Go Image Processing Software

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Browse free open source Go Image Processing Software and projects below. Use the toggles on the left to filter open source Go Image Processing Software by OS, license, language, programming language, and project status.

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  • 1
    imagor

    imagor

    Fast, secure image processing server and Go library, using libvips

    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.
    Downloads: 3 This Week
    Last Update:
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  • 2
    bild

    bild

    Image processing algorithms in pure Go

    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 kernel to an image. Package effect provides the functionality to manipulate images to achieve various looks. Package histogram provides basic histogram types and functions to analyze RGBA images. Package paint provides functions to edit a group of pixels on an image. Package parallel provides helper functions for the dispatching of parallel jobs.
    Downloads: 0 This Week
    Last Update:
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  • 3
    imaginary

    imaginary

    Fast, simple, scalable, Docker-ready HTTP microservice

    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, CORS, concurrency throttling, and fallback placeholder images. Docker and cloud deployment options make it suitable for distributed services. It supports common formats such as JPEG, PNG, WebP, and HEIF, with additional formats available through libvips builds.
    Downloads: 0 This Week
    Last Update:
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