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Description
Imagor is a high-performance and secure image processing server and Go library that leverages the capabilities of the highly efficient libvips library for image manipulation. It offers a broad array of image functions, such as resizing, cropping, rotating, flipping, and the application of various filters. Built to operate without maintaining state, Imagor can be seamlessly deployed via Docker containers. The system accommodates different storage solutions, including HTTP, AWS S3, Google Cloud Storage, and local file systems. Its highly customizable nature permits users to specify loaders, storages, and processors tailored to their particular requirements. Additionally, Imagor supports URL-safe image operations, facilitating real-time image transformations through URL parameters. Enhanced security is provided by HMAC-based URL signing, which safeguards against unauthorized access. Users benefit from its extensibility, allowing the integration of custom filters and processors to meet diverse needs. Furthermore, for generating video thumbnails, Imagor offers integration with ffmpeg through the imagorvideo extension, enabling the extraction of frames from videos for use as thumbnails. This versatility makes Imagor an ideal choice for various image processing tasks across different platforms.
Description
Scikit-image is an extensive suite of algorithms designed for image processing tasks. It is provided at no cost and without restrictions. Our commitment to quality is reflected in our peer-reviewed code, developed by a dedicated community of volunteers. This library offers a flexible array of image processing functionalities in Python. The development process is highly collaborative, with contributions from anyone interested in enhancing the library. Scikit-image strives to serve as the definitive library for scientific image analysis within the Python ecosystem. We focus on ease of use and straightforward installation to facilitate adoption. Moreover, we are judicious about incorporating new dependencies, sometimes removing existing ones or making them optional based on necessity. Each function in our API comes with comprehensive docstrings that clearly define expected inputs and outputs. Furthermore, arguments that share conceptual similarities are consistently named and positioned within function signatures. Our test coverage is nearly 100%, and every piece of code is scrutinized by at least two core developers prior to its integration into the library, ensuring robust quality control. Overall, scikit-image is committed to fostering a rich environment for scientific image analysis and ongoing community engagement.
API Access
Has API
API Access
Has API
Integrations
Akira AI
Amazon S3
Cython
Docker
Go
Google Cloud Storage
Label Studio
MLReef
PostgresML
Python
Integrations
Akira AI
Amazon S3
Cython
Docker
Go
Google Cloud Storage
Label Studio
MLReef
PostgresML
Python
Pricing Details
Free
Free Trial
Free Version
Pricing Details
Free
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
cshum
Country
China
Website
github.com/cshum/imagor
Vendor Details
Company Name
scikit-image
Country
United States
Website
scikit-image.org
