#Magic engine on raspberry pie how to#
In my research on how to optimize the Raspberry Pi for OpenCV I came across this excellent article by Sagi Zeevi.
My goal here to demonstrate that the optimizations are in fact much faster on the Raspberry Pi 3and you should not hesitate to use them in your own projects. In the remainder of this tutorial, we will discuss the optimizations we will leverage during our OpenCV installation, followed by walking through the seven installation steps.Īfter our optimized OpenCV compile is installed, we’ll run a few quick tests determine if our new OpenCV install is faster than the previous one. While we cannot train neural networks on the Raspberry Pi, we can deploy pre-trained networks to our Pi - provided we can optimize the Raspberry Pi sufficiently (and the network can fit into the limited memory of the Pi hardware).
However, I was left wondering if we could do better. The results were satisfactory, taking approximately 1.7 seconds to classify an image using GoogLeNet and 0.9 seconds for SqueezeNet, respectively. Looking for the source code to this post? Jump Right To The Downloads Section Optimizing OpenCV on the Raspberry PiĪ couple weeks ago I demonstrated how to deploy a deep neural network to your Raspberry Pi.
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