🖐💻 Hand Gesture Controlled Virtual Keyboard | Python + MediaPipe + OpenCV I’m excited to share my latest project — a Virtual Keyboard that works entirely through hand gestures! 👇 Using MediaPipe for hand tracking and OpenCV for real-time rendering, this project allows you to type on an on-screen keyboard simply by moving your fingers in front of your webcam — no physical keyboard needed. 🔍 Key Highlights ✅ Real-time hand landmark detection via MediaPipe ✅ On-screen virtual keyboard drawn with OpenCV ✅ Pinch gesture recognition to simulate key presses ✅ Works with any standard webcam ✅ Fully implemented in Python ⚙️ Tech Stack 🔹 Python 3.10+ 🔹 OpenCV 🔹 MediaPipe 🔹 NumPy 🧠 What I Learned - Gesture recognition using hand landmarks - Real-time computer vision pipelines - UI rendering in OpenCV - Calibrating physical gestures for digital input 🚀 Try it yourself Clone the project and run locally: 🔗 GitHub Repository: [https://lnkd.in/dbUaYkxm] 💬 Feedback Welcome I’d love to hear your thoughts or suggestions for improving it — such as adding voice feedback, better calibration, or multi-hand support. #Python #OpenCV #MediaPipe #ComputerVision #AI #MachineLearning #GestureRecognition #Innovation #ProjectShowcase

🎯 Built a real-time Green Color Detection System using Python and OpenCV! The program captures live video from the webcam and detects any green object in the frame. This helped me understand HSV color spaces, masking, and bitwise operations in computer vision. 💻 Tech Stack: Python | OpenCV | NumPy #OpenCV #Python #ComputerVision #AI #MachineLearning #DataScience #ColorDetection 💡 Detailed LinkedIn Description (Professional Style) 🚀 Real-Time Green Color Detection using OpenCV I recently developed a simple yet powerful computer vision project that detects green objects in real-time using a webcam feed. This project uses: OpenCV for video processing HSV color space to accurately detect green under different lighting Masking and bitwise operations to highlight green regions 💡 How it works: The webcam captures frames continuously. Each frame is converted from BGR to HSV color space. A mask is applied to isolate green pixels using color thresholds. The result displays only the green areas in a separate window. 🎯 Applications: Detecting plants or greenery in environmental monitoring Industrial color sorting systems Virtual green screen effects Traffic signal and object color recognition 🧠 Tech Stack: Python | OpenCV | NumPy Excited to build upon this by adding multi-color detection and integrating it with YOLO for object-based color tracking! #OpenCV #Python #DataScience #MachineLearning #ComputerVision #AIProjects #LearningByDoing

🖐️ Gesture Controlled Presentation project Excited to share my latest Computer Vision project — An app that lets you control presentation slides with just hand gestures. 🎯 Features: ✋Gesture Detection: Detects hand gestures using webcam. 💻 Slide Navigation ✏️ Annotation Mode: Draw or erase on slides using gestures. 🔴 Pointer Mode: Highlight content using your index & middle fingers. 🛠 Tech Stack: - Python - OpenCV - cvzone - NumPy 🎥 The webcam detects my hand gestures in real-time and updates the slides accordingly. GitHub Repository: https://lnkd.in/dcWpZCJv #AI #ComputerVision #OpenCV #Python #GestureControl #Innovation #cvzone #TechProject

🎉 Excited to share my latest project! I’ve developed and deployed a Chromosome Detection System using Python, Streamlit, and OpenCV. 🧬 This interactive web app lets users upload a biological image, automatically detect chromosomes, and download each one individually — or as a ZIP file for bulk analysis. Perfect for biological image processing and research workflows! 🔗 Try it live: https://lnkd.in/g8_sya5U 💻 GitHub Repository: https://lnkd.in/gEyBveVx 💡 Tech Stack: 🧠 Streamlit — for an intuitive and interactive web interface 🧩 OpenCV — for image processing and contour-based chromosome detection 🧮 NumPy & Pillow — for image manipulation and export This project strengthened my skills in computer vision, deployment, and problem-solving — showing how technology can meaningfully contribute to scientific discovery. Let’s connect and discuss how AI and Computer Vision can advance bioinformatics and scientific research! 🚀 #DataScience #ComputerVision #Python #MachineLearning #Bioinformatics #TechForGood #ProjectShowcase

"Press 1 for English or stay in the line" is dying, and I'm celebrating! Cartesia is making robotic agents obsolete. I put together a quick demo of how you can create a voice assistant using the following tech stack: • Python - The programming language • LiveKit - The agent orchestrating everything • Cartesia - Converts text to speech (the voice you hear) • OpenAI - Converts your voice to text • GPT4o-mini - The brains of the agent 65 lines of code, counting whitespaces. Source code below. You'll find instructions on how to run it in the README.md file. Here is a link to the repository: https://lnkd.in/eh5ew9WH

Just finished a fun project — an interactive digit recognition web app built using Streamlit and a Support Vector Machine (SVM) trained on the classic MNIST dataset. The app lets you upload any 28×28 handwritten digit image (even noisy ones), and it predicts the digit in real time 🧠✨ What makes this version special: ✅ Clean preprocessing pipeline using OpenCV (grayscale → threshold → crop → resize → pad → normalize) ✅ Adaptive thresholding for robust lighting correction ✅ Aspect-ratio preserving resize to match MNIST distribution ✅ Centered 28×28 output so the digit aligns just like MNIST samples ✅ Simple Streamlit UI with live preview of the processed image A small tweak — like fixing the image preprocessing — made a huge difference in accuracy and consistency. It’s always amazing how much impact proper data formatting has on model performance! 📦 Stack used: Python · Streamlit · OpenCV · NumPy · scikit-learn · Pandas 🧩 Next step: converting this into a CNN version for comparison. If anyone wants to explore or collaborate on extending it, I’d love to connect! 👇 #MachineLearning #Python #ComputerVision #Streamlit #SVM #OpenCV #AI #DataScience #MNIST #ProjectShowcase Gagan Puri 🙏 Mentor

🚀 Gesture-Controlled System Volume using Python 🖐️💻 I built a fun project that lets you control your PC’s volume using hand gestures — no keyboard or mouse required! 🙌 Using OpenCV and MediaPipe, the program tracks the distance between my thumb and index finger to adjust the system volume in real time. The rectangular bar on the screen dynamically shows the current volume level as the gesture changes. 🔹 Real-time hand tracking 🔹 Dynamic volume bar 🔹 Smooth, responsive UI 🔹 Touch-free volume control This was a great way to explore computer vision, gesture recognition, and a bit of UI design. Projects like this show how simple interactions can become powerful tools in everyday life. 🎥 Check out the demo in the video below. 📂 GitHub: https://lnkd.in/dxrUm-3M #Python #OpenCV #MediaPipe #ComputerVision #GestureControl #Innovation #MachineLearning #Coding #AI #LinkedInTech #Automation #OpenSource

⚡ Day 88 of #100DaysOfDSA – Next Permutation 🔁 📌 Problem. no 31: Implement an algorithm to rearrange numbers into the next lexicographically greater permutation of numbers. If no such arrangement exists, transform it into the lowest possible order (i.e., sorted in ascending order). 🚀 Runtime: 0 ms – Beats 💯% of Python submissions 💾 Memory: 12.43 MB – Beats 52.01% of solutions 💻 Language Used: Python ✅ Status: Accepted – All 266 test cases passed successfully! This problem was a great exercise in in-place array manipulation and understanding lexicographical ordering. It strengthened my skills in reverse traversal and efficient element swapping to achieve the next permutation sequence. 🔑 Key Learnings: ✔️ Learned how to find and swap pivot points efficiently ✔️ Improved logic-building for in-place array operations ✔️ Understood how permutations can be generated without extra space 🎯 Day 88 — A solid step forward in mastering array algorithms and improving my analytical approach to sequence transformations! 🔥💡 #100DaysOfCode #100DaysOfDSA #LeetCode #PythonProgramming #DataStructures #AlgorithmPractice #CodeNewbie #DailyCoding #ProblemSolving #TechJourney #WomenWhoCode #CodeLife #CodingChallenge #DSAChallenge #DeveloperLife #PythonDeveloper #LearnToCode #CodingCommunity #CodeEveryday #SoftwareEngineering #KathirCollegeOfEngineering #KathirCollege #BTechLife #AIDS #FutureEngineer #CodingMotivation #ProgrammingSkills

From idea to interactive tool in a single day Today I took a concept that’s usually buried in textbooks, space‑filling curves and Morton (Z‑order / Peano) codes, and turned it into a working, interactive Python package. It’s always exciting to see abstract math come alive visually, and even more exciting when others start cloning and exploring it the same day it’s published. (24 Unique Cloners within 2 hours of uploading files to the GitHub Repo) This touches so many areas: 🔹 Data visualization 🔹 Scientific computing 🔹 GIS & spatial indexing 🔹 Graphics & game development 🔹 High‑performance computing Big thanks to the open‑source ecosystem, especially Python, NumPy, and Matplotlib, for making it possible to go from concept to demo so quickly. If you’re in the worlds of data science, geospatial tech, or graphics/visualization, you’ll recognize how useful Morton codes can be. 👉 Stay tuned, more polish, demos, and maybe even a browser‑based version are on the way. #Python #OpenSource #DataVisualization #GIS #ScientificComputing #Graphics #HighPerformanceComputing

🎯 Exploring Digital Signal Processing Concepts Using Python & SymPy! Over the past few sessions, I worked on a series of practical DSP experiments to better understand the fundamentals of discrete-time systems, Z-transform, and filter design — all implemented using Python 🧠 Here’s what I explored 👇 🔹 1️⃣ Z-Transform Calculations Computed Z-transforms of common signals like aⁿu[n] and δ[n] using SymPy, along with their Region of Convergence (ROC). 🔹 2️⃣ Inverse Z-Transform Derived the time-domain signal from a given X(z) expression such as z/(z-2) — reinforcing how poles define system behavior. 🔹 3️⃣ Partial Fraction Expansion Used SciPy’s residuez() to perform partial fraction expansion of rational functions, identifying residues, poles, and direct terms — key in understanding system realization. 🔹 4️⃣ Poles and Zeros Visualization Found poles and zeros of transfer functions like (1 - 0.5z⁻¹) / (1 - 0.8z⁻¹) to analyze stability and frequency response characteristics. 🔹 5️⃣ System Properties & Testing (FIR System) Implemented a discrete-time system y[n] = 0.5x[n] + 0.8x[n−1] and verified: • Impulse & Step responses • Linearity and Time-Invariance tests • Frequency, Magnitude, and Phase responses • Stability check (BIBO) All visualized using Matplotlib 📈 🔹 6️⃣ Low-Pass Filter Design Designed a 2nd-order Butterworth low-pass filter, plotted its magnitude response, and expressed its transfer function H(z) symbolically. 🧩 Technologies Used: Python, NumPy, SciPy, SymPy, Matplotlib These experiments strengthened my understanding of Z-transforms, filter design, system stability, and signal analysis — bridging the gap between theoretical DSP and real-world implementation. #DigitalSignalProcessing #Python #SignalProcessing #Engineering #DSP #ZTransform #FilterDesign #SymPy #SciPy #LearningByDoing #Palaniappan Thillainathan #QIS College Of Engineering and Technology #ByteXplora Technology