View Source Evision.Stitcher (Evision v0.1.13)

Link to this section Summary

Types

t()

Type that represents an Evision.Stitcher struct.

Functions

Return

Has overloading in C++

These functions try to compose the given images (or images stored internally from the other function calls) into the final pano under the assumption that the image transformations were estimated before.

These functions try to compose the given images (or images stored internally from the other function calls) into the final pano under the assumption that the image transformations were estimated before.

Return
  • retval: double

Python prototype (for reference):

Creates a Stitcher configured in one of the stitching modes.

Creates a Stitcher configured in one of the stitching modes.

These functions try to match the given images and to estimate rotations of each camera.

These functions try to match the given images and to estimate rotations of each camera.

Return
  • retval: InterpolationFlags

Python prototype (for reference):

Return
  • retval: double

Python prototype (for reference):

Return
  • retval: double

Python prototype (for reference):

Return
  • retval: double

Python prototype (for reference):

Positional Arguments
  • resol_mpx: double

Python prototype (for reference):

Positional Arguments
  • interp_flags: InterpolationFlags

Python prototype (for reference):

Positional Arguments
  • conf_thresh: double

Python prototype (for reference):

Positional Arguments
  • resol_mpx: double

Python prototype (for reference):

Positional Arguments
  • resol_mpx: double

Python prototype (for reference):

Positional Arguments
  • flag: bool

Python prototype (for reference):

Positional Arguments
  • images: [Evision.Mat]
Return

Has overloading in C++

Variant 1:

These functions try to stitch the given images.

These functions try to stitch the given images.

Return
  • retval: bool

Python prototype (for reference):

Return
  • retval: double

Python prototype (for reference):

Link to this section Types

@type t() :: %Evision.Stitcher{ref: reference()}

Type that represents an Evision.Stitcher struct.

  • ref. reference()

    The underlying erlang resource variable.

Link to this section Functions

@spec composePanorama(t()) :: {integer(), Evision.Mat.t()} | {:error, String.t()}
Return

Has overloading in C++

Python prototype (for reference):

composePanorama([, pano]) -> retval, pano
Link to this function

composePanorama(self, images)

View Source
@spec composePanorama(t(), [Evision.Mat.maybe_mat_in()]) ::
  {integer(), Evision.Mat.t()} | {:error, String.t()}

These functions try to compose the given images (or images stored internally from the other function calls) into the final pano under the assumption that the image transformations were estimated before.

Positional Arguments
  • images: [Evision.Mat].

    Input images.

Return

Note: Use the functions only if you're aware of the stitching pipeline, otherwise use Stitcher::stitch. @return Status code.

Python prototype (for reference):

composePanorama(images[, pano]) -> retval, pano
Link to this function

composePanorama(self, images, opts)

View Source
@spec composePanorama(
  t(),
  [Evision.Mat.maybe_mat_in()],
  [{atom(), term()}, ...] | nil
) ::
  {integer(), Evision.Mat.t()} | {:error, String.t()}

These functions try to compose the given images (or images stored internally from the other function calls) into the final pano under the assumption that the image transformations were estimated before.

Positional Arguments
  • images: [Evision.Mat].

    Input images.

Return

Note: Use the functions only if you're aware of the stitching pipeline, otherwise use Stitcher::stitch. @return Status code.

Python prototype (for reference):

composePanorama(images[, pano]) -> retval, pano
@spec compositingResol(t()) :: number() | {:error, String.t()}
Return
  • retval: double

Python prototype (for reference):

compositingResol() -> retval
@spec create() :: t() | {:error, String.t()}

Creates a Stitcher configured in one of the stitching modes.

Keyword Arguments
  • mode: Mode.

    Scenario for stitcher operation. This is usually determined by source of images to stitch and their transformation. Default parameters will be chosen for operation in given scenario.

Return

@return Stitcher class instance.

Python prototype (for reference):

create([, mode]) -> retval
@spec create([{atom(), term()}, ...] | nil) :: t() | {:error, String.t()}

Creates a Stitcher configured in one of the stitching modes.

Keyword Arguments
  • mode: Mode.

    Scenario for stitcher operation. This is usually determined by source of images to stitch and their transformation. Default parameters will be chosen for operation in given scenario.

Return

@return Stitcher class instance.

Python prototype (for reference):

create([, mode]) -> retval
Link to this function

estimateTransform(self, images)

View Source
@spec estimateTransform(t(), [Evision.Mat.maybe_mat_in()]) ::
  integer() | {:error, String.t()}

These functions try to match the given images and to estimate rotations of each camera.

Positional Arguments
  • images: [Evision.Mat].

    Input images.

Keyword Arguments
  • masks: [Evision.Mat].

    Masks for each input image specifying where to look for keypoints (optional).

Return
  • retval: Status

Note: Use the functions only if you're aware of the stitching pipeline, otherwise use Stitcher::stitch. @return Status code.

Python prototype (for reference):

estimateTransform(images[, masks]) -> retval
Link to this function

estimateTransform(self, images, opts)

View Source
@spec estimateTransform(
  t(),
  [Evision.Mat.maybe_mat_in()],
  [{atom(), term()}, ...] | nil
) ::
  integer() | {:error, String.t()}

These functions try to match the given images and to estimate rotations of each camera.

Positional Arguments
  • images: [Evision.Mat].

    Input images.

Keyword Arguments
  • masks: [Evision.Mat].

    Masks for each input image specifying where to look for keypoints (optional).

Return
  • retval: Status

Note: Use the functions only if you're aware of the stitching pipeline, otherwise use Stitcher::stitch. @return Status code.

Python prototype (for reference):

estimateTransform(images[, masks]) -> retval
Link to this function

interpolationFlags(self)

View Source
@spec interpolationFlags(t()) :: integer() | {:error, String.t()}
Return
  • retval: InterpolationFlags

Python prototype (for reference):

interpolationFlags() -> retval
Link to this function

panoConfidenceThresh(self)

View Source
@spec panoConfidenceThresh(t()) :: number() | {:error, String.t()}
Return
  • retval: double

Python prototype (for reference):

panoConfidenceThresh() -> retval
@spec registrationResol(t()) :: number() | {:error, String.t()}
Return
  • retval: double

Python prototype (for reference):

registrationResol() -> retval
Link to this function

seamEstimationResol(self)

View Source
@spec seamEstimationResol(t()) :: number() | {:error, String.t()}
Return
  • retval: double

Python prototype (for reference):

seamEstimationResol() -> retval
Link to this function

setCompositingResol(self, resol_mpx)

View Source
@spec setCompositingResol(t(), number()) :: :ok | {:error, String.t()}
Positional Arguments
  • resol_mpx: double

Python prototype (for reference):

setCompositingResol(resol_mpx) -> None
Link to this function

setInterpolationFlags(self, interp_flags)

View Source
@spec setInterpolationFlags(t(), integer()) :: :ok | {:error, String.t()}
Positional Arguments
  • interp_flags: InterpolationFlags

Python prototype (for reference):

setInterpolationFlags(interp_flags) -> None
Link to this function

setPanoConfidenceThresh(self, conf_thresh)

View Source
@spec setPanoConfidenceThresh(t(), number()) :: :ok | {:error, String.t()}
Positional Arguments
  • conf_thresh: double

Python prototype (for reference):

setPanoConfidenceThresh(conf_thresh) -> None
Link to this function

setRegistrationResol(self, resol_mpx)

View Source
@spec setRegistrationResol(t(), number()) :: :ok | {:error, String.t()}
Positional Arguments
  • resol_mpx: double

Python prototype (for reference):

setRegistrationResol(resol_mpx) -> None
Link to this function

setSeamEstimationResol(self, resol_mpx)

View Source
@spec setSeamEstimationResol(t(), number()) :: :ok | {:error, String.t()}
Positional Arguments
  • resol_mpx: double

Python prototype (for reference):

setSeamEstimationResol(resol_mpx) -> None
Link to this function

setWaveCorrection(self, flag)

View Source
@spec setWaveCorrection(t(), boolean()) :: :ok | {:error, String.t()}
Positional Arguments
  • flag: bool

Python prototype (for reference):

setWaveCorrection(flag) -> None
@spec stitch(t(), [Evision.Mat.maybe_mat_in()]) ::
  {integer(), Evision.Mat.t()} | {:error, String.t()}
Positional Arguments
  • images: [Evision.Mat]
Return

Has overloading in C++

Python prototype (for reference):

stitch(images[, pano]) -> retval, pano
Link to this function

stitch(self, images, opts)

View Source
@spec stitch(t(), [Evision.Mat.maybe_mat_in()], [{atom(), term()}, ...] | nil) ::
  {integer(), Evision.Mat.t()} | {:error, String.t()}
@spec stitch(t(), [Evision.Mat.maybe_mat_in()], [Evision.Mat.maybe_mat_in()]) ::
  {integer(), Evision.Mat.t()} | {:error, String.t()}

Variant 1:

These functions try to stitch the given images.

Positional Arguments
  • images: [Evision.Mat].

    Input images.

  • masks: [Evision.Mat].

    Masks for each input image specifying where to look for keypoints (optional).

Return

@return Status code.

Python prototype (for reference):

stitch(images, masks[, pano]) -> retval, pano

Variant 2:

Positional Arguments
  • images: [Evision.Mat]
Return

Has overloading in C++

Python prototype (for reference):

stitch(images[, pano]) -> retval, pano
Link to this function

stitch(self, images, masks, opts)

View Source
@spec stitch(
  t(),
  [Evision.Mat.maybe_mat_in()],
  [Evision.Mat.maybe_mat_in()],
  [{atom(), term()}, ...] | nil
) :: {integer(), Evision.Mat.t()} | {:error, String.t()}

These functions try to stitch the given images.

Positional Arguments
  • images: [Evision.Mat].

    Input images.

  • masks: [Evision.Mat].

    Masks for each input image specifying where to look for keypoints (optional).

Return

@return Status code.

Python prototype (for reference):

stitch(images, masks[, pano]) -> retval, pano
@spec waveCorrection(t()) :: boolean() | {:error, String.t()}
Return
  • retval: bool

Python prototype (for reference):

waveCorrection() -> retval
@spec workScale(t()) :: number() | {:error, String.t()}
Return
  • retval: double

Python prototype (for reference):

workScale() -> retval