Modules
	Features Finding and Images Matching

	Rotation Estimation

	Autocalibration

	Images Warping

	Seam Estimation

	Exposure Compensation

	Image Blenders

Classes
struct	cv::detail::CameraParams
	Describes camera parameters. More...

class	cv::detail::DisjointSets

class	cv::detail::Graph

struct	cv::detail::GraphEdge

class	cv::Stitcher
	High level image stitcher. More...

class	cv::detail::Timelapser

class	cv::detail::TimelapserCrop

Functions
	cv::detail::GraphEdge::GraphEdge (int from, int to, float weight)

Ptr< Stitcher >	cv::createStitcher (bool try_use_gpu=false)

bool	cv::detail::overlapRoi (Point tl1, Point tl2, Size sz1, Size sz2, Rect &roi)

Rect	cv::detail::resultRoi (const std::vector< Point > &corners, const std::vector< UMat > &images)

Rect	cv::detail::resultRoi (const std::vector< Point > &corners, const std::vector< Size > &sizes)

Rect	cv::detail::resultRoiIntersection (const std::vector< Point > &corners, const std::vector< Size > &sizes)

Point	cv::detail::resultTl (const std::vector< Point > &corners)

void	cv::detail::selectRandomSubset (int count, int size, std::vector< int > &subset)

int &	cv::detail::stitchingLogLevel ()

Detailed Description

This figure illustrates the stitching module pipeline implemented in the Stitcher class. Using that class it's possible to configure/remove some steps, i.e. adjust the stitching pipeline according to the particular needs. All building blocks from the pipeline are available in the detail namespace, one can combine and use them separately.

The implemented stitching pipeline is very similar to the one proposed in [23] .

stitching pipeline

Camera models

There are currently 2 camera models implemented in stitching pipeline.

Homography model expecting perspective transformations between images implemented in cv::detail::BestOf2NearestMatcher cv::detail::HomographyBasedEstimator cv::detail::BundleAdjusterReproj cv::detail::BundleAdjusterRay
Affine model expecting affine transformation with 6 DOF or 4 DOF implemented in cv::detail::AffineBestOf2NearestMatcher cv::detail::AffineBasedEstimator cv::detail::BundleAdjusterAffine cv::detail::BundleAdjusterAffinePartial cv::AffineWarper

Homography model is useful for creating photo panoramas captured by camera, while affine-based model can be used to stitch scans and object captured by specialized devices. Use cv::Stitcher::create to get preconfigured pipeline for one of those models.

Note: Certain detailed settings of cv::Stitcher might not make sense. Especially you should not mix classes implementing affine model and classes implementing Homography model, as they work with different transformations.

Function Documentation

§ GraphEdge()

cv::detail::GraphEdge::GraphEdge	(	int	from,
		int	to,
		float	weight
	)

inline

§ createStitcher()

Ptr<Stitcher> cv::createStitcher ( bool try_use_gpu = false )

Python:
	retval	=	cv.createStitcher(	[, try_use_gpu]	)

§ overlapRoi()

bool cv::detail::overlapRoi	(	Point	tl1,
		Point	tl2,
		Size	sz1,
		Size	sz2,
		Rect &	roi
	)

§ resultRoi() [1/2]

Rect cv::detail::resultRoi	(	const std::vector< Point > &	corners,
		const std::vector< UMat > &	images
	)

§ resultRoi() [2/2]

Rect cv::detail::resultRoi	(	const std::vector< Point > &	corners,
		const std::vector< Size > &	sizes
	)

§ resultRoiIntersection()

Rect cv::detail::resultRoiIntersection	(	const std::vector< Point > &	corners,
		const std::vector< Size > &	sizes
	)

§ resultTl()

Point cv::detail::resultTl ( const std::vector< Point > & corners )

§ selectRandomSubset()

void cv::detail::selectRandomSubset	(	int	count,
		int	size,
		std::vector< int > &	subset
	)

§ stitchingLogLevel()

int& cv::detail::stitchingLogLevel ( )

Modules

Classes

Functions