✅ Building a Simple Calibration & Warp Tool in Python (Tkinter Canvas) Today I worked on a small but powerful feature: a 4‑point image calibration and rotation tool built using Python + Tkinter. 🔹 By simply clicking four points on an image, the tool automatically computes the transformation and warps the image on the Tkinter canvas. 🔹 This is especially useful for anyone working with scanned documents, camera feeds, or UI elements where manual alignment is needed. 🔹 The workflow is clean, intuitive, and requires no external heavy dependencies. This short demo video shows the tool in action, selecting points, applying rotation/warp, and instantly stabilising the image. I’m excited about how this can support future computer‑vision‑driven interfaces and interactive annotation tools. If anyone is exploring Tkinter‑based visual tools or lightweight calibration utilities, I’d love to connect and share ideas! #Python #Tkinter #ComputerVision #ImageProcessing #Calibration #ResearchTools #Automation

Setfos is Fast and Easy To Use Intuitive Graphical User Interface. • Easily design OPV and LED devices via the graphical user interface • Device structure visualization • Key graphs and reports are automatically generated • Run multiple simulations in parallel • Setfos multi threading ensures electrical and optical device properties can be calculated in a few seconds • Setfos includes fitting and optimization algorithms • Extract physical parameters and optimize the device layout • Easily import/export data and integrate with various frameworks (e.g., Python. Here is browser link: https://lnkd.in/g7iH-pXf

Efficient grid navigation often requires a clear understanding of how different components or "tiles" connect to one another. A common challenge in pathfinding is determining if a continuous route exists from a starting point to a destination based on specific connection rules for each cell. In this approach, I utilized a transition table to map how an incoming direction (Top, Right, Bottom, Left) translates to an outgoing direction based on the tile type. By defining these movements as a fixed set of rules, the algorithm can traverse the grid in constant space, O(1), excluding the input itself. The logic follows a simple "follow the pipe" strategy: Start from the initial cell and check both possible exit directions. Update the current position based on the permitted transition. If the next cell does not support the incoming direction, the path is invalid. The process continues until the target coordinates are reached or the path breaks. This method ensures high performance by avoiding complex recursion or auxiliary data structures while maintaining strict adherence to the grid's connectivity constraints. #Algorithms #Python #CodingEfficiency #ProblemSolving #DataStructures

A quick exercise for anyone working in computer vision: Implement the following in Python from scratch (no external libraries), within 50 minutes: • Brightness adjustment • Contrast enhancement • High-impact contrast & color enhancement • RGB to grayscale conversion • Image normalization The implementations themselves are straightforward. The real point is understanding how pixel intensities behave under transformation, and how small changes affect visual outcomes. A simple task on the surface—but a good check on fundamentals. I’d be happy if you share your code, results, and your thoughts and impressions during and after completing these tasks.

Sensitivity analysis: knowing what really matters in your design When performance varies, the real challenge is often simple: 👉 Which parameters actually drive the behavior of my system? With SIMBA + Python, sensitivity analysis makes this explicit. In this example, a simple RLC circuit is analyzed by: -perturbing each component value by ±10% -running transient simulations automatically -extracting a single performance metric (overshoot) -computing normalized sensitivities for each parameter 📊 The result is a clear ranking of parameters, showing immediately where engineering effort should be focused. 💡 Why it matters: -reduce trial‑and‑error -improve design robustness -focus on the parameters that truly impact performance -increase confidence before hardware validation 👉 Sensitivity analysis is not about running more simulations, it’s about making better design decisions. 🔗 Python script and SIMBA model available in the technical resources (link in first comment). #PowerElectronics #Simulation #SensitivityAnalysis #Python #SIMBA #DesignRobustness Jérôme Cornau Emmanuel Rutovic Guillaume Fontes

Excited to share my latest project: Page Replacement Algorithm Simulator Built using Python, this interactive application visualizes how memory management works in Operating Systems by simulating FIFO, LRU, LFU, and Optimal algorithms. It provides step-by-step execution, real-time metrics (page faults, hits, hit ratio), and insightful graphical analysis using matplotlib. With a clean dark-themed UI, playback controls, and comparative dashboards, the project transforms theoretical concepts into an intuitive, hands-on learning experience. This project helped me strengthen my understanding of OS concepts while also enhancing my skills in Python, GUI development, and data visualization. #Python #OperatingSystems #Algorithms #Projects #Learning #SoftwareDevelopment

Digital Noise Minimization The other day, I posted prototypes on Linkedin and on Github on various scientific topics related to image processing. This is one of the first reviews, which is more than 2 years old, thematically similar to recent examples. Now, based on it, I have created a prototype for minimizing digital noise. The file contains, as usual, the code, screenshots, and a full description of the prototype's functionality. https://lnkd.in/eyJNqaFi #Python #GUI #Digital #Noise #Minimization #Scientific #Visualization

Part 2: Vectorizing the Math – High-Performance Image Processing in Julia The content discusses using Julia for efficient mathematical iteration and matrix manipulation, showcasing its advantages over Python. It explains the use of Images.jl and Luxor.jl for image processing and rendering, detailing a loop that transforms RGB pixels into grayscale and outputs smooth SVG graphics. Future applications include developing a micro-SaaS backend....

Add Two Numbers: Linked List Digit Addition with Carry Process lists simultaneously like grade-school addition. Handle different lengths by treating missing nodes as 0. Carry propagates via loop condition carry > 0. Dummy simplifies head handling. Continue until lists exhausted AND carry zero. Carry Propagation: Including carry in loop condition ensures final carry digit added. Division/modulo elegantly extract carry and digit. This pattern applies to any multi-digit arithmetic simulation. Time: O(max(m, n)) | Space: O(max(m, n)) #LinkedList #DigitAddition #CarryPropagation #SimulationAlgorithm #Python #AlgorithmDesign #SoftwareEngineering

Longest Repeating Character Replacement: Window Valid When Replacements ≤ k The critical insight: window is valid when window_length - max_frequency ≤ k (replacements needed ≤ allowed). HashMap tracks character frequencies, max frequency determines how many characters need replacing. Expand window greedily, contract when replacement budget exceeded. Replacement Formula: length - max_freq = characters needing replacement. This transforms complex constraint into simple arithmetic check, enabling O(n) sliding window. Time: O(n) | Space: O(1) — max 26 chars #SlidingWindow #CharacterReplacement #FrequencyTracking #ConstraintValidation #Python #AlgorithmDesign #SoftwareEngineering