{"id":19773609,"url":"https://github.com/ultraflame4/tiledimage","last_synced_at":"2025-07-23T15:04:31.976Z","repository":{"id":64899083,"uuid":"392644866","full_name":"ultraflame4/TiledImage","owner":"ultraflame4","description":"This program stitches or \"Tiles\" a set of images (called tiles) together into one large composite image with likeness to a reference image","archived":false,"fork":false,"pushed_at":"2022-12-20T07:17:07.000Z","size":101,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"V3","last_synced_at":"2025-07-23T15:02:33.452Z","etag":null,"topics":["image-manipulation"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ultraflame4.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2021-08-04T10:24:37.000Z","updated_at":"2022-12-21T13:40:16.000Z","dependencies_parsed_at":"2023-01-30T00:01:03.870Z","dependency_job_id":null,"html_url":"https://github.com/ultraflame4/TiledImage","commit_stats":null,"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"purl":"pkg:github/ultraflame4/TiledImage","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultraflame4%2FTiledImage","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultraflame4%2FTiledImage/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultraflame4%2FTiledImage/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultraflame4%2FTiledImage/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ultraflame4","download_url":"https://codeload.github.com/ultraflame4/TiledImage/tar.gz/refs/heads/V3","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ultraflame4%2FTiledImage/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266699497,"owners_count":23970513,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-23T02:00:09.312Z","response_time":66,"last_error":null,"robots_txt_status":null,"robots_txt_updated_at":null,"robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["image-manipulation"],"created_at":"2024-11-12T05:10:21.972Z","updated_at":"2025-07-23T15:04:31.950Z","avatar_url":"https://github.com/ultraflame4.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# TilImg V3\nThis program composites many images into one big image using a set of images and a reference image\n![results](https://user-images.githubusercontent.com/34125174/208235487-44f5e641-e6eb-453a-a9db-25d93a093782.png)\n[Generated from Photo by Pixabay from Pexels: https://www.pexels.com/photo/dock-under-cloudy-sky-in-front-of-mountain-206359/ )\n\n### Installation \u0026 Usage\nCheck the [releases](https://github.com/ultraflame4/TiledImage/releases) page for the latest version and instructions on how to install it.\n\n### Image\nGenerates a tiled image from a set of images and a reference image.\nUsage: `timg img [OPTIONS] REFERENCE_IMAGEPATH OUT_PATH`\n\nUsage (Before v3.1.0): `timg REFERENCE_IMAGEPATH OUT_PATH`\n#### Arguments / Parameters\n##### Required Arguments\n- reference_imagepath: The path to the reference image which is used to determine the look of the final image \n- out_path: Where to save the results\n- tileset_path: The path to the folder containing the images to be used as tiles to construct the final image\n\n##### Optional Arguments\n- --resize-factor: The factor by which to resize the reference image. Default is -1 (auto, resizes based on tile size. Final image resolution will stay mostly the same)\n- --process-type: TilImg uses numba to speed up computation. This argument specifies the method used to do so. Default is guvectorize\n  - guvectorize: Uses numba's guvectorize to speed up computation. This is the default method\n  - njit: Uses numba's njit to speed up computation. This is known to be extremely slow\n  - cuda: Also uses numba's guvectorize but targets CUDA-enabled GPUs. This is known to be slighly faster than guvectorize but requires a CUDA-enabled GPU AND has some overhead costs\n  \n### Video\nAvailable form v3.1.0\nGenerates a video from a set of images and a reference video. Basically the video version of the image command.\nThis requires **ffmpeg to be installed** and to be **in the PATH!**\nIf you don't have ffmpeg installed, you can download it [here](https://ffmpeg.org/download.html)\n\nUsage: `timg vid [OPTIONS] SOURCE_PATH SAVE_PATH TILESET_PATHS...`\n\n##### Required Arguments\n- source_path: The path to the reference image which is used to determine the look of the final image \n- save_path: Where to save the results\n- tileset_path: The path to the folder containing the images to be used as tiles to construct the final image\n\n##### Optional Arguments\n- --resize-factor: The factor by which to resize the reference image. Default is -1 (auto, resizes based on tile size. Final image resolution will stay mostly the same)\n- --process-type: TilImg uses numba to speed up computation. This argument specifies the method used to do so. Default is guvectorize\n  - guvectorize: Uses numba's guvectorize to speed up computation. This is the default method\n  - ~~njit: Uses numba's njit to speed up computation. This is known to be extremely slow~~ Not available in video mode\n  - cuda: Also uses numba's guvectorize but targets CUDA-enabled GPUs. This is known to be slighly faster than guvectorize but requires a CUDA-enabled GPU AND has some overhead costs\n\n### Limitations\n- All images in the set must be of the same resolution and dimensions\n- The final product may have black bars on the side or have clipped textures because\u003cbr/\u003e\n  the dimensions of the reference image do not match dimensions of the images in the image set.\u003cbr/\u003e\n  The reference image dimensions has to be a multiple of the image set's dimensions to avoid this.\u003cbr/\u003e\n  \n- For video, ffmpeg must be installed and in the PATH!!!","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fultraflame4%2Ftiledimage","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fultraflame4%2Ftiledimage","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fultraflame4%2Ftiledimage/lists"}