官术网_书友最值得收藏!

Getting started with OpenCV

Being the avid user of OpenCV that I believe you are, I probably don't have to convince you about the power of OpenCV.

Built to provide a common infrastructure for computer vision applications, OpenCV has become a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. According to their own documentation, OpenCV has a user community of more than 47,000 people and has been downloaded over seven million times. That's pretty impressive! As an open-source project, it is very easy for researchers, businesses, and government bodies to utilize and modify already available code.

This being said, a number of open-source machine learning libraries have popped up since the recent machine learning boom that provide far more functionality than OpenCV. A prominent example is scikit-learn, which provides a number of state-of-the-art machine learning algorithms as well as a wealth of online tutorials and code snippets. As OpenCV was developed mainly to provide computer vision algorithms, its machine learning functionality is restricted to a single module, called ml. As we will see in this book, OpenCV still provides a number of state-of-the-art algorithms, but sometimes lacks a bit in functionality. In these rare cases, instead of reinventing the wheel, we will simply use scikit-learn for our purposes.

Last but not least, installing OpenCV using the Python Anaconda distribution is essentially a one-liner!

If you are a more advanced user who wants to build real-time applications, OpenCV's algorithms are well-optimized for this task, and Python provides several ways to speed up computations where it is necessary (using, for example, Cython or parallel processing libraries such as joblib or dask).
主站蜘蛛池模板: 海伦市| 大荔县| 左云县| 冷水江市| 邢台市| 密山市| 象山县| 玉山县| 洪湖市| 沙洋县| 建瓯市| 永康市| 金秀| 隆昌县| 凤翔县| 铅山县| 高碑店市| 新宾| 九龙县| 长丰县| 罗平县| 大同县| 广东省| 东丰县| 临沧市| 怀宁县| 同仁县| 呼玛县| 海口市| 海城市| 开阳县| 张北县| 全州县| 岐山县| 灵寿县| 安平县| 永顺县| 扶绥县| 简阳市| 菏泽市| 新津县|