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A collection of parallel image processing algorithms in pure Go. The aim of this project is simplicity in use and development over absolute high performance, but most algorithms are designed to be efficient and make use of parallelism when available. It uses packages from the standard library whenever possible to reduce dependency use and development abstractions. All operations return image types from the standard library. Package convolution provides the functionality to create and apply a...

Imagor is a fast, Docker-ready image processing server built in Go, ideal for on-the-fly image resizing, cropping, filtering, and optimization. It supports a wide variety of image formats and integrates seamlessly with cloud storage backends like AWS S3 and Google Cloud Storage. With support for HTTP and gRPC APIs, Imagor can be used in production environments to serve optimized images dynamically with high performance and low latency.

Imaginary is a high-performance HTTP microservice for image processing written in Go and powered by bimg and libvips. It can run as a private or public service and is designed for large-scale, low-memory image workloads. Images can be supplied through POST bodies, local files, or remote URLs. Supported operations include resizing, cropping, rotation, conversion, watermarking, and other transformations exposed through a simple HTTP API. Optional protections include API tokens, signed URLs,...

Primitive Pictures is an image processing command-line tool written in Go that reproduces images using geometric primitives (triangles, rectangles, ellipses, polygons, etc.). The core algorithm is iterative and “hill-climbing”: given a target image, it repeatedly finds the best single shape to add that will reduce the error between the current approximation and the target image, then draws that shape. Over time (e.g., adding 50-200 shapes) the output becomes an abstracted version of the...