GODUGUNURI ASHOK REDDY
Professional Summary
Python Developer with 0 years of experience building real-time computer vision prototypes during undergraduate projects and a Python Full Stack traineeship. Expertise in Python for algorithm implementation and OpenCV and NumPy for image processing and landmark-based detection. Applied Matplotlib to visualize fatigue and posture metrics and used SQLite and SQL to persist session data. Used Git and GitHub for version control and VS Code for development while documenting demos and usage guides. Practiced object-oriented design and systematic debugging to improve low-light reliability and reduce frame-processing bottlenecks. Delivered features such as eye-blink, yawn, posture detection, voice alerts and snapshot-based alerting across two capstone projects. Looking for an entry-level Python Developer role to contribute computer-vision experience and expand full-stack capabilities.
Technical Skills
Work Experience
- Built reusable Python modules and image-processing pipelines using OpenCV and NumPy to prototype real-time detection features for trainee projects.
- Integrated OpenCV-based eye, yawn and posture detection routines into demo applications and used Matplotlib to visualize detection metrics.
- Implemented relational data storage using SQLite and crafted SQL queries to persist detection logs, session metadata and user settings.
- Applied Git for version control and GitHub for collaborative code review; developed and debugged code in VS Code during iterative sprints.
- Debugged and profiled Python code to identify frame-processing bottlenecks and improved inference stability for low-light webcam feeds.
- Documented implementation details, authored usage guides for demo applications, and presented project demonstrations to instructors for validation.
Projects
- Developed a real-time driver drowsiness detection application in Python using OpenCV to detect eye-blinks and facial landmarks.
- Implemented yawn detection via facial landmark analysis and threshold-based heuristics to reduce false positives in continuous video streams.
- Designed a user interface to display alerts and snapshots; integrated Matplotlib to generate per-session fatigue-score graphs for analysis.
- Improved low-light detection by applying histogram equalization and adaptive thresholding to camera frames before landmark extraction.
- Debugged algorithm edge cases and captured alert snapshots for post-mortem analysis to refine detection thresholds and heuristics.
- Developed a real-time posture and focus monitoring tool using OpenCV to detect face position and estimate screen distance.
- Implemented posture classification and triggered voice alerts in Python when distance or posture thresholds indicated unsafe behavior.
- Integrated brightness and distance tracking into session logs and visualized trends with Matplotlib for user feedback.
- Conducted parameter tuning and debugging across multiple webcam models to reduce false triggers and improve detection stability.
- Captured user snapshots and generated notifications when unsafe conditions persisted to support further analysis and iteration.
Education
Certifications
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