def onSegmentGround():
groundPoints, scenePoints = segmentation.removeGround(
pointCloudObj.polyData)
vis.showPolyData(groundPoints,
'ground points',
color=[0, 1, 0],
parent='segmentation')
vis.showPolyData(scenePoints,
'scene points',
color=[1, 0, 1],
parent='segmentation')
pickedObj.setProperty('Visible', False)
def onSegmentGround():
groundPoints, scenePoints = segmentation.removeGround(pointCloudObj.polyData)
vis.showPolyData(groundPoints, 'ground points', color=[0,1,0], parent='segmentation')
vis.showPolyData(scenePoints, 'scene points', color=[1,0,1], parent='segmentation')
pickedObj.setProperty('Visible', False)
app = ConsoleApp()
# create a view
view = app.createView()
segmentation._defaultSegmentationView = view
robotStateModel, robotStateJointController = roboturdf.loadRobotModel('robot state model', view, parent='sensors', color=roboturdf.getRobotGrayColor(), visible=True)
segmentationroutines.SegmentationContext.initWithRobot(robotStateModel)
# load poly data
dataDir = app.getTestingDataDirectory()
polyData = ioUtils.readPolyData(os.path.join(dataDir, 'amazon-pod/01-small-changes.vtp'))
vis.showPolyData(polyData, 'pointcloud snapshot', visible=False)
# remove ground and clip to just the pod:
groundPoints, polyData = segmentation.removeGround(polyData)
vis.showPolyData(polyData, 'scene', visible=False)
polyData = segmentation.addCoordArraysToPolyData(polyData)
polyData = segmentation.thresholdPoints(polyData, 'y', [1, 1.6])
polyData = segmentation.thresholdPoints(polyData, 'x', [-1.2, 0.5])
vis.showPolyData(polyData, 'clipped', visible=False)
# remove outliers
polyData = segmentation.labelOutliers(polyData, searchRadius=0.03, neighborsInSearchRadius=40)
polyData = segmentation.thresholdPoints(polyData, 'is_outlier', [0, 0])
vis.showPolyData(polyData, 'inliers', visible=False)
# remove walls, and points behind temp:
polyData = removePlaneAndBeyond(polyData, expectedNormal=[0,1,0], filterRange=[-np.inf, -0.03], whichAxis=1, whichAxisLetter='y', percentile = 95)
polyData = removePlaneAndBeyond(polyData, expectedNormal=[1,0,0], filterRange=[-np.inf, -0.03], whichAxis=0, whichAxisLetter='x', percentile = 95)
polyData = removePlaneAndBeyond(polyData, expectedNormal=[1,0,0], filterRange=[0.03, np.inf], whichAxis=0, whichAxisLetter='x', percentile = 5)
robotStateModel, robotStateJointController = roboturdf.loadRobotModel(
'robot state model',
view,
parent='sensors',
color=roboturdf.getRobotGrayColor(),
visible=True)
segmentationroutines.SegmentationContext.initWithRobot(robotStateModel)
# load poly data
dataDir = app.getTestingDataDirectory()
polyData = ioUtils.readPolyData(
os.path.join(dataDir, 'amazon-pod/01-small-changes.vtp'))
vis.showPolyData(polyData, 'pointcloud snapshot', visible=False)
# remove ground and clip to just the pod:
groundPoints, polyData = segmentation.removeGround(polyData)
vis.showPolyData(polyData, 'scene', visible=False)
polyData = segmentation.addCoordArraysToPolyData(polyData)
polyData = segmentation.thresholdPoints(polyData, 'y', [1, 1.6])
polyData = segmentation.thresholdPoints(polyData, 'x', [-1.2, 0.5])
vis.showPolyData(polyData, 'clipped', visible=False)
# remove outliers
polyData = segmentation.labelOutliers(polyData,
searchRadius=0.03,
neighborsInSearchRadius=40)
polyData = segmentation.thresholdPoints(polyData, 'is_outlier', [0, 0])
vis.showPolyData(polyData, 'inliers', visible=False)
# remove walls, and points behind temp:
polyData = removePlaneAndBeyond(polyData,
def fitRunningBoardAtFeet(self):
# get stance frame
startPose = self.getPlanningStartPose()
stanceFrame = self.robotSystem.footstepsDriver.getFeetMidPoint(self.robotSystem.robotStateModel, useWorldZ=False)
stanceFrameAxes = transformUtils.getAxesFromTransform(stanceFrame)
# get pointcloud and extract search region covering the running board
polyData = segmentation.getCurrentRevolutionData()
polyData = segmentation.applyVoxelGrid(polyData, leafSize=0.01)
_, polyData = segmentation.removeGround(polyData)
polyData = segmentation.cropToBox(polyData, stanceFrame, [1.0, 1.0, 0.1])
if not polyData.GetNumberOfPoints():
print 'empty search region point cloud'
return
vis.updatePolyData(polyData, 'running board search points', parent=segmentation.getDebugFolder(), color=[0,1,0], visible=False)
# extract maximal points along the stance x axis
perpAxis = stanceFrameAxes[0]
edgeAxis = stanceFrameAxes[1]
edgePoints = segmentation.computeEdge(polyData, edgeAxis, perpAxis)
edgePoints = vnp.getVtkPolyDataFromNumpyPoints(edgePoints)
vis.updatePolyData(edgePoints, 'edge points', parent=segmentation.getDebugFolder(), visible=True)
# ransac fit a line to the edge points
linePoint, lineDirection, fitPoints = segmentation.applyLineFit(edgePoints)
if np.dot(lineDirection, stanceFrameAxes[1]) < 0:
lineDirection = -lineDirection
linePoints = segmentation.thresholdPoints(fitPoints, 'ransac_labels', [1.0, 1.0])
dists = np.dot(vnp.getNumpyFromVtk(linePoints, 'Points')-linePoint, lineDirection)
p1 = linePoint + lineDirection*np.min(dists)
p2 = linePoint + lineDirection*np.max(dists)
vis.updatePolyData(fitPoints, 'line fit points', parent=segmentation.getDebugFolder(), colorByName='ransac_labels', visible=False)
# compute a new frame that is in plane with the stance frame
# and matches the orientation and position of the detected edge
origin = np.array(stanceFrame.GetPosition())
normal = np.array(stanceFrameAxes[2])
# project stance origin to edge, then back to foot frame
originProjectedToEdge = linePoint + lineDirection*np.dot(origin - linePoint, lineDirection)
originProjectedToPlane = segmentation.projectPointToPlane(originProjectedToEdge, origin, normal)
zaxis = np.array(stanceFrameAxes[2])
yaxis = np.array(lineDirection)
xaxis = np.cross(yaxis, zaxis)
xaxis /= np.linalg.norm(xaxis)
yaxis = np.cross(zaxis, xaxis)
yaxis /= np.linalg.norm(yaxis)
d = DebugData()
d.addSphere(p1, radius=0.005)
d.addSphere(p2, radius=0.005)
d.addLine(p1, p2)
d.addSphere(originProjectedToEdge, radius=0.001, color=[1,0,0])
d.addSphere(originProjectedToPlane, radius=0.001, color=[0,1,0])
d.addLine(originProjectedToPlane, origin, color=[0,1,0])
d.addLine(originProjectedToEdge, origin, color=[1,0,0])
vis.updatePolyData(d.getPolyData(), 'running board edge', parent=segmentation.getDebugFolder(), colorByName='RGB255', visible=False)
# update the running board box affordance position and orientation to
# fit the detected edge
box = self.spawnRunningBoardAffordance()
boxDimensions = box.getProperty('Dimensions')
t = transformUtils.getTransformFromAxesAndOrigin(xaxis, yaxis, zaxis, originProjectedToPlane)
t.PreMultiply()
t.Translate(-boxDimensions[0]/2.0, 0.0, -boxDimensions[2]/2.0)
box.getChildFrame().copyFrame(t)
self.initialize()
def fitRunningBoardAtFeet(self):
# get stance frame
startPose = self.getPlanningStartPose()
stanceFrame = self.robotSystem.footstepsDriver.getFeetMidPoint(
self.robotSystem.robotStateModel, useWorldZ=False)
stanceFrameAxes = transformUtils.getAxesFromTransform(stanceFrame)
# get pointcloud and extract search region covering the running board
polyData = segmentation.getCurrentRevolutionData()
polyData = segmentation.applyVoxelGrid(polyData, leafSize=0.01)
_, polyData = segmentation.removeGround(polyData)
polyData = segmentation.cropToBox(polyData, stanceFrame,
[1.0, 1.0, 0.1])
if not polyData.GetNumberOfPoints():
print 'empty search region point cloud'
return
vis.updatePolyData(polyData,
'running board search points',
parent=segmentation.getDebugFolder(),
color=[0, 1, 0],
visible=False)
# extract maximal points along the stance x axis
perpAxis = stanceFrameAxes[0]
edgeAxis = stanceFrameAxes[1]
edgePoints = segmentation.computeEdge(polyData, edgeAxis, perpAxis)
edgePoints = vnp.getVtkPolyDataFromNumpyPoints(edgePoints)
vis.updatePolyData(edgePoints,
'edge points',
parent=segmentation.getDebugFolder(),
visible=True)
# ransac fit a line to the edge points
linePoint, lineDirection, fitPoints = segmentation.applyLineFit(
edgePoints)
if np.dot(lineDirection, stanceFrameAxes[1]) < 0:
lineDirection = -lineDirection
linePoints = segmentation.thresholdPoints(fitPoints, 'ransac_labels',
[1.0, 1.0])
dists = np.dot(
vnp.getNumpyFromVtk(linePoints, 'Points') - linePoint,
lineDirection)
p1 = linePoint + lineDirection * np.min(dists)
p2 = linePoint + lineDirection * np.max(dists)
vis.updatePolyData(fitPoints,
'line fit points',
parent=segmentation.getDebugFolder(),
colorByName='ransac_labels',
visible=False)
# compute a new frame that is in plane with the stance frame
# and matches the orientation and position of the detected edge
origin = np.array(stanceFrame.GetPosition())
normal = np.array(stanceFrameAxes[2])
# project stance origin to edge, then back to foot frame
originProjectedToEdge = linePoint + lineDirection * np.dot(
origin - linePoint, lineDirection)
originProjectedToPlane = segmentation.projectPointToPlane(
originProjectedToEdge, origin, normal)
zaxis = np.array(stanceFrameAxes[2])
yaxis = np.array(lineDirection)
xaxis = np.cross(yaxis, zaxis)
xaxis /= np.linalg.norm(xaxis)
yaxis = np.cross(zaxis, xaxis)
yaxis /= np.linalg.norm(yaxis)
d = DebugData()
d.addSphere(p1, radius=0.005)
d.addSphere(p2, radius=0.005)
d.addLine(p1, p2)
d.addSphere(originProjectedToEdge, radius=0.001, color=[1, 0, 0])
d.addSphere(originProjectedToPlane, radius=0.001, color=[0, 1, 0])
d.addLine(originProjectedToPlane, origin, color=[0, 1, 0])
d.addLine(originProjectedToEdge, origin, color=[1, 0, 0])
vis.updatePolyData(d.getPolyData(),
'running board edge',
parent=segmentation.getDebugFolder(),
colorByName='RGB255',
visible=False)
# update the running board box affordance position and orientation to
# fit the detected edge
box = self.spawnRunningBoardAffordance()
boxDimensions = box.getProperty('Dimensions')
t = transformUtils.getTransformFromAxesAndOrigin(
xaxis, yaxis, zaxis, originProjectedToPlane)
t.PreMultiply()
t.Translate(-boxDimensions[0] / 2.0, 0.0, -boxDimensions[2] / 2.0)
box.getChildFrame().copyFrame(t)
self.initialize()