plakakia

/πλακάκια

Python image tiling library for image processing, object detection, etc.

What is this? What is it going to be?

plakakia is an efficient image tiling tool designed to handle bounding boxes within images. It divides images into rectangular tiles based on specified parameters, seamlessly handling overlapping tiles. The tool assigns bounding boxes to tiles that fully contain them, and it also offers an option to eliminate duplicate bounding boxes. While the current version only supports fully contained bounding boxes, future updates will include support for partial overlap. plakakia can handle object detection and segmentation datasets.

Currently, the library offers online and offline modes for processing data (refer to the Usage section section below for more details):

• In the offline mode, one can use a config file and run a script once to process all data.
• In the online mode, the tile_image function allows processing of images of any dimension.

There are plans to expand plakakia's capabilities in the offline mode to handle images with more than 3 channels.

Performance

To ensure optimal performance, plakakia utilizes the multiprocessing and numpy libraries. This enables efficient processing of thousands of images without the use of nested for-loops commonly used in tiling tasks. For detailed benchmarks on various public datasets, please refer to the information provided below.

Installation

It is highly recommended that you create a new virtual environment for the installation:

1. Download and install Mamba (or Anaconda).
2. Create a virtual environment:
   mamba create -n plakakia jupyterlab nb_conda_kernels ipykernel ipywidgets pip -y
3. Activate the environment:
   mamba activate plakakia
4. Run the following command to install the library:
   pip install plakakia

Usage

A. Offline tile generation with a config file

make_tiles --config path/to/config.yaml

Here's an example config file.

B. Online tile generation

from plakakia.utils_tiling import tile_image

tiles, coordinates = tile_image(img, tile_size=100, step_size=100)

For more examples, check the examples folder.

Streamlit Demo App

You can run the demo app with the following command:

streamlit run demo/explore_tiling_output.py

And when you open http://localhost:8501 in your browser, you should see the following:

Benchmarks

Benchmarked on HP Laptop with specs: AMD Ryzen 5 PRO 6650U; 6 cores; 12 threads; 2.9 GHz

Dataset	Source	Formats (images/labels)	Number of images	tile_size	step_size	tiles generated	plakakia performance
Solar Panels v2	RoboFlow	jpg/COCO	112	150	50	3.075	1,11 sec
Traffic Signs	Kaggle	jpg/YOLO	741	300	200	1.695	2,8 sec
Hard Hat Workers v2	RoboFlow	jpg/YOLO	5.269	100	50	21.678	6,94 sec
Microsoft COCO dataset	RoboFlow	jpg/YOLO	121.408	200	150	177.039	3 min 4 sec

TODO list

☑️ Fix reading of classes from annotations (create a 'mapper' dictionary to map classes to numerical values).
☑️ Read settings from a file (e.g. json).
☑️ Removing all tiles with duplicate bounding boxes (that appear in other tiles).
☑️ Support other annotation formats (e.g. coco). (only input for now)
☑️ Provide tiling functionality without any labels needed.
☑️ Add support for segmentation tasks (tile both input images and masks).
☑️ Add a demo app for the users to be able to see the tiling applied on an image.
⬜️ Add less strict (flexible) duplicate removal methods to avoid missing bounding boxes.
⬜️ Consider bounding boxes in tiles if they partially belong to one.
⬜️ Support reading annotations from a dataframe/csv file.
⬜️ Make tiles with multidimensional data offline with config file (e.g. hdf5 hyperspectral images).

Want to contribute?

If you want to contribute to this project, please check the CONTRIBUTING.md file.