gopendrasharma89-tech/openclaw-workspace/image-utils

Name: image-utils
Author: gopendrasharma89-tech

Claude Code and agent skill by gopendrasharma89-tech/openclaw-workspace

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By gopendrasharma89-tech
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gopendrasharma89-tech/openclaw-workspace
skills/image-utils/SKILL.md
Updated 3 Oct 2026
Version 1.0.01 version recorded

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Classic image manipulation with Python Pillow - resize, crop, composite, format conversion, watermarks, brightness/contrast adjustments, and web optimization. Use this skill when post-processing AI-generated images, preparing images for web delivery, batch processing image directories, creating responsive image variants, or performing any deterministic pixel-level image operation. Works standalone or alongside bria-ai for post-processing generated images.

Install

npx vskill@latest install gopendrasharma89-tech/openclaw-workspace/image-utils

SKILL.md

Image Utilities

Pillow-based utilities for deterministic pixel-level image operations. Use for resize, crop, composite, format conversion, watermarks, and other standard image processing tasks.

When to Use This Skill

Post-processing AI-generated images: Resize, crop, optimize for web after generation
Format conversion: PNG ↔ JPEG ↔ WEBP with quality control
Compositing: Overlay images, paste subjects onto backgrounds
Batch processing: Resize to multiple sizes, add watermarks
Web optimization: Compress and resize for fast delivery
Social media preparation: Crop to platform-specific aspect ratios

When NOT to Use This Skill — Use `bria-ai` Instead

This skill handles deterministic pixel-level operations only. For any generative or AI-powered image work, use the bria-ai skill instead:

Generating images from text prompts → use bria-ai
AI background removal or replacement → use bria-ai
AI image editing (inpainting, object removal/addition) → use bria-ai
Style transfer or AI-driven visual effects → use bria-ai
Creating product lifestyle shots with AI → use bria-ai
Image upscaling with AI super-resolution → use bria-ai

Rule of thumb: If the task requires creating new visual content or understanding image semantics, use bria-ai. If the task requires transforming existing pixels (resize, crop, format convert, watermark), use this skill.

If bria-ai is not available, install it with:

bash

npx skills add bria-ai/bria-skill

Quick Reference

Operation	Method	Description
Loading	`load(source)`	Load from URL, path, bytes, or base64
	`load_from_url(url)`	Download image from URL
Saving	`save(image, path)`	Save with format auto-detection
	`to_bytes(image, format)`	Convert to bytes
	`to_base64(image, format)`	Convert to base64 string
Resizing	`resize(image, width, height)`	Resize to exact dimensions
	`scale(image, factor)`	Scale by factor (0.5 = half)
	`thumbnail(image, size)`	Fit within size, maintain aspect
Cropping	`crop(image, left, top, right, bottom)`	Crop to region
	`crop_center(image, width, height)`	Crop from center
	`crop_to_aspect(image, ratio)`	Crop to aspect ratio
Compositing	`paste(bg, fg, position)`	Overlay at coordinates
	`composite(bg, fg, mask)`	Alpha composite
	`fit_to_canvas(image, w, h)`	Fit onto canvas size
Borders	`add_border(image, width, color)`	Add solid border
	`add_padding(image, padding)`	Add whitespace padding
Transforms	`rotate(image, angle)`	Rotate by degrees
	`flip_horizontal(image)`	Mirror horizontally
	`flip_vertical(image)`	Flip vertically
Watermarks	`add_text_watermark(image, text)`	Add text overlay
	`add_image_watermark(image, logo)`	Add logo watermark
Adjustments	`adjust_brightness(image, factor)`	Lighten/darken
	`adjust_contrast(image, factor)`	Adjust contrast
	`adjust_saturation(image, factor)`	Adjust color saturation
	`blur(image, radius)`	Apply Gaussian blur
Web	`optimize_for_web(image, max_size)`	Optimize for delivery
Info	`get_info(image)`	Get dimensions, format, mode

Requirements

bash

pip install Pillow requests

Basic Usage

python

from image_utils import ImageUtils
# Load from URL
image = ImageUtils.load_from_url("https://example.com/image.jpg")
# Or load from various sources
image = ImageUtils.load("/path/to/image.png")         # File path
image = ImageUtils.load(image_bytes)                  # Bytes
image = ImageUtils.load("data:image/png;base64,...")  # Base64
# Resize and save
resized = ImageUtils.resize(image, width=800, height=600)
ImageUtils.save(resized, "output.webp", quality=90)
# Get image info
info = ImageUtils.get_info(image)
print(f"{info['width']}x{info['height']} {info['mode']}")

Resizing & Scaling

python

# Resize to exact dimensions
resized = ImageUtils.resize(image, width=800, height=600)
# Resize maintaining aspect ratio (fit within bounds)
fitted = ImageUtils.resize(image, width=800, height=600, maintain_aspect=True)
# Resize by width only (height auto-calculated)
resized = ImageUtils.resize(image, width=800)
# Scale by factor
half = ImageUtils.scale(image, 0.5)    # 50% size
double = ImageUtils.scale(image, 2.0)  # 200% size
# Create thumbnail
thumb = ImageUtils.thumbnail(image, (150, 150))

Cropping

python

# Crop to specific region
cropped = ImageUtils.crop(image, left=100, top=50, right=500, bottom=350)
# Crop from center
center = ImageUtils.crop_center(image, width=400, height=400)
# Crop to aspect ratio (for social media)
square = ImageUtils.crop_to_aspect(image, "1:1")      # Instagram
wide = ImageUtils.crop_to_aspect(image, "16:9")       # YouTube thumbnail
story = ImageUtils.crop_to_aspect(image, "9:16")      # Stories/Reels
# Control crop anchor
top_crop = ImageUtils.crop_to_aspect(image, "16:9", anchor="top")
bottom_crop = ImageUtils.crop_to_aspect(image, "16:9", anchor="bottom")

Compositing

python

# Paste foreground onto background
result = ImageUtils.paste(background, foreground, position=(100, 50))
# Alpha composite (foreground must have transparency)
result = ImageUtils.composite(background, foreground)
# Fit image onto canvas with letterboxing
canvas = ImageUtils.fit_to_canvas(
    image,
    width=1200,
    height=800,
    background_color=(255, 255, 255, 255),  # White
    position="center"  # or "top", "bottom"
)

Format Conversion

python

# Convert to different formats
png_bytes = ImageUtils.to_bytes(image, "PNG")
jpeg_bytes = ImageUtils.to_bytes(image, "JPEG", quality=85)
webp_bytes = ImageUtils.to_bytes(image, "WEBP", quality=90)
# Get base64 for data URLs
base64_str = ImageUtils.to_base64(image, "PNG")
data_url = ImageUtils.to_base64(image, "PNG", include_data_url=True)
# Returns: "data:image/png;base64,..."
# Save with format auto-detected from extension
ImageUtils.save(image, "output.png")
ImageUtils.save(image, "output.jpg", quality=85)
ImageUtils.save(image, "output.webp", quality=90)

Watermarks

python

# Text watermark
watermarked = ImageUtils.add_text_watermark(
    image,
    text="© 2024 My Company",
    position="bottom-right",  # bottom-left, top-right, top-left, center
    font_size=24,
    color=(255, 255, 255, 128),  # Semi-transparent white
    margin=20
)
# Logo/image watermark
logo = ImageUtils.load("logo.png")
watermarked = ImageUtils.add_image_watermark(
    image,
    watermark=logo,
    position="bottom-right",
    opacity=0.5,
    scale=0.15,  # 15% of image width
    margin=20
)

Adjustments

python

# Brightness (1.0 = original, <1 darker, >1 lighter)
bright = ImageUtils.adjust_brightness(image, 1.3)
dark = ImageUtils.adjust_brightness(image, 0.7)
# Contrast (1.0 = original)
high_contrast = ImageUtils.adjust_contrast(image, 1.5)
# Saturation (0 = grayscale, 1.0 = original, >1 more vivid)
vivid = ImageUtils.adjust_saturation(image, 1.3)
grayscale = ImageUtils.adjust_saturation(image, 0)
# Sharpness
sharp = ImageUtils.adjust_sharpness(image, 2.0)
# Blur
blurred = ImageUtils.blur(image, radius=5)

Transforms

python

# Rotate (counter-clockwise, degrees)
rotated = ImageUtils.rotate(image, 45)
rotated = ImageUtils.rotate(image, 90, expand=False)  # Don't expand canvas
# Flip
mirrored = ImageUtils.flip_horizontal(image)
flipped = ImageUtils.flip_vertical(image)

Borders & Padding

python

# Add solid border
bordered = ImageUtils.add_border(image, width=5, color=(0, 0, 0))
# Add padding (whitespace)
padded = ImageUtils.add_padding(image, padding=20)  # Uniform
padded = ImageUtils.add_padding(image, padding=(10, 20, 10, 20))  # left, top, right, bottom

Web Optimization

python

# Optimize for web delivery
optimized_bytes = ImageUtils.optimize_for_web(
    image,
    max_dimension=1920,  # Resize if larger
    format="WEBP",       # Best compression
    quality=85
)
# Save optimized
with open("optimized.webp", "wb") as f:
    f.write(optimized_bytes)

Integration with Bria AI

Use alongside the [bria-ai skill](https://clawhub.ai/galbria/bria-ai) to post-process AI-generated images. Generate or edit images with Bria's API, then use image-utils for resizing, cropping, watermarking, and web optimization.

python

import requests
from image_utils import ImageUtils
# Generate with Bria AI (see bria-ai skill for full API reference)
response = requests.post(
    "https://engine.prod.bria-api.com/v2/image/generate",
    headers={"api_token": BRIA_API_KEY, "Content-Type": "application/json"},
    json={"prompt": "product photo of headphones", "aspect_ratio": "1:1", "sync": True}
)
image_url = response.json()["result"]["image_url"]
# Download and post-process
image = ImageUtils.load_from_url(image_url)
# Create multiple sizes for responsive images
sizes = {
    "large": ImageUtils.resize(image, width=1200),
    "medium": ImageUtils.resize(image, width=600),
    "thumb": ImageUtils.thumbnail(image, (150, 150))
}
# Save all as optimized WebP
for name, img in sizes.items():
    ImageUtils.save(img, f"product_{name}.webp", quality=85)

Batch Processing Example

python

from pathlib import Path
from image_utils import ImageUtils
def process_catalog(input_dir, output_dir):
    """Process all images in a directory."""
    output_path = Path(output_dir)
    output_path.mkdir(exist_ok=True)
    for image_file in Path(input_dir).glob("*.{jpg,png,webp}"):
        image = ImageUtils.load(image_file)
        # Crop to square
        square = ImageUtils.crop_to_aspect(image, "1:1")
        # Resize to standard size
        resized = ImageUtils.resize(square, width=800, height=800)
        # Add watermark
        final = ImageUtils.add_text_watermark(resized, "© My Brand")
        # Save optimized
        output_file = output_path / f"{image_file.stem}.webp"
        ImageUtils.save(final, output_file, quality=85)
process_catalog("./raw_images", "./processed")

API Reference

See image_utils.py for complete implementation with docstrings.

Security

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Source

SKILL.md in the repositoryCheckingskills/image-utils/SKILL.md

Popularity

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Install

SKILL.md

Image Utilities

When to Use This Skill

When NOT to Use This Skill — Use bria-ai Instead

Quick Reference

Requirements

Basic Usage

Resizing & Scaling

Cropping

Compositing

Format Conversion

Watermarks

Adjustments

Transforms

Borders & Padding

Web Optimization

Integration with Bria AI

Batch Processing Example

API Reference

Security

Quality evaluation

Source

Popularity

Works with

When NOT to Use This Skill — Use `bria-ai` Instead