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Average Ratings 0 Ratings
Description
Create selections in various shapes, including rectangular, elliptical, or freeform styles, along with line and point selections. You can modify these selections and utilize the wand tool for automatic creation. Additionally, options are available to draw, fill, clear, filter, or measure selections effectively. Selections can be saved and transferred to different images, enhancing workflow flexibility. The toolset supports a range of image processing functions such as smoothing, sharpening, edge detection, median filtering, and thresholding for both 8-bit grayscale and RGB color images. Users can dynamically adjust the brightness and contrast settings of images in 8, 16, and 32-bit formats. Moreover, it allows for precise measurements of area, mean values, standard deviation, as well as minimum and maximum values for either the selected area or the entire image. Lengths and angles can also be measured, with the added capability of using real-world units like millimeters. Calibration is simplified through the use of density standards, and the software can generate detailed histograms and profile plots for thorough data analysis. This comprehensive set of features makes it an invaluable tool for image analysis and editing tasks.
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
Java
Label Studio
MLReef
PostgresML
Python
Yamak.ai
Yandex Data Proc
ZenML
Integrations
Akira AI
Cython
Java
Label Studio
MLReef
PostgresML
Python
Yamak.ai
Yandex Data Proc
ZenML
Pricing Details
No price information available.
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
ImageJ
Website
imagej.nih.gov/ij/features.html
Vendor Details
Company Name
scikit-image
Country
United States
Website
scikit-image.org