{"id":21597629,"url":"https://github.com/aadit3003/colour-palette-extraction","last_synced_at":"2025-03-18T11:26:25.923Z","repository":{"id":130392900,"uuid":"396199305","full_name":"Aadit3003/colour-palette-extraction","owner":"Aadit3003","description":"Extracted 5-colour palettes from a dataset of 541 images of Fall 2021 Couture Fashion shows and visualized them using unsupervised learning algorithms.","archived":false,"fork":false,"pushed_at":"2021-09-13T16:41:08.000Z","size":13782,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-24T17:45:16.657Z","etag":null,"topics":["colour-spaces","k-means-clustering","machine-learning","unsupervised-learning"],"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/Aadit3003.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}},"created_at":"2021-08-15T03:12:16.000Z","updated_at":"2023-04-12T11:42:53.000Z","dependencies_parsed_at":null,"dependency_job_id":"fb9e51bd-64ad-426a-8266-dd98ef76c03c","html_url":"https://github.com/Aadit3003/colour-palette-extraction","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/Aadit3003%2Fcolour-palette-extraction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Aadit3003%2Fcolour-palette-extraction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Aadit3003%2Fcolour-palette-extraction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Aadit3003%2Fcolour-palette-extraction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Aadit3003","download_url":"https://codeload.github.com/Aadit3003/colour-palette-extraction/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244209787,"owners_count":20416338,"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":["colour-spaces","k-means-clustering","machine-learning","unsupervised-learning"],"created_at":"2024-11-24T18:09:31.613Z","updated_at":"2025-03-18T11:26:25.915Z","avatar_url":"https://github.com/Aadit3003.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\n# Colour Palette extraction using Unsupervised Learning\nThis project explores innovative ways to explore relations between colour palettes in collections of images, using unsupervised machine learning algorithms from the Sci-kit library(K-Means clustering, Kernel PCA and t-SNE).\n## Code\nThe source code is split into two parts:\n1. Python code to extract a 5-Colour Hexadecimal Palette from an image.\n2. Python code to visualise the relation between a list of the above extracted palettes.\n## 1) [Palette Extraction](https://github.com/Aadit3003/Colour-Palette-Extraction/blob/850617ec95604604f6fe31d844a28ae39c065c1e/Palette%20Extraction.py)\nThe 5 colour palettes were extracted by converting the image to a 2D matrix of RGB Values(Nx3), which was used as input for the Mini Batch K-Means Clustering algorithm to return the colours(hex) from the five cluster centers. \n- *Here are some examples:*\n \n![Palette Examples](https://user-images.githubusercontent.com/82210227/129468364-f7f9a8a8-f2bb-491e-94bc-cf5d3c9d4b9b.png)\n- *(Top: Schiaparelli Fall 21 Couture (Look 23))*\n- *(Bottom: Van Gogh-Sunflowers (fourth version))*\n## 2) [Palette Visualisation](https://github.com/Aadit3003/Colour-Palette-Extraction/blob/eb8c0b731250722a71d42c2f2affc8b2def455e2/Palette%20Visualisation.py)\nA list of the 5 colour palettes obtained above, were visualised using 2 dimensionality reduction techniques. For the following visualisation, a dataset of 541 images, from multiple Fall 2021 Couture Fashion shows was used.\n## Kernel PCA\nThe RBF Kernel PCA algorithm was able to separate the palettes with darker colours from those with lighter colours, but grouped together palettes with saturated colours.\n![FW 21 Kernel PCA](https://user-images.githubusercontent.com/82210227/129468412-38535f23-6856-408b-8b58-d7238a69bb4e.png)\n## t-SNE\nThe t-SNE algorithm was more successful in grouping the palettes with similar colours and separating visually distinct palettes(such as the greens and pinks).\n![FW 21 t-SNE](https://user-images.githubusercontent.com/82210227/129468410-a9a90e88-7d1d-4ead-8e8e-cf7e7bcaccb1.png)\n\n## 3) Possible Applications\n### i) Streaming Service Recommendations\n- Colour palettes of video thumbnails could be used to recommend similar videos to users.\n- For the following visualisation, palettes from a random sample of 149 Netflix thumbnails were used.\n![Netflix](https://user-images.githubusercontent.com/82210227/129468416-0bec85cf-67e6-4532-a1ed-617b0455db08.png)\n- *(Top Left: RuPaul's Drag Race All Stars)*\n-  *(Bottom Right: Bo Bunrham: Inside)*\n### ii) Fashion Trend Visualisation\n- Designers could use colour palettes used in top fashion shows to gain insights about fashion trends.\n-  For the following visualisation, a dataset of 541 images, from multiple Fall 2021 Couture Fashion shows was used.\n![Fall 21 Couture](https://user-images.githubusercontent.com/82210227/129468419-04e7a777-a80c-4569-8131-e0c3a0dbd6d2.png)\n- *(Top Left: Valentino Fall Couture 21 Look 12)*\n- *(Bottom Right: Schiaparelli Fall Couture 21 Look 20)*\n### iii) Other Potential Applications\nThe concept explored in this project could have interesting applications in many fields like social media analytics, human computer interaction, art trends and so on. For example:\n1. Social media photo trend visualisation.\n2. Innovative visualisation of art pieces from different time periods.\n3. Classifying different kinds of flora by their colour. \n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faadit3003%2Fcolour-palette-extraction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faadit3003%2Fcolour-palette-extraction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faadit3003%2Fcolour-palette-extraction/lists"}