Projects using OpenCV in PunjabProjects using OpenCV in Punjab
Projects
People
Cover image for 👋 Hi, I'm Ahmad Yasin,
👋 Hi, I'm Ahmad Yasin, Founder & CEO of Nexariza AI — a global AI engineering and intelligent software company building AI agents, generative AI solutions, automation systems, computer vision applications, and custom SaaS products for businesses worldwide. In this short intro, I share who we are, what we do, and why we're passionate about helping businesses harness the power of AI to solve real problems. 🚀 What Nexariza AI does: ✅ Custom AI Agents & Automation ✅ Generative AI Solutions ✅ Computer Vision Systems ✅ Custom Software & SaaS Development 🌍 Work with us / Partner with us: 🔗 Website: https://nexariza.com (https://nexariza.com)📩 Email: contact@nexariza.com (mailto:contact@nexariza.com)💬 WhatsApp: +92 370 7348001 🔗 LinkedIn: linkedin.com/company/nexariza 🔗 All links: linktr.ee/nexariza_ai 📚 Also check out Nexariza Learning Hub for free AI, ML & programming tutorials — subscribe so you don't miss what's next! #NexarizaAI #AIAgency #ArtificialIntelligence #AIAutomation #FounderStory #TechEntrepreneur #AIAgents #GenerativeAI #SaaS #StartupJourney
0
12
Cover image for Automated Video Dehazing & Atmospheric
Automated Video Dehazing & Atmospheric Haze Simulation System 🚀 Project Overview This advanced Computer Vision project is designed to address visibility challenges in adverse weather conditions. The system features a dual-module architecture: it can synthetically inject realistic atmospheric fog/haze into crystal-clear video streams for dataset generation, and conversely, restore heavily degraded, foggy videos into crisp, high-visibility outputs in real-time. 🛠️ Core Functionality & Modules Module 1: Atmospheric Haze Simulation Purpose: Generates synthetic datasets to train and benchmark object detection models (like YOLO) for bad weather conditions. How it works: Implements mathematical scattering models to calculate depth maps and overlay a realistic layer of dense fog or smoke over clean video frames. Module 2: Real-Time Video Dehazing Purpose: Restores clarity and vivid color to video streams captured in low-visibility environments. How it works: Leverages physics-based Computer Vision algorithms (such as Dark Channel Prior - DCP) or Deep Learning frameworks to estimate atmospheric light, eliminate transmission noise, and reconstruct the scene's original contrast. 🎯 Use Cases & Applications Autonomous Vehicles: Enhances the sight and reliability of self-driving car sensors in dense fog. Smart Surveillance (CCTV): Improves security monitoring and facial recognition accuracy under harsh outdoor weather. Drone Navigation: Aids aerial drones in safely navigating through smoke, dust storms, or low-lying clouds. 💻 Tech Stack Used Language: Python Libraries: OpenCV, NumPy, Matplotlib, PyTorch / TensorFlow (if deep learning was applied) Concepts: Image Processing, Atmospheric Scattering Models, Feature Restoration, Video Pipeline Optimization
0
57
Cover image for I developed a real-time Air
I developed a real-time Air Drawing Application that uses Computer Vision to transform hand gestures into digital input. By utilizing a standard webcam, the app allows users to draw, erase, and select colors in mid-air without any physical contact with the screen. Key Technical Features: Hand Tracking: Integrated MediaPipe to track 21 specific hand landmarks with high precision and near-zero latency. Gesture Logic: Programmed custom state-switching—using a single index finger for "Drawing Mode" and a two-finger gesture for "Selection Mode" to navigate the UI. Real-time Rendering: Leveraged OpenCV for video processing and to create a dynamic, responsive canvas overlay. This project demonstrates my ability to build Human-Computer Interaction (HCI) tools and implement real-time AI solutions using Python.
0
37
Cover image for Satellite imagery is the ultimate
Satellite imagery is the ultimate stress test for Computer Vision. It is not just about detection; it is about architecting precision across massive, high-resolution datasets where every pixel counts. I have been building AI pipeline to segment parking lots, walkways, and structures from aerial data. The challenge is not just the mode, it is the Infrastructure: - Tiling & Inference: Processing massive dimensions without losing small-object context. - Robust Pre-processing: Using OpenCV to handle varying lighting and atmospheric "noise." - Cloud Orchestration: Turning heavy segmentation models into responsive, scale-ready tools. It is about turning raw pixels into a reliable data layer for the business. Currently opening 20 hours/week for long-term partnerships in Computer Vision & AI Systems.
1
105