图像拼接


模块

Features Finding and Images Matching
旋转估计
Autocalibration
Images Warping
Seam Estimation
Exposure Compensation
图像融合器

struct cv::detail::CameraParams
Describes camera parameters. 更多...
class cv::detail::DisjointSets
class cv::detail::Graph
struct cv::detail::GraphEdge
class cv::Stitcher
High level image stitcher. 更多...
class cv::detail::Timelapser
class cv::detail::TimelapserCrop

函数

cv::detail::GraphEdge::GraphEdge (int from , int to , float weight )
Ptr < Stitcher > cv::createStitcher (bool try_use_gpu=false)
Ptr < Stitcher > cv::createStitcherScans (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 ()

详细描述

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 [29] .

StitchingPipeline.jpg
stitching pipeline

Camera models

There are currently 2 camera models implemented in stitching pipeline.

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.

注意
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.

函数文档编制

GraphEdge()

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

createStitcher()

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

createStitcherScans()

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

overlapRoi()

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

resultRoi() [1/2]

Rect cv::detail::resultRoi ( const std::vector< Point > & corners ,
const std::vector< UMat > & images
)
Python:
retval = cv.detail.resultRoi( corners, images )
retval = cv.detail.resultRoi( corners, sizes )

resultRoi() [2/2]

Rect cv::detail::resultRoi ( const std::vector< Point > & corners ,
const std::vector< Size > & sizes
)
Python:
retval = cv.detail.resultRoi( corners, images )
retval = cv.detail.resultRoi( corners, sizes )

resultRoiIntersection()

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

resultTl()

Point cv::detail::resultTl ( const std::vector< Point > & corners )
Python:
retval = cv.detail.resultTl( corners )

selectRandomSubset()

void cv::detail::selectRandomSubset ( int count ,
int size ,
std::vector< int > & subset
)
Python:
None = cv.detail.selectRandomSubset( count, size, subset )

stitchingLogLevel()

int& cv::detail::stitchingLogLevel ( )
Python:
retval = cv.detail.stitchingLogLevel( )