TemplateTrackerMIInverseCompositional¶

class TemplateTrackerMIInverseCompositional(self)¶

Bases: TemplateTrackerMI

The algorithm implemented in this class is described in [12] and [31] .

Default constructor.

Methods

__init__	Default constructor.
initTemplateRefBspline	return: A tuple containing:
setMinimizationMethod
setUseTemplateSelect	Use only the strong gradient pixels to compute the Jabobian.

Inherited Methods

setPyramidal	Set the number of pyramid levels used in the multi-resolution scheme.
HESSIAN_YOUCEF
getdp
HESSIAN_NONSECOND
USE_HESSIEN_NORMAL
setBlur
getDiverge
resetTracker	Reset the tracker by freeing the memory allocated by the template tracker during the initialization.
setp
display	Overloaded function.
setSampling	Set the pixel sampling parameters along the rows and the columns.
setCovarianceComputation
setThresholdResidualDifference	Set the threshold used to stop optimization loop.
getMI256
HessienType	Hessian computation.
setApprocHessian
getp
initClick	Select the reference template in image I using mouse click.
getNbIteration
setCostFunctionVerification
setUseBrent	By default Brent usage is disabled.
initFromZone	Initialize the reference template from a vector of points.
getRatioPixelIn
HESSIAN_NEW
track	Track the template on image I .
getZoneRef	Return the reference template zone.
setHDes
setNbIterBrent
HESSIAN_d2I
BSPLINE_FOURTH_ORDER
HESSIAN_EXACT
setNc
BsplineType	Hessian computation.
HESSIAN_0
trackRobust
initFromPoints	Initialize the reference template from a vector of points.
setThresholdGradient
getG
getNbParam
getH
setHessianComputation
getNMI
setGain
USE_HESSIEN_DESIRE
setLambda
USE_HESSIEN_BEST_COND
setBspline
getCovarianceMatrix
HessienApproximationType	Hessian computation.
setGaussianFilterSize
getMI	Overloaded function.
setIterationMax	Set the maximum number of iteration of the estimation scheme.
BSPLINE_THIRD_ORDER

Operators

__doc__
__init__	Default constructor.
__module__

Attributes

BSPLINE_FOURTH_ORDER
BSPLINE_THIRD_ORDER
HESSIAN_0
HESSIAN_EXACT
HESSIAN_NEW
HESSIAN_NONSECOND
HESSIAN_YOUCEF
HESSIAN_d2I
USE_GRADIENT
USE_HESSIEN_BEST_COND
USE_HESSIEN_DESIRE
USE_HESSIEN_NORMAL
USE_LMA
USE_NEWTON
USE_QUASINEWTON
__annotations__

class BsplineType(self, value: int)¶

Bases: pybind11_object

Hessian computation.

Values:

• BSPLINE_THIRD_ORDER

• BSPLINE_FOURTH_ORDER

__and__(self, other: object) → object¶

__eq__(self, other: object) → bool¶

__ge__(self, other: object) → bool¶

__getstate__(self) → int¶

__gt__(self, other: object) → bool¶

__hash__(self) → int¶

__index__(self) → int¶

__init__(self, value: int)¶

__int__(self) → int¶

__invert__(self) → object¶

__le__(self, other: object) → bool¶

__lt__(self, other: object) → bool¶

__ne__(self, other: object) → bool¶

__or__(self, other: object) → object¶

__rand__(self, other: object) → object¶

__ror__(self, other: object) → object¶

__rxor__(self, other: object) → object¶

__setstate__(self, state: int) → None¶

__xor__(self, other: object) → object¶

property name : str¶

class HessienApproximationType(self, value: int)¶

Bases: pybind11_object

Hessian computation.

Values:

• BSPLINE_THIRD_ORDER

• BSPLINE_FOURTH_ORDER

__and__(self, other: object) → object¶

__eq__(self, other: object) → bool¶

__ge__(self, other: object) → bool¶

__getstate__(self) → int¶

__gt__(self, other: object) → bool¶

__hash__(self) → int¶

__index__(self) → int¶

__init__(self, value: int)¶

__int__(self) → int¶

__invert__(self) → object¶

__le__(self, other: object) → bool¶

__lt__(self, other: object) → bool¶

__ne__(self, other: object) → bool¶

__or__(self, other: object) → object¶

__rand__(self, other: object) → object¶

__ror__(self, other: object) → object¶

__rxor__(self, other: object) → object¶

__setstate__(self, state: int) → None¶

__xor__(self, other: object) → object¶

property name : str¶

class HessienType(self, value: int)¶

Bases: pybind11_object

Hessian computation.

Values:

• BSPLINE_THIRD_ORDER

• BSPLINE_FOURTH_ORDER

__and__(self, other: object) → object¶

__eq__(self, other: object) → bool¶

__ge__(self, other: object) → bool¶

__getstate__(self) → int¶

__gt__(self, other: object) → bool¶

__hash__(self) → int¶

__index__(self) → int¶

__init__(self, value: int)¶

__int__(self) → int¶

__invert__(self) → object¶

__le__(self, other: object) → bool¶

__lt__(self, other: object) → bool¶

__ne__(self, other: object) → bool¶

__or__(self, other: object) → object¶

__rand__(self, other: object) → object¶

__ror__(self, other: object) → object¶

__rxor__(self, other: object) → object¶

__setstate__(self, state: int) → None¶

__xor__(self, other: object) → object¶

property name : str¶

class MinimizationTypeMIInverseCompositional(self, value: int)¶

Bases: pybind11_object

Minimization method.

Values:

• USE_NEWTON

• USE_LMA

• USE_GRADIENT

• USE_QUASINEWTON

__and__(self, other: object) → object¶

__eq__(self, other: object) → bool¶

__ge__(self, other: object) → bool¶

__getstate__(self) → int¶

__gt__(self, other: object) → bool¶

__hash__(self) → int¶

__index__(self) → int¶

__init__(self, value: int)¶

__int__(self) → int¶

__invert__(self) → object¶

__le__(self, other: object) → bool¶

__lt__(self, other: object) → bool¶

__ne__(self, other: object) → bool¶

__or__(self, other: object) → object¶

__rand__(self, other: object) → object¶

__ror__(self, other: object) → object¶

__rxor__(self, other: object) → object¶

__setstate__(self, state: int) → None¶

__xor__(self, other: object) → object¶

property name : str¶

__init__(self)¶

Default constructor.

display(*args, **kwargs)¶

Overloaded function.

1. display(self: visp._visp.tt.TemplateTracker, I: visp._visp.core.ImageGray, col: visp._visp.core.Color = vpColor::green, thickness: int = 3) -> None

Display the warped reference template in an image.

The following code shows how to use display capabilities:

#include <visp3/tt/vpTemplateTrackerSSDInverseCompositional.h>
#include <visp3/tt/vpTemplateTrackerWarpHomography.h>

#ifdef ENABLE_VISP_NAMESPACE
using namespace VISP_NAMESPACE_NAME;
#endif

int main()
{
  vpImage<unsigned char> I;
  vpTemplateTrackerWarpHomography warp;
  vpTemplateTrackerSSDInverseCompositional tracker(&warp);
  vpTemplateTrackerZone zoneRef, zoneWarped;

  // Display the warped zone
  tracker.display(I, vpColor::red);

  // Display the reference zone
  zoneRef = tracker.getZoneRef();
  zoneRef.display(I, vpColor::green);

  // Display the warped zone
  vpColVector p = tracker.getp();
  warp.warpZone(zoneRef, p, zoneWarped);
  zoneWarped.display(I, vpColor::blue);
}

Parameters:

I

Image in which the warped zone has to be displayed.

col

Color used to draw the triangle edges.

thickness

Thickness of the lines.

2. display(self: visp._visp.tt.TemplateTracker, I: visp._visp.core.ImageRGBa, col: visp._visp.core.Color = vpColor::green, thickness: int = 3) -> None

Display the warped reference template in an image.

The following code shows how to use display capabilities:

#include <visp3/tt/vpTemplateTrackerSSDInverseCompositional.h>
#include <visp3/tt/vpTemplateTrackerWarpHomography.h>

#ifdef ENABLE_VISP_NAMESPACE
using namespace VISP_NAMESPACE_NAME;
#endif

int main()
{
  vpImage<vpRGBa> I;
  vpTemplateTrackerWarpHomography warp;
  vpTemplateTrackerSSDInverseCompositional tracker(&warp);
  vpTemplateTrackerZone zoneRef, zoneWarped;

  // Display the warped zone
  tracker.display(I, vpColor::red);

  // Display the reference zone
  zoneRef = tracker.getZoneRef();
  zoneRef.display(I, vpColor::green);

  // Display the warped zone
  vpColVector p = tracker.getp();
  warp.warpZone(zoneRef, p, zoneWarped);
  zoneWarped.display(I, vpColor::blue);
}

Parameters:

I

Image in which the warped zone has to be displayed.

col

Color used to draw the triangle edges.

thickness

Thickness of the lines.

getCovarianceMatrix(self) → visp._visp.core.Matrix¶

getDiverge(self) → bool¶

getG(self) → visp._visp.core.ColVector¶

getH(self) → visp._visp.core.Matrix¶

getMI(*args, **kwargs)¶

Overloaded function.

1. getMI(self: visp._visp.tt_mi.TemplateTrackerMI) -> float

2. getMI(self: visp._visp.tt_mi.TemplateTrackerMI, I: visp._visp.core.ImageGray, nc: int, bspline: int, tp: visp._visp.core.ColVector) -> tuple[float, int]

Returns:

A tuple containing:

• nc

getMI256(self, I: visp._visp.core.ImageGray, tp: visp._visp.core.ColVector) → float¶

getNMI(self) → float¶

getNbIteration(self) → int¶

getNbParam(self) → int¶

getRatioPixelIn(self) → float¶

getZoneRef(self) → visp._visp.tt.TemplateTrackerZone¶

Return the reference template zone.

getdp(self) → visp._visp.core.ColVector¶

getp(self) → visp._visp.core.ColVector¶

initClick(self, I: visp._visp.core.ImageGray, delaunay: bool = false) → None¶

Select the reference template in image I using mouse click.

Parameters:

I: visp._visp.core.ImageGray¶

Image containing the reference template.

delaunay: bool = false¶

Flag used to enable Delaunay triangulation.

• If true, from the image points selected by the user, a Delaunay triangulation is performed to initialize the reference template.

  • A left click select a image point;

  • A right click select the last image point and ends the initialisation stage.

• If false, the user select directly points as successive triangle corners. The size of v_ip vector should be a multiple of 3. It is not mandatory that triangles have one edge in common; they can define a discontinued area.

  • A left click select a triangle corner;

  • A right click select the last triangle corner and ends the initialisation stage. For example, to select the reference template as two triangles, the user has to left click five times and finish the selection on the sixth corner with a right click.

initFromPoints(self, I: visp._visp.core.ImageGray, v_ip: list[visp._visp.core.ImagePoint], delaunay: bool = false) → None¶

Initialize the reference template from a vector of points.

Parameters:

I: visp._visp.core.ImageGray¶

Image containing the reference template.

v_ip: list[visp._visp.core.ImagePoint]¶

Vector of image points defining the reference template.

delaunay: bool = false¶

• If true, from the image points defining the reference template enable Delaunay triangulation.

• If false, the vector of image points define the reference template as a list of triangles. The size of v_ip vector should be a multiple of 3.

initFromZone(self, I: visp._visp.core.ImageGray, zone: visp._visp.tt.TemplateTrackerZone) → None¶

Initialize the reference template from a vector of points.

Parameters:

I: visp._visp.core.ImageGray¶

Image containing the reference template.

zone: visp._visp.tt.TemplateTrackerZone¶

The zone that describes the reference template.

initTemplateRefBspline(self, ptIndex: int, et: float) → float¶

Returns:

A tuple containing:

• et

resetTracker(self) → None¶

Reset the tracker by freeing the memory allocated by the template tracker during the initialization.

setApprocHessian(self, approx: visp._visp.tt_mi.TemplateTrackerMI.HessienApproximationType) → None¶

setBlur(self, b: bool) → None¶

setBspline(self, newbs: visp._visp.tt_mi.TemplateTrackerMI.BsplineType) → None¶

setCostFunctionVerification(self, b: bool) → None¶

setCovarianceComputation(self, flag: bool) → None¶

setGain(self, g: float) → None¶

setGaussianFilterSize(self, new_taill: int) → None¶

setHDes(self, tH: visp._visp.core.Matrix) → None¶

setHessianComputation(self, type: visp._visp.tt_mi.TemplateTrackerMI.HessienType) → None¶

setIterationMax(self, n: int) → None¶

Set the maximum number of iteration of the estimation scheme.

Parameters:

n: int¶

Maximum number of iterations to stop the estimation scheme. A typical value is around 100.

setLambda(self, _l: float) → None¶

setMinimizationMethod(self, method: visp._visp.tt_mi.TemplateTrackerMIInverseCompositional.MinimizationTypeMIInverseCompositional) → None¶

setNbIterBrent(self, b: int) → None¶

setNc(self, newNc: int) → None¶

setPyramidal(self, nlevels: int = 2, level_to_stop: int = 1) → None¶

Set the number of pyramid levels used in the multi-resolution scheme. If nlevels > 1, the tracker uses a pyramidal approach.

Parameters:

nlevels: int = 2¶

Number of pyramid levels. Algorithm starts at level nlevels-1.

level_to_stop: int = 1¶

Last level of the pyramid that will be considered. Lowest level is zero.

setSampling(self, sample_i: int, sample_j: int) → None¶

Set the pixel sampling parameters along the rows and the columns.

Parameters:

sample_i: int¶

Sampling factor along the rows. If 1 all the lines are considered. If 2, consider one line over two.

sample_j: int¶

Sampling factor along the columns. If 1 all the columns are considered. If 2, consider one column over two.

setThresholdGradient(self, threshold: float) → None¶

setThresholdResidualDifference(self, threshold: float) → None¶

Set the threshold used to stop optimization loop. When the residual difference between two successive iterations becomes lower than the threshold we stop optimization loop.

Note

Increasing the default value allows to speed up the tracking.

Parameters:

threshold: float¶

Threshold used to stop optimization. Default value is set to 1e-4.

setUseBrent(self, b: bool) → None¶

By default Brent usage is disabled.

setUseTemplateSelect(self, b: bool) → None¶

Use only the strong gradient pixels to compute the Jabobian. By default this feature is disabled.

setp(self, tp: visp._visp.core.ColVector) → None¶

track(self, I: visp._visp.core.ImageGray) → None¶

Track the template on image I .

Parameters:

I: visp._visp.core.ImageGray¶

Image to process.

trackRobust(self, I: visp._visp.core.ImageGray) → None¶