centroid Class Reference

Used to track the centroid of a target object. More...

Inheritance diagram for centroid:

Collaboration diagram for centroid:

Public Member Functions
def	__init__ (self, iPt=None, params=CfgCentroid())
	Construction. More...
def	adapt (self)
	Adapt internal tracking parameters. More...
def	correct (self)
	Correct track state based on predication and measurement. More...
def	display_cv (self, I, ratio=None, window_name="track point ")
	Use opencv display routines to plot the trackpoint along with the given image. More...
def	displayDebugState (self, dbstate=None)
	Displays internally stored intermediate process output. More...
def	displayState (self, dstate=None, ax=None)
	Displays the current track pointer measurement. More...
def	emptyState (self)
	Return an empty state structure (in python a dataclass). More...
def	get (self, fname)
def	getState (self)
	Return the track-pointer state. More...
def	measure (self, I)
	Measure the track point from the given image. More...
def	offset (self, dp)
	Apply a vector offset to the track point. More...
def	predict (self)
	Predict next track state based on current track state. More...
def	process (self, I)
	Process the input image according to centroid tracking. More...
def	set (self, fname, fval)
def	setIfMissing (self, params, pname, pval)
	Set missing parameters in the registration parameters structure. More...
def	setState (self, dPt)
	Use a track state element to define internal track state. More...
def	transform (self, g)
	Apply a Lie group transformation (linear/affine) to the track point. More...

Public Attributes
	haveMeas
	params
	tpt

Detailed Description

Used to track the centroid of a target object.

Centroid track pointer interface object. Basically performs tracking of an object by computing its centroid.

There are two ways to perform centroid tracking. One is to presume that the object to track has already been binarized and it is simply a matter of computing the centroid. The other is that the binarization needs to first be computed, then the centroid calculated. If binarization of the input is desired, use the improcessor to perform the binarization as a pre-processing step. If the binarization is too complicated for an improcess procedure, then write a tracker wrapper around the binarization + tracking combination. It will usually then become a perceiver object.

More generically, the preprocessor can be used to take a non-track signal or more complex signal and recover only the "track point" equivalent sub-signal.

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			iPt = None,
			params = CfgCentroid()
	)

Construction.

Parameters

[in]	iPt	Initial track point, if known/available.
[in]	params	Configuration specification structure.

Reimplemented in centroidMulti.

Member Function Documentation

adapt()

def adapt

(

self

)

Adapt internal tracking parameters.

For base class, adaptation is a no-op as nothing is really known.

correct()

def correct

(

self

)

Correct track state based on predication and measurement.

For base class this is a no-op as there is no prediction.

display_cv()

def display_cv	(		self,
			I,
			ratio = None,
			window_name = "track point "
	)

Use opencv display routines to plot the trackpoint along with the given image.

Parameters

[in]	I	Image to display.
[in]	ratio	Resize ratio (no resize if not given)
[in]	window_name	Name for window. default="track point"

displayDebugState()

def displayDebugState	(		self,
			dbstate = None
	)

Displays internally stored intermediate process output.

Currently, there is no intermediate output, though that might change in the future.

displayState()

def displayState	(		self,
			dstate = None,
			ax = None
	)

Displays the current track pointer measurement.

Assumes that the current figure to plot to is activate. If the plot has existing elements that should remain, then hold should be enabled prior to invoking this function.

Parameters

[in]	dstate	State to display (otherwise, use internal state).
[in]	ax	Axis to display to (Uses matplotlibs I think?).

emptyState()

def emptyState

(

self

)

Return an empty state structure (in python a dataclass).

Returns

The empty state structure.

get()

def get	(		self,
			fname
	)

Reimplemented in centroidMulti.

getState()

def getState

(

self

)

Return the track-pointer state.

Parameters

[out]

tstate

The track point state structure.

Reimplemented in boardMeasure, and FromLayer.

measure()

def measure	(		self,
			I
	)

Measure the track point from the given image.

Parameters

[in]	I	The input image.
[out]	mstate	The measured state.

Reimplemented in centroidMulti.

offset()

def offset	(		self,
			dp
	)

Apply a vector offset to the track point.

Parameters

[in]

dp

The change in position to apply.

predict()

def predict

(

self

)

Predict next track state based on current track state.

For base version, this is a no-op. More complex versions, possibly with motion models, should override this.

process()

def process	(		self,
			I
	)

Process the input image according to centroid tracking.

Parameters

[in]	I	The input image.
	[out	tState The track state, if desired.

Reimplemented in centroidMulti.

set()

def set	(		self,
			fname,
			fval
	)

Reimplemented in centroidMulti.

setIfMissing()

def setIfMissing	(		self,
			params,
			pname,
			pval
	)

Set missing parameters in the registration parameters structure.

Parameters

[in]	params	The parameter structure. pname Name of parameter. pval Value of parameter.
[out]	params	The parameter structure.

setState()

def setState	(		self,
			dPt
	)

Use a track state element to define internal track state.

Parameters

[in]

dPt

Desired track point.

transform()

def transform	(		self,
			g
	)

Apply a Lie group transformation (linear/affine) to the track point.

Parameters

[in]

g

The Lie group class instance to apply.

Member Data Documentation

haveMeas

haveMeas

params

params

tpt

tpt

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The documentation for this class was generated from the following file:

• /home/mary/python/trackpointer/trackpointer/centroid.py