purdue-arc/sphero-swarm
Feb 2025 – Feb 2025
1 Month active
Languages Used
NumPyOpenCVPythonSciPy
Technical Skills
Computer VisionImage ProcessingObject DetectionObject TrackingOpenCVPython Development
Naunidha Singh
PROFILE
Naunidha Singh
Naman Sawhney enhanced the perception capabilities of the purdue-arc/sphero-swarm repository by developing robust LED detection and ball tracking features for autonomous control experiments. Leveraging Python, OpenCV, and NumPy, he implemented an OpenCV-based LED perception module and integrated object-level ball tracking to support experimental validation. He also introduced test and demo video assets, enabling rapid validation and benchmarking of perception algorithms. Updates to the LED perception script allowed for multiple video inputs and improved visualization, supporting reproducibility and streamlined experimentation. Minor path fixes further reduced runtime friction, reflecting a focused, iterative approach to improving experimental reliability and developer onboarding.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
1
Lines of code
190
Activity Months1
Your Network
116 people
Work History
February 2025
4 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for purdue-arc/sphero-swarm focused on enhancing perception capabilities for autonomous control and experimental validation. Delivered robust LED perception improvements with OpenCV-based detection, integrated ball tracking, and prepared perception-demo assets to accelerate experiments. Implemented small, targeted fixes to streamline runtime and improve reproducibility. The work aligns with our goals of reliable perception, repeatable experiments, and faster iteration cycles.
4 Commits • 1 Features
Feb 1, 2025February 2025 monthly summary for purdue-arc/sphero-swarm focused on enhancing perception capabilities for autonomous control and experimental validation. Delivered robust LED perception improvements with OpenCV-based detection, integrated ball tracking, and prepared perception-demo assets to accelerate experiments. Implemented small, targeted fixes to streamline runtime and improve reproducibility. The work aligns with our goals of reliable perception, repeatable experiments, and faster iteration cycles.
February 2025
Activity
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Quality Metrics
Correctness75.0%
Maintainability75.0%
Architecture60.0%
Performance50.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
NumPyOpenCVPythonSciPy
Technical Skills
Computer VisionImage ProcessingObject DetectionObject TrackingOpenCVPython DevelopmentVideo Processing
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