purdue-arc/sphero-swarm
Sep 2025 – Oct 2025
2 Months active
Languages Used
Python
Technical Skills
Computer VisionObject DetectionPython ScriptingScriptingVideo ProcessingAprilTag Detection
Quasi_Mot0
PROFILE
Quasi_mot0
Andrew Mollen developed perception and video processing utilities for the purdue-arc/sphero-swarm repository, focusing on data generation and real-time analytics for robotics workflows. He built a Python-based video splitting tool and a YOLO object detection test script, enabling efficient extraction and annotation of video frames for training and evaluation. In subsequent work, Andrew implemented real-time AprilTag detection using OpenCV, adding visualization overlays, multi-tag analytics, and perspective transforms to support calibration and debugging. His contributions emphasized robust automation, data organization, and analytics-ready outputs, demonstrating depth in computer vision, Python scripting, and the practical application of object detection techniques in research environments.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
168
Activity Months2
Your Network
116 people
Work History
October 2025
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for purdue-arc/sphero-swarm: Delivered real-time AprilTag detection via webcam with visualization overlays, enabling immediate tag localization and debugging. Enhanced visualization with multi-tag connection, precise outlines, and perspective-warped views for improved analytics. Commits 033cd5748fe155bcf4d5f9d5caf59af2027acaac (April Tag Stuff) and 6b99b58618e1ae9f297539e3df78a0a9f9b0e8ae (april stuff) documented. No major bugs fixed this month; the focus was on feature development, stabilization, and data capture readiness. Business value includes accelerated vision-assisted swarm control workflows, improved calibration/analysis data, and reduced debugging time. Technologies demonstrated include Python, OpenCV, real-time video processing, computer vision techniques (tag detection, drawing overlays, perspective transforms), and persistence of tag corner data.
2 Commits • 1 Features
Oct 1, 2025October 2025 monthly summary for purdue-arc/sphero-swarm: Delivered real-time AprilTag detection via webcam with visualization overlays, enabling immediate tag localization and debugging. Enhanced visualization with multi-tag connection, precise outlines, and perspective-warped views for improved analytics. Commits 033cd5748fe155bcf4d5f9d5caf59af2027acaac (April Tag Stuff) and 6b99b58618e1ae9f297539e3df78a0a9f9b0e8ae (april stuff) documented. No major bugs fixed this month; the focus was on feature development, stabilization, and data capture readiness. Business value includes accelerated vision-assisted swarm control workflows, improved calibration/analysis data, and reduced debugging time. Technologies demonstrated include Python, OpenCV, real-time video processing, computer vision techniques (tag detection, drawing overlays, perspective transforms), and persistence of tag corner data.
October 2025
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 — purdue-arc/sphero-swarm: Delivered a Video Splitting Utility and YOLO-based Object Detection Test Script to accelerate perception development and data generation. Enhancements include auto-creation of an annotated_test_frames folder, frame-skipping to save every Nth frame, and a final summary print of frames saved. These changes improve data quality, reduce storage and processing costs, and provide a repeatable testing workflow for live webcam scenarios. Key commits include 58cff9df0f227d7a5071ce4cc7c697e75e63951d (Adding the new yolo test and video splitter) and 14f5bba1c5a031d438712cda04b013c826f12b6a (vixed video splitter, automatically creates files called annotated_test_frames).
September 2025
2 Commits • 1 Features
Sep 1, 2025September 2025 — purdue-arc/sphero-swarm: Delivered a Video Splitting Utility and YOLO-based Object Detection Test Script to accelerate perception development and data generation. Enhancements include auto-creation of an annotated_test_frames folder, frame-skipping to save every Nth frame, and a final summary print of frames saved. These changes improve data quality, reduce storage and processing costs, and provide a repeatable testing workflow for live webcam scenarios. Key commits include 58cff9df0f227d7a5071ce4cc7c697e75e63951d (Adding the new yolo test and video splitter) and 14f5bba1c5a031d438712cda04b013c826f12b6a (vixed video splitter, automatically creates files called annotated_test_frames).
Activity
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Quality Metrics
Correctness80.0%
Maintainability80.0%
Architecture70.0%
Performance75.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
AprilTag DetectionAprilTagsComputer VisionObject DetectionOpenCVPython ScriptingScriptingVideo Processing
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