Luxand.cloud的动态

?? ?????????????????????????? ???????????? ???????????????????? & ?????? ?????????????????????? ???????????? ???? I just completed a new Medium story, accompanied by the GitHub repository for the same project! I found this project incredibly interesting and was highly motivated to share my thoughts about it. Anyway, have you ever wondered why your images sometimes look "off," especially when captured with wide-angle lenses? ?? Well, that's due to lens distortion—a phenomenon that occurs when the lens doesn’t perfectly map the 3D world onto a 2D image. ?? This distortion can be helpful in certain cases (like the fisheye lens, where the curvature adds an artistic and immersive effect), but it can also be problematic for regular cameras. ?? You may notice straight lines warping into curves and objects becoming unrecognizable at the edges. This can be especially troublesome when precision matters, like in robotics, augmented reality, or 3D reconstruction. So, how do we tackle this? The solution lies in Camera Calibration and Undistortion—two powerful steps to clean up the image and retrieve accurate data. ?? １． Ｃａｍｅｒａ Ｃａｌｉｂｒａｔｉｏｎ ?? Calibration is the process of estimating the internal (intrinsic) and external (extrinsic) parameters of the camera. By using a known pattern (like a chessboard), we can understand how much the camera distorts its view of the world. The Fisheye Model and Pinhole Model are the two most common approaches for camera calibration. ?????????????? ??????????: Great for cameras with minimal distortion (think of regular cameras). It assumes a simple, undistorted projection of the world. ?????????????? ??????????: Used for wide-angle lenses, capturing everything in a spherical view (like a fisheye camera). While it introduces more distortion, it allows us to capture more information in a single shot. ２． Ｃａｍｅｒａ Ｕｎｄｉｓｔｏｒｔｉｏｎ ??? Once we have calibrated the camera, we use the calibration data to undistort the image. With OpenCV or similar tools, the process involves remapping the distorted pixels back to their true positions, reducing the curvature and restoring straight lines. For Pinhole Cameras, a simple remapping suffices. For Fisheye Cameras, specialized techniques are required, considering the nonlinear distortion unique to wide-angle lenses. ?? The best part? This process isn’t limited to static photography. It can be applied in real-time applications where precise visual data is crucial. Why Does This Matter? Whether you’re working on self-driving cars, augmented reality, or 3D modeling, having a reliable understanding of camera calibration and undistortion is key to ensuring the accuracy of your visual data. ???????? ???????????? ????????: https://lnkd.in/eNubGZ32 #ComputerVision #OpenCV #CameraCalibration #ImageProcessing #3DReconstruction #MachineLearning #Robotics #ComputerVision #AI #Python #FisheyeCamera

Imagine a world where sound responds to human connection—where chaotic noise fades, replaced by tranquility, as people come closer together. That’s the vision behind????? ???????? (????), an interactive art installation I created to explore the profound link between affection and well-being. Training and using AI image recognition, and creative problem-solving, I designed a system that detects gestures like holding hands, hugging, or kissing, lowering stress-inducing noise and amplifying calming sounds in response. Here’s how I brought it to life: ???? ????????????????:?I fine-tuned YOLOv8, a fast object detection model, to classify various gestures of affection in real-time using a webcam. ???????????????? ??????????:?When existing image datasets fell short, I used Stable Diffusion to create hundreds of lifelike training images for gestures like hugging or holding hands, ensuring the model’s accuracy. ???????????????? ?????? ?????????????? ????????????????????:?I turned to Suno.ai to generate soothing, custom soundscapes when pre-existing options didn’t quite get the mood right. ???????????????????? ?????? ??????????????????????????:?With OpenVino, I optimized the system to run smoothly on a 2012 (!) MacBook Pro, ensuring interactivity without requiring high-end hardware. The goal of this project was simple but impactful: to encourage people to reflect on how physical connection can reduce stress and bring calm in today’s hectic, tech-driven world. Early feedback from participants confirmed its success, with many reporting they felt more connected and less stressed after engaging with and around the installation. I’ve made the source code publicly available on GitHub, and I hope this project inspires others to think about how we can use technology to enhance, not replace, our shared human experiences. Read more about it on my website: https://lnkd.in/ecZ9WTT6

?? Revolutionizing Human Pose Detection With Cutting-Edge Technology ?? Proud to showcase a project that combines the power of computer vision and machine learning to create a robust Human Pose Estimation Application. Built using Python, OpenCV, Mediapipe, and Streamlit, this project provides a seamless experience for detecting human poses from images, videos, or live webcam feeds. This project is a testament to how technology can turn complex tasks into accessible solutions, and it wouldn't have been possible without the incredible support and mentorship of Raja P Sir and Pavan Kumar U Sir. ? Frontend Features ? Interactive GUI: A simple yet powerful Streamlit-based interface to handle all inputs: images, videos, and live webcam feeds. ? File Upload: Upload images or videos directly in popular formats like JPG, PNG, MP4, AVI, etc. ? Real-Time Webcam Feed: Detect poses dynamically with a live webcam feed. ? Instant Results: Displays processed outputs—overlaying pose landmarks on inputs effortlessly. ?? Backend Workflow ?? Image Mode: Reads the uploaded image. Converts it to RGB and processes it using Mediapipe Pose Estimation. Returns a processed image with pose landmarks to the frontend. ?? Video Mode: Processes each video frame using Mediapipe Pose. Generates a processed video by overlaying pose landmarks frame-by-frame. Outputs the final video for seamless playback. ?? Webcam Mode: Captures live frames from the webcam. Applies Mediapipe Pose in real-time. Displays dynamic results on the frontend. ?? Highlights of the Project Real-Time Performance: Ensures minimal lag during webcam feed processing. User-Friendly: Even non-technical users can interact with ease. Modular Design: Scalable backend with multiple input options. ?? Potential Applications Fitness and sports motion analysis. Gesture recognition for interactive application. Motion tracking in gaming or animation. #HumanPoseEstimation #ComputerVision #AIForGood #PythonDevelopment #Streamlit #Mediapipe #InnovationThroughTechnology. https://lnkd.in/g95j6p-4

?????????? ????????????????????! ?? ??? ???????? ???????? ???? ??????????: ???????????????????? ?????????????????? ????????????? ?? ?????? ?????????????????? ??????????: It begins with a gentle breeze ???, where ???????? ???????????????? start to spin as the energy from the wind is captured. The turbines rotate, transferring this ?????????????? ???????????? into ???????????????????? ??????????????. This energy is then converted into ???????????????????? ?????????? by the generator ?. The electricity travels through ???????????????????????? ?????????? and ???????????? ??, crossing vast distances to reach the ???????????????? ?????????? near homes ??. Finally, it powers our houses with clean ?????????????????? ????????????! ?? ?? ???????? ?????????????????? ???????????? ???? ?? ?????????? ???????????? ????????????????: ?????????????? ?????? ????????, ?????????? ?????? ????????????!?? #WindEnergy, #RenewablePower, #Sustainability ???????????????????????? ?????????????? ???????? ???????? ???????????? ??? I’m excited to share a project I developed using OpenCV, creating an interactive drawing tool that allows users to draw and manipulate shapes in a pop-up window. ?? ???????????????????????? ????????????????? ?? ??????.??????????????(??????, ????????, ??????, ????????, ??????????????????, ??????????, ??????????????????): Adds text to the image with customizable font, size, color, and position. ?? ??????.??????????????????(??????, ??????, ??????, ??????????, ??????????????????): Draws rectangles by defining two opposite corner points, customizable color, and thickness. ????????.????????????(????????????????????, ??????):Displays the image in the specified window name. Used to show the canvas with the drawn shapes. ????????.????????????????????????????????(????????????????????, ??????????????, ??????????):Associates mouse events to the window, allowing real-time user interaction. ????????.??????????????(??????????): Waits for a specific key press to continue execution (or indefinitely if delay is set to 0). ?? ??????.??????????????????????????????????(): Closes all OpenCV windows, cleaning up after the execution. A special shoutout to Ayush Argonda , Sharon pathipati?and Harshitha Reddy for being incredible teammates and bringing their expertise to the project! ?? This project highlights OpenCV’s versatility in creating interactive graphics and event-driven applications. A great tool for both creative and practical uses! Innomatics Research Labs Raghu Ram Aduri I’m incredibly grateful to my amazing mentors SAXON K SHA, Lakshmi IlluriIlluri, and ,Lakshmi Vangapandu ?? for their invaluable guidance and unwavering support. You all inspire me every day! ?? ????????????????, ??????’?? ?????????????? ?????? ?????????? ???? ???????????????????? ???? ?????????? ???????????????????? ???????????? ?????? ???????? ?? ????????????????????!???? #OpenCV #ComputerVision #InteractiveGraphics #Programming #TechInnovation #Teamwork

?? Gesture-Based Painting This interactive experience is created using OpenCV, MediaPipe, and Python, where you can paint digitally by simply moving your hand in front of a webcam. Choose between multiple colors ??, clear the canvas with a wave ??, and watch as the app brings creativity to life in real-time. Key features: ?? Hand tracking with MediaPipe ?? Gesture-based color selection ? Smooth, real-time drawing with OpenCV Use Cases: ???Art Education: Enhance creativity in classrooms. ???Therapeutic Applications: Facilitate emotional expression in therapy. ???Remote Collaboration: Collaborate on art projects from anywhere. ????Interactive Exhibitions: Engage museum visitors in hands-on experiences. ???Gaming and Entertainment: Create immersive gaming environments. ???Content Creation for Social Media: Produce unique digital artworks. ??Accessibility for Artists with Disabilities: Empower creative expression. ???Corporate Branding and Marketing: Generate custom artwork for branding. This project showcases the power of computer vision in creating intuitive, hands-free interfaces. ?? Awesome work by: Akbar Sheikh ?? Stay tuned for more exciting developments and breakthroughs on the horizon! ? WISERLI Ultralytics OpenCV Roboflow YOLOvX Dr. Chandrakant Bothe Rohan Gupta Vishnu Mate Mohit Raj Sinha Prateeksha Tripathy P Shreyas Sinem ?elik Anu Bothe Saurabh Tople Glenn Jocher Muhammad Rizwan Munawar Nicolai Nielsen Harpreet Sahota ?? Florian Palatini Ritesh Kanjee Piotr Skalski Dragos Stan Arnaud Bastide Nicholas Nouri Timothy Goebel Shah Faisal #ComputerVision #AI #CreativeTech #Innovation #YOLOvX

?? Week 5/11 Project: Gesture-Controlled Drawing Canvas ?? Hello, LinkedIn family! ?? This week, I explored the exciting synergy of Artificial Intelligence and Human-Computer Interaction by building a Gesture-Controlled Drawing Canvas! Imagine drawing on a digital canvas without a pen, mouse, or touchscreen — just your hand gestures! Here's how it works: Index Finger Movement ???: Acts as your drawing tool to create patterns, shapes, or doodles. Fist Gesture ?: Pauses the drawing process to give you control when needed. Open Palm Gesture ???: Clears the canvas to start afresh. This interactive system uses real-time hand tracking to interpret gestures and translate them into actions on the canvas, powered by: ? MediaPipe for detecting and tracking hand landmarks in real-time. ? OpenCV for building the drawing interface and rendering visual feedback. ?? Key Features: 1?? Gesture-Based Interaction: Draw, pause, or clear the canvas using simple hand gestures. 2?? Real-Time Performance: Immediate feedback with smooth tracking of hand movements. 3?? Scalability: A foundation for gesture-based applications in education, accessibility, and creative tools. ??? Tech Stack: Python: Core programming for seamless implementation. MediaPipe: Advanced hand detection and tracking. OpenCV: Interactive canvas for drawing and feedback. NumPy: Efficient matrix operations for hand landmark processing. ?? Why This Project Matters: Gesture-based interfaces are reshaping how we interact with technology, offering natural, intuitive ways to engage with digital systems. Whether for artistic expression, accessible design, or next-gen applications, this project is a step toward creating tools that feel as human as possible. ?? GitHub Repository: Want to dive deeper into the code or try it out yourself? Check out the project repository here: ?? GitHub Link- https://lnkd.in/djcazs8t ?? What’s Next? I’m excited to explore additional features like: Multi-finger gestures for complex interactions. Shape recognition for automated drawing. Integration with AR/VR for immersive experiences. I’d love to hear your thoughts on this project! How would you envision using gesture-based interfaces in your field? Let’s connect and discuss! ?? #AI #GestureControl #MachineLearning #MediaPipe #OpenCV #Innovation #ProjectJourney #GitHub

?? Hand Gesture Controlled Scrolling Using OpenCV & MediaPipe ??? ?? Project Overview: Ever thought of controlling your computer screen with just your hand gestures? ?? Here's a simple project where I used OpenCV, MediaPipe, and PyAutoGUI to enable scrolling functionality using hand gestures captured through a webcam. This is an exciting step toward integrating computer vision with real-world applications! ?? ?? Key Features: Detects hand gestures using MediaPipe. Tracks the index and middle finger movements to control scrolling: Swipe up with fingers ?? Scroll up ?? Swipe down with fingers ?? Scroll down ?? Works seamlessly with a webcam to mimic touch-free interaction. ?? Technologies Used: Python ?? OpenCV for real-time video capture ?? MediaPipe for hand landmark detection ? PyAutoGUI for simulating scrolling actions ?? ??? How It Works: 1?? The webcam captures live video and detects hands in real time. 2?? Using MediaPipe's powerful hand landmark model, it tracks the Y-coordinates of the index and middle fingers. 3?? If the fingers move upwards/downwards together beyond a defined threshold, a scroll-up or scroll-down action is triggered using PyAutoGUI. ?? Applications: Touch-free navigation for presentations or reading long documents. Assisting individuals with limited mobility. Adding an interactive element to smart home systems. ?? What's Next? Looking forward to integrating more gestures for functionalities like zooming, switching tabs, and more! The possibilities with gesture-based interaction are endless! ? ?? Open for Collaboration: Got ideas for enhancing this project or exploring gesture-based controls? Let’s connect and innovate together! ?? #OpenCV #MediaPipe #GestureRecognition #PythonProjects #ComputerVision #AIInnovations

?? New Project: Virtual Mouse Control with Hand Gestures ???? Excited to share my updated Virtual Mouse Control project! Now, users can control screen actions via hand gestures with an intuitive Streamlit UI/UX. Here’s what’s new: ?? Overview: This project uses computer vision and Mediapipe for hand gesture tracking, detecting finger positions for precise actions like opening folders, directly interacting with the webcam. ?? Features: Hand Gesture Detection: Tracks hand landmarks and finger tips for movement. Dynamic Hotspot: Detects gestures in a hotspot zone to trigger actions. Webcam Integration: Streamlit’s camera input directly captures the feed for real-time gesture recognition. Streamlit UI/UX: Designed with a simple and interactive interface, users can easily adjust settings like hotspot size and smoothing factor through Streamlit sliders and buttons. ?? Tech Stack: Streamlit, Mediapipe, OpenCV, and NumPy for real-time webcam interaction. ?? New in This Version: Gesture actions are triggered in a hotspot area, making interaction smoother. Removed pyautogui for improved compatibility in cloud environments. Streamlit UI provides real-time feedback and settings control, improving user interaction. ?? Challenges: Optimized for smoother hand gesture tracking and managing webcam permissions in browsers. Designed the UI to ensure that users can easily interact with and adjust webcam settings in a hassle-free way. ? Next Steps: Expanding with more gestures and adding voice recognition for hands-free control. Would love to hear your thoughts! Connect with me for more updates. ?? #VirtualMouse #AI #ComputerVision #Mediapipe #HandGestures #Streamlit #Python #MachineLearning #TechInnovation #PythonDevelopment #AIProject #WebDev #UIUX Python Coding Ganpat University- V M Patel Institute of Management Google DeepMind Abdul Bari Mohammed Hitanshu Dineshkumar Patel CyberArk YHills

?? Face Detection in Images with OpenCV and MediaPipe This project showcases a robust face detection system that processes images to identify and highlight faces by drawing bounding boxes around them. By combining the power of OpenCV and MediaPipe's advanced face detection module, this implementation delivers accurate results with minimal effort. ?? Key Features: 1. MediaPipe Face Detection: Utilizes MediaPipe's efficient and lightweight face detection model, capable of detecting faces with high accuracy. 2. Bounding Box Visualization: Draws precise rectangles around detected faces, enabling easy visualization. 3. Batch Processing: Reads multiple image files from a directory, processes them sequentially, and displays the original and processed images side by side. 4. Customizable Detection Settings: Allows configuration of the face detection model and confidence threshold for flexible performance tuning. ?? How It Works: 1. Preprocessing: The input image is converted to RGB format to align with MediaPipe's requirements. 2. Face Detection: MediaPipe's FaceDetection class detects faces, returning bounding box coordinates relative to the image dimensions. 3. Bounding Box Drawing: Relative coordinates are converted to absolute pixel values, and rectangles are drawn around detected faces using OpenCV. 4. Image Display: The original image and the processed image with detected faces are displayed for comparison. ?? Applications: This project demonstrates foundational concepts that can be applied in: 1. Face-based authentication systems. 2. Attendance systems using facial recognition. 3. Image enhancement and analysis tools. 4. Social media filters and augmented reality applications. ?? What I Learned: 1. How to integrate MediaPipe's face detection capabilities with OpenCV. 2. Techniques for handling relative bounding box coordinates and converting them to pixel values. 3. Batch processing of images for computer vision tasks. ?? This project marks another milestone in my computer vision journey, showcasing a practical implementation of face detection with readily available tools and libraries. ?? ?? Project Link: https://lnkd.in/d7f9Nrzy #ComputerVision #OpenCV #RealTime #CyberSecurity #DeepLearning #AI #Python #Privacy #ComputerScience

?? Imagine This: You’re Capturing a Picture-Perfect Sunset... ?? The sky is painted with fiery reds, golden yellows, and calming purples. As breathtaking as it is to you, how does a computer see and interpret those colors? That’s where color models come in, forming the bridge between human perception and machine understanding. In my latest blog, "?????????? ???????????? ???? ?????????? ????????????????????: ?????????????????????????? ?????? ?????? ?????? ?????? ???????????????? ????????????", I delve into the fascinating world of how computers process and manipulate colors. Here’s what you’ll discover: ?? RGB: How red, green, and blue combine to bring digital screens to life. ?? HSV: A color model that’s closer to how we perceive hues, saturation, and brightness. ?? Why these models are key for computer vision ?? Read the full blog here: ?????????? ???????????? ???? ?????????? ????????????????????: https://lnkd.in/gvTrXGpU A special thanks to Krish Naik and Monal kumar for their invaluable guidance, which has been instrumental in my learning journey and in shaping this blog as part of my Computer Vision Series. Let’s uncover the vibrant science behind every pixel—your thoughts and feedback are always welcome! ?? #ComputerVision #ImageProcessing #ColorModels #AI #Python #RGB #HSV #MachineLearning #opencv #GenAi