DEVRhylme Foundation

🌟 Exciting Partnership Announcement! 🌟 We’re proud to partner with the SheBuilds Community, a women-centric platform dedicated to empowering women in tech, for their upcoming Meetup Series! 🚀 Happening in Chennai, Bangalore, Delhi, Noida, and Nagpur, these meetups are perfect for networking, learning, and collaboration. Don’t miss out on this incredible opportunity to connect with tech enthusiasts and leaders! 💻✨ 📅 Register now to secure your spot: https://lu.ma/w9mxwod7 #SheBuilds #WomenInTech #MeetupSeries #TechCommunity #Collaboration

In the context of computer vision, image resizing, cropping, and rotation are fundamental preprocessing techniques used to standardize images for analysis or to augment data for training machine learning models. Here’s a breakdown of each operation: ╰┈➤ 1. Resizing ⮞ Definition: Adjusting the dimensions (width and height) of an image while keeping its aspect ratio or allowing distortion. ⮞ Purpose: Images in a dataset often vary in size, but many computer vision models require a fixed input size. Resizing standardizes images to a specific resolution. ⮞ Considerations: ⚬ Aspect Ratio: Choose whether to preserve the aspect ratio (to avoid distortion) or adjust it to fit model requirements. ⚬ Interpolation Methods: Use techniques like Nearest Neighbor, Bilinear, or Bicubic interpolation to maintain image quality when resizing.  ● Tools: OpenCV (cv2.resize), PIL (Image.resize), TensorFlow, or PyTorch. ╰┈➤ 2. Cropping ⮞ Definition: Cutting out a rectangular region of an image.  ● Purpose: Focus on a region of interest (ROI), remove irrelevant areas, or create a uniform dataset. ⮞ Techniques: ⚬ Center Cropping: Crop the image centrally, often used to maintain focus on the main subject. ⚬ Random Cropping: Used in data augmentation to increase model robustness by showing various parts of the image during training. ⚬ Tools: OpenCV (cv2.crop), PIL (Image.crop), or directly manipulating arrays in frameworks like NumPy. ╰┈➤ 3. Rotation ⮞ Definition: Rotating the image by a specified angle around a center point. ⚬ Purpose: Augment data by generating multiple rotated versions of the same image to make models rotation-invariant. ⮞ Considerations: ⚬ Angle: Specify angles (e.g., 90°, 180°, or random angles). ⚬Filling Pixels: Decide how to handle pixels outside the original image bounds (e.g., padding, cropping, or filling with a specific color). ⚬ Tools: OpenCV (cv2.warpAffine), PIL (Image.rotate), or image transformation methods in TensorFlow/Keras. ➥ Use Cases in Computer Vision ⚬ Standardization: Resizing and cropping help create uniform datasets, making models more accurate. ⚬ Data Augmentation: Random rotations, crops, and resizes enhance dataset diversity, improving model generalization. ⚬ Feature Extraction: Cropping to focus on specific regions can help isolate features relevant to the task (e.g., face detection). #ImageResizing, #ImageCropping, #ImageRotation #DataPreprocessing and #DataAugmentation:

Get Started with OpenCV: Loading & Displaying Images 📸 Unlocking the world of computer vision with OpenCV starts with a fundamental skill: loading and displaying images. Let’s dive into how it works! 🌐 Step-by-Step Guide 📝 1. Install OpenCV: OpenCV is a powerful library for image processing and computer vision. To install, use: > pip install opencv-python 2. Load an Image: Use cv2.imread("path/to/image.jpg") to load an image from your file path. This command reads the image as an array of pixel values, creating a matrix representation in memory. > import cv2 > image = cv2.imread("path/to/image.jpg") Read Modes: ⚬ Color Image: cv2.IMREAD_COLOR (default) ⚬ Grayscale: cv2.IMREAD_GRAYSCALE ⚬ Unchanged: cv2.IMREAD_UNCHANGED (preserves alpha channel, if any) 3. Display the Image: Use cv2.imshow("Window Title", image) to open a display window: > cv2.imshow("Image Display", image) This creates a window titled “Image Display” with your image loaded. 4. Wait and Close: Keep the window open until a key is pressed with cv2.waitKey(0), then close it with cv2.destroyAllWindows(): > cv2.waitKey(0) > cv2.destroyAllWindows() ╰┈➤ Full Code Example: import cv2 # Load the image from file image = cv2.imread("path/to/image.jpg") # Display the image cv2.imshow("Loaded Image", image) # Wait for key press to close cv2.waitKey(0) cv2.destroyAllWindows() Quick Tips 💡 ⚬ Resize if Needed: Use cv2.resize() to fit large images to your screen: > resized_image = cv2.resize(image, (width, height)) ⚬ Grayscale for Simplicity: Load in grayscale to save memory and focus on structure: > gray_image = cv2.imread("path/to/image.jpg", cv2.IMREAD_GRAYSCALE) ⚬ Error Handling: Confirm the image loaded successfully: > if image is None: > print("Error: Could not load image.") With OpenCV basics like loading and displaying images under your belt, you’re ready to explore advanced processing techniques! Happy coding! 💻✨ #OpenCV #Python #ComputerVision #ImageProcessing

🔍 How Computers See Images: Pixels, Matrices & Digital Vision Have you ever wondered how computers "see" images? Unlike human vision, computers break down images into grids of pixels — tiny units of color and brightness. Here’s a look at how visual data is processed! 🔹 1. Pixels: The Basic Units of Digital Images A pixel (or "picture element") is the smallest unit in an image. Each holds data about color and brightness. Higher pixel counts mean more detail and resolution. 🔹 2. Grayscale Images: Simpler Matrices In grayscale images, each pixel represents a shade of gray on a scale of 0 (black) to 255 (white). This is often represented in a 2D matrix, ideal for basic processing tasks. Example Grayscale Matrix: [[0, 120, 200, 255], [30, 180, 60, 220], [90, 140, 210, 10], [255, 50, 30, 180]] 🔹 3. Color Images: RGB Matrices Color images use the RGB model (Red, Green, Blue), with each pixel storing three values for color intensity. A 4x4 color image needs three matrices, one per color channel, combining for millions of possible colors. Example RGB Channels: Red: [[255, 0, 100, 50], ...] Green: [[0, 255, 100, 50], ...] Blue: [[50, 100, 0, 255], ...] 🔹 4. Image Processing with Matrices Matrices are core to image processing, allowing algorithms to filter, detect edges, and recognize objects. Machine learning techniques use matrix math to analyze and interpret images, enabling technologies like facial recognition. 🔹 5. Resolution & Quality Resolution depends on pixel count. More pixels mean higher detail, but larger matrices require more processing power, creating a balance between quality and efficiency. ✨ Final Thoughts Understanding pixels and matrices reveals how computers process images, from simple grayscale to complex RGB, laying the foundation for computer vision and image editing. Next time you view an image, remember: it’s a matrix of numbers, analyzed and understood by algorithms! 🧑💻📊 #TechInsights #ImageProcessing #DigitalVision #ComputerScience #ComputerVision #HowComputersSeeImagesPixels

➥ What is Computer Vision? 👀 Computer vision is a field of artificial intelligence (AI) and computer science focused on enabling machines to interpret, analyze, and make decisions based on visual data from the world 🌎. This involves teaching computers to "see" by mimicking human vision, though often at much larger scales and with specialized applications. ➥ Key Components of Computer Vision ● Image Processing 🖼️: The first step is often preprocessing images to enhance their quality or transform them for better analysis. Techniques like resizing, filtering, color adjustments, and noise reduction help clean and prepare visual data. ● Feature Extraction 🔍: This step identifies key elements in an image, such as edges, shapes, colors, textures, or specific points, that represent meaningful information about the object or scene. ● Object Detection and Recognition 🧍🚗: Object detection involves locating specific objects (like people, vehicles, or animals) within an image, while object recognition identifies and classifies them. ● Image Classification and Segmentation 🐱🐶: Classification assigns an entire image a label, like "cat" or "dog," while segmentation breaks an image down into regions or objects for more granular analysis, such as identifying each pixel that belongs to a cat in a photo. ● 3D Vision and Depth Estimation 📏: Computer vision also extends to understanding depth and 3D structure, allowing machines to interpret the spatial arrangement of objects, as in 3D modeling and augmented reality (AR) applications. ➥ Applications of Computer Vision 🌟 » Facial Recognition 🧑💻: Used in security, social media, and more, to identify individuals. » Medical Imaging 🏥: Analyzes X-rays, MRIs, and CT scans to detect diseases. » Autonomous Vehicles 🚗: Enables self-driving cars to identify and respond to other vehicles, pedestrians, and road signs. » Manufacturing Quality Control 🏭: Detects defects in products on an assembly line. » Retail and Marketing 🛒: Powers cashier-less checkout systems and customer analytics. ➥ Technologies Used in Computer Vision ⚙️ Computer vision relies on machine learning and deep learning algorithms, especially Convolutional Neural Networks (CNNs), which are highly effective at processing image data. These networks learn from vast datasets, recognizing patterns and making predictions about new visual inputs. ➥ Why is Computer Vision Important? 💡 Computer vision has the potential to transform industries by automating visual tasks that typically require human expertise. By accurately analyzing and responding to visual information, machines can operate independently and efficiently in complex environments, leading to innovations in healthcare, transportation, retail, agriculture, and beyond 🌐. #ComputerVision #AI #MachineLearning #DeepLearning #FutureTech #Innovation #AutonomousVehicles #MedicalImaging #SmartManufacturing #RetailTech #AIinAction #TechForGood

Exciting News! 🚀 We are thrilled to announce that our organization has been accepted into the Microsoft for Startups Founders Hub! 🎉 Along with this incredible opportunity I’m excited to share that I also received Cloudflare credits worth up to $250000 USD! 💵✨ What does this mean for my journey? From Microsoft for Startups: 💰 $25000 in Azure Credits: Valid for two years once activated along with guidance from Priority Community Support on Microsoft Q&A. 🛠️ Quick-Start Templates: Easily Use common generative AI Answers through the "Construct with AI" tab. 📊 Microsoft 365 Business Premium: Collaborate organise and Examine with this subscription for one year. From Cloudflare: 🕒 Up to $250000 in Cloudflare Credits: I have up to 1 year to utilize these credits very importantly enhancing our infrastructure and capabilities. From IBM: 💡 Cloud Credits and AI Answers: Access to IBM powerful cloud infrastructure and AI tools to Improve our technological capabilities and accelerate growth. 🌐 Enterprise-Grade Security & Expandability: Leverage IBM security blockchain and Information Answers to ensure that our systems are scalable and secure. From Atlassian: 🛠️ Atlassian Tools & Credits: Collaborate and organise with tools like Jira Confluence and Trello helping to streamline our workflow and Improve team collaboration. 💼 Project Management Answers: Boost team productivity and Productivity with access to top-tier project management software. These Supplys from Microsoft Cloudflare IBM and Atlassian will empower us to Invent scale and Construct the future. amp big give thanks you to complete of these fabulous partners for support our travel 💪✨ let form the prospective collectively 🌍🚀 #MicrosoftForStartups #Cloudflare #IBMCloud #Atlassian #GenerativeAI #TechInnovation #Startups #AI #Cloud #FutureOfTech #ScalingUp

BAS Community Buildathon - Make BNB Greater 🚀 Excited to announce the BAS Community Buildathon with two cutting-edge tracks: 1️⃣ MPC-TLS Track 2️⃣ Data Sharing Track 🏆 5000 USDT prize pool for each track! 🏆 💡 Main Objective: Drive innovation using PADO SDK! 📈 Main Goal: Build a new economic model by combining off-chain data verification with on-chain proofs through PADO's advanced MPC-TLS protocol. 🔍 Challenge Overview: PADO leverages MPC-TLS to enable privacy-preserving user data authentication from internet sources. Developers have the chance to create pioneering dApps that utilize MPC-TLS for integrating identity, finance, or social data into Web3, fostering more secure and dynamic digital identities. In the Data Sharing Track, developers can unlock the power of Fully Homomorphic Encryption (FHE) for secure data-sharing across blockchain ecosystems. Imagine building a decentralized creator economy or secure, conditional access to sensitive data—all possible through PADO’s FHE! 💸 Rewards: 🏅 1st Place: 2000 USDT 🥈 2nd Place: 1500 USDT 🥉 3rd Place: 1000 USDT Rewards are distributed by track project partners, based on the following: 👨⚖️ Judging criteria: - Originality & Innovation - Ecosystem Integration - Technical Implementation - Usability & User Experience - Scalability & Performance - Implementation Completeness - End-to-End Functionality & UI/UX 👉 Don’t miss out: https://2ly.link/20klh Are you ready to make a real impact in Web3? 🌍 Dive into the BAS Community Buildathon, where your ideas and technical skills can transform the future of decentralized data sharing and privacy. 🔗 #BASBuildathon #BNBChain #PADO #Web3 #MPC_TLS #FHE #dAppDevelopment #CryptoInnovation #BlockchainEcosystem

Happy Diwali from DevRhylme Foundation! ✨ As we celebrate the Festival of Lights, we’re reminded of the power of knowledge, innovation, and community to illuminate the world. This Diwali, we’re grateful for our incredible community and partners who help us advance AI, Web3, and open-source initiatives. May this season bring new ideas, inspiration, and growth to all. Let’s continue to shine brighter together and work toward a future powered by technology and connection! 🌟 Wishing everyone a joyous, safe, and prosperous Diwali! 🪔 #HappyDiwali #FestivalOfLights #DevRhylmeFoundation #Innovation #Community #AI #Web3 #OpenSource #TechnologyForGood #Gratitude #Celebration #Progress #FutureForward

We are thrilled to announce that our organization accepted into the Microsoft for Startups Founders Hub! 🎉 Along with this incredible opportunity, I’m also excited to share that I’ve received Cloudflare credits worth up to $250,000 USD! 💵✨ What does this mean for my journey? From Microsoft for Startups: 💰 $25,000 in Azure Credits: Valid for two years once activated, along with guidance from Priority Community Support on Microsoft Q&A. 🛠️ Quick-Start Templates: Easily deploy common generative AI solutions through the "Build with AI" tab. 📊 Microsoft 365 Business Premium: Collaborate, organize, and analyze with this subscription for one year. From Cloudflare: 🕒 I have up to 1 year to utilize these credits, significantly enhancing our infrastructure and capabilities. These resources will empower us to innovate and scale effectively! A huge thank you to both Microsoft and Cloudflare for these incredible opportunities. Let’s build the future together! 💪✨ #MicrosoftForStartups #Cloudflare #Startup #Innovation #AI #CloudComputing #Entrepreneurship #TechJourney

Huge Milestone for DEVRhylme Foundation! 🎉 We’re thrilled to share three major updates that will propel us forward: 1️⃣ $100K Credit Funding from Datadog! This incredible support will allow us to scale our tech infrastructure, improve monitoring, and enhance our ability to deliver impactful projects in tech and education. 2️⃣ GitHub for Startups Approved! We now have access to 20 GitHub Enterprise seats, advanced tools, and dedicated support to accelerate our development and innovation. 3️⃣ 6 Months of Notion Plus + Unlimited AI! With Notion’s amazing toolkit—like Startup in a Box, Perks Dealbook, and Notion Academy—we’re ready to streamline operations and maximize productivity. A huge thank you to Datadog, GitHub, and Notion for empowering startups like ours. With these resources, we’re more driven than ever to push boundaries, build groundbreaking solutions, and make a real difference in our community! #DEVRhylme #Datadog #GitHubForStartups #Notion #AI #Innovation #Startups #TechForGood #Impact Let’s keep making waves together! 🌊