normSquared(se3.error(marker_transforms[marker],new_marker_transforms[marker].average))
else:
diff += vectorops.distanceSquared(marker_transforms[marker],new_marker_transforms[marker].average)
camera_transform = new_camera_transform.average
for marker in marker_id_list:
marker_transforms[marker] = new_marker_transforms[marker].average
if math.sqrt(diff) < tolerance:
#converged!
print "Converged with diff %g on iteration %d"%(math.sqrt(diff),iters)
break
print " ESTIMATE CHANGE:",math.sqrt(diff)
error = 0.0
for i in xrange(n):
marker = marker_ids[i]
Tclink = camera_link_transforms[i]
Tmlink = marker_link_transforms[i]
obs = marker_observations[i]
Tc = se3.mul(Tclink,camera_transform)
if marker_types[marker] == 't':
Tm = se3.mul(Tmlink,marker_transforms[marker])
error += vectorops.normSquared(se3.error(se3.mul(Tc,obs),Tm))
else:
Tm = se3.apply(Tmlink,marker_transforms[marker])
error += vectorops.distanceSquared(se3.apply(Tc,obs),Tm)
print " OBSERVATION ERROR:",math.sqrt(error)
#raw_input()
if DO_VISUALIZATION:
rgroup.setFrameCoordinates("camera estimate",camera_transform)
rgroup.setFrameCoordinates("marker estimate",marker_transforms.values()[0])
for i,obs in enumerate(marker_observations):
rgroup.setFrameCoordinates("obs"+str(i)+" estimate",obs)
vis.add("coordinates",rgroup)
vis.dialog()
if math.sqrt(diff) >= tolerance:
print "Max iters reached"
error = 0.0
for i in xrange(n):
marker = marker_ids[i]
Tclink = camera_link_transforms[i]
Tmlink = marker_link_transforms[i]
obs = marker_observations[i]
Tc = se3.mul(Tclink,camera_transform)
if marker_types[marker] == 't':
Tm = se3.mul(Tmlink,marker_transforms[marker])
error += vectorops.normSquared(se3.error(se3.mul(Tc,obs),Tm))
else:
Tm = se3.apply(Tmlink,marker_transforms[marker])
error += vectorops.distanceSquared(se3.apply(Tc,obs),Tm)
return (math.sqrt(error),camera_transform,marker_transforms)