Manages a complete matching context in EasyMatch. More...

Public Member Functions
None	__init__ (self)
	Constructs a matching context.
None	__init__ (self, int maxNumDOF)
	Constructs a matching context.
None	__init__ (self, eVision.EMatcher other)
	Constructs a matching context.
None	CopyTo (self, eVision.EMatcher other)
	Copies all the data of the current eVision.EMatcher object into another eVision.EMatcher object and returns it.
None	Save (self, str path)
	Saves the eVision.EMatcher.
None	Save (self, eVision.ESerializer\|None serializer)
	Saves the eVision.EMatcher.
None	Load (self, str path)
	Loads the eVision.EMatcher.
None	Load (self, eVision.ESerializer\|None serializer)
	Loads the eVision.EMatcher.
None	LearnPattern (self, eVision.EROIBW8\|None pattern)
	Learns a pattern to subsequently match in an image.
None	LearnPattern (self, eVision.EROIC24\|None pattern)
	Learns a pattern to subsequently match in an image.
None	LearnPattern (self, eVision.EROIBW8\|None pattern, eVision.ERegion region)
	Learns a pattern to subsequently match in an image.
None	LearnPattern (self, eVision.EROIC24\|None pattern, eVision.ERegion region)
	Learns a pattern to subsequently match in an image.
None	LearnPattern (self, eVision.EROIBW8\|None pattern, eVision.EFrame frame)
	Learns a pattern to subsequently match in an image.
None	LearnPattern (self, eVision.EROIC24\|None pattern, eVision.EFrame frame)
	Learns a pattern to subsequently match in an image.
None	LearnPattern (self, eVision.EROIBW8\|None pattern, eVision.ERegion region, eVision.EFrame frame)
	Learns a pattern to subsequently match in an image.
None	LearnPattern (self, eVision.EROIC24\|None pattern, eVision.ERegion region, eVision.EFrame frame)
	Learns a pattern to subsequently match in an image.
None	LearnPatterns (self, list[eVision._eVision.EBaseROI\|None] patterns)
	Learns multiple (at least 2) patterns.
None	LearnPatterns (self, list[eVision._eVision.EBaseROI\|None] patterns, list[eVision._eVision.ERegion\|None] regions)
	Learns multiple (at least 2) patterns.
None	LearnPatterns (self, list[eVision._eVision.EBaseROI\|None] patterns, list[eVision._eVision.ERegion\|None] regions, eVision.EFrame frame)
	Learns multiple (at least 2) patterns.
None	LearnPatterns (self, list[eVision._eVision.EBaseROI\|None] patterns, eVision.EFrame frame)
	Learns multiple (at least 2) patterns.
None	ClearImage (self)
	Releases the pointer to the image object that has been passed to the eVision.EMatcher object.
None	CopyLearntPattern (self, eVision.EImageBW8 image, int learntPatternIndex)
	Copies the learned pattern in the supplied image.
None	CopyLearntPattern (self, eVision.EImageBW8 image)
	Copies the learned pattern in the supplied image.
None	CopyLearntPattern (self, eVision.EImageC24 image, int learntPatternIndex)
	Copies the learned pattern in the supplied image.
None	CopyLearntPattern (self, eVision.EImageC24 image)
	Copies the learned pattern in the supplied image.
list[eVision._eVision.EFoundPattern]	Match (self, eVision.EROIBW8\|None image)
	Matches the pattern against an image.
list[eVision._eVision.EFoundPattern]	Match (self, eVision.EROIBW8\|None image, eVision.ERegion region)
	Matches the pattern against an image.
list[eVision._eVision.EFoundPattern]	Match (self, eVision.EROIC24\|None image)
	Matches the pattern against an image.
list[eVision._eVision.EFoundPattern]	Match (self, eVision.EROIC24\|None image, eVision.ERegion region)
	Matches the pattern against an image.
list[eVision._eVision.EFoundPattern]	Match (self, eVision.EROIBW8\|None image, eVision.EFrame frame)
	Matches the pattern against an image.
list[eVision._eVision.EFoundPattern]	Match (self, eVision.EROIBW8\|None image, eVision.ERegion region, eVision.EFrame frame)
	Matches the pattern against an image.
list[eVision._eVision.EFoundPattern]	Match (self, eVision.EROIC24\|None image, eVision.EFrame frame)
	Matches the pattern against an image.
list[eVision._eVision.EFoundPattern]	Match (self, eVision.EROIC24\|None image, eVision.ERegion region, eVision.EFrame frame)
	Matches the pattern against an image.
float	GetAngleStep (self, int reduction, int learntPatternIndex)
	Gets the angle step used at the given reduction.
float	GetAngleStep (self, int reduction)
	Gets the angle step used at the given reduction.
float	GetScaleStep (self, int reduction, int learntPatternIndex)
	Gets the scale step used at the given reduction.
float	GetScaleStep (self, int reduction)
	Gets the scale step used at the given reduction.
float	GetScaleXStep (self, int reduction, int learntPatternIndex)
	Gets the scale step used on the X axis at the given reduction.
float	GetScaleXStep (self, int reduction)
	Gets the scale step used on the X axis at the given reduction.
float	GetScaleYStep (self, int reduction, int learntPatternIndex)
	Gets the scale step used on the Y axis at the given reduction.
float	GetScaleYStep (self, int reduction)
	Gets the scale step used on the Y axis at the given reduction.
eVision.EMatchPosition	GetPosition (self, int index)
	Returns an eVision.EMatchPosition object containing the position coordinates.
None	DrawPosition (self, eVision.EDrawAdapter\|None graphicContext, int index, bool bCorner, float zoomX, float zoomY, float panX, float panY)
	Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPosition (self, eVision.EDrawAdapter\|None graphicContext, int index, bool bCorner, float zoomX, float zoomY, float panX)
	Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPosition (self, eVision.EDrawAdapter\|None graphicContext, int index, bool bCorner, float zoomX, float zoomY)
	Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPosition (self, eVision.EDrawAdapter\|None graphicContext, int index, bool bCorner, float zoomX)
	Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPosition (self, eVision.EDrawAdapter\|None graphicContext, int index, bool bCorner)
	Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPosition (self, eVision.EDrawAdapter\|None graphicContext, int index)
	Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPositions (self, eVision.EDrawAdapter\|None graphicContext, bool bCorner, float zoomX, float zoomY, float panX, float panY)
	Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPositions (self, eVision.EDrawAdapter\|None graphicContext, bool bCorner, float zoomX, float zoomY, float panX)
	Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPositions (self, eVision.EDrawAdapter\|None graphicContext, bool bCorner, float zoomX, float zoomY)
	Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPositions (self, eVision.EDrawAdapter\|None graphicContext, bool bCorner, float zoomX)
	Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPositions (self, eVision.EDrawAdapter\|None graphicContext, bool bCorner)
	Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
None	DrawPositions (self, eVision.EDrawAdapter\|None graphicContext)
	Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.
int	GetPatternWidth (self, int learntPatternIndex)
	Learned pattern width.
int	GetPatternHeight (self, int learntPatternIndex)
	Learned pattern height.
int	LearnTimeOut (self)
	Time-out for the eVision.EMatcher.LearnPattern and eVision.EMatcher.LearnPatterns methods.
	LearnTimeOut (self, int value)
	Time-out for the eVision.EMatcher.LearnPattern and eVision.EMatcher.LearnPatterns methods.
int	MatchTimeOut (self)
	Time-out for the eVision.EMatcher.Match method.
	MatchTimeOut (self, int value)
	Time-out for the eVision.EMatcher.Match method.
int	NumLearnedPatterns (self)
	Indicates the number of patterns learnt.
bool	PatternLearnt (self)
	Returns true after a learning operation has been successfully performed, indicating that the eVision.EMatcher object is ready for matching, and false otherwise.
bool	LearningHasRegion (self)
	Indicates whether a eVision.ERegion was used alongside the last learned pattern.
eVision.EIntSize	Extension (self)
	Sets/Gets the extension of the matching ROI, i.e.
	Extension (self, eVision.EIntSize value)
	Sets/Gets the extension of the matching ROI, i.e.
int	MinReducedArea (self)
	Minimum reduced area parameter.
	MinReducedArea (self, int value)
	Minimum reduced area parameter.
bool	AdvancedLearning (self)
	Toggle advanced learning.
	AdvancedLearning (self, bool value)
	Toggle advanced learning.
int	DontCareThreshold (self)
	"Don't care" threshold.
	DontCareThreshold (self, int value)
	"Don't care" threshold.
eVision.ECorrelationMode	CorrelationMode (self)
	Correlation mode.
	CorrelationMode (self, eVision.ECorrelationMode value)
	Correlation mode.
eVision.EMatchContrastMode	ContrastMode (self)
	Contrast mode.
	ContrastMode (self, eVision.EMatchContrastMode value)
	Contrast mode.
int	MaxPositions (self)
	Maximum number of positions.
	MaxPositions (self, int value)
	Maximum number of positions.
int	MaxInitialPositions (self)
	Maximum number of positions at the first stage of the matching process.
	MaxInitialPositions (self, int value)
	Maximum number of positions at the first stage of the matching process.
float	InitialMinScore (self)
	Minimum score applied as a selection criterion in the early stages of the matching process.
	InitialMinScore (self, float value)
	Minimum score applied as a selection criterion in the early stages of the matching process.
float	MinScore (self)
	Minimum score.
	MinScore (self, float value)
	Minimum score.
float	MinAngle (self)
	Minimum angle, in the current angle unit.
	MinAngle (self, float value)
	Minimum angle, in the current angle unit.
float	MaxAngle (self)
	Maximum angle, in the current angle unit.
	MaxAngle (self, float value)
	Maximum angle, in the current angle unit.
float	MinScale (self)
	Minimum scale factor for isotropic scaling.
	MinScale (self, float value)
	Minimum scale factor for isotropic scaling.
float	MaxScale (self)
	Maximum scale factor for isotropic scaling.
	MaxScale (self, float value)
	Maximum scale factor for isotropic scaling.
float	MinScaleX (self)
	Minimum horizontal scale factor for anisotropic scaling.
	MinScaleX (self, float value)
	Minimum horizontal scale factor for anisotropic scaling.
float	MaxScaleX (self)
	Maximum horizontal scale factor for anisotropic scaling.
	MaxScaleX (self, float value)
	Maximum horizontal scale factor for anisotropic scaling.
float	MinScaleY (self)
	Minimum vertical scale factor for anisotropic scaling.
	MinScaleY (self, float value)
	Minimum vertical scale factor for anisotropic scaling.
float	MaxScaleY (self)
	Maximum vertical scale factor for anisotropic scaling.
	MaxScaleY (self, float value)
	Maximum vertical scale factor for anisotropic scaling.
bool	IsotropicScale (self)
	Flag indicating whether isotropic (as opposed to anisotropic) scaling is used.
eVision.ESize	PixelDimensions (self)
	Gets/Sets the physical pixel dimensions.
	PixelDimensions (self, eVision.ESize value)
	Gets/Sets the physical pixel dimensions.
bool	Interpolate (self)
	Interpolation mode.
	Interpolate (self, bool value)
	Interpolation mode.
bool	EnableEarlyCandidateRejection (self)
	Whether to remove bad candidates in the early phases of the processing or not.
	EnableEarlyCandidateRejection (self, bool value)
	Whether to remove bad candidates in the early phases of the processing or not.
eVision.EFilteringMode	FilteringMode (self)
	Filtering mode.
	FilteringMode (self, eVision.EFilteringMode value)
	Filtering mode.
float	MaxOverlap (self)
	Overlapping tolerance.
	MaxOverlap (self, float value)
	Overlapping tolerance.
int	NumPositions (self)
	Number of good matches found, as defined by eVision.EMatcher.MinScore and eVision.EMatcher.MaxPositions properties.
list[eVision._eVision.EMatchPosition]	Positions (self)
	Returns a vector of eVision.EMatchPosition objects, each containing the position coordinates and other matching results.
int	NumReductions (self)
	Number of reduction steps used in the matching process.
eVision.EImageType	PatternType (self)
	Pixel type of the learned pattern.
int	FinalReduction (self)
	Index of the last reduction.
	FinalReduction (self, int value)
	Index of the last reduction.
int	PatternWidth (self)
	Learned pattern width.
int	PatternHeight (self)
	Learned pattern height.

Static Public Member Functions
int	GetVersion ()
	Version number of the eVision.EMatcher object.

Detailed Description

Manages a complete matching context in EasyMatch.

Remarks:

A matching context consists of a learned pattern and of the parameters required to locate one or more instances of the pattern in a search field.

License(s): EasyMatch

Constructor & Destructor Documentation

◆ init() [1/3]

None eVision.EMatcher.__init__ ( self )

Constructs a matching context.

Remarks:

With the default constructor (no argument), all parameters are initialized to their respective default values. The copy constructor constructs a matching context based on a pre-existing eVision.EMatcher object. The last constructor constructs a matching context with a specified number of degrees of freedom. maximumNumberOfDegreesOfFreedom is the maximum number of degrees of freedom that the eVision.EMatcher object being constructed will be allowed to use during its life. All other parameters are initialized to their respective default values. The degrees of freedom for a matching operation are the translation (2 modes), the rotation (1 mode), the isotropic scaling (1 mode) and the anisotropic scaling (1 more mode). There is no way to modify this parameter for an existing eVision.EMatcher object. The default value for maximumNumberOfDegreesOfFreedom is 5, that is the maximum value. The minimum value for the number of degrees of freedom is 2, in order to allow, at least, the pattern translation.

◆ init() [2/3]

None eVision.EMatcher.__init__	(		self,
		int	maxNumDOF )

Constructs a matching context.

Remarks:

With the default constructor (no argument), all parameters are initialized to their respective default values. The copy constructor constructs a matching context based on a pre-existing eVision.EMatcher object. The last constructor constructs a matching context with a specified number of degrees of freedom. maximumNumberOfDegreesOfFreedom is the maximum number of degrees of freedom that the eVision.EMatcher object being constructed will be allowed to use during its life. All other parameters are initialized to their respective default values. The degrees of freedom for a matching operation are the translation (2 modes), the rotation (1 mode), the isotropic scaling (1 mode) and the anisotropic scaling (1 more mode). There is no way to modify this parameter for an existing eVision.EMatcher object. The default value for maximumNumberOfDegreesOfFreedom is 5, that is the maximum value. The minimum value for the number of degrees of freedom is 2, in order to allow, at least, the pattern translation.

Parameters

maxNumDOF Maximum number of degrees of freedom (this number must be comprised between 2 and 5).

◆ init() [3/3]

None eVision.EMatcher.__init__	(		self,
		eVision.EMatcher	other )

Constructs a matching context.

Remarks:

With the default constructor (no argument), all parameters are initialized to their respective default values. The copy constructor constructs a matching context based on a pre-existing eVision.EMatcher object. The last constructor constructs a matching context with a specified number of degrees of freedom. maximumNumberOfDegreesOfFreedom is the maximum number of degrees of freedom that the eVision.EMatcher object being constructed will be allowed to use during its life. All other parameters are initialized to their respective default values. The degrees of freedom for a matching operation are the translation (2 modes), the rotation (1 mode), the isotropic scaling (1 mode) and the anisotropic scaling (1 more mode). There is no way to modify this parameter for an existing eVision.EMatcher object. The default value for maximumNumberOfDegreesOfFreedom is 5, that is the maximum value. The minimum value for the number of degrees of freedom is 2, in order to allow, at least, the pattern translation.

Parameters

other Another eVision.EMatcher object to be copied in the new eVision.EMatcher object.

Member Function Documentation

◆ AdvancedLearning() [1/2]

bool eVision.EMatcher.AdvancedLearning ( self )

Toggle advanced learning.

Remarks:

When enabled, the advanced learning process will try to optimize learning parameters like the Minimum Reduced Area. Advanced learning uses the whole image context (parent EImage for an EROI) to optimize the parameters. The learning will take more time (from 1x to 5x longer) but the matching probability could be improved. The improvement strongly depends on the pattern source image.

The advanced learning is automatically disabled when the method eVision.EMatcher.MinReducedArea is called. In addition, advanced learning does not work when using eVision.EMatcher.DontCareThreshold and a masked image. However, it is compatible with the overload of eVision.EMatcher.LearnPattern taking an eVision.ERegion, which allows to do the same as eVision.EMatcher.DontCareThreshold.

◆ AdvancedLearning() [2/2]

eVision.EMatcher.AdvancedLearning	(		self,
		bool	value )

Toggle advanced learning.

Remarks:

When enabled, the advanced learning process will try to optimize learning parameters like the Minimum Reduced Area. Advanced learning uses the whole image context (parent EImage for an EROI) to optimize the parameters. The learning will take more time (from 1x to 5x longer) but the matching probability could be improved. The improvement strongly depends on the pattern source image.

The advanced learning is automatically disabled when the method eVision.EMatcher.MinReducedArea is called. In addition, advanced learning does not work when using eVision.EMatcher.DontCareThreshold and a masked image. However, it is compatible with the overload of eVision.EMatcher.LearnPattern taking an eVision.ERegion, which allows to do the same as eVision.EMatcher.DontCareThreshold.

◆ ClearImage()

None eVision.EMatcher.ClearImage ( self )

Releases the pointer to the image object that has been passed to the eVision.EMatcher object.

Remarks:

It is the way to tell to the eVision.EMatcher object that its pointer is not valid anymore. The eVision.EMatcher.Match method keeps a copy of the image pointer given as parameter. So, if the user deletes this pointer, the eVision.EMatcher object should be informed.

◆ ContrastMode() [1/2]

eVision.EMatchContrastMode eVision.EMatcher.ContrastMode ( self )

Contrast mode.

Remarks:

By default, the contrast mode is set to eVision.EMatchContrastMode.Normal.

◆ ContrastMode() [2/2]

eVision.EMatcher.ContrastMode	(		self,
		eVision.EMatchContrastMode	value )

Contrast mode.

Remarks:

By default, the contrast mode is set to eVision.EMatchContrastMode.Normal.

◆ CopyLearntPattern() [1/4]

None eVision.EMatcher.CopyLearntPattern	(		self,
		eVision.EImageBW8	image )

Copies the learned pattern in the supplied image.

If no pattern has been learned, an exception with code eVision.EError.NoPatternLearnt will be thrown.

Parameters

image Pointer to the image in which the learned pattern will be returned.

◆ CopyLearntPattern() [2/4]

None eVision.EMatcher.CopyLearntPattern	(		self,
		eVision.EImageBW8	image,
		int	learntPatternIndex )

Copies the learned pattern in the supplied image.

If no pattern has been learned, an exception with code eVision.EError.NoPatternLearnt will be thrown.

Parameters

image	Pointer to the image in which the learned pattern will be returned.
learntPatternIndex	Index of the learnt pattern. Default: 0.

◆ CopyLearntPattern() [3/4]

None eVision.EMatcher.CopyLearntPattern	(		self,
		eVision.EImageC24	image )

Copies the learned pattern in the supplied image.

If no pattern has been learned, an exception with code eVision.EError.NoPatternLearnt will be thrown.

Parameters

image Pointer to the image in which the learned pattern will be returned.

◆ CopyLearntPattern() [4/4]

None eVision.EMatcher.CopyLearntPattern	(		self,
		eVision.EImageC24	image,
		int	learntPatternIndex )

Copies the learned pattern in the supplied image.

If no pattern has been learned, an exception with code eVision.EError.NoPatternLearnt will be thrown.

Parameters

image	Pointer to the image in which the learned pattern will be returned.
learntPatternIndex	Index of the learnt pattern. Default: 0.

◆ CopyTo()

None eVision.EMatcher.CopyTo	(		self,
		eVision.EMatcher	other )

Copies all the data of the current eVision.EMatcher object into another eVision.EMatcher object and returns it.

Parameters

other Reference to the eVision.EMatcher object in which the current eVision.EMatcher object parameters are to be copied.

◆ CorrelationMode() [1/2]

eVision.ECorrelationMode eVision.EMatcher.CorrelationMode ( self )

Correlation mode.

Remarks:

This property tells what normalization rule is used to correlate the pattern to the image. By default, the correlation mode is set to eVision.ECorrelationMode.Normalized.

◆ CorrelationMode() [2/2]

eVision.EMatcher.CorrelationMode	(		self,
		eVision.ECorrelationMode	value )

Correlation mode.

Remarks:

This property tells what normalization rule is used to correlate the pattern to the image. By default, the correlation mode is set to eVision.ECorrelationMode.Normalized.

◆ DontCareThreshold() [1/2]

int eVision.EMatcher.DontCareThreshold ( self )

"Don't care" threshold.

Remarks:

If the pattern cannot be inscribed in a rectangle because there are foreign objects in a close neighborhood, mismatches can be avoided by using "don't care" pixels: all the pattern pixels whose value is strictly below DontCareThreshold will be ignored. By default, this property is set to 0: no "don't care" pixel exists.

Note: When you use the "don't care" feature, either the pattern is well contrasted from its background -then set DontCareThreshold to an appropriate thresholding value- or it is not -then set the background pixels of the pattern to some low value (by a masking operation) and set DontCareThreshold to this low value plus one.

In addition, eVision.EMatcher.AdvancedLearning does not work when using a don't care threshold and a masked image. However, it is compatible with the overload of eVision.EMatcher.LearnPattern taking an eVision.ERegion, which allows to do the same as eVision.EMatcher.DontCareThreshold.

◆ DontCareThreshold() [2/2]

eVision.EMatcher.DontCareThreshold	(		self,
		int	value )

"Don't care" threshold.

Remarks:

If the pattern cannot be inscribed in a rectangle because there are foreign objects in a close neighborhood, mismatches can be avoided by using "don't care" pixels: all the pattern pixels whose value is strictly below DontCareThreshold will be ignored. By default, this property is set to 0: no "don't care" pixel exists.

Note: When you use the "don't care" feature, either the pattern is well contrasted from its background -then set DontCareThreshold to an appropriate thresholding value- or it is not -then set the background pixels of the pattern to some low value (by a masking operation) and set DontCareThreshold to this low value plus one.

In addition, eVision.EMatcher.AdvancedLearning does not work when using a don't care threshold and a masked image. However, it is compatible with the overload of eVision.EMatcher.LearnPattern taking an eVision.ERegion, which allows to do the same as eVision.EMatcher.DontCareThreshold.

◆ DrawPosition() [1/6]

None eVision.EMatcher.DrawPosition	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		int	index )

Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used.

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
index	Occurrence index, in range 0..NumPositions-1.

◆ DrawPosition() [2/6]

None eVision.EMatcher.DrawPosition	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		int	index,
		bool	bCorner )

Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used.

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
index	Occurrence index, in range 0..NumPositions-1.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)

◆ DrawPosition() [3/6]

None eVision.EMatcher.DrawPosition	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		int	index,
		bool	bCorner,
		float	zoomX )

Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used.

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
index	Occurrence index, in range 0..NumPositions-1.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)
zoomX	Horizontal zooming factor. By default, true scale is used.

◆ DrawPosition() [4/6]

None eVision.EMatcher.DrawPosition	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		int	index,
		bool	bCorner,
		float	zoomX,
		float	zoomY )

Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used.

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
index	Occurrence index, in range 0..NumPositions-1.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)
zoomX	Horizontal zooming factor. By default, true scale is used.
zoomY	Vertical zooming factor. If set to 0 (default), the horizontal zooming factor is used instead, so as to provide isotropic zooming.

◆ DrawPosition() [5/6]

None eVision.EMatcher.DrawPosition	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		int	index,
		bool	bCorner,
		float	zoomX,
		float	zoomY,
		float	panX )

Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used.

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
index	Occurrence index, in range 0..NumPositions-1.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)
zoomX	Horizontal zooming factor. By default, true scale is used.
zoomY	Vertical zooming factor. If set to 0 (default), the horizontal zooming factor is used instead, so as to provide isotropic zooming.
panX	Horizontal panning value expressed in pixels. By default, no panning occurs.

◆ DrawPosition() [6/6]

None eVision.EMatcher.DrawPosition	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		int	index,
		bool	bCorner,
		float	zoomX,
		float	zoomY,
		float	panX,
		float	panY )

Draws a graphical representation of a given occurrence of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used.

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
index	Occurrence index, in range 0..NumPositions-1.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)
zoomX	Horizontal zooming factor. By default, true scale is used.
zoomY	Vertical zooming factor. If set to 0 (default), the horizontal zooming factor is used instead, so as to provide isotropic zooming.
panX	Horizontal panning value expressed in pixels. By default, no panning occurs.
panY	Vertical panning value expressed in pixels. By default, no panning occurs.

◆ DrawPositions() [1/6]

None eVision.EMatcher.DrawPositions	(		self,
		eVision.EDrawAdapter \| None	graphicContext )

Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used for all occurrences (to vary the colors, draw the objects separately using the eVision.EMatcher.DrawPosition method instead).

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext Handle of the device context on which to draw.

◆ DrawPositions() [2/6]

None eVision.EMatcher.DrawPositions	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		bool	bCorner )

Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used for all occurrences (to vary the colors, draw the objects separately using the eVision.EMatcher.DrawPosition method instead).

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)

◆ DrawPositions() [3/6]

None eVision.EMatcher.DrawPositions	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		bool	bCorner,
		float	zoomX )

Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used for all occurrences (to vary the colors, draw the objects separately using the eVision.EMatcher.DrawPosition method instead).

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)
zoomX	Horizontal zooming factor. By default, true scale is used.

◆ DrawPositions() [4/6]

None eVision.EMatcher.DrawPositions	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		bool	bCorner,
		float	zoomX,
		float	zoomY )

Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used for all occurrences (to vary the colors, draw the objects separately using the eVision.EMatcher.DrawPosition method instead).

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)
zoomX	Horizontal zooming factor. By default, true scale is used.
zoomY	Vertical zooming factor. If set to 0 (default), the horizontal zooming factor is used instead, so as to provide isotropic zooming.

◆ DrawPositions() [5/6]

None eVision.EMatcher.DrawPositions	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		bool	bCorner,
		float	zoomX,
		float	zoomY,
		float	panX )

Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used for all occurrences (to vary the colors, draw the objects separately using the eVision.EMatcher.DrawPosition method instead).

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)
zoomX	Horizontal zooming factor. By default, true scale is used.
zoomY	Vertical zooming factor. If set to 0 (default), the horizontal zooming factor is used instead, so as to provide isotropic zooming.
panX	Horizontal panning value expressed in pixels. By default, no panning occurs.

◆ DrawPositions() [6/6]

None eVision.EMatcher.DrawPositions	(		self,
		eVision.EDrawAdapter \| None	graphicContext,
		bool	bCorner,
		float	zoomX,
		float	zoomY,
		float	panX,
		float	panY )

Draws a graphical representation of all occurrences of the pattern in the image, using a rectangle and possibly a small line segment in the upper-left corner.

When multiple patterns are learned, only the positions corresponding to the first pattern are drawn.

Remarks:

Drawing is done in the device context associated to the desired window. The current pen is used for all occurrences (to vary the colors, draw the objects separately using the eVision.EMatcher.DrawPosition method instead).

Deprecated

Use the eVision.EFoundPattern.Draw method of the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

graphicContext	Handle of the device context on which to draw.
bCorner	true if the corner mark is to be drawn. false by default. (This mark is useful when large rotations are allowed.)
zoomX	Horizontal zooming factor. By default, true scale is used.
zoomY	Vertical zooming factor. If set to 0 (default), the horizontal zooming factor is used instead, so as to provide isotropic zooming.
panX	Horizontal panning value expressed in pixels. By default, no panning occurs.
panY	Vertical panning value expressed in pixels. By default, no panning occurs.

◆ EnableEarlyCandidateRejection() [1/2]

bool eVision.EMatcher.EnableEarlyCandidateRejection ( self )

Whether to remove bad candidates in the early phases of the processing or not.

Enabled by default.

Remarks:

To reduce the number of candidates, those failing to fill some criterion are removed. This makes processing faster, a downside is that some good candidates can be erroneously removed. We recommend setting this parameter to false if you observe that a slight change in the input conditions make processing fail. For example: changing the search ROI in the image slightly.

After disabling the early candidate rejection, you will probably have to increase eVision.EMatcher.MaxInitialPositions as more candidates will now be kept.

◆ EnableEarlyCandidateRejection() [2/2]

eVision.EMatcher.EnableEarlyCandidateRejection	(		self,
		bool	value )

Whether to remove bad candidates in the early phases of the processing or not.

Enabled by default.

Remarks:

To reduce the number of candidates, those failing to fill some criterion are removed. This makes processing faster, a downside is that some good candidates can be erroneously removed. We recommend setting this parameter to false if you observe that a slight change in the input conditions make processing fail. For example: changing the search ROI in the image slightly.

After disabling the early candidate rejection, you will probably have to increase eVision.EMatcher.MaxInitialPositions as more candidates will now be kept.

◆ Extension() [1/2]

eVision.EIntSize eVision.EMatcher.Extension ( self )

Sets/Gets the extension of the matching ROI, i.e.

puts the horizontal and vertical distances, in pixels, that the found pattern occurrences may fall outside the matching ROI. Such occurrences partially outside the ROI have their score corrected by the ratio between the pattern area outside the ROI and the pattern area inside.

◆ Extension() [2/2]

eVision.EMatcher.Extension	(		self,
		eVision.EIntSize	value )

Sets/Gets the extension of the matching ROI, i.e.

puts the horizontal and vertical distances, in pixels, that the found pattern occurrences may fall outside the matching ROI. Such occurrences partially outside the ROI have their score corrected by the ratio between the pattern area outside the ROI and the pattern area inside.

◆ FilteringMode() [1/2]

eVision.EFilteringMode eVision.EMatcher.FilteringMode ( self )

Filtering mode.

Remarks:

To achieve acceptable time performance, EasyMatch works by sub-sampling the pattern in the early phases of the processing. The filtering mode parameter allows to select the pre-processing type applied to the image before the decimation: averaging or low-pass filtering. By default, this property is set to eVision.EFilteringMode.Uniform, whereas the eVision.EFilteringMode.LowPass mode is indicated if the image presents sharp gray-level transitions.

◆ FilteringMode() [2/2]

eVision.EMatcher.FilteringMode	(		self,
		eVision.EFilteringMode	value )

Filtering mode.

Remarks:

To achieve acceptable time performance, EasyMatch works by sub-sampling the pattern in the early phases of the processing. The filtering mode parameter allows to select the pre-processing type applied to the image before the decimation: averaging or low-pass filtering. By default, this property is set to eVision.EFilteringMode.Uniform, whereas the eVision.EFilteringMode.LowPass mode is indicated if the image presents sharp gray-level transitions.

◆ FinalReduction() [1/2]

int eVision.EMatcher.FinalReduction ( self )

Index of the last reduction.

Remarks:

The pattern matching process is comprised of a few passes (typically 4) during which the position accuracy is improved by a factor of 2 (for all degrees of freedom). This is called a reduction. By default, the computation continues until an accuracy of one pixel is obtained. To speed up the process, the last reduction passes can be dropped, so lowering the accuracy. Anyway, the interpolation mode can then still be used. By default, this property is set to 0 (pixel accuracy). Value 1 corresponds to 2-pixels accuracy, 2 to 4, and so on. The range of values that can be used for this property is 0 to NumReductions-1.

◆ FinalReduction() [2/2]

eVision.EMatcher.FinalReduction	(		self,
		int	value )

Index of the last reduction.

Remarks:

The pattern matching process is comprised of a few passes (typically 4) during which the position accuracy is improved by a factor of 2 (for all degrees of freedom). This is called a reduction. By default, the computation continues until an accuracy of one pixel is obtained. To speed up the process, the last reduction passes can be dropped, so lowering the accuracy. Anyway, the interpolation mode can then still be used. By default, this property is set to 0 (pixel accuracy). Value 1 corresponds to 2-pixels accuracy, 2 to 4, and so on. The range of values that can be used for this property is 0 to NumReductions-1.

◆ GetAngleStep() [1/2]

float eVision.EMatcher.GetAngleStep	(		self,
		int	reduction )

Gets the angle step used at the given reduction.

Parameters

reduction Offset of the reduction in the pyramid, must be between eVision.EMatcher.FinalReduction and eVision.EMatcher.NumReductions - 1.

◆ GetAngleStep() [2/2]

float eVision.EMatcher.GetAngleStep	(		self,
		int	reduction,
		int	learntPatternIndex )

Gets the angle step used at the given reduction.

Parameters

reduction	Offset of the reduction in the pyramid, must be between eVision.EMatcher.FinalReduction and eVision.EMatcher.NumReductions - 1.
learntPatternIndex	Index of the learnt pattern. Defaults to 0.

◆ GetPatternHeight()

int eVision.EMatcher.GetPatternHeight	(		self,
		int	learntPatternIndex )

Learned pattern height.

Parameters

learntPatternIndex Index of the learnt pattern.

◆ GetPatternWidth()

int eVision.EMatcher.GetPatternWidth	(		self,
		int	learntPatternIndex )

Learned pattern width.

Parameters

learntPatternIndex Index of the learnt pattern.

◆ GetPosition()

eVision.EMatchPosition eVision.EMatcher.GetPosition	(		self,
		int	index )

Returns an eVision.EMatchPosition object containing the position coordinates.

When multiple patterns are learned, the position corresponding to the first pattern at the given index is returned.

Deprecated

Use the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

Parameters

index 0-based index to the desired position. The positions are ordered by decreasing score.

◆ GetScaleStep() [1/2]

float eVision.EMatcher.GetScaleStep	(		self,
		int	reduction )

Gets the scale step used at the given reduction.

When multiple patterns are learned, the scale step corresponding to the first pattern at the given reduction is returned.

Parameters

reduction Offset of the reduction in the pyramid, must be between eVision.EMatcher.FinalReduction and eVision.EMatcher.NumReductions - 1.

◆ GetScaleStep() [2/2]

float eVision.EMatcher.GetScaleStep	(		self,
		int	reduction,
		int	learntPatternIndex )

Gets the scale step used at the given reduction.

When multiple patterns are learned, the scale step corresponding to the first pattern at the given reduction is returned.

Parameters

reduction	Offset of the reduction in the pyramid, must be between eVision.EMatcher.FinalReduction and eVision.EMatcher.NumReductions - 1.
learntPatternIndex	Index of the learnt pattern. Defaults to 0.

◆ GetScaleXStep() [1/2]

float eVision.EMatcher.GetScaleXStep	(		self,
		int	reduction )

Gets the scale step used on the X axis at the given reduction.

When multiple patterns are learned, the scale X step corresponding to the first pattern at the given reduction is returned.

Parameters

reduction Offset of the reduction in the pyramid, must be between eVision.EMatcher.FinalReduction and eVision.EMatcher.NumReductions - 1.

◆ GetScaleXStep() [2/2]

float eVision.EMatcher.GetScaleXStep	(		self,
		int	reduction,
		int	learntPatternIndex )

Gets the scale step used on the X axis at the given reduction.

When multiple patterns are learned, the scale X step corresponding to the first pattern at the given reduction is returned.

Parameters

reduction	Offset of the reduction in the pyramid, must be between eVision.EMatcher.FinalReduction and eVision.EMatcher.NumReductions - 1.
learntPatternIndex	Index of the learnt pattern. Defaults to 0.

◆ GetScaleYStep() [1/2]

float eVision.EMatcher.GetScaleYStep	(		self,
		int	reduction )

Gets the scale step used on the Y axis at the given reduction.

When multiple patterns are learned, the scale Y step corresponding to the first pattern at the given reduction is returned.

Parameters

reduction Offset of the reduction in the pyramid, must be between eVision.EMatcher.FinalReduction and eVision.EMatcher.NumReductions - 1.

◆ GetScaleYStep() [2/2]

float eVision.EMatcher.GetScaleYStep	(		self,
		int	reduction,
		int	learntPatternIndex )

Gets the scale step used on the Y axis at the given reduction.

When multiple patterns are learned, the scale Y step corresponding to the first pattern at the given reduction is returned.

Parameters

reduction	Offset of the reduction in the pyramid, must be between eVision.EMatcher.FinalReduction and eVision.EMatcher.NumReductions - 1.
learntPatternIndex	Index of the learnt pattern. Defaults to 0.

◆ GetVersion()

int eVision.EMatcher.GetVersion ( )

static

Version number of the eVision.EMatcher object.

◆ InitialMinScore() [1/2]

float eVision.EMatcher.InitialMinScore ( self )

Minimum score applied as a selection criterion in the early stages of the matching process.

Remarks:

When the search for matches starts, EasyMatch considers a set of candidate positions that it progressively refines. When they later appear to be bad candidates, they are rejected. Though it is the minimum score level that is used to reject bad matching positions at the final step of the matching process, the "initial minimum score" parameter is used to eliminate bad positions (whose score is not high enough) in the early stages of the matching processing. If the matching process is achieved in one step, only the minimum score parameter will be considered. By default, this property is set to -1.

◆ InitialMinScore() [2/2]

eVision.EMatcher.InitialMinScore	(		self,
		float	value )

Minimum score applied as a selection criterion in the early stages of the matching process.

Remarks:

When the search for matches starts, EasyMatch considers a set of candidate positions that it progressively refines. When they later appear to be bad candidates, they are rejected. Though it is the minimum score level that is used to reject bad matching positions at the final step of the matching process, the "initial minimum score" parameter is used to eliminate bad positions (whose score is not high enough) in the early stages of the matching processing. If the matching process is achieved in one step, only the minimum score parameter will be considered. By default, this property is set to -1.

◆ Interpolate() [1/2]

bool eVision.EMatcher.Interpolate ( self )

Interpolation mode.

Remarks:

By default, matching is done with a one-pixel precision for all degrees of freedom (translation, rotation and scaling). You can use an additional interpolation process to achieve sub-pixel accuracy. This generally leads to an improvement of the sub-pixel accuracy by a factor larger than 10. This is possible only when the found instances match closely the model. A score higher than 0.99 indicates that the instances are a close match of the model. In other words, the instance is considered to be more accurate when the score is higher. The added computational cost is low. By default, this property is set to false.

◆ Interpolate() [2/2]

eVision.EMatcher.Interpolate	(		self,
		bool	value )

Interpolation mode.

Remarks:

By default, matching is done with a one-pixel precision for all degrees of freedom (translation, rotation and scaling). You can use an additional interpolation process to achieve sub-pixel accuracy. This generally leads to an improvement of the sub-pixel accuracy by a factor larger than 10. This is possible only when the found instances match closely the model. A score higher than 0.99 indicates that the instances are a close match of the model. In other words, the instance is considered to be more accurate when the score is higher. The added computational cost is low. By default, this property is set to false.

◆ IsotropicScale()

bool eVision.EMatcher.IsotropicScale ( self )

Flag indicating whether isotropic (as opposed to anisotropic) scaling is used.

Remarks:

true if isotropic (as opposed to anisotropic) scaling is used, i.e. when the scale factors in both the X and Y directions are equal.

◆ LearningHasRegion()

bool eVision.EMatcher.LearningHasRegion ( self )

Indicates whether a eVision.ERegion was used alongside the last learned pattern.

Returns false if no learning was performed.

◆ LearnPattern() [1/8]

None eVision.EMatcher.LearnPattern	(		self,
		eVision.EROIBW8 \| None	pattern )

Learns a pattern to subsequently match in an image.

Remarks:

The maximum size for a pattern is \((\sqrt{Minimum Reduced Area} - 1) * 2^8\). For the default Minimum Reduced Area of 64, this corresponds to a 1792x1792 maximum size. Increasing the Minimum Reduced Area enables larger pattern size but reduces the processing speed.

When a region is given, the method will ignore the pixels outside the region by internally setting them to 0 and setting eVision.EMatcher.DontCareThreshold to 1 if it wasn't set by the user. Thus, when working with an eVision.ERegion, all pixels that are black in the pattern will be discarded.

Consequently, when using binary images, the value 0 shouldn't be used if you use an eVision.ERegion for learning. 0's in your images should be replaced by 1's. This can be done with the eVision.EArithmeticLogicOperation.Add mode of eVision.EasyImage.Oper.

Parameters

pattern Pattern to learn.

◆ LearnPattern() [2/8]

None eVision.EMatcher.LearnPattern	(		self,
		eVision.EROIBW8 \| None	pattern,
		eVision.EFrame	frame )

Learns a pattern to subsequently match in an image.

Remarks:

The maximum size for a pattern is \((\sqrt{Minimum Reduced Area} - 1) * 2^8\). For the default Minimum Reduced Area of 64, this corresponds to a 1792x1792 maximum size. Increasing the Minimum Reduced Area enables larger pattern size but reduces the processing speed.

When a region is given, the method will ignore the pixels outside the region by internally setting them to 0 and setting eVision.EMatcher.DontCareThreshold to 1 if it wasn't set by the user. Thus, when working with an eVision.ERegion, all pixels that are black in the pattern will be discarded.

Consequently, when using binary images, the value 0 shouldn't be used if you use an eVision.ERegion for learning. 0's in your images should be replaced by 1's. This can be done with the eVision.EArithmeticLogicOperation.Add mode of eVision.EasyImage.Oper.

Parameters

pattern	Pattern to learn.
frame	Reference frame of the learned image

◆ LearnPattern() [3/8]

None eVision.EMatcher.LearnPattern	(		self,
		eVision.EROIBW8 \| None	pattern,
		eVision.ERegion	region )

Learns a pattern to subsequently match in an image.

Remarks:

The maximum size for a pattern is \((\sqrt{Minimum Reduced Area} - 1) * 2^8\). For the default Minimum Reduced Area of 64, this corresponds to a 1792x1792 maximum size. Increasing the Minimum Reduced Area enables larger pattern size but reduces the processing speed.

When a region is given, the method will ignore the pixels outside the region by internally setting them to 0 and setting eVision.EMatcher.DontCareThreshold to 1 if it wasn't set by the user. Thus, when working with an eVision.ERegion, all pixels that are black in the pattern will be discarded.

Consequently, when using binary images, the value 0 shouldn't be used if you use an eVision.ERegion for learning. 0's in your images should be replaced by 1's. This can be done with the eVision.EArithmeticLogicOperation.Add mode of eVision.EasyImage.Oper.

Parameters

pattern	Pattern to learn.
region	Region in the pattern image to consider

◆ LearnPattern() [4/8]

None eVision.EMatcher.LearnPattern	(		self,
		eVision.EROIBW8 \| None	pattern,
		eVision.ERegion	region,
		eVision.EFrame	frame )

Learns a pattern to subsequently match in an image.

Remarks:

The maximum size for a pattern is \((\sqrt{Minimum Reduced Area} - 1) * 2^8\). For the default Minimum Reduced Area of 64, this corresponds to a 1792x1792 maximum size. Increasing the Minimum Reduced Area enables larger pattern size but reduces the processing speed.

When a region is given, the method will ignore the pixels outside the region by internally setting them to 0 and setting eVision.EMatcher.DontCareThreshold to 1 if it wasn't set by the user. Thus, when working with an eVision.ERegion, all pixels that are black in the pattern will be discarded.

Consequently, when using binary images, the value 0 shouldn't be used if you use an eVision.ERegion for learning. 0's in your images should be replaced by 1's. This can be done with the eVision.EArithmeticLogicOperation.Add mode of eVision.EasyImage.Oper.

Parameters

pattern	Pattern to learn.
region	Region in the pattern image to consider
frame	Reference frame of the learned image

◆ LearnPattern() [5/8]

None eVision.EMatcher.LearnPattern	(		self,
		eVision.EROIC24 \| None	pattern )

Learns a pattern to subsequently match in an image.

Remarks:

The maximum size for a pattern is \((\sqrt{Minimum Reduced Area} - 1) * 2^8\). For the default Minimum Reduced Area of 64, this corresponds to a 1792x1792 maximum size. Increasing the Minimum Reduced Area enables larger pattern size but reduces the processing speed.

When a region is given, the method will ignore the pixels outside the region by internally setting them to 0 and setting eVision.EMatcher.DontCareThreshold to 1 if it wasn't set by the user. Thus, when working with an eVision.ERegion, all pixels that are black in the pattern will be discarded.

Consequently, when using binary images, the value 0 shouldn't be used if you use an eVision.ERegion for learning. 0's in your images should be replaced by 1's. This can be done with the eVision.EArithmeticLogicOperation.Add mode of eVision.EasyImage.Oper.

Parameters

pattern Pattern to learn.

◆ LearnPattern() [6/8]

None eVision.EMatcher.LearnPattern	(		self,
		eVision.EROIC24 \| None	pattern,
		eVision.EFrame	frame )

Learns a pattern to subsequently match in an image.

Remarks:

The maximum size for a pattern is \((\sqrt{Minimum Reduced Area} - 1) * 2^8\). For the default Minimum Reduced Area of 64, this corresponds to a 1792x1792 maximum size. Increasing the Minimum Reduced Area enables larger pattern size but reduces the processing speed.

When a region is given, the method will ignore the pixels outside the region by internally setting them to 0 and setting eVision.EMatcher.DontCareThreshold to 1 if it wasn't set by the user. Thus, when working with an eVision.ERegion, all pixels that are black in the pattern will be discarded.

Consequently, when using binary images, the value 0 shouldn't be used if you use an eVision.ERegion for learning. 0's in your images should be replaced by 1's. This can be done with the eVision.EArithmeticLogicOperation.Add mode of eVision.EasyImage.Oper.

Parameters

pattern	Pattern to learn.
frame	Reference frame of the learned image

◆ LearnPattern() [7/8]

None eVision.EMatcher.LearnPattern	(		self,
		eVision.EROIC24 \| None	pattern,
		eVision.ERegion	region )

Learns a pattern to subsequently match in an image.

Remarks:

The maximum size for a pattern is \((\sqrt{Minimum Reduced Area} - 1) * 2^8\). For the default Minimum Reduced Area of 64, this corresponds to a 1792x1792 maximum size. Increasing the Minimum Reduced Area enables larger pattern size but reduces the processing speed.

When a region is given, the method will ignore the pixels outside the region by internally setting them to 0 and setting eVision.EMatcher.DontCareThreshold to 1 if it wasn't set by the user. Thus, when working with an eVision.ERegion, all pixels that are black in the pattern will be discarded.

Consequently, when using binary images, the value 0 shouldn't be used if you use an eVision.ERegion for learning. 0's in your images should be replaced by 1's. This can be done with the eVision.EArithmeticLogicOperation.Add mode of eVision.EasyImage.Oper.

Parameters

pattern	Pattern to learn.
region	Region in the pattern image to consider

◆ LearnPattern() [8/8]

None eVision.EMatcher.LearnPattern	(		self,
		eVision.EROIC24 \| None	pattern,
		eVision.ERegion	region,
		eVision.EFrame	frame )

Learns a pattern to subsequently match in an image.

Remarks:

The maximum size for a pattern is \((\sqrt{Minimum Reduced Area} - 1) * 2^8\). For the default Minimum Reduced Area of 64, this corresponds to a 1792x1792 maximum size. Increasing the Minimum Reduced Area enables larger pattern size but reduces the processing speed.

When a region is given, the method will ignore the pixels outside the region by internally setting them to 0 and setting eVision.EMatcher.DontCareThreshold to 1 if it wasn't set by the user. Thus, when working with an eVision.ERegion, all pixels that are black in the pattern will be discarded.

Consequently, when using binary images, the value 0 shouldn't be used if you use an eVision.ERegion for learning. 0's in your images should be replaced by 1's. This can be done with the eVision.EArithmeticLogicOperation.Add mode of eVision.EasyImage.Oper.

Parameters

pattern	Pattern to learn.
region	Region in the pattern image to consider
frame	Reference frame of the learned image

◆ LearnPatterns() [1/4]

None eVision.EMatcher.LearnPatterns	(		self,
		list[eVision._eVision.EBaseROI \| None]	patterns )

Learns multiple (at least 2) patterns.

Remarks:

Learning multiple patterns is useful when your object presents slight variations: shape deformation, lighting, perspective,... Each pattern is Learned and Matched independently and only the best patterns are kept. The options of the eVision.EMatcher are used for all of the patterns. We recommend setting a eVision.EMatcher.MaxOverlap when using this feature.

Parameters

patterns Vector of models to be learned (ROI).

◆ LearnPatterns() [2/4]

None eVision.EMatcher.LearnPatterns	(		self,
		list[eVision._eVision.EBaseROI \| None]	patterns,
		eVision.EFrame	frame )

Learns multiple (at least 2) patterns.

Remarks:

Learning multiple patterns is useful when your object presents slight variations: shape deformation, lighting, perspective,... Each pattern is Learned and Matched independently and only the best patterns are kept. The options of the eVision.EMatcher are used for all of the patterns. We recommend setting a eVision.EMatcher.MaxOverlap when using this feature.

Parameters

patterns	Vector of models to be learned (ROI).
frame	Reference frame of the learned image

◆ LearnPatterns() [3/4]

None eVision.EMatcher.LearnPatterns	(		self,
		list[eVision._eVision.EBaseROI \| None]	patterns,
		list[eVision._eVision.ERegion \| None]	regions )

Learns multiple (at least 2) patterns.

Remarks:

Learning multiple patterns is useful when your object presents slight variations: shape deformation, lighting, perspective,... Each pattern is Learned and Matched independently and only the best patterns are kept. The options of the eVision.EMatcher are used for all of the patterns. We recommend setting a eVision.EMatcher.MaxOverlap when using this feature.

Parameters

patterns	Vector of models to be learned (ROI).
regions	Vector of regions into the ROI where the learning is performed

◆ LearnPatterns() [4/4]

None eVision.EMatcher.LearnPatterns	(		self,
		list[eVision._eVision.EBaseROI \| None]	patterns,
		list[eVision._eVision.ERegion \| None]	regions,
		eVision.EFrame	frame )

Learns multiple (at least 2) patterns.

Remarks:

Learning multiple patterns is useful when your object presents slight variations: shape deformation, lighting, perspective,... Each pattern is Learned and Matched independently and only the best patterns are kept. The options of the eVision.EMatcher are used for all of the patterns. We recommend setting a eVision.EMatcher.MaxOverlap when using this feature.

Parameters

patterns	Vector of models to be learned (ROI).
regions	Vector of regions into the ROI where the learning is performed
frame	Reference frame of the learned image

◆ LearnTimeOut() [1/2]

int eVision.EMatcher.LearnTimeOut ( self )

Time-out for the eVision.EMatcher.LearnPattern and eVision.EMatcher.LearnPatterns methods.

Remarks:

If the processing time of one of these functions becomes longer than the set time-out, the processing is stopped and an exception is thrown. In that case, the error code of the exception is eVision.EError.TimeoutReached.

The time-out is set in microseconds.

This time-out is not a real time-out. The processing is stopped as soon as possible after the time-out has been reached. This means that the time elapsed effectively in the method can be greater than the time-out itself.

◆ LearnTimeOut() [2/2]

eVision.EMatcher.LearnTimeOut	(		self,
		int	value )

Time-out for the eVision.EMatcher.LearnPattern and eVision.EMatcher.LearnPatterns methods.

Remarks:

If the processing time of one of these functions becomes longer than the set time-out, the processing is stopped and an exception is thrown. In that case, the error code of the exception is eVision.EError.TimeoutReached.

The time-out is set in microseconds.

This time-out is not a real time-out. The processing is stopped as soon as possible after the time-out has been reached. This means that the time elapsed effectively in the method can be greater than the time-out itself.

◆ Load() [1/2]

None eVision.EMatcher.Load	(		self,
		eVision.ESerializer \| None	serializer )

Loads the eVision.EMatcher.

The given eVision.ESerializer must have been created for reading.

Parameters

serializer The serializer.

◆ Load() [2/2]

None eVision.EMatcher.Load	(		self,
		str	path )

Loads the eVision.EMatcher.

The given eVision.ESerializer must have been created for reading.

Parameters

path	The file path.

◆ Match() [1/8]

list[eVision._eVision.EFoundPattern] eVision.EMatcher.Match	(		self,
		eVision.EROIBW8 \| None	image )

Matches the pattern against an image.

Parameters

image Pointer to the image/ROI within which the pattern will be searched for.

◆ Match() [2/8]

list[eVision._eVision.EFoundPattern] eVision.EMatcher.Match	(		self,
		eVision.EROIBW8 \| None	image,
		eVision.EFrame	frame )

Matches the pattern against an image.

Parameters

image	Pointer to the image/ROI within which the pattern will be searched for.
frame	Reference frame of the image

◆ Match() [3/8]

list[eVision._eVision.EFoundPattern] eVision.EMatcher.Match	(		self,
		eVision.EROIBW8 \| None	image,
		eVision.ERegion	region )

Matches the pattern against an image.

Parameters

image	Pointer to the image/ROI within which the pattern will be searched for.
region	Region within which the pattern will be searched for.

◆ Match() [4/8]

list[eVision._eVision.EFoundPattern] eVision.EMatcher.Match	(		self,
		eVision.EROIBW8 \| None	image,
		eVision.ERegion	region,
		eVision.EFrame	frame )

Matches the pattern against an image.

Parameters

image	Pointer to the image/ROI within which the pattern will be searched for.
region	Region within which the pattern will be searched for.
frame	Reference frame of the image

◆ Match() [5/8]

list[eVision._eVision.EFoundPattern] eVision.EMatcher.Match	(		self,
		eVision.EROIC24 \| None	image )

Matches the pattern against an image.

Parameters

image Pointer to the image/ROI within which the pattern will be searched for.

◆ Match() [6/8]

list[eVision._eVision.EFoundPattern] eVision.EMatcher.Match	(		self,
		eVision.EROIC24 \| None	image,
		eVision.EFrame	frame )

Matches the pattern against an image.

Parameters

image	Pointer to the image/ROI within which the pattern will be searched for.
frame	Reference frame of the image

◆ Match() [7/8]

list[eVision._eVision.EFoundPattern] eVision.EMatcher.Match	(		self,
		eVision.EROIC24 \| None	image,
		eVision.ERegion	region )

Matches the pattern against an image.

Parameters

image	Pointer to the image/ROI within which the pattern will be searched for.
region	Region within which the pattern will be searched for.

◆ Match() [8/8]

list[eVision._eVision.EFoundPattern] eVision.EMatcher.Match	(		self,
		eVision.EROIC24 \| None	image,
		eVision.ERegion	region,
		eVision.EFrame	frame )

Matches the pattern against an image.

Parameters

image	Pointer to the image/ROI within which the pattern will be searched for.
region	Region within which the pattern will be searched for.
frame	Reference frame of the image

◆ MatchTimeOut() [1/2]

int eVision.EMatcher.MatchTimeOut ( self )

Time-out for the eVision.EMatcher.Match method.

Remarks:

If the processing time of one of these functions becomes longer than the set time-out, the processing is stopped and an exception is thrown. In that case, the error code of the exception is eVision.EError.TimeoutReached.

The time-out is set in microseconds.

This time-out is not a real time-out. The processing is stopped as soon as possible after the time-out has been reached. This means that the time elapsed effectively in the method can be greater than the time-out itself.

◆ MatchTimeOut() [2/2]

eVision.EMatcher.MatchTimeOut	(		self,
		int	value )

Time-out for the eVision.EMatcher.Match method.

Remarks:

If the processing time of one of these functions becomes longer than the set time-out, the processing is stopped and an exception is thrown. In that case, the error code of the exception is eVision.EError.TimeoutReached.

The time-out is set in microseconds.

This time-out is not a real time-out. The processing is stopped as soon as possible after the time-out has been reached. This means that the time elapsed effectively in the method can be greater than the time-out itself.

◆ MaxAngle() [1/2]

float eVision.EMatcher.MaxAngle ( self )

Maximum angle, in the current angle unit.

Remarks:

The rotation of the pattern is allowed within the range (-1 <= MinAngle < MaxAngle <= 1 revolution). By default, both remain 0.

◆ MaxAngle() [2/2]

eVision.EMatcher.MaxAngle	(		self,
		float	value )

Maximum angle, in the current angle unit.

Remarks:

The rotation of the pattern is allowed within the range (-1 <= MinAngle < MaxAngle <= 1 revolution). By default, both remain 0.

◆ MaxInitialPositions() [1/2]

int eVision.EMatcher.MaxInitialPositions ( self )

Maximum number of positions at the first stage of the matching process.

Remarks:

When the search for matches starts, EasyMatch considers a set of candidate positions that it progressively refines. When they later appear to be bad candidates, they are rejected. Eventually, a maximum of eVision.EMatcher.MaxPositions is returned. In some circumstances, when the image contains features roughly similar to the pattern, these can confuse the matching process, resulting in false matches. To overcome this situation, increasing the number of initial positions will help. By default, this property is set to 0, indicating that the value of eVision.EMatcher.MaxPositions should be used instead.

◆ MaxInitialPositions() [2/2]

eVision.EMatcher.MaxInitialPositions	(		self,
		int	value )

Maximum number of positions at the first stage of the matching process.

Remarks:

When the search for matches starts, EasyMatch considers a set of candidate positions that it progressively refines. When they later appear to be bad candidates, they are rejected. Eventually, a maximum of eVision.EMatcher.MaxPositions is returned. In some circumstances, when the image contains features roughly similar to the pattern, these can confuse the matching process, resulting in false matches. To overcome this situation, increasing the number of initial positions will help. By default, this property is set to 0, indicating that the value of eVision.EMatcher.MaxPositions should be used instead.

◆ MaxOverlap() [1/2]

float eVision.EMatcher.MaxOverlap ( self )

Overlapping tolerance.

Remarks:

0.0 means all found patterns must be disconnected, 1.0 means they can fully overlap. Default: 1.0.

◆ MaxOverlap() [2/2]

eVision.EMatcher.MaxOverlap	(		self,
		float	value )

Overlapping tolerance.

Remarks:

0.0 means all found patterns must be disconnected, 1.0 means they can fully overlap. Default: 1.0.

◆ MaxPositions() [1/2]

int eVision.EMatcher.MaxPositions ( self )

Maximum number of positions.

Remarks:

Indicates how many matching positions have to be returned at a maximum. This number corresponds to the expected maximum number of occurrences of the pattern (it is sometimes advisable to use a few additional positions). By default, this property is set to 1.

◆ MaxPositions() [2/2]

eVision.EMatcher.MaxPositions	(		self,
		int	value )

Maximum number of positions.

Remarks:

Indicates how many matching positions have to be returned at a maximum. This number corresponds to the expected maximum number of occurrences of the pattern (it is sometimes advisable to use a few additional positions). By default, this property is set to 1.

◆ MaxScale() [1/2]

float eVision.EMatcher.MaxScale ( self )

Maximum scale factor for isotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScale < MaxScale <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MaxScale() [2/2]

eVision.EMatcher.MaxScale	(		self,
		float	value )

Maximum scale factor for isotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScale < MaxScale <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MaxScaleX() [1/2]

float eVision.EMatcher.MaxScaleX ( self )

Maximum horizontal scale factor for anisotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScaleX < MaxScaleX <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MaxScaleX() [2/2]

eVision.EMatcher.MaxScaleX	(		self,
		float	value )

Maximum horizontal scale factor for anisotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScaleX < MaxScaleX <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MaxScaleY() [1/2]

float eVision.EMatcher.MaxScaleY ( self )

Maximum vertical scale factor for anisotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScaleY < MaxScaleY <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MaxScaleY() [2/2]

eVision.EMatcher.MaxScaleY	(		self,
		float	value )

Maximum vertical scale factor for anisotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScaleY < MaxScaleY <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MinAngle() [1/2]

float eVision.EMatcher.MinAngle ( self )

Minimum angle, in the current angle unit.

Remarks:

The rotation of the pattern is allowed within the range (-1 <= MinAngle < MaxAngle <= 1 revolution). By default, both remain 0.

◆ MinAngle() [2/2]

eVision.EMatcher.MinAngle	(		self,
		float	value )

Minimum angle, in the current angle unit.

Remarks:

The rotation of the pattern is allowed within the range (-1 <= MinAngle < MaxAngle <= 1 revolution). By default, both remain 0.

◆ MinReducedArea() [1/2]

int eVision.EMatcher.MinReducedArea ( self )

Minimum reduced area parameter.

Remarks:

To achieve acceptable time performance, EasyMatch works by under-sampling the pattern in the early phases of the processing. This property tells how many pixels of the pattern are kept, at a minimum. By default, this property is set to 64, which is the right choice in most situations. Higher values are not recommended. Lower values can speed up the processing, but sometimes cause the matching process fail to find the best matches. To circumvent this problem, you can use guard positions, that is find more matches than expected and keep the good ones only.

Note: Changing this property invalidates any previous learning.

◆ MinReducedArea() [2/2]

eVision.EMatcher.MinReducedArea	(		self,
		int	value )

Minimum reduced area parameter.

Remarks:

To achieve acceptable time performance, EasyMatch works by under-sampling the pattern in the early phases of the processing. This property tells how many pixels of the pattern are kept, at a minimum. By default, this property is set to 64, which is the right choice in most situations. Higher values are not recommended. Lower values can speed up the processing, but sometimes cause the matching process fail to find the best matches. To circumvent this problem, you can use guard positions, that is find more matches than expected and keep the good ones only.

Note: Changing this property invalidates any previous learning.

◆ MinScale() [1/2]

float eVision.EMatcher.MinScale ( self )

Minimum scale factor for isotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScale < MaxScale <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MinScale() [2/2]

eVision.EMatcher.MinScale	(		self,
		float	value )

Minimum scale factor for isotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScale < MaxScale <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MinScaleX() [1/2]

float eVision.EMatcher.MinScaleX ( self )

Minimum horizontal scale factor for anisotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScaleX < MaxScaleX <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MinScaleX() [2/2]

eVision.EMatcher.MinScaleX	(		self,
		float	value )

Minimum horizontal scale factor for anisotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScaleX < MaxScaleX <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MinScaleY() [1/2]

float eVision.EMatcher.MinScaleY ( self )

Minimum vertical scale factor for anisotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScaleY < MaxScaleY <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MinScaleY() [2/2]

eVision.EMatcher.MinScaleY	(		self,
		float	value )

Minimum vertical scale factor for anisotropic scaling.

Remarks:

Two scaling modes are allowed: in isotropic mode, the scale factor is identical in all directions; in anisotropic mode, the scale factors differ in the horizontal and vertical directions. To select the appropriate mode, it suffices to call the relevant Set member. The scaling of the pattern is recommended within the range (0.5 <= MinScaleY < MaxScaleY <= 2). By default, both remain 1. The same holds for anisotropic scale factors.

◆ MinScore() [1/2]

float eVision.EMatcher.MinScore ( self )

Minimum score.

Remarks:

This property indicates what score a match must reach to be considered as good, and to be returned in the list of positions; this selection criterion is applied at the final stage of the matching process. One good way to select the appropriate eVision.EMatcher.MinScore in a given context is to set it to -1 (any match will be retained), set eVision.EMatcher.MaxPositions to more than needed, and examine the returned scores after a matching. By default, this property is set to -1.

◆ MinScore() [2/2]

eVision.EMatcher.MinScore	(		self,
		float	value )

Minimum score.

Remarks:

This property indicates what score a match must reach to be considered as good, and to be returned in the list of positions; this selection criterion is applied at the final stage of the matching process. One good way to select the appropriate eVision.EMatcher.MinScore in a given context is to set it to -1 (any match will be retained), set eVision.EMatcher.MaxPositions to more than needed, and examine the returned scores after a matching. By default, this property is set to -1.

◆ NumLearnedPatterns()

int eVision.EMatcher.NumLearnedPatterns ( self )

Indicates the number of patterns learnt.

0 is returned if no learning was performed.

◆ NumPositions()

int eVision.EMatcher.NumPositions ( self )

Number of good matches found, as defined by eVision.EMatcher.MinScore and eVision.EMatcher.MaxPositions properties.

When multiple patterns are learned, the number of positions corresponding to the first pattern is returned.

Deprecated

Use the number of eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

◆ NumReductions()

int eVision.EMatcher.NumReductions ( self )

Number of reduction steps used in the matching process.

Remarks:

These depend on the actual pattern size, and on the MinReducedArea property. The FinalReduction property, used to speed up matching when coarse location is sufficient, must be set in range 0..NumReductions-1.

◆ PatternHeight()

int eVision.EMatcher.PatternHeight ( self )

Learned pattern height.

When multiple patterns are learned, the height of the first pattern is returned. Use eVision.EMatcher.GetPatternHeight for other learnt patterns.

◆ PatternLearnt()

bool eVision.EMatcher.PatternLearnt ( self )

Returns true after a learning operation has been successfully performed, indicating that the eVision.EMatcher object is ready for matching, and false otherwise.

◆ PatternType()

eVision.EImageType eVision.EMatcher.PatternType ( self )

Pixel type of the learned pattern.

◆ PatternWidth()

int eVision.EMatcher.PatternWidth ( self )

Learned pattern width.

When multiple patterns are learned, the width of the first pattern is returned. Use eVision.EMatcher.GetPatternWidth for other learnt patterns.

◆ PixelDimensions() [1/2]

eVision.ESize eVision.EMatcher.PixelDimensions ( self )

Gets/Sets the physical pixel dimensions.

Remarks:

When an image has been acquired in such a way that the pixels are "non-square" the physical width and height of the area covered by a pixel are unequal, a form of anisotropy results. When rotated, the objects become skewed; rectangles become parallelograms.

In such a situation, the pixel aspect ratio must be known to compensate it during matching. The aspect ratio is given by specifying the true width and height of a pixel.

The specification of the pixel dimensions is only useful when rotation is used.

Only the aspect ratio matters, so that relative width and height values can be given.

By default, the pixel width and height are set to 1.0.

◆ PixelDimensions() [2/2]

eVision.EMatcher.PixelDimensions	(		self,
		eVision.ESize	value )

Gets/Sets the physical pixel dimensions.

Remarks:

When an image has been acquired in such a way that the pixels are "non-square" the physical width and height of the area covered by a pixel are unequal, a form of anisotropy results. When rotated, the objects become skewed; rectangles become parallelograms.

In such a situation, the pixel aspect ratio must be known to compensate it during matching. The aspect ratio is given by specifying the true width and height of a pixel.

The specification of the pixel dimensions is only useful when rotation is used.

Only the aspect ratio matters, so that relative width and height values can be given.

By default, the pixel width and height are set to 1.0.

◆ Positions()

list[eVision._eVision.EMatchPosition] eVision.EMatcher.Positions ( self )

Returns a vector of eVision.EMatchPosition objects, each containing the position coordinates and other matching results.

When multiple patterns are learned, the positions corresponding to the first pattern are returned.

Deprecated

Use the eVision.EFoundPattern objects returned by eVision.EMatcher.Match instead.

Deprecated in version 25.2. Scheduled to be removed in version 27.6.

◆ Save() [1/2]

None eVision.EMatcher.Save	(		self,
		eVision.ESerializer \| None	serializer )

Saves the eVision.EMatcher.

The given eVision.ESerializer must have been created for writing.

Parameters

serializer The serializer.

◆ Save() [2/2]

None eVision.EMatcher.Save	(		self,
		str	path )

Saves the eVision.EMatcher.

The given eVision.ESerializer must have been created for writing.

Parameters

path	The file path.

Public Member Functions

Static Public Member Functions

Detailed Description

Constructor & Destructor Documentation

◆ __init__() [1/3]

◆ __init__() [2/3]

◆ __init__() [3/3]

Member Function Documentation

◆ AdvancedLearning() [1/2]

◆ AdvancedLearning() [2/2]

◆ ClearImage()

◆ ContrastMode() [1/2]

◆ ContrastMode() [2/2]

◆ CopyLearntPattern() [1/4]

◆ CopyLearntPattern() [2/4]

◆ CopyLearntPattern() [3/4]

◆ CopyLearntPattern() [4/4]

◆ CopyTo()

◆ CorrelationMode() [1/2]

◆ CorrelationMode() [2/2]

◆ DontCareThreshold() [1/2]

◆ DontCareThreshold() [2/2]

◆ DrawPosition() [1/6]

◆ DrawPosition() [2/6]

◆ DrawPosition() [3/6]

◆ DrawPosition() [4/6]

◆ DrawPosition() [5/6]

◆ DrawPosition() [6/6]

◆ DrawPositions() [1/6]

◆ DrawPositions() [2/6]

◆ DrawPositions() [3/6]

◆ DrawPositions() [4/6]

◆ DrawPositions() [5/6]

◆ DrawPositions() [6/6]

◆ EnableEarlyCandidateRejection() [1/2]

◆ EnableEarlyCandidateRejection() [2/2]

◆ Extension() [1/2]

◆ Extension() [2/2]

◆ FilteringMode() [1/2]

◆ FilteringMode() [2/2]

◆ FinalReduction() [1/2]

◆ FinalReduction() [2/2]

◆ GetAngleStep() [1/2]

◆ GetAngleStep() [2/2]

◆ GetPatternHeight()

◆ GetPatternWidth()

◆ GetPosition()

◆ GetScaleStep() [1/2]

◆ GetScaleStep() [2/2]

◆ GetScaleXStep() [1/2]

◆ GetScaleXStep() [2/2]

◆ GetScaleYStep() [1/2]

◆ GetScaleYStep() [2/2]

◆ GetVersion()

◆ InitialMinScore() [1/2]

◆ InitialMinScore() [2/2]

◆ Interpolate() [1/2]

◆ Interpolate() [2/2]

◆ IsotropicScale()

◆ LearningHasRegion()

◆ LearnPattern() [1/8]

◆ LearnPattern() [2/8]

◆ LearnPattern() [3/8]

◆ LearnPattern() [4/8]

◆ LearnPattern() [5/8]

◆ LearnPattern() [6/8]

◆ LearnPattern() [7/8]

◆ LearnPattern() [8/8]

◆ LearnPatterns() [1/4]

◆ LearnPatterns() [2/4]

◆ LearnPatterns() [3/4]

◆ LearnPatterns() [4/4]

◆ LearnTimeOut() [1/2]

◆ LearnTimeOut() [2/2]

◆ Load() [1/2]

◆ Load() [2/2]

◆ Match() [1/8]

◆ Match() [2/8]

◆ Match() [3/8]

◆ Match() [4/8]

◆ init() [1/3]

◆ init() [2/3]

◆ init() [3/3]