In this repository we implement a panorama stitching application in Python. The code is heavily based on OpenPano, which in turn is an open source implementation of AutoStitch. Compared to OpenPano, we add an exposure correction stage and better handling of the multi-band blending.
The code relies on OpenCV, Numpy, Scipy and (optionally) Numba.
It is suggested to use conda to install OpenCV, as it automatically handles
the installation of binary libraries and dependencies.
We reccomend to use a separate environment because OpenCV install several
dependent packages, that may interfere with your existing installation:
conda create -n pano python=3.7
conda activate pano
conda install opencv numbaNumba is not strictly necessary to run the application, but it significantly increases the speed on the crop stage.
The application entry point is stitcher.py, which implements the projection
and blending stages of panorama generation.
features.py contains the SIFT feature extraction, FLANN matching and
homography computation with RANSAC. We also include an implementation of
Multi-Scale Oriented Patches (MSOP)
that we didn't use because of the superior performance of SIFT.
bundle_adj.py includes the panorama discovery, focal and rotation estimation,
and the bundle adjustment stages of panorama generation.
The remaining modules are experiments or accessory functions.
pano_tests.py are the unit tests for different critical functions; run them
to ensure that the functionality is correct if you plan to modify the code.
profiler.py is a small self-contained Python profiler to guide the
optimization of the slow sections of the code.
blend.py is a set of experiments on Laplacian blending, Poisson blending and
and seam detection with graph cuts; the blending code actually used in panorama
generation has been moved to stitcher.
Run the application by passing the image path and options, e.g.:
python stitcher.py data/CMU2 -s 2The path is the only mandatory argument. The following optional arguments are available:
-s,--shrink SHRINK: downsample the images by a factor of SHRINK to speed up generation; stitching may fail for large factors because there aren't enough features to match.--ba {none,incr,last}: how to apply bundle adjustment; skip it (none), apply it incrementally after adding an image (incr, default) or only after adding all the images (last)--equalize,-e: compensate the exposure differences between images; disabled by default because it may lead to chromatic aberration.--crop,-c: crops the largest rectangle from the panorama; disabled by default-b,--blend {none,linear,multiband}: how to blend the images, either by simply pasting them on the mosaic (none), using linear blending (linear) or multi-band blending (multiband, default)-o OUT,--out OUT: saves the mosaic to the OUT file, with the format determined by the extension (JPG, PNG supported).
The application stores the features and matches in a Numpy NPZ file and the result of bundle adjustment (intrinsic and extrinsic camera parameters) in a PKL file, tagged by the dataset name and shrink factor; the application checks if the files exist and loads them instead of re-running the pipeline. Delete the files if you want to re-generate the panorama for scratch.
We tested the application on OpenPano's sequences CMU0, CMU1, CMU2, UAV and NSH in the example data; on the Lunch Room sequence in PASSTA; on the example data of c060604's panorama implementation and a sequence we acquired (Athens). All the sequences are stored in a shared folder, accessible from Google Drive and One Drive.