6.5 KiB
High level stitching API (Stitcher class)
Goal
In this tutorial you will learn how to:
- use the high-level stitching API for stitching provided by
- @ref cv::Stitcher
- learn how to use preconfigured Stitcher configurations to stitch images using different camera models.
Code
This tutorial code's is shown lines below. You can also download it from here.
@include samples/cpp/stitching.cpp
Explanation
The most important code part is:
@snippet cpp/stitching.cpp stitching
A new instance of stitcher is created and the @ref cv::Stitcher::stitch will do all the hard work.
@ref cv::Stitcher::create can create stitcher in one of the predefined
configurations (argument mode). See @ref cv::Stitcher::Mode for details. These
configurations will setup multiple stitcher properties to operate in one of
predefined scenarios. After you create stitcher in one of predefined
configurations you can adjust stitching by setting any of the stitcher
properties.
If you have cuda device @ref cv::Stitcher can be configured to offload certain
operations to GPU. If you prefer this configuration set try_use_gpu to true.
OpenCL acceleration will be used transparently based on global OpenCV settings
regardless of this flag.
Stitching might fail for several reasons, you should always check if
everything went good and resulting pano is stored in pano. See
@ref cv::Stitcher::Status documentation for possible error codes.
Camera models
There are currently 2 camera models implemented in stitching pipeline.
- Homography model expecting perspective transformations between images implemented in @ref 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 @ref 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.
@note Certain detailed settings of @ref 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.
Try it out
If you enabled building samples you can found binary under
build/bin/cpp-example-stitching. This example is a console application, run it without
arguments to see help. opencv_extra provides some sample data for testing all available
configurations.
to try panorama mode run:
./cpp-example-stitching --mode panorama <path to opencv_extra>/testdata/stitching/boat*
to try scans mode run (dataset from home-grade scanner):
./cpp-example-stitching --mode scans <path to opencv_extra>/testdata/stitching/newspaper*
or (dataset from professional book scanner):
./cpp-example-stitching --mode scans <path to opencv_extra>/testdata/stitching/budapest*
@note
Examples above expects POSIX platform, on windows you have to provide all files names explicitly
(e.g. boat1.jpg boat2.jpg...) as windows command line does not support * expansion.
Stitching detailed (python opencv >4.0.1)
If you want to study internals of the stitching pipeline or you want to experiment with detailed configuration you can use stitching_detailed source code available in C++ or python
stitching_detailed
@add_toggle_cpp [stitching_detailed.cpp](https://raw.githubusercontent.com/opencv/opencv/master/samples/cpp/stitching_detailed.cpp) @end_toggle@add_toggle_python stitching_detailed.py @end_toggle
stitching_detailed program uses command line to get stitching parameter. Many parameters exists. Above examples shows some command line parameters possible :
boat5.jpg boat2.jpg boat3.jpg boat4.jpg boat1.jpg boat6.jpg --work_megapix 0.6 --features orb --matcher homography --estimator homography --match_conf 0.3 --conf_thresh 0.3 --ba ray --ba_refine_mask xxxxx --save_graph test.txt --wave_correct no --warp fisheye --blend multiband --expos_comp no --seam gc_colorgrad
Pairwise images are matched using an homography --matcher homography and estimator used for transformation estimation too --estimator homography
Confidence for feature matching step is 0.3 : --match_conf 0.3. You can decrease this value if you have some difficulties to match images
Threshold for two images are from the same panorama confidence is 0. : --conf_thresh 0.3 You can decrease this value if you have some difficulties to match images
Bundle adjustment cost function is ray --ba ray
Refinement mask for bundle adjustment is xxxxx ( --ba_refine_mask xxxxx) where 'x' means refine respective parameter and '_' means don't. Refine one, and has the following format: fx,skew,ppx,aspect,ppy
Save matches graph represented in DOT language to test.txt ( --save_graph test.txt) : Labels description: Nm is number of matches, Ni is number of inliers, C is confidence
Perform wave effect correction is no (--wave_correct no)
Warp surface type is fisheye (--warp fisheye)
Blending method is multiband (--blend multiband)
Exposure compensation method is not used (--expos_comp no)
Seam estimation estimator is Minimum graph cut-based seam (--seam gc_colorgrad)
you can use those arguments on command line too :
boat5.jpg boat2.jpg boat3.jpg boat4.jpg boat1.jpg boat6.jpg --work_megapix 0.6 --features orb --matcher homography --estimator homography --match_conf 0.3 --conf_thresh 0.3 --ba ray --ba_refine_mask xxxxx --wave_correct horiz --warp compressedPlaneA2B1 --blend multiband --expos_comp channels_blocks --seam gc_colorgrad
You will get :
For images captured using a scanner or a drone ( affine motion) you can use those arguments on command line :
newspaper1.jpg newspaper2.jpg --work_megapix 0.6 --features surf --matcher affine --estimator affine --match_conf 0.3 --conf_thresh 0.3 --ba affine --ba_refine_mask xxxxx --wave_correct no --warp affine
You can find all images in https://github.com/opencv/opencv_extra/tree/master/testdata/stitching