This repository is all about computer vision

It includes small projects on various computer vision topics, such as object detection, object tracking, classical computer vision techniques, image segmentation, image classification, feature extraction algorithms, and more.

Each project includes an article that explains how the algorithm works, how the code is structured, provides general information about the algorithms, and much more.

Right now, you can find all articles on my personal website. I recommend reading them here: https://visionbrick.com/

Most of the projects have both C++ and Python implementations.

Deep Learning (PyTorch, TensorFlow)

Object Detection

YOLO Object Detection GUI	YOLO Model Inference in C++	Custom YOLO-NAS Training Pipeline
YOLO Object Tracking (ByteTrack & BoTSORT)	Faster R-CNN Model Training	DETR Object Detection

Image Segmentation

SAM2 Image Segmentation

Mask R-CNN Model Training

Detection (YOLO) + Segmentation (SAM)

Image Classification

PyTorch Image Classification Training

TensorFlow Image Classification Training

Vision Transformer (ViT) Model Training

Combined Pipelines

Detection (YOLO) + Classification (TensorFlow)

Blurring Faces with YOLO and SAM Models

Grounding DINO for Object Detection

Classical Computer Vision (Pure OpenCV Mostly)

Tracking Objects with SIFT algorithm	Tracking Objects with FAST algorithm	Tracking Objects with ORB algorithm
Tracking Objects with Kalman algorithm	Tracking Objects with Lucas Kanade algorithm	Tracking Objects with Mean shift algorithm
Tracking Objects with Background Subtraction	Text Extraction from Pages (OCR)	Perspective Transformation
Color-based Object Detection GUI	Object Detection with OpenCV Cascade Classifiers

Example Videos

To test these projects in your local environment with C++, delete the build folder and rebuild it. This is necessary because the folder paths will change, because of that you need to rebuild the project.
For python you can execute files directly