Scikit Image Alternatives

OpenCV is a real-time optimized Computer Vision library or tool which is developed to provide a common infrastructure for computer vision applications and to maximize the use of machine perception in commercial products. It contains more than 2,500 optimized algorithms, making you detect and recognize faces, extract #d models of objects, create 3D point clouds from stereo cameras and others.

It is written in C/C++ and used for computational efficiency, having a strong focus on real-time applications. The important feature includes C++, C, Python, and Java interfaces which are compatible with Windows, Linux, Mac OS, iOS, and Android. It aids you in making interactive art, mines inspection, stitching maps on the web, or through modern robotics.

SimpleCV is one of the advanced frameworks for creating computer vision applications to get access to several high-powered computer vision libraries like OpenCV without learning about bit depths, file formats, color spaces, buffer management, or matrix verses. After capturing an image from a webcam or another source, it applies a binary threshold that makes your image black and white and draws some text. As a result, the program then prints a result to a display.

The visually unique areas of an image are used in the reconstruction of 3D models and image matching tasks. The basic advantages include reliable for beginner programmers to write simple machine vision tests, cameras, video files, images, and video streams can be altered at any time, information present on the image can be extracted, sorted, and filtered easily, fast manipulation along with easy to remember names and linear algebra is strictly optional.

NumPy is a smart tool containing multidimensional array and matrix data structures that brings computational power to multiple languages like C or Fortron to Python. It is mostly used to operate a number of mathematical tasks on arrays such as trigonometric, statistical, and algebraic routines. The key feature of this platform is, it specifies the keyword argument dtype to convert each element into a float, which is not offered by the other traditional libraries.

Other hot functions include high-performance N-dimensional objects, having modern tools for integrating code from C/C++ and Fortran, a multidimensional container for generic data, and many others. It starts by initializing the NumPy arrays from Python lists by using nested lists for two or higher-dimensional data, making you easily access the elements in the array by adding square brackets.

Imutils is a tool that is developed to make basic image processing functions like translation, rotation, resizing, skeletonization, and display Matplotlib images with the help of OpenCV and Python. It translates the image by shifting its direction either in the X or Y direction. For translation, you need to supply the (x,y)-shift as (tx, ty) for generating the translation matrix. Through its resizing ability, it maintains the aspect ratio and gives keyword arguments like width and height, enabling the programmers to resize the image without disturbing the dimensions of quality.

Imutils empower you to construct a topological skeleton of an object in an image where the object is presumed to be white on a black background. It offers you a chance to construct a skeleton by using its morphological and binary functions.

IPSDK is one of the smart or efficient 2D/3D image processing tools that analyzes your images with the help of innovative and revolutionary modules based upon Machine learning techniques. It provides various solutions such as fast image processing, graphical interface, machine learning, helping you to maximize the core capabilities of your workstation. It automatically adjusts itself to your processor’s architecture and capabilities and compatible with SSE2, AVX, AVX2, and AVX523 accelerators.

IPSDK offers extensive and rigorous documentation of all image processing functions, and all the commands are accompanied by a visual to understand the parameters. Through its graphical repetition of processing time function, it draws the graphs of operations on datasets from 10 to 100Mb. In X-axis, the size of the dataset is displayed, and on Y-axis, the required time is observed, which is not present in other solutions.