image-filter-lab

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'
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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