Automatic Panorama Stitching software in MATLAB. Spherical, cyclindrical and planar projections stitching is supported in this version and can recognize multiple panoramas.
| Type | Images |
|---|---|
| Stitched image |  |
| Crop box |  |
| Cropped image |  |
| Type | Images |
|---|---|
| Stitched image |  |
| Cropped image |  |
MATLAB
MATLAB Computer Vision Toolbox
MATLAB Image Processing Toolbox
MATLAB Parallel Computing Toolbox
MATLAB Optimization Toolbox
Please use the Main_AutoPanoStitch.m to run the program. Change the folderPath = '../../../Data/Generic'; to your desired folder path. Also, change the folderName = ''; to a valid name. You can download the whole Generic folder datasets in AutoPanoStitch Stitching Datasets Compilation.
Change the hyper parameters accordingly if needed. But it is not required though.
%% Inputs 2
%--------------------------------------------------------------------------
% Parallel workers
input.numCores = str2double(getenv('NUMBER_OF_PROCESSORS')); % Number of cores for parallel processing
input.poolType = 'numcores'; % 'numcores' | 'Threads'
%% Inputs 3
% Warping
input.warpType = 'planar'; % 'spherical' | 'cylindrical' | 'planar' (projective)
% Focal length
input.fx = 2000; % focal length of camera in pixels
input.fy = 2000; % focal length of camera in pixels
% Distortion coefficients [k1, k2, k3, p1, p2]
input.DC = [0, 0, 0, 0, 0];
% Feature matching
input.detector = 'SIFT'; % 'HARRIS' | 'SIFT' | 'vl_SIFT' | 'FAST' | 'SURF' | 'BRISK' | 'ORB' | 'KAZE'
input.Matchingthreshold = 3.5; % 10.0 or 1.0 (default) | percent value in the range (0, 100] | depends on
% binary and non-binary features
input.Ratiothreshold = 0.6; % ratio in the range (0,1]
input.Sigma = 1.6; % Sigma of the Gaussian (1.4142135623)
input.NumLayersInOctave = 4; % Number of layers in each octave -- SIFT only
input.ContrastThreshold = 0.00133; % Contrast threshold for selecting the strongest features,
% specified as a non-negative scalar in the range [0,1].
% The threshold is used to filter out weak features in
% low-contrast regions of the image. -- SIFT only
input.EdgeThreshold = 6; % Edge threshold, specified as a non-negative scalar greater than or equal to 1.
% The threshold is used to filter out unstable edge-like features -- SIFT only
input.nearestFeaturesNum = 5; % Nearest images minimum number of features to filter
% distant image matches (filter gain overlap images to reduce complexity)
% Image matching (RANSAC)
input.Matchingmethod = 'Approximate'; %'Exhaustive' (default) | 'Approximate'
input.Inliersconfidence = 99.9; % Inlier confidence [0,100]
input.maxIter = 2000; % RANSAC maximum iterations
input.Transformationtype = 'affine'; % 'rigid' | 'similarity' | 'affine' | 'projective' | 'translation'
input.MaxDistance = 1.50; % Maximum distance (pixels) 1.5
% Image blending and panorama
input.gainDerivation = 1; % Two types of gain matrix derivations 1 or 2 (both leads to same results with some roundoffs)
input.sigmaN = 10; % Standard deviations of the normalised intensity error
input.sigmag = 0.01; % Standard deviations of the gain
input.resizeImage = 1; % Resize input images
input.resizeStitchedImage = 1; % Resize stitched image
input.maxPanoramaArea = 3e6; % Maximum panorama area
input.blending = 'multiband'; % 'multiband' | 'linear' | 'none'
input.bands = 6; % bands
input.MBBsigma = 5; % Multi-band Gaussian sigma
input.filtSize = [5,5]; % Gaussian kernel Filter size
input.parforSummation = true; % Gain diagonal elements summation by parfor
input.showPanoramaImgsNums = 0; % display the panorama images with numbers after tranform 0 or 1
% Post-processing
input.canvas_color = 'black'; % Panorama canvas color 'black' | 'white'
input.showCropBoundingBox = 1; % Display cropping bounding box 0 | 1
input.blackRange = 0; % Minimum dark pixel value to crop panaroma
input.whiteRange = 250; % Minimum bright pixel value to crop panaroma
input.showKeypointsPlot = 0; % Display keypoints plot (parfor suppresses this flag, so no use)
input.displayPanoramas = 0; % Display panoramas in figure
Depending on how your images are captured, panaroma being a spherical, cylindrical or planar should be selected judicially using the input.warpType and input.Transformationtype. Generally, spherical or cylindrical projections with affine or rigid transformation should work well in most of the cases. However, some panoramas specifically looks good in projective transformation, e.g. flatbed scanner or whiteboard (affine works well too) images.
Currently, spherical, cyclindrical and planar projections stitching is supported in this version and can recognize multiple panoramas. This work is in progress, further improvements such as the inclusion of a full view 360 x 180-degree panoramas stitching (everything visible from a point), automatic panorama straightening, runtime speed optimization and Graphical User Interface (GUI) are under development. Your patience will be appreciated.
Creating image stitching datasets takes a lot of time and effort. During my Ph.D. days, I tried to compile datasets that were comprehensive to have spherical, cylindrical or planar and full view 360 x 180-degree panoramas. These datasets posed a real challenge to the automatic stitching method. If all these datasets are stitched well, it definitely shows the robustness of your stitching method.
All these datasets are public! Some of them were from my Ph.D. studies (especially on cracks) and most of them were downloaded from the internet. I do not remember the individual names of the dataset providers. But I acknowledge their work and I am thankful to all of them! I hope you appreciate their efforts in making these datasets public to advance the research!
Below are some samples from the datasets. There are 98 panorama or image stitching/registration datasets in total. You can download them in AutoPanoStitch Stitching Datasets Compilation. If I come across any interesting and challenging datasets, I will expand this compilation.
| Type | Images |
|---|---|
| CMU |  |
| Grand Canyon |  |
| Shanghai |  |
| UCSB |  |
| Yellowstone |  |
| Rio |  |
Image stitching datasets for cracks are available to the public. If you use this specific dataset (related to cracks) in your research, please use the following BibTeX entry to cite:
@PhdThesis{preetham2021vision,
author = {{Aghalaya Manjunatha}, Preetham},
title = {Vision-Based and Data-Driven Analytical and Experimental Studies into Condition Assessment and Change Detection of Evolving Civil, Mechanical and Aerospace Infrastructures},
school = {University of Southern California},
year = 2021,
type = {Dissertations & Theses},
address = {3550 Trousdale Parkway Los Angeles, CA 90089},
month = {December},
note = {Condition assessment, Crack localization, Crack change detection, Synthetic crack generation, Sewer pipe condition assessment, Mechanical systems defect detection and quantification}
}- Bundle adjustment using the
lsqnonlinin MATLAB is computationally slow. - Panoramic images needs to be straightened, some of the panoramas will be skewed.
Bundle adjustment functions in MATLAB are adapted from the Kevin Luo's GitHub Repo and heavily improved.
The original implementation of the automatic panaroma stitching by Dr. Brown was written in C++ and is LICENSED under The University of British Columbia. This is being programmed and made available to public for academic / research purposes only. Please cite the relevant citations as provided in the main file.
To all the authors who made the image stitching datasets public.
Please rate and provide feedback for the further improvements.