Understanding Images as 2D Matrices with OpenCV

Built something that actually recognizes me. I developed a Face Recognition Attendance System using Python and OpenCV that works in real-time. • Detects faces using Haar Cascade • Trains on custom dataset using LBPH algorithm • Recognizes individuals with confidence scoring • Automatically logs attendance with timestamps What started as a simple idea turned into solving real challenges — handling bad data, improving accuracy, and making the system reliable. This project pushed me from just learning concepts to actually building systems. Next: making it product-level with GUI and database. #Python #OpenCV #ComputerVision #MachineLearning #Projects #LearningByDoing

Happy to announce the first release of 𝐉-𝐍𝐋𝐑, a 𝐉𝐀𝐗 native Python library for 𝐧𝐨𝐧-𝐥𝐢𝐧𝐞𝐚𝐫 𝐫𝐞𝐜𝐨𝐧𝐜𝐢𝐥𝐢𝐚𝐭𝐢𝐨𝐧, learning, and geometric analysis on constraint manifolds 🎉 The main capabilities of the library: ✅ Projection on arbitrary explicit (charts) or implicit manifolds 📐 ✅ Mesh generation on 3D manifolds 🌐 ✅ Efficient computation of geodesic paths and distances. 🐜 ✅ (Curvature-based) sampling of explicit/implicit manifolds 🥏 ✅ Dynamic visualisation tools to visualise 3D data 🧿 J-NLR leverages JAX’s hardware acceleration and auto-diff to make geometric analysis faster. Any feedback is welcome! #JAX #MachineLearning #DifferentialGeometry #Python #OpenSource #GeometricDeepLearning

Here is the research paper associated with the JNLR library. https://lnkd.in/gqZamwiq The library can be extended to compute Mean and Gaussian curvature by extracting max eigenvalues in the tangent space (max_tangent_eigenvalue ) #JAX #JNLR #ManifoldCurvature

I brought the Infinity Gauntlet to life with Python. 🫰✨ What started as a "just for fun" weekend project after re-watching Infinity War turned into a deep dive into real-time Hand Tracking and Computer Vision. I wanted to see if I could trigger the "Power Mode" using just my webcam and some math. The result? Total control over the universe (well, at least on my screen). ### How I built it: * 🖐️ **Hand Tracking:** Using MediaPipe to map 21 hand landmarks in real-time. * 🧠 **Gesture Logic:** Custom Python scripts to calculate finger distances and orientations. * ⚡ **Visual FX:** Dynamic overlays that scale and move with my hand coordinates. It's amazing how accessible AI tools have become. From a blank script to a working "Snap" in just a few hours. Want to play with the power of the stones yourself? 🚀 **Full Code on GitHub:** https://lnkd.in/geFGYfhu #ComputerVision #Python #ArtificialIntelligence #OpenCV #Marvel #DeepLearning #BuildInPublic

Excited to share my Computer Vision project: Smart Lane Detection System 🚗 This project focuses on real-time lane detection using Python, OpenCV, NumPy, Canny Edge Detection, Region of Interest (ROI) masking, and Hough Transform. The system is designed to detect road lane boundaries from video input and highlight lane lines for better road analysis and driving assistance. It demonstrates the practical implementation of Computer Vision and Image Processing techniques in real-time video analysis. 🔹 Key Features: • Real-time lane detection • Edge detection using Canny Algorithm • Lane line detection using Hough Transform • ROI (Region of Interest) masking • Video-based road lane analysis • Lane boundary highlighting 🔹 Technologies Used: Python | OpenCV | NumPy | Computer Vision | Image Processing 🔹 GitHub Repository: https://lnkd.in/dkbfaKvp I built this project as a self-learning initiative to strengthen my practical understanding of Computer Vision, image preprocessing, and real-time video processing. Looking forward to learning more and building advanced Computer Vision solutions. #ComputerVision #Python #OpenCV #NumPy #MachineLearning #ArtificialIntelligence #LaneDetection #ImageProcessing #GitHub #Projects #SoftwareEngineering #Developer

Digital Noise Minimization The other day, I posted prototypes on Linkedin and on Github on various scientific topics related to image processing. This is one of the first reviews, which is more than 2 years old, thematically similar to recent examples. Now, based on it, I have created a prototype for minimizing digital noise. The file contains, as usual, the code, screenshots, and a full description of the prototype's functionality. https://lnkd.in/eyJNqaFi #Python #GUI #Digital #Noise #Minimization #Scientific #Visualization

Why Python Feels Really Slow — And How I Got a 1500% Boost Python feels slow because the interpreter pays a tax on every loop, attribute access, and dynamic type check. I hit that wall building RoX, my AR spatial engine: 16 million voxels per object, millions of Python-level checks, and a pipeline that crawled. The fix was brutal and obvious—stop doing heavy numeric work in Python and let compiled layers do the work. Rewriting the hot path with NumPy vector ops and Numba’s njit turned a minute-long grind into something that runs in seconds. The result: from a 30-second render time to a measly 2 seconds while running three parallel pipelines—MediaPipe, OpenCV, and moderngl draw calls—in the background, roughly a 1500% speedup on the voxel pipeline. #XR #SpatialComputing #ComputerVision #MediaPipe #OpenCV #OpenGL #EdTech #GameDev #RoX

I built a Python mini-project that converts a normal photo into a realistic pencil sketch and others sketches using only NumPy, OpenCV, and Pillow - without any advanced computer vision libraries. This project focuses on applying core concepts of image processing by treating an image as a numerical matrix and performing mathematical transformations on pixel values. Key concepts used: Image as NumPy array (matrix of pixels) Grayscale conversion using weighted RGB values Image inversion (intensity transformation) Convolution-based blurring using a custom kernel Dodge blending technique to create sketch effect Contrast enhancement and visualization using CV2 This project helped me understand how images can be manipulated using pure mathematical operations and array processing in Python. fully open-source @ https://lnkd.in/eiCnU69C inspired by: "if a non-stochastic data-less approach can be taken to solving a problem, then machine learning should never be applied."

Search 2D Matrix: Two Binary Searches for O(log m + log n) Treating matrix as flat array requires index arithmetic. Cleaner approach: two binary searches — first finds row (compare against first/last elements), second searches within that row. Exploits both sorted dimensions independently. Two-Phase Search: Decompose 2D problem into sequential 1D searches when structure allows. Clearer logic than single pass with coordinate arithmetic. Time: O(log m + log n) | Space: O(1) #BinarySearch #2DMatrix #TwoPhaseSearch #SearchOptimization #Python #AlgorithmDesign #SoftwareEngineering

Built an Interactive Computer Vision Image Processing Toolkit using OpenCV and Streamlit. This project focuses on implementing core image processing operations and making them accessible through a simple web interface. Key functionalities: • Grayscale conversion • Canny edge detection for boundary extraction • Gaussian blur for noise reduction • Sepia filter using matrix transformation • Image rotation using affine transformations Tech Stack: Python, OpenCV, NumPy, Streamlit Key takeaways: • Improved understanding of image transformations and filtering • Learned practical challenges like BGR vs RGB inconsistencies • Built an interactive pipeline for real-time image processing GitHub: https://lnkd.in/gWXEGTNk Open to feedback and opportunities in Computer Vision / ML roles. #ComputerVision #OpenCV #Python #MachineLearning #SkillArbitrage #NSDC