Unsurprisingly, ANNs reside in the ml module of OpenCV.
Let's examine a dummy example, as a gentle introduction to ANNs:
import cv2 import numpy as np ann = cv2.ml.ANN_MLP_create() ann.setLayerSizes(np.array([9, 5, 9], dtype=np.uint8)) ann.setTrainMethod(cv2.ml.ANN_MLP_BACKPROP) ann.train(np.array([[1.2, 1.3, 1.9, 2.2, 2.3, 2.9, 3.0, 3.2, 3.3]], dtype=np.float32), cv2.ml.ROW_SAMPLE, np.array([[0, 0, 0, 0, 0, 1, 0, 0, 0]], dtype=np.float32)) print ann.predict(np.array([[1.4, 1.5, 1.2, 2., 2.5, 2.8, 3., 3.1, 3.8]], dtype=np.float32))
First, we create an ANN:
ann = cv2.ml.ANN_MLP_create()
You may wonder about the MLP acronym in the function name; it stands for multilayer perceptron. By now, you should know what a perceptron is.
After creating the network, we need to set its topology:
ann.setLayerSizes(np.array([9, 5, 9], dtype=np.uint8)) ann.setTrainMethod(cv2.ml.ANN_MLP_BACKPROP)
The layer sizes are defined by the NumPy array that is passed into the setLayerSizes method. The...