Class instance for extracting background that lies with the corner region of the RGB color cube, or the extremal regions of a color-space based on a planar cut. More...

Inheritance diagram for inCorner:

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Collaboration diagram for inCorner:

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Public Member Functions
def	__init__ (self, processor=None, bgMod=None)
	Constructor for corner color model target FG detector. More...

def	calc_margin (self, I)
	Run classifier scoring computation and return value. More...

def	measure (self, I)
	Apply the appearance detector to an image. More...

def	saveTo (self, fPtr)
	Empty method for saving internal information to HDF5 file. More...

def	selectMaskRegionFromPoint (self, pt)
	Select mask region from model class at given point. More...

def	selectMaskRegionFromPointAndMask (self, pt, mask)
	Select mask region from model class at given point. More...

def	set_model (self, pCutModel)
	Provide the background "classification" model to use for detection. More...

def	specify_model (self, n, d, tau, isVectorized=True)
	Specify the model by providing normal, distance, and threshold parameter(s). More...

Public Member Functions inherited from bgImage
def	__init__ (self, processor=None)

Public Member Functions inherited from inImage
def	info (self)
	Provide information about the current class implementation. More...

Public Member Functions inherited from Base
def	__init__ (self)
	Instantiate a detector Base activity class object. More...

def	adapt (self)
	Adapt any internal parameters based on activity state, signal, and any other historical information. More...

def	correct (self)
	Reconcile prediction and measurement as fitting. More...

def	detect (self, signal)
	Run detection only processing pipeline (no adaptation). More...

def	emptyDebug (self)
	Return empty debug state information. More...

def	emptyState (self)
	Return empty state. More...

def	getDebug (self)
	Return current/latest debug state information. More...

def	getState (self)
	Return current/latest state. More...

def	predict (self)
	Predict next state from current state. More...

def	process (self, signal)
	Process the new incoming signal on full detection pipeline. More...

def	save (self, fileName)
	Outer method for saving to a file given as a string. More...

Static Public Member Functions
def	build_model_blackBG (dist, tau, isVectorized=True)
	Build a black color-based background model. More...

def	build_model_whiteBG (dist, tau, isVectorized=True)
	Build a white color-based background model. More...

def	build_spherical_blackBG (dist, tau, isVectorized=True)
	Build a black color-based background model. More...

def	buildFromCfg (theConfig, processor=None)
	Build an inCorner instance from an algorithm configuration instance. More...

def	calibrate_from_data (bgI, fgI)

def	calibrate_from_image (I, bgI)

def	load (fileName)

def	loadFrom (fptr)
	Empty method for loading internal information from HDF5 file. More...

Static Public Member Functions inherited from Base
def	load (fileName, relpath=None)
	Outer method for loading file given as a string (with path). More...

Public Attributes
	bgModel

	Ip

Public Attributes inherited from inImage
	Ip

	processor

Public Attributes inherited from Base
	x
	Detection state. More...

Detailed Description

Class instance for extracting background that lies with the corner region of the RGB color cube, or the extremal regions of a color-space based on a planar cut.

The idea is similar to the fgmodel approach based on planar cuts but with the presumption that the background is what has the unique color profile. The presumption is that the background lies in a region of color-space that is extremal so that a planar surface separates it from the expected foreground colors.

Constructor & Destructor Documentation

◆ init()

def __init__	(	self,
		processor = `None`,
		bgMod = `None`
	)

Constructor for corner color model target FG detector.

Parameters

[in] appMod The model or parameters for the appearance detector.

Reimplemented in inCornerEstimator.

Member Function Documentation

◆ build_model_blackBG()

def build_model_blackBG	(	dist,
		tau,
		isVectorized = `True`
	)

static

Build a black color-based background model.

The assumption is that the background colors are in the black corner of the color cube and can be split from the foreground colors. In this case, the planar cut normal is known.

The good news about this kind of model is that it does not matter whether the image is RGB (typical) or BGR (OpenCV type). The corner region containing black is still around (0,0,0).

Parameters

[in]	dist	Locate of separating boundary (should be negative).
[in]	tau	The threshold / margin to apply.

◆ build_model_whiteBG()

def build_model_whiteBG	(	dist,
		tau,
		isVectorized = `True`
	)

static

Build a white color-based background model.

This is rare but provided just in case. The sign is flipped to cut off the more extremal white color values.

The good news about this kind of model is that it does not matter whether the image is RGB (typical) or BGR (OpenCV type). The corner region containing black is still around (0,0,0).

Parameters

[in]	dist	Locate of separating boundary (should be positive).
[in]	tau	The threshold / margin to apply.

◆ build_spherical_blackBG()

def build_spherical_blackBG	(	dist,
		tau,
		isVectorized = `True`
	)

static

Build a black color-based background model.

The assumption is that the background colors are in the black corner of the color cube and can be split from the foreground colors using a spherical cutting surface. The center is set to be the origin.

The good news about this kind of model is that it does not matter whether the image is RGB (typical) or BGR (OpenCV type). The corner region containing black is still around (0,0,0).

Parameters

[in]	dist	Locate of separating boundary.
[in]	tau	The threshold / margin to apply.

◆ buildFromCfg()

def buildFromCfg	(	theConfig,
		processor = `None`
	)

static

Build an inCorner instance from an algorithm configuration instance.

Parameters

[out] bgDet Instantiated inCorner background model detector.

◆ calc_margin()

def calc_margin	(	self,
		I
	)

Run classifier scoring computation and return value.

Parameters

[in]	I	Image to test on.
[out]	mI	The margin values (as an image/2D array).

◆ calibrate_from_data()

def calibrate_from_data	(	bgI,
		fgI
	)

static

◆ calibrate_from_image()

def calibrate_from_image	(	I,
		bgI
	)

static

◆ load()

def load ( fileName )

static

◆ loadFrom()

def loadFrom ( fPtr )

static

Empty method for loading internal information from HDF5 file.

Load data from given HDF5 pointer. Assumes in root from current file pointer location.

Reimplemented from Base.

◆ measure()

def measure	(	self,
		I
	)

Apply the appearance detector to an image.

Parameters

[in] I Image to test on.

Reimplemented from inImage.

Reimplemented in inCornerEstimator.

◆ saveTo()

def saveTo	(	self,
		fPtr
	)

Empty method for saving internal information to HDF5 file.

Save data to given HDF5 pointer. Puts in root.

Reimplemented from Base.

◆ selectMaskRegionFromPoint()

def selectMaskRegionFromPoint	(	self,
		pt
	)

Select mask region from model class at given point.

Permits single and multiple point selections. Multiple points return multiple mask slices. Relies on the detector having run and generated a background detection mask.

Parameters

[in] pt Point of interest (given column-wise)

Returns: A single mask slice or a collection of mask slices.

◆ selectMaskRegionFromPointAndMask()

def selectMaskRegionFromPointAndMask	(	self,
		pt,
		mask
	)

Select mask region from model class at given point.

Permits single and multiple point selections. Multiple points return multiple mask slices. Relies on the detector having run and generated a background detection mask.

Parameters

[in]	pt	Point of interest (given column-wise)
[in]	mask	Mask.

Returns: A single mask slice or a collection of mask slices.

◆ set_model()

def set_model	(	self,
		pCutModel
	)

Provide the background "classification" model to use for detection.

Parameters

[in] pCutModel Background planar cut model instance.

◆ specify_model()

def specify_model	(	self,
		n,
		d,
		tau,
		isVectorized = `True`
	)

Specify the model by providing normal, distance, and threshold parameter(s).

The distance offset and threshold operate additively, this it may be the case the one is set to zero and the other is not. Alternatively, it may be more intuitive to have them be separate since dist defines the location of the seprating plane and tau defines the detection margin/slack associated to the separating plane. It all depends on what is more intuitive to the designer. All options permitted in this case.

Parameters

[in]	n	Normal vector.
[in]	d	Distance offset.
[in]	tau	Threshold.

Member Data Documentation

◆ bgModel

bgModel

◆ Ip

Ip

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

/home/pvela/python/detector/detector/bgmodel/inCorner.py

Public Member Functions

Static Public Member Functions

Public Attributes

Detailed Description

Constructor & Destructor Documentation

◆ __init__()

Member Function Documentation

◆ build_model_blackBG()

◆ build_model_whiteBG()

◆ build_spherical_blackBG()

◆ buildFromCfg()

◆ calc_margin()

◆ calibrate_from_data()

◆ calibrate_from_image()

◆ load()

◆ loadFrom()

◆ measure()

◆ saveTo()

◆ selectMaskRegionFromPoint()

◆ selectMaskRegionFromPointAndMask()

◆ set_model()

◆ specify_model()

Member Data Documentation

◆ bgModel

◆ Ip

◆ init()