This repository contains a collection of computer vision projects covering a wide range of real-world problems, from fundamental image processing to advanced deep learning-based vision systems.
The goal of this repository is to demonstrate practical implementations of modern computer vision techniques using state-of-the-art frameworks and tools. Each project focuses on solving a specific problem and applying theoretical concepts to real-world applications.
This repository includes implementations and experiments related to the following computer vision tasks:
Detecting and localizing objects within images or video streams using bounding boxes.
Applications:
- Traffic monitoring systems
- Vehicle counting
- License plate detection
- Face detection
- Security surveillance systems
Assigning a label or category to an entire image.
Applications:
- Medical image diagnosis
- Defect detection in manufacturing
- Animal or plant species recognition
- Handwritten digit recognition
Dividing an image into meaningful regions at the pixel level.
Types:
- Semantic segmentation
- Instance segmentation
Applications:
- Autonomous driving
- Medical imaging (tumor detection)
- Background removal
- Scene understanding
Detecting human body landmarks and tracking movements in real time.
Applications:
- Fitness tracking systems
- Gesture recognition
- Human-computer interaction
- Activity recognition
Tracking detected objects across video frames while maintaining unique IDs.
Applications:
- Multi-object tracking in traffic
- Crowd analysis
- Sports analytics
Extracting text from images or video frames.
Applications:
- Automatic Number Plate Recognition (ANPR)
- Document digitization
- Invoice processing
Identifying or verifying individuals based on facial features.
Applications:
- Attendance systems
- Access control
- Emotion detection
Traditional computer vision techniques, including:
- Edge detection
- Thresholding
- Contour detection
- Morphological operations
- Color space transformations
- Filtering and smoothing
This repository leverages a wide ecosystem of computer vision and deep learning tools:
- OpenCV
- cvzone
- Pillow (PIL)
- scikit-image
- PyTorch
- TensorFlow
- Keras
- YOLO (Ultralytics implementation)
- Detectron2
- torchvision models
- MediaPipe
- OpenPose (conceptual or experimental)
- SORT (Simple Online and Realtime Tracking)
- Deep SORT
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Albumentations
- tqdm
Each project is organized in its own directory and typically contains:
- Source code
- Model weights (if required)
- Dataset configuration files
- Requirements file
- Documentation or usage instructions
The projects in this repository demonstrate how computer vision can be applied to:
- Intelligent transportation systems
- Smart city infrastructure
- Healthcare diagnostics
- Industrial automation
- Security and surveillance
- Human-computer interaction
- Sports and performance analytics
Many projects support:
- GPU acceleration using CUDA (when available)
- CPU fallback for environments without GPU
Performance depends on hardware specifications and model size.
Most projects require:
- Python 3.8+
- pip or conda for package management
Dependencies vary per project and are specified in individual requirements.txt files.
This repository serves as:
- A learning resource for computer vision practitioners
- A portfolio of applied AI projects
- A foundation for building production-ready vision systems
Makrious Ayman Riad Computer Science Student | Data Science Department Passionate about Computer Vision, Machine Learning, and AI Systems
If you find this repository useful or interesting, feel free to explore the projects and contribute.