#Devlog 1: The Vision

The Problem: Totally frustrated and bored with the slow, clunky Windows File Manager. The Goal: Build a “Ferrari” of file managers. Lightweight (< 5MB), Super Speed (Rust), and Instant Opening. The Stack:

Engine: Rust (Native performance, direct system calls).

Chassis: Tauri v2 (Uses native WebView2, no heavy Chrome bundle).

Dashboard: SolidJS (No Virtual DOM, raw speed).

##**but end up getting error only (what great progress **

📝 DEVLOG #4 - lets give him actual look

Working on globals.css - The Heart of Theming:
This is where I defined all the CSS custom properties that power the theme system. Using CSS variables means we can change the entire color scheme by just updating a few values:

/* app/globals.css */
@tailwind base;
@tailwind components;
@tailwind utilities;

// components/Providers.tsx

📝 DEVLOG #2 - Project Initialization

The research phase helped me understand the market gaps and define a clear vision. Without these two hours of planning, I would have built something mediocre instead of something exceptional.

After weighing and trying the options (React vs Vue vs Svelte, and different backend options), I decided on:

• Next.js 14 - Because I wanted to learn the new App Router, and it handles both frontend and API routes
• TypeScript - Because I’m tired of “undefined is not a function” errors at 2am
• Tailwind CSS - Because writing custom CSS for every element is 2019 energy
• TanStack Query - Because managing 11 parallel API calls manually would be insane

###White Box Era

WTF man , my mind blow up, This version… man, this version humbled me. i try swapped OpenCV for LaMa because the blur artifacts were getting embarrassing. LaMa is a beast—it hallucinates texture—but getting it to run on CPU via ONNX Runtime was a mission. I thought the model was broken. I re-downloaded weights, checked paths—nothing.

Turns out, it was a datatype error. The model output was normalized float32 (0.0 to 1.0).
If it’s > 1.0, leave it alone. If it’s < 1.0, scale it up. It was two lines of code to fix a bug that kept me up until 3 AM or may be 4AM i don’t remember al all.

go check out new release https://github.com/Y252marc/Watermark-remover-YOLO8/releases/tag/v5.0.1 (this is in a pre release for now)

##The White Box Disaster

for late several i am working on same thing which i called “The White Box Disaster”. The old OpenCV method (Telea) was just… meh. It was basically a glorified blur tool. If you had a watermark over a dog’s face, it would just smudge the fur, Unacceptable.
instead of removing the watermark, the app replaced it with pure white rectangles. The app also froze after processing one image. This is a model that actually “dreams” up new texture. It was mind-blowing… until we hit the “White Box” bug

“pain in the neck”(ROOT CAUSE ):-
Math Error: The AI model output pixel values in the 0-255 range, but our code multiplied them by 255 again, assuming they were 0-1. This exploded the values to white.
Memory Leak: Large images (4K) were being rendered directly to UI buttons, consuming RAM until the app froze.
Once I wrote a “Smart Scaling” fix to detect the range automatically

##check out new versions ::– https://github.com/Y252marc/Watermark-remover-YOLO8/releases (The app size is huge now (~300MB), but the quality jump is worth every byte)

##TRAINING AND LABELING

The YOLO model training completed successfully after running for 50 epochs without any errors or interruptions. The training process was stable, and the loss values showed a consistent downward trend across epochs, which indicates that the model learned effectively from the data. The bounding box loss, classification loss, and distribution focal loss all reduced gradually, showing improvements in object localization, class prediction, and overall detection quality.
GPU memory usage remained efficient at around 2 GB, confirming that the training was well within the limits of the NVIDIA RTX 3050 Laptop GPU.

The dataset used for training and validation consisted of 12,000 images containing a total of 31,915 labeled object instances.
AT BELOW YOU YOU CAN SEE DATA SET AND FINAL RESULT ( SEE YOU IN NEXT DEV LOG

UI improvement & ADD some more feature

we will use ttkbootstrap. It is a wrapper for Tkinter that instantly makes your app look like a modern Windows application (Dark mode, nice buttons, flat design) with minimal code.
New Features:

✂️ Crop Tool: A dedicated window allows you to drag-select an area before processing. It uses smart scaling so you don’t lose quality.

🎨 Modern UI: Uses the “Cyborg” (Dark Mode) theme.

🖼️ High-Res Pipeline: Keeps the original resolution safe in memory while showing you resized previews.

check out new releases :- https://github.com/Y252marc/Watermark-remover-YOLO8/releases/tag/v3.0.0

Createing a React component for a whiteboard canvas using the HTML5 Canvas API and Rough.js. The goal is to render ‘hand-drawn’ style shapes (rectangles, circles, and free-hand lines). Implement a basic state management system to store elements as an array of objects, each containing coordinates, shape type, and Rough.js options

2026-01-26 | Entry 009: The “Gold Master” & The Monkey Test
🏁 Objective
Finalize the application for public release. The goal was to produce a stable, standalone executable (.exe) that doesn’t just “work,” but produces high-quality results on complex images.
🐛 The “DLL Hell” Crisis (SOLVED)
The Issue: Even after fixing the code, the Windows executable kept crashing with ModuleNotFoundError: No module named ‘tkinter’. The Discovery: Windows 11 and Python 3.13 have a new directory structure that confuses PyInstaller. The standard build tools were failing to bundle the GUI engine (TCL/TK). The Fix: We implemented a “Nuclear Build” strategy.
We manually located the tcl86t.dll and tk86t.dll files in the system.
We used the –add-binary flag to force-feed these files into the executable.
Result: The app now launches instantly on any machine, with zero dependencies required.

The Pivot to v2.0: To fix this without bloating the app size with Generative AI, we tuned the classic CV pipeline:
Algorithm Swap: Switched from cv2.INPAINT_TELEA to cv2.INPAINT_NS (Navier-Stokes). This fluid-based method follows edges better, preserving the direction of the fur/hair.
Precision Masking: Reduced padding from 15px to 5px. We stopped asking the AI to hallucinate large chunks of data and focused strictly on the text pixels.
Texture Grabbing: Increased the inpaint radius from 3px to 7px, allowing the algorithm to pull more texture from the surrounding area.

📅 2026-01-24 | Entry 005: Video Pipeline & The “Motion Blur” Challenge
🎯 Objective
processing single static images , ensuring the watermark is removed from every frame while maintaining img quality.

🛠️ Actions Taken
Core Engine (src/process_video.py):

Implemented a frame-by-frame loop using cv2.VideoCapture.

Integrated the v16_master model to run inference on every individual frame.

Applied cv2.inpaint (Navier-Stokes algorithm) dynamically based on the detection mask.

Gate Drive: The gate of the NMOS (partially visible) is tied through R1 (1kΩ) to V_CC. Ensuring the V_GS threshold is met is critical here for efficient switching of the inductor.
The high-frequency switching and HV pulses could introduce significant noise; plan to add snubber circuits if ringing becomes an issue during prototyping.