Remove Gemini logos and watermarks from images using OpenCV inpainting.
npx skills add https://github.com/bear2u/my-skills --skill gemini-logo-remover
Please help me install this Agent Skill. Skill: gemini-logo-remover Source: https://github.com/bear2u/my-skills/tree/main/skills/gemini-logo-remover Command: npx skills add https://github.com/bear2u/my-skills --skill gemini-logo-remover
This Skill removes Gemini logos, watermarks, or AI-generated image markers from images using OpenCV inpainting, saving time and preserving visuals.
Run the skill with your image input and save the result to the outputs folder. Example: process 'input.png' to 'clean.png' in outputs.
High-intent search queries and answers about installing and using this skill.
Logo and watermark removal uses OpenCV inpainting to detect and blend out unwanted regions. This Skill applies the TELEA inpainting algorithm to reconstruct pixels in masked areas, preserving image quality while eliminating branding marks, logos, or AI-generated watermarks from screenshots and photos.
Image inpainting is a technique that reconstructs missing or masked image regions by analyzing surrounding pixels. OpenCV's inpainting algorithm intelligently fills removed areas with content that matches the background, making it effective for seamless logo removal without visible artifacts.
Yes, this Skill supports both corner-based and rectangular mask removal. You define the region—such as a bottom-right corner or arbitrary rectangle—and the inpainting algorithm removes the logo while blending the cleaned area with surrounding pixels for a natural result.
Logo removal requires Python with OpenCV (cv2), NumPy, and Pillow libraries. It processes standard image formats and outputs cleaned images to your specified path, making it compatible with most image workflows and automation pipelines.
Inpainting is ideal when you need automated, pixel-aware removal that blends naturally with surrounding content. Unlike manual cropping or simple masking, inpainting reconstructs the removed region intelligently, making it best for preserving image composition while eliminating logos or watermarks.
This Skill handles individual image processing with Python and OpenCV. Batch processing requires automation scripting that iterates the removal across multiple input images, saving cleaned outputs to your designated folder for each file.
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