Skip to main content

IconMatch

Easily select icons on the screen in any environment.

Showcasing bounding boxes and original image Showcasing candidate boxes functionality Showcasing realtime demo

This is part of the Hands Free Computing project. Built with OpenCV 3.12 and Python 3.8.

💜 Sponsors:

Sponsor us and we can add your link, banner or other promo materials!

💜 Follow NativeSensors:

Subscribe on Polar

Table of Contents

Installation

  1. Install from PyPI:
$ pip install iconmatch

Usage

You can use the functions as shown in demo.py as a default entry point.

In the below example, the main set of functions is called within a callback function, as this allows the threshold value to be controlled from a GUI in OpenCV.

Image Scanner:

import cv2 as cv

import IconMatch.IconMatch from ImageScanner

src = cv.imread("source to your image file")
scanner = ImageScanner(thersh = 100)

detected_rectangles = scanner.scan(src)
# list of [(x,y,w,h),(x,y,w,h), ... , (x,y,w,h)]

Screen Scanner:

import IconMatch.IconMatch from ScreenScanner

scanner = ScreenScanner(thersh = 100)

detected_rectangles = scanner.scan(bbox = (x,y,w,h))
# list of [(x,y,w,h),(x,y,w,h), ... , (x,y,w,h)]

RealTime demo:

python rt_demo.py

Key Features

  • Detection of areas with a high likelihood of being clickable icons.
  • Detection of closest rectangle to point of interest (be it gaze, or mouse as in the examples)

API

The current available APIs encompass what your image processing pipeline should contain. Both APIs are currently still experimental as I learn more about OpenCV and optimize code.

ImageScanner

Performs Canny detection on passed images and group overlapping rectangles

ScreenScanner

Scans your display, take screnshoots and call ImageScanners

Roadmap

  • Detect regions of interest with moderate accuracy
  • Detect candidate region based on proximity
  • Detect icon-like objects on the screen
  • [?] Context provision into regions of interest

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are genuinely appreciated.

  1. Fork the Project
  2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
  3. Commit your Changes (git commit -m 'Add some AmazingFeature')
  4. Push to the Branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Original Creator: Luis Zugasti - @luis__zugasti Original Creator blog: https://luiszugasti.me

Current Maintainer: Piotr Walas - @Piotr__Walas

Project Link: https://github.com/NativeSensors/IconMatch

Metadata

Release files for IconMatch 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for IconMatch 0.2.0
File Size Uploaded
iconmatch-0.2.0.tar.gz 11.4 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for IconMatch 0.2.0
File Interpreter ABI Platform
iconmatch-0.2.0-py3-none-any.whl Python 3 none any Details

Total release size: 23.3 kB

Release files / iconmatch-0.2.0.tar.gz

Download URL iconmatch-0.2.0.tar.gz
Size 11.4 kB
Tags Source
SHA-256 checksum
How to use checksums
c3c7f06ce272e84d0e913c14ff551592063912cf96671d18b6bf40e697fff3b3
BLAKE2b-256 checksum
How to use checksums
dfd40fc96784490e602450544f74908c6f8085c403ab55741867677b207ee02d
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.10.6

Release files / iconmatch-0.2.0-py3-none-any.whl

Download URL iconmatch-0.2.0-py3-none-any.whl
Size 11.9 kB
Tags Python 3
SHA-256 checksum
How to use checksums
063e5577b60b5ed71c37bb536f16dc2671f3a55f5edcc5650a847283c5b564f4
BLAKE2b-256 checksum
How to use checksums
51b9c42e2d4c1d950ec7d2cc28c563986510230180c5adc9c7f1706da5fa13c3
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.0 CPython/3.10.6

Release history Release notifications | RSS feed

This release

0.2.0 This release

2 release files

0.0.2

2 release files

0.0.1

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page