eVision.EImageStitcher Class Reference

Stitch calibrated images together, forming a seamless panoramic image. More...

Public Member Functions
None	__init__ (self)
	Constructs a eVision.EImageStitcher.
None	__init__ (self, eVision.EImageStitcher other)
	Copy operator.
None	Stitch (self, list[eVision._eVision.EImageC24] images, eVision.EImageC24 output, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageC24] images, eVision.EImageC24 output, int rowCount, int colCount)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageC24] images, eVision.EImageC24 output, int rowCount)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageC24] images, eVision.EImageC24 output)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageBW8] images, eVision.EImageBW8 output, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageBW8] images, eVision.EImageBW8 output, int rowCount, int colCount)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageBW8] images, eVision.EImageBW8 output, int rowCount)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageBW8] images, eVision.EImageBW8 output)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, eVision.EImageC24 output, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, eVision.EImageC24 output, int rowCount, int colCount)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, eVision.EImageC24 output, int rowCount)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, eVision.EImageC24 output)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, eVision.EImageBW8 output, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, eVision.EImageBW8 output, int rowCount, int colCount)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, eVision.EImageBW8 output, int rowCount)
	Stitch calibrated images together.
None	Stitch (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, eVision.EImageBW8 output)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageC24] images, eVision.EImageC24 output, int referenceImageIndex, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageC24] images, eVision.EImageC24 output, int referenceImageIndex, int rowCount, int colCount)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageC24] images, eVision.EImageC24 output, int referenceImageIndex, int rowCount)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageC24] images, eVision.EImageC24 output, int referenceImageIndex)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageBW8] images, eVision.EImageBW8 output, int referenceImageIndex, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageBW8] images, eVision.EImageBW8 output, int referenceImageIndex, int rowCount, int colCount)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageBW8] images, eVision.EImageBW8 output, int referenceImageIndex, int rowCount)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageBW8] images, eVision.EImageBW8 output, int referenceImageIndex)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, eVision.EImageC24 output, int referenceImageIndex, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, eVision.EImageC24 output, int referenceImageIndex, int rowCount, int colCount)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, eVision.EImageC24 output, int referenceImageIndex, int rowCount)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, eVision.EImageC24 output, int referenceImageIndex)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, eVision.EImageBW8 output, int referenceImageIndex, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, eVision.EImageBW8 output, int referenceImageIndex, int rowCount, int colCount)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, eVision.EImageBW8 output, int referenceImageIndex, int rowCount)
	Stitch calibrated images together.
None	StitchWithReferenceImage (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, eVision.EImageBW8 output, int referenceImageIndex)
	Stitch calibrated images together.
None	StartIncrementalStitching (self, eVision.EImageType imageType, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Puts the stitcher into incremental stitching mode.
None	StartIncrementalStitching (self, eVision.EImageType imageType, int rowCount, int colCount)
	Puts the stitcher into incremental stitching mode.
None	StartIncrementalStitching (self, eVision.EImageType imageType, int rowCount)
	Puts the stitcher into incremental stitching mode.
None	StartIncrementalStitching (self, eVision.EImageType imageType)
	Puts the stitcher into incremental stitching mode.
None	ProcessImage (self, eVision.EImageC24 image)
	When in incremental stitching mode, eVision.EImageStitcher.ProcessImage can be called multiple times to successively add image to the final image.
None	ProcessImage (self, eVision.EImageBW8 image)
	When in incremental stitching mode, eVision.EImageStitcher.ProcessImage can be called multiple times to successively add image to the final image.
None	ProcessImage (self, eVision.EImageC24 image, eVision.ERegion region)
	When in incremental stitching mode, eVision.EImageStitcher.ProcessImage can be called multiple times to successively add image to the final image.
None	ProcessImage (self, eVision.EImageBW8 image, eVision.ERegion region)
	When in incremental stitching mode, eVision.EImageStitcher.ProcessImage can be called multiple times to successively add image to the final image.
None	FinalizeIncrementalStitching (self, eVision.EImageC24 output)
	Finalizes the stitching.
None	FinalizeIncrementalStitching (self, eVision.EImageBW8 output)
	Finalizes the stitching.
None	DrawPoints (self, eVision.EDrawAdapter|None drawAdapter, int n)
	Draw features points used to stitch the n-th image.
None	DrawTransformations (self, eVision.EDrawAdapter|None drawAdapter, float zoomX, float zoomY, float panX, float panY)
	For each stitched patch, draw a rectangle with the related transformation applied.
None	DrawTransformations (self, eVision.EDrawAdapter|None drawAdapter, float zoomX, float zoomY, float panX)
	For each stitched patch, draw a rectangle with the related transformation applied.
None	DrawTransformations (self, eVision.EDrawAdapter|None drawAdapter, float zoomX, float zoomY)
	For each stitched patch, draw a rectangle with the related transformation applied.
None	DrawTransformations (self, eVision.EDrawAdapter|None drawAdapter, float zoomX)
	For each stitched patch, draw a rectangle with the related transformation applied.
None	DrawTransformations (self, eVision.EDrawAdapter|None drawAdapter)
	For each stitched patch, draw a rectangle with the related transformation applied.
list[float]	GetMatrix (self, int n)
	Returns the affine matrix applied to input image n-th to compose the final image.
None	Save (self, str path)
	Saves the eVision.EImageStitcher.
None	Save (self, eVision.ESerializer|None serializer)
	Saves the eVision.EImageStitcher.
None	Load (self, str path)
	Loads the eVision.EImageStitcher.
None	Load (self, eVision.ESerializer|None serializer)
	Loads the eVision.EImageStitcher.
None	LearnBW8 (self, list[eVision._eVision.EImageBW8] images, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Computes the necessary transformations to apply on each input images.
None	LearnBW8 (self, list[eVision._eVision.EImageBW8] images, int rowCount, int colCount)
	Computes the necessary transformations to apply on each input images.
None	LearnBW8 (self, list[eVision._eVision.EImageBW8] images, int rowCount)
	Computes the necessary transformations to apply on each input images.
None	LearnBW8 (self, list[eVision._eVision.EImageBW8] images)
	Computes the necessary transformations to apply on each input images.
None	LearnBW8 (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Computes the necessary transformations to apply on each input images.
None	LearnBW8 (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, int rowCount, int colCount)
	Computes the necessary transformations to apply on each input images.
None	LearnBW8 (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, int rowCount)
	Computes the necessary transformations to apply on each input images.
None	LearnBW8 (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions)
	Computes the necessary transformations to apply on each input images.
None	LearnC24 (self, list[eVision._eVision.EImageC24] images, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Computes the necessary transformations to apply on each input images.
None	LearnC24 (self, list[eVision._eVision.EImageC24] images, int rowCount, int colCount)
	Computes the necessary transformations to apply on each input images.
None	LearnC24 (self, list[eVision._eVision.EImageC24] images, int rowCount)
	Computes the necessary transformations to apply on each input images.
None	LearnC24 (self, list[eVision._eVision.EImageC24] images)
	Computes the necessary transformations to apply on each input images.
None	LearnC24 (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	Computes the necessary transformations to apply on each input images.
None	LearnC24 (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, int rowCount, int colCount)
	Computes the necessary transformations to apply on each input images.
None	LearnC24 (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, int rowCount)
	Computes the necessary transformations to apply on each input images.
None	LearnC24 (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions)
	Computes the necessary transformations to apply on each input images.
None	LearnWithReferenceImageBW8 (self, list[eVision._eVision.EImageBW8] images, int referenceImageIndex, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageBW8 (self, list[eVision._eVision.EImageBW8] images, int referenceImageIndex, int rowCount, int colCount)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageBW8 (self, list[eVision._eVision.EImageBW8] images, int referenceImageIndex, int rowCount)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageBW8 (self, list[eVision._eVision.EImageBW8] images, int referenceImageIndex)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageBW8 (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, int referenceImageIndex, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageBW8 (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, int referenceImageIndex, int rowCount, int colCount)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageBW8 (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, int referenceImageIndex, int rowCount)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageBW8 (self, list[eVision._eVision.EImageBW8] images, list[eVision._eVision.ERegion] regions, int referenceImageIndex)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageC24 (self, list[eVision._eVision.EImageC24] images, int referenceImageIndex, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageC24 (self, list[eVision._eVision.EImageC24] images, int referenceImageIndex, int rowCount, int colCount)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageC24 (self, list[eVision._eVision.EImageC24] images, int referenceImageIndex, int rowCount)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageC24 (self, list[eVision._eVision.EImageC24] images, int referenceImageIndex)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageC24 (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, int referenceImageIndex, int rowCount, int colCount, eVision.EStitchingGridOrder gridOrder)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageC24 (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, int referenceImageIndex, int rowCount, int colCount)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageC24 (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, int referenceImageIndex, int rowCount)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	LearnWithReferenceImageC24 (self, list[eVision._eVision.EImageC24] images, list[eVision._eVision.ERegion] regions, int referenceImageIndex)
	See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.
None	Reset (self)
	Resets the current state of this eVision.EImageStitcher (learn or incremental mode namely).
bool	FeatureScalingEnabled (self)
	Gets whether images should be scaled down before the feature points are computed.
	FeatureScalingEnabled (self, bool value)
	Gets whether images should be scaled down before the feature points are computed.
float	GridOverlapX (self)
	Returns the size of the horizontal overlap between two adjacent patches.
	GridOverlapX (self, float value)
	Returns the size of the horizontal overlap between two adjacent patches.
float	GridOverlapY (self)
	Returns the size of the vertical overlap between two adjacent patches.
	GridOverlapY (self, float value)
	Returns the size of the vertical overlap between two adjacent patches.
eVision.EStitchingAlgorithm	Algorithm (self)
	Sets or Gets the algorithm used by the eVision.EImageStitcher.
	Algorithm (self, eVision.EStitchingAlgorithm value)
	Sets or Gets the algorithm used by the eVision.EImageStitcher.
bool	IsLearned (self)
	Returns the current learning state of eVision.EImageStitcher.

Detailed Description

Stitch calibrated images together, forming a seamless panoramic image.

Remarks:

License(s): EasyImage

Constructor & Destructor Documentation

__init__() [1/2]

None eVision.EImageStitcher.__init__

(

self

)

Constructs a eVision.EImageStitcher.

__init__() [2/2]

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

Copy operator.

Parameters

other

Another eVision.EImageStitcher.

Member Function Documentation

Algorithm() [1/2]

eVision.EStitchingAlgorithm eVision.EImageStitcher.Algorithm

(

self

)

Sets or Gets the algorithm used by the eVision.EImageStitcher.

One can either prioritize speed or robustness. Speed is prioritized by default.

Algorithm() [2/2]

eVision.EImageStitcher.Algorithm	(		self,
		eVision.EStitchingAlgorithm	value )

Sets or Gets the algorithm used by the eVision.EImageStitcher.

One can either prioritize speed or robustness. Speed is prioritized by default.

DrawPoints()

None eVision.EImageStitcher.DrawPoints	(		self,
		eVision.EDrawAdapter | None	drawAdapter,
		int	n )

Draw features points used to stitch the n-th image.

Must be called after a stitching. This method does not draw the n-th image. It has to be done beforehand.

Parameters

drawAdapter	Draw adapter to draw onto.
n	Index of the image whose feature points should be drawn.

DrawTransformations() [1/5]

None eVision.EImageStitcher.DrawTransformations	(		self,
		eVision.EDrawAdapter | None	drawAdapter )

For each stitched patch, draw a rectangle with the related transformation applied.

Must be called after a stitching. This method doesn't draw the final image. It has to be done beforehand.

Parameters

drawAdapter

Draw adapter to draw onto.

DrawTransformations() [2/5]

None eVision.EImageStitcher.DrawTransformations	(		self,
		eVision.EDrawAdapter | None	drawAdapter,
		float	zoomX )

For each stitched patch, draw a rectangle with the related transformation applied.

Must be called after a stitching. This method doesn't draw the final image. It has to be done beforehand.

Parameters

drawAdapter	Draw adapter to draw onto.
zoomX	Horizontal zooming factor. By default, true scale is used.

DrawTransformations() [3/5]

None eVision.EImageStitcher.DrawTransformations	(		self,
		eVision.EDrawAdapter | None	drawAdapter,
		float	zoomX,
		float	zoomY )

For each stitched patch, draw a rectangle with the related transformation applied.

Must be called after a stitching. This method doesn't draw the final image. It has to be done beforehand.

Parameters

drawAdapter	Draw adapter to draw onto.
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, to provide isotropic zooming.

DrawTransformations() [4/5]

None eVision.EImageStitcher.DrawTransformations	(		self,
		eVision.EDrawAdapter | None	drawAdapter,
		float	zoomX,
		float	zoomY,
		float	panX )

For each stitched patch, draw a rectangle with the related transformation applied.

Must be called after a stitching. This method doesn't draw the final image. It has to be done beforehand.

Parameters

drawAdapter	Draw adapter to draw onto.
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, to provide isotropic zooming.
panX	Horizontal panning value expressed in pixels. By default, no panning occurs.

DrawTransformations() [5/5]

None eVision.EImageStitcher.DrawTransformations	(		self,
		eVision.EDrawAdapter | None	drawAdapter,
		float	zoomX,
		float	zoomY,
		float	panX,
		float	panY )

For each stitched patch, draw a rectangle with the related transformation applied.

Must be called after a stitching. This method doesn't draw the final image. It has to be done beforehand.

Parameters

drawAdapter	Draw adapter to draw onto.
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, 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.

FeatureScalingEnabled() [1/2]

bool eVision.EImageStitcher.FeatureScalingEnabled

(

self

)

Gets whether images should be scaled down before the feature points are computed.

FeatureScalingEnabled() [2/2]

eVision.EImageStitcher.FeatureScalingEnabled	(		self,
		bool	value )

Gets whether images should be scaled down before the feature points are computed.

FinalizeIncrementalStitching() [1/2]

None eVision.EImageStitcher.FinalizeIncrementalStitching	(		self,
		eVision.EImageBW8	output )

Finalizes the stitching.

Computes and returns the final image.

Parameters

output

Output image. This image will be resized by this method and its content lost.

FinalizeIncrementalStitching() [2/2]

None eVision.EImageStitcher.FinalizeIncrementalStitching	(		self,
		eVision.EImageC24	output )

Finalizes the stitching.

Computes and returns the final image.

Parameters

output

Output image. This image will be resized by this method and its content lost.

GetMatrix()

list[float] eVision.EImageStitcher.GetMatrix	(		self,
		int	n )

Returns the affine matrix applied to input image n-th to compose the final image.

The returned vector is a row-ordered 3x2 matrix Must be called after a successful stitching.

Parameters

n	Index of the image whose affine matrix should be returned.

GridOverlapX() [1/2]

float eVision.EImageStitcher.GridOverlapX

(

self

)

Returns the size of the horizontal overlap between two adjacent patches.

The overlap value is relative to the image width and is expressed in percentage.

GridOverlapX() [2/2]

eVision.EImageStitcher.GridOverlapX	(		self,
		float	value )

Returns the size of the horizontal overlap between two adjacent patches.

The overlap value is relative to the image width and is expressed in percentage.

GridOverlapY() [1/2]

float eVision.EImageStitcher.GridOverlapY

(

self

)

Returns the size of the vertical overlap between two adjacent patches.

The overlap value is relative to the image height and is expressed in percentage.

GridOverlapY() [2/2]

eVision.EImageStitcher.GridOverlapY	(		self,
		float	value )

Returns the size of the vertical overlap between two adjacent patches.

The overlap value is relative to the image height and is expressed in percentage.

IsLearned()

bool eVision.EImageStitcher.IsLearned

(

self

)

Returns the current learning state of eVision.EImageStitcher.

eVision.EImageStitcher.Reset resets this to false.

LearnBW8() [1/8]

None eVision.EImageStitcher.LearnBW8	(		self,
		list[eVision._eVision.EImageBW8]	images )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageStitcher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images

Input images. Each image must belong to the same plan.

LearnBW8() [2/8]

None eVision.EImageStitcher.LearnBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		int	rowCount )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageStitcher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
rowCount	Number of rows.

LearnBW8() [3/8]

None eVision.EImageStitcher.LearnBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		int	rowCount,
		int	colCount )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageStitcher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
rowCount	Number of rows.
colCount	Number of columns.

LearnBW8() [4/8]

None eVision.EImageStitcher.LearnBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageStitcher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

LearnBW8() [5/8]

None eVision.EImageStitcher.LearnBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageStitcher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.

LearnBW8() [6/8]

None eVision.EImageStitcher.LearnBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		int	rowCount )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageStitcher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
rowCount	Number of rows.

LearnBW8() [7/8]

None eVision.EImageStitcher.LearnBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		int	rowCount,
		int	colCount )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageStitcher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
rowCount	Number of rows.
colCount	Number of columns.

LearnBW8() [8/8]

None eVision.EImageStitcher.LearnBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageStitcher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

LearnC24() [1/8]

None eVision.EImageStitcher.LearnC24	(		self,
		list[eVision._eVision.EImageC24]	images )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageSticher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images

Input images. Each image must belong to the same plan.

LearnC24() [2/8]

None eVision.EImageStitcher.LearnC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		int	rowCount )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageSticher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
rowCount	Number of rows.

LearnC24() [3/8]

None eVision.EImageStitcher.LearnC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		int	rowCount,
		int	colCount )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageSticher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
rowCount	Number of rows.
colCount	Number of columns.

LearnC24() [4/8]

None eVision.EImageStitcher.LearnC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageSticher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

LearnC24() [5/8]

None eVision.EImageStitcher.LearnC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageSticher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.

LearnC24() [6/8]

None eVision.EImageStitcher.LearnC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		int	rowCount )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageSticher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
rowCount	Number of rows.

LearnC24() [7/8]

None eVision.EImageStitcher.LearnC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		int	rowCount,
		int	colCount )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageSticher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
rowCount	Number of rows.
colCount	Number of columns.

LearnC24() [8/8]

None eVision.EImageStitcher.LearnC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Computes the necessary transformations to apply on each input images.

Subsequent calls to the Stitch methods will use these transformations instead of re-computing them. Thus, they are expected to be faster.

It is assumed that all the set of input images (in Learn and in next Stitch methods) are correctly ordered, have the same size, and that all their images must have the same dimension.

Learning to stitch on a pattern with strong feature points to maximize the precision and then apply the stitching on images in production is a valable usage. See the New eVision Studio example where the EImageSticher is applied on a piece of tissue (low number of reliable feature points) after being learned on a pattern (high number of reliable feature points).

Throw an exception if the learning failed (the same as if a regular stitching operation fails).

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

LearnWithReferenceImageBW8() [1/8]

None eVision.EImageStitcher.LearnWithReferenceImageBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		int	referenceImageIndex )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
referenceImageIndex	Index of the image to start the stitching from.

LearnWithReferenceImageBW8() [2/8]

None eVision.EImageStitcher.LearnWithReferenceImageBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		int	referenceImageIndex,
		int	rowCount )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.

LearnWithReferenceImageBW8() [3/8]

None eVision.EImageStitcher.LearnWithReferenceImageBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.

LearnWithReferenceImageBW8() [4/8]

None eVision.EImageStitcher.LearnWithReferenceImageBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

LearnWithReferenceImageBW8() [5/8]

None eVision.EImageStitcher.LearnWithReferenceImageBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		int	referenceImageIndex )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
referenceImageIndex	Index of the image to start the stitching from.

LearnWithReferenceImageBW8() [6/8]

None eVision.EImageStitcher.LearnWithReferenceImageBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		int	referenceImageIndex,
		int	rowCount )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.

LearnWithReferenceImageBW8() [7/8]

None eVision.EImageStitcher.LearnWithReferenceImageBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.

LearnWithReferenceImageBW8() [8/8]

None eVision.EImageStitcher.LearnWithReferenceImageBW8	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

LearnWithReferenceImageC24() [1/8]

None eVision.EImageStitcher.LearnWithReferenceImageC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		int	referenceImageIndex )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
referenceImageIndex	Index of the image to start the stitching from.

LearnWithReferenceImageC24() [2/8]

None eVision.EImageStitcher.LearnWithReferenceImageC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		int	referenceImageIndex,
		int	rowCount )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.

LearnWithReferenceImageC24() [3/8]

None eVision.EImageStitcher.LearnWithReferenceImageC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.

LearnWithReferenceImageC24() [4/8]

None eVision.EImageStitcher.LearnWithReferenceImageC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

LearnWithReferenceImageC24() [5/8]

None eVision.EImageStitcher.LearnWithReferenceImageC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		int	referenceImageIndex )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
referenceImageIndex	Index of the image to start the stitching from.

LearnWithReferenceImageC24() [6/8]

None eVision.EImageStitcher.LearnWithReferenceImageC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		int	referenceImageIndex,
		int	rowCount )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.

LearnWithReferenceImageC24() [7/8]

None eVision.EImageStitcher.LearnWithReferenceImageC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.

LearnWithReferenceImageC24() [8/8]

None eVision.EImageStitcher.LearnWithReferenceImageC24	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

See eVision.EImageStitcher.LearnBW8 (or eVision.EImageStitcher.LearnC24) and eVision.EImageStitcher.StitchWithReferenceImage.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Regions returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

Load() [1/2]

None eVision.EImageStitcher.Load	(		self,
		eVision.ESerializer | None	serializer )

Loads the eVision.EImageStitcher.

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

Parameters

serializer

The serializer.

Load() [2/2]

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

Loads the eVision.EImageStitcher.

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

Parameters

path	The file path.

ProcessImage() [1/4]

None eVision.EImageStitcher.ProcessImage	(		self,
		eVision.EImageBW8	image )

When in incremental stitching mode, eVision.EImageStitcher.ProcessImage can be called multiple times to successively add image to the final image.

Significant part of the computation happens in eVision.EImageStitcher.ProcessImage. eVision.EImageStitcher.ProcessImage is a blocking function. Handling the asynchronousity or any kind of multi-threading is left to the user. eVision.EImageStitcher.ProcessImage is not multi-thread safe.

Parameters

image

Image to add.

ProcessImage() [2/4]

None eVision.EImageStitcher.ProcessImage	(		self,
		eVision.EImageBW8	image,
		eVision.ERegion	region )

When in incremental stitching mode, eVision.EImageStitcher.ProcessImage can be called multiple times to successively add image to the final image.

Significant part of the computation happens in eVision.EImageStitcher.ProcessImage. eVision.EImageStitcher.ProcessImage is a blocking function. Handling the asynchronousity or any kind of multi-threading is left to the user. eVision.EImageStitcher.ProcessImage is not multi-thread safe.

Parameters

image	Image to add.
region	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.

ProcessImage() [3/4]

None eVision.EImageStitcher.ProcessImage	(		self,
		eVision.EImageC24	image )

When in incremental stitching mode, eVision.EImageStitcher.ProcessImage can be called multiple times to successively add image to the final image.

Significant part of the computation happens in eVision.EImageStitcher.ProcessImage. eVision.EImageStitcher.ProcessImage is a blocking function. Handling the asynchronousity or any kind of multi-threading is left to the user. eVision.EImageStitcher.ProcessImage is not multi-thread safe.

Parameters

image

Image to add.

ProcessImage() [4/4]

None eVision.EImageStitcher.ProcessImage	(		self,
		eVision.EImageC24	image,
		eVision.ERegion	region )

When in incremental stitching mode, eVision.EImageStitcher.ProcessImage can be called multiple times to successively add image to the final image.

Significant part of the computation happens in eVision.EImageStitcher.ProcessImage. eVision.EImageStitcher.ProcessImage is a blocking function. Handling the asynchronousity or any kind of multi-threading is left to the user. eVision.EImageStitcher.ProcessImage is not multi-thread safe.

Parameters

image	Image to add.
region	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.

Reset()

None eVision.EImageStitcher.Reset

(

self

)

Resets the current state of this eVision.EImageStitcher (learn or incremental mode namely).

Frees temporary resources.

Save() [1/2]

None eVision.EImageStitcher.Save	(		self,
		eVision.ESerializer | None	serializer )

Saves the eVision.EImageStitcher.

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

Parameters

serializer

The serializer.

Save() [2/2]

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

Saves the eVision.EImageStitcher.

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

Parameters

path	The file path.

StartIncrementalStitching() [1/4]

None eVision.EImageStitcher.StartIncrementalStitching	(		self,
		eVision.EImageType	imageType )

Puts the stitcher into incremental stitching mode.

During incremental stitching mode, it is assumed that the time between two image captures is significant. It essentially separates eVision.EImageStitcher.Stitch into multiple function calls. Thus, it is possible to distribute some parts of the stitching computation between image captures by asynchronously calling eVision.EImageStitcher.ProcessImage. Simple stitching mode happens in this order:

1. Capture image 1.
2. Capture image 2. ...
3. Stitch all images.

While incremental stitching mode happens in this order:

1. Capture image 1 (asynchronously submit it to eVision.EImageStitcher with eVision.EImageStitcher.ProcessImage).
2. Capture image 2 (asynchronously submit it to eVision.EImageStitcher with eVision.EImageStitcher.ProcessImage). ...
3. Finalize stitching.

Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Parameters

imageType

Image type of the output and of each input images.

StartIncrementalStitching() [2/4]

None eVision.EImageStitcher.StartIncrementalStitching	(		self,
		eVision.EImageType	imageType,
		int	rowCount )

Puts the stitcher into incremental stitching mode.

During incremental stitching mode, it is assumed that the time between two image captures is significant. It essentially separates eVision.EImageStitcher.Stitch into multiple function calls. Thus, it is possible to distribute some parts of the stitching computation between image captures by asynchronously calling eVision.EImageStitcher.ProcessImage. Simple stitching mode happens in this order:

1. Capture image 1.
2. Capture image 2. ...
3. Stitch all images.

While incremental stitching mode happens in this order:

1. Capture image 1 (asynchronously submit it to eVision.EImageStitcher with eVision.EImageStitcher.ProcessImage).
2. Capture image 2 (asynchronously submit it to eVision.EImageStitcher with eVision.EImageStitcher.ProcessImage). ...
3. Finalize stitching.

Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Parameters

imageType	Image type of the output and of each input images.
rowCount	Number of rows.

StartIncrementalStitching() [3/4]

None eVision.EImageStitcher.StartIncrementalStitching	(		self,
		eVision.EImageType	imageType,
		int	rowCount,
		int	colCount )

Puts the stitcher into incremental stitching mode.

During incremental stitching mode, it is assumed that the time between two image captures is significant. It essentially separates eVision.EImageStitcher.Stitch into multiple function calls. Thus, it is possible to distribute some parts of the stitching computation between image captures by asynchronously calling eVision.EImageStitcher.ProcessImage. Simple stitching mode happens in this order:

1. Capture image 1.
2. Capture image 2. ...
3. Stitch all images.

While incremental stitching mode happens in this order:

1. Capture image 1 (asynchronously submit it to eVision.EImageStitcher with eVision.EImageStitcher.ProcessImage).
2. Capture image 2 (asynchronously submit it to eVision.EImageStitcher with eVision.EImageStitcher.ProcessImage). ...
3. Finalize stitching.

Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Parameters

imageType	Image type of the output and of each input images.
rowCount	Number of rows.
colCount	Number of columns.

StartIncrementalStitching() [4/4]

None eVision.EImageStitcher.StartIncrementalStitching	(		self,
		eVision.EImageType	imageType,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Puts the stitcher into incremental stitching mode.

During incremental stitching mode, it is assumed that the time between two image captures is significant. It essentially separates eVision.EImageStitcher.Stitch into multiple function calls. Thus, it is possible to distribute some parts of the stitching computation between image captures by asynchronously calling eVision.EImageStitcher.ProcessImage. Simple stitching mode happens in this order:

1. Capture image 1.
2. Capture image 2. ...
3. Stitch all images.

While incremental stitching mode happens in this order:

1. Capture image 1 (asynchronously submit it to eVision.EImageStitcher with eVision.EImageStitcher.ProcessImage).
2. Capture image 2 (asynchronously submit it to eVision.EImageStitcher with eVision.EImageStitcher.ProcessImage). ...
3. Finalize stitching.

Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Parameters

imageType	Image type of the output and of each input images.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

Stitch() [1/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageBW8]	images,
		eVision.EImageBW8	output )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.

Stitch() [2/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageBW8]	images,
		eVision.EImageBW8	output,
		int	rowCount )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.

Stitch() [3/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageBW8]	images,
		eVision.EImageBW8	output,
		int	rowCount,
		int	colCount )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.
colCount	Number of columns.

Stitch() [4/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageBW8]	images,
		eVision.EImageBW8	output,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

Stitch() [5/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageBW8	output )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.

Stitch() [6/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageBW8	output,
		int	rowCount )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.

Stitch() [7/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageBW8	output,
		int	rowCount,
		int	colCount )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.
colCount	Number of columns.

Stitch() [8/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageBW8	output,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

Stitch() [9/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageC24]	images,
		eVision.EImageC24	output )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.

Stitch() [10/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageC24]	images,
		eVision.EImageC24	output,
		int	rowCount )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.

Stitch() [11/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageC24]	images,
		eVision.EImageC24	output,
		int	rowCount,
		int	colCount )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.
colCount	Number of columns.

Stitch() [12/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageC24]	images,
		eVision.EImageC24	output,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

Stitch() [13/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageC24	output )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.

Stitch() [14/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageC24	output,
		int	rowCount )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.

Stitch() [15/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageC24	output,
		int	rowCount,
		int	colCount )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.
colCount	Number of columns.

Stitch() [16/16]

None eVision.EImageStitcher.Stitch	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageC24	output,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Stitch calibrated images together.

Stitching happens iteratively from the reference image which defines the overall orientation of the final image. The reference image is automatically inferred after computing matching possibilities. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching. When in grid mode, the image as assumed to be taken in the following fashion:

1	2	3
4	5	6
7	8	9

Precision depends on your images. Stitching works by finding and matching common feature points between a pair of images. These pairs of points can then be used to compute the transformation to be applied to the images to compose the final image. Therefore, the precision of stitching can be increased by :

• Increasing the number of feature points shared between two images
• Increasing the number of unique features points, to avoid false positive matches. To minimize the number of false positives, the Overlap parameter can be adjusted. A smaller overlap will yield a smaller number of points.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

StitchWithReferenceImage() [1/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageBW8]	images,
		eVision.EImageBW8	output,
		int	referenceImageIndex )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.

StitchWithReferenceImage() [2/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageBW8]	images,
		eVision.EImageBW8	output,
		int	referenceImageIndex,
		int	rowCount )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.

StitchWithReferenceImage() [3/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageBW8]	images,
		eVision.EImageBW8	output,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.

StitchWithReferenceImage() [4/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageBW8]	images,
		eVision.EImageBW8	output,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

StitchWithReferenceImage() [5/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageBW8	output,
		int	referenceImageIndex )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.

StitchWithReferenceImage() [6/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageBW8	output,
		int	referenceImageIndex,
		int	rowCount )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.

StitchWithReferenceImage() [7/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageBW8	output,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.

StitchWithReferenceImage() [8/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageBW8]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageBW8	output,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

StitchWithReferenceImage() [9/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageC24]	images,
		eVision.EImageC24	output,
		int	referenceImageIndex )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.

StitchWithReferenceImage() [10/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageC24]	images,
		eVision.EImageC24	output,
		int	referenceImageIndex,
		int	rowCount )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.

StitchWithReferenceImage() [11/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageC24]	images,
		eVision.EImageC24	output,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.

StitchWithReferenceImage() [12/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageC24]	images,
		eVision.EImageC24	output,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.

StitchWithReferenceImage() [13/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageC24	output,
		int	referenceImageIndex )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.

StitchWithReferenceImage() [14/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageC24	output,
		int	referenceImageIndex,
		int	rowCount )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.

StitchWithReferenceImage() [15/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageC24	output,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.

StitchWithReferenceImage() [16/16]

None eVision.EImageStitcher.StitchWithReferenceImage	(		self,
		list[eVision._eVision.EImageC24]	images,
		list[eVision._eVision.ERegion]	regions,
		eVision.EImageC24	output,
		int	referenceImageIndex,
		int	rowCount,
		int	colCount,
		eVision.EStitchingGridOrder	gridOrder )

Stitch calibrated images together.

Stitching happens iteratively from the base image which defines the overall orientation of the final image. The reference image is explicitly given. If the images were taken in a grid fashion, the shape of the grid can be referenced by specifying its number of rows and columns. Doing so speeds up the matching process and can improve the result. Specifying a number of rows and columns of zero defaults to not using a grid when matching.

Parameters

images	Input images. Each image must belong to the same plan.
regions	Region returned by the eVision.ECameraCalibration.Unwarp or eVision.Legacy.EWorldShape.Unwarp methods. Indicates which pixel belong to the original image during blending.
output	Output image. This image will be resized by this method and its content lost.
referenceImageIndex	Index of the image to start the stitching from.
rowCount	Number of rows.
colCount	Number of columns.
gridOrder	Grid traversal definition.