#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:

# 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()

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

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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