Dr. Farshid Pirahansiah CV
My Products
- 3D Camera Calibration
- AI Realtime GUI OpenCV
- AI Local ToDo List telegram mini app
- NuGet OpenCV5 Static Library for VS2019
- NuGet OpenCV5 Static Library for VS2022
- LLM VSCode Extensions Farshid
- cvtest: Computer Vision Test
- AI Model Cost Calculator: Optimizing Costs for Computer Vision and Multimodal AI Solutions
My custom ChatGPT
- Image Processing / Computer Vision Developer: Expert in Python, OpenCV for image processing and computer vision applications.
- MLOps & DevOps: An expert MLOps engineer assisting in DevOps and pipeline optimization.
- Career Companion: A dedicated job assistant for CV enhancement, interview prep, and job matching.
- German TutorBot: A German teacher for text correction and simple translations.
- Simpli3D Creator: Image-to-3D model conversion, no text involved.
- Simpli3D Style Transfer
- Image Inspirer
- Image 3D Transformer
My Social
Index
Camera Calibration Expertise
I am an expert in camera calibration, with several publications, applications, and solutions developed around this area. My work spans both single-camera and multi-camera systems, where I have experience with a range of calibration techniques, from simple fixed patterns (like chessboards) to more complex, automated calibration methods.
Expertise Areas:
- Standard RGB cameras: Calibration for common imaging tasks.
- High-resolution cameras: Used in industries requiring precision and high-detail imaging.
- Depth cameras: Calibration for stereo vision systems, and 3D reconstruction.
- Infrared cameras: Applied to thermal imaging, night vision, and more.
- IoT-connected camera systems: Solutions for real-time monitoring and smart environments.
- Robotic vision systems: Integrating multiple camera feeds for dynamic environments like autonomous navigation and industrial robotics.
- Medical imaging systems: Precise calibration for tools
Calibration Techniques:
- Fixed calibration patterns: Using methods like chessboards for simpler applications.
- Dynamic and automated calibration: Solutions tailored for real-time and mobile platforms, allowing cameras to adapt to changing environments.
I have worked with various industries such as robotics, IoT, medical technology, and industrial automation, providing robust calibration solutions that ensure accuracy across multiple environments and platforms.
https://lnkd.in/d97ypMcc
Computer Vision + LLM
AI Model Cost Calculator: Optimizing Costs for Computer Vision and Multimodal AI Solutions
My AI Model Cost Calculator with the latest pricing for GPT-4 Turbo, Google Gemini 1.5 Pro, and Claude 3 Opus! 💡 Calculate text and image processing costs easily with real-time estimates. Check it out! #AI #MachineLearning #CostCalculator #GPT4 #Claude #GoogleGemini
OpenCV 5 NuGet Package – Simplified Setup for Visual Studio
Get started with OpenCV 5 effortlessly! My custom NuGet packages allow you to integrate the static library into your Visual Studio 2019 or 2022 projects in just a few minutes.
Key Features:
- NuGet Packages for OpenCV 5: Optimized for both Visual Studio 2019 and 2022.
- Quick Setup: Install and configure your computer vision project with OpenCV in less than 5 minutes.
- Static Library: Eliminate the hassle of manual configurations by using the NuGet Package Manager for seamless integration.
Step-by-Step Tutorial:
Check out my detailed guide on how to install and set up your OpenCV project on Visual Studio 2022. Watch it here:
Install OpenCV on Visual Studio in Minutes
NuGet Package Links:
-
Visual Studio 2019: OpenCV5 Static Library for VS2019
Install via:
Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 -
Visual Studio 2022: OpenCV5 Static Library for VS2022
Install via:
Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7
Source Code:
Download the complete project on GitHub.
#OpenCV5 #C++ #ComputerVision #VisualStudio
cvtest: Computer Vision Test
Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning
Do you want to test the output of your computer vision application, whether it’s video or images?
In computer vision development, there are no standard tests readily available, so I’ve created and shared a few.
Key Features:
- Standard Tests for Computer Vision Applications: Since no official standards exist, I’ve written custom tests to check processing time, memory usage, CPU usage, and more.
- Output Validation: For image-based applications, I’ve developed tests that compare output images to ground truth, using methods like PSNR, SSIM, and other image quality metrics (e.g., distortion, brightness, sharpness).
- Hardware-Specific Tests: I’ve also written tests for different hardware architectures to evaluate their impact on performance.
- Common Questions Solved:
- Does your program adjust image brightness automatically and correctly?
- Is your generic sharpening kernel removing blurriness effectively?
- How do you check FPS (frames per second) for image processing?
- Which OCR system works best for your input image?
Find more details in the README file on GitHub.
Download the Source Code:
object detection and tracking on Edge and cloud Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning
LLM Developers: VSCode Extensions Farshid
VS Marketplace Link: VSCode Extensions Farshid
VSCode Extensions Farshid is a pack of essential tools designed for Computer Vision (CV), Machine Learning (ML), Large Language Models (LLM), and Personal Knowledge Management (PKM) projects.
Enhance your workflow with tools like:
- Better Comments for clean code annotations
- Prettier for code formatting
- Python, Jupyter, and Docker extensions to boost productivity in AI and data science tasks.
🔗 Get it on the Marketplace: VSCode Extensions Farshid
💬 Feedback: Check out the GitHub repo for issues, feedback, or contributions.
Happy coding! 💻✨
Extension Details:
- Name: VSCode Extensions Farshid
- ID: vscode-extensions-farshid.vscode-extensions-farshid
- Description: A curated list of essential extensions for CV, ML, LLM, and PKM projects.
- Version: 0.0.1
- Publisher: VSCode Extensions Farshid
#VSCode #DeveloperTools #Programming #MachineLearning #DataScience
farshid/portfolio/projects/GUI_OpenCV/realtime_GUI_OpenCV.py
Excited to share my new Real-time OpenCV Function Tester built with PyQt5! 🎉
🔹 Apply OpenCV functions on images with a user-friendly GUI. 🔹 Safe code execution environment. 🔹 Undo functionality for easy experimentation. 🔹 Perfect for learning and prototyping in computer vision.
Check it out! 🖥️✨
#OpenCV #Python #PyQt5 #ComputerVision #Coding
farshid/portfolio/projects/GUI_OpenCV/realtime_GUI_OpenCV.py
Todo App Overview and Features
t.me/de_mini_app_bot @de_mini_app_bot Innovating Task Management with Local Storage-Powered Todo App | Full-Stack Developer | Telegram Bot & Mini App
Data Flow:
- Loading Data: Upon loading, the app retrieves todos from IndexedDB for persistence.
- Initializing App: Updates calendars and renders todos across all sections.
- User Interaction:
- Adding Todos: Users can add todos to any section, which are saved with relevant date metadata.
- Navigating Calendars: Changing the
selectedDateupdates the calendars and re-renders todos. - Managing Todos: Users can mark todos as done/undone or delete them. Changes are saved to IndexedDB.
- Saving Data: Any modifications trigger
saveToIndexedDB(), ensuring data persistence.
Date Management:
- Selected Date: Central to the app, determines which todos are displayed.
- Date Metadata: Each todo contains date, weekNumber, month, and year for filtering.
- Week Calculation: Follows ISO standards, starting weeks on Monday.
Compatibility and Limitations:
- Browsers: Compatible with modern browsers supporting IndexedDB.
- Devices: Works across desktops, tablets, smartphones, and smart TVs.
- Limitations:
- IndexedDB Support: Older browsers without IndexedDB support may not retain data.
- Data Storage: Storage limits vary by browser and device.
Usage Instructions:
- Adding Todos:
- Enter the task in the input field of the desired section.
- Click “Add” or press “Enter” to save.
- Navigating Dates:
- Click on calendar elements (days, weeks, months, years) to change the
selectedDate. - The app will refresh to show relevant todos.
- Click on calendar elements (days, weeks, months, years) to change the
- Managing Todos:
- Click on a todo to toggle completion status.
- Click the “Delete” button to remove a todo.
Conclusion:
This Todo App offers a simple, efficient way to manage tasks across different time frames with automatic data persistence. By leveraging IndexedDB, the app ensures data retention without manual saving. With cross-device compatibility, the app is accessible to a wide range of users.
Shortcuts:
- Automatic Saving: Data is saved automatically without any manual effort.
- Cross-Device Compatibility: Works seamlessly across multiple devices.
- Simple UI: Clean, intuitive interface for ease of use.
Potential Enhancements:
- Synchronization: Implement cloud sync for multi-device support.
- Notifications: Add reminders for due tasks.
- Customization: Allow users to customize themes or layouts.
Technical Features of the Todo App:
- Multi-View Calendar: Seamlessly switch between day, week, month, and year views for comprehensive task tracking.
- Task Management: Easily add, edit, and complete tasks, with real-time updates.
- Responsive Design: Consistent user experience across desktop, tablet, and smartphone devices.
- Local Storage: Offline capability ensures data availability and reliability.
- Telegram Bot Integration: Real-time updates and notifications via Telegram Bot.
- OpenAI API: Leverage AI-powered automation for smarter task management.
Technical Architecture:
- Frontend: Built with HTML, CSS, and JavaScript, offering a dynamic, lightweight interface.
- Backend Simulation: Mimics backend functionalities using local storage, ideal for limited server-side access.
- Security: Data is saved locally on the user’s device; caution is advised for sensitive data.
- Deployment: Easy integration and deployment on any static hosting service, with minimal maintenance required.
#TaskManagement #AWSLambda #JavaScript #HTML #CSS #TelegramBot #OpenAI #WebDevelopment #CalendarIntegration #OfflineApp #ResponsiveDesign #ProductivityTools
@pirahansiahbot: AWS
ASK from My resume Farshid Pirahansiah Telegram miniapp:
Building and Deploying a Fine-Tuned ChatGPT Model for Telegram Integration Using AWS Lambda. Creating a Powerful AI-Driven Telegram Bot to Answer CV Queries: A Comprehensive Guide.
Project Overview:
- Creating an AI-Driven Telegram Bot for CV Queries:
This project focuses on building a Telegram bot that answers queries about my CV using AI, hosted on AWS Lambda. The bot, available at https://t.me/pirahansiahbot, utilizes a fine-tuned GPT-4 Mini model to provide accurate, context-driven responses based on a pre-loaded text file.
Key Steps and Technologies:
- Dataset Preparation:
- Custom Dataset: Created and cleaned a dataset reflecting my professional experience.
- Data Augmentation: Enhanced the dataset for better model generalization.
- Fine-Tuning:
- Fine-Tuned GPT-4 Mini model using “gpt-4o-mini-2024-07-18” as the base.
- Model Optimization:
- Hyperparameter Tuning: Optimized model parameters for performance.
- Validation: Ensured the model accurately handles diverse CV-related queries.
- AWS Lambda Integration:
- Custom Layers: Developed layers to integrate OpenAI API, Telegram, and file system operations.
- File Handling: Stored CV content within Lambda for quick access during queries.
- Telegram Bot Development:
- Bot Creation: Set up and activated the bot using the setWebhook API to link with AWS Lambda.
- AI-Powered Responses: The bot provides detailed answers to CV-related queries using the fine-tuned model.
Project Summary:
This project demonstrates how AI and serverless computing can be combined to create a responsive Telegram bot that can answer any question related to my CV. The bot, found at https://t.me/pirahansiahbot, showcases expertise in data preparation, model fine-tuning, and AWS Lambda deployment.
Hashtags:
#AIPoweredBot #TelegramBot #AWSLambda #OpenAI #Serverless #GPT4Mini #FineTuning #DataAugmentation #ModelOptimization #CVAssistant #ChatGPT
@image_processing_farshid_bot:
🚀 My new Image Processing Bot! 🤖🎨
📸 Send an image, apply advanced OpenCV functions like cv2.Canny(img), and get instant results! Perfect for quick edits and AI-driven enhancements.
💰 Support the bot via TonCoin or purchase stars.
Bot Link: https://t.me/image_processing_farshid_bot
Hashtags:
#AI #ImageProcessing #Python #OpenCV #TelegramBot
🚀 Just deployed a robust Telegram bot on AWS Lambda!
From setting up webhooks to handling image processing and integrating payments, this guide covers it all. Learn how to optimize your bot for serverless environments!
#AWS #TelegramBot #Serverless #AI #CloudComputing
Project Overview:
This guide provides a comprehensive overview of setting up, deploying, and troubleshooting a Telegram bot on AWS Lambda, including webhook configuration, Lambda layer creation, and common development issues.
@item2cook_bot: AWS
Transform your photos into beautiful pencil sketches instantly! Just send an image, and SketchBot will do the rest.
https://t.me/farshidpirahansiahbot
Key Features:
- Creative Transformations: Coding transformations, advanced OpenCV functions, and creative image manipulation.
- Payment Integration: Pay with TON or purchase stars for premium features.
- Telegram Mini App Integration: Seamless integration of Telegram Mini Apps for enhanced functionality.
Technical Architecture:
This AI bot:
- Accepts an image and shows function options from
a_cv_functions.py. - Applies the function to the image and displays the result.
- Handles text-based image modification requests through
a_cv_handler_main.py. - Saves all data in a database, checks user activity every 1 minute (
a_database_handler.py), and handles payments througha_payment_handler.py.