Inspiration
Color vision deficiency is the reduced ability to identify or differentiate colors. People having color vision deficiency have several difficulties in performing day to day activities from basic ones like reading traffic lights and signals, purchasing groceries or clothes to advanced ones like accessing technology and education. It leads to undesirable outcomes for instance; color confusion, unintended purchasing, incorrect interpretation of color encoded information, difficulty in learning and technology use. People going through color vision deficiency could experience frustration and stress associated with it.
What it does
Color Tag's motive is to alleviate the color confusion by labelling the colors of the image. When a user hovers over the image, Color Tag labels the color of the area of the image that the user is currently pointing to. This enable the individuals experiencing color vision deficiency to make more informed decisions, make technology and education easier to access and more accurate interpretation of color encoded information(maps, charts, graphs etc).
How we built it
This application is primarily built using python using computer vision library OpenCV. Pandas, numpy and matplotlib are the other libraries used.
Challenges we ran into
I have been programming with Python, but this is the first time I am using OpenCV and Flask. Challenges were in identifying and applying the specific APIs per the project needs within the timespan of the hackathon event.
Accomplishments that we're proud of
The greatest accomplishment was identifying a problem statement and develop a fully functional solution within the timeframe of the hackathon. The application is highly responsive and easy to use which were the primary goals.
What we learned
The positive impact this solution could have in a color vision deficient person's life.
What's next for Color Tag
As the next level,
- I am planning to integrate this application with cameras, scanners and spectacles.
- This feature could be enabled/disabled on need basis.
- Fine tune color classification depending on specific types of color vision deficiencies including Duetan, Protan, Tritan, and Achromatopsia.
- Develop ML and Deep Learning models for dynamic pictures.
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