Since OpenCV 3.1, there has been a deep neural networks (DNN) module in the library, implementing forward pass (inferencing) with deep networks, pre-trained using some popular deep learning frameworks, such as Caffe (http://caffe.berkeleyvision.org/), TensorFlow (https://www.tensorflow.org/), Torch/Pytorch (http://torch.ch/), Darknet (https://pjreddie.com/darknet/), and models in ONNX (https://onnx.ai/) format. In OpenCV 3.3, the module has been promoted from an opencv_contrib repository to the main repository (https://github.com/opencv/opencv/tree/master/modules/dnn) and has been accelerated significantly. Therefore, since OpenCV 3.3, pre-trained networks can be used to make a prediction within our application, and many of the popular network architectures, which were introduced in the previous section, are compatible with OpenCV 3.3.
In this section...
