VisionPilot

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  in local

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  in demo link

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  the public url

Inspiration

Traditional computer vision systems often rely on fixed pipelines that process every image in the same way, even when image quality varies. We wanted to explore a more adaptive approach: a vision system that evaluates the input, identifies useful processing steps, and helps users understand what should happen next.

What it does

VisionPilot is an experimental, agentic computer vision application built with OpenCV. It provides an interactive interface for analyzing images, evaluating image sharpness, identifying image regions, and exploring processing decisions through an agent-style workflow.

The goal is to make computer vision more accessible and adaptable by combining visual analysis with decision-oriented processing.

How we built it

We built VisionPilot using Python, OpenCV, and Streamlit. OpenCV handles image-processing operations, while Streamlit provides the interactive application interface. The project includes image-quality analysis, region-based analysis, and an agent-decision workflow.

We also prepared the project with cloud integration in mind, including AWS as part of the competition's technology ecosystem.

Challenges we ran into

One of our main challenges was connecting image-analysis results to the agent's decision-making workflow. Integrating the different components required careful handling of processing outputs, application state, and errors. We also had to distinguish between features that were working and those that still required further implementation and testing.

Accomplishments that we're proud of

We created a working foundation for an interactive computer vision application, bringing image analysis and a user-friendly interface together. We also established a foundation for experimenting with more adaptive, decision-driven vision workflows.

What we learned

We gained practical experience with OpenCV-based image processing, building interactive applications with Streamlit, debugging integration issues, and designing software around an agentic workflow. We also learned the importance of validating each component independently before combining it into a larger system.

What's next for VisionPilot

Our next steps are to strengthen the agent's decision logic, integrate and validate object detection and deep-learning capabilities, expand video-processing support, and complete the AWS integration. We aim to evolve VisionPilot into a more capable system that can select and execute appropriate computer vision operations based on the input and the task.

Built With

• ai
• computingagentic
• intelligenceawscloud
• machine-learning
• processingartificial
• pythonopencvcomputer
• visionstreamlitimage