{"id":18548984,"url":"https://github.com/danthe1st/convolut0r","last_synced_at":"2025-08-08T14:16:44.821Z","repository":{"id":112509436,"uuid":"587806929","full_name":"danthe1st/Convolut0r","owner":"danthe1st","description":"Convolut0r allows to manipulate images using (custom and preset) Convolutions.","archived":false,"fork":false,"pushed_at":"2023-01-24T20:34:24.000Z","size":47,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-02-17T09:45:08.557Z","etag":null,"topics":["convolution","image-processing"],"latest_commit_sha":null,"homepage":"","language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/danthe1st.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-01-11T16:24:31.000Z","updated_at":"2023-04-09T06:29:17.000Z","dependencies_parsed_at":"2023-05-15T07:30:40.242Z","dependency_job_id":null,"html_url":"https://github.com/danthe1st/Convolut0r","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danthe1st%2FConvolut0r","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danthe1st%2FConvolut0r/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danthe1st%2FConvolut0r/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/danthe1st%2FConvolut0r/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/danthe1st","download_url":"https://codeload.github.com/danthe1st/Convolut0r/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254301620,"owners_count":22047907,"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","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":["convolution","image-processing"],"created_at":"2024-11-06T20:36:22.688Z","updated_at":"2025-05-15T08:14:46.504Z","avatar_url":"https://github.com/danthe1st.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Convolut0r\nConvolut0r allows to manipulate images using (custom and preset) Convolutions.\n\n## Convolutions\nA Convolution is a multi-dimensional array (kernel) which is applied on images (or other kinds of multidimensional data) in a certain way.\nFor the output image, each pixel is calculated by multiplying pixels of the input image at the corresponding position with the kernel and summing up the results.\nThe kernel \"slides\" over the whole image producing the output image.\n\nAs an illustration, take a look at the following exemplary image:\n\n[![Convolutions](https://user-images.githubusercontent.com/34687786/212144189-053015ca-23e5-4aba-b409-c30dafb5f349.png)](https://user-images.githubusercontent.com/34687786/212144189-053015ca-23e5-4aba-b409-c30dafb5f349.png)\n\nHere, a 3x3 kernel \"slides\" over a 4x4 image where each position of the kernel in the image is marked by a rectangle.\nFor each pixel in the resulting (2x2) image, the kernel is applied to the specific area and the resulting pixel will be the sum of the all corresponding input pixels multiplied with the corresponding value of the kernel.\n\nAside from \"normal\" image processing, convolutions are widely used in Convoluted Neural Networks (CNNs) which are particularly good at working with high-dimensional data like images.\n\n## Options\n\n### Striding\nStriding defines how fast the kernel \"slides\" over the image. A value of `1` means that it moves one pixel to the right each time.\nUsing a higher value than `1` for striding significantly decreases the resolution.\n\n### Padding\nWithout a padding, the output image would have a lower resolution than the input image (see the exemplary image for before). It is possible to add some padding to the input image in order to increase the number of pixels in the output image.\n\n# Download\nPrebuilt artifacts can be downloaded [from GitHub Actions](https://github.com/danthe1st/Convolut0r/actions).\n\nSelect a completed workflow run, download the artifact, extract it and run the installer.\n\n# Building the project\n\nOnce [Maven](https://maven.apache.org/) is installed, the project can be built and run using the command `mvn javafx:run`.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanthe1st%2Fconvolut0r","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdanthe1st%2Fconvolut0r","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdanthe1st%2Fconvolut0r/lists"}