-->Numpy: What is NumPy?
NumPy is a python library used for working with arrays. It also has functions for working in domain of linear algebra, fourier transform, and matrices. NumPy was created in 2005 by Travis Oliphant. It is an open source project and you can use it freely. NumPy stands for Numerical Python.
Why Use NumPy ?
In Python we have lists that serve the purpose of arrays, but they are slow to process. NumPy aims to provide an array object that is up to 50x faster that traditional Python lists. The array object in NumPy is called ndarray, it provides a lot of supporting functions that make working with ndarray very easy. Arrays are very frequently used in data science, where speed and resources are very important.
More information can be found at : https://numpy.org/
-->Matplotlib:
Matplotlib is an amazing visualization library in Python for 2D plots of arrays. Matplotlib is a multi-platform data visualization library built on NumPy arrays and designed to work with the broader SciPy stack. It was introduced by John Hunter in the year 2002. One of the greatest benefits of visualization is that it allows us visual access to huge amounts of data in easily digestible visuals. Matplotlib consists of several plots like line, bar, scatter, histogram etc.
More information can be found at : https://matplotlib.org/
-->Pandas:
Pandas is a high-level data manipulation tool developed by Wes McKinney. It is built on the Numpy package and its key data structure is called the DataFrame. DataFrames allow you to store and manipulate tabular data in rows of observations and columns of variables.pandas is well suited for many different kinds of data:
1).Tabular data with heterogeneously-typed columns, as in an SQL table or Excel spreadsheet 2).Ordered and unordered (not necessarily fixed-frequency) time series data. 3).Arbitrary matrix data (homogeneously typed or heterogeneous) with row and column labels 4).Any other form of observational / statistical data sets. The data actually need not be labeled at all to be placed into a pandas data structure
The two primary data structures of pandas, Series (1-dimensional) and DataFrame (2-dimensional), handle the vast majority of typical use cases in finance, statistics, social science, and many areas of engineering.
More information can be found at : https://pandas.pydata.org/docs/
-->OpenCV:
OpenCV is a library of programming functions mainly aimed at real-time computer vision. Originally developed by Intel, it was later supported by Willow Garage then Itseez. The library is cross-platform and free for use under the open-source BSD license. Applications openFrameworks running the OpenCV add-on example
OpenCV's application areas include:
1).2D and 3D feature toolkits 2).Egomotion estimation 3).Facial recognition system 4).Gesture recognition 5).Human–computer interaction (HCI) 6).Mobile robotics 7).Motion understanding 8).Object identification 9).Segmentation and recognition 10).Stereopsis stereo vision: depth perception from 2 cameras 11).Structure from motion (SFM) 12).Motion tracking 13).Augmented reality
More information can be found at : https://docs.opencv.org/2.4/doc/tutorials/tutorials.html