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labelling\u003c/h3\u003e\n\u003c/p\u003e\n\n# Iris\n\u003e A browser based UI for labelling image files that tracks the labels using a database, rather than using folders and paths\n\n![Iris](https://github.com/ismailuddin/iris/raw/master/artwork/iris_preview.png)\n\n## Why use Iris?\n* Label thousands of images (or possibly millions?) easily without having to use a file manager that slows down with too many files.\n  * Iris uses pagination to view subsets of your data, so the platform doesn't slow down with large quantities of images.\n* Keep track of all your labels using a database.\n  * Iris stores the labels for your data in an SQLite database file in a `.iris` directory that is created in the root of your data folder. This decouples labels from the directory paths, helping to avoid mistakes.\n* Simple and easy to use drag and drop UI.\n  * Images can be labelled using simple drag and drop interactions, making it easy for anyone to use.\n\n## Installation\nTo install **iris**, use the following command:\n\n```shell\n$   pip install iris-image-labelling\n```\n\n## Usage\nOnce installed, **iris** registers as a command in your terminal that is accessible from any directory. \n\n**Iris** presumes your data is initially organised in a directory that has sub-directories for every category of your data **may** take on.\n\n```\ndata/\n    |---category_1/\n        |---file_1.png\n        |---file_2.png\n    |---category_2/\n        |---file_3.png\n        |---file_4.png\n```\n\nThe folders are initially used to deduce what categories your data can take on. They **do not** need to contain any data / images in them.\n\nWith the following project structure set up, navigate to the parent directory of `data/` and then launch **iris** as follows from the terminal:\n\n\n```shell\n$   iris launch -f data\n```\n\n**Iris** launches by default on port 5000, from where you can go on and begin labelling your data.\n\nArgument | Description\n--|--\n-f | Folder to build database from\n-h | The host to run the server on\n-p | The port number to run the server on\n\n### Reorganise data\nTo avoid any confusion between the re-labelled data, and the original categories inferred by the file paths of the images, a button labelled 'Reorganise data' is present in the toolbar.\nThis will re-organise the files amongst the folders according to the new labels.\n\n\n### Tags\nImages can also take on tags, which are initially inferred based on the sub-directories. That is any directory below the top level directory. For example:\n\n```\ndata/\n    tag_1/\n        another_tag/\n            file_1.png\n            file_2.png\n            file_3.png\n    tag_2/\n        file_4.png\n        file_5.png\n        file_6.png\n```\n\nThese tags show up in the browser based UI after hovering over the **Tag** label.\n\n### Programmatic API\nOnce **iris** has been launched inside a directory and the `.iris` folder has been\nsetup, you can use the Python API to get the labels and make any more changes\nprogrammatically.\n\n#### Getting the list of files\nThe complete list of files can be returned using the following snippet. The `folder` argument to the `Query` class is the relative path to the folder where your images are stored.\n\n```python\nfrom iris.api.files import Query\n\nq = Query(folder=\"../images/\")\ndf = q.get_all_files()\n```\n\nExample output:\n\nid |\tpath |\tfilename |\tcategory |\ttags\n--|--|--|--|--|\n270 |\timages/heti/clf/ignore/2Sph8IbCgnU.png |\t2Sph8IbCgnU.png\t|heti |\t[clf, ignore]\n271 |\timages/heti/clf/ignore/1aiDVT31RRE.png |\t1aiDVT31RRE.png\t|heti |\t[clf, ignore]\n272 |\timages/heti/clf/ignore/4GLI-k4wmFg.png |\t4GLI-k4wmFg.png\t|heti |\t[clf, ignore]\n273 |\timages/heti/clf/ignore/-wuFyjSeLec.png |\t-wuFyjSeLec.png\t|meji |\t[clf, ignore]\n\n#### Modifying a file's attributes\nYou can modify any or all of the attributes of a file using the following snippet.\n\n```python\nfrom iris.api.files import Query\n\nq = Query(folder=\"../images/\")\nq.update_file(270, {\"category\": \"new_category\"})\n```\n\nThe second argument, `file_kwargs`, is a dictionary whose keys should correspond to one of the columns in the DataFrame. If the key provided does not exist, it will be ignored.\n\n## To do\n- [ ] Ability to change tags using drag and drop interface\n- [ ] Add new categories using browser UI\n- [ ] Write unit tests for JS frontend\n- [ ] Write unit tests for Python backend","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fismailuddin%2Firis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fismailuddin%2Firis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fismailuddin%2Firis/lists"}