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https://github.com/smrfeld/dash-annotate-cv

Dash components for computer vision annotation tasks
https://github.com/smrfeld/dash-annotate-cv

annotations computer-vision dash data-labeling machine-learning plotly plotly-dash

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Dash components for computer vision annotation tasks

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# Dash Annotate CV - A dash library for computer vision annotation tasks



`dash_annotate_cv` is a Python Dash library for computer vision annotation tasks. It exposes reusable annotation components in a library format for `dash`.



![](readme_gif_image_labels.gif)

*Example of whole-image label annotations*



![](readme_gif_bboxs.gif)

*Example of bounding box annotations*



Supported tasks:

- [x] Annotation of images (whole image labels)

- [x] Annotating multiple labels per image

- [x] Annotating bounding boxes including labels

- [x] Support for COCO format



Roadmap for future tasks:

- [ ] Support for more annotation formats: YOLO, etc.

- [ ] Support for image segmentation tasks.

- [ ] Support for skeleton annotation tasks.

- [ ] Annotating video events

- [ ] Annotating video tags



**Note**: this library is not meant for production usage. It is meant to be used for quick prototyping and testing of annotation tasks.



## Getting started



1. (Recommended) Create a new conda environment.



    ```bash

    conda create -n dash_annotate_cv python=3.11

    conda activate dash_annotate_cv

    ```



2. Install using pip:



    ```bash

    pip install dash_annotate_cv

    ```



    Or local install from the repository:



    ```bash

    git clone https://github.com/smrfeld/dash-annotate-cv 

    cd dash-annotate-cv

    pip install -e .

    ```



3. Run the example as described next.



## Examples



The app can be launched in two ways:

1. A simple command line utility lets you get up and running quickly.

2. The app can be launched by writing a short Python script, which lets you use the annotation components in a flexible way in your own Dash app. We follow the practices of resuable components in Dash described in [All-in-one components for Dash](https://dash.plotly.com/all-in-one-components).



Each is described here briefly. You can also check out the [examples](examples).



### Command line utility



To get started labeling immediately, you can use a simple command line utility. You just need to write a config YAML file. You can see the [examples](examples) directory for more examples - as simple example is as follows:



```yaml

# Annotation mode

# image_labels = Annotate images with whole-image labels

# bboxs = Annotate bounding boxes in each image

mode: image_labels



# Label source

label_source:

  labels:

  - astronaut

  - camera

  - cat



# Image source

image_source:



  # Image source type

  source_type: list_of_files



  # List of files

  list_of_files:

  - ex_chelsea.jpg

  - ex_astronaut.jpg

  - ex_camera.jpg



# Storage (optional)

storage:

  storage_type: json

  json_file: example.json



# Options (optional)

options:



  # How many labels can be selected per image

  selection_mode: single

```



To launch the app, simply run:



```bash

dacv conf.yml

```



Navigate to the default address `http://127.0.0.1:8050/` in your browser to use the app.



Use `dacv -h` for a complete list of options. Or check out the [examples](examples) directory for more examples.



### Python



You can use the library in your own Dash app. This is recommended as a more flexible way to create your own annotation interface. The following example shows how to use the library to annotate images with labels.



```python

# Import the library

import dash_annotate_cv as dac



# Other imports

from dash import Dash, html

import dash_bootstrap_components as dbc

from skimage import data



# Load some images

images = [ ("chelsea",data.chelsea()), ("astronaut",data.astronaut()), ("camera",data.camera()) ] # type: ignore

images_pil = [ (name,Image.fromarray(image)) for name,image in images ]



# Set up the image and label sources

image_source = dac.ImageSource(images=images_pil)

label_source = dac.LabelSource(labels=["astronaut", "camera", "cat"])



# Set up writing

storage = dac.AnnotationStorage(storage_types=[dac.StorageType.JSON], json_file="annotations.json")



app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])

app.layout = dbc.Container([

    html.H1("Annotate Images"),

    dac.AnnotateImageLabelsAIO(label_source, image_source, annotation_storage=storage)

    ])

app.run(debug=True)

```



The result is shown in the gif above. Check out the [examples](examples) directory for more examples.



### Loading the annotations & format



Loading the annotations is as simple as:



```python

with open("annotations.json","r") as f:

    annotations = dac.ImageAnnotations.from_dict(json.load(f))

```



See the `ImageAnnotations` class for more information on the format.



## Dev



Some useful references:



* [All-in-one components for Dash](https://dash.plotly.com/all-in-one-components)

* [Image annotation in Dash](https://dash.plotly.com/annotations)