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Dismiss alert {{ message }} C-Naoki / image-stitcher Public * Notifications You must be signed in to change notification settings * Fork 1 * Star 77 This is a python implementation for stitching images. zenn.dev/naoki0103/articles/image-stitcher-application License MIT license 77 stars 1 fork Branches Tags Activity Star Notifications You must be signed in to change notification settings * Code * Issues 0 * Pull requests 0 * Actions * Projects 0 * Security * Insights Additional navigation options * Code * Issues * Pull requests * Actions * Projects * Security * Insights C-Naoki/image-stitcher This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. main BranchesTags Go to file Code Folders and files Name Name Last commit Last commit message date Latest commit History 17 Commits .github/workflows .github/ workflows .vscode .vscode data data docs docs notebooks notebooks src src .gitignore .gitignore LICENSE LICENSE Makefile Makefile README.md README.md poetry.lock poetry.lock poetry.toml poetry.toml pyproject.toml pyproject.toml View all files Repository files navigation * README * MIT license image-stitcher Python 3.10 License: MIT RubyDoc This is a python implementation for stitching images by automatically searching for overlap region. * Usage * Preview of results * Main Idea * [?] Support * [?] Contact Usage from src.main import main from src.utils.visualizer import result_visualize merged_image, cand = main( image1=image1, # The first image to be combined image2=image2, # The second image to be combined min_overlap=(5, 5), # The minimum overlap region verbose=False, # Whether to print the log ) result_visualize( image1=image1, # The first image to be combined image2=image2, # The second image to be combined merged_image=merged_image, # The output image cand=cand, # The parameters ) Preview of results The results using CIFAR-10 are shown below. I would refer you to tutorial.ipynb for detailed results. [input] Figure 1. The example of input images. The red area represents an empty region. This application can combine these images while considering their rotation. [rotated] Figure 2. The preprocessed input images. This rotation process is necessary to accurately combine the images. The green frame represents the overlap region between the input images. [result] Figure 3. The output image. Main Idea [case1] Figure 4. The overview of this application in limited case. This application is designed based on the overlap region's width $w_c$ and height $h_c$. Thanks to this idea, we can simply limit the search space, thus preventing it from capturing overly small, suboptimal overlap region. [case2] Figure 5. The overview of this application. However, the above approach is not always applicable, specifically when $\min(h_1, h_2) < h_c$ or $\min(w_1, w_2) < w_c$. To address this issue, I change the perspective of $w_c$ and $h_c$ like the above figure. Therefore, this application can handle images of arbitrary sizes. [?] Support If you like this app, give it a and share it with friends! [?] Contact For questions or issues, feel free to open an issue. I appreciate your feedback and look forward to hearing from you! About This is a python implementation for stitching images. zenn.dev/naoki0103/articles/image-stitcher-application Topics python images image-analysis Resources Readme License MIT license Activity Stars 77 stars Watchers 1 watching Forks 1 fork Report repository Releases No releases published Packages 0 No packages published Contributors 2 * @C-Naoki C-Naoki Naoki Chihara * @actions-user actions-user Languages * Jupyter Notebook 96.8% * Python 2.9% * Makefile 0.3% Footer (c) 2024 GitHub, Inc. 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