官术网_书友最值得收藏!

Having a look at supervised learning in OpenCV

Knowing how supervised learning works is pretty if we can't put it into practice. Thankfully, OpenCV provides a pretty straightforward interface for all its statistical learning models, which includes all supervised learning models.

In OpenCV, every machine learning model derives from the cv::ml::StatModel base class. This is fancy talk for saying that if we want to be a machine learning model in OpenCV, we have to provide all the functionality that StatModel tells us to. This includes a method to train the model (called train) and a method to measure the performance of the model (called calcError).

In object-oriented programming (OOP), functions are often called objects or classes. An object can itself consist of a number of functions, called methods, as well as variables, called members or attributes. You can learn more about OOP in Python at https://docs.python.org/3/tutorial/classes.html.

Thanks to this organization of the software, setting up a machine learning model in OpenCV always follows the same logic:

  • Initialization: We call the model by name to create an empty instance of the model.
  • Set parameters: If the model needs some parameters, we can set them via setter methods, which can be different for every model. For example, in order for a k-NN algorithm to work, we need to specify its open parameter, k (as we will find out later).
  • Train the model: Every model must provide a method called train, used to fit the model to some data.
  • Predict new labels: Every model must provide a method called predict, used to predict the labels of new data.
  • Score the model: Every model must provide a method called calcError, used to measure performance. This calculation might be different for every model.
Because OpenCV is a vast and community-driven project, not every algorithm follows these rules to the extent that we as users might expect. For example, the k-NN algorithm does most of its work in a findNearest method, although predict still works. We will make sure to point out these discrepancies as we work through different examples.

As we will make the occasional use of scikit-learn to implement some machine learning algorithms that OpenCV does not provide, it is worth pointing out that learning algorithms in scikit-learn follow an almost identical logic. The most notable difference is that scikit-learn sets all the required model parameters in the initialization step. In addition, it calls the training function fit instead of train, and the scoring function score instead of calcError.

主站蜘蛛池模板: 女性| 巴彦淖尔市| 江山市| 盐亭县| 同心县| 兰坪| 阿克| 麟游县| 布尔津县| 房产| 宣威市| 达州市| 淅川县| 诸城市| 黑山县| 太原市| 天柱县| 江阴市| 淳化县| 古交市| 增城市| 侯马市| 榕江县| 手游| 罗平县| 秦安县| 南康市| 贵州省| 荣昌县| 寻甸| 宣化县| 淄博市| 含山县| 雷波县| 甘德县| 梅河口市| 仁化县| 会同县| 乌苏市| 绥江县| 象州县|