- Machine Learning for OpenCV
- Michael Beyeler
- 196字
- 2021-07-02 19:47:17
Dealing with data using OpenCV and Python
Although raw data can come from a variety of sources and in a wide range of formats, it will help us to think of all data fundamentally as arrays of numbers. For example, images can be thought of as simply 2D arrays of numbers representing pixel brightness across an area. Sound clips can be thought of 1D arrays of intensity over time. For this reason, efficient storage and manipulation of numerical arrays is absolutely fundamental to machine learning.
If you have mostly been using OpenCV's C++ application programming interface (API) and plan on continuing to do so, you might find that dealing with data in C++ can be a bit of a pain. Not only will you have to deal with the syntactic overhead of the C++ language, but you will also have to wrestle with different data types and cross-platform compatibility issues.
This process is radically simplified if you use OpenCV's Python API because you automatically get access to a large number of open-source packages from the Scientific Python (SciPy) community. Case in point is the Numerical Python (NumPy) package, around which most scientific computing tools are built.
- Instant Node Package Manager
- Data Visualization with D3 4.x Cookbook(Second Edition)
- Java程序設(shè)計與開發(fā)
- UML和模式應(yīng)用(原書第3版)
- ASP.NET Core Essentials
- C/C++算法從菜鳥到達(dá)人
- Three.js開發(fā)指南:基于WebGL和HTML5在網(wǎng)頁上渲染3D圖形和動畫(原書第3版)
- Java技術(shù)手冊(原書第7版)
- Programming ArcGIS 10.1 with Python Cookbook
- Unity 5 for Android Essentials
- ElasticSearch Cookbook(Second Edition)
- Getting Started with Nano Server
- Learning JavaScript Data Structures and Algorithms(Second Edition)
- 從Power BI到Analysis Services:企業(yè)級數(shù)據(jù)分析實戰(zhàn)
- 深入分析GCC