purdue-arc/sphero-swarm
Oct 2025 – Oct 2025
1 Month active
Languages Used
Python
Technical Skills
Argument ParsingComputer VisionDeep LearningDepthAINetwork CommunicationObject Detection
Jack-Crowley
PROFILE
Jack-crowley
During October 2025, John Crowley developed real-time camera integration for the purdue-arc/sphero-swarm repository, enabling object detection and tracking using YOLOv8 and DepthAI. He implemented a system to freeze initial object IDs, supporting stable tracking for robotics perception pipelines. John improved Sphero Spotter’s stability by addressing thread synchronization and reorganizing module paths for clearer code structure. He enhanced input handling by adding argument parsing, supporting both webcam and video file streams, and refactored the main logic to use these streams. Leveraging Python, computer vision, and network communication, his work increased reliability, flexibility, and maintainability for real-time perception systems.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
697
Activity Months1
Your Network
18 people
Work History
October 2025
3 Commits • 2 Features
Oct 1, 2025Month: 2025-10 | Repository: purdue-arc/sphero-swarm Concise monthly summary focusing on business value and technical achievements: - Key features delivered: - Real-time Camera Integration with YOLOv8 Object Detection and DepthAI-based Tracking, enabling live perception with optional freezing of initial object IDs for consistent tracking. This supports monitoring and labeling of objects in perception pipelines. - (Reference commit: 59a56600be14aa4ade7c6796708f47f0848ee8e7) - Major bugs fixed: - Sphero Spotter stability improvements: fixed thread synchronization by ensuring the main thread waits for the spawned thread to complete, and reorganized the SpheroCoordinate module path for clarity. (Reference commit: 922c5b3c68d7a5450bbc58541b9256099d08b571) - Input handling and usage enhancements: - Added argument parsing, established connections for Sphero Spotter, introduced new input stream classes for webcams and video files, refactored main logic to use streams and arguments, and updated the test client to communicate via JSON. This enables flexible data ingestion and easier testing. (Reference commit: b8cf2f2ee21d52e48b8576d812035ff7838d3f53) - Overall impact and accomplishments: - Improved real-time perception capabilities and reliability for perception stacks used in robotics demos and deployments. - Enhanced stability and maintainability through clearer code organization and synchronization fixes. - Expanded data ingestion options and testing workflow, accelerating integration with new data sources. - Technologies/skills demonstrated: - YOLOv8, DepthAI, real-time object detection and tracking - Thread synchronization and concurrency handling - Command-line argument parsing, input streams for camera/video, JSON-based communication - Code organization and test/client tooling improvements Business value: - Faster time-to-perception demonstrations with robust tracking. - Fewer runtime threading issues and clearer module structure reduce maintenance costs. - Flexible data ingestion enables broader testing and easier integration with external perception pipelines.
3 Commits • 2 Features
Oct 1, 2025Month: 2025-10 | Repository: purdue-arc/sphero-swarm Concise monthly summary focusing on business value and technical achievements: - Key features delivered: - Real-time Camera Integration with YOLOv8 Object Detection and DepthAI-based Tracking, enabling live perception with optional freezing of initial object IDs for consistent tracking. This supports monitoring and labeling of objects in perception pipelines. - (Reference commit: 59a56600be14aa4ade7c6796708f47f0848ee8e7) - Major bugs fixed: - Sphero Spotter stability improvements: fixed thread synchronization by ensuring the main thread waits for the spawned thread to complete, and reorganized the SpheroCoordinate module path for clarity. (Reference commit: 922c5b3c68d7a5450bbc58541b9256099d08b571) - Input handling and usage enhancements: - Added argument parsing, established connections for Sphero Spotter, introduced new input stream classes for webcams and video files, refactored main logic to use streams and arguments, and updated the test client to communicate via JSON. This enables flexible data ingestion and easier testing. (Reference commit: b8cf2f2ee21d52e48b8576d812035ff7838d3f53) - Overall impact and accomplishments: - Improved real-time perception capabilities and reliability for perception stacks used in robotics demos and deployments. - Enhanced stability and maintainability through clearer code organization and synchronization fixes. - Expanded data ingestion options and testing workflow, accelerating integration with new data sources. - Technologies/skills demonstrated: - YOLOv8, DepthAI, real-time object detection and tracking - Thread synchronization and concurrency handling - Command-line argument parsing, input streams for camera/video, JSON-based communication - Code organization and test/client tooling improvements Business value: - Faster time-to-perception demonstrations with robust tracking. - Fewer runtime threading issues and clearer module structure reduce maintenance costs. - Flexible data ingestion enables broader testing and easier integration with external perception pipelines.
October 2025
Activity
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Quality Metrics
Correctness73.4%
Maintainability73.4%
Architecture73.4%
Performance73.4%
AI Usage33.4%
Skills & Technologies
Programming Languages
Python
Technical Skills
Argument ParsingComputer VisionDeep LearningDepthAINetwork CommunicationObject DetectionObject TrackingPythonPython DevelopmentReal-time SystemsWeb DevelopmentYOLOv8
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