PartialSpaceDijkstra¶

Inheritance diagram¶

Classes¶

PartialSpaceDijkstra¶

class ORSModel.ors.PartialSpaceDijkstra¶

Bases: ORSModel.ors.Dijkstra

extractBoundaryFromROI(self, aVolROI: ORSModel.ors.ROI) → None¶

Parameters:	aVolROI (ORSModel.ors.ROI) –

getClassNameStatic() → str¶

getClassNameStatic

Returns:	output (str) –

none() → PartialSpaceDijkstra¶

Returns:	output (PartialSpaceDijkstra) –

setRadiusOfInterest(self, radius: int) → None¶

Parameters:	radius (int) –

Dijkstra¶

class ORSModel.ors.Dijkstra

Bases: ORSModel.ors.Unmanaged

cleanDistanceMapChannel(self, outputChannel: ORSModel.ors.Channel) → None

Removes boundaries or non reached value from a distance map channel.

Parameters:	outputChannel (ORSModel.ors.Channel) – a distance map channel (an Channel)

createDistanceMap(self, lOutputChannelDistanceMap: ORSModel.ors.Channel, lOutputChannelTraceBack: ORSModel.ors.Channel, lOutputChannelLabel: ORSModel.ors.Channel) → None

Creates a distance map starting from all the providedROI sources.

Parameters:	lOutputChannelDistanceMap (ORSModel.ors.Channel) – the distance map generated by the Dijkstra algorithm (an Channel) lOutputChannelTraceBack (ORSModel.ors.Channel) – a traceback channel, can be NULL (an Channel) lOutputChannelLabel (ORSModel.ors.Channel) – a label channel, can be NULL (an Channel)

createDistanceMapForMaxDistance(self, lOutputChannelDistanceMap: ORSModel.ors.Channel, maxDistance: float, lOutputChannelTraceBack: ORSModel.ors.Channel, lOutputChannelLabel: ORSModel.ors.Channel, continueDijkstra: bool) → None

Creates a distance map until a providedDijkstra distance is reached, starting from all the provided ROI sources.

Parameters:

lOutputChannelDistanceMap (ORSModel.ors.Channel) – the distance map generated by the Dijkstra algorithm (an Channel)
maxDistance (float) – the Dijkstra distance to reach before stopping to process new voxels (a double)
lOutputChannelTraceBack (ORSModel.ors.Channel) – a traceback channel, can be NULL (an Channel)
lOutputChannelLabel (ORSModel.ors.Channel) – a label channel, can be NULL (an Channel)
continueDijkstra (bool) – TRUE if it is not the first time this call is done on this instance of Dijkstra with the same distance map and that the algorithm must continue, FALSE to start over

createDistanceMapForNBIteration(self, lOutputChannelDistanceMap: ORSModel.ors.Channel, forNbIteration: int, lOutputChannelTraceBack: ORSModel.ors.Channel, lOutputChannelLabel: ORSModel.ors.Channel, autoUpdateROI: bool, continueDijkstra: bool) → None

Creates a distance map for a given number of voxels, starting from all the providedROI sources.

Parameters:

lOutputChannelDistanceMap (ORSModel.ors.Channel) – the distance map generated by the Dijkstra algorithm (an Channel)
forNbIteration (int) – the number of voxels to be processed by the Dijkstra algorithm (an unsigned int)
lOutputChannelTraceBack (ORSModel.ors.Channel) – a traceback channel, can be NULL (an Channel)
lOutputChannelLabel (ORSModel.ors.Channel) – a label channel, can be NULL (an Channel)
autoUpdateROI (bool) – TRUE if the source ROIs should be updated with their diffusion result, FALSE otherwise
continueDijkstra (bool) – TRUE if it is not the first time this call is done on this instance of Dijkstra with the same distance map and that the algorithm must continue, FALSE to start over

createDistanceMapUntilAnyPointInTargetRoiIsReached(self, lOutputChannelDistanceMap: ORSModel.ors.Channel, aTargetVolumeROI: ORSModel.ors.ROI, lOutputChannelTraceBack: ORSModel.ors.Channel, continueDijkstra: bool) → None

Creates a distance map until any stop point provided in aROI are reached, starting from all the provided ROI sources.

Parameters:	lOutputChannelDistanceMap (ORSModel.ors.Channel) – the distance map generated by the Dijkstra algorithm (an Channel) aTargetVolumeROI (ORSModel.ors.ROI) – TODO DOCUMENT_ME lOutputChannelTraceBack (ORSModel.ors.Channel) – a traceback channel, can be None (a Channel) continueDijkstra (bool) – a label channel, can be None (a Channel)

createDistanceMapUntilPointInWorldCoordinatesIsReached(self, lOutputChannelDistanceMap: ORSModel.ors.Channel, xStopPointInWorld: float, yStopPointInWorld: float, zStopPointInWorld: float, lOutputChannelTraceBack: ORSModel.ors.Channel, lOutputChannelLabel: ORSModel.ors.Channel, continueDijkstra: bool) → None

Parameters:

lOutputChannelDistanceMap (ORSModel.ors.Channel) – Z position of a world coordinate
xStopPointInWorld (float) – the number of voxels to be processed after the stop condition is matched (an uint32_t)
yStopPointInWorld (float) – a traceback channel, can be NULL (an Channel)
zStopPointInWorld (float) – a label channel, can be NULL (an Channel)
lOutputChannelTraceBack (ORSModel.ors.Channel) – TRUE if it is not the first time this call is done on this instance of Dijkstra with the same distance map and that the algorithm must continue, FALSE to start over
lOutputChannelLabel (ORSModel.ors.Channel) –
continueDijkstra (bool) –

createDistanceMapUntilPointsAreReached(self, lOutputChannelDistanceMap: ORSModel.ors.Channel, positionTripleInSourceRef: int, nbPosition: int, waitForNIndex: int, lOutputChannelTraceBack: ORSModel.ors.Channel, lOutputChannelLabel: ORSModel.ors.Channel, breakForAny: bool, continueDijkstra: bool) → None

Creates a distance map until any or all stop points provided are reached, starting from all the providedROI sources.

Parameters:

lOutputChannelDistanceMap (ORSModel.ors.Channel) – the distance map generated by the Dijkstra algorithm (an Channel)
positionTripleInSourceRef (int) – a collection of x,y,z triplets stop points in input channel referential (an uint32_t*)
nbPosition (int) – the number of triplets present in the collection of triplets stop points (an uint32_t)
waitForNIndex (int) – the number of voxels to be processed after the stop condition is matched (an uint32_t)
lOutputChannelTraceBack (ORSModel.ors.Channel) – a traceback channel, can be NULL (an Channel)
lOutputChannelLabel (ORSModel.ors.Channel) – a label channel, can be NULL (an Channel)
breakForAny (bool) – TRUE if the algorithm has to stop for any points, FALSE if all the points have to be reached to stop
continueDijkstra (bool) – TRUE if it is not the first time this call is done on this instance of Dijkstra with the same distance map and that the algorithm must continue, FALSE to start over

getAlphaParameter(self) → float

Gets the alpha parameter for the metric == 1.

Note

Only useful when metric is set to 1.

Returns:	output (float) – the alpha parameter (a double)

getClassNameStatic() → str

getClassNameStatic

Returns:	output (str) –

getEuclideanBias(self) → float

Gets the Euclidean bias that will be the minimumDijkstra distance between voxels.

Note

Neighbors of distance 1 will have a bias of spacialTerm

Note

Neighbors of distance sqrt(2) will have a bias of sqrt(2)*spacialTerm

Note

Neighbors of distance sqrt(3) will have a bias of sqrt(3)*spacialTerm

Returns:	output (float) – the minimum distance between voxels (a double)

getGaussianPeakCenterValue(self) → float

Sets the peak position of the gaussian used if the metric == 1.

Note

Only useful when metric is set to 1.

Returns:	output (float) – peak center of the gaussian (a double)

getIndexOfStopPointReach(self) → int

Retrieve the index reached by theDijkstra algorithm.

Note

Only useful when distance map was generated using methods createDistanceMapUntilAnyPointInTargetRoiIsReached, createDistanceMapUntilPointInWorldCoordinatesIsReached or createDistanceMapUntilPointsAreReached.

Returns:	output (int) – index in the input channel (a int64_t)

getKappa(self) → float

Gets the kappa parameter for the metric == 1.

Note

Only useful when metric is set to 1.

Returns:	output (float) – the kappa parameter (a double)

getMetric(self) → int

Gets the metric used by theDijkstra algorithm.

Note

If the metric chosen is 0, the metric function will be: DijkstraDistanceBetween(a,b) = (Ia-Ib)^2 + EuclideanBias*EuclideanDistance(a,b);

Note

If the metric chosen is 1, the metric function will be: DijkstraDistanceBetween(a,b) = (Ia-Ib)^2 + EuclideanBias*EuclideanDistance(a,b) + alpha(1.0 - exp(-(b-gaussianPeakCenterValue)^2/kappa*sigmaPow2)); where a and b are Neighbor, Ia and Ib are intensity at a and b

Returns:	output (int) – 0 or 1

getNeighborCount(self) → int

Gets the number of neighbors used by theDijkstra algorithm (the connectivity).

Note

Can be 6, 18 or 26

Returns:	output (int) – the number of neighbors (an char)

getROI(self, index: int) → ROI

Retrieves a particularROI from the index specified slot.

Note

A maximum of 10 ROIs can be provided. The ROIs provided must be of the same shape as the input channel.

Parameters:	index (int) – the slot index (an unsigned char)
Returns:	output (ORSModel.ors.ROI) – the ROI associated with this slot index (an ROI), or NULL if no ROI is at that slot

getROICount(self) → int

Returns the number of ROIs that have been set as sources.

Note

A maximum of 10 ROI can be provided.

Returns:	output (int) – the number of ROIs that have been provided (an char)

getSigmaPow2(self) → float

Gets the variance used by the gaussian used if the metric == 1.

Note

Only useful when metric is set to 1.

Returns:	output (float) – variance of the gaussian (a double)

none() → Dijkstra

Returns:	output (Dijkstra) –

resetROIs(self) → None: Empties all the sourceROI slots.

setAlphaParameter(self, alpha: float) → None

Sets the alpha parameter for the metric == 1.

Note

Only useful when metric is set to 1.

Parameters:	alpha (float) – the alpha parameter (a double)

setEuclideanBias(self, EuclideanBias: float) → None

Provides an Euclidean bias that will be the minimumDijkstra distance between voxels.

Note

Neighbors of distance 1 will have a bias of spacialTerm.

Note

Neighbors of distance sqrt(2) will have a bias of sqrt(2)*spacialTerm.

Note

Neighbors of distance sqrt(3) will have a bias of sqrt(3)*spacialTerm.

Parameters:	EuclideanBias (float) – the minimum distance between voxels (a double)

setGaussianPeakCenterValue(self, aPeakCenter: float) → None

Sets the peak position of the gaussian used if the metric == 1.

Note

Only useful when metric is set to 1.

Parameters:	aPeakCenter (float) – peak center of the gaussian (a double)

setInputChannelAndWorkingArea(self, inputChannel: ORSModel.ors.Channel, minX: int, minY: int, minZ: int, maxX: int, maxY: int, maxZ: int, currentT: int) → None

Sets the channel that will be used by theDijkstra algorithm to calculate distance.

Note

The min and max boundaries must not describe a space bigger than the input channel.

Parameters:

inputChannel (ORSModel.ors.Channel) – the input channel (an Channel)
minX (int) – the minimum X index in the input channel (an unsigned short)
minY (int) – the minimum Y index in the input channel (an unsigned short)
minZ (int) – the minimum Z index in the input channel (an unsigned short)
maxX (int) – the maximum X index in the input channel (an unsigned short)
maxY (int) – the maximum Y index in the input channel (an unsigned short)
maxZ (int) – the maximum Z index in the input channel (an unsigned short)
currentT (int) –

setInputLabelsChannel(self, aInputLabelsChannel: ORSModel.ors.Channel) → None

Parameters:	aInputLabelsChannel (ORSModel.ors.Channel) –

setInputMultiROI(self, aInputMultiROI: ORSModel.ors.MultiROI) → None

Parameters:	aInputMultiROI (ORSModel.ors.MultiROI) –

setKappa(self, kappa: float) → None

Sets the kappa parameter for the metric == 1.

Note

Only useful when metric is set to 1.

Parameters:	kappa (float) – the kappa parameter (a double)

setMaskROI(self, IMaskROI: ORSModel.ors.ROI) → None

Parameters:	IMaskROI (ORSModel.ors.ROI) –

setMetric(self, metricType: int) → None

Selects the metric to be used by theDijkstra algorithm.

Note

If the metric chosen is 0, the metric function will be: DijkstraDistanceBetween(a,b) = (Ia-Ib)^2 + EuclideanBias*EuclideanDistance(a,b);

Note

If the metric chosen is 1, the metric function will be: DijkstraDistanceBetween(a,b) = (Ia-Ib)^2 + EuclideanBias*EuclideanDistance(a,b) + alpha(1.0 - exp(-(b-gaussianPeakCenterValue)^2/kappa*sigmaPow2)); where a and b are Neighbors, Ia and Ib are intensity at a and b

Parameters:	metricType (int) – 0 or 1

setNeighborCountTo18(self) → None: Sets the number of neighbors used by theDijkstra algorithm to 6 ( Neighbor distance == 1).

setNeighborCountTo26(self) → None: Sets the number of neighbors used by theDijkstra algorithm to 6 ( Neighbor distance == 1).

setNeighborCountTo6(self) → None: Sets the number of neighbors used by theDijkstra algorithm to 6 ( Neighbor distance == 1).

setProgressObject(self, IProgress: ORSModel.ors.Progress) → None

Parameters:	IProgress (ORSModel.ors.Progress) –

setROI(self, index: int, aVolROI: ORSModel.ors.ROI) → None

Fills a particularROI slot to be used as a source for the Dijkstra algorithm.

Note

A maximum of 10 ROIs can be provided. The ROIs provided must be of the same shape as the input channel.

Parameters:	index (int) – the slot index (an unsigned char) aVolROI (ORSModel.ors.ROI) – the ROI associated with this slot index (an ROI)

setSigmaPow2(self, aLimit: float) → None

Sets the variance used by the gaussian used if the metric == 1.

Note

Only useful when metric is set to 1.

Parameters:	aLimit (float) – variance of the gaussian (a double)

tracebackCPU(self, aROI: ORSModel.ors.ROI, linputChannelTraceBack: ORSModel.ors.Channel, aPath: ORSModel.ors.VisualPath) → None

Uses a traceback channel to fill a path from aROI to the nearest source ROI.

Parameters:	aROI (ORSModel.ors.ROI) – a trace back start (an ROI) linputChannelTraceBack (ORSModel.ors.Channel) – a trace back channel (an ROI) aPath (ORSModel.ors.VisualPath) –

updateDistanceMapForMaxDistance(self, lOutputChannelDistanceMap: ORSModel.ors.Channel, maxDistance: float, lOutputChannelTraceBack: ORSModel.ors.Channel, lOutputChannelLabel: ORSModel.ors.Channel) → None

Parameters:	lOutputChannelDistanceMap (ORSModel.ors.Channel) – maxDistance (float) – lOutputChannelTraceBack (ORSModel.ors.Channel) – lOutputChannelLabel (ORSModel.ors.Channel) –

Unmanaged¶

class ORSModel.ors.Unmanaged

Bases: ORSModel.ors.ORSBaseClass

brief_description: Abstract class for objects that are not managed by the core library. author: Eric Fournier. All other members of ORS participated. version: 1.0 date: Jan 2005

atomicLoad(sFilename: str) → Unmanaged

Creates an object from a file where an object was saved.

Parameters:	sFilename (str) – path of the file to load
Returns:	output (Unmanaged) – a managed object, or none() if the load fails

atomicSave(self, aFilename: str) → int

Saves the object to a file.

Parameters:	aFilename (str) – path of the file to save
Returns:	output (int) – 0 if successful, otherwise an error code

createFromPythonRepresentation(aPythonRepresentation: str) → Unmanaged

Create aUnmanaged Object from a python representation a static method.

Parameters:	aPythonRepresentation (str) –
Returns:	output (ORSModel.ors.Unmanaged) –

fromPythonRepresentation(self, aPythonRepresentation: str) → bool

Create aUnmanaged object from a Python string representation.

Parameters:	aPythonRepresentation (str) – a Python evaluable string representation (a string)
Returns:	output (bool) – TRUE if parsing worked, FALSE otherwise (a bool)

classmethod getAllSubclasses(outputCollection=None)

classmethod getClassDenomination()

static getClassFromProgId(progId)

getClassName(self) → str

Retrieves the class name of the core object wrapped by this Interface object.

Returns:	output (str) –

getClassNameStatic() → str

getClassNameStatic

Returns:	output (str) –

getDataChecksum(self) → str

Returns:	output (str) –

getIsInstanceOf(self, pProgId: str) → bool

Queries the object to know if it is an instance of a certain class.

Parameters:	pProgId (str) –
Returns:	output (bool) –

classmethod getIsSubclassOf(parentClass)

getPythonRepresentation(self) → str

Gets a Python evaluable string representation.

Returns:	output (str) –

isNone(self) → bool

Checks if the receiver is none.

Returns:	output (bool) –

isNotNone(self) → bool

Checks if the receiver is not none.

Returns:	output (bool) –

none() → Unmanaged

Returns:	output (Unmanaged) –

ORSBaseClass¶

class ORSModel.ors.ORSBaseClass

brief_description: An abstract class from which all objects issued from the author: Eric Fournier. All other members of ORS participated. version: 1.0 date: Jan 2005

getPythonTraceBack() → typing.List[str]

Set the python traceback for a call from python.

Returns:	output (typing.List[str]) –

isManaged(self) → bool

Returns:	output (bool) –

isNone(self) → bool

Returns:	output (bool) –

setPythonTraceBack(tb: ORSModel.ors.typing.List[str]) → None

Set the python traceback for a call from python.

Parameters:	tb (typing.List[str]) –

PartialSpaceDijkstra¶

Inheritance diagram¶

Classes¶