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Description
This image processing solution is incredibly straightforward and can save you countless hours of development effort. We handle your image requests instantly and only require the original files. To get images resized to specific dimensions or altered in various ways, all you need to do is append the appropriate parameters to the URL. For instance, if you want to adjust an image to fill a 200 x 200 pixel area, you simply need to construct the right URL. We recognize that certain organizations face distinct challenges, often due to compliance issues, which prevent them from using publicly available image processing solutions. Our focus is solely on processing and delivering images; thus, if your needs include cloud storage or file sharing, we may not be the best fit. To crop an image effectively, you just need to provide four key parameters: the x and y coordinates for the top left corner of the crop area, along with the width and height of the desired rectangle. This streamlined approach ensures that you can get precisely the images you need without unnecessary complications.
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
Cython
Label Studio
MLReef
PostgresML
Python
Yamak.ai
Yandex Data Proc
ZenML
Integrations
Akira AI
Cython
Label Studio
MLReef
PostgresML
Python
Yamak.ai
Yandex Data Proc
ZenML
Pricing Details
$ 15 Per month
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
Libpixel
Website
www.libpixel.com
Vendor Details
Company Name
scikit-image
Country
United States
Website
scikit-image.org
