def fitSwitchBox(self, polyData, points):
boxPosition = points[0]
wallPoint = points[1]
# find a frame that is aligned with wall
searchRadius = 0.2
planePoints, normal = segmentation.applyLocalPlaneFit(polyData, points[0], searchRadius=np.linalg.norm(points[1] - points[0]), searchRadiusEnd=1.0)
obj = vis.updatePolyData(planePoints, 'wall plane points', color=[0,1,0], visible=False)
obj.setProperty('Point Size', 7)
viewDirection = segmentation.SegmentationContext.getGlobalInstance().getViewDirection()
if np.dot(normal, viewDirection) < 0:
normal = -normal
origin = segmentation.computeCentroid(planePoints)
zaxis = [0,0,1]
xaxis = normal
yaxis = np.cross(zaxis, xaxis)
yaxis /= np.linalg.norm(yaxis)
zaxis = np.cross(xaxis, yaxis)
zaxis /= np.linalg.norm(zaxis)
t = transformUtils.getTransformFromAxes(xaxis, yaxis, zaxis)
# translate that frame to the box position
t.PostMultiply()
t.Translate(boxPosition)
boxFrame = transformUtils.copyFrame(t)
self.switchPlanner.spawnBoxAffordanceAtFrame(boxFrame)
def fitSwitchBox(self, polyData, points):
boxPosition = points[0]
wallPoint = points[1]
# find a frame that is aligned with wall
searchRadius = 0.2
planePoints, normal = segmentation.applyLocalPlaneFit(polyData, points[0], searchRadius=np.linalg.norm(points[1] - points[0]), searchRadiusEnd=1.0)
obj = vis.updatePolyData(planePoints, 'wall plane points', color=[0,1,0], visible=False)
obj.setProperty('Point Size', 7)
viewDirection = segmentation.SegmentationContext.getGlobalInstance().getViewDirection()
if np.dot(normal, viewDirection) < 0:
normal = -normal
origin = segmentation.computeCentroid(planePoints)
zaxis = [0,0,1]
xaxis = normal
yaxis = np.cross(zaxis, xaxis)
yaxis /= np.linalg.norm(yaxis)
zaxis = np.cross(xaxis, yaxis)
zaxis /= np.linalg.norm(zaxis)
t = transformUtils.getTransformFromAxes(xaxis, yaxis, zaxis)
# translate that frame to the box position
t.PostMultiply()
t.Translate(boxPosition)
boxFrame = transformUtils.copyFrame(t)
self.switchPlanner.spawnBoxAffordanceAtFrame(boxFrame)
def fitObjectsOnShelf(polyData, maxHeight = 0.25):
# find the shelf plane:
polyDataWithoutFront, _ = segmentation.removeMajorPlane(polyData, distanceThreshold=0.02)
polyDataPlaneFit, origin, normal = segmentation.applyPlaneFit(polyDataWithoutFront, expectedNormal=np.array([0.0,0.0,1.0]), perpendicularAxis=np.array([0.0,0.0,1.0]), returnOrigin=True)
vis.updatePolyData(polyDataPlaneFit, 'polyDataPlaneFit', parent='segmentation', visible=False)
shelfSurfacePoints = segmentation.thresholdPoints(polyDataPlaneFit, 'dist_to_plane', [-0.01, 0.01])
shelfCenter = segmentation.computeCentroid(shelfSurfacePoints)
shelfFrame = transformUtils.getTransformFromOriginAndNormal(shelfCenter, normal, normalAxis=2)
vis.showFrame(shelfFrame, 'shelfFrame', parent='segmentation', scale=0.15 , visible=False)
# find the points near to the shelf plane and find objects on it:
points = vnp.getNumpyFromVtk(polyData, 'Points')
dist = np.dot(points - origin, normal)
vnp.addNumpyToVtk(polyData, dist, 'dist_to_plane')
shelfPoints = segmentation.thresholdPoints(polyData, 'dist_to_plane', [-0.01, maxHeight])
vis.updatePolyData(shelfPoints, 'shelf', parent='segmentation', visible=False)
data = segmentation.segmentTableScene(shelfPoints, shelfCenter, filterClustering = False )
vis.showClusterObjects(data.clusters + [data.table], parent='segmentation')
# remove the points that we considered from the orginal cloud
dists = vnp.getNumpyFromVtk(polyData, 'dist_to_plane')
diffShelf = ( ((dists > maxHeight) + (dists < -0.01))) + 0.1 -0.1
vnp.addNumpyToVtk(polyData, diffShelf, 'diff_shelf')
polyData = segmentation.thresholdPoints(polyData, 'diff_shelf', [1, 1])
vis.updatePolyData(polyData, 'rest', parent='segmentation', visible=False)
return polyData
def segmentTableObjects(self):
tableCentroid = segmentation.computeCentroid(self.tableData.box)
self.tableData.frame.TransformPoint(tableCentroid, tableCentroid)
data = segmentation.segmentTableScene(self.getInputPointCloud(), tableCentroid)
data.clusters = self.sortClustersOnTable(data.clusters)
self.clusterObjects = vis.showClusterObjects(data.clusters, parent='segmentation')
self.segmentationData = data
def run(self):
polyData = self.getPointCloud()
annotation = self.getAnnotationInput()
annotationPoint = annotation.annotationPoints[0]
mesh = segmentation.fitShelfItem(polyData, annotationPoint, clusterTolerance=self.properties.getProperty('Cluster tolerance'))
annotation.setProperty('Visible', False)
om.removeFromObjectModel(om.findObjectByName('shelf item'))
obj = vis.showPolyData(mesh, 'shelf item', color=[0,1,0])
t = transformUtils.frameFromPositionAndRPY(segmentation.computeCentroid(mesh), [0,0,0])
segmentation.makeMovable(obj, t)
def fitObjectsOnShelf(polyData, maxHeight=0.25):
# find the shelf plane:
polyDataWithoutFront, _ = segmentation.removeMajorPlane(
polyData, distanceThreshold=0.02)
polyDataPlaneFit, origin, normal = segmentation.applyPlaneFit(
polyDataWithoutFront,
expectedNormal=np.array([0.0, 0.0, 1.0]),
perpendicularAxis=np.array([0.0, 0.0, 1.0]),
returnOrigin=True)
vis.updatePolyData(polyDataPlaneFit,
'polyDataPlaneFit',
parent='segmentation',
visible=False)
shelfSurfacePoints = segmentation.thresholdPoints(polyDataPlaneFit,
'dist_to_plane',
[-0.01, 0.01])
shelfCenter = segmentation.computeCentroid(shelfSurfacePoints)
shelfFrame = transformUtils.getTransformFromOriginAndNormal(shelfCenter,
normal,
normalAxis=2)
vis.showFrame(shelfFrame,
'shelfFrame',
parent='segmentation',
scale=0.15,
visible=False)
# find the points near to the shelf plane and find objects on it:
points = vnp.getNumpyFromVtk(polyData, 'Points')
dist = np.dot(points - origin, normal)
vnp.addNumpyToVtk(polyData, dist, 'dist_to_plane')
shelfPoints = segmentation.thresholdPoints(polyData, 'dist_to_plane',
[-0.01, maxHeight])
vis.updatePolyData(shelfPoints,
'shelf',
parent='segmentation',
visible=False)
data = segmentation.segmentTableScene(shelfPoints,
shelfCenter,
filterClustering=False)
vis.showClusterObjects(data.clusters + [data.table], parent='segmentation')
# remove the points that we considered from the orginal cloud
dists = vnp.getNumpyFromVtk(polyData, 'dist_to_plane')
diffShelf = (((dists > maxHeight) + (dists < -0.01))) + 0.1 - 0.1
vnp.addNumpyToVtk(polyData, diffShelf, 'diff_shelf')
polyData = segmentation.thresholdPoints(polyData, 'diff_shelf', [1, 1])
vis.updatePolyData(polyData, 'rest', parent='segmentation', visible=False)
return polyData
def run(self):
polyData = self.getPointCloud()
annotation = self.getAnnotationInput()
annotationPoint = annotation.annotationPoints[0]
mesh = segmentation.fitShelfItem(
polyData,
annotationPoint,
clusterTolerance=self.properties.getProperty('Cluster tolerance'))
annotation.setProperty('Visible', False)
om.removeFromObjectModel(om.findObjectByName('shelf item'))
obj = vis.showPolyData(mesh, 'shelf item', color=[0, 1, 0])
t = transformUtils.frameFromPositionAndRPY(
segmentation.computeCentroid(mesh), [0, 0, 0])
segmentation.makeMovable(obj, t)
def findMinimumBoundingRectangle(self, polyData, linkFrame):
'''
Find minimum bounding rectangle.
The input is assumed to be a rectangular point cloud of cinder blocks
Returns transform of far right corner (pointing away from robot)
'''
# TODO: for non-z up surfaces, this needs work
# TODO: return other parameters
# Originally From: https://github.com/dbworth/minimum-area-bounding-rectangle
polyData = segmentation.applyVoxelGrid(polyData, leafSize=0.02)
#vis.updatePolyData( polyData, 'block top', parent='cont debug', visible=False)
polyDataCentroid = segmentation.computeCentroid(polyData)
pts =vtkNumpy.getNumpyFromVtk( polyData , 'Points' )
xy_points = pts[:,[0,1]]
vis.updatePolyData( get2DAsPolyData(xy_points) , 'xy_points', parent='cont debug', visible=False)
hull_points = qhull_2d.qhull2D(xy_points)
vis.updatePolyData( get2DAsPolyData(hull_points) , 'hull_points', parent='cont debug', visible=False)
# Reverse order of points, to match output from other qhull implementations
hull_points = hull_points[::-1]
# print 'Convex hull points: \n', hull_points, "\n"
# Find minimum area bounding rectangle
(rot_angle, rectArea, rectDepth, rectWidth, center_point, corner_points_ground) = min_bounding_rect.minBoundingRect(hull_points)
vis.updatePolyData( get2DAsPolyData(corner_points_ground) , 'corner_points_ground', parent='cont debug', visible=False)
cornerPoints = np.vstack((corner_points_ground.T, polyDataCentroid[2]*np.ones( corner_points_ground.shape[0]) )).T
cornerPolyData = vtkNumpy.getVtkPolyDataFromNumpyPoints(cornerPoints)
# Create a frame at the far right point - which points away from the robot
farRightCorner = self.findFarRightCorner(cornerPolyData , linkFrame)
viewDirection = segmentation.SegmentationContext.getGlobalInstance().getViewDirection()
robotYaw = math.atan2( viewDirection[1], viewDirection[0] )*180.0/np.pi
blockAngle = rot_angle*(180/math.pi)
#print "robotYaw ", robotYaw
#print "blockAngle ", blockAngle
blockAngleAll = np.array([blockAngle , blockAngle+90 , blockAngle+180, blockAngle+270])
#print blockAngleAll
for i in range(0,4):
if(blockAngleAll[i]>180):
blockAngleAll[i]=blockAngleAll[i]-360
#print blockAngleAll
values = abs(blockAngleAll - robotYaw)
#print values
min_idx = np.argmin(values)
if ( (min_idx==1) or (min_idx==3) ):
#print "flip rectDepth and rectWidth as angle is not away from robot"
temp = rectWidth ; rectWidth = rectDepth ; rectDepth = temp
#print "best angle", blockAngleAll[min_idx]
rot_angle = blockAngleAll[min_idx]*math.pi/180.0
# Optional: overwrite all of the above with the yaw of the robt when it was standing in front of the blocks:
if (self.fixBlockYawWithInitial):
rot_angle = self.initialRobotYaw
cornerTransform = transformUtils.frameFromPositionAndRPY( farRightCorner , [0,0, np.rad2deg(rot_angle) ] )
#print "Minimum area bounding box:"
#print "Rotation angle:", rot_angle, "rad (", rot_angle*(180/math.pi), "deg )"
#print "rectDepth:", rectDepth, " rectWidth:", rectWidth, " Area:", rectArea
#print "Center point: \n", center_point # numpy array
#print "Corner points: \n", cornerPoints, "\n" # numpy array
return cornerTransform, rectDepth, rectWidth, rectArea
def findMinimumBoundingRectangle(self, polyData, linkFrame):
'''
Find minimum bounding rectangle.
The input is assumed to be a rectangular point cloud of cinder blocks
Returns transform of far right corner (pointing away from robot)
'''
# TODO: for non-z up surfaces, this needs work
# TODO: return other parameters
# Originally From: https://github.com/dbworth/minimum-area-bounding-rectangle
polyData = segmentation.applyVoxelGrid(polyData, leafSize=0.02)
#vis.updatePolyData( polyData, 'block top', parent='cont debug', visible=False)
polyDataCentroid = segmentation.computeCentroid(polyData)
pts =vtkNumpy.getNumpyFromVtk( polyData , 'Points' )
xy_points = pts[:,[0,1]]
vis.updatePolyData( get2DAsPolyData(xy_points) , 'xy_points', parent='cont debug', visible=False)
hull_points = qhull_2d.qhull2D(xy_points)
vis.updatePolyData( get2DAsPolyData(hull_points) , 'hull_points', parent='cont debug', visible=False)
# Reverse order of points, to match output from other qhull implementations
hull_points = hull_points[::-1]
# print 'Convex hull points: \n', hull_points, "\n"
# Find minimum area bounding rectangle
(rot_angle, rectArea, rectDepth, rectWidth, center_point, corner_points_ground) = min_bounding_rect.minBoundingRect(hull_points)
vis.updatePolyData( get2DAsPolyData(corner_points_ground) , 'corner_points_ground', parent='cont debug', visible=False)
cornerPoints = np.vstack((corner_points_ground.T, polyDataCentroid[2]*np.ones( corner_points_ground.shape[0]) )).T
cornerPolyData = vtkNumpy.getVtkPolyDataFromNumpyPoints(cornerPoints)
# Create a frame at the far right point - which points away from the robot
farRightCorner = self.findFarRightCorner(cornerPolyData , linkFrame)
viewDirection = segmentation.SegmentationContext.getGlobalInstance().getViewDirection()
robotYaw = math.atan2( viewDirection[1], viewDirection[0] )*180.0/np.pi
blockAngle = rot_angle*(180/math.pi)
#print "robotYaw ", robotYaw
#print "blockAngle ", blockAngle
blockAngleAll = np.array([blockAngle , blockAngle+90 , blockAngle+180, blockAngle+270])
#print blockAngleAll
for i in range(0,4):
if(blockAngleAll[i]>180):
blockAngleAll[i]=blockAngleAll[i]-360
#print blockAngleAll
values = abs(blockAngleAll - robotYaw)
#print values
min_idx = np.argmin(values)
if ( (min_idx==1) or (min_idx==3) ):
#print "flip rectDepth and rectWidth as angle is not away from robot"
temp = rectWidth ; rectWidth = rectDepth ; rectDepth = temp
#print "best angle", blockAngleAll[min_idx]
rot_angle = blockAngleAll[min_idx]*math.pi/180.0
# Optional: overwrite all of the above with the yaw of the robt when it was standing in front of the blocks:
if (self.fixBlockYawWithInitial):
rot_angle = self.initialRobotYaw
cornerTransform = transformUtils.frameFromPositionAndRPY( farRightCorner , [0,0, np.rad2deg(rot_angle) ] )
#print "Minimum area bounding box:"
#print "Rotation angle:", rot_angle, "rad (", rot_angle*(180/math.pi), "deg )"
#print "rectDepth:", rectDepth, " rectWidth:", rectWidth, " Area:", rectArea
#print "Center point: \n", center_point # numpy array
#print "Corner points: \n", cornerPoints, "\n" # numpy array
return cornerTransform, rectDepth, rectWidth, rectArea