Freelancers using OpenCV in Punjab

Freelancers using OpenCV in PunjabFreelancers using OpenCV in Punjab

Projects People

proMultan, Pakistan

Flutter Mobile Apps & AI/ML/RAG Engineer | Computer Vision

$5k+: Earned

4x: Hired

5.0: Rating

40: Followers

Flutter Mobile Apps & AI/ML/RAG Engineer | Computer Vision

Cover image for AI Gym Rep Counter: Automatic Workout Tracking System

AI Gym Rep Counter: Automatic Workout Tracking System

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Cover image for Real-Time Restaurant Table Monitoring System - Computer Vision

Real-Time Restaurant Table Monitoring System - Computer Vision

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Cover image for ProposalAI: Streamlining Proposal Writing with AI

ProposalAI: Streamlining Proposal Writing with AI

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Cover image for AI Customer Support Chatbot Development

AI Customer Support Chatbot Development

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Rawalpindi, Pakistan

AI Specilist, AI Automation, Chatbots, Business, Workflow

New to Contra

AI Specilist, AI Automation, Chatbots, Business, Workflow

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

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Cover image for E-Assistant is an autonomous AI

E-Assistant is an autonomous AI shopping agent designed to streamline the consumer decision-making process. By simultaneously querying multiple e-commerce platforms, it utilizes a proprietary value-ranking algorithm to provide real-time product comparisons based on price, rating, and review volume.

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Cover image for Automated Multi-Agent AI Support &

Automated Multi-Agent AI Support & Lead Triage Pipeline Are your high-ticket clients waiting hours for an email response? This intelligent multi-agent n8n workflow instantly screens, analyzes, and responds to customer emails in real-time, utilizing advanced RAG (Retrieval-Augmented Generation) to deliver human-like support instantly. Project Overview: This is an enterprise-grade AI automation system designed to eliminate manual customer support queues. Instead of simple auto-replies, it uses a multi-agent routing structure combined with a dynamic knowledge base to handle complex inquiries autonomously. How It Works (Under the Hood): Instant Inbound Triage: A Gmail Trigger catches incoming emails instantly, extracting raw content for processing. AI Intent Classification: An initial OpenAI model acts as a gatekeeper, analyzing the email to determine if it is a valid customer support request or irrelevant noise. Conditional Routing: An advanced router splits the path: non-support emails receive a polite automated Telegram update, while actual support tickets are routed to the main AI engine. Context-Aware AI Agent: The core Customer Support Agent is equipped with an OpenAI Chat Model, conversational memory, and a custom Vector Store Tool. Pinecone RAG Integration: The agent queries a Pinecone Vector Database (powered by OpenAI Text Embeddings) to fetch real-time, accurate company documentation and context, eliminating hallucinations. Automated Action & Response: Once the resolution is drafted, the system automatically creates a draft in Gmail for review and sends an instant internal notification via Telegram. Why This Wins Clients (The Value Pitch): Zero Hallucinations: Connected to a live vector database (Pinecone) so the AI only speaks from approved company data. Reduced Overhead: Cuts down customer support response times from hours to under 60 seconds. Production-Ready Architecture: Designed with modern n8n AI sub-nodes, structured tools, and modular scaling capabilities.

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Cover image for Real Time SMS Spam Detection/Classification

Real Time SMS Spam Detection/Classification System

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Lahore, Pakistan

AI Engineer and Machine Learning Expert

New to Contra

AI Engineer and Machine Learning Expert

Cover image for The system continuously monitors examination

The system continuously monitors examination halls through live camera feeds, analyzes student behavior, and flags suspicious activities such as mobile phone usage, abnormal head movements, gaze deviation, multiple faces, and unauthorized interactions.

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Cover image for AI Surveillance & Activity Recognition

AI Surveillance & Activity Recognition System Built a real-time AI-powered surveillance platform for intelligent monitoring, object detection, and security analytics. The system leverages deep learning and computer vision to detect people, vehicles, unattended objects, and suspicious activities across multiple camera streams. Key Features Real-time person, vehicle, and object detection Automatic Number Plate Recognition (ANPR) Facial recognition and identity verification Multi-object tracking using ByteTrack Unattended baggage and boundary breach detection Crowd monitoring and suspicious activity recognition Real-time alerts and monitoring dashboard Optimized for high-performance GPU deployment

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Cover image for Urdu News AI Summarization &

Urdu News AI Summarization & Search System Designed and developed an end-to-end AI platform for processing Urdu news from television broadcasts and digital media. The system automatically transcribes speech, extracts text from news tickers, generates concise summaries, and enables intelligent semantic search across large collections of Urdu news content.

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Cover image for AI Chatbot with RAG &

AI Chatbot with RAG & Multi-Agent System

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Syed Asad Iftikhar

Rawalpindi, Pakistan

AI Automation Engineer | n8n, Flutter & Voice AI Specialist.

New to Contra

AI Automation Engineer | n8n, Flutter & Voice AI Specialist.

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.

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Cover image for I engineered a high-performance, browser-based

I engineered a high-performance, browser-based 3D experience that allows users to manipulate a system of 15,000+ particles using real-time hand gestures. This project explores the intersection of Web Graphics and Computer Vision, turning a standard webcam into a spatial controller. The Technical Challenge The main hurdle was achieving a "haptic" feel ensuring the particles reacted instantly and smoothly to physical movements without lag. This required mapping 2D webcam coordinates into a 3D coordinate system and calculating volumetric transformations on the fly. The Solution & Workflow High-Performance Rendering: Used Three.js and WebGL with additive blending and custom color mapping to render a massive particle count at a consistent 60 FPS. Real-time Inference: Integrated Google MediaPipe to track 21 3D landmarks per hand directly in the client-side browser, eliminating the need for a backend. Dynamic Math: Implemented custom lerping (Linear Interpolation) and gesture thresholds to create fluid movements, such as a "fist-clench" collapsing the particles into a sphere and an "open-palm" expanding them into a galaxy. The Results Created a fully immersive, zero-latency interface that demonstrates the power of the modern web for interactive AI applications.

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Cover image for I built a custom AI

I built a custom AI Voice Agent for a service-based business to automate their appointment booking process. The system handles inbound/outbound calls, qualifies customers, and manages scheduling without any human intervention. The Technical Challenge The goal was to create a "robust" agent that could handle natural human speech patterns including stammers or mid-sentence changes while maintaining the conversational flow. I needed to bridge the gap between high-fidelity voice synthesis and real-time database management. The Solution & Workflow Voice & Logic: Integrated ElevenLabs for realistic vocal performance and Retell AI (or similar) for low-latency conversational handling. Automation Engine: Built complex workflows in n8n to process the data gathered during the call. Real-time Integration: The system automatically cross-references and updates Google Calendar, sending instant confirmations to both the client and the customer. The Results 100% Automation: Successfully moved the client from manual phone bookings to a fully autonomous system. Error Handling: Programmed the agent to stay on track even when users provide vague or self-correcting input.

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Cover image for I developed Chezify, a high-performance

I developed Chezify, a high-performance mobile application tailored for a local fast-food restaurant. The goal was to bridge the gap between a traditional physical storefront and a modern digital presence by providing a seamless, branded ordering interface. The Technical Challenge The focus was on creating a Modular Architecture that allows the business to scale its menu effortlessly. I designed a library of reusable Flutter widgets (FoodCards, DealCards) that ensure UI consistency across the entire app while maintaining a strict 60 FPS performance target for a premium user experience. The Results Optimized Performance: Achieved silky-smooth scrolling and transitions, even with high-resolution imagery. Scalable Design: Built a custom UI/UX from scratch using Dart, avoiding generic templates to ensure brand uniqueness. Business Integration: Developed an "Exclusive Deals" logic to drive higher order values through targeted combo offers.

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Rawalpindi, Pakistan

Backend & AI engineer who ships systems fast and scale.

New to Contra

Backend & AI engineer who ships systems fast and scale.

Cover image for Yeah, this one is a

Yeah, this one is a bit out there, but it was super fun. I built an AI that actually understands memes. It looks at the image and the text together using PyTorch, TensorFlow, and GPT 4. I used early and late fusion techniques to combine vision and language, plus Tesseract OCR to grab text from the image itself. The model hits over 95% accuracy on meme classification. It’s not your typical corporate project, but it shows how multimodal AI can understand humor, culture, and context. Definitely one of my favorite experiments. For more details, please visit: https://arham-nexus.vercel.app/work/memechecker

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Cover image for This was a really cool

This was a really cool EdTech project. The goal was to automate grading and student verification. I built a Flask backend that hooks into NLP models, speech to text, text to speech, and even computer vision with OpenCV. Students can give live demos, and the system checks their face, asks them questions out loud, and grades everything automatically. No more manual grading or worrying about cheating. It’s 100% automated, and teachers love how transparent it is. The AI responds in under a second, so it feels almost magical. For more details, please visit: https://arham-nexus.vercel.app/work/evaluasysai

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Cover image for This is a classic enterprise

This is a classic enterprise academic portal, but built solid. It uses Web Forms, ASP.NET, C#, and SQL Server. The main challenge was role based access control because you have admins, faculty, TAs, and lab demonstrators all needing different permissions. I designed a three tier architecture with stored procedures and optimistic concurrency control so data doesn’t get messed up when people edit at the same time. Over 200 users per semester use it for task assignments and progress tracking. It’s not flashy, but it works perfectly. For more details, please visit: https://arham-nexus.vercel.app/work/talabportal

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Cover image for Skill Swap is a peer

Skill Swap is a peer to peer learning platform where you can be both a student and a teacher. I built the backend with Java Spring Boot, real time chat with WebSockets, and video calls with WebRTC. The matching algorithm finds people based on skills, ratings, and availability. You can switch roles anytime. It also has a trust based review system so fake reviews don’t ruin it. This was an MVP, but it proved that real time learning communities can work really well. For more details, please visit: https://arham-nexus.vercel.app/work/skillswap

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Lahore, Pakistan

Full Stack Developer and AI Automation Developer

Full Stack Developer and AI Automation Developer

Cover image for Lie Detector

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Cover image for Chic Dreams

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Cover image for Tabsform

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Lahore, Pakistan

AI Developer & ML Engineer: Top-notch Expertise

AI Developer & ML Engineer: Top-notch Expertise

Cover image for Yolo v10 - Object Detection and tracking

Yolo v10 - Object Detection and tracking

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Cover image for Computer Vision - Detection and Segmentation with Yolo V9

Computer Vision - Detection and Segmentation with Yolo V9

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Cover image for LLM Agents Cybersecurity workflow

LLM Agents Cybersecurity workflow

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Cover image for AI Agents workflow

AI Agents workflow

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Lahore, Pakistan

I build production-ready AI systems that actually ship

I build production-ready AI systems that actually ship

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.

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Cover image for AI-based Video Generation Pipeline

AI-based Video Generation Pipeline

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Cover image for I designed and delivered an

I designed and delivered an end-to-end MVP for a real-time AI talking avatar system, where live browser speech is converted into an AI-generated response and rendered through a lip-synced avatar video on GPU hardware. The objective was to validate technical feasibility, latency characteristics, and perceived real-time interaction before committing to production hardening. The system integrates speech-to-text, LLM-based reasoning, text-to-speech, and video synthesis into a single, runnable pipeline, deployed on an A100 GPU.

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Cover image for At Formulatrix, I led the

At Formulatrix, I led the development of a computer vision pipeline for RockMaker, a biotech product used in crystallization experiments. I built and deployed object detection and image classification models that improved scoring accuracy by 13%, reducing manual effort for scientists and increasing customer satisfaction. The solution was productionized with Docker and CI/CD pipelines, ensuring scalability and reliability across client sites.

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