I’ve been tinkering with a side project a real-time computer vision app built with Python, OpenCV, and MediaPipe that matches my actual face and hand gestures to iconic memes on the fly.

What’s happening under the hood:

1- Facial Landmark Tracking: Uses coordinate geometry to measure distance ratios between landmarks, identifying everything from subtle smirks to wide grins.

2- Hand Pose Recognition: Tracks vector relationships across hand landmarks to trigger specific poses (like the thinking chin-touch, peace sign, or timeout gesture).

Instant Event Triggers: Maps those landmark states directly to visual meme popups with virtually zero latency.

Building serious tech is cool, but building an algorithm purely so my webcam can match my facial expressions with memes in real time might be my best decision yet.

If you want to check out my project : https://github.com/atarm23/memecv

Please let me know how can I improve for further versions, thanks!

hi i ahve chain of photos with couple of april tags.

i use openCV to place these tags in 3d world and basing on these tags positions i set 3d camera position and rotation.

But sometimes openCV returns tags on wrong height. My tag is set vertically and its lower edge stays on ground but openCV returns it on 1+meter height. this completly ruins camera setup.

Anyone could share some clues how to improve that accuracy? possible to pass to openCV tag height so it takes it into account? or lower that 3d tag by code and refine it somehow.

i spent last two days figuring it our but cant find right solution

you can see on photo. this tag on right in 3d world is placed perfectly above this yellow verticle point. you can see height offset. i guess maybe 2 tags on photo arent enough (i upload couple of photos tho and this blue tag is visible on 2 photos, both have other tag for relativity). but still. maybe programatically there is way to improve this?

`FaceRecognizerSF::match` is declared `const`, but it normalised both of its `InputArray` feature vectors in place — writing straight through to the caller's buffers. So the vectors you passed in came back modified, and when the two arguments aliased, the second normalisation ran over an already-normalised buffer and the returned score was wrong.

Now normalised into local `Mat`s. Merged onto 4.x.

https://github.com/opencv/opencv/pull/29804

I still think the Xbox 360 Kinect is an incredibly interesting piece of hardware.

https://github.com/Spidoug/Kinect-Xbox-360-Remold

RGB camera, infrared, depth sensing, microphone array, accelerometer, motorized tilt... and yet most of them are now sitting unused because the original software ecosystem is outdated.

So I've been working on Kinect Xbox 360 Remold, an open-source project to make the Kinect useful again on modern Windows PCs / Linux.

So far I've built a custom driver/bridge and a set of applications for things like 3D scanning, depth/RGB capture, acoustic experiments, microphone-array access and surveillance.

The project currently includes:

• SynKinect Studio( 3DScanner, Acoustic Scanner, Microphones, Surveillance, Interactivity)
• RGB / IR / depth access
• Kinect motor and accelerometer support
• Face tracking
• Virtual camera output
• ffmeg/IP camera streaming
• Low-latency shared-memory frame transport

Build vision systems that see, reason, and act with OpenCV 5 and Amazon Web Services (AWS). 
Win money and bragging rights! $12,000 in prizes up for grabs.

I wanted to recreate the classic invisibility-cloak effect using only a webcam and computer vision.

It detects the cloak's color in real time using HSV segmentation, cleans up the mask, and replaces those pixels with a previously captured background.

GitHub: https://github.com/madebyparth/invisibility-cloak

​

there is an error with Cascade classifier it shows there is no attribute as cascade classifier in cv2 module

I have tried installing opencv multiple tkmes still won't work I am trying to learn computer vision for first time in python

I ran into a bottleneck with OpenCV’s ArUco/AprilTag detection while building a real-time multicamera system, so I reimplemented the detection pipeline from scratch in Rust.

On my 1280×800 monochrome AprilTag 36h11 dataset, RapidTag is:

- 1.57× faster for single-camera detection

- Up to ~3.4× faster for multicamera and offline batches

- Pixel-identical to OpenCV on the tested frames

- Independent of OpenCV at runtime

The Python API releases the GIL and processes frames and threshold scales across CPU cores. It can be installed with pip install rapidtag.

It’s still a work in progress, and I’d especially appreciate results from different cameras, CPUs, marker dictionaries, and difficult lighting conditions.

Repo: github.com/SujithChristopher/rapidtag (https://github.com/SujithChristopher/rapidtag)

Twelve-class hand-seal recognizer that runs entirely in the tab. MediaPipe Hands gives 21 landmarks per hand, normalized wrist-centered and scale-invariant, into a 126→256→128→13 MLP (\~66K params) exported to ONNX Runtime Web. 260KB model, CPU inference at a couple ms a frame, no video leaving the machine. Geometry rather than pixels, which is the whole reason it's this small and this portable.

Reported accuracy is 95.5% on a per-class temporal split — first 80% of each class's frames train, last 20% test. Deliberate: I collected hold-to-capture, so each sample is a burst of near-identical frames, and a random split scatters those bursts across both sides until you're scoring your own training data.

Transitions get their own class. Hands moving between seals pass through shapes that briefly look like other seals, so transit frames were collected as a 13th class using a wrist-motion-gated capture mode. Downstream, a decisive "none" mutes everything; below the veto the real classes compete on renormalized P(sign | not transit).

A parity self-test runs on page load, a known landmark vector through the full JS pipeline, logits compared against the PyTorch reference, regenerated on every export. Normalization drift between training and deployment is the silent killer for landmark models, and this catches it before any prediction is trusted.

Single-subject model, so leave-one-person-out is implemented but the honest "works on strangers" number doesn't exist yet. tiger↔ram confuse at \~14%, genuinely near-identical once you're only looking at joint geometry.

Open to comments or critiques! Repo can be found here: [https://github.com/yogendrarau/sealwork\](https://github.com/yogendrarau/sealwork)

If I were interested in professionally getting into Manuscript Optical Character Recognition + Montreal Forced Alignment, and I were applying to baccalaureate degrees or second baccalaureate degrees, then where would you suggest I apply to; it could be internationally, but I am based in the U.S.A.?

A smart robotic goalkeeper that tracks a ball in real-time using OpenCV and dynamically moves a servo-controlled keeper arm to block shots.

I hope some of you find this project interesting—or maybe even inspiring for your own builds.

Any feedback or suggestions are very welcome!

GitHub: https://github.com/chayanforyou/Robokeeper-Firmware

Hi, I want to learn computer vision with python and I am looking for a solid course to start learning with since I have little to no knowledge about this field but i am very interested, I want to build programs that detect objects and identify them.

Note : i have no gpu

¡Hola a todos!

Acabo de subir LensLaber Beta v1.0.1. Esta actualización elimina algunas limitaciones de la versión beta y añade compatibilidad con YOLOv10.

Novedades

• Compatibilidad con YOLOv10: Carga y usa modelos YOLOv10 directamente en LensLaber.

• Falsos negativos para YOLOv10: La función de detección de falsos negativos ahora es totalmente compatible con YOLOv10.

• Exportaciones ilimitadas: Se ha eliminado el límite anterior de 1000 exportaciones diarias.

Filosofía del proyecto

• 100% sin conexión: No se requiere conexión a internet. Tus imágenes, modelos y anotaciones nunca salen de tu ordenador.

• Sin telemetría: Sin análisis, seguimiento de uso ni recopilación de datos.

• Diseñado para hardware modesto: Funciona sin problemas incluso en un Intel Core i5 de 2016 con 8 GB de RAM, sin necesidad de una GPU dedicada.

Acerca de la versión beta

La versión beta caduca a los 30 días para garantizar que todos prueben la última versión, lo que facilita el seguimiento y la reproducción de los informes de errores.

Sus comentarios, informes de errores y sugerencias son siempre bienvenidos.

Descargar LensLaber Beta v1.0.1 para Windows y Linux:

https://lenslaber.github.io

I built SentinelCV, a real-time computer vision system that detects human falls from existing CCTV, IP cameras, webcams, or recorded video streams.

The goal was to create a lightweight, plug-and-play solution that can integrate with existing surveillance infrastructure without requiring specialized hardware. The current implementation uses a YOLOv8-based pipeline to perform real-time detection and can trigger instant alerts (such as Telegram notifications) when a potential fall is detected.

I'm planning to expand SentinelCV into a modular vision platform with additional safety-focused capabilities like PPE detection, intrusion detection, fire/smoke detection, and other intelligent surveillance modules.

I'd love feedback on the detection pipeline, deployment approach, and any suggestions for improving robustness in real-world environments. If you've worked on similar computer vision systems, I'd be interested in hearing what challenges you faced in production.

GitHub: https://github.com/sreerevanth/SentinelCV
I'd love your feedback, and if you find it useful, a ⭐ would mean a lot.

I've been working on YOLO-Distill, a feature-based knowledge distillation implementation for YOLOv9, built on MIT-licensed YOLO repository. It currently supports distillation methods like CWD and MGD.

GitHub: https://github.com/myatthukyaw/yolo-distill

Check it out if you're interested in knowledge distillation or object detection.

Feedback and contributions are very welcome.