Machine Learning for OpenCV 4 - Second Edition

Building the support vector machine

In OpenCV, SVMs are built, trained, and scored the same exact way as every other learning algorithm we have encountered so far, using the following four steps:

1. Call the create method to construct a new SVM:

In [6]: import cv2...     svm = cv2.ml.SVM_create()

As shown in the following command, there are different modes in which we can operate an SVM. For now, all we care about is the case we discussed in the previous example: an SVM that tries to partition the data with a straight line. This can be specified with the setKernel method:

In [7]: svm.setKernel(cv2.ml.SVM_LINEAR)

2. Call the classifier's train method to find the optimal decision boundary:

In [8]: svm.train(X_train, cv2.ml.ROW_SAMPLE, y_train) ...