https://github.com/ismailuddin/iris
🖥 Web platform for easy labelling and management of 🏞 image data labelling
https://github.com/ismailuddin/iris
data-labeling data-management machine-learning python
Last synced: 3 months ago
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🖥 Web platform for easy labelling and management of 🏞 image data labelling
- Host: GitHub
- URL: https://github.com/ismailuddin/iris
- Owner: ismailuddin
- License: mit
- Created: 2020-03-01T17:31:30.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-01-05T11:01:24.000Z (over 3 years ago)
- Last Synced: 2025-12-15T22:28:02.636Z (6 months ago)
- Topics: data-labeling, data-management, machine-learning, python
- Language: JavaScript
- Homepage:
- Size: 10.3 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 18
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Iris - Data labelling
# Iris
> A browser based UI for labelling image files that tracks the labels using a database, rather than using folders and paths
## Why use Iris?
* Label thousands of images (or possibly millions?) easily without having to use a file manager that slows down with too many files.
* Iris uses pagination to view subsets of your data, so the platform doesn't slow down with large quantities of images.
* Keep track of all your labels using a database.
* 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.
* Simple and easy to use drag and drop UI.
* Images can be labelled using simple drag and drop interactions, making it easy for anyone to use.
## Installation
To install **iris**, use the following command:
```shell
$ pip install iris-image-labelling
```
## Usage
Once installed, **iris** registers as a command in your terminal that is accessible from any directory.
**Iris** presumes your data is initially organised in a directory that has sub-directories for every category of your data **may** take on.
```
data/
|---category_1/
|---file_1.png
|---file_2.png
|---category_2/
|---file_3.png
|---file_4.png
```
The folders are initially used to deduce what categories your data can take on. They **do not** need to contain any data / images in them.
With the following project structure set up, navigate to the parent directory of `data/` and then launch **iris** as follows from the terminal:
```shell
$ iris launch -f data
```
**Iris** launches by default on port 5000, from where you can go on and begin labelling your data.
Argument | Description
--|--
-f | Folder to build database from
-h | The host to run the server on
-p | The port number to run the server on
### Reorganise data
To 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.
This will re-organise the files amongst the folders according to the new labels.
### Tags
Images 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:
```
data/
tag_1/
another_tag/
file_1.png
file_2.png
file_3.png
tag_2/
file_4.png
file_5.png
file_6.png
```
These tags show up in the browser based UI after hovering over the **Tag** label.
### Programmatic API
Once **iris** has been launched inside a directory and the `.iris` folder has been
setup, you can use the Python API to get the labels and make any more changes
programmatically.
#### Getting the list of files
The 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.
```python
from iris.api.files import Query
q = Query(folder="../images/")
df = q.get_all_files()
```
Example output:
id | path | filename | category | tags
--|--|--|--|--|
270 | images/heti/clf/ignore/2Sph8IbCgnU.png | 2Sph8IbCgnU.png |heti | [clf, ignore]
271 | images/heti/clf/ignore/1aiDVT31RRE.png | 1aiDVT31RRE.png |heti | [clf, ignore]
272 | images/heti/clf/ignore/4GLI-k4wmFg.png | 4GLI-k4wmFg.png |heti | [clf, ignore]
273 | images/heti/clf/ignore/-wuFyjSeLec.png | -wuFyjSeLec.png |meji | [clf, ignore]
#### Modifying a file's attributes
You can modify any or all of the attributes of a file using the following snippet.
```python
from iris.api.files import Query
q = Query(folder="../images/")
q.update_file(270, {"category": "new_category"})
```
The 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.
## To do
- [ ] Ability to change tags using drag and drop interface
- [ ] Add new categories using browser UI
- [ ] Write unit tests for JS frontend
- [ ] Write unit tests for Python backend