Annotate Images Like a Pro: Python Image Annotation Tool Walkthrough

Image Annotation Made Easy with DigitalSreeni's Python Tool
In this video, I walk you through my Python-based image annotation application and its associated tools, providing a step-by-step demo to help you get started.
Topics Covered:
--Installation of the Python library for image annotation, along with setting up Anaconda and configuring your environment.
- Creating new projects and adding 2D and multi-dimensional images (TIFF, CZI).
- Manual annotation of 2D images and slices from multi-dimensional images using polygon and rectangle tools.
- Semi-automatic annotations with the Segment Anything Model (SAM).
- Renaming and assigning colors to classes for better organization.
- Exporting annotations to various formats: COCO JSON, YOLO v8, labeled images, semantic images, Pascal VOC bounding boxes.
- Verifying exported annotations by reloading them into the program.
Additional Tools:
- Annotation statistics
- Combining JSON annotations
- Data splitting
- Patch extraction
- Data augmentation of images and annotations
Links:
GitHub repository: github.com/bns...
PyPI for pip install info: pypi.org/proje...
To Install:
pip install digitalsreeni-image-annotator
Once installed, simply type sreeni in your command prompt within the correct environment to launch the application.
You can download SAM models from the following links. Please be cautious about the large model on systems with limited memory.
github.com/ult...
github.com/ult...
github.com/ult...
github.com/ult...
It is recommended to place the SAM models in a directory from where you normally start the application to avoid multiple downloads of the same models from the Ultralytics server.

[@jesussoto8628]

This tool looks great Thanks for sharing

[@Brickkzz]

This is by far the best tool I’ve used. It’s much easier and more flexible than other online options. The interface is intuitive, and integrates smoothly with my YOLOv8 training workflows. Highly recommend for anyone in computer vision! Few very minor improvements: (1) add automated labelling (e.g. DINO+SAM, or custom YOLO that was pretrained on the dataset), (2) add an option Save As... for the projects, (3) editing the polygon of SAM-labelled instances (for minor corrections).

[@ponyspitsfire3035]

Awesome tool! Have you considered adding „auto“ bbox detection using a model such as Grounding DINO?

[@Xamy-]

Feedback is put a desaturated white green background and darkened green for the font color. Common trick to enhance readability.
Add a confirmation to deleting classes.
Allowing a user to choose a yolo model to assist with labelling in the same way as SAM
Otherwise interesting stuff :)

[@DigitalSreeni]

Thanks for your feedback. When you were referring to darkened green font color, I assume you men for the slices. I fixed that part and also added confirmation for deleting a class. It used to be there, but somehow lost that functionality. I will release the updated version after a few tests.
Using alternate, customized model for annotation assist is on my wish list. I personally like Mask R-CNN (Detectron2).

[@victorsilvadossantos2769]

Great piece of work! Thanks for sharing this!

[@srivathsansanthanam639]

I DIDN'T FIND A FREEWARE WHICH WAS USER FREINDLY WITH A GOOD UI. I WAS PLANNING TO DESIGN ONE EXACTLY SIMILAR TO THIS AND GIVE IT FREE TO THE COMMUNITY IN A YEAR OR SO.
YOU JUST DID IT

[@tahirak.7565]

Thankyou.Saved for later. Could you please make Video on yolo 11.

[@raghvendrabhargava8313]

This is a awesome job. I am going to use this right away.

[@cyberhard]

Very nice! Thanks for the release and the video.
The auto segmentation is a great tool. Auto labeling using one's own object detection model would be a great addition.
Have you considered supporting ONNX and OpenVINO? They both provide an increase in interference speed over the PyTorch model.

[@DigitalSreeni]

Auto-labeling using own trained model is something on my wish list. In fact, I had it for a couple of versions and had to remove it as it wasn't working well on Linux or mac. Thanks for the suggestion, reaffirms my wishes :)
I am relying on Ultralytics for SAM which uses Pytorch, hence the need for it.

[@akshatbhatnagar3571]

This is great tool. Thank you

[@ngocthienle8828]

Thanks for sharing this tool.

[@coder_zero]

This is sooooooo coooollllllll 😍🤩😍

[@TheTimtimtimtam]

Bless your wonderful work Sir, Thank you kindly.

[@DigitalSreeni]

You are very welcome

[@sanumioluwafemi7247]

Great tool. Can I use this with .bmp images?

[@sanumioluwafemi7247]

Well, I tried it and it worked perfectly

[@DigitalSreeni]

Yes, of course

[@inquisitiverakib5844]

can we get JSON format annotation from it?

[@DigitalSreeni]

Yes.