This is a demo of the Dash interactive Python framework developed by Plotly.
Dash abstracts away all of the technologies and protocols required to build an interactive web-based application and is a simple and effective way to bind a user interface around your Python code. To learn more check out our documentation.
Try out the demo app here.
This demo lets you interactively explore different image processing filters. You can upload your own image or use the one provided with the demo.
Filters can be applied either to the whole image, or to selections created with one of the selection tools.
First create a virtual environment with conda or venv inside a temp folder, then activate it.
virtualenv dash-image-processing-venv
# Windows
dash-image-processing-venv\Scripts\activate
# Or Linux
source venv/bin/activate
Clone the git repo, then install the requirements with pip
git clone https://github.com/plotly/dash-image-processing.git
cd dash-image-processing
pip install -r requirements.txt
Run the app
python app.py
This app uses S3 to store user input images. To use this app locally, make sure to create a .env file in the root directory with the following content:
BUCKETEER_AWS_SECRET_ACCESS_KEY=***********
BUCKETEER_AWS_ACCESS_KEY_ID=***********
BUCKETEER_BUCKET_NAME=**********
All this information is given when you create a bucket in AWS. For Plotly devs, the bucket name is dash-image-processing-bucket.
If you are looking to host this app on the Dash Deployment Server, make sure:
- That you have linked a Redis database to your app.
- To configure S3 storage by adding the content of
.envas environment variables (in Settings).
This app wraps Pillow, a powerful image processing library in Python, and abstracts all the operations through an easy-to-use GUI. All the computation is done on the back-end through Dash, and image transfer is optimized through session-based Redis caching and S3 storage.
Recently, while we were experimenting with ImageJ, an image processing app in Java, we wondered if it was possible to bring two changes: port the app into a browser interface, and shift the computation to the backend (so that extremely large images can also be processed).
This is how we thought about making a Dash app that would wrap Pillow, the modern version of the Python Imaging Library. This was the natural thing to do because Dash itself is already based on Flask, and Plotly already has the graph objects for manipulating images. Adding S3 storage to keep the uploaded file and caching the operations with Redis were absolutely painless because of the easy integration with Python.
- Dash - Main server and interactive components
- Plotly Python - Used to create the interactive plots
- Pillow - Apply operations to images
- Boto S3 - Store User inputted images
- Redis - Cache the user input
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us. Although only a subset of Pillow is currently present, you are welcome to add any type of plugins, e.g. ML-based image processing. Just visit the project repo and make a PR with your addition: https://github.com/plotly/dash-image-processing
See also the list of contributors who participated in this project.
This project is licensed under the MIT License - see the LICENSE.md file for details
