Let's discuss , know and apply some useful Python libraries and make Amazing project. So get ready to start

1. Pillow

[Related to image manipulation]

Pillow is actually a fork of PIL – Python Image Library. At first, pillow was mainly based on the PIL code-structure. But later, it transformed into something more friendly and better. Experts say Pillow is actually a modern version of PIL. However, pillow is your trusted company while working with images or any type of image format.

Features Of Pillow

*Using Pillow, you can not only open and save images but also influence the environment of images as well.

*Pillow supports a lot of file types such as PDF, WebP, PCX, PNG, JPEG, GIF, PSD, WebP, PCX, GIF, IM, EPS, ICO, BMP, and many others as well.

*With Pillow, you can easily create thumbnails for images. Thumbnails bear most of the valuable aspects of your image.

*Pillow supports a collection of image filters – FIND_EDGES, DETAIL, SMOOTH, BLUR, CONTOUR, SHARPEN, SMOOTH_MORE, and others.

2. Matplotlib

{Related to math}

Matplotlib is a Python library that uses Python Script to write 2-dimensional graphs and plots. Often mathematical or scientific applications require more than single axes in a representation. This library helps us to build multiple plots at a time. You can, however, use Matplotlib to manipulate different characteristics of figures as well.

Features Of Matplotlib

*Matplotlib can create such quality figures that are really good for publication. Figures you create with Matplotlib are available in hardcopy formats across different interactive platforms.

*You can use MatPlotlib with different toolkits such as Python Scripts, IPython Shells, Jupyter Notebook, and many other four graphical user interfaces.

*A number of third-party libraries can be integrated with Matplotlib applications. Such as seaborn, ggplot, and other projection and mapping toolkits such as basemap.

*An active community of developers is dedicated to helping you with any of your inquiries with Matplotlib. Their contribution to Matplotlib is highly praisable.

*Good thing is that you can track any bugs, new patches, and feature requests on the issue tracker page from Github. It is an official page for featuring different issues related to Matplotlib.

3. OpenCV

{Related to camera use}

OpenCV, a.k.a Open Source Computer Vision is a python package for image processing. It monitors overall functions that are focused on instant computer vision. Although OpenCV has no proper documentation, according to many developers, it is one of the hardest libraries to learn. However, it does provide many inbuilt functions through which you learn Computer vision easily

Features Of OpenCV

OpenCV is an ideal image processing package that allows you to both read and write images at the same time.

*Computer Vision allows you to rebuild, interrupt, and comprehend a 3D environment from its respective 2D environment.

*This package allows you to diagnose special objects in any videos or images. Objects such as faces, eyes, trees, etc.

*You can also save and capture any moment of a video and also analyze its different properties such as motion, background, etc. OpenCV is compatible with many operating systems such as Windows, OS-X, Open BSD, and many others.

4. Numpy

{Related to array}

Numpy is a popular array – processing package of Python. It provides good support for different dimensional array objects as well as for matrices. Numpy is not only confined to providing arrays only, but it also provides a variety of tools to manage these arrays. It is fast, efficient, and really good for managing matrice and arrays.

Features Of Numpy

*Arrays of Numpy offer modern mathematical implementations on huge amount of data. Numpy makes the execution of these projects much easier and hassle-free.

*Numpy provides masked arrays along with general array objects. It also comes with functionalities such as manipulation of logical shapes, discrete Fourier transform, general linear algebra, and many more.

*While you change the shape of any N-dimensional arrays, Numpy will create new arrays for that and delete the old ones. This python package provides useful tools for integration. You can easily integrate Numpy with programming languages such as C, C++, and Fortran code.

*Numpy provides such functionalities that are comparable to MATLAB. They both allow users to get faster with operations.