{"id":18946868,"url":"https://github.com/klaudiozdrava/image-analysis","last_synced_at":"2026-05-16T11:32:59.320Z","repository":{"id":257481878,"uuid":"553269015","full_name":"klaudiozdrava/Image-Analysis","owner":"klaudiozdrava","description":"A Python project that was developed as a university assignment and the goal is to colorize an grayscale image using machine learning techniques.","archived":false,"fork":false,"pushed_at":"2022-10-18T01:29:45.000Z","size":7,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-03T23:34:46.477Z","etag":null,"topics":["colorization","image-processing","kmeans-clustering","numpy","svm"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/klaudiozdrava.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null}},"created_at":"2022-10-18T01:10:10.000Z","updated_at":"2022-10-18T01:34:56.000Z","dependencies_parsed_at":"2024-09-20T13:00:53.232Z","dependency_job_id":null,"html_url":"https://github.com/klaudiozdrava/Image-Analysis","commit_stats":null,"previous_names":["klaudiozdrava/image-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/klaudiozdrava/Image-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/klaudiozdrava%2FImage-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/klaudiozdrava%2FImage-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/klaudiozdrava%2FImage-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/klaudiozdrava%2FImage-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/klaudiozdrava","download_url":"https://codeload.github.com/klaudiozdrava/Image-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/klaudiozdrava%2FImage-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33100859,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-16T04:41:52.686Z","status":"ssl_error","status_checked_at":"2026-05-16T04:41:52.009Z","response_time":115,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["colorization","image-processing","kmeans-clustering","numpy","svm"],"created_at":"2024-11-08T13:08:13.941Z","updated_at":"2026-05-16T11:32:59.315Z","avatar_url":"https://github.com/klaudiozdrava.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Image-Analysis\nA Python project that was developed as a university assignment for the subject of Image Processing. \nThe program takes an input image and a reference dataset of photos. The goal is to colorize the greyscale image using a trained support vector machine.\nTo achieve that, we have implemented a variety of image processing techniques.\nFirst, we change color spaces from RGB to LAB. Then, we apply the SLIC algorithm to find the group of superpixels for each image.\nThese segments along with SIFT and GABOR features are given as input for the SVM.\nUsing scikit-learn, we use machine learning techniques to predict the color of a superpixel using the dataset superpixels as reference. \nThe output of the program returns the colorized version of the input image.\nTo run the algorithm user should provide at runtime the absolute path of the folder that contains training images and the path of testing image.\n\n\n![git](https://user-images.githubusercontent.com/47723760/196314012-701d8a0c-54d9-48f8-8f3a-ade85f824bb7.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fklaudiozdrava%2Fimage-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fklaudiozdrava%2Fimage-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fklaudiozdrava%2Fimage-analysis/lists"}