# OpenCV Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/opencv
> Markdown URL: https://aitinkerers.org/technologies/opencv.md
> Technology record last updated: 2026-09-18T15:13:38Z
> Generated: 2026-09-23T01:46:49Z

OpenCV (Open Source Computer Vision Library) is the world’s largest, Apache 2-licensed library for real-time computer vision and machine learning.

OpenCV is the definitive, cross-platform library for computer vision, packing over 2500 optimized algorithms for tasks like object detection, 3D reconstruction, and image processing. It’s built on C++ but offers robust interfaces for Python, Java, and MATLAB, supporting real-time applications across Windows, Linux, macOS, and mobile platforms. Originally an Intel initiative, it’s now managed by the non-profit Open Source Vision Foundation. With over 40 million estimated monthly downloads, it’s the go-to tool for major companies (Google, Microsoft, IBM) and startups alike, driving everything from automated surveillance to advanced robotics.

- Official technology site: https://opencv.org
- Public AI Tinkerers demos and talks: 26
- Result page: 1 of 2

## Recent Public Talks and Demos

### [AI powered software for Physics Laboratories](https://lahore.aitinkerers.org/talks/rsvp_MpYgRd0NfCY)

The project is about building a software that integrates a streamlined UI with AI tools like object tracking and OCR, to help young physics laboratory students validate physical theories and principles from lab video footage. I'll demo the software live, either on my laptop or a windows machine. I'll walk through the software functions using actual lab videos. I'll walk through the code design and main components of the software.

- Event context: AI Tinkerers Lahore: September 19, 2026 — 2026-09-19 — Lahore
- Public talk page: https://lahore.aitinkerers.org/talks/rsvp_MpYgRd0NfCY

### [From Rehabilitation to Flapping Wings: Real-Time Perception Driving Physical Systems](https://tokyo.aitinkerers.org/talks/rsvp_c8643kvumj0)

I build AI perception systems that drive physical robots. Three projects, one thread: cameras and sensors feeding real-time decisions into motors, navigation, and flight. First, the AI motion-evaluation module I built during my internship at Genrobotics for G-KAI, a 4-DOF rehab arm, a camera-based system that scores a patient's movement in real time and drives the multi-motor control loop. Second, an amphibious robot I built independently (grant-funded, IEEE-published) that fuses LiDAR and camera for autonomous SLAM navigation on land and water. Third, a biomimetic ornithopter (flapping-wing UAV, design patent filed) from my startup, Ornistra Intelligence. On stage, I'll show a live recreation of the motion-tracking pipeline on a laptop, real SLAM visualization from the amphibious robot, architecture diagrams for each system, and photos of the actual ornithopter prototype.

- Event context: AI Tinkerers Tokyo - September 1st Meetup — 2026-09-01 — Tokyo
- Public talk page: https://tokyo.aitinkerers.org/talks/rsvp_c8643kvumj0

### [Edge-AI Kindergarten Attendance: Automating Check-ins with Computer Vision & Local LLM Workflows](https://wellington.aitinkerers.org/talks/rsvp_s0e9vu8YVco)

I built an automated, privacy-first facial recognition attendance system for daycares to replace manual paper check-in logs, utilizing a local Python, OpenCV, DeepFace, Flask, and SQLite stack. To bridge computer vision with generative AI, the system integrates a local open-weights LLM via Ollama and Qwen. During the live demo, I will showcase how real-time camera frames trigger automated face verification, log attendance locally into SQLite, and instantly invoke the local LLM endpoint (/api/ai-summary) to generate professional natural language daily attendance reports, pattern summaries, and administrative notes completely offline. Example Pipeline & Output: The system serializes recent database rows into a structured markdown text log (e.g., - Time: 10:05:05 | Guardian: Mahesh Endla | Child: Hrithvik Endla) and passes it through strict prompt constraints. This produces professional markdown reports featuring daily attendance patterns, anomaly flags, and administrative notes: Markdown **Daily Summary Report** **Date:** August 11, 2026 **Attendance Patterns:** Today was a busy day with multiple check-ins for Hrithvik Endla at approximately 10:05 AM by his guardian, Mahesh Endla. **Administrative Note:** Please confirm with the guardian regarding check-in frequency to ensure records are accurately reflected.

- Event context: AI Tinkerers Wellington: Inaugural Meetup · 26 August 2026 — 2026-08-26 — Wellington
- Public talk page: https://wellington.aitinkerers.org/talks/rsvp_s0e9vu8YVco

### [Your Webcam Knows the Geometry - Real-Time Relighting & Single-Shot Novel Views](https://lausanne.aitinkerers.org/talks/rsvp_cMMuinG2WV4)

Two single-view inverse-rendering systems that reconstruct scene geometry from one camera and re-render it under conditions never captured — one changes the light, the other changes the viewpoint, both fast enough to ship. The Relighting part decomposes a live webcam frame into geometry + material (normals/albedo/shading) and re-renders it under any lighting in real time (~24 FPS), showcasing a variety of lighting conditions (multiple colored lights, orbiting and rainbow-rotating lights) The Novel View Synthesis builds a 3D representation of the scene using Gaussian Splatting and renders it from a different angle. It currently runs at 5 FPS but we are hoping to get closer to real time soon.

- Event context: AI Tinkerers Lausanne June 2026 Meetup — 2026-06-25 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_cMMuinG2WV4

### [Porting Segment Anything: SAM2 in ONNX/C++, SAM3 in thin scripts](https://lausanne.aitinkerers.org/talks/rsvp_HLLk1mziQvk)

I built a portable interactive segmentation stack around Segment Anything: a C++/ONNX Runtime wrapper for SAM2 plus thin SAM3 image/video demo scripts that run on Windows CUDA or macOS Apple Silicon. In the demo I’ll show prompt-driven segmentation and video propagation, then pop the hood on how I split the model/runtime pieces so the same workflow runs outside research notebooks.

- Event context: AI Tinkerers Lausanne April 2026 Meetup — 2026-04-30 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_HLLk1mziQvk

### [Reverse-Engineering Video Editing Styles into Programmatic Templates via AI](https://dublin.aitinkerers.org/talks/rsvp_6EoN2x9ohBc)

I am building a feature for my video automation platform (shablon.co) that eliminates the manual toil of replicating complex video formats. Instead of manually adjusting cuts and keyframes, a user inputs a reference video. The system processes the file, analyzes the visual and auditory components (cut frequency, typography, motion graphics, transitions, and pacing), and extracts these parameters into a structured, programmatic template. It essentially reverse-engineers a final render back into a parameterized blueprint that can be instantly applied to fresh raw footage.

- Event context: AI Tinkerers Dublin Meetup — Baseline, April 9, 2026 — 2026-04-09 — Dublin
- Public talk page: https://dublin.aitinkerers.org/talks/rsvp_6EoN2x9ohBc

### [CyberRunner: How We Open-Sourced the AI That Beat Humans](https://zurich.aitinkerers.org/talks/rsvp_95B2B3fk6Sc)

CyberRunner is the first AI-driven robotic system to learn and master the physical "Labyrinth" marble game through reinforcement learning, achieving times that surpass the world record set by humans. This talk explores the transition from a laboratory experiment to a fully open-sourced hardware and software stack designed to democratize high-speed, high-precision robotic control.

- Event context: AI Tinkerers Zurich April 9th — 2026-04-09 — Zürich
- Public talk page: https://zurich.aitinkerers.org/talks/rsvp_95B2B3fk6Sc

### [Herbie The Love Bug](https://columbus.aitinkerers.org/talks/rsvp_uy62C-hYfr0)

I’ll be presenting Herbie, a kids ride on car converted to look like the Herbie beetle car from the old movie. Its an autonomous sidewalk vehicle built on NVIDIA Jetson that follows people. The project combines classical computer vision (OpenCV and YOLO v11) with real-time control logic to enable vision-only navigation. I’ll walk through the hardware stack, perception pipeline, control challenges at higher speeds, and how I’m exploring learned models for smoother trajectory control.

- Event context: AI Tinkerers - Columbus March Meetup — 2026-03-02 — Columbus
- Public talk page: https://columbus.aitinkerers.org/talks/rsvp_uy62C-hYfr0

### [I gave my reachy mini a brain](https://nyc.aitinkerers.org/talks/rsvp_DmKV1qTMbSw)

Reachy Mini robot with an OpenClaw brain

- Event context: 🦞Demo Night: OpenClaw ft Convex — 2026-02-17 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_DmKV1qTMbSw

### [Smart dombila](https://conakry.aitinkerers.org/talks/rsvp_Fp7qxkgADuk)

Poubelle intelligente

- Event context: AI Tinkerers – Conakry: Inaugural AI Conference @ Afrinov Tech Expo — 2026-02-11 — Conakry
- Public talk page: https://conakry.aitinkerers.org/talks/rsvp_Fp7qxkgADuk

### [The Real Santa Claus by Minds & Models](https://prague.aitinkerers.org/talks/rsvp_jzk6DE6Kn10)

We will present our system for real time content personalization of ad screens.

- Event context: AI Tinkerers Prague: Annual Christmas Meetup 2025 — 2025-12-16 — Prague
- Public talk page: https://prague.aitinkerers.org/talks/rsvp_jzk6DE6Kn10

### [Blob Oracle - What if the world's smartest AI met the world's smartest slime mold...](https://lausanne.aitinkerers.org/talks/rsvp_4Gv-hcD5kco)

I'm really not convinced that language is synonymous with intelligence, so, instead of asking ChatGPT for life advice, I decided to ask.... A slime mold. Physarum polycephalum (The Blob) is a unicellular organism that is weirdly intelligent, considering that it doesn't have a brain or nervous system. Soooo what if we combined it with an LLM? 👀

- Event context: AI Tinkerers Lausanne December 2025 Meetup — 2025-12-03 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_4Gv-hcD5kco

### [Super Coool Super Awesome Tennis Serve Analyzer](https://montreal.aitinkerers.org/talks/rsvp_4lDuqbxrR9s)

I love playing tennis, my serve is wildly inconsitent and cost me a lot of games. Instructors are pricey and I'm cheap and don't want to shell out 100$ an hour for someone to feeed me a handle full of tips that I could have googled myself. logically to improve ones serve on might actually just go and practice but that would be far to simple. instead I thought the best way to improve my tennis serve would be to sit behind a keyboard and build an web-app powered by AI to tell me how I'm messing up my serve. It got cold and snow came so I wasn't able to actually use it :( but In the presentation I will walk through the implementaiton details and give a demo

- Event context: AI Tinkerers Montreal: Demo Night — November 20, 2025 — 2025-11-20 — Montreal
- Public talk page: https://montreal.aitinkerers.org/talks/rsvp_4lDuqbxrR9s

### [Gourd Grief! My Pumpkin Won't Stop Talking (Thanks, LLMs)](https://chicago.aitinkerers.org/talks/rsvp_DgOGZm0-4T0)

Ever wondered what a pumpkin would say if it could see and speak? In this demo, I'll show you how I combined a Raspberry Pi , computer vision, and large language models to create an interactive talking jack-o'-lantern that responds to what it sees. Just in time for Halloween.

- Event context: AI Tinkerers Chicago October Meetup ft Programmers Inc — 2025-10-14 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_DgOGZm0-4T0

### [Instant Custom Jewelry: A Vision + LLM pipeline that sizes rings, generates CAD, and prices in under 90 seconds](https://dc.aitinkerers.org/talks/rsvp_5CVz16ExsNA)

I’ll demo a working pipeline that turns a quick hand scan into a priced, manufacturable ring design—live, no slides. Flow (end-to-end): (1) WebRTC camera feed captures the customer’s hand; (2) a small CV model + OpenCV infers ring size from finger landmarks and scale cues; (3) a JSON spec is composed via an LLM prompt (style, metal, stone size, budget); (4) parametric CAD is generated (OpenSCAD) and exported to STL; (5) a geometry pass computes volume/weight for price; (6) a Shopify draft order is created with thumbnails and options. What I’ll show: latency breakdowns, prompt templates, failure modes (skin tone/lighting, hand pose), and tradeoffs between on-device vs. API inference. I’ll open the repo and walk through the exact modules so builders can replicate this for any mass-customization retail flow.

- Event context: AI Tinkerers x Google: DC Metro Meetup (October 2, 2025) — 2025-10-02 — DC
- Public talk page: https://dc.aitinkerers.org/talks/rsvp_5CVz16ExsNA

### [Computer Vision](https://bogota.aitinkerers.org/talks/rsvp_JgUAbfmsmqM)

Este programa implementa un sistema de detección de objetos en tiempo real utilizando un modelo preentrenado de TensorFlow (SSD MobileNet v2) y la librería OpenCV para la visualización.

- Event context: AI Tinkerers Bogotá Septiembre 2025 — 2025-09-25 — Bogotá
- Public talk page: https://bogota.aitinkerers.org/talks/rsvp_JgUAbfmsmqM

### [Structured Outputs & Batch Processing w/AI](https://nyc.aitinkerers.org/talks/rsvp_G38IYdS2Bb8)

A short overview of some code that does batch processing and when and where you'd want to build it into your workflows. Structured outputs bring their own development challenges, I'll speak to a couple of things I've noticed from using structured outputs over the last 12 months.

- Event context: Building AI Agents with Google Cloud AI — 2025-06-25 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_G38IYdS2Bb8

### [3D reconstruction from monocular images](https://nyc.aitinkerers.org/talks/rsvp_5lZNDxFUnzY)

A workflow that takes in 2D pictures of a meal and creates real-world accurate 3D meshes and a scene. Why? 3D reconstruction from monocular images is a rapidly evolving research area in computer vision with significant applications in food image analysis. The ability to reconstruct 3D food models from single 2D eating occasion images in real-world physical units allows users to share food experiences in three dimensions and provides crucial information about food portions, facilitating the tracking of individual nutrition intake. However, 3D reconstruction presents unique challenges that make it particularly valuable to evaluate the robustness and capability of existing computer vision algorithms.

- Event context: Demos and Dim Sum with Deel and Apollo — 2025-06-03 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_5lZNDxFUnzY

### [SpectroCVT-Net: Diagnóstico de Alzheimer con IA](https://manizales.aitinkerers.org/talks/rsvp_RYM62co_wN0)

Presentaré SpectroCVT-Net, una nueva arquitectura de aprendizaje profundo que desarrollamos para clasificar la enfermedad de Alzheimer (EA) y otras demencias utilizando espectrogramas de EEG. Haré una demostración del código del modelo y explicaré cómo combina capas convolucionales para la extracción de características locales con un Vision Transformer para el análisis del contexto global. La demostración cubrirá la arquitectura, el proceso de entrenamiento y mostrará cómo utilizamos Grad-CAM para la interpretabilidad, resaltando qué partes del espectrograma el modelo se enfoca para hacer predicciones.

- Event context: Tercer Encuentro de AI Tinkerers Manizales — 2025-04-30 — Manizales
- Public talk page: https://manizales.aitinkerers.org/talks/rsvp_RYM62co_wN0

### [SoftballCV](https://nashville.aitinkerers.org/talks/rsvp_SVObkZgf6tg)

An opencv and YOLO implementation that tracks a softball game in order to "score" the game and keep detailed softball statistics.

- Event context: AI Tinkerers Nashville – April 9th, 2025 — 2025-04-09 — Nashville
- Public talk page: https://nashville.aitinkerers.org/talks/rsvp_SVObkZgf6tg

### [Wildfire prediction through Mobile Device Pings](https://toronto.aitinkerers.org/talks/rsvp_rB4CJR7UnJI)

Wildfires are a growing threat, with devastating consequences for communities and ecosystems. Inspired by recent events in LA, I'm working on a system that uses ConvLSTM (Convolutional Long Short-Term Memory) to predict wildfire spread based on crowd-sourced mobile device location data. The demo will showcase a live walkthrough of the model in action. I’ll start by showing how location pings and time-series data from nearby users are preprocessed into spatial-temporal inputs. Then, I'll dive into the ConvLSTM code, explaining how it processes this data to generate wildfire likelihood heatmaps over time. To bring the predictions to life, I’ll visualize the output heatmaps as dynamic animations, representing how a wildfire might spread in real-time. I’ll also briefly highlight how external factors like wind direction, temperature, and vegetation type can be incorporated using public APIs and how they influence predictions. This isn't a polished product, but a tinkerer's attempt to explore how accessible data and open-source AI tools can solve real-world problems!

- Event context: AI Tinkerers Toronto - January 2025 Meetup at Google **sold out** — 2025-01-30 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_rB4CJR7UnJI

### [PitchPerfect](https://bengaluru.aitinkerers.org/talks/rsvp_s2EmAktEju8)

PitchPerfect is an AI Agent for dating on Hinge

- Event context: AI Tinkerers - Bengaluru - January Meetup — 2025-01-23 — Bengaluru
- Public talk page: https://bengaluru.aitinkerers.org/talks/rsvp_s2EmAktEju8

### [Seguridad Infantil: El Uso de Cámaras de Video e IA para el Cuidado de Niños](https://bogota.aitinkerers.org/talks/rsvp_GHOgUtTJPL0)

El proyecto "Seguridad Infantil" tiene como meta principal el desarrollo de un sistema de monitoreo inteligente que emplea cámaras de video equipadas con inteligencia artificial (IA) para salvaguardar la seguridad y el bienestar de los niños. Este sistema está diseñado para ofrecer a padres y cuidadores una herramienta efectiva que les permita supervisar a los menores, detectar situaciones de riesgo y responder de manera ágil ante posibles emergencias. El proyecto incluye varios procesos clave, tales como: Redes Neuronales Convolucionales (CNN): Estas son fundamentales para la detección y clasificación de imágenes. Se emplearán técnicas de transferencia de aprendizaje, utilizando conjuntos de datos específicos para entrenar modelos que mejoren la precisión al identificar situaciones de riesgo. Análisis de Video en Tiempo Real: La implementación de IA permitirá analizar transmisiones de video en vivo, facilitando la identificación de comportamientos peligrosos en tiempo real. Análisis Predictivo: Se aplicarán modelos de aprendizaje automático que examinarán datos históricos para anticipar comportamientos de riesgo e identificar patrones relacionados con la victimización. Estos modelos se alimentarán con información sobre interacciones reales de peligro. Modelos de Redes Neuronales Recurrentes (RNN): Se utilizarán RNN para el análisis de series temporales, lo que permitirá comprender las variaciones en el comportamiento de un niño a lo largo del tiempo y detectar señales tempranas de riesgo. Privacidad Diferencial: Se garantizará que los sistemas de IA cumplan con normativas de protección de datos, como el GDPR o el COPPA, mediante la implementación de técnicas de privacidad diferencial. Esto permitirá a las organizaciones obtener información valiosa sin poner en riesgo la privacidad de los menores.

- Event context: AI Tinkerers Bogotá - Women Edition — 2024-11-28 — Bogotá
- Public talk page: https://bogota.aitinkerers.org/talks/rsvp_GHOgUtTJPL0

### [Applying 4o Vision Finetuning to Chemistry Diagrams](https://singapore.aitinkerers.org/talks/rsvp_7w9joR3W_oI)

The task is to extract student's attempts for chemistry diagram questions. These diagrams are a graph with nodes and edges. Using VLLMs out of the box often results in the model correcting the chemistry equations or missing key notation. Here we explore Vision Finetuning, and see how far we can go with less than 10 hand-labelled examples. Kuang Wen and I will show the data we have, the augmentation techniques, and our current demo app comparing finetuned with non-finetuned.

- Event context: AI Tinkerers Singapore: 3rd Meetup - November 19th, 2024 — 2024-11-19 — Singapore
- Public talk page: https://singapore.aitinkerers.org/talks/rsvp_7w9joR3W_oI

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## More Results

- Next: https://aitinkerers.org/technologies/opencv.md?page=2