Intel OpenVINO on Raspberry Pi: 2018 harvest
You can write about programming not only with prose, but also with verses. The latter, of course, does not happen often - say, on an Intel blog, this happened a little less than never. However, as an experiment today, we decided to allow ourselves; how it happened is up to you. So...
Last quarter of the year, to
be precise, last week,
Intel developers presented a new
OpenVINO toolkit release on software .
What's new is done - refer to changelog'am,
I can’t be given exact details,
I’ll tell you a little
about this post: About vision, Intel and raspberry wine.
***
OpenVINO is essentially a set of tools,
libraries and examples of solutions.
With a special acceleration on Intel's hardware,
popular computer vision problems.
Who has not heard about computer vision -
This is a field of computer science,
Forcing to understand the machine,
Like man, the world around.
The source of information is mainly images,
and the problem is to create an algorithm
for extracting useful signs
and making decisions on them.
***
The first in our mini-review is the OpenCV library,
How often it is called, the traditional
Machine Vision and Learning,
Reading from the camera and on the drawing screen,
Cross-platform and living on GitHub,
Got 30502 stars at the time of writing.
In the package for Raspberry, we will also find
OpenCV Public, and with it
NEON optimization, wrappers in Python,
GStreamer for cameras and for GTK windows.
***
Already seven years in the world.
To solve our problems.
Deep neural networks are used.
Those that need to be trained with a teacher.
Caffe, PyTorch - it's all about training,
sometimes taking a couple of weeks.
OpenVINO solves the second problem:
Run the trained grids as quickly as possible.
From the best solutions for many platforms,
For known scenarios, needs and resources,
In OpenVINO deep learning - a separate engine,
With one interface on different devices.
***
To work with your toolkit.
Install Raspbian 9 on Raspberry Pi,
Connect Movidius stick, power supply,
Check that the CPU version is at least seven.
Read more in the corresponding guide ,
And as a demo, I propose a code that
works with the camera and turns off the
LED in the direction of view.
Download two networks: for persons finding
and predicting the position of the head ,
Use a USB camera and note,
What is in the pin code is number 2 by default.
***
Dedicated to all those who are enthusiastic.
We have been working on our project all year.
Let everything accelerate in the upcoming,
OpenVINO on Raspberry - the same IoT!
import cv2 as cv
from gpiozero import LED
from math import cos, sin, pi
winName = 'OpenVINO on Raspberry Pi'
cv.namedWindow(winName, cv.WINDOW_NORMAL)
faceDetectionNet = cv.dnn.readNet('face-detection-retail-0004.xml',
'face-detection-retail-0004.bin')
headPoseNet = cv.dnn.readNet('head-pose-estimation-adas-0001.xml',
'head-pose-estimation-adas-0001.bin')
faceDetectionNet.setPreferableTarget(cv.dnn.DNN_TARGET_MYRIAD)
headPoseNet.setPreferableTarget(cv.dnn.DNN_TARGET_MYRIAD)
cap = cv.VideoCapture(0)
led = LED(2)
led.on()
while cv.waitKey(1) != 27:
hasFrame, frame = cap.read()
ifnot hasFrame:
break
frameHeight, frameWidth = frame.shape[0], frame.shape[1]
# Detect faces on the image.
blob = cv.dnn.blobFromImage(frame, size=(300, 300), ddepth=cv.CV_8U)
faceDetectionNet.setInput(blob)
detections = faceDetectionNet.forward()
for detection in detections.reshape(-1, 7):
confidence = float(detection[2])
if confidence > 0.5:
xmin = int(detection[3] * frameWidth)
ymin = int(detection[4] * frameHeight)
xmax = int(detection[5] * frameWidth)
ymax = int(detection[6] * frameHeight)
xmax = max(1, min(xmax, frameWidth - 1))
ymax = max(1, min(ymax, frameHeight - 1))
xmin = max(0, min(xmin, xmax - 1))
ymin = max(0, min(ymin, ymax - 1))
# Run head pose estimation network.
face = frame[ymin:ymax+1, xmin:xmax+1]
blob = cv.dnn.blobFromImage(face, size=(60, 60), ddepth=cv.CV_8U)
headPoseNet.setInput(blob)
headPose = headPoseNet.forward(['angle_p_fc', 'angle_r_fc', 'angle_y_fc'])
p, r, y = headPose[0][0], headPose[1][0], headPose[2][0]
cos_r = cos(r * pi / 180)
sin_r = sin(r * pi / 180)
sin_y = sin(y * pi / 180)
cos_y = cos(y * pi / 180)
sin_p = sin(p * pi / 180)
cos_p = cos(p * pi / 180)
x = int((xmin + xmax) / 2)
y = int((ymin + ymax) / 2)
# center to right
cv.line(frame, (x,y), (x+int(50*(cos_r*cos_y+sin_y*sin_p*sin_r)), y+int(50*cos_p*sin_r)), (0, 0, 255), thickness=3)
# center to top
cv.line(frame, (x, y), (x+int(50*(cos_r*sin_y*sin_p+cos_y*sin_r)), y-int(50*cos_p*cos_r)), (0, 255, 0), thickness=3)
# center to forward
cv.line(frame, (x, y), (x + int(50*sin_y*cos_p), y + int(50*sin_p)), (255, 0, 0), thickness=3)
cv.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 255, 255))
if abs(cos_y * cos_p) > 0.9:
cv.putText(frame, 'FORWARD', (0, 30), cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), thickness=2)
led.off()
else:
led.on()
cv.imshow(winName, frame)- Face Detection Network:
- Network to assess the position of the head: