FastMarching

Inheritance diagram

Classes

FastMarching

class ORSModel.ors.FastMarching(self)

Bases: Unmanaged

cleanSpeedMapChannel(self, outputChannel: ORSModel.ors.Channel)

Remove boundary or non reached value from a speed mapChannel.

Parameters:

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

continueDistanceMapForNBIteration(self, lOutputChannelSpeedMap: ORSModel.ors.Channel, forNbIteration: int, autoUpdateROI: bool)

Parameters:

• lOutputChannelSpeedMap (ORSModel.ors.Channel) –

• forNbIteration (int) –

• autoUpdateROI (bool) –

createDistanceMap(self, inChannelDistanceMap: ORSModel.ors.Channel, positionTripleInSourceRef: int, nbPosition: int, lMaskChannel: ORSModel.ors.Channel, traceBackChannel: ORSModel.ors.Channel) → ORSModel.ors.Channel

Parameters:

• inChannelDistanceMap (ORSModel.ors.Channel) –

• positionTripleInSourceRef (int) –

• nbPosition (int) –

• lMaskChannel (ORSModel.ors.Channel) –

• traceBackChannel (ORSModel.ors.Channel) –

Returns:

output (ORSModel.ors.Channel) –

createDistanceMapForNBIteration(self, lOutputChannelSpeedMap: ORSModel.ors.Channel, forNbIteration: int, autoUpdateROI: bool, lMaskChannel: ORSModel.ors.Channel)

Parameters:

• lOutputChannelSpeedMap (ORSModel.ors.Channel) –

• forNbIteration (int) –

• autoUpdateROI (bool) –

• lMaskChannel (ORSModel.ors.Channel) –

createDistanceMapWithMask(self, inChannelDistanceMap: ORSModel.ors.Channel, lMaskChannel: ORSModel.ors.Channel) → ORSModel.ors.Channel

Parameters:

• inChannelDistanceMap (ORSModel.ors.Channel) –

• lMaskChannel (ORSModel.ors.Channel) –

Returns:

output (ORSModel.ors.Channel) –

getClassNameStatic() → str

getClassNameStatic

Returns:

output (str) –

getEuclideanBias(self) → float

get the Euclidean bias that will be the minimumDijkstra distance between voxels

Note

Neighbor of distance 1 will have a bias of spacialTerm

Note

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

Note

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

Returns:

output (float) – the minimum distance between voxel (a double)

getForcedMeanValue(self) → float

Returns:

output (float) –

getIndexOfStopPointReach(self) → int

Returns:

output (int) –

getMaxValueToConsider(self) → float

Returns:

output (float) –

getMinValueToConsider(self) → float

Returns:

output (float) –

getROI(self, index: int) → ORSModel.ors.ROI

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) –

Returns:

output (ORSModel.ors.ROI) –

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 unsigned char)

getStopValue(self) → float

Returns:

output (float) –

getStopWhenValueIsEncountered(self) → bool

Returns:

output (bool) –

getUsedForcedMean(self) → bool

Returns:

output (bool) –

none() → FastMarching

Returns:

output (FastMarching) –

recomputeValueWindow(self, aVolumeROI: ORSModel.ors.ROI)

Parameters:

aVolumeROI (ORSModel.ors.ROI) –

resetROIs(self)

Empties all the sourceROI slots.

setEuclideanBias(self, EuclideanBias: float)

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)

setForcedMeanValue(self, aVal: float)

Parameters:

aVal (float) –

setInputChannelAndWorkingArea(self, inputChannel: ORSModel.ors.Channel, minX: int, minY: int, minZ: int, maxX: int, maxY: int, maxZ: int, currentT: int)

Sets the channel that will be used by theFastMarching 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 (a uint32_t)

• minY (int) – the minimum Y index in the input channel (a uint32_t) TODO DOCUMENT_ME: Should this be removed?

• minZ (int) – the minimum Z index in the input channel (a uint32_t)

• maxX (int) – the maximum X index in the input channel (a uint32_t)

• maxY (int) – the maximum Y index in the input channel (a uint32_t) TODO DOCUMENT_ME

• maxZ (int) – the maximum Z index in the input channel (an unsigned short)

• currentT (int) – the current time point (auint32_t)

setMaxValueToConsider(self, maxValue: float)

Parameters:

maxValue (float) –

setMinValueToConsider(self, minValue: float)

Parameters:

minValue (float) –

setROI(self, index: int, aVolROI: ORSModel.ors.ROI)

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) –

• aVolROI (ORSModel.ors.ROI) –

setStopPosition(self, xP: int, yP: int, zP: int)

Parameters:

• xP (int) –

• yP (int) –

• zP (int) –

setStopValue(self, stopValue: float)

Parameters:

stopValue (float) –

setStopWhenValueIsEncountered(self, aF: bool)

Parameters:

aF (bool) –

setUseValueWindow(self, aF: bool)

Parameters:

aF (bool) –

setUsedForcedMean(self, aF: bool)

Parameters:

aF (bool) –

useDijkstraMetric(self, aF: bool)

Parameters:

aF (bool) –

Unmanaged

class ORSModel.ors.Unmanaged

Bases: ORSBaseClass

Abstract class for objects that are not managed by the core library. Unmanaged objects are transient objects.

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) – an unmanaged 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) → ORSModel.ors.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)

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) –

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

An abstract class from which all objects issued from the ORS Core Library inherit.

getPythonTraceBack() → List[str]

Set the python traceback for a call from python.

Returns:

output (List[str]) –

isManaged(self) → bool

Returns:

output (bool) –

isNone(self) → bool

Returns:

output (bool) –

setPythonTraceBack(tb: List[str])

Set the python traceback for a call from python.

Parameters:

tb (List[str]) –