What is image-filter-lab?

Image Filter Lab is a comprehensive Claude skill designed for professional-grade image enhancement and artistic transformation. It enables users to apply a wide array of filters—including vintage, sepia, B&W, and Gaussian blur—while providing fine-grained control over color adjustments like saturation, brightness, and contrast. Supporting both Python API and CLI interfaces, it features batch processing capabilities and custom preset creation, making it an essential tool for automating visual content styling and maintaining aesthetic consistency across large image datasets.

When should I use image-filter-lab?

image-filter-lab is useful in the following scenarios: • Social Media Branding: Automatically apply consistent 'Instagram-style' or 'Noir' presets to a batch of photos to maintain a unified brand aesthetic across platforms. • E-commerce Image Optimization: Batch process product photography to standardize brightness, contrast, and sharpness for professional web catalogs. • Content Repurposing: Quickly transform standard images into stylized assets using film grain, vignettes, or artistic blurs for blog posts and marketing materials. • Automated Design Pipelines: Integrate the Python API into developer workflows to programmatically generate processed thumbnails or stylized background images. • Photography Post-Processing: Use the CLI to rapidly apply complex filter chains (e.g., sharpen + vintage + vignette) to entire directories of high-resolution photos.

name	image-filter-lab
description	Apply artistic filters to images including vintage, sepia, B&W, blur, sharpen, vignette, and color adjustments. Create custom filter presets.

Image Filter Lab

Apply professional filters and effects to images.

Features

Color Filters: Sepia, B&W, color tint, saturation
Blur Effects: Gaussian, motion, radial blur
Artistic Filters: Vintage, film grain, vignette
Enhancements: Sharpen, contrast, brightness
Custom Presets: Save and apply filter combinations
Batch Processing: Apply filters to multiple images

Quick Start

from image_filter import ImageFilterLab

lab = ImageFilterLab()
lab.load("photo.jpg")

# Apply vintage filter
lab.vintage()
lab.save("photo_vintage.jpg")

# Chain multiple effects
lab.load("photo.jpg")
lab.brightness(1.2).contrast(1.1).saturation(0.8).vignette()
lab.save("photo_edited.jpg")

CLI Usage

# Apply single filter
python image_filter.py --input photo.jpg --filter vintage --output result.jpg

# Apply multiple filters
python image_filter.py -i photo.jpg --sepia --vignette --sharpen -o result.jpg

# Adjust parameters
python image_filter.py -i photo.jpg --brightness 1.2 --contrast 1.1 -o result.jpg

# Batch process
python image_filter.py --batch photos/ --filter vintage --output-dir filtered/

API Reference

ImageFilterLab Class

class ImageFilterLab:
    def __init__(self)

    # Loading
    def load(self, filepath: str) -> 'ImageFilterLab'

    # Color Filters
    def grayscale(self) -> 'ImageFilterLab'
    def sepia(self, intensity: float = 1.0) -> 'ImageFilterLab'
    def negative(self) -> 'ImageFilterLab'
    def tint(self, color: Tuple, intensity: float = 0.3) -> 'ImageFilterLab'

    # Adjustments
    def brightness(self, factor: float) -> 'ImageFilterLab'
    def contrast(self, factor: float) -> 'ImageFilterLab'
    def saturation(self, factor: float) -> 'ImageFilterLab'
    def hue(self, shift: int) -> 'ImageFilterLab'
    def temperature(self, value: int) -> 'ImageFilterLab'

    # Blur Effects
    def blur(self, radius: int = 5) -> 'ImageFilterLab'
    def motion_blur(self, size: int = 15, angle: int = 0) -> 'ImageFilterLab'
    def radial_blur(self, amount: int = 10) -> 'ImageFilterLab'

    # Sharpen
    def sharpen(self, factor: float = 1.0) -> 'ImageFilterLab'
    def unsharp_mask(self, radius: int = 2, percent: int = 150) -> 'ImageFilterLab'

    # Artistic
    def vintage(self) -> 'ImageFilterLab'
    def film_grain(self, amount: int = 25) -> 'ImageFilterLab'
    def vignette(self, radius: float = 0.8, intensity: float = 0.5) -> 'ImageFilterLab'
    def posterize(self, levels: int = 4) -> 'ImageFilterLab'
    def solarize(self, threshold: int = 128) -> 'ImageFilterLab'

    # Presets
    def apply_preset(self, preset: str) -> 'ImageFilterLab'
    def save_preset(self, name: str, operations: List) -> None

    # Output
    def save(self, filepath: str, quality: int = 95) -> str
    def reset(self) -> 'ImageFilterLab'

    # Batch
    def batch_process(self, input_dir: str, output_dir: str,
                     filter_func: callable) -> List[str]

Built-in Presets

vintage: Sepia tint, reduced saturation, vignette
film: Slight desaturation, grain, crushed blacks
instagram: High contrast, warm tint, vignette
noir: High contrast B&W, strong vignette
warm: Warm color temperature, increased saturation
cool: Cool color temperature, slightly desaturated
dramatic: High contrast, deep shadows
dreamy: Soft blur, bright highlights, low contrast

Dependencies

pillow>=10.0.0
opencv-python>=4.8.0
numpy>=1.24.0

image-filter-lab

When & Why to Use This Skill

Use Cases