TrackPointer Class Reference

Inheritance diagram for TrackPointer:

Collaboration diagram for TrackPointer:

Public Member Functions
def	__init__ (self, iState=None, trackCfg=None)
	Constructor for layered puzzle scene tracker. More...
def	adapt (self)
	Adapt the layer detection models. More...
def	correct (self)
	Apply correction process to the individual detectors. More...
def	display_cv (self, I, ratio=None, window_name="trackpoints")
def	emptyDebug (self)
def	emptyState (self)
	Get and empty state to recover its basic structure. More...
def	getDebug (self)
def	getState (self)
	Get the complete detector state, which involves the states of the individual layer detectors. More...
def	info (self)
def	measure (self, I)
	Apply detection to the source image pass. More...
def	predict (self)
	Generate prediction of expected measurement. More...
def	process (self, I)
	Apply entire predict to adapt process to source image(s). More...

Public Attributes
	hand

Constructor & Destructor Documentation

__init__()

def __init__	(		self,
			iState = None,
			trackCfg = None
	)

Constructor for layered puzzle scene tracker.

Parameters

[in]	iState	Initial state of tracks.
[in]	trackCfg	Trackpointer(s) configuration.

Member Function Documentation

adapt()

def adapt

(

self

)

Adapt the layer detection models.

There is no adaptation.

correct()

def correct

(

self

)

Apply correction process to the individual detectors.

Apply naive correction on a per detector basis. As a layered system, there might be interdependencies that would impact the correction step. Ignoring that for now since it does not immediately come to mind what needs to be done.

display_cv()

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

emptyDebug()

def emptyDebug

(

self

)

emptyState()

def emptyState

(

self

)

Get and empty state to recover its basic structure.

Parameters

[out]

estate

The empty state.

getDebug()

def getDebug

(

self

)

getState()

def getState

(

self

)

Get the complete detector state, which involves the states of the individual layer detectors.

Parameters

[out]

state

The detector state for each layer, by layer.

info()

def info

(

self

)

measure()

def measure	(		self,
			I
	)

Apply detection to the source image pass.

Parameters

[in]

I

Layered detection image instance (structure/dataclass).

predict()

def predict

(

self

)

Generate prediction of expected measurement.

The detectors are mostly going to be static models, which means that prediction does nothing. Just in case though, the prediction methods are called for them.

process()

def process	(		self,
			I
	)

Apply entire predict to adapt process to source image(s).

Parameters

[in]

I

Layered detection image instance (structure/dataclass).

Member Data Documentation

hand

hand

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

• /home/pvela/python/surveillance/Surveillance/layers/HoveringHand.py