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

composePanorama

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: Status
• pano: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

composePanorama([, pano]) -> retval, pano

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

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

Variant 1:

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

• self: Evision.Stitcher.t()

• images: [Evision.Mat].

  Input images.

Return

• retval: Status

• pano: Evision.Mat.t().

  Final pano.

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

Python prototype (for reference only):

composePanorama(images[, pano]) -> retval, pano

Variant 2:

composePanorama

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: Status
• pano: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

composePanorama([, pano]) -> retval, pano

@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

• self: Evision.Stitcher.t()

• images: [Evision.Mat].

  Input images.

Return

• retval: Status

• pano: Evision.Mat.t().

  Final pano.

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

Python prototype (for reference only):

composePanorama(images[, pano]) -> retval, pano

@spec compositingResol(t()) :: number() | {:error, String.t()}

compositingResol

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: double

Python prototype (for reference only):

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

• retval: Evision.Stitcher.t()

@return Stitcher class instance.

Python prototype (for reference only):

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

• retval: Evision.Stitcher.t()

@return Stitcher class instance.

Python prototype (for reference only):

create([, mode]) -> retval

@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

• self: Evision.Stitcher.t()

• 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 only):

estimateTransform(images[, masks]) -> retval

@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

• self: Evision.Stitcher.t()

• 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 only):

estimateTransform(images[, masks]) -> retval

@spec interpolationFlags(t()) :: integer() | {:error, String.t()}

interpolationFlags

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: InterpolationFlags

Python prototype (for reference only):

interpolationFlags() -> retval

@spec panoConfidenceThresh(t()) :: number() | {:error, String.t()}

panoConfidenceThresh

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: double

Python prototype (for reference only):

panoConfidenceThresh() -> retval

@spec registrationResol(t()) :: number() | {:error, String.t()}

registrationResol

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: double

Python prototype (for reference only):

registrationResol() -> retval

@spec seamEstimationResol(t()) :: number() | {:error, String.t()}

seamEstimationResol

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: double

Python prototype (for reference only):

seamEstimationResol() -> retval

@spec setCompositingResol(t(), number()) :: t() | {:error, String.t()}

setCompositingResol

Positional Arguments

• self: Evision.Stitcher.t()
• resol_mpx: double

Python prototype (for reference only):

setCompositingResol(resol_mpx) -> None

@spec setInterpolationFlags(t(), integer()) :: t() | {:error, String.t()}

setInterpolationFlags

Positional Arguments

• self: Evision.Stitcher.t()
• interp_flags: InterpolationFlags

Python prototype (for reference only):

setInterpolationFlags(interp_flags) -> None

@spec setPanoConfidenceThresh(t(), number()) :: t() | {:error, String.t()}

setPanoConfidenceThresh

Positional Arguments

• self: Evision.Stitcher.t()
• conf_thresh: double

Python prototype (for reference only):

setPanoConfidenceThresh(conf_thresh) -> None

@spec setRegistrationResol(t(), number()) :: t() | {:error, String.t()}

setRegistrationResol

Positional Arguments

• self: Evision.Stitcher.t()
• resol_mpx: double

Python prototype (for reference only):

setRegistrationResol(resol_mpx) -> None

@spec setSeamEstimationResol(t(), number()) :: t() | {:error, String.t()}

setSeamEstimationResol

Positional Arguments

• self: Evision.Stitcher.t()
• resol_mpx: double

Python prototype (for reference only):

setSeamEstimationResol(resol_mpx) -> None

@spec setWaveCorrection(t(), boolean()) :: t() | {:error, String.t()}

setWaveCorrection

Positional Arguments

• self: Evision.Stitcher.t()
• flag: bool

Python prototype (for reference only):

setWaveCorrection(flag) -> None

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

stitch

Positional Arguments

• self: Evision.Stitcher.t()
• images: [Evision.Mat]

Return

• retval: Status
• pano: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

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

@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

• self: Evision.Stitcher.t()

• images: [Evision.Mat].

  Input images.

• masks: [Evision.Mat].

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

Return

• retval: Status

• pano: Evision.Mat.t().

  Final pano.

@return Status code.

Python prototype (for reference only):

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

Variant 2:

stitch

Positional Arguments

• self: Evision.Stitcher.t()
• images: [Evision.Mat]

Return

• retval: Status
• pano: Evision.Mat.t().

Has overloading in C++

Python prototype (for reference only):

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

@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

• self: Evision.Stitcher.t()

• images: [Evision.Mat].

  Input images.

• masks: [Evision.Mat].

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

Return

• retval: Status

• pano: Evision.Mat.t().

  Final pano.

@return Status code.

Python prototype (for reference only):

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

@spec waveCorrection(t()) :: boolean() | {:error, String.t()}

waveCorrection

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: bool

Python prototype (for reference only):

waveCorrection() -> retval

@spec workScale(t()) :: number() | {:error, String.t()}

workScale

Positional Arguments

• self: Evision.Stitcher.t()

Return

• retval: double

Python prototype (for reference only):

workScale() -> retval