Machine Learning for OpenCV

Manually implementing cross-validation in OpenCV

The easiest way to perform cross-validation in OpenCV is to do the data splits by hand.

For example, in order to implement two-fold cross-validation, we would follow the following procedure:

1. Load the dataset:

      In [1]: from sklearn.datasets import load_iris      ...     import numpy as np      ...     iris = load_iris()      ...     X = iris.data.astype(np.float32)      ...     y = iris.target

2. Split the data into two equally sized parts:

      In [2]: from sklearn.model_selection import model_selection      ...     X_fold1, X_fold2, y_fold1, y_fold2 = train_test_split(      ...         X, y, random_state=37, train_size=0.5      ...     )

3. Instantiate the classifier:

      In [3]: import cv2      ...     knn = cv2.ml.KNearest_create()      ...     knn.setDefaultK(1)

4. Train the ...