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.
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.
license
MIT
metadata
{"author":"Bria AI","version":"1.3.4"}
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
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 image work, use the skill instead:
generative or AI-powered
bria-ai
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:
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
pip install Pillow requests
Basic Usage
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
# 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
# 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
# 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
# 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)
# Optimize for web delivery
optimized_bytes = ImageUtils.optimize_for_web(
image,
max_dimension=1920, # Resize if largerformat="WEBP", # Best compression
quality=85
)
# Save optimizedwithopen("optimized.webp", "wb") as f:
f.write(optimized_bytes)
Integration with Bria AI
Use alongside the bria-ai skill 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.
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 WebPfor name, img in sizes.items():
ImageUtils.save(img, f"product_{name}.webp", quality=85)
