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

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:

	# perform face detection
	# display detected face frame
	# display FPS info in webcam video feed

	# This is the official sample demo file desribed in the installer documentation
	# Date: 2020 01 26
	# Install OpenVINO™ toolkit for Raspbian* OS
	# http://docs.openvinotoolkit.org/2019_R1/_docs_install_guides_installing_openvino_raspbian.html

	import cv2
	import time
	import imutils

	# Load the model.
	net = cv2.dnn.readNet('face-detection-adas-0001.xml',
	'face-detection-adas-0001.bin')
	# Specify target device.
	# ERROR net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV)
	# OK net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
	# ERROR net.setPreferableBackend(cv2.dnn.DNN_BACKEND_HALIDE)

	net.setPreferableBackend(cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE)
	net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)

	# open video frame
	video_capture = cv2.VideoCapture(0)

	while True:
	start_time = time.time()
	ret, frame = video_capture.read()

	# frame resize to improve performance
	frame = imutils.resize(frame, width=648, height=480)

	rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

	# Prepare input blob and perform an inference.
	blob = cv2.dnn.blobFromImage(rgb_frame, size=(640, 480), ddepth=cv2.CV_8U)
	net.setInput(blob)
	out = net.forward()

	# Draw detected faces on the frame.
	for detection in out.reshape(–1, 7):
	confidence = float(detection[2])
	xmin = int(detection[3] * frame.shape[1])
	ymin = int(detection[4] * frame.shape[0])
	xmax = int(detection[5] * frame.shape[1])
	ymax = int(detection[6] * frame.shape[0])
	if confidence > 0.5:
	cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, 255, 0))

	#display FPS
	fpsInfo = "FPS: " + str(1.0 / (time.time() – start_time)) # FPS = 1 / time to process loop
	print(fpsInfo)
	font = cv2.FONT_HERSHEY_DUPLEX
	cv2.putText(frame, fpsInfo, (10, 20), font, 0.4, (255, 255, 255), 1)

	cv2.imshow('Video', frame)

	if cv2.waitKey(1) & 0xFF == ord('q'):
	break

	video_capture.release()
	cv2.destroyAllWindows()

view raw RaspberryPiFaceDetectionIntelNeuralStick2.py hosted with ❤ by GitHub

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

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

References

My posts on Raspberry Pi ⚡🐲⚡

Dev posts for Raspberry Pi

Tools and Apps for Raspberry Pi

Setup the device

Hardware

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