YOLOvX的动态

?? **Excited to Share Our Minor Project!** ?? We, a team of enthusiastic developers—Sriman vara prasad Illingi, Tanniru Sravani, Nithin Peddalodi and Soumya Akuthota —are thrilled to present our project: ?? Air Canvas: Gesture-Driven Virtual Drawing Using OpenCV and Mediapipe ?? This project is an innovative virtual drawing tool that leverages Computer Vision and AI-powered Gesture Recognition to allow users to draw on a digital canvas using only hand movements—no physical touch required! ???? ?? Key Features: - Real-time hand tracking and landmark detection using Mediapipe. - Intuitive gesture-based controls for drawing, color selection, and canvas clearing. - Developed with OpenCV for image processing. ?? This project showcases the seamless integration of AI and computer vision technologies in creating interactive user experiences. ???? Special Thanks to our guide, Dr.P.Krupa sagar for his support and guidance throughout the project. ?? Looking Forward: We hope to expand and refine this project further and explore more possibilities in gesture-based interactions! We’d love to hear your feedback, thoughts, or suggestions! ?? Please checkout the GitHub link: https://lnkd.in/ggKgfqyv #Teamwork #ComputerVision #MinorProject #OpenCV #Mediapipe #Innovation #JNTUHUCES

**LinkedIn Post Description for Your Minor Project** ?? **Excited to Share Our Minor Project!** ?? We, a team of enthusiastic developers—Sriman vara prasad Illingi , Tanniru Sravani , Nithin Peddalodi and Soumya Akuthota —are thrilled to present our project: ?? **Air Canvas: Gesture-Driven Virtual Drawing Using OpenCV and Mediapipe** ?? This project is an innovative virtual drawing tool that leverages **Computer Vision** and **AI-powered Gesture Recognition** to allow users to draw on a digital canvas using only hand movements—no physical touch required! ???? ?? **Key Features**: - Real-time hand tracking and landmark detection using **Mediapipe**. - Intuitive gesture-based controls for drawing, color selection, and canvas clearing. - Developed with **OpenCV** for image processing. ?? This project showcases the seamless integration of AI and computer vision technologies in creating interactive user experiences. ???? **Special Thanks** to our guide, Dr.P.Krupa sagar , for his support and guidance throughout the project. ?? **Looking Forward**: We hope to expand and refine this project further and explore more possibilities in gesture-based interactions! ?? [https://lnkd.in/gtixDEVS] We’d love to hear your feedback, thoughts, or suggestions! ?? #Teamwork #ComputerVision #MinorProject #OpenCV #Mediapipe #Innovation #JNTUHUCES

?? Excited to Share Our Minor Project! ?? We, a team of enthusiastic developers—Sriman vara prasad Illingi , Tanniru Sravani , Nithin Peddalodi, and Soumya Akuthota —are thrilled to present our project: ?? Air Canvas: Gesture-Driven Virtual Drawing Using OpenCV and Mediapipe ?? This project is an innovative virtual drawing tool that leverages Computer Vision and AI-powered Gesture Recognition to allow users to draw on a digital canvas using only hand movements—no physical touch required! ???? ?? Key Features: - Real-time hand tracking and landmark detection using Mediapipe. - Intuitive gesture-based controls for drawing, color selection, and canvas clearing. - Developed with OpenCV for image processing. ?? This project showcases the seamless integration of AI and computer vision technologies in creating interactive user experiences. ???? Special Thanks to our guide, Dr.P.Krupa sagar , for his support and guidance throughout the project. ?? Looking Forward: We hope to expand and refine this project further and explore more possibilities in gesture-based interactions! ?? https://lnkd.in/grez24wa We’d love to hear your feedback, thoughts, or suggestions! ?? #Teamwork #ComputerVision #MinorProject #OpenCV #Mediapipe #Innovation #JNTUHUCES

?? "Excited to Share Our Minor Project!" ?? We, a team of enthusiastic developers— Sriman Vara Prasad, Tanniru Sravani, Peddalodi Nithin and Akuthota Soumya—are thrilled to present our project: ?? "Air Canvas: Gesture-Driven Virtual Drawing Using OpenCV and Mediapipe" ?? This project is an innovative virtual drawing tool that leverages Computer Vision and AI-powered Gesture Recognition to allow users to draw on a digital canvas using only hand movements—no physical touch required! ???? ?? Key Features: - Real-time hand tracking and landmark detection using Mediapipe. - Intuitive gesture-based controls for drawing, color selection, and canvas clearing. - Developed with OpenCV for image processing. ?? This project showcases the seamless integration of AI and computer vision technologies in creating interactive user experiences. ???? Special Thanks to our guide Dr.P.Krupa sagar , for his support and guidance throughout the project. ?? Looking Forward: We hope to expand and refine this project further and explore more possibilities in gesture-based interactions! ?? [https://lnkd.in/g3xSnb_Y] We’d love to hear your feedback, thoughts, or suggestions! ?? #Teamwork #ComputerVision #MinorProject #OpenCV #Mediapipe #Innovation #JNTUHUCES

Blogging: Third Blog (3/15) ?? Excited to share my latest blog post on Real-time Multimodal Hand & Face Landmark Tracking with Dynamic AR Overlays using OpenCV and MediaPipe link : https://lnkd.in/df6tqr5g Introducting my fun project: Imagine being able to see cool special effects on your screen in real time just by moving your hands or face! In this project, we’ll use Computer Vision to do exactly that. We’ll create a fun application that can recognize hand movements and facial features to trigger different effects, such as placing a superhero logo on your hand or making it look like your face is on fire. We’ll use a library called MediaPipe, which helps detect and track hands and faces using your webcam. By combining this with OpenCV (a powerful tool for image processing), we’ll be able to overlay images and animations onto the live video feed from your camera. Here’s what our application will do: 1. Detect your hands and face in real-time using your webcam. 2. Show buttons on the screen that you can “press” by moving your hand over them. 3. Depending on which button you press, it will overlay different effects: 4. Dr. Strange: Shows a magic symbol on your hand. 5. Spider-Man: Puts a Spider-Man mask on your face. 6. Heatblast: Adds a fire animation around your face. Dive into my blog to learn the steps involved in building this interactive application and how you can bring these cool effects to your screen. Let's innovate together and push the boundaries of what's possible in computer vision and augmented reality! About the challenge: To boost my creativity and expand my knowledge, I've set myself a 15-blog challenge! Over the next few weeks, I'll be posting new articles every 1-2 days. As a beginner, I might make a few mistakes along the way, so I welcome any corrections or suggestions. Feel free to share topics you'd like me to explore. Let's grow together! Crimson Tech #ComputerVision #AI #MachineLearning #DeepLearning #TechInConstruction #InnovationInTech #BlogChallenge #LearningJourney #CrimsonTech #MediaPipe #OpenCV #AugmentedReality #SpecialEffects #InteractiveTech #RealTimeRendering #HandTracking #FaceTracking #SuperheroEffects #Heatblast Ultralytics NVIDIA Hugging Face Meta Marvel Entertainment Marvel Studios IBM Machine Learning Augmented Reality Python Coding #python #crimsontech

My latest blog post on?Hunyuan3D 2.0: Scaling Diffusion Models for High-Resolution Textured 3D Assets Generation! ??? ?? https://lnkd.in/gXBgeUvt In this deep dive, I explore how?Hunyuan3D 2.0?leverages cutting-edge diffusion models to revolutionize the creation of high-resolution, textured 3D assets. From gaming ?? to virtual reality ??? and film production ??, this framework opens up incredible possibilities for generating realistic and detailed 3D models. Here’s what you’ll find in the blog: ??Understanding Diffusion Models: Learn how diffusion models work and why they’re perfect for 3D asset generation. ??Hunyuan3D 2.0 Architecture: A breakdown of its scalable design, texture generation modules, and multi-resolution training. ??Step-by-Step Coding Example: A hands-on implementation in PyTorch, complete with explanations! ??Future Directions: Insights into where this technology is headed next. Whether you’re a researcher, developer, or just curious about the future of 3D content creation, this blog has something for you. Check it out and let me know your thoughts! ?? #AI #3DModeling #DiffusionModels #GenerativeAI #MachineLearning #PyTorch #TechInnovation #VirtualReality #GameDev #3DArt

?? Exploring Image Edges with Prewitt Operator Hey everyone! ?? Curious about finding edges in pictures? Let's explore the simple but effective Prewitt operator technique. ?? Understanding the Code: We start by loading an image and converting it to grayscale. Then, we apply the Prewitt masks in both horizontal and vertical directions to find gradients using convolution. ?? What's Convolution? Convolution is like a sliding window that overlays the image. At each position, it multiplies the pixel values of the image covered by the mask and sums them up. This process helps in detecting patterns like edges. ?? How Prewitt Works: The Prewitt masks are small matrices that we slide over the image. They emphasize sudden changes in intensity. For example, the horizontal mask highlights vertical edges, and the vertical mask highlights horizontal edges. ?? Calculating Magnitude: To get a holistic view of edges, we calculate the magnitude of gradients in both directions. The combined magnitude gives us a clearer picture of where the edges are. ?? Visualizing Edges: Finally, we normalize the magnitude to the 0-255 range and display the edges. Brighter pixels indicate stronger edges, while darker ones represent weaker or no edges. ?? Why Share This? Understanding edge detection is crucial in various fields like image processing, computer vision, and even in everyday photography apps. ?? Try It Yourself: Feel free to experiment with different images or even create your own masks to detect edges in unique ways! ?? Code: https://lnkd.in/eaAgrDtm Let's explore the fascinating world of image processing together! Feel free to ask questions or share your insights. Happy coding! ??

?? **I Implemented Autofocus in C++ using OpenCV Library** In the realm of digital photography, autofocus is not just a feature—it's the linchpin that captures life's moments with impeccable clarity. As a passionate developer with a penchant for image processing, I embarked on a journey to demystify the mechanics behind this pivotal technology. ?? **The Challenge**: Autofocus systems in digital cameras, from the ubiquitous smartphone to the sophisticated SLR, rely on a symphony of calculations. These systems momentarily blur the vision, only to recalibrate and sharpen the image, ensuring that every detail is in perfect focus. ??? **The Implementation**: Leveraging the power of OpenCV and C++, I've implemented an autofocus algorithm that mirrors the precision of modern cameras. This algorithm sifts through a video sequence of a static scene to pinpoint the sharpest frame—a cornerstone for any autofocusing system. ?? **The Focus**: The crux of my project lies in two robust measures of focus: - **Variance of Absolute Values of Laplacian (var_abs_laplacian)**: Harnessing the Laplacian operator's sensitivity to edges, this function computes the variance, serving as a focus measure. - **Sum Modified Laplacian (SML)**: An enhanced version of the Laplacian, the SML accentuates the focus measure, ensuring even greater accuracy. ?? **The Precision**: To refine the autofocus, I've introduced a manual specification of the Region of Interest (ROI). By focusing on a designated flower region within the frame, the algorithm simulates the selective focus akin to professional photography. ?? **The Insight**: This project not only honed my skills in computer vision but also provided a profound understanding of how autofocus systems operate in real-world scenarios. ?? **The Vision**: As I continue to explore the vast landscape of image processing and machine learning, my goal is to contribute to innovations that enhance our interaction with technology. ?? **Connect with Me**: I'm eager to collaborate and exchange ideas with fellow enthusiasts in the field of computer vision and AI. Let's push the boundaries of what's possible together! #OpenCV #ComputerVision #ImageProcessing #MachineLearning #Autofocus #Innovation #Technology #CPlusPlus

Thrilled to share my latest AI-driven creation—AI Virtual Paint! This interactive tool uses OpenCV and Mediapipe for real-time hand tracking, allowing users to paint on the screen without touching any hardware." I wanted to explore how computer vision can create a seamless, touch-free painting experience—an innovation that can be used in creative industries, virtual classrooms, or just for fun! Using hand tracking, the system detects finger positions to let users draw, choose different colors, and even erase—all with simple gestures. The code integrates OpenCV for image processing and Mediapipe for precise hand movement detection. One key challenge was to ensure accurate hand detection and smooth drawing, even at 60 FPS. The solution came from refining my algorithms for processing hand gestures and improving performance through frame optimization. This technology can evolve into educational tools, interactive displays, or even digital art platforms. I’m excited to add new features, such as multi-user drawing and advanced gesture recognition. If you’re passionate about AI, computer vision, or innovative interfaces, I’d love to hear your thoughts on how we can take this further. #ComputerVision #OpenCV #Mediapipe #AIProjects #InteractiveDesign #Innovation #VirtualArt #PythonProjects #GestureControl

hi guys so I wanna talk about sth crazy everyone knows that AI is already controlling most things in the world specially computer related jobs like programming, graphics design, video editing, motion graphics, even UI/UX design but its not as perfect as we think of it I'm telling you this from my experience I checked like 2 AI websites to convert an image into a video so I gave it the picture with some caption but you have no idea how much time it took to convert the image into a video plus the result is so embarrassing the only difference between these 2 AI websites is how much time they took to do their job try it guys and prove me wrong if you see any perfection I will mention the websites on the bottom line.. 1. https://lnkd.in/dZBYQUJe 2. https://lnkd.in/dBMEPFyT