{"id":21358344,"url":"https://github.com/stm/imagefluency","last_synced_at":"2025-07-26T18:04:14.976Z","repository":{"id":46262547,"uuid":"78774174","full_name":"stm/imagefluency","owner":"stm","description":"Image Fluency Scores in R","archived":false,"fork":false,"pushed_at":"2024-02-26T10:51:31.000Z","size":23468,"stargazers_count":8,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2024-10-30T06:00:04.108Z","etag":null,"topics":["fluency","image-analysis","image-analytics","r","r-package","rstats"],"latest_commit_sha":null,"homepage":"https://imagefluency.com","language":"R","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/stm.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"funding":null,"license":null,"code_of_conduct":".github/CODE_OF_CONDUCT.md","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":"2017-01-12T18:23:32.000Z","updated_at":"2024-08-06T10:44:55.000Z","dependencies_parsed_at":"2022-09-11T03:10:39.363Z","dependency_job_id":"e8054fc3-ef55-4ab0-97a4-319e3cb50d72","html_url":"https://github.com/stm/imagefluency","commit_stats":{"total_commits":182,"total_committers":2,"mean_commits":91.0,"dds":"0.43956043956043955","last_synced_commit":"bfddc96b97484a6d6de7d62fff7b6daa3d701606"},"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stm%2Fimagefluency","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stm%2Fimagefluency/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stm%2Fimagefluency/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/stm%2Fimagefluency/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/stm","download_url":"https://codeload.github.com/stm/imagefluency/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":225844790,"owners_count":17533161,"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":["fluency","image-analysis","image-analytics","r","r-package","rstats"],"created_at":"2024-11-22T05:15:45.657Z","updated_at":"2024-11-22T05:15:46.302Z","avatar_url":"https://github.com/stm.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"## imagefluency: Image Statistics Based on Processing Fluency \u003cimg src=\"man/figures/logo.png\" align=\"right\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](http://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/)\n[![R-CMD-check](https://github.com/stm/imagefluency/actions/workflows/check-standard.yaml/badge.svg)](https://github.com/stm/imagefluency/actions/workflows/check-standard.yaml)\n[![codecov test coverage](https://codecov.io/gh/stm/imagefluency/branch/master/graph/badge.svg)](https://app.codecov.io/gh/stm/imagefluency?branch=main)\n[![CRAN status](https://www.r-pkg.org/badges/version/imagefluency)](https://cran.r-project.org/package=imagefluency)\n[![CRAN downloads](http://cranlogs.r-pkg.org/badges/imagefluency)](https://cran.r-project.org/package=imagefluency)\n[![CRAN downloads](http://cranlogs.r-pkg.org/badges/grand-total/imagefluency)](https://cran.r-project.org/package=imagefluency)\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5614665.svg)](https://doi.org/10.5281/zenodo.5614665)\n\u003c!-- badges: end --\u003e\n\n## Overview\n\n**imagefluency** is a simple R package for image fluency scores. The\npackage allows to get scores for several basic aesthetic principles that\nfacilitate fluent cognitive processing of images.\nIf you want to try it out before installing, you can find an interactive Shiny app [here](https://mayer.shinyapps.io/imagefluency/) (alpha version).\n    \nThe main functions are:\n\n* `img_contrast()`  to get the visual contrast of an image.\n* `img_complexity()`  to get the visual complexity of an image (equals\n   1 minus image simplicity)\n* `img_self_similarity()`  to get the visual self-similarity of an image\n* `img_simplicity()`  function to get the visual simplicity of an image (equals\n   1 minus image complexity).\n* `img_symmetry()`  to get the vertical and horizontal symmetry of an\n   image.\n* `img_typicality()`  to get the visual typicality of a list of images relative\n   to each other\n\nOther helpful functions are:\n\n* `img_read()`  wrapper function to read images into R using `read.bitmap()` from the\n  [readbitmap](https://github.com/jefferis/readbitmap) package\n* `rgb2gray()`  convert images from RGB into grayscale (might speed up computation)\n* `run_imagefluency()`  to launch a Shiny app locally on your computer for an interactive demo of the\n   main functions\n\n\nThe main author is [Stefan Mayer](https://github.com/stm/).\n\n## Installation\n\nYou can install the current stable version from CRAN.\n```r\ninstall.packages('imagefluency')\n```\n\nTo download the latest development version from Github use the `install_github` function of the `remotes` package.\n```r\n# install remotes if necessary\nif (!require('remotes')) install.packages('remotes')\n# install imagefluency from github\nremotes::install_github('stm/imagefluency')\n```\n\nOptionally, if you have `rmarkdown` installed, you can also have your system build the the vignettes when downloading from GitHub.\n```r\n# install from github with vignettes (needs rmarkdown installed)\nremotes::install_github('stm/imagefluency', build_vignettes = TRUE)\n```\n\nUse the following link to report bugs/issues: \u003chttps://github.com/stm/imagefluency/issues\u003e\n\n## Example usage\n\n```r\n# visual contrast\n#\n# example image file (from package): bike.jpg\nbike_location \u003c- system.file('example_images', 'bike.jpg', package = 'imagefluency')\n# read image from file\nbike \u003c- img_read(bike_location)\n# get contrast\nimg_contrast(bike)\n\n# visual symmetry\n#\n# read image\nrails \u003c- img_read(system.file('example_images', 'rails.jpg', package = 'imagefluency'))\n# get only vertical symmetry\nimg_symmetry(rails, horizontal = FALSE)\n```\n\n## Documentation\n\nSee the [getting started vignette](https://imagefluency.com/articles/getting-started.html) for a detailed introduction and the [reference page](https://imagefluency.com/reference/index.html) for details on each function. \n\nIf you are analyzing a larger number of images, make sure to read the tutorial on how to [analyze multiple images at once](https://imagefluency.com/articles/batch-processing.html).\n\n## Citation\n\nTo cite **imagefluency** in publications use:\n\n\u003e Mayer, S. (2024). *imagefluency: Image Statistics Based on Processing Fluency*. R package version 0.2.5. doi:  [10.5281/zenodo.5614665](https://doi.org/10.5281/zenodo.5614665)\n\nA BibTeX entry is:\n```\n@software{,\n  author       = {Stefan Mayer},\n  title        = {imagefluency: Image Statistics Based on Processing Fluency},\n  year         = 2024,\n  version      = {0.2.5},\n  doi          = {10.5281/zenodo.5614665},\n  url          = {https://imagefluency.com}\n}\n```\n## Dependencies\nThe `img_complexity` function relies on the packages [R.utils](https://cran.r-project.org/package=R.utils) and [magick](https://github.com/ropensci/magick). The `img_self_similarity` function relies on the packages [OpenImageR](https://github.com/mlampros/OpenImageR), [pracma](https://cran.r-project.org/package=pracma), and [quadprog](https://cran.r-project.org/package=quadprog). The `img_read` function relies on the [readbitmap](https://github.com/jefferis/readbitmap) package. The `run_imagefluency` shiny app depends on [shiny](https://github.com/rstudio/shiny).\n\n## Further references\n\nTo learn more about the different image fluency metrics, see the following publications:\n\n* Mayer, S. \u0026 Landwehr, J, R. (2018). Quantifying Visual Aesthetics\nBased on Processing Fluency Theory: Four Algorithmic Measures for\nAntecedents of Aesthetic Preferences. *Psychology of Aesthetics,\nCreativity, and the Arts*, *12*(4), 399--431. \ndoi: [10.1037/aca0000187](https://doi.org/10.1037/aca0000187)\n\n* Mayer, S. \u0026 Landwehr, J. R. (2018). Objective measures of design\ntypicality. *Design Studies*, *54*, 146--161.\ndoi: [10.1016/j.destud.2017.09.004](https://doi.org/10.1016/j.destud.2017.09.004)\n\n## Code of Conduct\n\nPlease note that this project is released with a [Contributor Code of Conduct](https://imagefluency.com/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstm%2Fimagefluency","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstm%2Fimagefluency","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstm%2Fimagefluency/lists"}