A tool for processing screenshots to ensure consistent sizing, formatting, and padding for documentation and tutorials.
This Python script helps standardize screenshots taken from different Mac devices with varying screen resolutions. It solves common documentation problems by:
- Resizing images to consistent dimensions
- Standardizing DPI settings
- Adding consistent padding around UI elements
- Optimizing data visualizations (UMAPs, scatter plots, etc.)
- Preventing accidental file overwrites
Perfect for teams working on documentation where screenshots come from multiple sources and need visual consistency.
- Python 3.6+
- Pillow library
# Create a virtual environment (optional but recommended)
python -m venv your-venv
source your-venv/bin/activate # On Windows: your-venv\Scripts\activate
# Install required dependency
pip install Pillow# Process a single file
python screenshot_standardizer.py --input input.png
# Process a directory of images
python screenshot_standardizer.py --input ./screenshots
# Process with 200px padding around UI elements
python screenshot_standardizer.py --input input.png --padding 200
# Process as data visualization with enhanced quality
python screenshot_standardizer.py --input plot.png --type viz
# Process at large size with high DPI
python screenshot_standardizer.py --input input.png --size large --dpi 600
# Force overwrite existing files
python screenshot_standardizer.py --input input.png --force
# Save to a specific output directory
python screenshot_standardizer.py --input input.png --outdir ~/Desktop/processed_images
# Use a different image format
python screenshot_standardizer.py --input input.png --format jpg
# Specify a custom output file (single file mode only)
python screenshot_standardizer.py --input input.png --output ~/Desktop/processed_image.png
# Enable verbose mode for detailed processing information
python screenshot_standardizer.py --input input.png --verbose--input,-i: Input file or directory (required)--output,-o: Output file path (for single file processing only)--outdir,-d: Output directory for processed images (default: processed-screenshots)--size,-s: Choose preset sizes: small (1024px), medium (1440px), or large (1920px)--type,-t: Processing type: default or viz (optimized for data visualizations)--padding,-p: Add consistent padding around elements (in pixels)--dpi,-dp: Set DPI for output images (default: 144)--no-border: Disable borders on processed images--force,-f: Force overwrite existing files--format,-fm: Output image format: png, jpg/jpeg, or webp (default: png)--verbose,-v: Print detailed processing information
- Consistency: Creates uniform visuals in documentation despite screenshots coming from different devices
- Professionalism: Properly formatted images with consistent padding improve documentation quality
- Efficiency: Batch process multiple screenshots at once
- Visualization Optimization: Special processing for data plots improves clarity and readability
Processed images are saved to a processed-screenshots directory by default. The naming convention adds the size preset to the filename, like original_medium.png.
To test how processed images look in a documentation context, create a sample markdown file:
mkdir -p test_docs/images
# Process example images
python screenshot_standardizer.py --input path/to/umap_image.png --output test_docs/images/umap_processed.png --type viz --padding 200
python screenshot_standardizer.py --input path/to/color_picker.png --output test_docs/images/colorpicker_processed.png --padding 200
# Create test markdown
touch test_docs/sample_documentation.mdExample markdown content:
# Gene Expression Analysis Tool
## UMAP Visualization
The UMAP visualization allows researchers to explore gene expression patterns across samples.
## Color Picker Interface
The color picker lets users customize the visualization palette according to their preferences.
## Comparing Both Features
When using the visualization tool, researchers can adjust the color scale to highlight specific expression levels in the UMAP plot.This test will help you verify that your processed images have consistent visual appearance in real documentation.