Published November 1, 2014

Face Recognition & Face Tracking via OpenCV and UDOO

Make a screen move towards your face when it moves in the camera range

Full instructions provided8,106

Face Recognition & Face Tracking via OpenCV and UDOO

Things used in this project

Hardware components

UDOO DUAL

MicroSD

with Ubuntu pre-installed

UDOO Camera Module

or Compatible USB Camera

UDOO Screen

Servos (Tower Pro MG996R)

Software apps and online services

OpenCV

Story

In this tutorial we’ll see how to implement an OpenCV App with Python and an Arduino sketch that reads OpenCV data and moves a UDOO screen when you move your face in the UDOO camera range.

OpenCV is an open source computer vision and machine learning software library that makes possible to process images and to do face tracking, face detection, shape detection and other cool stuff. It has C, C++, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android. OpenCV is written in optimized C/C++, so that the library can take advantage of multi-core processing.

OpenCV library is pre-installed on UDOObuntu.

What we need is:

UDOO screen

If you’ve met all the above requirements, let’s boot UDOObuntu! And let’s understand how the OpenCV library works and how the Sam3x Arduino controller is connected to IMX6, where UDOObuntu runs.

Remember that is necessary to enable the UDOO cam before doing something else; to enable it to take a look at the guide on this site.
In this simple project we used Python but you can also use Java or C/C++, just pay attention to import cv2 (as OpenCV library) in you project.

Remember that to use the serial port in Python you must import the serial library in the header of the script.
Before importing the serial library, you have to install it. To do this you have to open a terminal window and execute this instruction:

So let’s start and check the code together!
This is what you need to run the App:
Python file, for example webcam.py

So let’s start and check some code!

These are the needed components of the App:
Python file, for example webcam.py

Code

Download

import cv2
import sys
import serial

# Create an object Serial with ttymxc3 port
ser = serial.Serial(
    port='/dev/ttymxc3',
    baudrate=115200,
    parity=serial.PARITY_ODD, # Optional
    stopbits=serial.STOPBITS_ONE, # Optional
    bytesize=serial.EIGHTBITS # Optional
)

# Check if Serial is Open, close it then re-open (to avoid exception)
if ser.isOpen():
    ser.close()

ser.open()
cascPath = sys.argv[1]
faceCascade = cv2.CascadeClassifier(cascPath)

# Set video source (UDOO Camera) and resolution
video_capture = cv2.VideoCapture(7)
video_capture.set(3,320)
video_capture.set(4,240)

while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=(30, 30),
        flags=cv2.cv.CV_HAAR_SCALE_IMAGE
    )

    # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        print ("x: ",x,", y: ",y)
        prov=str(x)
        prov = prov + 'x'
        ser.write(prov)
        prov=str(y)
        prov = prov + 'x'
        ser.write(prov)
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow('Video', frame)

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

# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()

And here you can check the Arduino sketch:

Code

Download

#include  
 
int data[3], v[2];
int i=0, j=0, n=0, prov=320, num, plus=320, plus1=240, aum=45, aum1=45;
char c;
Servo servo1;
Servo servo2;
 
void setup()
{
  servo1.attach(2); 
  servo2.attach(3);  
  Serial.begin(9600);
}
 
void loop()
{  
  //Read Serial bus data received from Python and write them into servo position
  if(Serial.available())
  {
    prov=Serial.read()-48;
    if(prov!=72)
    {
      data[i]=prov;
      i++;
    }
    else
    {
      if(i==0)
        num=0;
      if(i==1)
        num=data[0];
      if(i==2)
        num=data[1]+data[0]*10;
      if(i==3)
        num=data[2]+data[1]*10+data[0]*100;
      i=0;
      v[n]=num;
      n++;
      if(n==2)
      {
        //Start X axis
        while(plus>0)
        {
          if((v[0]<=plus) && (v[0]>(plus-10)))
          {
            servo1.write(aum-5);
            delay(15);
            plus=0;
          }
          else
          {
            plus=plus-10;
            aum=aum+3;
          }
        }
        plus=320;
        aum=45;
        //Start Y axis
        while(plus1>0)
        {
          if((v[1]<=plus1) && (v[1]>(plus1-10)))
          {
            servo2.write(aum1);
            delay(15);
            plus1=0;
          }
          else
          {
            plus1=plus1-10;
            aum1=aum1+3;
          }
        }
        plus1=240;
        aum1=45;
        n=0;
      }
    }
  }
}

After this simple code, you have to download an xml file that describe a figure or an object: this xml file is the most used and accurate to detect face but actually is not very performant.

Here is a prototype:

Finally to run your python script you have to open a terminal window and write

In this case:

Here you can download the complete code of this example demo:

So, here’s the final result!

import cv2
import sys
import serial

# Create an object Serial with ttymxc3 port
ser = serial.Serial(
    port='/dev/ttymxc3',
    baudrate=115200,
    parity=serial.PARITY_ODD, # Optional
    stopbits=serial.STOPBITS_ONE, # Optional
    bytesize=serial.EIGHTBITS # Optional
)

# Check if Serial is Open, close it then re-open (to avoid exception)
if ser.isOpen():
    ser.close()

ser.open()
cascPath = sys.argv[1]
faceCascade = cv2.CascadeClassifier(cascPath)

# Set video source (UDOO Camera) and resolution
video_capture = cv2.VideoCapture(7)
video_capture.set(3,320)
video_capture.set(4,240)

while True:
    # Capture frame-by-frame
    ret, frame = video_capture.read()
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=(30, 30),
        flags=cv2.cv.CV_HAAR_SCALE_IMAGE
    )

    # Draw a rectangle around the faces
    for (x, y, w, h) in faces:
        print ("x: ",x,", y: ",y)
        prov=str(x)
        prov = prov + 'x'
        ser.write(prov)
        prov=str(y)
        prov = prov + 'x'
        ser.write(prov)
        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)

    # Display the resulting frame
    cv2.imshow('Video', frame)

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

# When everything is done, release the capture
video_capture.release()
cv2.destroyAllWindows()

#include  
 
int data[3], v[2];
int i=0, j=0, n=0, prov=320, num, plus=320, plus1=240, aum=45, aum1=45;
char c;
Servo servo1;
Servo servo2;
 
void setup()
{
  servo1.attach(2); 
  servo2.attach(3);  
  Serial.begin(9600);
}
 
void loop()
{  
  //Read Serial bus data received from Python and write them into servo position
  if(Serial.available())
  {
    prov=Serial.read()-48;
    if(prov!=72)
    {
      data[i]=prov;
      i++;
    }
    else
    {
      if(i==0)
        num=0;
      if(i==1)
        num=data[0];
      if(i==2)
        num=data[1]+data[0]*10;
      if(i==3)
        num=data[2]+data[1]*10+data[0]*100;
      i=0;
      v[n]=num;
      n++;
      if(n==2)
      {
        //Start X axis
        while(plus>0)
        {
          if((v[0]<=plus) && (v[0]>(plus-10)))
          {
            servo1.write(aum-5);
            delay(15);
            plus=0;
          }
          else
          {
            plus=plus-10;
            aum=aum+3;
          }
        }
        plus=320;
        aum=45;
        //Start Y axis
        while(plus1>0)
        {
          if((v[1]<=plus1) && (v[1]>(plus1-10)))
          {
            servo2.write(aum1);
            delay(15);
            plus1=0;
          }
          else
          {
            plus1=plus1-10;
            aum1=aum1+3;
          }
        }
        plus1=240;
        aum1=45;
        n=0;
      }
    }
  }
}

Credits

Marco Mancini

2 projects • 14 followers

#Maker, student and beer consumer at @UDOO_Board dev team. Founder of #GDGCamerino, the @google dev group of my own town. Love #robotics, #DIY, #Drones.

Face Recognition & Face Tracking via OpenCV and UDOO

Things used in this project

Hardware components

Software apps and online services

Story

Code

Code

Code

haarcascade_frontalface_default.xml

Code

Code

Code

code.txt

code.txt

code.txt

code.txt

code.txt

Github

Credits

Marco Mancini

Comments

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Face Recognition & Face Tracking via OpenCV and UDOO

Face Recognition & Face Tracking via OpenCV and UDOO

Things used in this project

Hardware components

Software apps and online services

Story

Code

Code

Code

haarcascade_frontalface_default.xml

Code

Code

Code

code.txt

code.txt

code.txt

code.txt

code.txt

Github

Credits

Marco Mancini

Comments

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