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Firesize

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6
+ 1
0
scikit-image

250
127
+ 1
11
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Firesize vs scikit-image: What are the differences?

Firesize: On the fly image resizing. No code required. Built-in CDN. Firesize is a hosted image processing proxy. It's focused on doing one thing fast: resizing and cropping images on demand. It's a simple url based API allows you to quickly generate images on demand; scikit-image: Image processing in Python. scikit-image is a collection of algorithms for image processing.

Firesize and scikit-image can be categorized as "Image Processing and Management" tools.

Some of the features offered by Firesize are:

  • Processing options specified via URL
  • URL signing (for security)
  • Process images from anywhere (no uploading to Firesize required)

On the other hand, scikit-image provides the following key features:

  • Provides I/O, filtering, morphology, transformations, measurement, annotation, color conversions, test data sets, etc.
  • Written in Python with a well-commented source code
  • Has had 5,709 commits made by 116 contributors representing 29,953 lines of code

Firesize and scikit-image are both open source tools. It seems that scikit-image with 3.1K GitHub stars and 1.31K forks on GitHub has more adoption than Firesize with 92 GitHub stars and 40 GitHub forks.

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Pros of Firesize
Pros of scikit-image
    Be the first to leave a pro
    • 6
      More powerful
    • 4
      Anaconda compatibility
    • 1
      Great documentation

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    What is Firesize?

    Firesize is a hosted image processing proxy. It's focused on doing one thing fast: resizing and cropping images on demand. It's a simple url based API allows you to quickly generate images on demand.

    What is scikit-image?

    scikit-image is a collection of algorithms for image processing.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Firesize?
    What companies use scikit-image?
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      What tools integrate with Firesize?
      What tools integrate with scikit-image?
      What are some alternatives to Firesize and scikit-image?
      OpenCV
      OpenCV was designed for computational efficiency and with a strong focus on real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing. Enabled with OpenCL, it can take advantage of the hardware acceleration of the underlying heterogeneous compute platform.
      Cloudinary
      Cloudinary is a cloud-based service that streamlines websites and mobile applications' entire image and video management needs - uploads, storage, administration, manipulations, and delivery.
      Pillow
      It adds image processing capabilities to your Python interpreter. It provides extensive file format support, an efficient internal representation, and fairly powerful image processing capabilities.
      FFMPEG
      The universal multimedia toolkit.
      imgix
      imgix is the leading platform for end-to-end visual media processing. With robust APIs, SDKs, and integrations, imgix empowers developers to optimize, transform, manage, and deliver images and videos at scale through simple URL parameters.
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