wimg

A fast and efficient command-line image processing tool written in Go. Convert images between formats and compress them with ease.

Features

• Format Conversion: Convert between JPEG, PNG, WebP, TIFF, and AVIF formats
• Image Compression: Compress images to create .compressed files with configurable quality
• Fast Processing: Built with high-performance image processing libraries
• Simple CLI: Easy-to-use command-line interface
• Cross-platform: Works on Linux, macOS, and Windows

Installation

From Source

git clone https://github.com/d3xfoo/wimg.git
cd wimg
go build -o wimg cmd/wimg/main.go
sudo mv wimg /usr/local/bin/

Usage

Basic Commands

# Convert image to WebP format
wimg image.jpg .webp

# Compress image (creates image.compressed.jpg)
wimg image.jpg

# Compress and replace original with compressed version
wimg -r image.jpg

Examples

# Convert JPEG to PNG
wimg photo.jpg .png

# Convert to WebP (great for web)
wimg photo.jpg .webp

# Convert to AVIF (modern format)
wimg photo.jpg .avif

# Compress a large image (creates large-photo.compressed.jpg)
wimg large-photo.jpg

# Compress and replace original (keeps only compressed version)
wimg -r photo.jpg

Supported Formats

• Input: JPEG, PNG, WebP, TIFF, AVIF
• Output: JPEG, PNG, WebP, TIFF, AVIF

Compression Behavior

When compressing an image without the -r flag:

• Creates a new file with .compressed extension
• Original file is preserved
• Example: wimg photo.jpg → creates photo.compressed.jpg

When using the -r flag:

• Creates compressed version
• Removes the original file
• Renames compressed file to original name
• Example: wimg -r photo.jpg → replaces photo.jpg with compressed version

Dependencies

• Go 1.24.5 or later
• bimg - Image processing library

Development

Building

make build

Testing

make test

Release

make release

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• bimg for the excellent image processing capabilities
• The Go community for the robust ecosystem