The Mill’s Post

🚀 Excited to Share My Latest Project: Real-Time Pose Estimation with OpenCV and MediaPipe! 🤖📹 I’ve recently been working on an exciting project that combines OpenCV and MediaPipe for real-time human pose estimation. This application captures video from a webcam or pass a video path to it, processes each frame to detect and annotate key body landmarks, and displays the results in real-time. 🔍 Key Features: Real-Time Processing: Uses MediaPipe’s pose detection model to identify and track body landmarks with impressive accuracy. Dynamic Visualization: Draws detected pose landmarks directly on the video feed, providing an intuitive view of the model’s predictions. User Interaction: Allows for easy termination of the session by pressing the 'q' key, ensuring a smooth user experience. This project demonstrates the power of combining computer vision libraries to create interactive and insightful applications. It's a great example of how technology can be used to analyze and understand human movement in real-time. Feel free to check out the code and see how these technologies work together to bring pose estimation to life! #MachineLearning #ComputerVision #OpenCV #MediaPipe #PoseEstimation #RealTimeProcessing #AI #TechInnovation Link for code:https://lnkd.in/gN8XpqSh

[𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝟭/𝟱] 🚀 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗦𝗲𝗿𝗶𝗲𝘀 After months of diving deep into 𝗰𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝘃𝗶𝘀𝗶𝗼𝗻, 𝗱𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 and 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜. I'm thrilled to share my exciting project: 𝗣𝗶𝗻𝗴 𝗣𝗼𝗻𝗴 𝗩𝗶𝘀𝗶𝗼𝗻. This project combines cutting-edge AI technologies to analyze and visualize ping pong gameplay in real-time. 🎾 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗼𝗳 𝗣𝗶𝗻𝗴 𝗣𝗼𝗻𝗴 𝗩𝗶𝘀𝗶𝗼𝗻: 𝟭. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗕𝗮𝗹𝗹 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴:   Built with 𝗬𝗢𝗟𝗢𝘃𝟴, creating stunning visualizations of ball trajectories with a unique glowing trail effect that captures the poetry of the game in motion. 𝟮. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗧𝗮𝗯𝗹𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀:  Implemented 𝘀𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝘀𝗲𝗴𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 and 𝗸𝗲𝘆𝗽𝗼𝗶𝗻𝘁 𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻 for precise table boundary tracking, enabling accurate bounce detection and gameplay analysis. 𝟯. 𝗠𝘂𝗹𝘁𝗶-𝗩𝗶𝗲𝘄 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗦𝘆𝘀𝘁𝗲𝗺:  - Real-time gameplay footage  - Top-down tactical view  - Ball bounce off position and trajectory visualization  - Table segmentation overlay 4. 𝗛𝗶𝗴𝗵-𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: What makes this project special is how it handles real-time processing. I've built a multi-threaded system with CUDA acceleration that processes multiple video frames simultaneously, achieving smooth 20+ FPS performance. The architecture uses thread-safe queues and efficient memory management to ensure every ball movement is captured without dropping frames Tech Stack: - YOLOv8: Real-time ball detection - U-Net: Table segmentation - PyTorch: Deep learning backbone - OpenCV: Video processing - CUDA: GPU acceleration I've made this project open source! Check it out here: https://lnkd.in/eHjQf8W9 I'm always looking to improve and would love to hear your thoughts on making it even better. Whether you're into sports tech, computer vision, or just curious about AI, let's connect and discuss! PS: This project represents countless hours of learning, experimenting, and optimizing. But seeing that glowing ball trail track perfectly in real-time makes it all worth it! 🔥😅(Don't miss the end of the video!) #ComputerVision #UNET #SAM #CNN #AI #DeepLearning #SportsTech #Innovation #MachineLearning

Exploring the Basics of Human-Computer Interaction! I recently worked on a basic project that combines hand gesture recognition and face emotion detection using DeepFace, MediaPipe, and OpenCV. This project helped me: Recognize and process simple hand gestures in real time. Detect basic facial emotions for intuitive responses. Understand the fundamentals of integrating these two features. This was a great learning experience that introduced me to the potential of AI in creating interactive systems. Excited to explore more in this field! #LearningJourney #ArtificialIntelligence #ComputerVision #HandGestureRecognition #FaceEmotionDetection"

🎄 Tech Day Fun: Building a Christmas-Themed Game with Generative AI 🎮 For the 6th year in a row, our Tech Day tradition brought the team together for an exciting challenge. This year’s mission? Create a Christmas-themed game using as much Generative AI as possible — in just 4 hours! We split into four teams to bring the vision to life: ✨ Santa Simulation: Developed the game’s simulation engine. ✨ Santa Experience: Designed the front-end, music, and animations. ✨ Santa AI Agent: Crafted GPT prompts to guide Santa through a maze to find presents and the Christmas tree. ✨ Santa World: Built API to generate the maze using GPT prompts. Leveraging AI, we created: ✅ Music and pixel art graphics. ✅ Algorithm snippets to speed up coding. ✅ A real-time maze generator and pathfinder based on user prompts (though somewhat dependent on OpenAI response times). 🎥 Check out the video — built in just hours, it’s a bit rough but full of creativity! #Hyarchis #Engineering #TechDay #AI #GameDevelopment

Exploring the possibilities of neural networks led me to experiment with creating 3D isometric game assets for city-builder or farm-style games. I have experience and understand that, often, the final output required is just an image without the 3D model itself, making this approach feasible. By preparing a Depth map in 3D Max and configuring ControlNet in ComfyUI, I was able to maintain the 126° angle necessary for this type of game. For example, I can switch a lemon cake to a cherry one just by changing the prompt. By adjusting the silhouette of the Depth map, I can generate new geometry. I'm interested in developing AI applications in different fields, so if you or anyone in your network is looking for specialists in this area, I'd be happy to connect! #AI #GameDevelopment #3DIsometric #CityBuilder #FarmGame #ComfyUI #DepthMap #3DAssets #ProceduralGeneration #GameArt #IsometricDesign #GameDev #AIArt

Excited to share my recent blog post about our journey with AI-generated game worlds! 🎮 In my Machine Learning class project, we dived into the realm of Game World Generation, utilizing #ML models to create playable environments. From ideation to implementation in Unity, I've documented our process, including initial prototypes, model training, hosting on Render, and #Unity implementation. Check out the article for insights and a guide on setting up your own ML model. Very special thanks to all my teammates on this project Asheen Mathasing, Prajwal Shetty Vijaykumar, and Hritick Buragohain #GameDev #AI #MachineLearning #Unity3D

Remembering the COVID-19 era, I’m reminded of how technology helped us navigate those challenging times , I’m sharing my latest project that addresses a key issue from that period – **Face Mask Detection**! 😷 🔍 ||Project Overview||: I’ve developed a deep learning-CNN model using VGG16 with transfer learning to detect whether individuals are wearing face masks. This project aims to assist in promoting safety in public spaces, helping enforce mask mandates when it mattered the most.Also implemented face localization before classification using OpenCV detection techniques. 🔧 Technical Highlights: - Model: VGG16 CNN architecture pre-trained on ImageNet - Dataset: 7553 images from the Kaggle Face Mask Detection dataset - Tools & Techniques: OpenCV, Model evaluation, performance visualization, and transfer learning 💡 Now ,What’s Next? - Experimenting with other models like MobileNet and ResNet - Expanding the dataset for increased diversity I’m excited about the future possibilities in Computer vision and eager to explore more applications in this field! 🌟 If you’re passionate about AI and deep learning or would like to collaborate, let’s connect! #ComputerVision #DeepLearning #FaceMaskDetection #VGG16 #AI #MachineLearning #TechInnovation #ProjectShowcase #SafetyTech #Innovation #OpenCV

Looks amazing! Generative AI in Premiere Pro https://lnkd.in/dFaG6tb4

GPT-2 Agent Benchmark Score with 1-Click Solution We are excited to announce that we have achieved best-in-class performance using a one-click, uniform setting targeting 3D physics environments like MuJoCo (PaperswithCode sota: https://lnkd.in/gS7Gimiq). Small GPT models (GPT-2) are used to attain top scores in these 3D environments. Check out our progress here: https://lnkd.in/gFq_b_yd Key Highlights: 1. 5x Fewer Steps: Significantly reduced the number of simulation steps, cutting down gameplay costs. Reducing steps is crucial since many updates must be performed sequentially and cannot be parallelized. 2. No Tuning Required: From simple one-leg Hopper to complex Humanoid agents, training parameters (like gamma and lambda) are dynamically learned. We aim for no tuning across general scales of games. 3. Unified Model Configuration: Utilized uniform GPT-2 models with the same configuration across all tasks to save time during testing and deployment. While our SDK is on the way, we invite you to explore our algorithm and the details of how it works: https://lnkd.in/gXzT8QpR What's Next: - Preparing benchmark scores on 2D games such as Atari by adding an extra image encoder in this 1-click setting. - Testing results with different computational scales in cloud services and planning to offer this high-performing AI agent modeling as a cloud service soon.

🚨 Exciting news! 🚨 I just created a Proof of Concept for hand detection and classification using the childhood game we all know and love: Rock, Paper, Scissors! 🎉 What if we could teach computers to detect and recognize for us? Well, now we can! I'm sharing a pre-trained model and repository that you can use directly. Check it out on GitHub: https://lnkd.in/g_Br5ThP Object Detection and Classification is one of the most mature technologies in the AI field. For common objects or natural images, it's a non-trivial problem anymore, as long as you have the annotations and the data. For specific tasks, such as medical imaging, a different approach might be needed, but I'm always up for a challenge! What should I create next? If you have any difficulties in Detection and Classification tasks, I'm here to help. In return, I'll create it as one of my content! Just hit me up. Let's learn together! 🤓 I am using Ultralytics work, which is an awesome wrapper with complete metrics generated during training and evaluation. What a production grade indeed! #LearnWithHanry #melekAI #PyTorch #HandDetection #HandRecognition