The ml (machine learning) module in OpenCV

The ml (machine learning) module in OpenCV provides functions for training and evaluating machine learning models, as well as for making predictions using trained models. It includes support for various types of machine learning algorithms, such as support vector machines (SVMs), decision trees, and neural networks.

Here is an example of how to use the ml module to train and evaluate a decision tree classifier using the Iris dataset:

import cv2
import numpy as np

# Load the Iris dataset
data = np.genfromtxt('iris.csv', delimiter=',')
X = data[:, :4]
y = data[:, 4]

# Split the data into training and test sets
X_train, X_test, y_train, y_test = cv2.trainTestSplit(X, y, test_size=0.2)

# Create the decision tree classifier
classifier = cv2.ml.DTrees_create()

# Train the classifier
classifier.train(X_train, cv2.ml.ROW_SAMPLE, y_train)

# Evaluate the classifier
accuracy = classifier.calcError(X_test, cv2.ml.ROW_SAMPLE, y_test)
print('Accuracy:', accuracy)

# Make predictions using the trained classifier
predictions = classifier.predict(X_test)
print('Predictions:', predictions)

In this example, the Iris dataset is loaded from a CSV file and split into training and test sets. The decision tree classifier is created and trained using the training data, and the calcError function is used to evaluate the classifier on the test data. The predict function is then used to make predictions using the trained classifier.

There are many other functions and capabilities available in the ml module, including support for different types of machine learning algorithms and data. You can find more information and examples in the official documentation and various online tutorials.