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gemini-logo-remover

Uses computer vision to automatically remove watermarks and logos from images.

Install

mkdir -p .claude/skills/gemini-logo-remover && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/360" && unzip -o skill.zip -d .claude/skills/gemini-logo-remover && rm skill.zip

Installs to .claude/skills/gemini-logo-remover

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Remove Gemini logos, watermarks, or AI-generated image markers using OpenCV inpainting. Use this skill when the user asks to remove Gemini logo, AI watermark, or any logo/watermark from images.
193 chars✓ has a “when” trigger
Beginner

Key capabilities

  • Remove logos or watermarks using OpenCV inpainting
  • Target specific image regions via pixel coordinates
  • Identify and remove watermarks from image corners
  • Process images for professional presentations
  • Save cleaned images to designated output directories

How it works

The skill uses OpenCV's Telea inpainting algorithm to fill in masked regions based on surrounding pixel data. It allows users to define the removal area either by explicit coordinates or by relative corner ratios.

Inputs & outputs

You give it
Path to an image and target coordinates or corner location
You get back
An image file with the specified region inpainted

When to use gemini-logo-remover

  • Removing AI watermarks
  • Deleting unwanted brand logos
  • Cleaning image background noise
  • Processing images for presentations

About this skill

Gemini Logo Remover

Remove Gemini logos and watermarks from AI-generated images using inpainting.

Setup

pip install opencv-python numpy pillow --break-system-packages

Usage

By Coordinates

import cv2
import numpy as np

def remove_region(input_path, output_path, x1, y1, x2, y2, radius=5):
    """Remove rectangular region using inpainting."""
    img = cv2.imread(input_path)
    h, w = img.shape[:2]
    
    mask = np.zeros((h, w), dtype=np.uint8)
    cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
    
    result = cv2.inpaint(img, mask, radius, cv2.INPAINT_TELEA)
    cv2.imwrite(output_path, result)

# Example: remove region at coordinates
remove_region('/mnt/user-data/uploads/img.png', 
              '/mnt/user-data/outputs/clean.png',
              x1=700, y1=650, x2=800, y2=720)

By Corner

def remove_corner_logo(input_path, output_path, corner='bottom_right', 
                       w_ratio=0.1, h_ratio=0.1, padding=10):
    """Remove logo from corner. corner: top_left, top_right, bottom_left, bottom_right"""
    img = cv2.imread(input_path)
    h, w = img.shape[:2]
    
    lw, lh = int(w * w_ratio), int(h * h_ratio)
    
    coords = {
        'bottom_right': (w - lw - padding, h - lh - padding, w - padding, h - padding),
        'bottom_left': (padding, h - lh - padding, lw + padding, h - padding),
        'top_right': (w - lw - padding, padding, w - padding, lh + padding),
        'top_left': (padding, padding, lw + padding, lh + padding)
    }
    x1, y1, x2, y2 = coords[corner]
    
    mask = np.zeros((h, w), dtype=np.uint8)
    cv2.rectangle(mask, (x1, y1), (x2, y2), 255, -1)
    
    result = cv2.inpaint(img, mask, 5, cv2.INPAINT_TELEA)
    cv2.imwrite(output_path, result)

# Example: remove bottom-right logo
remove_corner_logo('/mnt/user-data/uploads/img.png',
                   '/mnt/user-data/outputs/no_logo.png',
                   corner='bottom_right', w_ratio=0.08, h_ratio=0.08)

Find Coordinates

img = cv2.imread(input_path)
h, w = img.shape[:2]
print(f"Size: {w}x{h}")

# Gemini 별 로고는 보통 이미지 우하단 모서리에서 약간 안쪽에 위치
# 일반적인 좌표: x1=w-150, y1=h-100, x2=w-130, y2=h-55
# 정확한 위치는 이미지마다 다르므로 조정 필요

Output

Always save to /mnt/user-data/outputs/ and use present_files tool.

Notes

  • Inpainting works best for small areas with uniform backgrounds
  • Gemini logo is typically in bottom-right corner
  • Adjust coordinates/ratios based on actual logo position and size

When not to use it

  • When the watermark covers a large or complex area of the image
  • When the image background is highly textured or non-uniform

Prerequisites

pip install opencv-python numpy pillow

Limitations

  • Inpainting performance degrades on large or complex backgrounds
  • Requires manual adjustment of coordinates for precise logo removal

How it compares

It provides a programmatic, repeatable way to clean specific image regions compared to manual editing tools.

Compared to similar skills

gemini-logo-remover side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
gemini-logo-remover (this skill)98moReviewBeginner
milimo-storyboard-analyst05moNo flagsAdvanced
data-engineering137moReviewAdvanced
crawl4ai218moReviewIntermediate

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