#RaspberryPi – Performance differences in #FaceRecognition using #OpenVino (code with @code!)

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Hi !

I’ve been looking to use the amazing Intel Neural Stick 2 for a while, and one of the 1st ideas that I have was to check how fast my Raspberry Pi 4 can run using this device.

The Intel team released a nice step by step process installation for Raspberry Pi. And it works great, there are a couple of minor glitches that you need to figure out, like the latest package version, everything else works great.

Note: I downloaded my openvino toolkit from here (https://download.01.org/opencv/2019/openvinotoolkit/R3/), and the downloaded file is (l_openvino_toolkit_runtime_raspbian_p_2019.3.334.tgz).

Once installed, the 1st python sample is a face recognition one. This sample analyzes a image file using OpenCV to detect faces, and creates a new output file with the detected images. As I said, is very straight forward.

So, I decided to create a new python sample to run live face detection using the camera feed and also display the FPS. This is the output code:

The code is very straight forward and the main matters are

  • It uses 2 models from the Intel Zoo to perform the face detection: face-detection-adas-0001.xml and face-detection-adas-0001.bin
  • Lines 22 and 23 are key to define that OpenCV will load and use the models in the Intel device
  • I use imutils to resize the image to 640×480. Feel free to use any other library for this, even OpenCV
  • Also, it works also with smaller resolutions, however 640×480 is good for this demo

And the final app running analyzing almost 8 frames per second (8 FPS).

Which is almost 10 times faster that the 0.7 FPS without Intel NCS2

And, I already wrote about running Visual Studio Code in the Raspberry Pi (see references) is an amazing experience. I did all my Python in VSCode coding remote accesing my device via VNC. Python runs like a charm!

You can download the code from https://github.com/elbruno/rpiopenvino/tree/master/facedetection


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